The deep space network

NASA Technical Reports Server (NTRS)

1977-01-01

A Deep Space Network progress report is presented dealing with in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

Deep Space Network equipment performance, reliability, and operations management information system

NASA Technical Reports Server (NTRS)

Cooper, T.; Lin, J.; Chatillon, M.

2002-01-01

The Deep Space Mission System (DSMS) Operations Program Office and the DeepSpace Network (DSN) facilities utilize the Discrepancy Reporting Management System (DRMS) to collect, process, communicate and manage data discrepancies, equipment resets, physical equipment status, and to maintain an internal Station Log. A collaborative effort development between JPL and the Canberra Deep Space Communication Complex delivered a system to support DSN Operations.

The deep space network

NASA Technical Reports Server (NTRS)

1980-01-01

The functions and facilities of the Deep Space Network are considered. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.

The deep space network

NASA Technical Reports Server (NTRS)

1979-01-01

Progress is reported in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. The functions and facilities of the Deep Space Network are emphasized.

The deep space network

NASA Technical Reports Server (NTRS)

1979-01-01

A report is given of the Deep Space Networks progress in (1) flight project support, (2) tracking and data acquisition research and technology, (3) network engineering, (4) hardware and software implementation, and (5) operations.

The deep space network

NASA Technical Reports Server (NTRS)

1977-01-01

The facilities, programming system, and monitor and control system for the deep space network are described. Ongoing planetary and interplanetary flight projects are reviewed, along with tracking and ground-based navigation, communications, and network and facility engineering.

Considerations on communications network protocols in deep space

NASA Technical Reports Server (NTRS)

Clare, L. P.; Agre, J. R.; Yan, T.

2001-01-01

Communications supporting deep space missions impose numerous unique constraints that impact the architectural choices made for cost-effectiveness. We are entering the era where networks that exist in deep space are needed to support planetary exploration. Cost-effective performance will require a balanced integration of applicable widely used standard protocols with new and innovative designs.

Deep space network support of the manned space flight network for Apollo, volume 3. [support for Apollo 14, 15, 16, and 17 flights

NASA Technical Reports Server (NTRS)

Hartley, R. B.

1974-01-01

The Deep Space Network (DSN) activities in support of Project Apollo during the period of 1971 and 1972 are reported. Beginning with the Apollo 14 mission and concluding with the Apollo 17 mission, the narrative includes, (1) a mission description, (2) the NASA support requirements placed on the DSN, and, (3) a comprehensive account of the support activities provided by each committed DSN deep space communication station. Associated equipment and activities of the three elements of the DSN (the Deep Space Instrumentation Facility (DSIF), the Space Flight Operations Facility (SFOF), and the Ground Communications Facility (GCF)) used in meeting the radio-metric and telemetry demands of the missions are documented.

Office of Tracking and Data Acquisition. [deep space network and spacecraft tracking

NASA Technical Reports Server (NTRS)

1975-01-01

The Office of Tracking and Data Acquisition (OTDA) and its two worldwide tracking network facilities, the Spaceflight Tracking and Data Network and the Deep Space Network, are described. Other topics discussed include the NASA communications network, the tracking and data relay satellite system, other OTDA tracking activities, and OTDA milestones.

The Deep Space Network: The challenges of the next 20 years - The 21st century

NASA Technical Reports Server (NTRS)

Dumas, L. N.; Edwards, C. D.; Hall, J. R.; Posner, E. C.

1990-01-01

The Deep Space Network (DSN) has been the radio navigation and communications link between NASA's lunar and deep space missions for 30 years. In this paper, new mission opportunities over the next 20 years are discussed. The system design drivers and the DSN architectural concepts for those challenges are briefly considered.

The deep space network. [tracking and communication support for space probes

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization of the deep space network are summarized. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. Interface support for the Mariner Venus Mercury 1973 flight and Pioneer 10 and 11 missions is included.

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.

The deep space network, volume 6

NASA Technical Reports Server (NTRS)

1971-01-01

Progress on Deep Space Network (DSN) supporting research and technology is presented, together with advanced development and engineering, implementation, and DSN operations of flight projects. The DSN is described. Interplanetary and planetary flight projects and radio science experiments are discussed. Tracking and navigational accuracy analysis, communications systems and elements research, and supporting research are considered. Development of the ground communications and deep space instrumentation facilities is also presented. Network allocation schedules and angle tracking and test development are included.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1977-01-01

The various systems and subsystems are discussed for the Deep Space Network (DSN). A description of the DSN is presented along with mission support, program planning, facility engineering, implementation and operations.

The deep space network, Volume 11

NASA Technical Reports Server (NTRS)

1972-01-01

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

Deep Space Network Antenna Monitoring Using Adaptive Time Series Methods and Hidden Markov Models

NASA Technical Reports Server (NTRS)

Smyth, Padhraic; Mellstrom, Jeff

1993-01-01

The Deep Space Network (DSN)(designed and operated by the Jet Propulsion Laboratory for the National Aeronautics and Space Administration (NASA) provides end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system.

Major technological innovations introduced in the large antennas of the Deep Space Network

NASA Technical Reports Server (NTRS)

Imbriale, W. A.

2002-01-01

The NASA Deep Space Network (DSN) is the largest and most sensitive scientific, telecommunications and radio navigation network in the world. Its principal responsibilities are to provide communications, tracking, and science services to most of the world's spacecraft that travel beyond low Earth orbit. The network consists of three Deep Space Communications Complexes. Each of the three complexes consists of multiple large antennas equipped with ultra sensitive receiving systems. A centralized Signal Processing Center (SPC) remotely controls the antennas, generates and transmits spacecraft commands, and receives and processes the spacecraft telemetry.

The deep space network, volume 15

NASA Technical Reports Server (NTRS)

1973-01-01

The DSN progress is reported in flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations. Topics discussed include: DSN functions and facilities, planetary flight projects, tracking and ground-based navigation, communications, data processing, network control system, and deep space stations.

The Deep Space Network, volume 39

NASA Technical Reports Server (NTRS)

1977-01-01

The functions, facilities, and capabilities of the Deep Space Network and its support of the Pioneer, Helios, and Viking missions are described. Progress in tracking and data acquisition research and technology, network engineering and modifications, as well as hardware and software implementation and operations are reported.

Deep space network Mark 4A description

NASA Technical Reports Server (NTRS)

Wallace, R. J.; Burt, R. W.

1986-01-01

The general system configuration for the Mark 4A Deep Space Network is described. The arrangement and complement of antennas at the communications complexes and subsystem equipment at the signal processing centers are described. A description of the Network Operations Control Center is also presented.

Deep Space Telecommunications

NASA Technical Reports Server (NTRS)

Kuiper, T. B. H.; Resch, G. M.

2000-01-01

The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

Sub-microradian pointing for deep space optical telecommunications network

NASA Technical Reports Server (NTRS)

Ortiz, G.; Lee, S.; Alexander, J.

2001-01-01

This presentation will cover innovative hardware, algorithms, architectures, techniques and recent laboratory results that are applicable to all deep space optical communication links, such as the Mars Telecommunication Network to future interstellar missions.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1975-01-01

Work accomplished on the Deep Space Network (DSN) was described, including the following topics: supporting research and technology, advanced development and engineering, system implementation, and DSN operations pertaining to mission-independent or multiple-mission development as well as to support of flight projects.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1988-01-01

The Deep Space Network (DSN) is the largest and most sensitive scientific telecommunications and radio navigation network in the world. Its principal responsibilities are to support unmanned interplanetary spacecraft missions and to support radio and radar astronomy observations in the exploration of the solar system and the universe. The DSN facilities and capabilities as of January 1988 are described.

The deep space network, volume 12

NASA Technical Reports Server (NTRS)

1972-01-01

Progress in the development of the DSN is reported along with TDA research and technology, network engineering, hardware, and software implementation. Included are descriptions of the DSN function and facilities, Helios mission support, Mariner Venus/Mercury 1973 mission support, Viking mission support, tracking and ground-based navigation, communications, network control and data processing, and deep space stations.

Propagation Effects of Importance to the NASA/JPL Deep Space Network (DSN)

NASA Technical Reports Server (NTRS)

Slobin, Steve

1999-01-01

This paper presents Propagation Effects of Importance To The NASA/JPL Deep Space Network (DSN). The topics include: 1) DSN Antennas; 2) Deep Space Telecom Link Basics; 3) DSN Propagation Region of Interest; 4) Ka-Band Weather Effects Models and Examples; 5) Existing Goldstone Ka-Band Atmosphere Attenuation Model; 6) Existing Goldstone Atmosphere Noise Temperature Model; and 7) Ka-Band delta (G/T) Relative to Vacuum Condition. This paper summarizes the topics above.

The Network Information Management System (NIMS) in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wales, K. J.

1983-01-01

In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

The Telecommunications and Data Acquisition Report. [Deep Space Network

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

This publication, one of a series formerly titled The Deep Space Network Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

The deep space network, volume 19

NASA Technical Reports Server (NTRS)

1974-01-01

The progress is reported in the DSN for Nov. and Dec. 1973. Research is described for the following areas: functions and facilities, mission support for flight projects, tracking and ground-based navigation, spacecraft/ground communication, network control and operations technology, and deep space stations.

The Telecommunications and Data Acquisition Report. [Deep Space Network

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported. Also included is TDA funded activity at JPL on data and information systems and reimbursable DSN work performed for other space agencies through NASA.

Neural network based satellite tracking for deep space applications

NASA Technical Reports Server (NTRS)

Amoozegar, F.; Ruggier, C.

2003-01-01

The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

High-power transmitter automation. [deep space network

NASA Technical Reports Server (NTRS)

Gosline, R.

1980-01-01

The current status of the transmitter automation development applicable to all transmitters in the deep space network is described. Interface and software designs are described that improve reliability and reduce the time required for subsystem turn-on and klystron saturation to less than 10 minutes.

Operability engineering in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wilkinson, Belinda

1993-01-01

Many operability problems exist at the three Deep Space Communications Complexes (DSCC's) of the Deep Space Network (DSN). Four years ago, the position of DSN Operability Engineer was created to provide the opportunity for someone to take a system-level approach to solving these problems. Since that time, a process has been developed for personnel and development engineers and for enforcing user interface standards in software designed for the DSCC's. Plans are for the participation of operations personnel in the product life-cycle to expand in the future.

Preliminary Concept of Operations for the Deep Space Array-Based Network

NASA Astrophysics Data System (ADS)

Bagri, D. S.; Statman, J. I.

2004-05-01

The Deep Space Array-Based Network (DSAN) will be an array-based system, part of a greater than 1000 times increase in the downlink/telemetry capability of the Deep Space Network. The key function of the DSAN is provision of cost-effective, robust telemetry, tracking, and command services to the space missions of NASA and its international partners. This article presents an expanded approach to the use of an array-based system. Instead of using the array as an element in the existing Deep Space Network (DSN), relying to a large extent on the DSN infrastructure, we explore a broader departure from the current DSN, using fewer elements of the existing DSN, and establishing a more modern concept of operations. For example, the DSAN will have a single 24 x 7 monitor and control (M&C) facility, while the DSN has four 24 x 7 M&C facilities. The article gives the architecture of the DSAN and its operations philosophy. It also briefly describes the customer's view of operations, operations management, logistics, anomaly analysis, and reporting.

The deep space network, volume 10

NASA Technical Reports Server (NTRS)

1972-01-01

Progress on the Deep Space Network (DSN) supporting research and technology is reported. The objectives, functions and facilities of the DSN are described along with the mission support for the following: interplanetary flight projects, planetary flight projects, and manned space flight projects. Work in advanced engineering and communications systems is reported along with changes in hardware and software configurations in the DSN/MSFN tracking stations.

Iris Transponder-Communications and Navigation for Deep Space

NASA Technical Reports Server (NTRS)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Microwave analog fiber-optic link for use in the deep space network

NASA Technical Reports Server (NTRS)

Logan, R. T., Jr.; Lutes, G. F.; Maleki, L.

1990-01-01

A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.

Evolutionary Scheduler for the Deep Space Network

NASA Technical Reports Server (NTRS)

Guillaume, Alexandre; Lee, Seungwon; Wang, Yeou-Fang; Zheng, Hua; Chau, Savio; Tung, Yu-Wen; Terrile, Richard J.; Hovden, Robert

2010-01-01

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

Summary of DSN (Deep Space Network) reimbursable launch support

NASA Technical Reports Server (NTRS)

Fanelli, N. A.; Wyatt, M. E.

1988-01-01

The Deep Space Network is providing ground support to space agencies of foreign governments as well as to NASA and other agencies of the Federal government which are involved in space activities. DSN funding for support of missions other than NASA are on either a cooperative or a reimbursable basis. Cooperative funding and support are accomplished in the same manner as NASA sponsored missions. Reimbursable launch funding and support methods are described.

The Deep Space Network: An instrument for radio astronomy research

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Levy, G. S.; Kuiper, T. B. H.; Walken, P. R.; Chandlee, R. C.

1988-01-01

The NASA Deep Space Network operates and maintains the Earth-based two-way communications link for unmanned spacecraft exploring the solar system. It is NASA's policy to also make the Network's facilities available for radio astronomy observations. The Network's microwave communication systems and facilities are being continually upgraded. This revised document, first published in 1982, describes the Network's current radio astronomy capabilities and future capabilities that will be made available by the ongoing Network upgrade. The Bibliography, which includes published papers and articles resulting from radio astronomy observations conducted with Network facilities, has been updated to include papers to May 1987.

Deep space communication - Past, present, and future

NASA Technical Reports Server (NTRS)

Posner, E. C.; Stevens, R.

1984-01-01

This paper reviews the progress made in deep space communication from its beginnings until now, describes the development and applications of NASA's Deep Space Network, and indicates directions for the future. Limiting factors in deep space communication are examined using the upcoming Voyager encounter with Uranus, centered on the downlink telemetry from spacecraft to earth, as an example. A link calculation for Voyager at Uranus over Australia is exhibited. Seven basic deep space communication functions are discussed, and technical aspects of spacecraft communication equipment, ground antennas, and ground electronics and processing are considered.

A federated information management system for the Deep Space Network. M.S. Thesis - Univ. of Southern California

NASA Technical Reports Server (NTRS)

Dobinson, E.

1982-01-01

General requirements for an information management system for the deep space network (DSN) are examined. A concise review of available database management system technology is presented. It is recommended that a federation of logically decentralized databases be implemented for the Network Information Management System of the DSN. Overall characteristics of the federation are specified, as well as reasons for adopting this approach.

Temperature control simulation for a microwave transmitter cooling system. [deep space network

NASA Technical Reports Server (NTRS)

Yung, C. S.

1980-01-01

The thermal performance of a temperature control system for the antenna microwave transmitter (klystron tube) of the Deep Space Network antenna tracking system is discussed. In particular the mathematical model is presented along with the details of a computer program which is written for the system simulation and the performance parameterization. Analytical expressions are presented.

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

NASA Technical Reports Server (NTRS)

Navarro, Robert

2006-01-01

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1990-01-01

Archival reports on developments in programs managed by the Jet Propulsion Laboratory's (JPL) Office of Telecommunications and Data Acquisition (TDA) are given. Space communications, radio navigation, radio science, and ground-based radio and radar astronomy, activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, supporting research and technology, implementation, and operations are reported. Also included is TDA-funded activity at JPL on data and information systems and reimbursable Deep Space Network (DSN) work performed for other space agencies through NASA.

Automating Deep Space Network scheduling and conflict resolution

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Clement, Bradley

2005-01-01

The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.

Publications of the Jet Propulsion Laboratory, January through December 1974. [deep space network, Apollo project, information theory, and space exploration

NASA Technical Reports Server (NTRS)

1975-01-01

Formalized technical reporting is described and indexed, which resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. The five classes of publications included are technical reports, technical memorandums, articles from the bimonthly Deep Space Network Progress Report, special publications, and articles published in the open literature. The publications are indexed by author, subject, and publication type and number.

The Deep Impact Network Experiment Operations Center

NASA Technical Reports Server (NTRS)

Torgerson, J. Leigh; Clare, Loren; Wang, Shin-Ywan

2009-01-01

Delay/Disruption Tolerant Networking (DTN) promises solutions in solving space communications challenges arising from disconnections as orbiters lose line-of-sight with landers, long propagation delays over interplanetary links, and other phenomena. DTN has been identified as the basis for the future NASA space communications network backbone, and international standardization is progressing through both the Consultative Committee for Space Data Systems (CCSDS) and the Internet Engineering Task Force (IETF). JPL has developed an implementation of the DTN architecture, called the Interplanetary Overlay Network (ION). ION is specifically implemented for space use, including design for use in a real-time operating system environment and high processing efficiency. In order to raise the Technology Readiness Level of ION, the first deep space flight demonstration of DTN is underway, using the Deep Impact (DI) spacecraft. Called the Deep Impact Network (DINET), operations are planned for Fall 2008. An essential component of the DINET project is the Experiment Operations Center (EOC), which will generate and receive the test communications traffic as well as "out-of-DTN band" command and control of the DTN experiment, store DTN flight test information in a database, provide display systems for monitoring DTN operations status and statistics (e.g., bundle throughput), and support query and analyses of the data collected. This paper describes the DINET EOC and its value in the DTN flight experiment and potential for further DTN testing.

Optical ground station site diversity for Deep Space Optical Communications the Mars Telecom Orbiter optical link

NASA Technical Reports Server (NTRS)

Wilson, K.; Parvin, B.; Fugate, R.; Kervin, P.; Zingales, S.

2003-01-01

Future NASA deep space missions will fly advanced high resolution imaging instruments that will require high bandwidth links to return the huge data volumes generated by these instruments. Optical communications is a key technology for returning these large data volumes from deep space probes. Yet to cost effectively realize the high bandwidth potential of the optical link will require deployment of ground receivers in diverse locations to provide high link availability. A recent analysis of GOES weather satellite data showed that a network of ground stations located in Hawaii and the Southwest continental US can provide an average of 90% availability for the deep space optical link. JPL and AFRL are exploring the use of large telescopes in Hawaii, California, and Albuquerque to support the Mars Telesat laser communications demonstration. Designed to demonstrate multi-Mbps communications from Mars, the mission will investigate key operational strategies of future deep space optical communications network.

A history of the deep space network

NASA Technical Reports Server (NTRS)

Corliss, W. R.

1976-01-01

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

Tracks of a Giant

NASA Image and Video Library

2010-08-25

The giant, 70-meter-wide antenna at NASA Deep Space Network complex in Goldstone, Calif., tracks a spacecraft on Nov. 17, 2009. This antenna, officially known as Deep Space Station 14, is also nicknamed the Mars antenna.

High-Capacity Ground Communications to Support Future Space Missions: A Forecast of Ground Communications Challenges in the 2010-2020 Period

NASA Technical Reports Server (NTRS)

Markley, Richard W.

2003-01-01

The purpose of this presentation is to identify major challenges involved in space ground communications networks to support space flight missions over the next 20 years. The presentation focus is on the Deep Space Network and its customers, but the forecast is applicable to all space ground communications networks.

Operation's Concept for Array-Based Deep Space Network

NASA Technical Reports Server (NTRS)

Bagri, Durgadas S.; Statman, Joseph I.; Gatti, Mark S.

2005-01-01

The Array-based Deep Space Network (DSNArray) will be a part of more than 10(exp 3) times increase in the downlink/telemetry capability of the Deep space Network (DSN). The key function of the DSN-Array is to provide cost-effective, robust Telemetry, Tracking and Command (TT&C) services to the space missions of NASA and its international partners. It provides an expanded approach to the use of an array-based system. Instead of using the array as an element in the existing DSN, relying to a large extent on the DSN infrastructure, we explore a broader departure from the current DSN, using fewer elements of the existing DSN, and establishing a more modern Concept of Operations. This paper gives architecture of DSN-Array and its operation's philosophy. It also describes customer's view of operations, operations management and logistics - including maintenance philosophy, anomaly analysis and reporting.

NASA deep space network operations planning and preparation

NASA Technical Reports Server (NTRS)

Jensen, W. N.

1982-01-01

The responsibilities and structural organization of the Operations Planning Group of NASA Deep Space Network (DSN) Operations are outlined. The Operations Planning group establishes an early interface with a user's planning organization to educate the user on DSN capabilities and limitations for deep space tracking support. A team of one or two individuals works through all phases of the spacecraft launch and also provides planning and preparation for specific events such as planetary encounters. Coordinating interface is also provided for nonflight projects such as radio astronomy and VLBI experiments. The group is divided into a Long Range Support Planning element and a Near Term Operations Coordination element.

The Deep Space Network as an instrument for radio science research

NASA Technical Reports Server (NTRS)

Asmar, S. W.; Renzetti, N. A.

1993-01-01

Radio science experiments use radio links between spacecraft and sensor instrumentation that is implemented in the Deep Space Network. The deep space communication complexes along with the telecommunications subsystem on board the spacecraft constitute the major elements of the radio science instrumentation. Investigators examine small changes in the phase and/or amplitude of the radio signal propagating from a spacecraft to study the atmospheric and ionospheric structure of planets and satellites, planetary gravitational fields, shapes, masses, planetary rings, ephemerides of planets, solar corona, magnetic fields, cometary comae, and such aspects of the theory of general relativity as gravitational waves and gravitational redshift.

How We Get Pictures from Space. NASA Facts (Revised Edition).

ERIC Educational Resources Information Center

Haynes, Robert

This booklet discusses image processing from spacecraft in deep space. The camera system on board the spacecraft, the Deep Space Network (DSN), and the image processing system are described. A table listing photographs taken by unmanned spacecraft from 1959-1977 is provided. (YP)

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1993-01-01

This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The papers included in this document cover satellite tracking and ground-based navigation, spacecraft-ground communications, and optical communication systems for the Deep Space Network.

A Heavy-Duty Jack for a Giant Task

NASA Image and Video Library

2010-11-03

A major refurbishment of the giant Mars antenna at NASA Deep Space Network Goldstone Deep Space Communications Complex in California Mojave Desert required workers to jack up millions of pounds of delicate scientific equipment.

Simple gain probability functions for large reflector antennas of JPL/NASA

NASA Technical Reports Server (NTRS)

Jamnejad, V.

2003-01-01

Simple models for the patterns as well as their cumulative gain probability and probability density functions of the Deep Space Network antennas are developed. These are needed for the study and evaluation of interference from unwanted sources such as the emerging terrestrial system, High Density Fixed Service, with the Ka-band receiving antenna systems in Goldstone Station of the Deep Space Network.

Flight Software Implementation of the Beacon Monitor Expreiment On the NASA New Millennium Deep Space 1 (DS-1) Mission

NASA Technical Reports Server (NTRS)

Foster, R.; Schlutsmeyer, A.

1997-01-01

A new technology that can lower the cost of mission operations on future spacecraft will be tested on the NASA New Millennium Deep Space 1 (DS-1) Mission. This technology, the Beacon Monitor Experiment (BMOX), can be used to reduce the Deep Space Network (DSN) tracking time and its associated costs on future missions.

Ka-band (32 GHz) allocations for deep space

NASA Technical Reports Server (NTRS)

Degroot, N. F.

1987-01-01

At the 1979 World Administrative Conference, two new bands were allocated for deep space telecommunications: 31.8 to 32.3 GHz, space-to-Earth, and 34.2 to 34.7 GHz, Earth-to-space. These bands provide opportunity for further development of the Deep Space Network and its support of deep space research. The history of the process by which JPL/NASA developed the rationale, technical background, and statement of requirement for the bands are discussed. Based on this work, United States proposals to the conference included the bands, and subsequent U.S. and NASA participation in the conference led to successful allocations for deep space telecommunications in the 30 GHz region of the spectrum. A detailed description of the allocations is included.

DSMS science operations concept

NASA Technical Reports Server (NTRS)

Connally, M. J.; Kuiper, T. B.

2001-01-01

The Deep Space Mission System (DSMS) Science Operations Concept describes the vision for enabling the use of the DSMS, particularly the Deep Space Network (DSN) for direct science observations in the areas of radio astronomy, planetary radar, radio science and VLBI.

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

NASA Technical Reports Server (NTRS)

Lu, Thomas; Pham, Timothy; Liao, Jason

2011-01-01

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

Estimating the Deep Space Network modification costs to prepare for future space missions by using major cost drivers

NASA Technical Reports Server (NTRS)

Remer, Donald S.; Sherif, Josef; Buchanan, Harry R.

1993-01-01

This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The paper focuses on the costs required to modify and/or enhance the DSN to prepare for future space missions. The model is a function of eight major mission cost drivers and estimates both the total cost and the annual costs of a similar future space mission. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Estimates derived from the model are tested against actual cost data for two independent missions, Viking and Mariner Jupiter/Saturn (MJS).

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.

The JPL optical communications telescope laboratory (OCTL) test bed for the future optical Deep Space Network

NASA Technical Reports Server (NTRS)

Wilson, K. E.; Page, N.; Wu, J.; Srinivasan, M.

2003-01-01

Relative to RF, the lower power-consumption and lower mass of high bandwidth optical telecommunications make this technology extremely attractive for returning data from future NASA/JPL deep space probes.

Using The Global Positioning System For Earth Orbiter and Deep Space Network

NASA Technical Reports Server (NTRS)

Lichten, Stephen M.; Haines, Bruce J.; Young, Lawrence E.; Dunn, Charles; Srinivasan, Jeff; Sweeney, Dennis; Nandi, Sumita; Spitzmesser, Don

1994-01-01

The Global Positioning System (GPS) can play a major role in supporting orbit and trajectory determination for spacecraft in a wide range of applications, including low-Earth, high-earth, and even deep space (interplanetary) tracking.

The role of the deep space network's frequency and timing system in the detection of gravitational waves

NASA Technical Reports Server (NTRS)

Mankins, J. C.

1982-01-01

A review of the Deep Space Network's (DSN) use of precision Doppler-tracking of deep space vehicles is presented. The review emphasizes operational and configurational aspects and considers: the projected configuration of the DSN's frequency and timing system; the environment within the DSN provided by the precision atomic standards within the frequency and timing system--both current and projected; and the general requirements placed on the DSN and the frequency and timing system for both the baseline and the nominal gravitational wave experiments. A comment is made concerning the current probability that such an experiment will be carried out in the foreseeable future.

Range Measurement as Practiced in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.; Kinman, Peter W.

2007-01-01

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

Recycling used lubricating oil at the deep space stations

NASA Technical Reports Server (NTRS)

Koh, J. L.

1981-01-01

A comparison is made of the lubricating oil recycling methods used in the Deep Space Station 43 test and the basic requirements which could favor recycling of oil for continuous reuse. The basic conditions for successful recycling are compared to the conditions that exist in the Deep Space Network (DSN). This comparison shows that to recycle used oil in the DSN would not only be expensive but also nonproductive.

Deep Space Network-Wide Portal Development: Planning Service Pilot Project

NASA Technical Reports Server (NTRS)

Doneva, Silviya

2011-01-01

The Deep Space Network (DSN) is an international network of antennas that supports interplanetary spacecraft missions and radio and radar astronomy observations for the exploration of the solar system and the universe. DSN provides the vital two-way communications link that guides and controls planetary explorers, and brings back the images and new scientific information they collect. In an attempt to streamline operations and improve overall services provided by the Deep Space Network a DSN-wide portal is under development. The project is one step in a larger effort to centralize the data collected from current missions including user input parameters for spacecraft to be tracked. This information will be placed into a principal repository where all operations related to the DSN are stored. Furthermore, providing statistical characterization of data volumes will help identify technically feasible tracking opportunities and more precise mission planning by providing upfront scheduling proposals. Business intelligence tools are to be incorporated in the output to deliver data visualization.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Developments in Earth-based ratio technology as applied to the Deep Space Network are reported. Topics include ratio astronomy and spacecraft tracking networks. Telemetric methods and instrumentation are described. Station control and system technology for space communication is discussed. Special emphasis is placed on network data processing.

Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby. II. Doppler tracking: Formulation of observed and computed values, and noise budget

NASA Astrophysics Data System (ADS)

Bocanegra-Bahamón, T. M.; Molera Calvés, G.; Gurvits, L. I.; Duev, D. A.; Pogrebenko, S. V.; Cimò, G.; Dirkx, D.; Rosenblatt, P.

2018-01-01

Context. Closed-loop Doppler data obtained by deep space tracking networks, such as the NASA Deep Space Network (DSN) and the ESA tracking station network (Estrack), are routinely used for navigation and science applications. By shadow tracking the spacecraft signal, Earth-based radio telescopes involved in the Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data only when the dedicated deep space tracking facilities are operating in closed-loop mode. Aims: We explain the data processing pipeline in detail and discuss the capabilities of the technique and its potential applications in planetary science. Methods: We provide the formulation of the observed and computed values of the Doppler data in PRIDE tracking of spacecraft and demonstrate the quality of the results using an experiment with the ESA Mars Express spacecraft as a test case. Results: We find that the Doppler residuals and the corresponding noise budget of the open-loop Doppler detections obtained with the PRIDE stations compare to the closed-loop Doppler detections obtained with dedicated deep space tracking facilities.

Networks Technology Conference

NASA Technical Reports Server (NTRS)

Tasaki, Keiji K. (Editor)

1993-01-01

The papers included in these proceedings represent the most interesting and current topics being pursued by personnel at GSFC's Networks Division and supporting contractors involved in Space, Ground, and Deep Space Network (DSN) technical work. Although 29 papers are represented in the proceedings, only 12 were presented at the conference because of space and time limitations. The proceedings are organized according to five principal technical areas of interest to the Networks Division: Project Management; Network Operations; Network Control, Scheduling, and Monitoring; Modeling and Simulation; and Telecommunications Engineering.

Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

NASA Astrophysics Data System (ADS)

Zucker, Shay; Giryes, Raja

2018-04-01

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

Challenges for deep space communications in the 1990s

NASA Technical Reports Server (NTRS)

Dumas, Larry N.; Hornstein, Robert M.

1991-01-01

The discussion of NASA's Deep Space Network (DSN) examines the evolving character of aerospace missions and the corresponding changes in the DSN architecture. Deep space missions are reviewed, and it is noted that the two 34-m and the 70-m antenna subnets of the DSN are heavily loaded and more use is expected. High operational workload and the challenge of network cross-support are the design drivers for a flexible DSN architecture configuration. Incorporated in the design are antenna arraying for aperture augmentation, beam-waveguide antennas for frequency agility, and connectivity with non-DSN sites for cross-support. Compatibility between spacecraft and ground-facility designs is important for establishing common international standards of communication and data-system specification.

Environmental projects. Volume 7: Environmental resources document

NASA Technical Reports Server (NTRS)

Kushner, Len; Kroll, Glenn

1988-01-01

The Goldstone Deep Space Communications Complex (GDSCC) in Barstow, California, is part of the NASA Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Goldstone is managed, directed and operated by the Jet Propulsion Laboratory of Pasadena, California. The GDSCC includes five distinct operational sites: Echo, Venus, Mars, Apollo, and Mojave Base. Within each site is a Deep Space Station (DPS), consisting of a large dish antenna and its support facilities. As required by NASA directives concerning the implementation of the National Environmental Policy Act, each NASA field installation is to publish an Environmental Resources Document describing the current environment at the installation, including any adverse effects that NASA operations may have on the local environment.

A Deep Space Network Portable Radio Science Receiver

NASA Technical Reports Server (NTRS)

Jongeling, Andre P.; Sigman, Elliott H.; Chandra, Kumar; Trinh, Joseph T.; Navarro, Robert; Rogstad, Stephen P.; Goodhart, Charles E.; Proctor, Robert C.; Finley, Susan G.; White, Leslie A.

2009-01-01

The Radio Science Receiver (RSR) is an open-loop receiver installed in NASA s Deep Space Network (DSN), which digitally filters and records intermediate-frequency (IF) analog signals. The RSR is an important tool for the Cassini Project, which uses it to measure perturbations of the radio-frequency wave as it travels between the spacecraft and the ground stations, allowing highly detailed study of the composition of the rings, atmosphere, and surface of Saturn and its satellites.

Dishing Up the Data: The Role of Australian Space Tracking and Radioastronomy Facilities in the Exploration of the Solar System

NASA Astrophysics Data System (ADS)

Dougherty, K.; Sarkissian, J.

2002-01-01

The recent Australian film, The Dish, highlighted the role played by the Parkes Radio Telescope in tracking and communicating with the Apollo 11 mission. However the events depicted in this film represent only a single snapshot of the role played by Australian radio astronomy and space tracking facilities in the exploration of the Solar System. In 1960, NASA established its first deep space tracking station outside the United States at Island Lagoon, near Woomera in South Australia. From 1961 until 1972, this station was an integral part of the Deep Space Network, responsible for tracking and communicating with NASA's interplanetary spacecraft. It was joined in 1965 by the Tidbinbilla tracking station, located near Canberra in eastern Australia, a major DSN facility that is still in operation today. Other NASA tracking facilities (for the STADAN and Manned Space Flight networks) were also established in Australia during the 1960s, making this country home to the largest number of NASA tracking facilities outside the United States. At the same time as the Island Lagoon station was being established in South Australia, one of the world's major radio telescope facilities was being established at Parkes, in western New South Wales. This 64-metre diameter dish, designed and operated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), was also well-suited for deep space tracking work: its design was, in fact, adapted by NASA for the 64-metre dishes of the Deep Space Network. From Mariner II in 1962 until today, the Parkes Radio Telescope has been contracted by NASA on many occasions to support interplanetary spacecraft, as well as the Apollo lunar missions. This paper will outline the role played by both the Parkes Radio Telescope and the NASA facilities based in Australia in the exploration of the Solar System between 1960 and 1976, when the Viking missions landed on Mars. It will outline the establishment and operation of the Deep Space Network in Australia and consider the joint US-Australian agreement under which it was managed. It will also discuss the relationship of the NASA stations to the Parkes Radio Telescope and the integration of Parkes into the NASA network to support specific space missions. The particular involvement of Australian facilities in significant space missions will be highlighted and assessed.

Receivers

NASA Technical Reports Server (NTRS)

Donnelly, H.

1983-01-01

Before discussing Deep Space Network receivers, a brief description of the functions of receivers and how they interface with other elements of the Network is presented. Different types of receivers are used in the Network for various purposes. The principal receiver type is used for telemetry and tracking. This receiver provides the capability, with other elements of the Network, to track the space probe utilizing Doppler and range measurements, and to receive telemetry, including both scientific data from the onboard experiments and engineering data pertaining to the health of the probe. Another type of receiver is used for radio science applications. This receiver measures phase perturbations on the carrier signal to obtain information on the composition of solar and planetary atmospheres and interplanetary space. A third type of receiver utilizes very long baseline interferometry (VLBI) techniques for both radio science and spacecraft navigation data. Only the telemetry receiver is described in detail in this document. The integration of the Receiver-Exciter subsystem with other portions of the Deep Space Network is described.

Representational Distance Learning for Deep Neural Networks

PubMed Central

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

Representational Distance Learning for Deep Neural Networks.

PubMed

McClure, Patrick; Kriegeskorte, Nikolaus

2016-01-01

Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

Ramp time synchronization. [for NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Hietzke, W.

1979-01-01

A new method of intercontinental clock synchronization has been developed and proposed for possible use by NASA's Deep Space Network (DSN), using a two-way/three-way radio link with a spacecraft. Analysis of preliminary data indicates that the real-time method has an uncertainty of 0.6 microsec, and it is very likely that further work will decrease the uncertainty. Also, the method is compatible with a variety of nonreal-time analysis techniques, which may reduce the uncertainty down to the tens of nanosecond range.

Deep space network resource scheduling approach and application

NASA Technical Reports Server (NTRS)

Eggemeyer, William C.; Bowling, Alan

1987-01-01

Deep Space Network (DSN) resource scheduling is the process of distributing ground-based facilities to track multiple spacecraft. The Jet Propulsion Laboratory has carried out extensive research to find ways of automating this process in an effort to reduce time and manpower costs. This paper presents a resource-scheduling system entitled PLAN-IT with a description of its design philosophy. The PLAN-IT's current on-line usage and limitations in scheduling the resources of the DSN are discussed, along with potential enhancements for DSN application.

Relocation of the Deep Space Network Maintenance Center

NASA Technical Reports Server (NTRS)

Beutler, K. F.

1981-01-01

The Jet Propulsion Laboratory maintains a Deep Space Network (DSN) maintenance center (DMC), whose task is to engineer and manage the repair and calibration program for the electronic and mechanical equipment used in the tracking stations located at Madrid, Spain, and Canberra, Australia. The DMC also manages the Goldstone complex maintenance facility (GCMF), whose task is to repair and calibrate the Goldstone electronic and mechanical equipment. The rationale for moving the facility to Barstow, California, and the benefits derived from the move are discussed.

Application of the Deep Space Network (DSN) to the testing of general relativity

NASA Technical Reports Server (NTRS)

Anderson, J. D.; Levy, G. S.; Renzetti, N. A.

1986-01-01

The NASA Deep Space Network, a precision telecommunications and radio navigation facility, is described in detail. The first spacecraft relativity test with Mariner 6 and Mariner 7 at solar conjunction is discussed as well as more accurate tests using the Mariner 9 anchored to Mars. Consideration is also given to solar system tests of relativistic celestial mechanics and future prospects. It is noted that the NASA Mars Observer orbital mission is under development and is expected to reach Mars in 1991.

The Deep Space Atomic Clock Mission

NASA Technical Reports Server (NTRS)

Ely, Todd A.; Koch, Timothy; Kuang, Da; Lee, Karen; Murphy, David; Prestage, John; Tjoelker, Robert; Seubert, Jill

2012-01-01

The Deep Space Atomic Clock (DSAC) mission will demonstrate the space flight performance of a small, low-mass, high-stability mercury-ion atomic clock with long term stability and accuracy on par with that of the Deep Space Network. The timing stability introduced by DSAC allows for a 1-Way radiometric tracking paradigm for deep space navigation, with benefits including increased tracking via utilization of the DSN's Multiple Spacecraft Per Aperture (MSPA) capability and full ground station-spacecraft view periods, more accurate radio occultation signals, decreased single-frequency measurement noise, and the possibility for fully autonomous on-board navigation. Specific examples of navigation and radio science benefits to deep space missions are highlighted through simulations of Mars orbiter and Europa flyby missions. Additionally, this paper provides an overview of the mercury-ion trap technology behind DSAC, details of and options for the upcoming 2015/2016 space demonstration, and expected on-orbit clock performance.

Development of a prototype real-time automated filter for operational deep space navigation

NASA Technical Reports Server (NTRS)

Masters, W. C.; Pollmeier, V. M.

1994-01-01

Operational deep space navigation has been in the past, and is currently, performed using systems whose architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational data flow has been developed. This system can allow for more rapid response as well as much reduced staffing to support mission navigation operations.

NASA's next generation all-digital deep space network breadboard receiver

NASA Technical Reports Server (NTRS)

Hinedi, Sami

1993-01-01

This paper describes the breadboard advanced receiver (ARX) that is currently being built for future use in NASA's deep space network (DSN). This receiver has unique requirements in having to operate with very weak signals from deep space probes and provide high quality telemetry and tracking data. The hybrid analog/digital receiver performs multiple functions including carrier, subcarrier and symbol synchronization. Tracking can be achieved for either residual, suppressed or hybrid carriers and for both sinusoidal and square wave subcarriers. System requirements are specified and a functional description of the ARX is presented. The various digital signal processing algorithms used are also discussed and illustrated with block diagrams. Other functions such as time tagged Doppler extraction and monitor/control are also discussed including acquisition algorithms and lock detection schemes.

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel

2012-01-01

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

1980-01-01

Progress in the development and operations of the Deep Space Network is reported. Developments in Earth based radio technology as applied to geodynamics, astrophysics, and radio astronomy's use of the deep space stations for a radio search for extraterrestrial intelligence in the microwave region of the electromagnetic spectrum are reported.

Madrid space station

NASA Technical Reports Server (NTRS)

Fahnestock, R. J.; Renzetti, N. A.

1975-01-01

The Madrid space station, operated under bilateral agreements between the governments of the United States and Spain, is described in both Spanish and English. The space station utilizes two tracking and data acquisition networks: the Deep Space Network (DSN) of the National Aeronautics and Space Administration and the Spaceflight Tracking and Data Network (STDN) operated under the direction of the Goddard Space Flight Center. The station, which is staffed by Spanish employees, comprises four facilities: Robledo 1, Cebreros, and Fresnedillas-Navalagamella, all with 26-meter-diameter antennas, and Robledo 2, with a 64-meter antenna.

Spacecraft Will Communicate "on the Fly"

NASA Technical Reports Server (NTRS)

Laufenberg, Lawrence

2003-01-01

As NASA probes deeper into space, the distance between sensor and scientist increases, as does the time delay. NASA needs to close that gap, while integrating more spacecraft types and missions-from near-Earth orbit to deep space. To speed and integrate communications from space missions to scientists on Earth and back again. NASA needs a comprehensive, high-performance communications network. To this end, the CICT Programs Space Communications (SC) Project is providing technologies for building the Space Internet which will consist of large backbone network, mid-size access networks linked to the backbones, and smaller, ad-hoc network linked to the access network. A key component will be mobile, wireless networks for spacecraft flying in different configurations.

Distributed Interplanetary Delay/Disruption Tolerant Network (DTN) Monitor and Control System

NASA Technical Reports Server (NTRS)

Wang, Shin-Ywan

2012-01-01

The main purpose of Distributed interplanetary Delay Tolerant Network Monitor and Control System as a DTN system network management implementation in JPL is defined to provide methods and tools that can monitor the DTN operation status, detect and resolve DTN operation failures in some automated style while either space network or some heterogeneous network is infused with DTN capability. In this paper, "DTN Monitor and Control system in Deep Space Network (DSN)" exemplifies a case how DTN Monitor and Control system can be adapted into a space network as it is DTN enabled.

The Deep Impact Network Experiment Operations Center Monitor and Control System

NASA Technical Reports Server (NTRS)

Wang, Shin-Ywan (Cindy); Torgerson, J. Leigh; Schoolcraft, Joshua; Brenman, Yan

2009-01-01

The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.

Networks consolidation program: Maintenance and Operations (M&O) staffing estimates

NASA Technical Reports Server (NTRS)

Goodwin, J. P.

1981-01-01

The Mark IV-A consolidate deep space and high elliptical Earth orbiter (HEEO) missions tracking and implements centralized control and monitoring at the deep space communications complexes (DSCC). One of the objectives of the network design is to reduce maintenance and operations (M&O) costs. To determine if the system design meets this objective an M&O staffing model for Goldstone was developed which was used to estimate the staffing levels required to support the Mark IV-A configuration. The study was performed for the Goldstone complex and the program office translated these estimates for the overseas complexes to derive the network estimates.

The Deep Space Network information system in the year 2000

NASA Technical Reports Server (NTRS)

Markley, R. W.; Beswick, C. A.

1992-01-01

The Deep Space Network (DSN), the largest, most sensitive scientific communications and radio navigation network in the world, is considered. Focus is made on the telemetry processing, monitor and control, and ground data transport architectures of the DSN ground information system envisioned for the year 2000. The telemetry architecture will be unified from the front-end area to the end user. It will provide highly automated monitor and control of the DSN, automated configuration of support activities, and a vastly improved human interface. Automated decision support systems will be in place for DSN resource management, performance analysis, fault diagnosis, and contingency management.

Gravitational wave searches using the DSN (Deep Space Network)

NASA Technical Reports Server (NTRS)

Nelson, S. J.; Armstrong, J. W.

1988-01-01

The Deep Space Network Doppler spacecraft link is currently the only method available for broadband gravitational wave searches in the 0.01 to 0.001 Hz frequency range. The DSN's role in the worldwide search for gravitational waves is described by first summarizing from the literature current theoretical estimates of gravitational wave strengths and time scales from various astrophysical sources. Current and future detection schemes for ground based and space based detectors are then discussed. Past, present, and future planned or proposed gravitational wave experiments using DSN Doppler tracking are described. Lastly, some major technical challenges to improve gravitational wave sensitivities using the DSN are discussed.

The Deep Space Network Array

NASA Technical Reports Server (NTRS)

Gatti, Mark S.

2006-01-01

This document is a viewgraph presentation that reviews the costs, and technological processing required to replace the current network of Deep Space Antennas. The concept of using an array for space communications is much less of a concern than the cost of implementing and operating such an array. Within the cost question, the cost uncertainty of the front-end components (repeated n-times) is of most importance. The activities at JPL have focused on both these aspects of the cost. A breadboard array of three antennas at JPL has been the vehicle to perform many investigations into the development of the new DSN. Several pictures of the antennas at JPL are shown.

Three-Dimensional Analysis of Deep Space Network Antenna Coverage

NASA Technical Reports Server (NTRS)

Kegege, Obadiah; Fuentes, Michael; Meyer, Nicholas; Sil, Amy

2012-01-01

There is a need to understand NASA s Deep Space Network (DSN) coverage gaps and any limitations to provide redundant communication coverage for future deep space missions, especially for manned missions to Moon and Mars. The DSN antennas are required to provide continuous communication coverage for deep space flights, interplanetary missions, and deep space scientific observations. The DSN consists of ground antennas located at three sites: Goldstone in USA, Canberra in Australia, and Madrid in Spain. These locations are not separated by the exactly 120 degrees and some DSN antennas are located in the bowl-shaped mountainous terrain to shield against radiofrequency interference resulting in a coverage gap in the southern hemisphere for the current DSN architecture. To analyze the extent of this gap and other coverage limitations, simulations of the DSN architecture were performed. In addition to the physical properties of the DSN assets, the simulation incorporated communication forward link calculations and azimuth/elevation masks that constrain the effects of terrain for each DSN antenna. Analysis of the simulation data was performed to create coverage profiles with the receiver settings at a deep space altitudes ranging from 2 million to 10 million km and a spherical grid resolution of 0.25 degrees with respect to longitude and latitude. With the results of these simulations, two- and three-dimensional representations of the area without communication coverage and area with coverage were developed, showing the size and shape of the communication coverage gap projected in space. Also, the significance of this communication coverage gap is analyzed from the simulation data.

A distributed data base management system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Bryan, A. I.

1975-01-01

Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.

Knowledge engineering for temporal dependency networks as operations procedures. [in space communication

NASA Technical Reports Server (NTRS)

Fayyad, Kristina E.; Hill, Randall W., Jr.; Wyatt, E. J.

1993-01-01

This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

Development of Autonomous Aerobraking - Phase 2

NASA Technical Reports Server (NTRS)

Murri, Daniel G.

2013-01-01

Phase 1 of the Development of Autonomous Aerobraking (AA) Assessment investigated the technical capability of transferring the processes of aerobraking maneuver (ABM) decision-making (currently performed on the ground by an extensive workforce and communicated to the spacecraft via the deep space network) to an efficient flight software algorithm onboard the spacecraft. This document describes Phase 2 of this study, which was a 12-month effort to improve and rigorously test the AA Development Software developed in Phase 1. Aerobraking maneuver; Autonomous Aerobraking; Autonomous Aerobraking Development Software; Deep Space Network; NASA Engineering and Safety Center

The 26-meter antenna s-x conversion project. [Deep Space Network

NASA Technical Reports Server (NTRS)

1982-01-01

Programmatic and management aspects of converting an existing 26-meter S-band subnet to a 34-meter S- and X-band subnet of the Deep Space Network are described. The stations involved were DSS 12 near Barstow, DSS 44 in Australia, and DSS 62 in Spain. The main subsystems affected by the conversion were the antenna mechanical, antenna microwave, and receiver-exciter. Antenna mechanial modifications and electronic additions and changes are described. The design and analysis of critical areas are considered and antenna performance is discussed.

Preliminary design work on a DSN VLBI correlator. [Deep Space Network

NASA Technical Reports Server (NTRS)

Lushbaugh, W. A.; Layland, J. W.

1978-01-01

The Deep Space Network is in the process of fielding high-density digital instrumentation recorders for support of the Pioneer Venus 1978 entry experiment and other related tasks. It has long been obvious that these recorders would also serve well as the recording medium for very long base interferometry (VLBI) experiments with relatively weak radio sources, provided that a suitable correlation processor for these tape recordings could be established. The overall design and current status of a VLBI correlator designed to mate with these tape recorders are described.

DSN radio science system design and testing for Voyager-Neptune encounter

NASA Technical Reports Server (NTRS)

Ham, N. C.; Rebold, T. A.; Weese, J. F.

1989-01-01

The Deep Space Network (DSN) Radio Science System presently implemented within the Deep Space Network was designed to meet stringent requirements imposed by the demands of the Voyager-Neptune encounter and future missions. One of the initial parameters related to frequency stability is discussed. The requirement, specification, design, and methodology for measuring this parameter are described. A description of special instrumentation that was developed for the test measurements and initial test data resulting from the system tests performed at Canberra, Australia and Usuda, Japan are given.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

The deep space network, volume 14

NASA Technical Reports Server (NTRS)

1973-01-01

DSN progress during Jan. and Feb. 1973 is reported. Areas of accomplishment include: flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations.

Implementation of the 64-meter-diameter Antennas at the Deep Space Stations in Australia and Spain

NASA Technical Reports Server (NTRS)

Bartos, K. P.; Bell, H. B.; Phillips, H. P.; Sweetser, B. M.; Rotach, O. A.

1975-01-01

The management and construction aspects of the Overseas 64-m Antenna Project in which two 64-m antennas were constructed at the Tidbinbilla Deep Space Communications Complex in Australia, and at the Madrid Deep Space Communications Complex in Spain are described. With the completion of these antennas the Deep Space Network is equipped with three 64-m antennas spaced around the world to maintain continuous coverage of spacecraft operations. These antennas provide approximately a 7-db gain over the capabilities of the existing 26-m antenna nets. The report outlines the project organization and management, resource utilization, fabrication, quality assurance, and construction methods by which the project was successfully completed. Major problems and their solutions are described as well as recommendations for future projects.

Frequency Domain Beamforming for a Deep Space Network Downlink Array

NASA Technical Reports Server (NTRS)

Navarro, Robert

2012-01-01

This paper describes a frequency domain beamformer to array up to 8 antennas of NASA's Deep Space Network currently in development. The objective of this array is to replace and enhance the capability of the DSN 70m antennas with multiple 34m antennas for telemetry, navigation and radio science use. The array will coherently combine the entire 500 MHz of usable bandwidth available to DSN receivers. A frequency domain beamforming architecture was chosen over a time domain based architecture to handle the large signal bandwidth and efficiently perform delay and phase calibration. The antennas of the DSN are spaced far enough apart that random atmospheric and phase variations between antennas need to be calibrated out on an ongoing basis in real-time. The calibration is done using measurements obtained from a correlator. This DSN Downlink Array expands upon a proof of concept breadboard array built previously to develop the technology and will become an operational asset of the Deep Space Network. Design parameters for frequency channelization, array calibration and delay corrections will be presented as well a method to efficiently calibrate the array for both wide and narrow bandwidth telemetry.

Optical subnet concepts for the deep space network

NASA Technical Reports Server (NTRS)

Shaik, K.; Wonica, D.; Wilhelm, M.

1993-01-01

This article describes potential enhancements to the Deep Space Network, based on a subnet of receiving stations that will utilize optical communications technology in the post-2010 era. Two optical subnet concepts are presented that provide full line-of-sight coverage of the ecliptic, 24 hours a day, with high weather availability. The technical characteristics of the optical station and the user terminal are presented, as well as the effects of cloud cover, transmittance through the atmosphere, and background noise during daytime or nighttime operation on the communications link. In addition, this article identifies candidate geographic sites for the two network concepts and includes a link design for a hypothetical Pluto mission in 2015.

Tracking and data system support for the Viking 1975 mission to Mars. Volume 3: Planetary operations

NASA Technical Reports Server (NTRS)

Mudgway, D. J.

1977-01-01

The support provided by the Deep Space Network to the 1975 Viking Mission from the first landing on Mars July 1976 to the end of the Prime Mission on November 15, 1976 is described and evaluated. Tracking and data acquisition support required the continuous operation of a worldwide network of tracking stations with 64-meter and 26-meter diameter antennas, together with a global communications system for the transfer of commands, telemetry, and radio metric data between the stations and the Network Operations Control Center in Pasadena, California. Performance of the deep-space communications links between Earth and Mars, and innovative new management techniques for operations and data handling are included.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

Deep Space Network and Systems topics addressed include: tracking and ground-base navigation; communications, spacecraft-ground; station control and system technology; capabilities for existing projects; and network upgrading and sustaining.

How Phoenix Talks to Earth

NASA Technical Reports Server (NTRS)

2008-01-01

[figure removed for brevity, see original site] Click on the image for the animation

This animation shows how NASA's Phoenix Mars Lander stays in contact with Earth. As NASA's Mars Odyssey orbiter passes overhead approximately every two hours, Phoenix transmits images and scientific data from the surface to the orbiter, which then relays the data to NASA's Deep Space Network of antennas on Earth. Similarly, NASA's Deep Space Network transmits instructions from Earth to Odyssey, which then relays the information to Phoenix.

The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

The Challenges and Opportunities for International Cooperative Radio Science; Experience with Mars Express and Venus Express Missions

NASA Technical Reports Server (NTRS)

Holmes, Dwight P.; Thompson, Tommy; Simpson, Richard; Tyler, G. Leonard; Dehant, Veronique; Rosenblatt, Pascal; Hausler, Bernd; Patzold, Martin; Goltz, Gene; Kahan, Daniel;

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

Amateur Radio Communications with a Deep Space Probe (Yes, It's Possible)

NASA Astrophysics Data System (ADS)

Cudnik, Brian; Rahman, Mahmudur; Saganti, Seth; Erickson, Gary M.; Saganti, Premkumar

2015-05-01

Prairie View A&M University through the collaboration with NASA-Johnson Space Center has partnered with the Kyushu Institute of Technology (KIT), Japan and developed a payload for the Shinen-2 spacecraft that was launched from Japan on December 3, 2014 as part of the Hayabusa2 mission. The main purpose of the Shinen-2 spacecraft is deep space communication experiment to test the feasibility of deep-space radio communications from the spacecraft to Earth without the need of the Deep Space Network (DSN) of NASA. This presents an opportunity to the wider community of amateur astronomers, ham radio operators, and other research personnel in that they will have the opportunity to work with deep space communication such as Shinen-2 spacecraft. It should be possible to detect a signal as an increased strength from Shinen-2 spacecraft at a rest frequency of 437.385 MHz, using commercially available equipment procured at low-cost, when the spacecraft approaches to within 3,000,000 km of the Earth during December 2015.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

Space Network Time Distribution and Synchronization Protocol Development for Mars Proximity Link

NASA Technical Reports Server (NTRS)

Woo, Simon S.; Gao, Jay L.; Mills, David

2010-01-01

Time distribution and synchronization in deep space network are challenging due to long propagation delays, spacecraft movements, and relativistic effects. Further, the Network Time Protocol (NTP) designed for terrestrial networks may not work properly in space. In this work, we consider the time distribution protocol based on time message exchanges similar to Network Time Protocol (NTP). We present the Proximity-1 Space Link Interleaved Time Synchronization (PITS) algorithm that can work with the CCSDS Proximity-1 Space Data Link Protocol. The PITS algorithm provides faster time synchronization via two-way time transfer over proximity links, improves scalability as the number of spacecraft increase, lowers storage space requirement for collecting time samples, and is robust against packet loss and duplication which underlying protocol mechanisms provide.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

The Office of Space Operation (OSO) tasks addressed include: Deep Space Network (DSN) advanced systems and systems implementation. The Office of Space Science and Applications (OSSA) tasks discussed include SETI data controllers and simulated performance for narrowband signal detection.

A Ten-Meter Ground-Station Telescope for Deep-Space Optical Communications: A Preliminary Design

NASA Technical Reports Server (NTRS)

Britcliffe, M.; Hoppe, D.; Roberts, W.; Page, N.

2001-01-01

This article describes a telescope design for a 10-m optical ground station for deep-space communications. The design for a direct-detection optical communications telescope differs dramatically from a telescope for imaging applications. In general, the requirements for optical manufacturing and tracking performance are much less stringent for direct detection of optical signals. The technical challenge is providing a design that will operate in the daytime/nighttime conditions required for a Deep Space Network tracking application. The design presented addresses these requirements. The design will provide higher performance at lower cost than existing designs.

Near Earth Architectural Options for a Future Deep Space Optical Communications Network

NASA Technical Reports Server (NTRS)

Edwards, B. L.; Liebrecht, P. E.; Fitzgerald, R. J.

2004-01-01

In the near future the National Aeronautics and Space Administration anticipates a significant increase in demand for long-haul communications services from deep space to Earth. Distances will range from 0.1 to 40 AU, with data rate requirements in the 1's to 1000's of Mbits/second. The near term demand is driven by NASA's Space Science Enterprise which wishes to deploy more capable instruments onboard spacecraft and increase the number of deep space missions. The long term demand is driven by missions with extreme communications challenges such as very high data rates from the outer planets, supporting sub-surface exploration, or supporting NASA's Human Exploration and Development of Space Enterprise beyond Earth orbit. Laser communications is a revolutionary communications technology that will dramatically increase NASA's ability to transmit information across the solar system. Lasercom sends information using beams of light and optical elements, such as telescopes and optical amplifiers, rather than RF signals, amplifiers, and antennas. This paper provides an overview of different network options at Earth to meet NASA's deep space lasercom requirements. It is based mainly on work done for the Mars Laser Communications Demonstration Project, a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory, California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL). It reports preliminary conclusions from the Mars Lasercom Study conducted at MIT/LL and on additional work done for the Tracking and Data Relay Satellite System Continuation Study at GSFC. A lasercom flight terminal will be flown on the Mars Telesat Orbiter (MTO) to be launched by NASA in 2009, and will be the first high rate deep space demonstration of this revolutionary technology.

Analysis of Near-field of Circular Aperture Antennas with Application to Study of High Intensity Radio Frequency (HIRF) Hazards to Aviation from JPL/NASA Deep Space Network Antennas

NASA Technical Reports Server (NTRS)

Jamnejad, Vahraz; Statman, Joseph

2013-01-01

This work includes a simplified analysis of the radiated near to mid-field from JPL/NASA Deep Space Network (DSN) reflector antennas and uses an averaging technique over the main beam region and beyond for complying with FAA regulations in specific aviation environments. The work identifies areas that require special attention, including the implications of the very narrow beam of the DSN transmitters. The paper derives the maximum averaged power densities allowed and identifies zones where mitigation measures are required.

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Deep Space Network (DSN) Data Systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit DSN software life cycle statistics. The estimation model output scales a standard DSN Work Breakdown Structure skeleton, which is then input into a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

Tracking and data systems support for the Helios project. Volume 1: Project development through end of mission, phase 2

NASA Technical Reports Server (NTRS)

Goodwin, P. S.; Traxler, M. R.; Meeks, W. G.; Flanagan, F. M.

1976-01-01

The overall evolution of the Helios Project is summarized from its conception through to the completion of the Helios-1 mission phase 2. Beginning with the project objectives and concluding with the Helios-1 spacecraft entering its first superior conjunction (end of mission phase 2), descriptions of the project, the mission and its phases, international management and interfaces, and Deep Space Network-spacecraft engineering development in telemetry, tracking, and command systems to ensure compatibility between the U.S. Deep Space Network and the German-built spacecraft are included.

The Future of NASA's Deep Space Network and Applications to Planetary Probe Missions

NASA Technical Reports Server (NTRS)

Deutsch, Leslie J.; Preston, Robert A.; Vrotsos, Peter

2010-01-01

NASA's Deep Space Network (DSN) has been an invaluable tool in the world's exploration of space. It has served the space-faring community for more than 45 years. The DSN has provided a primary communication pathway for planetary probes, either through direct- to-Earth links or through intermediate radio relays. In addition, its radiometric systems are critical to probe navigation and delivery to target. Finally, the radio link can also be used for direct scientific measurement of the target body ('radio science'). This paper will examine the special challenges in supporting planetary probe missions, the future evolution of the DSN and related spacecraft technology, the advantages and disadvantages of radio relay spacecraft, and the use of the DSN radio links for navigation and scientific measurements.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Delay/Disruption Tolerant Networking for the International Space Station (ISS)

NASA Technical Reports Server (NTRS)

Schlesinger, Adam; Willman, Brett M.; Pitts, Lee; Davidson, Suzanne R.; Pohlchuck, William A.

2017-01-01

Disruption Tolerant Networking (DTN) is an emerging data networking technology designed to abstract the hardware communication layer from the spacecraft/payload computing resources. DTN is specifically designed to operate in environments where link delays and disruptions are common (e.g., space-based networks). The National Aeronautics and Space Administration (NASA) has demonstrated DTN on several missions, such as the Deep Impact Networking (DINET) experiment, the Earth Observing Mission 1 (EO-1) and the Lunar Laser Communication Demonstration (LLCD). To further the maturation of DTN, NASA is implementing DTN protocols on the International Space Station (ISS). This paper explains the architecture of the ISS DTN network, the operational support for the system, the results from integrated ground testing, and the future work for DTN expansion.

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor A.

2012-01-01

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

Alamos: An International Collaboration to Provide a Space Based Environmental Monitoring Solution for the Deep Space Network

NASA Astrophysics Data System (ADS)

Kennedy, S. O.; Dunn, A.; Lecomte, J.; Buchheim, K.; Johansson, E.; Berger, T.

2018-02-01

This abstract proposes the advantages of an externally mounted instrument in support of the human physiology, space biology, and human health and performance key science area. Alamos provides Space-Based Environmental Monitoring capabilities.

KSC-04PD-2671B

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. A worker at Astrotech Space Operations in Titusville, Fla., begins fueling the Deep Impact spacecraft. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2669

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., suit up before fueling the Deep Impact spacecraft. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2668

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., suit up before fueling the Deep Impact spacecraft. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2671A

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. A worker at Astrotech Space Operations in Titusville, Fla., begins fueling the Deep Impact spacecraft. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

Automated monitor and control for deep space network subsystems

NASA Technical Reports Server (NTRS)

Smyth, P.

1989-01-01

The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.

The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

NASA Technical Reports Server (NTRS)

Webb, W. A.

1978-01-01

The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

Architectural Options for a Future Deep Space Optical Communications Network

NASA Technical Reports Server (NTRS)

Edwards, B. L.; Benjamin, T.; Scozzafava, J.; Khatri, F.; Sharma, J.; Parvin, B.; Liebrecht, P. E.; Fitzgerald, R. J.

2004-01-01

This paper provides an overview of different options at Earth to provide Deep Space optical communication services. It is based mainly on work done for the Mars Laser Communications Demonstration (MLCD) Project, a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory, California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL). It also reports preliminary conclusions from the Tracking and Data Relay Satellite System Continuation Study at GSFC. A lasercom flight terminal will be flown on the Mars Telecommunications Orbiter (MTO) to be launched by NASA in 2009, and will be the first high rate deep space demonstration of this revolutionary technology.

SBIR Technology Applications to Space Communications and Navigation (SCaN)

NASA Technical Reports Server (NTRS)

Liebrecht, Phil; Eblen, Pat; Rush, John; Tzinis, Irene

2010-01-01

This slide presentation reviews the mission of the Space Communications and Navigation (SCaN) Office with particular emphasis on opportunities for technology development with SBIR companies. The SCaN office manages NASA's space communications and navigation networks: the Near Earth Network (NEN), the Space Network (SN), and the Deep Space Network (DSN). The SCaN networks nodes are shown on a world wide map and the networks are described. Two types of technologies are described: Pull technology, and Push technologies. A listing of technology themes is presented, with a discussion on Software defined Radios, Optical Communications Technology, and Lunar Lasercom Space Terminal (LLST). Other technologies that are being investigated are some Game Changing Technologies (GCT) i.e., technologies that offer the potential for improving comm. or nav. performance to the point that radical new mission objectives are possible, such as Superconducting Quantum Interference Filters, Silicon Nanowire Optical Detectors, and Auto-Configuring Cognitive Communications

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1984-01-01

Activities in space communication, radio navigation, radio science, and ground-based astronomy are reported. Advanced systems for the Deep Space Network and its Ground-Communications Facility are discussed including station control and system technology. Network sustaining as well as data and information systems are covered. Studies of geodynamics, investigations of the microwave spectrum, and the search for extraterrestrial intelligence are reported.

Long-range planning cost model for support of future space missions by the deep space network

NASA Technical Reports Server (NTRS)

Sherif, J. S.; Remer, D. S.; Buchanan, H. R.

1990-01-01

A simple model is suggested to do long-range planning cost estimates for Deep Space Network (DSP) support of future space missions. The model estimates total DSN preparation costs and the annual distribution of these costs for long-range budgetary planning. The cost model is based on actual DSN preparation costs from four space missions: Galileo, Voyager (Uranus), Voyager (Neptune), and Magellan. The model was tested against the four projects and gave cost estimates that range from 18 percent above the actual total preparation costs of the projects to 25 percent below. The model was also compared to two other independent projects: Viking and Mariner Jupiter/Saturn (MJS later became Voyager). The model gave cost estimates that range from 2 percent (for Viking) to 10 percent (for MJS) below the actual total preparation costs of these missions.

The deep space network, volume 16

NASA Technical Reports Server (NTRS)

1973-01-01

The objectives, functions, and organization of the DSN are summarized, and the instrumentation facility, ground communication facility, and the network control system are described. The requirements for supporting planetary flight projects are discussed along with the research and technology for tracking, navigation, network control, and data processing.

Evolving Deep Networks Using HPC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Steven R.; Rose, Derek C.; Johnston, Travis

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus oftenmore » a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.« less

Monitor and Control of the Deep-Space network via Secure Web

NASA Technical Reports Server (NTRS)

Lamarra, N.

1997-01-01

(view graph) NASA lead center for robotic space exploration. Operating division of Caltech/Jet Propulsion Laboratory. Current missions, Voyagers, Galileo, Pathfinder, Global Surveyor. Upcoming missions, Cassini, Mars and New Millennium.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1984-01-01

Developments in space communications, radio navigation, radio science, ground-base radio astronomy, reports on the Deep Space Network (DSN) and its Ground Communications Facility (GCF), and applications of radio interferometry at microwave frequencies are discussed.

Effective Utilization of Resources and Infrastructure for a Spaceport Network Architecture

NASA Technical Reports Server (NTRS)

Gill, Tracy; Larson, Wiley; Mueller, Robert; Roberson, Luke

2012-01-01

Providing routine, affordable access to a variety of orbital and deep space destinations requires an intricate network of ground, planetary surface, and space-based spaceports like those on Earth (land and sea), in various Earth orbits, and on other extraterrestrial surfaces. Advancements in technology and international collaboration are critical to establish a spaceport network that satisfies the requirements for private and government research, exploration, and commercial objectives. Technologies, interfaces, assembly techniques, and protocols must be adapted to enable mission critical capabilities and interoperability throughout the spaceport network. The conceptual space mission architecture must address the full range of required spaceport services, from managing propellants for a variety of spacecraft to governance structure. In order to accomplish affordability and sustainability goals, the network architecture must consider deriving propellants from in situ planetary resources to the maximum extent possible. Water on the Moon and Mars, Mars' atmospheric CO2, and O2 extracted from lunar regolith are examples of in situ resources that could be used to generate propellants for various spacecraft, orbital stages and trajectories, and the commodities to support habitation and human operations at these destinations. The ability to use in-space fuel depots containing in situ derived propellants would drastically reduce the mass required to launch long-duration or deep space missions from Earth's gravity well. Advances in transformative technologies and common capabilities, interfaces, umbilicals, commodities, protocols, and agreements will facilitate a cost-effective, safe, reliable infrastructure for a versatile network of Earth- and extraterrestrial spaceports. Defining a common infrastructure on Earth, planetary surfaces, and in space, as well as deriving propellants from in situ planetary resources to construct in-space propellant depots to serve the spaceport network, will reduce exploration costs due to standardization of infrastructure commonality and reduction in number and types of interfaces and commodities.

Comparative Studies of Prediction Strategies for Solar X-ray Time Series

NASA Astrophysics Data System (ADS)

Muranushi, T.; Hattori, T.; Jin, Q.; Hishinuma, T.; Tominaga, M.; Nakagawa, K.; Fujiwara, Y.; Nakamura, T.; Sakaue, T.; Takahashi, T.; Seki, D.; Namekata, K.; Tei, A.; Ban, M.; Kawamura, A. D.; Hada-Muranushi, Y.; Asai, A.; Nemoto, S.; Shibata, K.

2016-12-01

Crucial virtues for operational space weather forecast are real-timeforecast ability, forecast precision and customizability to userneeds. The recent development of deep-learning makes it veryattractive to space weather, because (1) it learns gradually incomingdata, (2) it exhibits superior accuracy over conventional algorithmsin many fields, and (3) it makes the customization of the forecasteasier because it accepts raw images.However, the best deep-learning applications are only attainable bycareful human designers that understands both the mechanism of deeplearning and the application field. Therefore, we need to foster youngresearchers to enter the field of machine-learning aided forecast. So,we have held a seminar every Monday with undergraduate and graduatestudents from May to August 2016.We will review the current status of space weather science and theautomated real-time space weather forecast engine UFCORIN. Then, weintroduce the deep-learning space weather forecast environments wehave set up using Python and Chainer on students' laptop computers.We have started from simple image classification neural network, thenimplemented space-weather neural network that predicts future X-rayflux of the Sun based on the past X-ray lightcurve and magnetic fieldline-of-sight images.In order to perform each forecast faster, we have focused on simplelightcurve-to-lightcurve forecast, and performed comparative surveysby changing following parameters: The size and topology of the neural network Batchsize Neural network hyperparameters such as learning rates to optimize the preduction accuracy, and time for prediction.We have found how to design compact, fast but accurate neural networkto perform forecast. Our forecasters can perform predictionexperiment for four-year timespan in a few minutes, and achieveslog-scale errors of the order of 1. Our studies is ongoing, and inour talk we will review our progress till December.

Space Programs Summary 37-33. Volume 3. The Deep Space Network for the period 1 March-30 April 1965

DTIC Science & Technology

1965-05-31

designed to communicate To improve the data rate and distance capability, a 210-ft with, and permit control of, spacecraft designed for deep antenna is...51 experienced doppler problems. It was neces- tracking momentarily to make this change. It was de - sary to determine the bias oscillator frequencies...is being designed and constructed for the Mars site of the Gold- stone space communications station. The operating fre- quency of the AAS will be at

Single-mode fiber systems for deep space communication network

NASA Technical Reports Server (NTRS)

Lutes, G.

1982-01-01

The present investigation is concerned with the development of single-mode optical fiber distribution systems. It is pointed out that single-mode fibers represent potentially a superior medium for the distribution of frequency and timing reference signals and wideband (400 MHz) IF signals. In this connection, single-mode fibers have the potential to improve the capability and precision of NASA's Deep Space Network (DSN). Attention is given to problems related to precise time synchronization throughout the DSN, questions regarding the selection of a transmission medium, and the function of the distribution systems, taking into account specific improvements possible by an employment of single-mode fibers.

Cryogenic, low-noise high electron mobility transistor amplifiers for the Deep Space Network

NASA Technical Reports Server (NTRS)

Bautista, J. J.

1993-01-01

The rapid advances recently achieved by cryogenically cooled high electron mobility transistor (HEMT) low-noise amplifiers (LNA's) in the 1- to 10-GHz range are making them extremely competitive with maser amplifiers. In order to address future spacecraft navigation, telemetry, radar, and radio science needs, the Deep Space Network is investing both maser and HEMT amplifiers for its Ka-band (32-GHz) downlink capability. This article describes the current state cryogenic HEMT LNA development at Ka-band for the DSN. Noise performance results at S-band (2.3 GHz) and X-band (8.5 GHz) for HEMT's and masers are included for completeness.

The JPL Resource Allocation Planning and Scheduling Office (RAPSO) process

NASA Technical Reports Server (NTRS)

Morris, D. G.; Burke, E. S.

2002-01-01

The Jet Propulsion Laboratory's Resource Allocation Planning and Scheduling Office is chartered to divide the limited amount of tracking hours of the Deep Space Network amongst the various missions in as equitable allotment as can be achieved. To best deal with this division of assets and time, an interactive process has evolved that promotes discussion with agreement by consensus between all of the customers that use the Deep Space Network (DSN). Aided by a suite of tools, the task of division of asset time is then performed in three stages of granularity. Using this approach, DSN loads are either forecasted or scheduled throughout a moving 10-year window.

Tracking and data system support for the Viking 1975 mission to Mars. Volume 2: Launch through landing of Viking 1

NASA Technical Reports Server (NTRS)

Mudgway, D. J.; Traxler, M. R.

1977-01-01

Problems inherent in the deployment and management of a worldwide tracking and data acquisition network to support the two Viking Orbiters and two Viking Landers simultaneously over 320 million kilometers (200 million miles) of deep space are discussed. Activities described include tracking coverage of the launch phase, the deep space operations during the long cruise phase that occupied approximately 11 months, and the implementation of the a vast worldwide network of tracking sttions and global communications systems. The performance of the personnel, hardware, and software involved in this vast undertaking are evaluated.

DCMDN: Deep Convolutional Mixture Density Network

NASA Astrophysics Data System (ADS)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

NASA Integrated Space Communications Network

NASA Technical Reports Server (NTRS)

Tai, Wallace; Wright, Nate; Prior, Mike; Bhasin, Kul

2012-01-01

The NASA Integrated Network for Space Communications and Navigation (SCaN) has been in the definition phase since 2010. It is intended to integrate NASA s three existing network elements, i.e., the Space Network, Near Earth Network, and Deep Space Network, into a single network. In addition to the technical merits, the primary purpose of the Integrated Network is to achieve a level of operating cost efficiency significantly higher than it is today. Salient features of the Integrated Network include (a) a central system element that performs service management functions and user mission interfaces for service requests; (b) a set of common service execution equipment deployed at the all stations that provides return, forward, and radiometric data processing and delivery capabilities; (c) the network monitor and control operations for the entire integrated network are conducted remotely and centrally at a prime-shift site and rotating among three sites globally (a follow-the-sun approach); (d) the common network monitor and control software deployed at all three network elements that supports the follow-the-sun operations.

Maintenance of time and frequency in the Jet Propulsion Laboratory's Deep Space Network using the Global Positioning System

NASA Technical Reports Server (NTRS)

Clements, P. A.; Borutzki, S. E.; Kirk, A.

1984-01-01

The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, must maintain time and frequency within specified limits in order to accurately track the spacecraft engaged in deep space exploration. Various methods are used to coordinate the clocks among the three tracking complexes. These methods include Loran-C, TV Line 10, Very Long Baseline Interferometry (VLBI), and the Global Positioning System (GPS). Calculations are made to obtain frequency offsets and Allan variances. These data are analyzed and used to monitor the performance of the hydrogen masers that provide the reference frequencies for the DSN Frequency and Timing System (DFT). Areas of discussion are: (1) a brief history of the GPS timing receivers in the DSN, (2) a description of the data and information flow, (3) data on the performance of the DSN master clocks and GPS measurement system, and (4) a description of hydrogen maser frequency steering using these data.

Delta-DOR: The One-Nanoradian Navigation Measurement System of the Deep Space Network --- History, Architecture, and Componentry

NASA Astrophysics Data System (ADS)

Curkendall, D. W.; Border, J. S.

2013-05-01

Doppler and range data alone supported navigation for the earliest missions into deep space. Though extremely precise in line-of-sight coordinates, the navigation system built on these data had a weakness for determining the spacecraft declination component. To address this, the Deep Space Network (DSN) developed the capability for very long baseline interferometry measurements beginning in the late 1970s. Both the implementation of the interferometric system and the importance of such measurements to flight projects have evolved significantly over the past three decades. Innovations introduced through research and development programs have led to continuous improvements in performance. Today's system provides data approaching one-nanoradian accuracy with reliability of 98 percent. This article provides an overview of the development and use of interferometric tracking techniques in the DSN starting with the Viking era and continuing with a description of the current system and its planned use to support interplanetary cruise navigation of the Mars Science Laboratory spacecraft.

Report on the survey for electrostatic discharges on Mars using NASA's Deep Space Network (DSN)

NASA Astrophysics Data System (ADS)

Arabshahi, S.; Majid, W.; Geldzahler, B.; Kocz, J.; Schulter, T.; White, L.

2017-12-01

Mars atmosphere has strong dust activity. It is suggested that the larger regional storms are capable of producing electric fields large enough to initiate electrostatic discharges. The storms have charging process similar to terrestrial dust devils and have hot cores and complicated vortex winds similar to terrestrial thunderstorms. However, due to uncertainties in our understanding of the electrical environment of the storms and absence of related in-situ measurements, the existence (or non-existence) of such electrostatic discharges on the planet is yet to be confirmed. Knowing about the electrical activity on Mars is essential for future human explorations of the planet. We have recently launched a long-term monitoring campaign at NASA's Madrid Deep Space Communication Complex (MDSCC) to search for powerful discharges on Mars. The search occurs during routine tracking of Mars orbiting spacecraft by Deep Space Network (DSN) radio telescope. In this presentation, we will report on the result of processing and analysis of the data from the first six months of our campaign.

Deep Space Network and Lunar Network Communication Coverage of the Moon

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2006-01-01

In this article, we describe the communication coverage analysis for the lunar network and the Earth ground stations. The first part of this article focuses on the direct communication coverage of the Moon from the Earth's ground stations. In particular, we assess the coverage performance of the Moon based on the existing Deep Space Network (DSN) antennas and the complimentary coverage of other potential stations at Hartebeesthoek, South Africa and at Santiago, Chile. We also address the coverage sensitivity based on different DSN antenna scenarios and their capability to provide single and redundant coverage of the Moon. The second part of this article focuses on the framework of the constrained optimization scheme to seek a stable constellation six relay satellites in two planes that not only can provide continuous communication coverage to any users on the Moon surface, but can also deliver data throughput in a highly efficient manner.

Internet Technologies for Space-based Communications: State of the Art and Challenges

NASA Technical Reports Server (NTRS)

Bhasin, K.; DePaula, R.; Edwards, C.

2000-01-01

The Internet is rapidly changing the ways we communicate information around the globe today. The desire to provide Internet-based services to anyone, anywhere, anytime has brought satellite communications to the forefront to become an integral part of the Internet. In spite of the distances involved, satellite links are proving to be capable of providing Internet services based on Internet protocol (TCP/IP) stack. This development has led to the question particularly at NASA; can satellites and other space platforms become an Internet-node in space? This will allow the direct transfer of information directly from space to the users on Earth and even be able to control the spacecraft and its instruments. NASA even wants to extend the near earth space Internet to deep space applications where scientists and the public here on Earth may view space exploration in real time via the Internet. NASA's future solar system exploration will involve intensive in situ investigations of planets, moons, asteroids, and comets. While past missions typically involved a single fly-by or orbiting science spacecraft, future missions will begin to use fleets of small, highly intelligent robotic vehicles to carry out collaborative investigations. The resulting multi-spacecraft topologies will effectively create a wide area network spanning the solar system. However, this will require significant development in Internet technologies for space use. This paper provides the status'of the Internet for near earth applications and the potential extension of the Internet for use in deep space planetary exploration. The paper will discuss the overall challenges of implementing the space Internet and how the space Internet will integrate into the complex terrestrial systems those forms the Internet of today in a hybrid set of networks. Internet. We envision extending to the deep space environment such Internet concepts as a well-designed layered architecture. This effort will require an ability to develop and infuse new physical layer technology to increase network bandwidth at very low-bit error rates. In addition, we identify network technologies such as routers and switches needed to maintain standard application layer interfaces, while providing low-cost, efficient, modular networking solutions. We will describe the overall architectural approach to extending the concept of the Internet to space and highlight the important technological challenges and initiatives that will make it a reality.

The Parkes front-end controller and noise-adding radiometer

NASA Technical Reports Server (NTRS)

Brunzie, T. J.

1990-01-01

A new front-end controller (FEC) was installed on the 64-m antenna in Parkes, Australia, to support the 1989 Voyager 2 Neptune encounter. The FEC was added to automate operation of the front-end microwave hardware as part of the Deep Space Network's Parkes-Canberra Telemetry Array. Much of the front-end hardware was refurbished and reimplemented from a front-end system installed in 1985 by the European Space Agency for the Uranus encounter; however, the FEC and its associated noise-adding radiometer (NAR) were new Jet Propulsion Laboratory (JPL) designs. Project requirements and other factors led to the development of capabilities not found in standard Deep Space Network (DSN) controllers and radiometers. The Parkes FEC/NAR performed satisfactorily throughout the Neptune encounter and was removed in October 1989.

Pubface: Celebrity face identification based on deep learning

NASA Astrophysics Data System (ADS)

Ouanan, H.; Ouanan, M.; Aksasse, B.

2018-05-01

In this paper, we describe a new real time application called PubFace, which allows to recognize celebrities in public spaces by employs a new pose invariant face recognition deep neural network algorithm with an extremely low error rate. To build this application, we make the following contributions: firstly, we build a novel dataset with over five million faces labelled. Secondly, we fine tuning the deep convolutional neural network (CNN) VGG-16 architecture on our new dataset that we have built. Finally, we deploy this model on the Raspberry Pi 3 model B using the OpenCv dnn module (OpenCV 3.3).

A growth path for deep space communications

NASA Technical Reports Server (NTRS)

Layland, J. W.; Smith, J. G.

1987-01-01

Increased Deep Space Network (DPN) receiving capability far beyond that now available for Voyager is achievable through a mix of increased antenna aperture and increased frequency of operation. In this note a sequence of options are considered: adding midsized antennas for arraying with the existing network at X-band; converting to Ka-band and adding array elements; augmenting the DSN with an orbiting Ka-band station; and augmenting the DSN with an optical receiving capability, either on the ground or in space. Costs of these options are compared as means of achieving significantly increased receiving capability. The envelope of lowest costs projects a possible path for moving from X-band to Ka-band and thence to optical frequencies, and potentially for moving from ground-based to space-based apertures. The move to Ka-band is clearly of value now, with development of optical communications technology a good investment for the future.

KSC-04PD-2670

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., get ready to begin fueling the Deep Impact spacecraft, seen wrapped in a protective cover in the background. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2673

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., begin fueling operations of the Deep Impact spacecraft, seen wrapped in a protective cover in the background. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2674

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., begin fueling operations of the Deep Impact spacecraft, seen wrapped in a protective cover in the background. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0128

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II rocket carrying the Deep Impact spacecraft stands out against an early dawn sky. Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0124

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II rocket carrying the Deep Impact spacecraft is bathed in light waiting for tower rollback before launch. Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2671

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Workers at Astrotech Space Operations in Titusville, Fla., get ready to begin fueling the Deep Impact spacecraft, seen wrapped in a protective cover in the background. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

The optical antenna system design research on earth integrative network laser link in the future

NASA Astrophysics Data System (ADS)

Liu, Xianzhu; Fu, Qiang; He, Jingyi

2014-11-01

Earth integrated information network can be real-time acquisition, transmission and processing the spatial information with the carrier based on space platforms, such as geostationary satellites or in low-orbit satellites, stratospheric balloons or unmanned and manned aircraft, etc. It is an essential infrastructure for China to constructed earth integrated information network. Earth integrated information network can not only support the highly dynamic and the real-time transmission of broadband down to earth observation, but the reliable transmission of the ultra remote and the large delay up to the deep space exploration, as well as provide services for the significant application of the ocean voyage, emergency rescue, navigation and positioning, air transportation, aerospace measurement or control and other fields.Thus the earth integrated information network can expand the human science, culture and productive activities to the space, ocean and even deep space, so it is the global research focus. The network of the laser communication link is an important component and the mean of communication in the earth integrated information network. Optimize the structure and design the system of the optical antenna is considered one of the difficulty key technologies for the space laser communication link network. Therefore, this paper presents an optical antenna system that it can be used in space laser communication link network.The antenna system was consisted by the plurality mirrors stitched with the rotational paraboloid as a substrate. The optical system structure of the multi-mirror stitched was simulated and emulated by the light tools software. Cassegrain form to be used in a relay optical system. The structural parameters of the relay optical system was optimized and designed by the optical design software of zemax. The results of the optimal design and simulation or emulation indicated that the antenna system had a good optical performance and a certain reference value in engineering. It can provide effective technical support to realize interconnection of earth integrated laser link information network in the future.

Architecture and System Engineering Development Study of Space-Based Satellite Networks for NASA Missions

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2003-01-01

Traditional NASA missions, both near Earth and deep space, have been stovepipe in nature and point-to-point in architecture. Recently, NASA and others have conceptualized missions that required space-based networking. The notion of networks in space is a drastic shift in thinking and requires entirely new architectures, radio systems (antennas, modems, and media access), and possibly even new protocols. A full system engineering approach for some key mission architectures will occur that considers issues such as the science being performed, stationkeeping, antenna size, contact time, data rates, radio-link power requirements, media access techniques, and appropriate networking and transport protocols. This report highlights preliminary architecture concepts and key technologies that will be investigated.

Exciter For X-Band Transmitter And Receiver

NASA Technical Reports Server (NTRS)

Johns, Carl E.

1989-01-01

Report describes developmental X-band exciter for X-band uplink subsystem of Deep Space Network. X-band transmitter-exciting signal expected to have fractional frequency stability of 5.2 X 10 to negative 15th power during 1,000-second integration period. Generates coherent test signals for S- and X-band Block III translator of Deep Space Network, Doppler-reference signal for associated Doppler-extractor system, first-local-oscillator signal for associated receiver, and reference signal for associated ranging subsystem. Tests of prototype exciter show controlling and monitoring and internal phase-correcting loops perform according to applicable design criteria. Measurements of stability of frequency and of single-sideband noise spectral density of transmitter-exciting signal made subsequently.

Mission Control, 1964

NASA Image and Video Library

2016-10-27

This archival image was released as part of a gallery comparing JPL's past and present, commemorating the 80th anniversary of NASA's Jet Propulsion Laboratory on Oct. 31, 2016. When spacecraft in deep space "phone home," they do it through NASA's Deep Space Network. Engineers in this room at NASA's Jet Propulsion Laboratory -- known as Mission Control -- monitor the flow of data. This image was taken in May 1964, when the building this nerve center is in, the Space Flight Operations Facility (Building 230), was dedicated at JPL. http://photojournal.jpl.nasa.gov/catalog/PIA21120

Stochastic availability analysis of operational data systems in the Deep Space Network

NASA Technical Reports Server (NTRS)

Issa, T. N.

1991-01-01

Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

KSC-05PD-0133

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. From the nearby Press Site at Cape Canaveral Air Force Station, Fla., photographers capture the exciting launch of the Deep Impact spacecraft at 1:47 p.m. EST. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0134

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Erupting from the flames and smoke beneath it, NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0131

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Erupting from the flames and smoke beneath it, NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0135

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Erupting from the flames and smoke beneath it, NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0136

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Engulfed by flames and smoke, NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0130

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. With a burst of flames, NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

Telecommunications and data acquisition

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Environmental projects. Volume 16: Waste minimization assessment

NASA Technical Reports Server (NTRS)

1994-01-01

The Goldstone Deep Space Communications Complex (GDSCC), located in the MoJave Desert, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network (DSN), the world's largest and most sensitive scientific telecommunications and radio navigation network. The Goldstone Complex is operated for NASA by the Jet Propulsion Laboratory. At present, activities at the GDSCC support the operation of nine parabolic dish antennas situated at five separate locations known as 'sites.' Each of the five sites at the GDSCC has one or more antennas, called 'Deep Space Stations' (DSS's). In the course of operation of these DSS's, various hazardous and non-hazardous wastes are generated. In 1992, JPL retained Kleinfelder, Inc., San Diego, California, to quantify the various streams of hazardous and non-hazardous wastes generated at the GDSCC. In June 1992, Kleinfelder, Inc., submitted a report to JPL entitled 'Waste Minimization Assessment.' This present volume is a JPL-expanded version of the Kleinfelder, Inc. report. The 'Waste Minimization Assessment' report did not find any deficiencies in the various waste-management programs now practiced at the GDSCC, and it found that these programs are being carried out in accordance with environmental rules and regulations.

Environmental projects. Volume 13: Underground storage tanks, removal and replacement. Goldstone Deep Space Communications Complex

NASA Technical Reports Server (NTRS)

Bengelsdorf, Irv

1991-01-01

The Goldstone Deep Space Communications Complex (GDSCC), located in the Mojave Desert about 40 miles north of Barstow, California, and about 160 miles northeast of Pasadena, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. As a large-scale facility located in a remote, isolated desert region, the GDSCC operations require numerous on-site storage facilities for gasoline, diesel oil, hydraulic oil, and waste oil. These fluids are stored in underground storage tanks (USTs). This present volume describes what happened to the 26 USTs that remained at the GDSCC. Twenty-four of these USTs were constructed of carbon steel without any coating for corrosion protection, and without secondary containment or leak detection. Two remaining USTs were constructed of fiberglass-coated carbon steel but without secondary containment or leak protection. Of the 26 USTs that remained at the GDSCC, 23 were cleaned, removed from the ground, cut up, and hauled away from the GDSCC for environmentally acceptable disposal. Three USTs were permanently closed (abandoned in place).

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1989-01-01

Archival reports on developments in programs managed by the Jet Propulsion Laboratory's Office of Telecommunications and Data Acquisition are provided. Space communications, radio navigation, radio science, and ground based radio and radio astronomy are discussed. Deep Space Network projects are also discussed.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1985-01-01

Developments in programs managed by the Jet Propulsion Laboratory's Office of Telecommunications and Data acquisition are discussed. Space communications, radio antennas, the Deep Space Network, antenna design, Project SETI, seismology, coding, very large scale integration, downlinking, and demodulation are among the topics covered.

The Telecommunications and Data Acquisition

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Progress in the development and operations of the Deep Space Network is reported including developments in Earth based radio technology as applied to other research programs. These programs include application of radio interferometry at microwave frequencies to geodetic measurements and geodynamics, use of deep space stations individually and in pairs as an interferometer by radio astronomers for astrophysics research by direct observations of radio sources, and radio search for extraterrestrial intelligence in the microwave region of the electromagnetic spectrum.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

The design and implementation of the Technical Facilities Controller (TFC) for the Goldstone deep space communications complex

NASA Technical Reports Server (NTRS)

Killian, D. A.; Menninger, F. J.; Gorman, T.; Glenn, P.

1988-01-01

The Technical Facilities Controller is a microprocessor-based energy management system that is to be implemented in the Deep Space Network facilities. This system is used in conjunction with facilities equipment at each of the complexes in the operation and maintenance of air-conditioning equipment, power generation equipment, power distribution equipment, and other primary facilities equipment. The implementation of the Technical Facilities Controller was completed at the Goldstone Deep Space Communications Complex and is now operational. The installation completed at the Goldstone Complex is described and the utilization of the Technical Facilities Controller is evaluated. The findings will be used in the decision to implement a similar system at the overseas complexes at Canberra, Australia, and Madrid, Spain.

Experimental Evaluation of Optically Polished Aluminum Panels on the Deep Space Network's 34 Meter Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, V.

2011-01-01

The potential development of large aperture ground?based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation is to polish the aluminum reflector panels of 34?meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by state of?the?art polished aluminum panels. Theoretical analyses of receiving antenna pointing, temporal synchronization and data detection have been addressed in previous papers. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. Two polished aluminum panels (a standard DSN panel polished to high reflectance, and a custom designed aluminum panel with much better surface quality) have been mounted on the 34 meter research antenna at Deep?Space Station 13 (DSS?13), and a remotely controlled CCD camera with a large CCD sensor in a weather?proof container has been installed next to the subreflector, pointed directly at the custom polished panel. The point?spread function (PSF) generated by the Vertex polished panel has been determined to be smaller than the sensor of the CCD camera, hence a detailed picture of the PSF can be obtained every few seconds, and the sensor array data processed to determine the center of the intensity distribution. In addition to estimating the center coordinates, expected communications performance can also been evaluated with the recorded data. The results of preliminary pointing experiments with the Vertex polished panel receiver using the planet Jupiter to simulate the PSF generated by a deep?space optical transmitter are presented and discussed in this paper.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A.

1980-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implemention, and operations is documented. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is documented. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

Adaptation of a software development methodology to the implementation of a large-scale data acquisition and control system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Madrid, G. A.; Westmoreland, P. T.

1983-01-01

A progress report is presented on a program to upgrade the existing NASA Deep Space Network in terms of a redesigned computer-controlled data acquisition system for channelling tracking, telemetry, and command data between a California-based control center and three signal processing centers in Australia, California, and Spain. The methodology for the improvements is oriented towards single subsystem development with consideration for a multi-system and multi-subsystem network of operational software. Details of the existing hardware configurations and data transmission links are provided. The program methodology includes data flow design, interface design and coordination, incremental capability availability, increased inter-subsystem developmental synthesis and testing, system and network level synthesis and testing, and system verification and validation. The software has been implemented thus far to a 65 percent completion level, and the methodology being used to effect the changes, which will permit enhanced tracking and communication with spacecraft, has been concluded to feature effective techniques.

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

NASA Astrophysics Data System (ADS)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Preliminary performance analysis of an interplanetary navigation system using asteroid based beacons

NASA Technical Reports Server (NTRS)

Jee, J. Rodney; Khatib, Ahmad R.; Muellerschoen, Ronald J.; Williams, Bobby G.; Vincent, Mark A.

1988-01-01

A futuristic interplanetary navigation system using transmitters placed on selected asteroids is introduced. This network of space beacons is seen as a needed alternative to the overly burdened Deep Space Network. Covariance analyses on the potential performance of these space beacons located on a candidate constellation of eight real asteroids are initiated. Simplified analytic calculations are performed to determine limiting accuracies attainable with the network for geometric positioning. More sophisticated computer simulations are also performed to determine potential accuracies using long arcs of range and Doppler data from the beacons. The results from these computations show promise for this navigation system.

The deep space network, volume 9

NASA Technical Reports Server (NTRS)

1972-01-01

Progress on DSN supporting research and technology is reported. Topics discussed include: descriptions of the objectives, functions, organization, facilities, and communication; Pioneer support; and advanced engineering.

The NASA SETI sky survey - Recent developments

NASA Technical Reports Server (NTRS)

Klein, Michael J.; Gulkis, Samuel; Olsen, Edward T.; Renzetti, Nicholas A.

1988-01-01

NASA's Search for Extraterrestrial Intelligence (SETI) project utilizes two complimentary search strategies: a sky survey and a targeted search. The SETI team at the Jet Propulsion Laboratory have primary responsibility to develop and carry out the sky survey part of the Microwave Observing Project. The paper describes progress that has been made to develop the major elements of the survey including a two-million channel wideband spectrum analyzer system that is being developed and constructed by JPL for the Deep Space Network. The new system will be a multiuser instrument that will serve as a prototype for the SETI Sky Survey processor. This system will be used to test the signal detection and observational strategies on deep-space network antennas in the near future.

Simulating Autonomous Telecommunication Networks for Space Exploration

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.

2008-01-01

Currently, most interplanetary telecommunication systems require human intervention for command and control. However, considering the range from near Earth to deep space missions, combined with the increase in the number of nodes and advancements in processing capabilities, the benefits from communication autonomy will be immense. Likewise, greater mission science autonomy brings the need for unscheduled, unpredictable communication and network routing. While the terrestrial Internet protocols are highly developed their suitability for space exploration has been questioned. JPL has developed the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to help characterize network designs and protocols. The results will allow future mission planners to better understand the trade offs of communication protocols. This paper discusses various issues with interplanetary network and simulation results of interplanetary networking protocols.

Telecommunications Systems Design Techniques Handbook

NASA Technical Reports Server (NTRS)

Edelson, R. E. (Editor)

1972-01-01

The Deep Space Network (DSN) increasingly supports deep space missions sponsored and managed by organizations without long experience in DSN design and operation. The document is intended as a textbook for those DSN users inexperienced in the design and specification of a DSN-compatible spacecraft telecommunications system. For experienced DSN users, the document provides a reference source of telecommunication information which summarizes knowledge previously available only in a multitude of sources. Extensive references are quoted for those who wish to explore specific areas more deeply.

WENESSA, Wide Eye-Narrow Eye Space Simulation fo Situational Awareness

NASA Astrophysics Data System (ADS)

Albarait, O.; Payne, D. M.; LeVan, P. D.; Luu, K. K.; Spillar, E.; Freiwald, W.; Hamada, K.; Houchard, J.

In an effort to achieve timelier indications of anomalous object behaviors in geosynchronous earth orbit, a Planning Capability Concept (PCC) for a “Wide Eye-Narrow Eye” (WE-NE) telescope network has been established. The PCC addresses the problem of providing continuous and operationally robust, layered and cost-effective, Space Situational Awareness (SSA) that is focused on monitoring deep space for anomalous behaviors. It does this by first detecting the anomalies with wide field of regard systems, and then providing reliable handovers for detailed observational follow-up by another optical asset. WENESSA will explore the added value of such a system to the existing Space Surveillance Network (SSN). The study will assess and quantify the degree to which the PCC completely fulfills, or improves or augments, these deep space knowledge deficiencies relative to current operational systems. In order to improve organic simulation capabilities, we will explore options for the federation of diverse community simulation approaches, while evaluating the efficiencies offered by a network of small and larger aperture, ground-based telescopes. Existing Space Modeling and Simulation (M&S) tools designed for evaluating WENESSA-like problems will be taken into consideration as we proceed in defining and developing the tools needed to perform this study, leading to the creation of a unified Space M&S environment for the rapid assessment of new capabilities. The primary goal of this effort is to perform a utility assessment of the WE-NE concept. The assessment will explore the mission utility of various WE-NE concepts in discovering deep space anomalies in concert with the SSN. The secondary goal is to generate an enduring modeling and simulation environment to explore the utility of future proposed concepts and supporting technologies. Ultimately, our validated simulation framework would support the inclusion of other ground- and space-based SSA assets through integrated analysis. Options will be explored using at least two competing simulation capabilities, but emphasis will be placed on reasoned analyses as supported by the simulations.

KSC-05PP-0138

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Emerging through the smoke and steam, the Boeing Delta II rocket carrying NASAs Deep Impact spacecraft lifts off at 1:47 p.m. EST from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0137

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. After a perfect liftoff at 1:47 p.m. EST today from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II rocket with Deep Impact spacecraft aboard soars through the clear blue sky. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05pp0138

NASA Image and Video Library

2005-01-12

KENNEDY SPACE CENTER, FLA. - Emerging through the smoke and steam, the Boeing Delta II rocket carrying NASA’s Deep Impact spacecraft lifts off at 1:47 p.m. EST from Launch Pad 17-B, Cape Canaveral Air Force Station, Fla. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impact’s flyby spacecraft will reveal the secrets of the comet’s interior by collecting pictures and data of how the crater forms, measuring the crater’s depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-05PD-0132

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Guests of NASA gather near the launch site at Cape Canaveral Air Force Station, Fla., to watch the Deep Impact spacecraft as it speeds through the air after a perfect launch at 1:47 p.m. EST. A NASA Discovery mission, Deep Impact is heading for space and a rendezvous 83 million miles from Earth with Comet Tempel 1. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1985-01-01

Deep Space Network (DSN) progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operation is discussed. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

KSC-05PD-0126

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., shadows paint the Boeing Delta II rocket carrying the Deep Impact spacecraft as the mobile service tower at left is rolled back before launch.Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0125

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II rocket carrying the Deep Impact spacecraft looms into the night sky as the mobile service tower at right is rolled back before launch. Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0127

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., the Boeing Delta II carrying the Deep Impact spacecraft rocket shines under spotlights in the early dawn hours as it waits for launch. Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0129

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The sun rises behind Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., where the Boeing Delta II rocket carrying the Deep Impact spacecraft waits for launch. Gray clouds above the horizon belie the favorable weather forecast for the afternoon launch. Scheduled for liftoff at 1:47 p.m. EST today, Deep Impact will head for space and a rendezvous with Comet Tempel 1 when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile (impactor) to crash onto the surface July 4, 2005, Deep Impacts flyby spacecraft will reveal the secrets of the comets interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

A Service Portal for the Integrated SCaN Network

NASA Technical Reports Server (NTRS)

Marx, Sarah R.

2012-01-01

The Space Communication and Navigation (SCaN) program office owns the assets and services provided by the Deep Space Network (DSN), Near Earth Network (NEN), and Space Network (SN). At present, these individual networks are operated by different NASA centers--JPL for DSN--and Goddard Space Flight Center (GSFC) for NEN and SN--with separate commitments offices for each center. In the near future, SCaN's program office would like to deploy an integrated service portal which would merge the two commitments offices with the goal of easing the task of user planning for space missions requiring services of two or more of these networks. Following interviews with subject matter experts in this field, use cases were created to include the services and functionality mission users would like to see in this new integrated service portal. These use cases provide a guideline for a mock-up of the design of the user interface for the portal. The benefit of this work will ease the time required and streamline/standardize the process for planning and scheduling SCAN's services for future space missions.

Expected antenna utilization and overload

NASA Technical Reports Server (NTRS)

Posner, Edward C.

1991-01-01

The trade-offs between the number of antennas at Deep Space Network (DSN) Deep-Space Communications Complex and the fraction of continuous coverage provided to a set of hypothetical spacecraft, assuming random placement of the space craft passes during the day. The trade-offs are fairly robust with respect to the randomness assumption. A sample result is that a three-antenna complex provides an average of 82.6 percent utilization of facilities and coverage of nine spacecraft that each have 8-hour passes, whereas perfect phasing of the passes would yield 100 percent utilization and coverage. One key point is that sometimes fewer than three spacecraft are visible, so an antenna is idle, while at other times, there aren't enough antennas, and some spacecraft do without service. This point of view may be useful in helping to size the network or to develop a normalization for a figure of merit of DSN coverage.

How to Take 30 Years Off the Life of an Earth-Orbiter Network

NASA Technical Reports Server (NTRS)

Berner, C. D.; Perkins, R. C.; Baker, N.

1995-01-01

In the mid 1960's the NASA/JPL Deep Space Network installed a global 26-meter antenna network to support a large group of Low Earth Orbiters and Highly Elliptical Orbiters. Although this network was equipped with then state-of-the-art equipment, operations were labor- intensive. A study is discussed which takes a close look at re- engineering the 26-meter antenna network from all aspects.

On Applications of Disruption Tolerant Networking to Optical Networking in Space

NASA Technical Reports Server (NTRS)

Hylton, Alan Guy; Raible, Daniel E.; Juergens, Jeffrey; Iannicca, Dennis

2012-01-01

The integration of optical communication links into space networks via Disruption Tolerant Networking (DTN) is a largely unexplored area of research. Building on successful foundational work accomplished at JPL, we discuss a multi-hop multi-path network featuring optical links. The experimental test bed is constructed at the NASA Glenn Research Center featuring multiple Ethernet-to-fiber converters coupled with free space optical (FSO) communication channels. The test bed architecture models communication paths from deployed Mars assets to the deep space network (DSN) and finally to the mission operations center (MOC). Reliable versus unreliable communication methods are investigated and discussed; including reliable transport protocols, custody transfer, and fragmentation. Potential commercial applications may include an optical communications infrastructure deployment to support developing nations and remote areas, which are unburdened with supporting an existing heritage means of telecommunications. Narrow laser beam widths and control of polarization states offer inherent physical layer security benefits with optical communications over RF solutions. This paper explores whether or not DTN is appropriate for space-based optical networks, optimal payload sizes, reliability, and a discussion on security.

Processing of chromatic information in a deep convolutional neural network.

PubMed

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

New tracking implementation in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.

2001-01-01

As part of the Network Simplification Project, the tracking system of the Deep Space Network is being upgraded. This upgrade replaces the discrete logic sequential ranging system with a system that is based on commercial Digital Signal Processor boards. The new implementation allows both sequential and pseudo-noise types of ranging. The other major change is a modernization of the data formatting. Previously, there were several types of interfaces, delivering both intermediate data and processed data (called 'observables'). All of these interfaces were bit-packed blocks, which do not allow for easy expansion, and many of these interfaces required knowledge of the specific hardware implementations. The new interface supports four classes of data: raw (direct from the measuring equipment), derived (the observable data), interferometric (multiple antenna measurements), and filtered (data whose values depend on multiple measurements). All of the measurements are reported at the sky frequency or phase level, so that no knowledge of the actual hardware is required. The data is formatted into Standard Formatted Data Units, as defined by the Consultative Committee for Space Data Systems, so that expansion and cross-center usage is greatly enhanced.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Noé, Frank

2018-06-01

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1982-01-01

Developments in Earth-based radio technology are reported. The Deep Space Network is discussed in terms of its advanced systems, network and facility engineering and implementation, operations, and energy sources. Problems in pulse communication and radio frequency interference are addressed with emphasis on pulse position modulation and laser beam collimation.

Mark 4A DSN receiver-exciter and transmitter subsystems

NASA Technical Reports Server (NTRS)

Wick, M. R.

1986-01-01

The present configuration of the Mark 4A DSN Receiver-Exciter and Transmitter Subsystems is described. Functional requirements and key characteristics are given to show the differences in the capabilities required by the Networks Consolidation task for combined High Earth Orbiter and Deep Space Network tracking support.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1994-01-01

This quarterly publication provides archival reports on developments in programs in space communications, radio navigation, radio science, and ground-based radio and radar astronomy. It reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standardization activities at the Jet Propulsion Laboratory for space data and information systems.

Development of realtime connected element interferometry at the Goldstone Deep Space Communications Complex

NASA Technical Reports Server (NTRS)

Edwards, C. D.

1990-01-01

Connected-element interferometry (CEI) has the potential to provide high-accuracy angular spacecraft tracking on short baselines by making use of the very precise phase delay observable. Within the Goldstone Deep Space Communications Complex (DSCC), one of three tracking complexes in the NASA Deep Space Network, baselines of up to 21 km in length are available. Analysis of data from a series of short-baseline phase-delay interferometry experiments are presented to demonstrate the potential tracking accuracy on these baselines. Repeated differential observations of pairs of angularly close extragalactic radio sources were made to simulate differential spacecraft-quasar measurements. Fiber-optic data links and a correlation processor are currently being developed and installed at Goldstone for a demonstration of real-time CEI in 1990.

Unified Simulation and Analysis Framework for Deep Space Navigation Design

NASA Technical Reports Server (NTRS)

Anzalone, Evan; Chuang, Jason; Olsen, Carrie

2013-01-01

As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.

Search for Extraterrestrial Intelligence (SETI)

NASA Technical Reports Server (NTRS)

Billingham, John

1993-01-01

Various aspects of project SETI are discussed. Some of the topics discussed include spectrum analyzers, signal processing, sky surveys, radiotelescopes, high resolution microwave survey, Deep Space Network, and signal detection.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Link monitor and control operator assistant: A prototype demonstrating semiautomated monitor and control

NASA Technical Reports Server (NTRS)

Lee, L. F.; Cooper, L. P.

1993-01-01

This article describes the approach, results, and lessons learned from an applied research project demonstrating how artificial intelligence (AI) technology can be used to improve Deep Space Network operations. Configuring antenna and associated equipment necessary to support a communications link is a time-consuming process. The time spent configuring the equipment is essentially overhead and results in reduced time for actual mission support operations. The NASA Office of Space Communications (Code O) and the NASA Office of Advanced Concepts and Technology (Code C) jointly funded an applied research project to investigate technologies which can be used to reduce configuration time. This resulted in the development and application of AI-based automated operations technology in a prototype system, the Link Monitor and Control Operator Assistant (LMC OA). The LMC OA was tested over the course of three months in a parallel experimental mode on very long baseline interferometry (VLBI) operations at the Goldstone Deep Space Communications Center. The tests demonstrated a 44 percent reduction in pre-calibration time for a VLBI pass on the 70-m antenna. Currently, this technology is being developed further under Research and Technology Operating Plan (RTOP)-72 to demonstrate the applicability of the technology to operations in the entire Deep Space Network.

Rock images classification by using deep convolution neural network

NASA Astrophysics Data System (ADS)

Cheng, Guojian; Guo, Wenhui

2017-08-01

Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

Delay/Disruption Tolerance Networking (DTN) Implementation and Utilization Options on the International Space Station

NASA Technical Reports Server (NTRS)

Holbrook, Mark; Pitts, Robert Lee; Gifford, Kevin K.; Jenkins, Andrew; Kuzminsky, Sebastian

2010-01-01

The International Space Station (ISS) is in an operational configuration and nearing final assembly. With its maturity and diverse payloads onboard, the opportunity exists to extend the orbital lab into a facility to exercise and demonstrate Delay/Disruption Tolerant Networking (DTN). DTN is an end-to-end network service providing communications through environments characterized by intermittent connectivity, variable delays, high bit error rates, asymmetric links and simplex links. The DTN protocols, also known as bundle protocols, provide a store-and-forward capability to accommodate end-to-end network services. Key capabilities of the bundling protocols include: the Ability to cope with intermittent connectivity, the Ability to take advantage of scheduled and opportunistic connectivity (in addition to always up connectivity), Custody Transfer, and end-to-end security. Colorado University at Boulder and the Huntsville Operational Support Center (HOSC) have been developing a DTN capability utilizing the Commercial Generic Bioprocessing Apparatus (CGBA) payload resources onboard the ISS, at the Boulder Payload Operations Center (POC) and at the HOSC. The DTN capability is in parallel with and is designed to augment current capabilities. The architecture consists of DTN endpoint nodes on the ISS and at the Boulder POC, and a DTN node at the HOSC. The DTN network is composed of two implementations; the Interplanetary Overlay Network (ION) and the open source DTN2 implementation. This paper presents the architecture, implementation, and lessons learned. By being able to handle the types of environments described above, the DTN technology will be instrumental in extending networks into deep space to support future missions to other planets and other solar system points of interest. Thus, this paper also discusses how this technology will be applicable to these types of deep space exploration missions.

Project Report: Design and Analysis for the Deep Space Network BWG Type 2 Antenna Feed Platform

NASA Technical Reports Server (NTRS)

Crawford, Andrew

2011-01-01

The following report explains in detail the solid modeling design process and structural analysis of the LNA (Low Noise Amplifier) feed platform to be constructed and installed on the new BWG (Beam Wave Guide) Type-2 tracking antenna in Canberra, Australia, as well as all future similar BWG Type-2 antennas builds. The Deep Space Networks new BWG Type-2 antennas use beam waveguides to funnel and 'extract' the desired signals received from spacecraft, and the feed platform supports and houses the LNA(Low Noise Amplifier) feed-cone and cryogenic cooling equipment used in the signal transmission and receiving process. The mandated design and construction of this platform to be installed on the new tracking antenna will be used and incorporated on all future similar antenna builds.

Software cost/resource modeling: Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. J.

1980-01-01

A parametric software cost estimation model prepared for JPL deep space network (DSN) data systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models, such as those of the General Research Corporation, Doty Associates, IBM (Walston-Felix), Rome Air Force Development Center, University of Maryland, and Rayleigh-Norden-Putnam. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software lifecycle statistics. The estimation model output scales a standard DSN work breakdown structure skeleton, which is then input to a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

Deep Space Network Capabilities for Receiving Weak Probe Signals

NASA Technical Reports Server (NTRS)

Asmar, Sami; Johnston, Doug; Preston, Robert

2005-01-01

Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

Environmental projects. Volume 3: Environmental compliance audit

NASA Technical Reports Server (NTRS)

1987-01-01

The Goldstone Deep Space Communications Complex is part of NASA's Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at Goldstone are carried out in support of six large parabolic dish antennas. In support of the national goal of the preservation of the environment and the protection of human health and safety, NASA, JPL and Goldstone have adopted a position that their operating installations shall maintain a high level of compliance with Federal, state, and local laws governing the management of hazardous substances, abestos, and underground storage tanks. A JPL version of a document prepared as an environmental audit of Goldstone operations is presented. Both general and specific items of noncompliance at Goldstone are identified and recommendations are provided for corrective actions.

Computer simulated building energy consumption for verification of energy conservation measures in network facilities

NASA Technical Reports Server (NTRS)

Plankey, B.

1981-01-01

A computer program called ECPVER (Energy Consumption Program - Verification) was developed to simulate all energy loads for any number of buildings. The program computes simulated daily, monthly, and yearly energy consumption which can be compared with actual meter readings for the same time period. Such comparison can lead to validation of the model under a variety of conditions, which allows it to be used to predict future energy saving due to energy conservation measures. Predicted energy saving can then be compared with actual saving to verify the effectiveness of those energy conservation changes. This verification procedure is planned to be an important advancement in the Deep Space Network Energy Project, which seeks to reduce energy cost and consumption at all DSN Deep Space Stations.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1985-01-01

Reports on developments in space communications, radio navigation, radio science, and ground-based radio astronomy are presented. Activities of the Deep Space Network (DSN) are reported in the areas of planning, supporting research and technology, implementation and operations. The application of radio interferometry at microwave frequencies for geodynamic measurements is also discussed.

Detection Performance of Upgraded "Polished Panel" Optical Receiver Concept on the Deep-Space Network's 34 Meter Research Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor A.

2012-01-01

The development and demonstration of a "polished panel" optical receiver concept on the 34 meter research antenna of the Deep Space Network (DSN) has been the subject of recent papers. This concept would enable simultaneous reception of optical and microwave signals by retaining the original shape of the main reflector for microwave reception, but with the aluminum panels polished to high reflectivity to enable focusing of optical signal energy as well. A test setup has been installed on the DSN's 34 meter research antenna at Deep Space Station 13 (DSS-13) of NASA's Goldstone Communications Complex in California, and preliminary experimental results have been obtained. This paper describes the results of our latest efforts to improve the point-spread function (PSF) generated by a custom polished panel, in an attempt to reduce the dimensions of the PSF, thus enabling more precise tracking and improved detection performance. The design of the new mechanical support structure and its operation are described, and the results quantified in terms of improvements in collected signal energy and optical communications performance, based on data obtained while tracking the planet Jupiter with the 34 meter research antenna at DSS-13.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1985-01-01

Reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition are presented. Emphasis is placed on activities of the Deep Space Network and its associated ground facilities.

The NASA Space Communications Data Networking Architecture

NASA Technical Reports Server (NTRS)

Israel, David J.; Hooke, Adrian J.; Freeman, Kenneth; Rush, John J.

2006-01-01

The NASA Space Communications Architecture Working Group (SCAWG) has recently been developing an integrated agency-wide space communications architecture in order to provide the necessary communication and navigation capabilities to support NASA's new Exploration and Science Programs. A critical element of the space communications architecture is the end-to-end Data Networking Architecture, which must provide a wide range of services required for missions ranging from planetary rovers to human spaceflight, and from sub-orbital space to deep space. Requirements for a higher degree of user autonomy and interoperability between a variety of elements must be accommodated within an architecture that necessarily features minimum operational complexity. The architecture must also be scalable and evolvable to meet mission needs for the next 25 years. This paper will describe the recommended NASA Data Networking Architecture, present some of the rationale for the recommendations, and will illustrate an application of the architecture to example NASA missions.

DSN command system Mark III-78. [data processing

NASA Technical Reports Server (NTRS)

Stinnett, W. G.

1978-01-01

The Deep Space Network command Mark III-78 data processing system includes a capability for a store-and-forward handling method. The functions of (1) storing the command files at a Deep Space station; (2) attaching the files to a queue; and (3) radiating the commands to the spacecraft are straightforward. However, the total data processing capability is a result of assuming worst case, failure-recovery, or nonnominal operating conditions. Optional data processing functions include: file erase, clearing the queue, suspend radiation, command abort, resume command radiation, and close window time override.

Optimizing the G/T ratio of the DSS-13 34-meter beam-waveguide antenna

NASA Technical Reports Server (NTRS)

Esquivel, M. S.

1992-01-01

Calculations using Physical Optics computer software were done to optimize the gain-to-noise-temperature (G/T) ratio of Deep Space Station (DSS)-13, the Deep Space Network's (DSN's) 34-m beam-waveguide antenna, at X-band for operation with the ultra-low-noise amplifier maser system. A better G/T value was obtained by using a 24.2-dB far-field-gain smooth-wall dual-mode horn than by using the standard X-band 22.5-dB-gain corrugated horn.

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

NASA Technical Reports Server (NTRS)

Connell, Andrea M.

2011-01-01

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

1980-01-01

Progress in the development and operations of the Deep Space Network along with developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the search for extraterrestrial intelligence are reported.

Deep Space Ka-band Link Management and the MRO Demonstration: Long-term Weather Statistics Versus Forecasting

NASA Technical Reports Server (NTRS)

Davarian, Faramaz; Shambayati, Shervin; Slobin, Stephen

2004-01-01

During the last 40 years, deep space radio communication systems have experienced a move toward shorter wavelengths. In the 1960s a transition from L- to S-band occurred which was followed by a transition from S- to X-band in the 1970s. Both these transitions provided deep space links with wider bandwidths and improved radio metrics capability. Now, in the 2000s, a new change is taking place, namely a move to the Ka-band region of the radio frequency spectrum. Ka-band will soon replace X-band as the frequency of choice for deep space communications providing ample spectrum for the high data rate requirements of future missions. The low-noise receivers of deep space networks have a great need for link management techniques that can mitigate weather effects. In this paper, three approaches for managing Ka-band Earth-space links are investigated. The first approach uses aggregate annual statistics, the second one uses monthly statistics, and the third is based on the short-term forecasting of the local weather. An example of weather forecasting for Ka-band link performance prediction is presented. Furthermore, spacecraft commanding schemes suitable for Ka-band link management are investigated. Theses schemes will be demonstrated using NASA's Mars Reconnaissance Orbiter (MRO) spacecraft in the 2007 to 2008 time period, and the demonstration findings will be reported in a future publication.

The Deep Space Network in the Common Platform Era: A Prototype Implementation at DSS-13

NASA Technical Reports Server (NTRS)

Davarian, F.

2013-01-01

To enhance NASA's Deep Space Network (DSN), an effort is underway to improve network performance and simplify its operation and maintenance. This endeavor, known as the "Common Platform," has both short- and long-term objectives. The long-term work has not begun yet; however, the activity to realize the short-term goals has started. There are three goals for the long-term objective: 1. Convert the DSN into a digital network where signals are digitized at the output of the down converters at the antennas and are distributed via a digital IF switch to the processing platforms. 2. Employ a set of common hardware for signal processing applications, e.g., telemetry, tracking, radio science and Very Long Baseline Interferometry (VLBI). 3. Minimize in-house developments in favor of purchasing commercial off-the-shelf (COTS) equipment. The short-term goal is to develop a prototype of the above at NASA's experimental station known as DSS-13. This station consists of a 34m beam waveguide antenna with cryogenically cooled amplifiers capable of handling deep space research frequencies at S-, X-, and Ka-bands. Without the effort at DSS-13, the implementation of the long-term goal can potentially be risky because embarking on the modification of an operational network without prior preparations can, among other things, result in unwanted service interruptions. Not only are there technical challenges to address, full network implementation of the Common Platform concept includes significant cost uncertainties. Therefore, a limited implementation at DSS-13 will contribute to risk reduction. The benefits of employing common platforms for the DSN are lower cost and improved operations resulting from ease of maintenance and reduced number of spare parts. Increased flexibility for the user is another potential benefit. This paper will present the plans for DSS-13 implementation. It will discuss key issues such as the Common Platform architecture, choice of COTS equipment, and the standard for radio frequency (RF) to digital interface.

Pattern-recognition techniques applied to performance monitoring of the DSS 13 34-meter antenna control assembly

NASA Technical Reports Server (NTRS)

Mellstrom, J. A.; Smyth, P.

1991-01-01

The results of applying pattern recognition techniques to diagnose fault conditions in the pointing system of one of the Deep Space network's large antennas, the DSS 13 34-meter structure, are discussed. A previous article described an experiment whereby a neural network technique was used to identify fault classes by using data obtained from a simulation model of the Deep Space Network (DSN) 70-meter antenna system. Described here is the extension of these classification techniques to the analysis of real data from the field. The general architecture and philosophy of an autonomous monitoring paradigm is described and classification results are discussed and analyzed in this context. Key features of this approach include a probabilistic time-varying context model, the effective integration of signal processing and system identification techniques with pattern recognition algorithms, and the ability to calibrate the system given limited amounts of training data. Reported here are recognition accuracies in the 97 to 98 percent range for the particular fault classes included in the experiments.

A common-aperture X- and S-band four-function feedcone. [hornfeed design for antennas of Deep Space Network

NASA Technical Reports Server (NTRS)

Withington, J. R.; Williams, W. F.

1982-01-01

Williams and Withington (1979) have considered a prototype X-S-band feedhorn which enabled simultaneous X- and S-band reception from a Cassegrain antenna. This feedhorn has quite successfully demonstrated an alternate method to the standard Deep Space Network (DSN) system of multiple subreflectors and dichroic plate for dual-band reception. In connection with a Network Consolidation Program, involving centralized control of existing antennas and construction of new reflector antennas, a second-generation feedhorn/combiner was conceived to show that this common-aperture feedhorn system was capable of performing all necessary functions the DSN would be called upon to perform with existing and future X-S-band spacecraft. Attention is given to the feedhorn concept, the combiner concept, the first and the second generation of the horn, Sand X-band tuning, and planned capabilities. The feedhorn greatly extends the state of the art in DSN performance and will enhance DSN capabilities in the future.

KSC-04PD-2699

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., the Deep Impact spacecraft is mated to the Boeing Delta II third stage. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2693

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. Boeing technicians at Astrotech Space Operations in Titusville, Fla., prepare the third stage of a Delta II rocket for mating with the Deep Impact spacecraft. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

Lifting the Runners

NASA Image and Video Library

2010-08-25

Under the unflinching summer sun, workers at NASA Deep Space Network complex in Goldstone, Calif., use a crane to lift a runner segment that is part of major surgery on a giant, 70-meter-wide antenna.

The deep space network, volume 8

NASA Technical Reports Server (NTRS)

1972-01-01

Progress is reported on DSN supporting research and technology, advanced development and engineering, implementation, and operations which pertain to mission-independent or multiple-mission development as well as to support of flight projects.

NASA Integrated Network Monitor and Control Software Architecture

NASA Technical Reports Server (NTRS)

Shames, Peter; Anderson, Michael; Kowal, Steve; Levesque, Michael; Sindiy, Oleg; Donahue, Kenneth; Barnes, Patrick

2012-01-01

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation office (SCaN) has commissioned a series of trade studies to define a new architecture intended to integrate the three existing networks that it operates, the Deep Space Network (DSN), Space Network (SN), and Near Earth Network (NEN), into one integrated network that offers users a set of common, standardized, services and interfaces. The integrated monitor and control architecture utilizes common software and common operator interfaces that can be deployed at all three network elements. This software uses state-of-the-art concepts such as a pool of re-programmable equipment that acts like a configurable software radio, distributed hierarchical control, and centralized management of the whole SCaN integrated network. For this trade space study a model-based approach using SysML was adopted to describe and analyze several possible options for the integrated network monitor and control architecture. This model was used to refine the design and to drive the costing of the four different software options. This trade study modeled the three existing self standing network elements at point of departure, and then described how to integrate them using variations of new and existing monitor and control system components for the different proposed deployments under consideration. This paper will describe the trade space explored, the selected system architecture, the modeling and trade study methods, and some observations on useful approaches to implementing such model based trade space representation and analysis.

Deep Space Network Revitalization: Operations for the 21st Century

NASA Technical Reports Server (NTRS)

Statman, Joseph I.

1999-01-01

The National Aeronautics and Space Administration (NASA) supports unmanned space missions through a Deep Space Network (DSN) that is developed and operated by the Jet Propulsion Laboratory (JPL and its subcontractors. The DSN capabilities have been incrementally upgraded since its establishment in the late '50s and are delivered from three Deep Space Communications Complexes (DSCC's) near Goldstone, California, Madrid, Spain, and Canberra, Australia. At present each DSCC includes large antennas with diameters from 11 meters to 70 meters, that operate largely in S-band and X-band frequencies. In addition each DSCC includes all the associated electronics to receive and process the low-level telemetry signals, and radiate the necessary command with high-power transmitters. To accommodate support of the rapidly increasing number of missions by NASA and other space agencies, and to facilitate maintaining and increasing the level of service in a shrinking budget environment, JPL has initiated a bold road map with three key components: 1. A Network Simplification Project (NSP) to upgrade aging electronics, replacing them with modem commercially based components. NSP and related replacement tasks are projected to reduce the cost of operating the DSN by 50% relative to the 1997 levels. 2. Upgrade of all 34-m and 70-m antennas to provision of Ka-Band telemetry downlink capability, complemented by an existing X-band uplink capability. This will increase the effective telemetry downlink capacity by a factor of 4, without building any new antennas. 3. Establishment of an optical communications network to support for high data rate unmanned missions that cannot be accommodated with radiofrequency (RF) communications, as well as establish a path toward support of manned missions at Mars. In this paper we present the mission loading projected for 1998-2008 and the elements of the JPL road map that will enable supporting it with a reduced budget. Particular emphasis will be on streamlining the architecture and to reduce the DSN cost for operations, maintenance and sustaining engineering while at the same time also simplifying and reducing the operations cost for the flight missions.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1989-01-01

Developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. Activities of the Deep Space Network and its associated Ground Communications Facility in planning, in supporting research and technology, in implementation, and in operations are reported in space communications, radio navigation, radio science, and ground-based radio and radar astronomy.

The Telecommunications and Data Acquisition Progress Report 42-123

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1995-01-01

The progress of research programs monitored by the Jet Propulsion Laboratory's Telecommunications and Mission Operations Directorate (TMOD) are presented in this quarterly document. Areas monitored include space communications, radio navigation, radio science, ground-based radio and radar astronomy, information systems, and all other communication and research technology activities for the Deep Space Network (DSN).

Self-learning Monte Carlo with deep neural networks

NASA Astrophysics Data System (ADS)

Shen, Huitao; Liu, Junwei; Fu, Liang

2018-05-01

The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1989-01-01

Deep Space Network advanced systems, very large scale integration architecture for decoders, radar interface and control units, microwave time delays, microwave antenna holography, and a radio frequency interference survey are among the topics discussed.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1992-01-01

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information. In the search for extraterrestrial intelligence (SETI), the TDA Progress Report reports on implementation and operations for searching the microwave spectrum. Topics covered include tracking and ground-based navigation; communications, spacecraft-ground; station control and system technology; capabilities for new projects; network upgrade and sustaining; network operations and operations support; and TDA program management and analysis.

Uplink-Downlink: A History of the Deep Space Network, 1957-1997

NASA Technical Reports Server (NTRS)

Mudgway, Douglas J.; Launius, Roger (Technical Monitor)

2001-01-01

In these pages, the informed reader will discover a simple description of what the Deep Space Network (DSN) is about, and how it works an aspect of NASA's spectacular planetary program that seldom found its way into the popular media coverage of those major events. Future historical researchers will find a complete record of the origin and birth of the DSN, its subsequent development and expansion over the ensuing four decades, and a description of the way in which the DSN was used to fulfill the purpose for which it was created. At the same time, the specialist reader is provided with an abundant source of technical references that address every aspect of the advanced telecommunications technology on which the success of the DSN depended. And finally, archivists, educators, outreach managers, and article writers will have ready recourse to the inner workings of the DSN and how they related to the more publicly visible events of the planetary space program.

System security in the space flight operations center

NASA Technical Reports Server (NTRS)

Wagner, David A.

1988-01-01

The Space Flight Operations Center is a networked system of workstation-class computers that will provide ground support for NASA's next generation of deep-space missions. The author recounts the development of the SFOC system security policy and discusses the various management and technology issues involved. Particular attention is given to risk assessment, security plan development, security implications of design requirements, automatic safeguards, and procedural safeguards.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

This publication, one of a series formerly titled The Deep Space Network (DSN) Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

This publication, one of a series formerly titled The Deep Space Network Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1993-01-01

This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA.

Enhanced Communication Network Solution for Positive Train Control Implementation

NASA Technical Reports Server (NTRS)

Fatehi, M. T.; Simon, J.; Chang, W.; Chow, E. T.; Burleigh, S. C.

2011-01-01

The commuter and freight railroad industry is required to implement Positive Train Control (PTC) by 2015 (2012 for Metrolink), a challenging network communications problem. This paper will discuss present technologies developed by the National Aeronautics and Space Administration (NASA) to overcome comparable communication challenges encountered in deep space mission operations. PTC will be based on a new cellular wireless packet Internet Protocol (IP) network. However, ensuring reliability in such a network is difficult due to the "dead zones" and transient disruptions we commonly experience when we lose calls in commercial cellular networks. These disruptions make it difficult to meet PTC s stringent reliability (99.999%) and safety requirements, deployment deadlines, and budget. This paper proposes innovative solutions based on space-proven technologies that would help meet these challenges: (1) Delay Tolerant Networking (DTN) technology, designed for use in resource-constrained, embedded systems and currently in use on the International Space Station, enables reliable communication over networks in which timely data acknowledgments might not be possible due to transient link outages. (2) Policy-Based Management (PBM) provides dynamic management capabilities, allowing vital data to be exchanged selectively (with priority) by utilizing alternative communication resources. The resulting network may help railroads implement PTC faster, cheaper, and more reliably.

Adaptive template generation for amyloid PET using a deep learning approach.

PubMed

Kang, Seung Kwan; Seo, Seongho; Shin, Seong A; Byun, Min Soo; Lee, Dong Young; Kim, Yu Kyeong; Lee, Dong Soo; Lee, Jae Sung

2018-05-11

Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research. © 2018 Wiley Periodicals, Inc.

The telecommunications and data acquisition progress report 42-64

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Progress in the development and operations of the Deep Space Network is reported. Developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are included.

The telecommunications and data acquisition

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1980-01-01

Radio astronomy and radio interferometry at microwave frequencies are discussed. Other topics concerning the Deep Space Network include program planning, planetary and interplanetary mission support, tracking and ground based navigation, communications, and station control and system technology.

Ocean Inside Saturn Moon Enceladus

NASA Image and Video Library

2014-04-03

Gravity measurements by NASA Cassini spacecraft and Deep Space Network suggest that Saturn moon Enceladus, which has jets of water vapor and ice gushing from its south pole, also harbors a large interior ocean beneath an ice shell.

Planning a DSN support section technical library

NASA Technical Reports Server (NTRS)

Bailey, T.; Chatburn, C. C.

1980-01-01

The planning procedure being used to establish a technical library for the Deep Space Network support section is described. The inventory and survey methods employed are described and the preliminary results of these methods are discussed.

Wideband waveguide polarizer development for SETI

NASA Technical Reports Server (NTRS)

Lee, P.; Stanton, P.

1991-01-01

A wideband polarizer for the Deep Space Network (DSN) 34 meter beam waveguide antenna is needed for the Search for Extraterrestrial Intelligence (SETI) project. The results of a computer analysis of a wideband polarizer are presented.

Limitations of shallow nets approximation.

PubMed

Lin, Shao-Bo

2017-10-01

In this paper, we aim at analyzing the approximation abilities of shallow networks in reproducing kernel Hilbert spaces (RKHSs). We prove that there is a probability measure such that the achievable lower bound for approximating by shallow nets can be realized for all functions in balls of reproducing kernel Hilbert space with high probability, which is different with the classical minimax approximation error estimates. This result together with the existing approximation results for deep nets shows the limitations for shallow nets and provides a theoretical explanation on why deep nets perform better than shallow nets. Copyright © 2017 Elsevier Ltd. All rights reserved.

NASA Integrated Network COOP

NASA Technical Reports Server (NTRS)

Anderson, Michael L.; Wright, Nathaniel; Tai, Wallace

2012-01-01

Natural disasters, terrorist attacks, civil unrest, and other events have the potential of disrupting mission-essential operations in any space communications network. NASA's Space Communications and Navigation office (SCaN) is in the process of studying options for integrating the three existing NASA network elements, the Deep Space Network, the Near Earth Network, and the Space Network, into a single integrated network with common services and interfaces. The need to maintain Continuity of Operations (COOP) after a disastrous event has a direct impact on the future network design and operations concepts. The SCaN Integrated Network will provide support to a variety of user missions. The missions have diverse requirements and include anything from earth based platforms to planetary missions and rovers. It is presumed that an integrated network, with common interfaces and processes, provides an inherent advantage to COOP in that multiple elements and networks can provide cross-support in a seamless manner. The results of trade studies support this assumption but also show that centralization as a means of achieving integration can result in single points of failure that must be mitigated. The cost to provide this mitigation can be substantial. In support of this effort, the team evaluated the current approaches to COOP, developed multiple potential approaches to COOP in a future integrated network, evaluated the interdependencies of the various approaches to the various network control and operations options, and did a best value assessment of the options. The paper will describe the trade space, the study methods, and results of the study.

The Telecommunications and Data Acquisition

NASA Technical Reports Server (NTRS)

Posner, Edward C. (Editor)

1992-01-01

This quarterly publication provides archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC).

GEODSS Overview

NASA Astrophysics Data System (ADS)

Bruck, R.

2014-09-01

Ground-based Electro-Optical Deep Space Surveillance (GEODSS) is an optical telescope system that passively collects visible wavelength data for the Space Surveillance Network (SSN). The GEODSS generated data is used by the Joint Space Operations Center (JSpOC) located at, both Vandenberg AFB California and Colorado Springs, Colorado. GEODSS data is also used by National Air and Space Intelligence Center (NASIC) situated on Wright-Patterson Air Force Base outside of Dayton Ohio, There are three geographically dispersed GEODSS sites; Socorro, NM on White Sands Missile Range (WSMR), Diego Garcia, British Indian Ocean Territory, and Haleakala on the island of Maui, Hawaii. Each of the sites is equipped with three telescopes of identical design. GEODSS Telescopes are primarily used to observe individually tasked deep space artificial satellites in the period range of 225 minutes and beyond using Charge Coupling Device (CCD) technology.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1983-01-01

Archival reports on developments in programs managed by JPL's office of Telecommunications and Data Acquisition (TDA) are presented. In space communications, radio navigation, radio science, and ground-based radio astronomy, it reports on activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, in supporting research and technology, in implementation, and in operations.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, Edward C. (Editor)

1993-01-01

Reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other agencies through NASA.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1994-01-01

Reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are provided. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other agencies through NASA.

Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data

NASA Astrophysics Data System (ADS)

Hon, Marc; Stello, Dennis; Yu, Jie

2018-05-01

Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower frequency resolution spectra expected from these missions. Additionally, we provide new classifications for 8633 Kepler red giants, out of which 426 have previously not been classified using asteroseismology. This brings the total to 14983 Kepler red giants classified with our new neural network. We also verify that our classifiers are remarkably robust to suboptimal data, including low signal-to-noise and incorrect training truth labels.

KSC-04PD-2460

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B at Cape Canaveral Air Force Station, the second stage of the Boeing Delta II rocket arrives at the top of the mobile service tower. The element will be mated to the Delta II, which will launch NASAs Deep Impact spacecraft. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing an impactor on a course to hit the comets sunlit side, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measure the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determine the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

Tracking and data systems support for the Helios project. Volume 2: DSN support of Project Helios April 1975 - May 1976

NASA Technical Reports Server (NTRS)

Goodwin, P. S.; Traxler, M. R.; Meeks, W. G.; Flanagan, F. M.

1977-01-01

Deep Space Network activities in the development of the Helios B mission from planning through entry of Helios 2 into first superior conjunction (end of Mission Phase II) are summarized. Network operational support activities for Helios 1 from first superior conjunction through entry into third superior conjunction are included.

DSN Network e-VLBI Calibration of Earth Orientation

NASA Technical Reports Server (NTRS)

Zhang, Liwei Dennis; Steppe, A.; Lanyi, G.; Jacobs, C.

2006-01-01

This viewgraph presentation reviews the calibration of the Earth's orientation by using the Deep Space Network (DSN) e Very Large Base Integration (VLBI). The topics include: 1) Background: TEMPO; 2) Background: UT1 Knowledge Error; 3) e-VLBI: WVSR TEMPO Overview; 4) e-VLBI: WVSR TEMPO Turnaround; 5) e-VLBI: WVSR TEMPO R&D Tests; and 6) WVSR TEMPO Test Conclusion.

The GPS Topex/Poseidon precise orbit determination experiment - Implications for design of GPS global networks

NASA Technical Reports Server (NTRS)

Lindqwister, Ulf J.; Lichten, Stephen M.; Davis, Edgar S.; Theiss, Harold L.

1993-01-01

Topex/Poseidon, a cooperative satellite mission between United States and France, aims to determine global ocean circulation patterns and to study their influence on world climate through precise measurements of sea surface height above the geoid with an on-board altimeter. To achieve the mission science aims, a goal of 13-cm orbit altitude accuracy was set. Topex/Poseidon includes a Global Positioning System (GPS) precise orbit determination (POD) system that has now demonstrated altitude accuracy better than 5 cm. The GPS POD system includes an on-board GPS receiver and a 6-station GPS global tracking network. This paper reviews early GPS results and discusses multi-mission capabilities available from a future enhanced global GPS network, which would provide ground-based geodetic and atmospheric calibrations needed for NASA deep space missions while also supplying tracking data for future low Earth orbiters. Benefits of the enhanced global GPS network include lower operations costs for deep space tracking and many scientific and societal benefits from the low Earth orbiter missions, including improved understanding of ocean circulation, ocean-weather interactions, the El Nino effect, the Earth thermal balance, and weather forecasting.

Team Collaboration Software

NASA Technical Reports Server (NTRS)

Wang, Yeou-Fang; Schrock, Mitchell; Baldwin, John R.; Borden, Charles S.

2010-01-01

The Ground Resource Allocation and Planning Environment (GRAPE 1.0) is a Web-based, collaborative team environment based on the Microsoft SharePoint platform, which provides Deep Space Network (DSN) resource planners tools and services for sharing information and performing analysis.

Prototype real-time baseband signal combiner. [deep space network

NASA Technical Reports Server (NTRS)

Howard, L. D.

1980-01-01

The design and performance of a prototype real-time baseband signal combiner, used to enhance the received Voyager 2 spacecraft signals during the Jupiter flyby, is described. Hardware delay paths, operating programs, and firmware are discussed.

Deep Space Network capabilities for receiving weak probe signals

NASA Technical Reports Server (NTRS)

Asmar, Sami; Johnston, Doug; Preston, Robert

2004-01-01

This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

Environmental projects. Volume 12: Friable asbestos abatement, GDSCC

NASA Technical Reports Server (NTRS)

1990-01-01

The Goldstone Deep Space Communications Complex (GDSCC) is part of the NASA Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. These activities may give rise to a variety of environmental hazards, particularly the danger of exposure of GDSCC personnel to asbestos fibers that have been shown to be responsible for such serious ailments as asbestosis, lung cancer, and mesothelioma. Asbestos-containing materials (ACM's) were used in the construction of many of the approximately 100 buildings and structures that were built at the GDSCC during a 30-year period from the 1950s through 1980s. The friable asbestos-abatement program at the GDSCC is presented which consists of text, illustrations, and tables that describe the friable asbestos abatement carried out at the GDSCC from December 21, 1988 through May 11, 1989.

Single- and dual-carrier microwave noise abatement in the deep space network. [microwave antennas

NASA Technical Reports Server (NTRS)

Bathker, D. A.; Brown, D. W.; Petty, S. M.

1975-01-01

The NASA/JPL Deep Space Network (DSN) microwave ground antenna systems are presented which simultaneously uplink very high power S-band signals while receiving very low level S- and X-band downlinks. Tertiary mechanisms associated with elements give rise to self-interference in the forms of broadband noise burst and coherent intermodulation products. A long-term program to reduce or eliminate both forms of interference is described in detail. Two DSN antennas were subjected to extensive interference testing and practical cleanup program; the initial performance, modification details, and final performance achieved at several planned stages are discussed. Test equipment and field procedures found useful in locating interference sources are discussed. Practices deemed necessary for interference-free operations in the DSN are described. Much of the specific information given is expected to be easily generalized for application in a variety of similar installations. Recommendations for future investigations and individual element design are given.

Application of inertial instruments for DSN antenna pointing and tracking

NASA Technical Reports Server (NTRS)

Eldred, D. B.; Nerheim, N. M.; Holmes, K. G.

1990-01-01

The feasibility of using inertial instruments to determine the pointing attitude of the NASA Deep Space Network antennas is examined. The objective is to obtain 1 mdeg pointing knowledge in both blind pointing and tracking modes to facilitate operation of the Deep Space Network 70 m antennas at 32 GHz. A measurement system employing accelerometers, an inclinometer, and optical gyroscopes is proposed. The initial pointing attitude is established by determining the direction of the local gravity vector using the accelerometers and the inclinometer, and the Earth's spin axis using the gyroscopes. Pointing during long-term tracking is maintained by integrating the gyroscope rates and augmenting these measurements with knowledge of the local gravity vector. A minimum-variance estimator is used to combine measurements to obtain the antenna pointing attitude. A key feature of the algorithm is its ability to recalibrate accelerometer parameters during operation. A survey of available inertial instrument technologies is also given.

Antennas for the array-based Deep Space Network: current status and future designs

NASA Technical Reports Server (NTRS)

Imbriale, William A.; Gama, Eric

2005-01-01

Development of very large arrays1,2 of small antennas has been proposed as a way to increase the downlink capability of the NASA Deep Space Network DSN) by two or three orders of magnitude thereby enabling greatly increased science data from currently configured missions or enabling new mission concepts. The current concept is for an array of 400 x 12-m antennas at each of three longitudes. The DSN array will utilize radio astronomy sources for phase calibration and will have wide bandwidth correlation processing for this purpose. NASA has undertaken a technology program to prove the performance and cost of a very large DSN array. Central to that program is a 3-element interferometer to be completed in 2005. This paper describes current status of the low cost 6-meter breadboard antenna to be used as part of the interferometer and the RF design of the 12-meter antenna.

An OSI architecture for the deep space network

NASA Technical Reports Server (NTRS)

Heuser, W. Randy; Cooper, Lynne P.

1993-01-01

The flexibility and robustness of a monitor and control system are a direct result of the underlying inter-processor communications architecture. A new architecture for monitor & Control at the Deep Space Network Communications Complexes has been developed based on the Open System Interconnection (OSI) standards. The suitability of OSI standards for DSN M&C has been proven in the laboratory. The laboratory success has resulted in choosing an OSI-based architecture for DSS-13 M&C. DSS-13 is the DSN experimental station and is not part of the 'operational' DSN; it's role is to provide an environment to test new communications concepts can be tested and conduct unique science experiments. Therefore, DSS-13 must be robust enough to support operational activities, while also being flexible enough to enable experimentation. This paper describes the M&C architecture developed for DSS-13 and the results from system and operational testing.

The Deep Space Network stability analyzer

NASA Technical Reports Server (NTRS)

Breidenthal, Julian C.; Greenhall, Charles A.; Hamell, Robert L.; Kuhnle, Paul F.

1995-01-01

A stability analyzer for testing NASA Deep Space Network installations during flight radio science experiments is described. The stability analyzer provides realtime measurements of signal properties of general experimental interest: power, phase, and amplitude spectra; Allan deviation; and time series of amplitude, phase shift, and differential phase shift. Input ports are provided for up to four 100 MHz frequency standards and eight baseband analog (greater than 100 kHz bandwidth) signals. Test results indicate the following upper bounds to noise floors when operating on 100 MHz signals: -145 dBc/Hz for phase noise spectrum further than 200 Hz from carrier, 2.5 x 10(exp -15) (tau =1 second) and 1.5 x 10(exp -17) (tau =1000 seconds) for Allan deviation, and 1 x 10(exp -4) degrees for 1-second averages of phase deviation. Four copies of the stability analyzer have been produced, plus one transportable unit for use at non-NASA observatories.

Deep Space Wide Area Search Strategies

NASA Astrophysics Data System (ADS)

Capps, M.; McCafferty, J.

There is an urgent need to expand the space situational awareness (SSA) mission beyond catalog maintenance to providing near real-time indications and warnings of emerging events. While building and maintaining a catalog of space objects is essential to SSA, this does not address the threat of uncatalogued and uncorrelated deep space objects. The Air Force therefore has an interest in transformative technologies to scan the geostationary (GEO) belt for uncorrelated space objects. Traditional ground based electro-optical sensors are challenged in simultaneously detecting dim objects while covering large areas of the sky using current CCD technology. Time delayed integration (TDI) scanning has the potential to enable significantly larger coverage rates while maintaining sensitivity for detecting near-GEO objects. This paper investigates strategies of employing TDI sensing technology from a ground based electro-optical telescope, toward providing tactical indications and warnings of deep space threats. We present results of a notional wide area search TDI sensor that scans the GEO belt from three locations: Maui, New Mexico, and Diego Garcia. Deep space objects in the NASA 2030 debris catalog are propagated over multiple nights as an indicative data set to emulate notional uncatalogued near-GEO orbits which may be encountered by the TDI sensor. Multiple scan patterns are designed and simulated, to compare and contrast performance based on 1) efficiency in coverage, 2) number of objects detected, and 3) rate at which detections occur, to enable follow-up observations by other space surveillance network (SSN) sensors. A step-stare approach is also modeled using a dedicated, co-located sensor notionally similar to the Ground-Based Electro-Optical Deep Space Surveillance (GEODSS) tower. Equivalent sensitivities are assumed. This analysis quantifies the relative benefit of TDI scanning for the wide area search mission.

Remote observing with NASA's Deep Space Network

NASA Astrophysics Data System (ADS)

Kuiper, T. B. H.; Majid, W. A.; Martinez, S.; Garcia-Miro, C.; Rizzo, J. R.

2012-09-01

The Deep Space Network (DSN) communicates with spacecraft as far away as the boundary between the Solar System and the interstellar medium. To make this possible, large sensitive antennas at Canberra, Australia, Goldstone, California, and Madrid, Spain, provide for constant communication with interplanetary missions. We describe the procedures for radioastronomical observations using this network. Remote access to science monitor and control computers by authorized observers is provided by two-factor authentication through a gateway at the Jet Propulsion Laboratory (JPL) in Pasadena. To make such observations practical, we have devised schemes based on SSH tunnels and distributed computing. At the very minimum, one can use SSH tunnels and VNC (Virtual Network Computing, a remote desktop software suite) to control the science hosts within the DSN Flight Operations network. In this way we have controlled up to three telescopes simultaneously. However, X-window updates can be slow and there are issues involving incompatible screen sizes and multi-screen displays. Consequently, we are now developing SSH tunnel-based schemes in which instrument control and monitoring, and intense data processing, are done on-site by the remote DSN hosts while data manipulation and graphical display are done at the observer's host. We describe our approaches to various challenges, our experience with what worked well and lessons learned, and directions for future development.

Interplanetary CubeSat Navigational Challenges

NASA Technical Reports Server (NTRS)

Martin-Mur, Tomas J.; Gustafson, Eric D.; Young, Brian T.

2015-01-01

CubeSats are miniaturized spacecraft of small mass that comply with a form specification so they can be launched using standardized deployers. Since the launch of the first CubeSat into Earth orbit in June of 2003, hundreds have been placed into orbit. There are currently a number of proposals to launch and operate CubeSats in deep space, including MarCO, a technology demonstration that will launch two CubeSats towards Mars using the same launch vehicle as NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) Mars lander mission. The MarCO CubeSats are designed to relay the information transmitted by the InSight UHF radio during Entry, Descent, and Landing (EDL) in real time to the antennas of the Deep Space Network (DSN) on Earth. Other CubeSatts proposals intend to demonstrate the operation of small probes in deep space, investigate the lunar South Pole, and visit a near Earth object, among others. Placing a CubeSat into an interplanetary trajectory makes it even more challenging to pack the necessary power, communications, and navigation capabilities into such a small spacecraft. This paper presents some of the challenges and approaches for successfully navigating CubeSats and other small spacecraft in deep space.

KSC-04PD-2697

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians oversee the final movement of the Deep Impact spacecraft being lowered onto the Delta II third stage for mating. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2698

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians oversee the final movement of the Deep Impact spacecraft being lowered onto the Delta II third stage for mating. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0010

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., the Deep Impact spacecraft is secure in the canister for its move to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2696

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians watch as an overhead crane lowers the Deep Impact spacecraft onto the Delta II third stage for mating. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3- foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2695

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians watch as an overhead crane lifts the Deep Impact spacecraft, which is being moved for mating to the Delta II third stage. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2694

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians attach a crane to the Deep Impact spacecraft in order to move it to the Delta II third stage at left for mating. When the spacecraft and third stage are mated, they will be moved to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There they will be mated to the Delta II rocket and the fairing installed around them for protection during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

Cost and Performance Comparison of an Earth-Orbiting Optical Communication Relay Transceiver and a Ground-Based Optical Receiver Subnet

NASA Technical Reports Server (NTRS)

Wilson, K. E.; Wright, M.; Cesarone, R.; Ceniceros, J.; Shea, K.

2003-01-01

Optical communications can provide high-data-rate telemetry from deep-space probes with subsystems that have lower mass, consume less power, and are smaller than their radio frequency (RF) counterparts. However, because optical communication is more a.ected by weather than is RF communication, it requires groundstation site diversity to mitigate the adverse e.ects of inclement weather on the link. An optical relay satellite is not a.ected by weather and can provide 24-hour coverage of deep-space probes. Using such a relay satellite for the deep-space link and an 8.4-GHz (X-band) link to a ground station would support high-data-rate links from small deep-space probes with very little link loss due to inclement weather. We have reviewed past JPL-funded work on RF and optical relay satellites, and on proposed clustered and linearly dispersed optical subnets. Cost comparisons show that the life cycle costs of a 7-m optical relay station based on the heritage of the Next Generation Space Telescope is comparable to that of an 8-station subnet of 10- m optical ground stations. This makes the relay link an attractive option vis- a-vis a ground-station network.

Cost and Performance Comparison of an Earth-Orbiting Optical Communication Relay Transceiver and a Ground-Based Optical Receiver Subnet

NASA Astrophysics Data System (ADS)

Wilson, K. E.; Wright, M.; Cesarone, R.; Ceniceros, J.; Shea, K.

2003-01-01

Optical communications can provide high-data-rate telemetry from deep-space probes with subsystems that have lower mass, consume less power, and are smaller than their radio frequency (RF) counterparts. However, because optical communication is more affected by weather than is RF communication, it requires ground station site diversity to mitigate the adverse effects of inclement weather on the link. An optical relay satellite is not affected by weather and can provide 24-hour coverage of deep-space probes. Using such a relay satellite for the deep-space link and an 8.4-GHz (X-band) link to a ground station would support high-data-rate links from small deep-space probes with very little link loss due to inclement weather. We have reviewed past JPL-funded work on RF and optical relay satellites, and on proposed clustered and linearly dispersed optical subnets. Cost comparisons show that the life cycle costs of a 7-m optical relay station based on the heritage of the Next Generation Space Telescope is comparable to that of an 8-station subnet of 10-m optical ground stations. This makes the relay link an attractive option vis-a-vis a ground station network.

Disruption Tolerant Networking Flight Validation Experiment on NASA's EPOXI Mission

NASA Technical Reports Server (NTRS)

Wyatt, Jay; Burleigh, Scott; Jones, Ross; Torgerson, Leigh; Wissler, Steve

2009-01-01

In October and November of 2008, the Jet Propulsion Laboratory installed and tested essential elements of Delay/Disruption Tolerant Networking (DTN) technology on the Deep Impact spacecraft. This experiment, called Deep Impact Network Experiment (DINET), was performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. During DINET some 300 images were transmitted from the JPL nodes to the spacecraft. Then they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. All transmitted bundles were successfully received, without corruption. The DINET experiment demonstrated DTN readiness for operational use in space missions. This activity was part of a larger NASA space DTN development program to mature DTN to flight readiness for a wide variety of mission types by the end of 2011. This paper describes the DTN protocols, the flight demo implementation, validation metrics which were created for the experiment, and validation results.

Absolute flux density calibrations of radio sources: 2.3 GHz

NASA Technical Reports Server (NTRS)

Freiley, A. J.; Batelaan, P. D.; Bathker, D. A.

1977-01-01

A detailed description of a NASA/JPL Deep Space Network program to improve S-band gain calibrations of large aperture antennas is reported. The program is considered unique in at least three ways; first, absolute gain calibrations of high quality suppressed-sidelobe dual mode horns first provide a high accuracy foundation to the foundation to the program. Second, a very careful transfer calibration technique using an artificial far-field coherent-wave source was used to accurately obtain the gain of one large (26 m) aperture. Third, using the calibrated large aperture directly, the absolute flux density of five selected galactic and extragalactic natural radio sources was determined with an absolute accuracy better than 2 percent, now quoted at the familiar 1 sigma confidence level. The follow-on considerations to apply these results to an operational network of ground antennas are discussed. It is concluded that absolute gain accuracies within + or - 0.30 to 0.40 db are possible, depending primarily on the repeatability (scatter) in the field data from Deep Space Network user stations.

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

NASA Astrophysics Data System (ADS)

Zhang, Linfeng; Wang, Han; E, Weinan

2018-03-01

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.

KSC-04PD-2180

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Joe Galamback mounts a bracket on a solar panel on the Deep Impact spacecraft. Galamback is a lead mechanic technician with Ball Aerospace and Technologies Corp. in Boulder, Colo. The spacecraft is undergoing verification testing after its long road trip from Colorado.A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3- foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. The spacecraft is scheduled to launch Dec. 30, 2004, aboard a Boeing Delta II rocket from Launch Complex 17 at Cape Canaveral Air Force Station, Fla.

Learning Search Control Knowledge for Deep Space Network Scheduling

NASA Technical Reports Server (NTRS)

Gratch, Jonathan; Chien, Steve; DeJong, Gerald

1993-01-01

While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

Earth Rotation Parameters from DSN VLBI: 1994

NASA Technical Reports Server (NTRS)

Steppe, J. A.; Oliveau, S. H.; Sovers, O. J.

1994-01-01

In this report, Earth Rotation Parameter (ERP) estimates ahve been obtained from an analysis of Deep Space Network (DSN) VLBI data that directly aligns its celestial and terrestrial reference frames with those of the International Earth Rotation Service (IERS).

Publications of the Jet Propulsion Laboratory 1982

NASA Technical Reports Server (NTRS)

1983-01-01

A bibliography of articles concerning topics on the deep space network, data acquisition, telecommunication, and related aerospace studies is presented. A sample of the diverse subjects include, solar energy remote sensing, computer science, Earth resources, astronomy, and satellite communication.

Triple Asteroid System Triples Asteroid Observers Interest

NASA Image and Video Library

2009-08-06

NASA Deep Space Network, Goldstone radar images show triple asteroid 1994 CC, which consists of a central object approximately 700 meters 2,300 feet in diameter and two smaller moons that orbit the central body. Animation available at the Photojournal

Animated software training via the internet: lessons learned

NASA Technical Reports Server (NTRS)

Scott, C. J.

2000-01-01

The Mission Execution and Automation Section, Information Technologies and Software Systems Division at the Jet Propulsion Laboratory, recently delivered an animated software training module for the TMOD UPLINK Consolidation Task for operator training at the Deep Space Network.

Maintenance and operations cost model for DSN subsystems

NASA Technical Reports Server (NTRS)

Burt, R. W.; Lesh, J. R.

1977-01-01

A procedure is described which partitions the recurring costs of the Deep Space Network (DSN) over the individual DSN subsystems. The procedure results in a table showing the maintenance, operations, sustaining engineering and supportive costs for each subsystems.

Cryogenic, X-band and Ka-band InP HEMT based LNAs for the Deep Space Network

NASA Technical Reports Server (NTRS)

Bautista, J. J.; Bowen, J. G.; Fernandez, J. E.; Fujiwara, B.; Loreman, J.; Petty, S.; Prater, J. L.

2000-01-01

This paper presents an overview of this development process with emphasis on comparison between modeled and measured, LNA modules, front-end receiver packages employing these modules, and antennae employing these packages.

Distrubtion Tolerant Network Technology Flight Validation Report: DINET

NASA Technical Reports Server (NTRS)

Jones, Ross M.

2009-01-01

In October and November of 2008, the Jet Propulsion Laboratory installed and tested essential elements of Delay/Disruption Tolerant Networking (DTN) technology on the Deep Impact spacecraft. This experiment, called Deep Impact Network Experiment (DINET), was performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. During DINET some 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. All transmitted bundles were successfully received, without corruption. The DINET experiment demonstrated DTN readiness for operational use in space missions.

Distribution Tolerant Network Technology Flight Validation Report: DINET

NASA Technical Reports Server (NTRS)

Jones, Ross M.

2009-01-01

In October and November of 2008, the Jet Propulsion Laboratory installed and tested essential elements of Delay/Disruption Tolerant Networking (DTN) technology on the Deep Impact spacecraft. This experiment, called Deep Impact Network Experiment (DINET), was performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. During DINET some 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. All transmitted bundles were successfully received, without corruption. The DINET experiment demonstrated DTN readiness for operational use in space missions.

Supply support of NASA tracking networks

NASA Technical Reports Server (NTRS)

1973-01-01

The extent which supply support for Jet Propulsion Laboratory's Deep Space Network and Goddard Space Flight Center's Space Flight Tracking and Data Network should be consolidated is considered along with the Identification of opportunities for improvements in each of the supply systems without regard to consolidation. There is a considerable amount of commonality between the items in the stock catalogs at the two network depots, 58% for federal stock number items and 30% overall. The workload at the DSIF Supply Depot (DSD) is small (less than 20%) compared to the Network Logistics Depot (NLD). A number of important benefits in supply support would result from a consolidation of DSD into NLD. LMI found that a consolidation as is, without any changes in inventory management techniques, would reduce annual operating costs by from $208,000 to $358,000. However, if the consolidation were coupled with a change to use of economic order quantities, the annual operating cost reduction would range from $930,000 to $1,078,000.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1991-01-01

This quarterly reports on space communications, radio navigation, radio science, and ground based radio and radar astronomy in connection with the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and in operations. Also included is standards activity at JPL for space data and information systems and DSN work. Specific areas of research are: Tracking and ground based navigation; Spacecraft and ground communications; Station control and system technology; DSN Systems Implementation; and DSN Operations.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, Edward C. (Editor)

1991-01-01

This quarterly publication provides archival reports on developments in programs managed by the Jet Propulsion Laboratory's (JPL's) Office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on the activities of the Deep Space Network (DSN) in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data, information systems, and reimbursable DSN work performed for other space agencies through NASA.

Current status of the HAL/S compiler on the Modcomp classic 7870 computer

NASA Technical Reports Server (NTRS)

Lytle, P. J.

1981-01-01

A brief history of the HAL/S language, including the experience of other users of the language at the Jet Propulsion Laboratory is presented. The current status of the compiler, as implemented on the Modcomp 7870 Classi computer, and future applications in the Deep Space Network (DSN) are discussed. The primary applications in the DSN will be in the Mark IVA network.

Artificial Intelligence in planetary spectroscopy

NASA Astrophysics Data System (ADS)

Waldmann, Ingo

2017-10-01

The field of exoplanetary spectroscopy is as fast moving as it is new. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain. This is true for both: the data analysis of observations as well as the theoretical modelling of their atmospheres.Issues of low signal-to-noise data and large, non-linear parameter spaces are nothing new and commonly found in many fields of engineering and the physical sciences. Recent years have seen vast improvements in statistical data analysis and machine learning that have revolutionised fields as diverse as telecommunication, pattern recognition, medical physics and cosmology.In many aspects, data mining and non-linearity challenges encountered in other data intensive fields are directly transferable to the field of extrasolar planets. In this conference, I will discuss how deep neural networks can be designed to facilitate solving said issues both in exoplanet atmospheres as well as for atmospheres in our own solar system. I will present a deep belief network, RobERt (Robotic Exoplanet Recognition), able to learn to recognise exoplanetary spectra and provide artificial intelligences to state-of-the-art atmospheric retrieval algorithms. Furthermore, I will present a new deep convolutional network that is able to map planetary surface compositions using hyper-spectral imaging and demonstrate its uses on Cassini-VIMS data of Saturn.

Layered virus protection for the operations and administrative messaging system

NASA Technical Reports Server (NTRS)

Cortez, R. H.

2002-01-01

NASA's Deep Space Network (DSN) is critical in supporting the wide variety of operating and plannedunmanned flight projects. For day-to-day operations it relies on email communication between the three Deep Space Communication Complexes (Canberra, Goldstone, Madrid) and NASA's Jet Propulsion Laboratory. The Operations & Administrative Messaging system, based on the Microsoft Windows NTand Exchange platform, provides the infrastructure that is required for reliable, mission-critical messaging. The reliability of this system, however, is threatened by the proliferation of email viruses that continue to spread at alarming rates. A layered approach to email security has been implemented across the DSN to protect against this threat.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

In-Situ Environmental Monitoring and Science Investigations Enabled by the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Clark, P. E.; Collier, M. R.; Farrell, W. M.

2018-02-01

A distributed network of instrument packages in an ARTEMIS-like orbit will serve as the much-needed basis for on-going monitoring of cislunar environmental dynamics, critical for a successful human presence on the Moon.

Array signal processing in the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Pham, Timothy T.; Jongeling, Andre P.

2004-01-01

In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.

Precision of radio science instrumentation for planetary exploration

NASA Technical Reports Server (NTRS)

Asmar, S. W.; Armstrong, J. W.; Iess, L.; Tortora, P.

2004-01-01

The Deep Space Network is the largest and most sensitive scientific telecommunications facility Primary function: providing two-way communication between the Earth and spacecraft exploring the solar system Instrumented with large parabolic reflectors, high-power transmitters, low-noise amplifiers & receivers.

Deep learning for studies of galaxy morphology

NASA Astrophysics Data System (ADS)

Tuccillo, D.; Huertas-Company, M.; Decencière, E.; Velasco-Forero, S.

2017-06-01

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

Telecommunications and data acquisition systems support for Voyager missions to Jupiter and Saturn, 1972-1981, prelaunch through Saturn encounter

NASA Technical Reports Server (NTRS)

Traxler, M. R.; Beauchamp, D. F.

1983-01-01

The Deep Space Network has supported the Voyager Project for approximately nine years, during which time implementation, testing, and operational support was provided. Four years of this time involved testing prior to launch; the final five years included network operations support and additional network implementation. Intensive and critical support intervals included launch and four planetary encounters. The telecommunications and data acquisition support for the Voyager Missions to Jupiter and Saturn are summarized.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Lee, Charles H.; Cheung, Kar-Ming

2012-01-01

In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

Architecture for Cognitive Networking within NASA's Future Space Communications Infrastructure

NASA Technical Reports Server (NTRS)

Clark, Gilbert; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

2016-01-01

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, GEO, MEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes an architecture enabling the development and deployment of cognitive networking capabilities into the envisioned future NASA space communications infrastructure. We begin by discussing the need for increased automation, including inter-system discovery and collaboration. This discussion frames the requirements for an architecture supporting cognitive networking for future missions and relays, including both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture, and results of implementation and initial testing of a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

Architecture for Cognitive Networking within NASAs Future Space Communications Infrastructure

NASA Technical Reports Server (NTRS)

Clark, Gilbert J., III; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

2016-01-01

Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

Tracking Performance of Upgraded "Polished Panel" Optical Receiver on NASA's 34 Meter Research Antenna

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor

2013-01-01

There has been considerable interest in developing and demonstrating a hybrid "polished panel" optical receiver concept that would replace the microwave panels on the Deep Space Network's (DSN) 34 meter antennas with highly polished aluminum panels, thus enabling simultaneous opticaland microwave reception. A test setup has been installed on the 34 meter research antenna at DSS-13 (Deep Space Station 13) at NASA's Goldstone Deep Space Communications Complex in California in order to assess the feasibility of this concept. Here we describe the results of a recent effort todramatically reduce the dimensions of the point-spread function (PSF) generated by a custom polished panel, thus enabling improved optical communications performance. The latest results are compared to the previous configuration in terms of quantifiable PSF improvement. In addition, the performance of acquisition and tracking algorithms designed specifically for the polished panel PSF are evaluated and compared, based on data obtained from real-time tracking of planets and bright stars with the 34 meter research antenna at DSS-13.

Tracking and data system support for the Mariner Mars 1971 mission. Volume 3: Orbit insertion through end of primary mission

NASA Technical Reports Server (NTRS)

Barnum, P. W.; Renzetti, N. A.; Textor, G. P.; Kelly, L. B.

1973-01-01

The Tracking and Data System (TDS) Support for the Mariner Mars 1971 Mission final report contains the deep space tracking and data acquisition activities in support of orbital operations. During this period a major NASA objective was accomplished: completion of the 180th revolution and 90th day of data gathering with the spacecraft about the planet Mars. Included are presentations of the TDS flight support pass chronology data for each of the Deep Space Stations used, and performance evaluation for the Deep Space Network Telemetry, Tracking, Command, and Monitor Systems. With the loss of Mariner 8 at launch, Mariner 9 assumed the mission plan of Mariner 8, which included the TV mapping cycles and a 12-hr orbital period. The mission plan was modified as a result of a severe dust storm on the surface of Mars, which delayed the start of the TV mapping cycles. Thus, the end of primary mission date was extended to complete the TV mapping cycles.

Periodically Launched, Dedicated CubeSats/SmallSats for Space Situational Awareness Through NASA Communications Networks

NASA Astrophysics Data System (ADS)

Stromberg, E. M.; Shaw, H.; Estabrook, P.; Neilsen, T. L.; Gunther, J.; Swenson, C.; Fish, C. S.; Schaire, S. H.

2014-12-01

Space Situational Awareness (SSA) is an area where spaceflight activities and missions can directly influence the quality of life on earth. The combination of space weather, near earth orbiting objects, atmospheric conditions at the space boundary, and other phenomena can have significant short-term and long-term implications for the inhabitants of this planet. The importance of SSA has led to increased activity in this area from both space and ground based platforms. The emerging capability of CubeSats and SmallSats provides an opportunity for these low-cost, versatile platforms to augment the SSA infrastructure. The CubeSats and SmallSats can be launched opportunistically with shorter lead times than larger missions. They can be organized both as constellations or individual sensor elements. Combining CubeSats and SmallSats with the existing NASA communications networks (TDRS Space Network, Deep Space Network and the Near Earth Network) provide a backbone structure for SSA which can be tied to a SSA portal for data distribution and management. In this poster we will describe the instruments and sensors needed for CubeSat and SmallSat SSA missions. We will describe the architecture and concept of operations for a set of opportunistic, periodically launched, SSA CubeSats and SmallSats. We will also describe the integrated communications infrastructure to support end-to-end data delivery and management to a SSA portal.

JPL Closeup

NASA Technical Reports Server (NTRS)

1983-01-01

Voyager, Infrared Astronomical Satellite, Galileo, Viking, Solar Mesosphere Explorer, Wide-field/Planetary Camera, Venus Mapper, International Solar Polar Mission - Solar Interplanetary Satellite, Extreme Ultraviolet Explores, Starprobe, International Halley Watch, Marine Mark II, Samex, Shuttle Imaging Radar-A, Deep Space Network, Biomedical Technology, Ocean Studies and Robotics are summarized.

High-power transmitter automation, part 2

NASA Technical Reports Server (NTRS)

Gregg, M. A.

1981-01-01

The current status of the transmitter automation development is reported. The work described is applicable to all transmitters in the Deep Space Network. New interface and software designs are described which improve reliability and reduce the time required for subsystem turn on and klystron saturation.

Phylogenetic convolutional neural networks in metagenomics.

PubMed

Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

2018-03-08

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

KSC-05PD-0001

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft waits at Astrotech Space Operations in Titusville, Fla., for placement of a protective cover before the canister is installed around it. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0004

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians place the lower segments of a protective canister around the Deep Impact spacecraft. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0007

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., technicians lower the upper canister toward the Deep Impact spacecraft. It will be attached to the lower segments already surrounding the spacecraft. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0005

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians roll the Deep Impact spacecraft into another area where the upper canister can be lowered around it. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0002

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., a protective cover is being lowered over the Deep Impact spacecraft to protect it before the canister is installed around it. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0011

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft leaves Astrotech Space Operations in Titusville, Fla., in the pre-dawn hours on a journey to Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. There the spacecraft will be attached to the second stage of the Boeing Delta II rocket. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0003

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians lower a protective cover over the Deep Impact spacecraft to protect it before the canister is installed around it. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3- foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0006

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., technicians install a crane onto the upper canister before lifting it to install around the Deep Impact spacecraft. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0009

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., Boeing technicians attach the upper canister with the lower segments surrounding the Deep Impact spacecraft. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0008

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. At Astrotech Space Operations in Titusville, Fla., technicians lower the upper canister toward the Deep Impact spacecraft. It will be attached to the lower segments already surrounding the spacecraft. Once the spacecraft is completely covered, it will be transferred to Launch Pad 17-B on Cape Canaveral Air Force Station, Fla. Then, in the mobile service tower, the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

Integrated Network Architecture for Sustained Human and Robotic Exploration

NASA Technical Reports Server (NTRS)

Noreen, Gary; Cesarone, Robert; Deutsch, Leslie; Edwards, Charles; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazolla, Sabino;

Deep Space Networking Experiments on the EPOXI Spacecraft

NASA Technical Reports Server (NTRS)

Jones, Ross M.

2011-01-01

NASA's Space Communications & Navigation Program within the Space Operations Directorate is operating a program to develop and deploy Disruption Tolerant Networking [DTN] technology for a wide variety of mission types by the end of 2011. DTN is an enabling element of the Interplanetary Internet where terrestrial networking protocols are generally unsuitable because they rely on timely and continuous end-to-end delivery of data and acknowledgments. In fall of 2008 and 2009 and 2011 the Jet Propulsion Laboratory installed and tested essential elements of DTN technology on the Deep Impact spacecraft. These experiments, called Deep Impact Network Experiment (DINET 1) were performed in close cooperation with the EPOXI project which has responsibility for the spacecraft. The DINET 1 software was installed on the backup software partition on the backup flight computer for DINET 1. For DINET 1, the spacecraft was at a distance of about 15 million miles (24 million kilometers) from Earth. During DINET 1 300 images were transmitted from the JPL nodes to the spacecraft. Then, they were automatically forwarded from the spacecraft back to the JPL nodes, exercising DTN's bundle origination, transmission, acquisition, dynamic route computation, congestion control, prioritization, custody transfer, and automatic retransmission procedures, both on the spacecraft and on the ground, over a period of 27 days. The first DINET 1 experiment successfully validated many of the essential elements of the DTN protocols. DINET 2 demonstrated: 1) additional DTN functionality, 2) automated certain tasks which were manually implemented in DINET 1 and 3) installed the ION SW on nodes outside of JPL. DINET 3 plans to: 1) upgrade the LTP convergence-layer adapter to conform to the international LTP CL specification, 2) add convergence-layer "stewardship" procedures and 3) add the BSP security elements [PIB & PCB]. This paper describes the planning and execution of the flight experiment and the validation results.

Simulator of Space Communication Networks

NASA Technical Reports Server (NTRS)

Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

2005-01-01

Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

Quantum-chemical insights from deep tensor neural networks

PubMed Central

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems. PMID:28067221

Quantum-chemical insights from deep tensor neural networks.

PubMed

Schütt, Kristof T; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R; Tkatchenko, Alexandre

2017-01-09

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol -1 ) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

Quantum-chemical insights from deep tensor neural networks

NASA Astrophysics Data System (ADS)

Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre

2017-01-01

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol-1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

KSC-05PD-0019

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. From a vantage point above, a worker observes the Deep Impact spacecraft exposed after removal of the canister and protective cover. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2404

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., a second Solid Rocket Booster (SRB) is raised off a transporter to be lifted up the mobile service tower. It will be attached to the Boeing Delta II launch vehicle for launch of the Deep Impact spacecraft. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact project management is handled by the Jet Propulsion Laboratory in Pasadena, Calif. The spacecraft is scheduled to launch Dec. 30, 2004.

KSC-05PD-0075

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft waits inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., for fairing installation. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0079

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., the partly enclosed Deep Impact spacecraft (background) waits while the second half of the fairing (foreground left) moves toward it. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0076

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., the first half of the fairing is moved toward the Deep Impact spacecraft for installation. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0078

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., the first half of the fairing is moved into place around the Deep Impact spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0074

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft waits inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., for fairing installation. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0077

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., the first half of the fairing is moved into place around the Deep Impact spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0073

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft waits inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., for fairing installation. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0080

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. Inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air force Station, Fla., workers attach the two halves of the fairing around the Deep Impact spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth nosecone, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-04PD-2413

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. On Launch Pad 17-B, Cape Canaveral Air Force Station, Fla., a crane begins lifting the third in a set of three Solid Rocket Boosters (SRBs). The SRBs will be hoisted up the mobile service tower and join three others already mated to the Boeing Delta II rocket that will launch the Deep Impact spacecraft. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing an impactor on a course to hit the comets sunlit side, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measure the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determine the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-04PD-2664

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. This view from inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air Force Station, shows the Boeing Delta II second stage as it reaches the top. The component will be reattached to the interstage adapter on the Delta II. The rocket is the launch vehicle for the Deep Impact spacecraft, scheduled for liftoff no earlier than Jan. 12. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-04PD-2662

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. At Launch Pad 17-B, Cape Canaveral Air Force Station, the Boeing Delta II second stage reaches the top of the mobile service tower. The component will be reattached to the interstage adapter on the Delta II. The rocket is the launch vehicle for the Deep Impact spacecraft, scheduled for liftoff no earlier than Jan. 12. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KSC-04PD-2663

NASA Technical Reports Server (NTRS)

2004-01-01

KENNEDY SPACE CENTER, FLA. This view from inside the mobile service tower on Launch Pad 17-B, Cape Canaveral Air Force Station, shows the Boeing Delta II second stage as it reaches the top. The component will be reattached to the interstage adapter on the Delta II. The rocket is the launch vehicle for the Deep Impact spacecraft, scheduled for liftoff no earlier than Jan. 12. A NASA Discovery mission, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth, and reveal the secrets of its interior. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will collect pictures and data of how the crater forms, measuring the craters depth and diameter, as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network.

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

PubMed

Eo, Taejoon; Jun, Yohan; Kim, Taeseong; Jang, Jinseong; Lee, Ho-Joon; Hwang, Dosik

2018-04-06

To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a deep CNN operating on an image domain (ICNN), and (3) an interleaved data consistency operations. These components are alternately applied, and each CNN is trained to minimize the loss between the reconstructed and corresponding fully sampled k-spaces. The final reconstructed image is obtained by forward-propagating the undersampled k-space data through the entire network. Performances of K-net (KCNN with inverse Fourier transform), I-net (ICNN with interleaved data consistency), and various combinations of the 2 different networks were tested. The test results indicated that K-net and I-net have different advantages/disadvantages in terms of tissue-structure restoration. Consequently, the combination of K-net and I-net is superior to single-domain CNNs. Three MR data sets, the T 2 fluid-attenuated inversion recovery (T 2 FLAIR) set from the Alzheimer's Disease Neuroimaging Initiative and 2 data sets acquired at our local institute (T 2 FLAIR and T 1 weighted), were used to evaluate the performance of 7 conventional reconstruction algorithms and the proposed cross-domain CNNs, which hereafter is referred to as KIKI-net. KIKI-net outperforms conventional algorithms with mean improvements of 2.29 dB in peak SNR and 0.031 in structure similarity. KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling. © 2018 International Society for Magnetic Resonance in Medicine.

Ending Year in Space: NASA Goddard Network Maintains Communications from Space to Ground

NASA Image and Video Library

2016-03-01

NASA's Goddard Space Flight Center in Greenbelt, Maryland, will monitor the landing of NASA Astronaut Scott Kelly and Russian Cosmonaut Mikhail Kornienko from their #YearInSpace Mission. Goddard's Networks Integration Center, pictured above, leads all coordination for space-to-ground communications support for the International Space Station and provides contingency support for the Soyuz TMA-18M 44S spacecraft, ensuring complete communications coverage through NASA's Space Network. The Soyuz 44S spacecraft will undock at 8:02 p.m. EST this evening from the International Space Station. It will land approximately three and a half hours later, at 11:25 p.m. EST in Kazakhstan. Both Kelly and Kornienko have spent 340 days aboard the International Space Station, preparing humanity for long duration missions and exploration into deep space. Read more: www.nasa.gov/feature/goddard/2016/ending-year-in-space-na... Credit: NASA/Goddard/Rebecca Roth NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Ending Year in Space: NASA Goddard Network Maintains Communications from Space to Ground

NASA Image and Video Library

2017-12-08

NASA's Goddard Space Flight Center in Greenbelt, Maryland, will monitor the landing of NASA Astronaut Scott Kelly and Russian Cosmonaut Mikhail Kornienko from their #YearInSpace Mission. Goddard's Networks Integration Center, pictured above, leads all coordination for space-to-ground communications support for the International Space Station and provides contingency support for the Soyuz TMA-18M 44S spacecraft, ensuring complete communications coverage through NASA's Space Network. The Soyuz 44S spacecraft will undock at 8:02 p.m. EST this evening from the International Space Station. It will land approximately three and a half hours later, at 11:25 p.m. EST in Kazakhstan. Both Kelly and Kornienko have spent 340 days aboard the International Space Station, preparing humanity for long duration missions and exploration into deep space. Read more: www.nasa.gov/feature/goddard/2016/ending-year-in-space-na... Credit: NASA/Goddard/Rebecca Roth NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, Edward C. (Editor)

1991-01-01

A compilation is presented of articles on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition. In space communications, radio navigation, radio science, and ground based radio and radar astronomy, activities of the Deep Space Network are reported in planning, in supporting research and technology, in implementation, and in operations. Also included is standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. In the search for extraterrestrial intelligence (SETI), implementation and operations are reported for searching the microwave spectrum.

Frame synchronization for the Galileo code

NASA Technical Reports Server (NTRS)

Arnold, S.; Swanson, L.

1991-01-01

Results are reported on the performance of the Deep Space Network's frame synchronizer for the (15,1/4) convolutional code after Viterbi decoding. The threshold is found that optimizes the probability of acquiring true sync within four frames using a strategy that requires next frame verification.

Probes for measuring noise current in an electronic cable

NASA Technical Reports Server (NTRS)

Lundy, C. C.

1974-01-01

Electromagnetic interference in deep-space network receiver is often caused by stray coupling from power lines. These stray signals create potential differences between ground terminals, which leads to excessive noise in receiver circuits. Pair of probes detect and measure noise currents in conductors.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1982-01-01

Progress in the development and operations of the Deep Space Network is reported. Developments in Earth-based radio technology as applied to other research programs are also reported. These programs include geodynamics, astrophysics, and radio searching for extraterrestrial intelligence in the microwave region of the electromagnetic spectrum.

Radio astronomy

NASA Technical Reports Server (NTRS)

Taylor, R. M.; Manchester, R. N.

1980-01-01

The activities of the Deep Space Network in support of radio and radar astronomy operations during July and August 1980 are reported. A brief update on the OSS-sponsored planetary radio astronomy experiment is provided. Also included are two updates, one each from Spain and Australia on current host country activities.

The Telecommunications and Data Acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Progress in the development and operations of the Deep Space Network is reported including develoments in Earth-based radio technology as applied to other research programs. These programs are: geodynamics, astrophysics, and the radio search for extraterrestrial intelligence in the microwave region of the electromagnetic spectrum.

Viking Mars encounter

NASA Technical Reports Server (NTRS)

1976-01-01

Various phases of planetary operations related to the Viking mission to Mars are described. Topics discussed include: approach phase, Mars orbit insertion, prelanding orbital activities, separation, descent and landing, surface operations, surface sampling and operations starting, orbiter science and radio science, Viking 2, Deep Space Network and data handling.

Design and progress report for compact cryocooled sapphire oscillator 'VCSO'

NASA Technical Reports Server (NTRS)

Dick, G. John; Wang, Rabi T.; Tjoelker, Robert L.

2005-01-01

We report on the development of a compact cryocooled sapphiere oscillator 'VCSO', designed as a higher-performance replacement for ultra-stable quartz oscillators in local oscillator, cleanup, and flywheel applications in the frequency generation and distribution subsystems of NASA's Deep Space Network (DSN).

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1987-01-01

Developments in programs managed by the Jet Propulsion Laboratory's Office of Telecommunications and Data Acquisition are discussed. Topics discussed include sorption compression/mechanical expanded hybrid refrigeration, calculated 70-meter antenna performance for offset L-band, systolic arrays and stack decoding, and calibrations of Deep Space Network antennas.

Regionalized Lunar South Pole Surface Navigation System Analysis

NASA Technical Reports Server (NTRS)

Welch, Bryan W.

2008-01-01

Apollo missions utilized Earth-based assets for navigation because the landings took place at lunar locations in constant view from the Earth. The new exploration campaign to the lunar south pole region will have limited Earth visibility, but the extent to which a navigation system comprised solely of Earth-based tracking stations will provide adequate navigation solutions in this region is unknown. This report presents a dilution-of-precision (DoP)-based, stationary surface navigation analysis of the performance of multiple lunar satellite constellations, Earth-based deep space network assets, and combinations thereof. Results show that kinematic and integrated solutions cannot be provided by the Earth-based deep space network stations. Also, the stationary surface navigation system needs to be operated either as a two-way navigation system or as a one-way navigation system with local terrain information, while the position solution is integrated over a short duration of time with navigation signals being provided by a lunar satellite constellation.

Table-driven configuration and formatting of telemetry data in the Deep Space Network

NASA Technical Reports Server (NTRS)

Manning, Evan

1994-01-01

With a restructured software architecture for telemetry system control and data processing, the NASA/Deep Space Network (DSN) has substantially improved its ability to accommodate a wide variety of spacecraft in an era of 'better, faster, cheaper'. In the new architecture, the permanent software implements all capabilities needed by any system user, and text tables specify how these capabilities are to be used for each spacecraft. Most changes can now be made rapidly, outside of the traditional software development cycle. The system can be updated to support a new spacecraft through table changes rather than software changes, reducing the implementation, test, and delivery cycle for such a change from three months to three weeks. The mechanical separation of the text table files from the program software, with tables only loaded into memory when that mission is being supported, dramatically reduces the level of regression testing required. The format of each table is a different compromise between ease of human interpretation, efficiency of computer interpretation, and flexibility.

Earth's gravity field to the eighteenth degree and geocentric coordinates for 104 stations from satellite and terrestrial data

NASA Technical Reports Server (NTRS)

Gaposchkin, E. M.

1973-01-01

Geodetic parameters describing the earth's gravity field and the positions of satellite-tracking stations in a geocentric reference frame were computed. These parameters were estimated by means of a combination of five different types of data: routine and simultaneous satellite observations, observations of deep-space probes, measurements of terrestrial gravity, and surface-triangulation data. The combination gives better parameters than does any subset of data types. The dynamic solution used precision-reduced Baker-Nunn observations and laser range data of 25 satellites. Data from the 49-station National Oceanic and Atmospheric Administration BC-4 network, the 19-station Smithsonian Astrophysical Observatory Baker-Nunn network, and independent camera stations were employed in the geometrical solution. Data from the tracking of deep-space probes were converted to relative longitudes and distances to the earth's axis of rotation of the tracking stations. Surface-gravity data in the form of 550-km squares were derived from 19,328 1 deg X 1 deg mean gravity anomalies.

Remote Operations of the Deep Space Network Radio Science Subsystem

NASA Astrophysics Data System (ADS)

Caetta, J.; Asmar, S.; Abbate, S.; Connally, M.; Goltz, G.

1998-04-01

The capability for scientists to remotely control systems located at the Deep Space Network facilities only recently has been incorporated in the design and implementation of new equipment. However, time lines for the implementation, distribution, and operational readiness of such systems can extend much farther into the future than the users can wait. The Radio Science Systems Group was faced with just that circumstance; new hardware was not scheduled to become operational for several years, but the increasing number of experiments and configurations for Cassini, Galileo, Mars missions, and other flight projects made that time frame impractical because of the associated increasing risk of not acquiring critical data. Therefore, a method of interfacing with the current radio science subsystem has been developed and used with a high degree of success, although with occasional problems due to this capability not having been originally designed into the system. This article discusses both the method and the problems involved in integrating this new (remote) method of control with a legacy system.

The Deep Space Network: Noise temperature concepts, measurements, and performance

NASA Technical Reports Server (NTRS)

Stelzried, C. T.

1982-01-01

The use of higher operational frequencies is being investigated for improved performance of the Deep Space Network. Noise temperature and noise figure concepts are used to describe the noise performance of these receiving systems. The ultimate sensitivity of a linear receiving system is limited by the thermal noise of the source and the quantum noise of the receiver amplifier. The atmosphere, antenna and receiver amplifier of an Earth station receiving system are analyzed separately and as a system. Performance evaluation and error analysis techniques are investigated. System noise temperature and antenna gain parameters are combined to give an overall system figure of merit G/T. Radiometers are used to perform radio ""star'' antenna and system sensitivity calibrations. These are analyzed and the performance of several types compared to an idealized total power radiometer. The theory of radiative transfer is applicable to the analysis of transmission medium loss. A power series solution in terms of the transmission medium loss is given for the solution of the noise temperature contribution.

Telecommunications and navigation systems design for manned Mars exploration missions

NASA Astrophysics Data System (ADS)

Hall, Justin R.; Hastrup, Rolf C.

1989-06-01

This paper discusses typical manned Mars exploration needs for telecommunications, including preliminary navigation support functions. It is a brief progress report on an ongoing study program within the current NASA JPL Deep Space Network (DSN) activities. A typical Mars exploration case is defined, and support approaches comparing microwave and optical frequency performance for both local in situ and Mars-earth links are described. Optical telecommunication and navigation technology development opportunities in a Mars exploration program are also identified. A local Mars system telecommunication relay and navigation capability for service support of all Mars missions has been proposed as part of an overall solar system communications network. The effects of light-time delay and occultations on real-time mission decision-making are discussed; the availability of increased local mass data storage may be more important than increasing peak data rates to earth. The long-term frequency use plan will most likely include a mix of microwave, millimeter-wave and optical link capabilities to meet a variety of deep space mission needs.

Telecommunications and navigation systems design for manned Mars exploration missions

NASA Technical Reports Server (NTRS)

Hall, Justin R.; Hastrup, Rolf C.

1989-01-01

This paper discusses typical manned Mars exploration needs for telecommunications, including preliminary navigation support functions. It is a brief progress report on an ongoing study program within the current NASA JPL Deep Space Network (DSN) activities. A typical Mars exploration case is defined, and support approaches comparing microwave and optical frequency performance for both local in situ and Mars-earth links are described. Optical telecommunication and navigation technology development opportunities in a Mars exploration program are also identified. A local Mars system telecommunication relay and navigation capability for service support of all Mars missions has been proposed as part of an overall solar system communications network. The effects of light-time delay and occultations on real-time mission decision-making are discussed; the availability of increased local mass data storage may be more important than increasing peak data rates to earth. The long-term frequency use plan will most likely include a mix of microwave, millimeter-wave and optical link capabilities to meet a variety of deep space mission needs.

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

Networked Operations of Hybrid Radio Optical Communications Satellites

NASA Technical Reports Server (NTRS)

Hylton, Alan; Raible, Daniel

2014-01-01

In order to address the increasing communications needs of modern equipment in space, and to address the increasing number of objects in space, NASA is demonstrating the potential capability of optical communications for both deep space and near-Earth applications. The Integrated Radio Optical Communications (iROC) is a hybrid communications system that capitalizes on the best of both the optical and RF domains while using each technology to compensate for the other's shortcomings. Specifically, the data rates of the optical links can be higher than their RF counterparts, whereas the RF links have greater link availability. The focus of this paper is twofold: to consider the operations of one or more iROC nodes from a networking point of view, and to suggest specific areas of research to further the field. We consider the utility of Disruption Tolerant Networking (DTN) and the Virtual Mission Operation Center (VMOC) model.

Noncoherent Doppler tracking: first flight results

NASA Astrophysics Data System (ADS)

DeBoy, Christopher C.; Robert Jensen, J.; Asher, Mark S.

2005-01-01

Noncoherent Doppler tracking has been devised as a means to achieve highly accurate, two-way Doppler measurements with a simple, transceiver-based communications system. This technique has been flown as an experiment on the Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) spacecraft, (launched 7 December 2001), as the operational technique for Doppler tracking on CONTOUR, and is baselined on several future deep space missions at JHU/APL. This paper reports on initial results from a series of successful tests of this technique between the TIMED spacecraft and NASA ground stations in the Deep Space Network. It also examines the advantages that noncoherent Doppler tracking and a transceiver-based system may offer to small satellite systems, including reduced cost, mass, and power.

Analysis on Tracking Schedule and Measurements Characteristics for the Spacecraft on the Phase of Lunar Transfer and Capture

NASA Astrophysics Data System (ADS)

Song, Young-Joo; Choi, Su-Jin; Ahn, Sang-il; Sim, Eun-Sup

2014-03-01

In this work, the preliminary analysis on both the tracking schedule and measurements characteristics for the spacecraft on the phase of lunar transfer and capture is performed. To analyze both the tracking schedule and measurements characteristics, lunar transfer and capture phases¡¯ optimized trajectories are directly adapted from former research, and eleven ground tracking facilities (three Deep Space Network sties, seven Near Earth Network sites, one Daejeon site) are assumed to support the mission. Under these conceptual mission scenarios, detailed tracking schedules and expected measurement characteristics during critical maneuvers (Trans Lunar Injection, Lunar Orbit Insertion and Apoapsis Adjustment Maneuver), especially for the Deajeon station, are successfully analyzed. The orders of predicted measurements' variances during lunar capture phase according to critical maneuvers are found to be within the order of mm/s for the range and micro-deg/s for the angular measurements rates which are in good agreement with the recommended values of typical measurement modeling accuracies for Deep Space Networks. Although preliminary navigation accuracy guidelines are provided through this work, it is expected to give more practical insights into preparing the Korea's future lunar mission, especially for developing flight dynamics subsystem.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1994-01-01

This quarterly publication provides archival reports on developments in programs managed by JPL's Telecommunications and Mission Operations Directorate (TMOD), which now includes the former Telecommunications and Data Acquisition (TDA) Office. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DS) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC).

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Yuen, Joseph H. (Editor)

1995-01-01

This quarterly publication provides archival reports on developments in programs managed by JPL's Telecommunications and Mission Operations Directorate (TMOD), which now includes the former Telecommunications and Data Acquisition (TDA) Office. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC).

Direct Communication to Earth from Probes

NASA Technical Reports Server (NTRS)

Bolton, Scott J.; Folkner, William M.; Abraham, Douglas S.

2005-01-01

A viewgraph presentation on outer planetary probe communications to Earth is shown. The topics include: 1) Science Rational for Atmospheric Probes to the Outer Planets; 2) Controlling the Scientific Appetite; 3) Learning more about Jupiter before we send more probes; 4) Sample Microwave Scan From Juno; 5) Jupiter s Deep Interior; 6) The Square Kilometer Array (SKA): A Breakthrough for Radio Astronomy; 7) Deep Space Array-based Network (DSAN); 8) Probe Direct-to-Earth Data Rate Calculations; 9) Summary; and 10) Enabling Ideas.

Network Information Management Subsystem

NASA Technical Reports Server (NTRS)

Chatburn, C. C.

1985-01-01

The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.

Communicating with Voyager

NASA Technical Reports Server (NTRS)

Dumas, Larry N.; Hornstein, Robert M.

1990-01-01

The Deep Space Network for receiving Voyager 2 data is discussed. The functions of the earth-Voyager radio link are examined, including radiometrics, transmission of commands to the spacecraft, radio sciences, and the transmission of telemetry from the spacecraft to earth. The use of ranging, Doppler, and VLBI measurements to maintain position and velocity data on Voyager 2 is described. Emphasis is placed on the international tracking network for obtaining Voyager 2 data on Neptune and Triton.

Earth-Mars Telecommunications and Information Management System (TIMS): Antenna Visibility Determination, Network Simulation, and Management Models

NASA Technical Reports Server (NTRS)

Odubiyi, Jide; Kocur, David; Pino, Nino; Chu, Don

1996-01-01

This report presents the results of our research on Earth-Mars Telecommunications and Information Management System (TIMS) network modeling and unattended network operations. The primary focus of our research is to investigate the feasibility of the TIMS architecture, which links the Earth-based Mars Operations Control Center, Science Data Processing Facility, Mars Network Management Center, and the Deep Space Network of antennae to the relay satellites and other communication network elements based in the Mars region. The investigation was enhanced by developing Build 3 of the TIMS network modeling and simulation model. The results of several 'what-if' scenarios are reported along with reports on upgraded antenna visibility determination software and unattended network management prototype.

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Deep Space Network operations, engineering, and implementation are reported. Developments in Earth-based radiotechnology as applied to other research programs in the fields of Geodynamics, Astrophysics, and programs related to radio searchers (instrumentation and methods) in extraterrestrial areas in the microwave region of the electromagnetic spectrum are also presented.

DSS 13 antenna monitor system. [Deep Space Network

NASA Technical Reports Server (NTRS)

Siev, B.; Bayergo, D.

1979-01-01

The development of a monitor system for the DSS 13 antenna is presented. The system checks for accumulator pressures, differential pressures, wind velocity, power supplies, fluid temperatures, and fluid levels. It was concluded that the system performed properly in high winds and correctly reported all malfunctions.

Multipurpose exciter with low phase noise

NASA Technical Reports Server (NTRS)

Conroy, B.; Le, D.

1989-01-01

Results of an effort to develop a lower-cost exciter with high stability, low phase noise, and controllable phase and frequency for use in Deep Space Network and Goldstone Solar System Radar applications are discussed. Included is a discussion of the basic concept, test results, plans, and concerns.

Increasingly complex representations of natural movies across the dorsal stream are shared between subjects.

PubMed

Güçlü, Umut; van Gerven, Marcel A J

2017-01-15

Recently, deep neural networks (DNNs) have been shown to provide accurate predictions of neural responses across the ventral visual pathway. We here explore whether they also provide accurate predictions of neural responses across the dorsal visual pathway, which is thought to be devoted to motion processing and action recognition. This is achieved by training deep neural networks to recognize actions in videos and subsequently using them to predict neural responses while subjects are watching natural movies. Moreover, we explore whether dorsal stream representations are shared between subjects. In order to address this question, we examine if individual subject predictions can be made in a common representational space estimated via hyperalignment. Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies, revealing a correspondence in representations of DNN layers and dorsal stream areas. It is also demonstrated that models operating in a common representational space can generalize to responses of multiple or even unseen individual subjects to novel spatio-temporal stimuli in both encoding and decoding settings, suggesting that a common representational space underlies dorsal stream responses across multiple subjects. Copyright © 2015 Elsevier Inc. All rights reserved.

A Journey with MOM

NASA Technical Reports Server (NTRS)

Helfrich, Cliff; Berry, David S.; Bhat, Ramachandra; Border, James; Graat, Eric; Halsell, Allen; Kruizinga, Gerhard; Lau, Eunice; Mottinger, Neil; Rush, Brian;

Goldstone Tracking the Echo Satelloon.

NASA Image and Video Library

2016-10-27

This archival image was released as part of a gallery comparing JPL’s past and present, commemorating the 80th anniversary of NASA’s Jet Propulsion Laboratory on Oct. 31, 2016. This photograph shows the first pass of Echo 1, NASA's first communications satellite, over the Goldstone Tracking Station managed by NASA's Jet Propulsion Laboratory, in Pasadena, California, in the early morning of Aug. 12, 1960. The movement of the antenna, star trails (shorter streaks), and Echo 1 (the long streak in the middle) are visible in this image. Project Echo bounced radio signals off a 10-story-high, aluminum-coated balloon orbiting the Earth. This form of "passive" satellite communication -- which mission managers dubbed a "satelloon" -- was an idea conceived by an engineer from NASA's Langley Research Center in Hampton, Virginia, and was a project managed by NASA's Goddard Space Flight Center in Greenbelt, Maryland. JPL's role involved sending and receiving signals through two of its 85-foot-diameter (26-meter-diameter) antennas at the Goldstone Tracking Station in California's Mojave Desert. The Goldstone station later became part of NASA's Deep Space Network. JPL, a division of Caltech in Pasadena, California, manages the Deep Space Network for NASA. http://photojournal.jpl.nasa.gov/catalog/PIA21114

PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics

NASA Astrophysics Data System (ADS)

Lutich, Andrey

2017-07-01

This research considers the problem of generating compact vector representations of physical design patterns for analytics purposes in semiconductor patterning domain. PatterNet uses a deep artificial neural network to learn mapping of physical design patterns to a compact Euclidean hyperspace. Distances among mapped patterns in this space correspond to dissimilarities among patterns defined at the time of the network training. Once the mapping network has been trained, PatterNet embeddings can be used as feature vectors with standard machine learning algorithms, and pattern search, comparison, and clustering become trivial problems. PatterNet is inspired by the concepts developed within the framework of generative adversarial networks as well as the FaceNet. Our method facilitates a deep neural network (DNN) to learn directly the compact representation by supplying it with pairs of design patterns and dissimilarity among these patterns defined by a user. In the simplest case, the dissimilarity is represented by an area of the XOR of two patterns. Important to realize that our PatterNet approach is very different to the methods developed for deep learning on image data. In contrast to "conventional" pictures, the patterns in the CAD world are the lists of polygon vertex coordinates. The method solely relies on the promise of deep learning to discover internal structure of the incoming data and learn its hierarchical representations. Artificial intelligence arising from the combination of PatterNet and clustering analysis very precisely follows intuition of patterning/optical proximity correction experts paving the way toward human-like and human-friendly engineering tools.

DEEP-South: Preliminary Photometric Results from the KMTNet-CTIO

NASA Astrophysics Data System (ADS)

Kim, Myung-Jin; Moon, Hong-Kyu; Choi, Young-Jun; Yim, Hong-Suh; Bae, Young-Ho; Roh, Dong-Goo; Park, Jin Tae; Moon, Bora

2016-01-01

Korea Astronomy and Space Science Institute (KASI) successfully completed the development of Korea Microlensing Telescope Network (KMTNet, Park et al. 2012) in mid-2015, following which it conducted test runs for several months. `DEep Ecliptic Patrol of the Southern sky' (DEEP-South, Moon et al. 2015), which will be used for asteroid and comet studies, will not only characterize targeted asteroids, carrying out blind surveys toward the sweet spots, but will also mine the data of such bodies using the KMTNet archive. We report preliminary lightcurves of four Potentially Hazardous Asteroids (PHAs) from test runs at KMTNet-CTIO in the February - May 2015 period.

Coherent Frequency Reference System for the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Tucker, Blake C.; Lauf, John E.; Hamell, Robert L.; Gonzaler, Jorge, Jr.; Diener, William A.; Tjoelker, Robert L.

2010-01-01

The NASA Deep Space Network (DSN) requires state-of-the-art frequency references that are derived and distributed from very stable atomic frequency standards. A new Frequency Reference System (FRS) and Frequency Reference Distribution System (FRD) have been developed, which together replace the previous Coherent Reference Generator System (CRG). The FRS and FRD each provide new capabilities that significantly improve operability and reliability. The FRS allows for selection and switching between frequency standards, a flywheel capability (to avoid interruptions when switching frequency standards), and a frequency synthesis system (to generate standardized 5-, 10-, and 100-MHz reference signals). The FRS is powered by redundant, specially filtered, and sustainable power systems and includes a monitor and control capability for station operations to interact and control the frequency-standard selection process. The FRD receives the standardized 5-, 10-, and 100-MHz reference signals and distributes signals to distribution amplifiers in a fan out fashion to dozens of DSN users that require the highly stable reference signals. The FRD is also powered by redundant, specially filtered, and sustainable power systems. The new DSN Frequency Distribution System, which consists of the FRS and FRD systems described here, is central to all operational activities of the NASA DSN. The frequency generation and distribution system provides ultra-stable, coherent, and very low phase-noise references at 5, l0, and 100 MHz to between 60 and 100 separate users at each Deep Space Communications Complex.

Establishment of a Spaceport Network Architecture

NASA Technical Reports Server (NTRS)

Larson, Wiley J.; Gill, Tracy R.; Mueller, Robert P.; Brink, Jeffrey S.

2012-01-01

Since the beginning of the space age, the main actors in space exploration have been governmental agencies, enabling a privileged access to space, but with very restricted and rare missions. The last decade has seen the rise of space tourism, and the founding of ambitious private space mining companies, showing the beginnings of a new exploration era, that is based on a more generalized and regular access to space and which is not limited to the Earth's vicinity. However, the cost of launching sufficient mass into orbit to sustain these inspiring challenges is prohibitive, and the necessary infrastructures to support these missions is still lacking. To provide easy and affordable access into orbital and deep space destinations, there is the need to create a network of spaceports via specific waypoint locations coupled with the use of natural resources, or In Situ Resource Utilization (ISRU), to provide a more economical solution. As part of the International Space University Space Studies Program 2012, the international and intercultural team of Operations and Service Infrastructure for Space (OASIS) proposes an interdisciplinary answer to the problem of economical space access and transportation. This paper presents a summary of a detailed report [1] of the different phases of a project for developing a network of spaceports throughout the Solar System in a timeframe of 50 years. The requirements, functions, critical technologies and mission architecture of this network of spaceports are outlined in a roadmap of the important steps and phases. The economic and financial aspects are emphasized in order to allow a sustainable development of the network in a public-private partnership via the formation of an International Spaceport Authority (ISPA). The approach includes engineering, scientific, financial, legal, policy, and societal aspects. Team OASIS intends to provide guidelines to make the development of space transportation via a spaceports logistics network feasible, and believes that this pioneering effort will revolutionize space exploration, science and commerce, ultimately contributing to permanently expand humanity into space.

The Development of a Simulator System and Hardware Test Bed for Deep Space X-Ray Navigation

NASA Astrophysics Data System (ADS)

Doyle, Patrick T.

2013-03-01

Currently, there is a considerable interest in developing technologies that will allow using photon measurements from celestial x-ray sources for deep space navigation. The impetus for this is that many envisioned future space missions will require spacecraft to have autonomous navigation capabilities. For missions close to Earth, Global Navigation Satellite Systems (GNSS) such as GPS are readily available for use, but for missions far from Earth, other alternatives must be provided. While existing systems such as the Deep Space Network (DSN) can be used, latencies associated with servicing a fleet of vehicles may not be compatible with some autonomous operations requiring timely updates of their navigation solution. Because of their somewhat predictable emissions, pulsars are the ideal candidates for x-ray sources that can be used to provide key parameters for navigation. Algorithms and simulation tools that will enable designing and analyzing x-ray navigation concepts are presented. The development of a compact x-ray detector system is pivotal to the eventual deployment of such navigation systems. Therefore, results of a high altitude balloon test to evaluate the design of a compact x-ray detector system are described as well.

DEEP ATTRACTOR NETWORK FOR SINGLE-MICROPHONE SPEAKER SEPARATION.

PubMed

Chen, Zhuo; Luo, Yi; Mesgarani, Nima

2017-03-01

Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.

On sampling band-pass signals

NASA Technical Reports Server (NTRS)

Sadr, R.; Shahshahani, M.

1989-01-01

Four techniques for uniform sampling of band-bass signals are examined. The in-phase and quadrature components of the band-pass signal are computed in terms of the samples of the original band-pass signal. The relative implementation merits of these techniques are discussed with reference to the Deep Space Network (DSN).

TIGER reliability analysis in the DSN

NASA Technical Reports Server (NTRS)

Gunn, J. M.

1982-01-01

The TIGER algorithm, the inputs to the program and the output are described. TIGER is a computer program designed to simulate a system over a period of time to evaluate system reliability and availability. Results can be used in the Deep Space Network for initial spares provisioning and system evaluation.

Reducing cost with autonomous operations of the Deep Space Network radio science receiver

NASA Technical Reports Server (NTRS)

Asmar, S.; Anabtawi, A.; Connally, M.; Jongeling, A.

2003-01-01

This paper describes the Radio Science Receiver system and the savings it has brought to mission operations. The design and implementation of remote and autonomous operations will be discussed along with the process of including user feedback along the way and lessons learned and procedures avoided.

Goldstone radio spectrum protection. [deep space network

NASA Technical Reports Server (NTRS)

Gaudian, B. A.; Cushman, R. B.

1980-01-01

Potential electromagnetic interference to the Goldstone tracking receivers due to neighboring military installations is discussed. Coordination of the military and NASA Goldstone activities in the Mojave Desert area is seen to be an effective method to protect the Goldstone radio spectrum while maintaining compatible operations for the military and Goldstone.

Mission and Assets Database

NASA Technical Reports Server (NTRS)

Baldwin, John; Zendejas, Silvino; Gutheinz, Sandy; Borden, Chester; Wang, Yeou-Fang

2009-01-01

Mission and Assets Database (MADB) Version 1.0 is an SQL database system with a Web user interface to centralize information. The database stores flight project support resource requirements, view periods, antenna information, schedule, and forecast results for use in mid-range and long-term planning of Deep Space Network (DSN) assets.

Emergency Communications for NASA's Deep Space Missions

NASA Technical Reports Server (NTRS)

Shambayati, Shervin; Lee, Charles H.; Morabito, David D.; Cesarone, Robert J.; Abraham, Douglas S.

2011-01-01

The ability to communicate with spacecraft during emergencies is a vital service that NASA's Deep Space Network (DSN) provides to all deep space missions. Emergency communications is characterized by low data rates(typically is approximately10 bps) with the spacecraft using either a low-gain antenna (LGA, including omnidirectional antennas) or,in some cases, a medium-gain antenna (MGA). Because of the use of LGAs/MGAs for emergency communications, the transmitted power requirements both on the spacecraft andon the ground are substantially greater than those required for normal operations on the high-gain antenna (HGA) despite the lower data rates. In this paper, we look at currentand future emergency communications capabilities available to NASA's deep-space missions and discuss their limitations in the context of emergency mode operations requirements.These discussions include the use of the DSN 70-m diameter antennas, the use of the 34-m diameter antennas either alone or arrayed both for the uplink (Earth-to-spacecraft) and the downlink (spacecraft-to-Earth), upgrades to the ground transmitters, and spacecraft power requirements both with unitygain (0 dB) LGAs and with antennas with directivity (>0 dB gain, either LGA or MGA, depending on the gain). Also discussed are the requirements for forward-error-correctingcodes for both the uplink and the downlink. In additional, we introduce a methodology for proper selection of a directionalLGA/MGA for emergency communications.

Space Surveillance Network (SSN) Optical Augmentation (SOA)

DTIC Science & Technology

1999-04-01

physical characteristics, and the geocentric and topocentric positions of each satellite in the deep space object catalog. The SKYMAP propagator...maintains the geocentric and topocentric positions and recomputes the position of each object several times a minute. For each scheduling...AINTENANCE Mission Personnel ( Staffing ) Officers 0.0 0.0 0.0 0.0 $90K/person (0) Enlisted 0.0 0.0 0.0 0.0 $45K/person (0) Contractor 20.0

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1983-01-01

Developments in programs in telecommunication and data acquisition in space communications, radio navigation, radio science, and ground based radio astronomy are reported. Activities of the deep space network (DSN) and its associated ground communication facility (GCF) in planning, supporting research and technology, implementation, and in operations are outlined. The publication of reports on the application of radio interferometry at microwave frequencies for geodynamic measurements are presented. Implementation and operation for searching the microwave spectrum is reported.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Utilization of the Deep Space Atomic Clock for Europa Gravitational Tide Recovery

NASA Technical Reports Server (NTRS)

Seubert, Jill; Ely, Todd

2015-01-01

Estimation of Europa's gravitational tide can provide strong evidence of the existence of a subsurface liquid ocean. Due to limited close approach tracking data, a Europa flyby mission suffers strong coupling between the gravity solution quality and tracking data quantity and quality. This work explores utilizing Low Gain Antennas with the Deep Space Atomic Clock (DSAC) to provide abundant high accuracy uplink-only radiometric tracking data. DSAC's performance, expected to exhibit an Allan Deviation of less than 3e-15 at one day, provides long-term stability and accuracy on par with the Deep Space Network ground clocks, enabling one-way radiometric tracking data with accuracy equivalent to that of its two-way counterpart. The feasibility of uplink-only Doppler tracking via the coupling of LGAs and DSAC and the expected Doppler data quality are presented. Violations of the Kalman filter's linearization assumptions when state perturbations are included in the flyby analysis results in poor determination of the Europa gravitational tide parameters. B-plane targeting constraints are statistically determined, and a solution to the linearization issues via pre-flyby approach orbit determination is proposed and demonstrated.

Standard interface: Twin-coaxial converter

NASA Technical Reports Server (NTRS)

Lushbaugh, W. A.

1976-01-01

The network operations control center standard interface has been adopted as a standard computer interface for all future minicomputer based subsystem development for the Deep Space Network. Discussed is an intercomputer communications link using a pair of coaxial cables. This unit is capable of transmitting and receiving digital information at distances up to 600 m with complete ground isolation between the communicating devices. A converter is described that allows a computer equipped with the standard interface to use the twin coaxial link.

The NASA Deep Space Network (DSN) Array

NASA Technical Reports Server (NTRS)

Gatti, Mark

2006-01-01

The DSN Array Project is currently working with Senior Management at both JPL and NASA to develop strategies towards starting a major implementation project. Several studies within NASA are concluding, all of which recommend that any future DSN capability include arraying of antennas to increase performance. Support of Deep Space, Lunar, and CEV (crewed exploration vehicle) missions is possible. High data rate and TDRSS formatting is being investigated. Any future DSN capacity must include Uplink. Current studies ongoing to investigate and develop technologies for uplink arraying; provides advantages in three ways: 1) N2 effect. EIRP grows as N2(-vs-N for a downlink array); 2) Improved architectural options (can separate uplink and downlink); and 3) Potential for more cost effective transmitters for fixed EIRP.

Enhancing the Radio Astronomy Capabilities at NASA's Deep Space Network

NASA Astrophysics Data System (ADS)

Lazio, Joseph; Teitelbaum, Lawrence; Franco, Manuel M.; Garcia-Miro, Cristina; Horiuchi, Shinji; Jacobs, Christopher; Kuiper, Thomas; Majid, Walid

2015-08-01

NASA's Deep Space Network (DSN) is well known for its role in commanding and communicating with spacecraft across the solar system that produce a steady stream of new discoveries in Astrophysics, Heliophysics, and Planetary Science. Equipped with a number of large antennas distributed across the world, the DSN also has a history of contributing to a number of leading radio astronomical projects. This paper summarizes a number of enhancements that are being implemented currently and that are aimed at increasing its capabilities to engage in a wide range of science observations. These enhancements include* A dual-beam system operating between 18 and 27 GHz (~ 1 cm) capable of conducting a variety of molecular line observations, searches for pulsars in the Galactic center, and continuum flux density (photometry) of objects such as nearby protoplanetary disks* Enhanced spectroscopy and pulsar processing backends for use at 1.4--1.9 GHz (20 cm), 18--27 GHz (1 cm), and 38--50 GHz (0.7 cm)* The DSN Transient Observatory (DTN), an automated, non-invasive backend for transient searching* Larger bandwidths (>= 0.5 GHz) for pulsar searching and timing; and* Improved data rates (2048 Mbps) and better instrumental response for very long baseline interferometric (VLBI) observations with the new DSN VLBI processor (DVP), which is providing unprecedented sensitivity for maintenance of the International Celestial Reference Frame (ICRF) and development of future versions.One of the results of these improvements is that the 70~m Deep Space Station 43 (DSS-43, Tidbinbilla antenna) is now the most sensitive radio antenna in the southern hemisphere. Proposals to use these systems are accepted from the international community.Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics & Space Administration.

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

KSC-05PD-0013

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft is lifted from its transporter into the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. the spacecraft will be attached to the second stage of the Boeing Delta II rocket. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0012

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft arrives before dawn at the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. The spacecraft will be attached to the second stage of the Boeing Delta II rocket. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0017

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. In the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla., workers stand by as the canister is lifted away from the Deep Impact spacecraft. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0018

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. In the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla., workers watch as the protective cover surrounding the Deep Impact spacecraft is lifted away. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0015

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. In the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla., workers begin lowering the Deep Impact spacecraft toward the second stage of the Boeing Delta II launch vehicle below for mating. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0016

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. In the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla., workers attach the third stage motor, connected to the Deep Impact spacecraft, to the spin table on the second stage of the Boeing Delta II launch vehicle below. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

KSC-05PD-0014

NASA Technical Reports Server (NTRS)

2005-01-01

KENNEDY SPACE CENTER, FLA. The Deep Impact spacecraft is lifted into the top of the mobile service tower on Launch Pad 17-B at Cape Canaveral Air Force Station, Fla. the spacecraft will be attached to the second stage of the Boeing Delta II rocket. Next the fairing will be installed around the spacecraft. The fairing is a molded structure that fits flush with the outside surface of the Delta II upper stage booster and forms an aerodynamically smooth joint, protecting the spacecraft during launch and ascent. Scheduled for liftoff Jan. 12, Deep Impact will probe beneath the surface of Comet Tempel 1 on July 4, 2005, when the comet is 83 million miles from Earth. After releasing a 3- by 3-foot projectile to crash onto the surface, Deep Impacts flyby spacecraft will reveal the secrets of its interior by collecting pictures and data of how the crater forms, measuring the craters depth and diameter as well as the composition of the interior of the crater and any material thrown out, and determining the changes in natural outgassing produced by the impact. It will send the data back to Earth through the antennas of the Deep Space Network. Deep Impact is a NASA Discovery mission.

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mastering the game of Go with deep neural networks and tree search.

PubMed

Silver, David; Huang, Aja; Maddison, Chris J; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis

2016-01-28

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

Initial Characterization of Optical Communications with Disruption-Tolerant Network Protocols

NASA Technical Reports Server (NTRS)

Schoolcraft, Joshua; Wilson, Keith

2011-01-01

Disruption-tolerant networks (DTNs) are groups of network assets connected with a suite of communication protocol technologies designed to mitigate the effects of link delay and disruption. Application of DTN protocols to diverse groups of network resources in multiple sub-networks results in an overlay network-of-networks with autonomous data routing capability. In space environments where delay or disruption is expected, performance of this type of architecture (such as an interplanetary internet) can increase with the inclusion of new communications mediums and techniques. Space-based optical communication links are therefore an excellent building block of space DTN architectures. When compared to traditional radio frequency (RF) communications, optical systems can provide extremely power-efficient and high bandwidth links bridging sub-networks. Because optical links are more susceptible to link disruption and experience the same light-speed delays as RF, optical-enabled DTN architectures can lessen potential drawbacks and maintain the benefits of autonomous optical communications over deep space distances. These environment-driven expectations - link delay and interruption, along with asymmetric data rates - are the purpose of the proof-of-concept experiment outlined herein. In recognizing the potential of these two technologies, we report an initial experiment and characterization of the performance of a DTN-enabled space optical link. The experiment design employs a point-to-point free-space optical link configured to have asymmetric bandwidth. This link connects two networked systems running a DTN protocol implementation designed and written at JPL for use on spacecraft, and further configured for higher bandwidth performance. Comparing baseline data transmission metrics with and without periodic optical link interruptions, the experiment confirmed the DTN protocols' ability to handle real-world unexpected link outages while maintaining capability of reliably delivering data at relatively high rates. Finally, performance characterizations from this data suggest performance optimizations to configuration and protocols for future optical-specific DTN space link scenarios.

A Multi-Center Space Data System Prototype Based on CCSDS Standards

NASA Technical Reports Server (NTRS)

Rich, Thomas M.

2016-01-01

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing RF propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. The maturity level of this protocol set is, however, insufficient for mission inclusion at this time. This prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of these protocols..

SMC: SCENIC Model Control

NASA Technical Reports Server (NTRS)

Srivastava, Priyaka; Kraus, Jeff; Murawski, Robert; Golden, Bertsel, Jr.

2015-01-01

NASAs Space Communications and Navigation (SCaN) program manages three active networks: the Near Earth Network, the Space Network, and the Deep Space Network. These networks simultaneously support NASA missions and provide communications services to customers worldwide. To efficiently manage these resources and their capabilities, a team of student interns at the NASA Glenn Research Center is developing a distributed system to model the SCaN networks. Once complete, the system shall provide a platform that enables users to perform capacity modeling of current and prospective missions with finer-grained control of information between several simulation and modeling tools. This will enable the SCaN program to access a holistic view of its networks and simulate the effects of modifications in order to provide NASA with decisional information. The development of this capacity modeling system is managed by NASAs Strategic Center for Education, Networking, Integration, and Communication (SCENIC). Three primary third-party software tools offer their unique abilities in different stages of the simulation process. MagicDraw provides UMLSysML modeling, AGIs Systems Tool Kit simulates the physical transmission parameters and de-conflicts scheduled communication, and Riverbed Modeler (formerly OPNET) simulates communication protocols and packet-based networking. SCENIC developers are building custom software extensions to integrate these components in an end-to-end space communications modeling platform. A central control module acts as the hub for report-based messaging between client wrappers. Backend databases provide information related to mission parameters and ground station configurations, while the end user defines scenario-specific attributes for the model. The eight SCENIC interns are working under the direction of their mentors to complete an initial version of this capacity modeling system during the summer of 2015. The intern team is composed of four students in Computer Science, two in Computer Engineering, one in Electrical Engineering, and one studying Space Systems Engineering.

Architecture Study on Telemetry Coverage for Immediate Post-Separation Phase

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles; Kellogg, Kent; Stocklin, Frank; Zillig, David; Fielhauer, Karl

2008-01-01

This document is the viewgraphs that accompanies a paper that presents the preliminary results of an architecture study that provides continuous telemetry coverage for NASA missions for immediate post-separation phase. After launch when the spacecraft separated from the upper stage, the spacecraft typically executes a number of mission-critical operations prior to the deployment of solar panels and the activation of the primary communication subsystem. JPL, GSFC, and APL have similar design principle statements that require continuous coverage of mission-critical telemetry during the immediate post-separation phase. To conform to these design principles, an architecture that consists of a separate spacecraft transmitter and a robust communication network capable of tracking the spacecraft signals is needed. The main results of this study are as follows: 1) At low altitude (< 10000 km) when most post-separation critical operations are executed, Earth-based network (e.g. Deep Space Network (DSN)) can only provide limited coverage, whereas space-based network (e.g. Space Network (SN)) can provide continuous coverage. 2) Commercial-off-the-shelf SN compatible transmitters are available for small satellite applications. In this paper we present the detailed coverage analysis of Earth-based and Space-based networks. We identify the key functional and performance requirements of the architecture, and describe the proposed selection criteria of the spacecraft transmitter. We conclude the paper with a proposed forward plan.

The exploration of outer space with cameras: A history of the NASA unmanned spacecraft missions

NASA Astrophysics Data System (ADS)

Mirabito, M. M.

The use of television cameras and other video imaging devices to explore the solar system's planetary bodies with unmanned spacecraft is chronicled. Attention is given to the missions and the imaging devices, beginning with the Ranger 7 moon mission, which featured the first successfully operated electrooptical subsystem, six television cameras with vidicon image sensors. NASA established a network of parabolic, ground-based antennas on the earth (the Deep Space Network) to receive signals from spacecraft travelling farther than 16,000 km into space. The image processing and enhancement techniques used to convert spacecraft data transmissions into black and white and color photographs are described, together with the technological requirements that drove the development of the various systems. Terrestrial applications of the planetary imaging systems are explored, including medical and educational uses. Finally, the implementation and functional characteristics of CCDs are detailed, noting their installation on the Space Telescope.

Environmental projects, volume 11. Environmental assessment: Addition to operations building, Mars site

NASA Technical Reports Server (NTRS)

1990-01-01

An Environmental Assessment was performed of the proposed addition to building G-86 at the Mars Site, which will provide space for new electronic equipment to consolidate the Deep Space Network (DSN) support facilities from other Goldstone Deep Space Communication Complex (GDSCC) sites at the Mars Site, and will include a fifth telemetry and command group with its associated link monitor, control processor, and operator consoles. The addition of these facilities will increase the capability of the DSN to support future sophisticated NASA spacecraft missions such as the International Solar and Terrestrial Physics (ISTP) Program. The planned construction of this building addition requires an Environmental Assessment (EA) document that records the existing environmental conditions at the Mars Site, that analyzes the environmental effects that possibly could be expected from the construction and use of the new building addition, and that recommends measures to be taken to mitigate any possible deleterious environmental effects.

High-speed railway real-time localization auxiliary method based on deep neural network

NASA Astrophysics Data System (ADS)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

ChemTS: an efficient python library for de novo molecular generation.

PubMed

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

ChemTS: an efficient python library for de novo molecular generation

NASA Astrophysics Data System (ADS)

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-12-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

Large Phased Array Radar Using Networked Small Parabolic Reflectors

NASA Technical Reports Server (NTRS)

Amoozegar, Farid

2006-01-01

Multifunction phased array systems with radar, telecom, and imaging applications have already been established for flat plate phased arrays of dipoles, or waveguides. In this paper the design trades and candidate options for combining the radar and telecom functions of the Deep Space Network (DSN) into a single large transmit array of small parabolic reflectors will be discussed. In particular the effect of combing the radar and telecom functions on the sizes of individual antenna apertures and the corresponding spacing between the antenna elements of the array will be analyzed. A heterogeneous architecture for the DSN large transmit array is proposed to meet the radar and telecom requirements while considering the budget, scheduling, and strategic planning constrains.

The U.S. Rosetta Project : eighteen months in flight

NASA Technical Reports Server (NTRS)

Alexander, Claudia J.; Gulkis, Samuel; Frerking, Margaret A.; Holmes, Dwight P.; Weissman, Paul A.; Burch, J.; Stern, A.; Goldstein, R.; Parker, J.; Cravens, T.;

(abstract) Spacecraft Doppler Tracking with the Deep Space Network in the Search for Gravitational Waves

NASA Technical Reports Server (NTRS)

Asmar, Sami; Renzetti, Nicholas

1994-01-01

The Deep Space Network generates accurate radio science data observables for investigators who use radio links between spacecraft and the Earth to examine small changes in the phase and/or amplitude of the signal to study a wide variety of structures and phenomena in space. Several such studies are directed at aspects of the theory of general relativity such as gravitational redshift and gravitational waves. A gravitational wave is a propagating, polarized gravitational field, a ripple in the curvature of space-time. In Einstein's theory of general relativity, the waves are propagating solutions of the Einstein field equations. Their amplitudes are dimensionless strain amplitudes that change the fractional difference in distance between test masses and the rates at which separated clocks keep time. Predicted by all relativistic theories of gravity, they are extremely weak (the ratio of gravitational forces to electrical forces is about 10(sup -40)) and are generated at detectable levels only by astrophysical sources - very massive sources under violent dynamical conditions. The waves have never been detected but searches in the low-frequency band using Doppler tracking of many spacecraft have been conducted and others are being planned. Upper limits have been placed on the gravitational wave strength with the best sensitivities to date are for periodic waves being 7 x 10(sup -15).

Sequence-of-events-driven automation of the deep space network

NASA Technical Reports Server (NTRS)

Hill, R., Jr.; Fayyad, K.; Smyth, C.; Santos, T.; Chen, R.; Chien, S.; Bevan, R.

1996-01-01

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Sequence-of-Events-Driven Automation of the Deep Space Network

NASA Technical Reports Server (NTRS)

Hill, R., Jr.; Fayyad, K.; Smyth, C.; Santos, T.; Chen, R.; Chien, S.; Bevan, R.

1996-01-01

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

The Dark Side of Saturn's Gravity

NASA Astrophysics Data System (ADS)

Iess, L.; Racioppa, P.; Durante, D.; Mariani, M., Jr.; Anabtawi, A.; Armstrong, J. W.; Gomez Casajus, L.; Tortora, P.; Zannoni, M.

2017-12-01

On July 19, 2017 the Cassini spacecraft successfully completed its sixth and last pericenter pass devoted to the investigation of Saturn's interior structure and rings. During each pass the spacecraft was tracked for about 24 hours by the antennas of NASA's Deep Space Network and ESA's ESTRACK network, providing high quality measurements of the spacecraft range rate. We report on a preliminary estimate of Saturn's gravity field and ring mass inferred from range rate observables, and discuss the surprising features of our findings.

A comparison of frame synchronization methods. [Deep Space Network

NASA Technical Reports Server (NTRS)

Swanson, L.

1982-01-01

Different methods are considered for frame synchronization of a concatenated block code/Viterbi link. Synchronization after Viterbi decoding, synchronization before Viterbi decoding based on hard-quantized channel symbols are all compared. For each scheme, the probability under certain conditions of true detection of sync within four 10,000 bit frames is tabulated.

Publications of the Jet Propulsion Laboratory, 1981

NASA Technical Reports Server (NTRS)

1982-01-01

Over 500 externally distributed technical reports released during 1981 that resulted from scientific and engineering work performed, or managed by Jet Propulsion Laboratory are listed by primary author. Of the total number of entries, 311 are from the bimonthly Deep Space Network Progress Report, and its successor, the Telecommunications and Data Acquisition Progress Report.

76 FR 32360 - Information Collection Being Reviewed by the Federal Communications Commission

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-06

... do so within the period of time allowed by this notice, you should advise the contact listed below as... other for profit. Number of Respondents and Responses: 158 respondents; 2,406 responses. Estimated Time... Satellite Digital Audio Radio Service (SDARS), Aeronautical Mobile Telemetry (AMT), and Deep Space Network...

Air & Space Power Journal. Volume 27, Number 1, January-February 2013

DTIC Science & Technology

2013-02-01

Chernobyl ” if the program had released the uranium gas in the centri- fuges instead of causing degradation.38 Though operations had previ- ously taken...Langner, Stuxnet Deep Dive. 38. Ellen Messmer, “Stuxnet Could Have Caused ‘New Chernobyl ,’ Russian Ambassador Says,” Network World, 27 January 2011

Multiple-Feed Design For DSN/SETI Antenna

NASA Technical Reports Server (NTRS)

Slobin, S. D.; Bathker, D. A.

1988-01-01

Frequency bands changed with little interruption of operation. Modification of feedhorn mounting on existing 34-m-diameter antenna in Deep Space Network (DSN) enables antenna to be shared by Search for Extra-Terrestrial Intelligence (SET) program with minimal interruption of DSN spacecraft tracking. Modified antenna useful in terrestrial communication systems requiring frequent changes of operating frequencies.

Publications of the Jet Propulsion Laboratory, January - December 1973. [bibliography

NASA Technical Reports Server (NTRS)

1974-01-01

Five classes of publications are included in this bibliography: (1) Technical Reports in which the information is complete for a specific accomplishment and is intended for a wide audience. (2) Articles from the bimonthly Deep Space Network Progress Report. Each volume's collection of articles presents a periodical survey of current accomplishments by the Deep Space Network. (3) Technical Memorandums, in which the information is complete for a specific accomplishment but is intended for a limited audience to satisfy unique requirements. (4) Articles from the JPL Quarterly Technical Review. Each article summarizes a recent important development, interim or final results, or an advancement in the state of the art in a scientific or engineering endeavor, This publication has been discontinued, and the issues indexed in this bibliography are the last to be published. (5) Articles published in the open literature. The publications are indexed by: (1) author, (2) subject, and (3) publication type and number. A descriptive entry appears under the name of each author of each publication; an abstract is included with the entry for the primary (first-listed) author.

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

NASA Technical Reports Server (NTRS)

Vilnrotter, V.; Tsao, P.; Lee, D.; Cornish, T.; Jao, J.; Slade, M.

2011-01-01

A coherent uplink array consisting of up to three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R-4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

Planetary Radar Imaging with the Deep-Space Network's 34 Meter Uplink Array

NASA Technical Reports Server (NTRS)

Vilnrotter, Victor; Tsao, P.; Lee, D.; Cornish, T.; Jao, J.; Slade, M.

2011-01-01

A coherent Uplink Array consisting of two or three 34-meter antennas of NASA's Deep Space Network has been developed for the primary purpose of increasing EIRP at the spacecraft. Greater EIRP ensures greater reach, higher uplink data rates for command and configuration control, as well as improved search and recovery capabilities during spacecraft emergencies. It has been conjectured that Doppler-delay radar imaging of lunar targets can be extended to planetary imaging, where the long baseline of the uplink array can provide greater resolution than a single antenna, as well as potentially higher EIRP. However, due to the well known R4 loss in radar links, imaging of distant planets is a very challenging endeavor, requiring accurate phasing of the Uplink Array antennas, cryogenically cooled low-noise receiver amplifiers, and sophisticated processing of the received data to extract the weak echoes characteristic of planetary radar. This article describes experiments currently under way to image the planets Mercury and Venus, highlights improvements in equipment and techniques, and presents planetary images obtained to date with two 34 meter antennas configured as a coherently phased Uplink Array.

Parallel Distributed Processing Theory in the Age of Deep Networks.

PubMed

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

The NASA data systems standardization program - Radio frequency and modulation

NASA Technical Reports Server (NTRS)

Martin, W. L.

1983-01-01

The modifications being considered by the NASA-ESA Working Group (NEWG) for space-data-systems standardization to maximize the commonality of the NASA and ESA RF and modulation systems linking spaceborne scientific experiments with ground stations are summarized. The first phase of the NEWG project shows that the NASA MK-IVA Deep Space Network and Shuttle Interrogator (SI) systems in place or planned for 1985 are generally compatible with the ESA Network, but that communications involving the Tracking and Data Relay Satellite (TDRS) are incompatible due to its use of spread-spectrum modulation, pseudonoise ranging, multiple-access channels, and Mbit/s data rates. Topics under study for the post-1985 period include low-bit-rate capability for the ESA Network, an optional 8-kHz command subcarrier for the SI, fixing the spacecraft-transponder frequency-multiplication ratios for possible X-band uplinks or X-band nondeep-space downlinks, review of incompatible TDRS features, and development of the 32-GHz band.

A Multi-Center Space Data System Prototype Based on CCSDS Standards

NASA Technical Reports Server (NTRS)

Rich, Thomas M.

2016-01-01

Deep space missions beyond earth orbit will require new methods of data communications in order to compensate for increasing Radio Frequency (RF) propagation delay. The Consultative Committee for Space Data Systems (CCSDS) standard protocols Spacecraft Monitor & Control (SM&C), Asynchronous Message Service (AMS), and Delay/Disruption Tolerant Networking (DTN) provide such a method. However, the maturity level of this protocol stack is insufficient for mission inclusion at this time. This Space Data System prototype is intended to provide experience which will raise the Technical Readiness Level (TRL) of this protocol set. In order to reduce costs, future missions can take advantage of these standard protocols, which will result in increased interoperability between control centers. This prototype demonstrates these capabilities by implementing a realistic space data system in which telemetry is published to control center applications at the Jet Propulsion Lab (JPL), the Marshall Space Flight Center (MSFC), and the Johnson Space Center (JSC). Reverse publishing paths for commanding from each control center are also implemented. The target vehicle consists of realistic flight computer hardware running Core Flight Software (CFS) in the integrated Power, Avionics, and Power (iPAS) Pathfinder Lab at JSC. This prototype demonstrates a potential upgrade path for future Deep Space Network (DSN) modification, in which the automatic error recovery and communication gap compensation capabilities of DTN would be exploited. In addition, SM&C provides architectural flexibility by allowing new service providers and consumers to be added efficiently anywhere in the network using the common interface provided by SM&C's Message Abstraction Layer (MAL). In FY 2015, this space data system was enhanced by adding telerobotic operations capability provided by the Robot API Delegate (RAPID) family of protocols developed at NASA. RAPID is one of several candidates for consideration and inclusion in a new international standard being developed by the CCSDS Telerobotic Operations Working Group. Software gateways for the purpose of interfacing RAPID messages with the existing SM&C based infrastructure were developed. Telerobotic monitor, control, and bridge applications were written in the RAPID framework, which were then tailored to the NAO telerobotic test article hardware, a product of Aldebaran Robotics.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Passive Thermal Design Approach for the Space Communications and Navigation (SCaN) Testbed Experiment on the International Space Station (ISS)

NASA Technical Reports Server (NTRS)

Siamidis, John; Yuko, Jim

2014-01-01

The Space Communications and Navigation (SCaN) Program Office at NASA Headquarters oversees all of NASAs space communications activities. SCaN manages and directs the ground-based facilities and services provided by the Deep Space Network (DSN), Near Earth Network (NEN), and the Space Network (SN). Through the SCaN Program Office, NASA GRC developed a Software Defined Radio (SDR) testbed experiment (SCaN testbed experiment) for use on the International Space Station (ISS). It is comprised of three different SDR radios, the Jet Propulsion Laboratory (JPL) radio, Harris Corporation radio, and the General Dynamics Corporation radio. The SCaN testbed experiment provides an on-orbit, adaptable, SDR Space Telecommunications Radio System (STRS) - based facility to conduct a suite of experiments to advance the Software Defined Radio, Space Telecommunications Radio Systems (STRS) standards, reduce risk (Technology Readiness Level (TRL) advancement) for candidate Constellation future space flight hardware software, and demonstrate space communication links critical to future NASA exploration missions. The SCaN testbed project provides NASA, industry, other Government agencies, and academic partners the opportunity to develop and field communications, navigation, and networking technologies in the laboratory and space environment based on reconfigurable, software defined radio platforms and the STRS Architecture.The SCaN testbed is resident on the P3 Express Logistics Carrier (ELC) on the exterior truss of the International Space Station (ISS). The SCaN testbed payload launched on the Japanese Aerospace Exploration Agency (JAXA) H-II Transfer Vehicle (HTV) and was installed on the ISS P3 ELC located on the inboard RAM P3 site. The daily operations and testing are managed out of NASA GRC in the Telescience Support Center (TSC).

Deep learning with domain adaptation for accelerated projection-reconstruction MR.

PubMed

Han, Yoseob; Yoo, Jaejun; Kim, Hak Hee; Shin, Hee Jung; Sung, Kyunghyun; Ye, Jong Chul

2018-09-01

The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory requires a large number of radial lines for high-resolution reconstruction. Increasing the number of radial lines causes longer acquisition time, making it more difficult for routine clinical use. On the other hand, if we reduce the number of radial lines, streaking artifact patterns are unavoidable. To solve this problem, we propose a novel deep learning approach with domain adaptation to restore high-resolution MR images from under-sampled k-space data. The proposed deep network removes the streaking artifacts from the artifact corrupted images. To address the situation given the limited available data, we propose a domain adaptation scheme that employs a pre-trained network using a large number of X-ray computed tomography (CT) or synthesized radial MR datasets, which is then fine-tuned with only a few radial MR datasets. The proposed method outperforms existing compressed sensing algorithms, such as the total variation and PR-FOCUSS methods. In addition, the calculation time is several orders of magnitude faster than the total variation and PR-FOCUSS methods. Moreover, we found that pre-training using CT or MR data from similar organ data is more important than pre-training using data from the same modality for different organ. We demonstrate the possibility of a domain-adaptation when only a limited amount of MR data is available. The proposed method surpasses the existing compressed sensing algorithms in terms of the image quality and computation time. © 2018 International Society for Magnetic Resonance in Medicine.

Software for Allocating Resources in the Deep Space Network

NASA Technical Reports Server (NTRS)

Wang, Yeou-Fang; Borden, Chester; Zendejas, Silvino; Baldwin, John

2003-01-01

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Next-Generation Ground Network Architecture for Communications and Tracking of Interplanetary Smallsats

NASA Astrophysics Data System (ADS)

Cheung, K.-M.; Abraham, D.; Arroyo, B.; Basilio, E.; Babuscia, A.; Duncan, C.; Lee, D.; Oudrhiri, K.; Pham, T.; Staehle, R.; Waldherr, S.; Welz, G.; Wyatt, J.; Lanucara, M.; Malphrus, B.; Bellardo, J.; Puig-Suari, J.; Corpino, S.

2015-08-01

As small spacecraft venture out of Earth orbit, they will encounter challenges not experienced or addressed by the numerous low Earth orbit (LEO) CubeSat and smallsat missions staged to date. The LEO CubeSats typically use low-cost, proven CubeSat radios, antennas, and university ground stations with small apertures. As more ambitious yet cost-constrained space mission concepts to the Moon and beyond are being developed, CubeSats and smallsats have the potential to provide a more affordable platform for exploring deep space and performing the associated science. Some of the challenges that have, so far, slowed the proliferation of small interplanetary spacecraft are those of communications and navigation. Unlike Earth-orbiting spacecraft that navigate via government services such as North American Aerospace Defense Command's (NORAD's) tracking elements or the Global Positioning Satellite (GPS) system, interplanetary spacecraft would have to operate in a fundamentally different manner that allows the deep-space communications link to provide both command/telemetry and the radiometric data needed for navigation. Another challenge occurs when smallsat and CubeSat missions would involve multiple spacecraft that require near-simultaneous communication and/or navigation, but have a very limited number of ground antenna assets, as well as available spectrum, to support their links. To address these challenges, the Jet Propulsion Laboratory (JPL) and the Deep Space Network (DSN) it operates for NASA are pursuing the following efforts: (1) Developing a CubeSat-compatible, DSN-compatible transponder -- Iris -- which a commercial vendor can then make available as a product line. (2) Developing CubeSat-compatible high-gain antennas -- deployable reflectors, reflectarrays, and inflatable antennas. (3) Streamlining access and utilization processes for DSN and related services such as the Advanced Multi-Mission Operations System (AMMOS). (4) Developing methodologies for tracking and operating multiple spacecraft simultaneously, including spectrum coordination. (5) Coordination and collaboration with non-DSN facilities. This article further describes the communications and tracking challenges facing interplanetary smallsats and CubeSats, and the next-generation ground network architecture being evolved to mitigate those challenges.

Study of Jovian synchrotron emission with the NASA's Deep Space Network for Juno mission

NASA Astrophysics Data System (ADS)

Garcia-Miro, Cristina; Horiuchi, Shinji; Levin, Steve; Orton, Glenn S.; Bolton, Scott; Jauncey, David; Kuiper, T. B. H.; Teitelbaum, Lawrence

2016-10-01

We are monitoring Jupiter's synchrotron emission with the purpose of connecting the measurements of the Juno mission's MicroWave Radiometer (MWR) experiment to the historical baseline of non-thermal emission, using NASA's Deep Space Network (DSN). The DSN has the most sensitive network of antennas dedicated to tracking spacecraft that are exploring deep space, whose state-of-the-art receivers are considered among the best radio telescopes in the world. Availability for radio astronomy studies is subject to demand from space projects using the DSN. These antennas have previously contributed to the study of the Jovian non-thermal synchroton emission [1].NASA's New Frontiers Juno mission was placed into a nominal orbit on the 4th of July, 2016, allowing it to begin a detailed exploration of Jupiter. Among its scientific objectives is the characterization and exploration of the 3D structure of Jupiter's polar magnetosphere and auroras. It is important to provide a means to connect these detailed MWR measurements with the historical record of synchrotron emission. Ideally, these measurements should be performed on a regular basis during the whole extent of the mission. The DSN has the advantage of being able to perform uninterrupted 24-hour observations using antennas from the different complexes located in USA, Australia and Spain.Additionally, this monitoring program links with and validates the Jupiter observations currently performed by the triplet of educational programs GAVRT, STARS and PARTNeR in USA, Australia and Spain, respectively. These educational programs are partially supported by the DSN and use some of its antennas for teaching purposes, involving students in professional research and exploration.We will describe the DSN single-dish continuum observations of Jupiter in detail: the antennas, receivers and the equipment used to collect the data, the observing procedure, and the data-reduction process. Preliminary results of the Jupiter beaming curve will also be presented.References[1] Bolton, S.J., Janssen, M., Thorne, R., et al.: Ultra-relativistic electrons in Jupiter's radiation belts, Nature, 415, 2002.

Mars Express Interplanetary Navigation from Launch to Mars Orbit Insertion: The JPL Experience

NASA Technical Reports Server (NTRS)

Han, Dongsuk; Highsmith, Dolan; Jah, Moriba; Craig, Diane; Border, James; Kroger, Peter

2004-01-01

The National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) played a significant role in supporting the safe arrival of the European Space Agency (ESA) Mars Express (MEX) orbiter to Mars on 25 December 2003. MEX mission is an international collaboration between member nations of the ESA and NASA, where NASA is supporting partner. JPL's involvement included providing commanding and tracking service with JPL's Deep Space Network (DSN), in addition to navigation assurance. The collaborative navigation effort between European Space Operations Centre (ESOC) and JPL is the first since ESA's last deep space mission, Giotto, and began many years before the MEX launch. This paper discusses the navigational experience during the cruise and final approach phase of the mission from JPL's perspective. Topics include technical challenges such as orbit determination using non-DSN tracking data and media calibrations, and modeling of spacecraft physical properties for accurate representation of non-gravitational dynamics. Also mentioned in this paper is preparation and usage of DSN Delta Differential Oneway Range ((Delta)DOR) measurements, a key element to the accuracy of the orbit determination.

Trajectory design for the Deep Space Program Science Experiment (DSPSE) mission

NASA Astrophysics Data System (ADS)

Carrington, D.; Carrico, J.; Jen, J.; Roberts, C.; Seacord, A.; Sharer, P.; Newman, L.; Richon, K.; Kaufman, B.; Middour, J.

In 1994, the Deep Space Program Science Experiment (DSPSE) spacecraft will become the first spacecraft to perform, in succession, both a lunar orbiting mission and a deep-space asteroid encounter mission. The primary mission objective is to perform a long-duration flight-test of various new-technology lightweight components, such as sensors, in a deep-space environment. The mission has two secondary science objectives: to provide high-resolution imaging of the entire lunar surface for mapping purposes and flyby imaging of the asteroid 1620 Geographos. The DSPSE mission is sponsored by the Strategic Defense Initiative Organization (SDIO). As prime contractor, the Naval Research Laboratory (NRL) is building the spacecraft and will conduct mission operations. The Goddard Space Flight Center's (GSFC) Flight Dynamics Division is supporting NRL in the areas of The Deep Space Network (DSN) will provide tracking support. The DSPSE mission will begin with a launch from the Western Test Range in late January 1994. Following a minimum 1.5-day stay in a low-Earth parking orbit, a solid kick motor burn will boost DSPSE into an 18-day, 2.5-revolution phasing orbit transfer trajectory to the Moon. Two burns to insert DSPSE into a lunar polar orbit suitable for the mapping mission will be followed by mapping orbit maintenance and adjustment operations over a period of 2 sidereal months. In May 1994, a lunar orbit departure maneuver, in conjunction with a lunar swingby 26 days later, will propel DSPSE onto a heliocentric transfer that will intercept Geographos on September 1, 1994. This paper presents the characteristics, deterministic delta-Vs, and design details of each trajectory phase of this unique mission, together with the requirements, constraints, and design considerations to which each phase is subject. Numerous trajectory plots and tables of significant trajectory events are included. Following a discussion of the results of a preliminary launch window analysis, a summary of the deterministic impulsive delta-V budget required to establish the baseline mission trajectory design is presented.

Directivity of a Sparse Array in the Presence of Atmospheric-Induced Phase Fluctuations for Deep Space Communications

NASA Technical Reports Server (NTRS)

Nessel, James A.; Acosta, Robert J.

2010-01-01

Widely distributed (sparse) ground-based arrays have been utilized for decades in the radio science community for imaging celestial objects, but have only recently become an option for deep space communications applications with the advent of the proposed Next Generation Deep Space Network (DSN) array. But whereas in astronomical imaging, observations (receive-mode only) are made on the order of minutes to hours and atmospheric-induced aberrations can be mostly corrected for in post-processing, communications applications require transmit capabilities and real-time corrections over time scales as short as fractions of a second. This presents an unavoidable problem with the use of sparse arrays for deep space communications at Ka-band which has yet to be successfully resolved, particularly for uplink arraying. In this paper, an analysis of the performance of a sparse antenna array, in terms of its directivity, is performed to derive a closed form solution to the expected array loss in the presence of atmospheric-induced phase fluctuations. The theoretical derivation for array directivity degradation is validated with interferometric measurements for a two-element array taken at Goldstone, California. With the validity of the model established, an arbitrary 27-element array geometry is defined at Goldstone, California, to ascertain its performance in the presence of phase fluctuations. It is concluded that a combination of compact array geometry and atmospheric compensation is necessary to ensure high levels of availability.

Precise estimation of tropospheric path delays with GPS techniques

NASA Technical Reports Server (NTRS)

Lichten, S. M.

1990-01-01

Tropospheric path delays are a major source of error in deep space tracking. However, the tropospheric-induced delay at tracking sites can be calibrated using measurements of Global Positioning System (GPS) satellites. A series of experiments has demonstrated the high sensitivity of GPS to tropospheric delays. A variety of tests and comparisons indicates that current accuracy of the GPS zenith tropospheric delay estimates is better than 1-cm root-mean-square over many hours, sampled continuously at intervals of six minutes. These results are consistent with expectations from covariance analyses. The covariance analyses also indicate that by the mid-1990s, when the GPS constellation is complete and the Deep Space Network is equipped with advanced GPS receivers, zenith tropospheric delay accuracy with GPS will improve further to 0.5 cm or better.

Deep Space Optical Link ARQ Performance Analysis

NASA Technical Reports Server (NTRS)

Clare, Loren; Miles, Gregory

2016-01-01

Substantial advancements have been made toward the use of optical communications for deep space exploration missions, promising a much higher volume of data to be communicated in comparison with present -day Radio Frequency (RF) based systems. One or more ground-based optical terminals are assumed to communicate with the spacecraft. Both short-term and long-term link outages will arise due to weather at the ground station(s), space platform pointing stability, and other effects. To mitigate these outages, an Automatic Repeat Query (ARQ) retransmission method is assumed, together with a reliable back channel for acknowledgement traffic. Specifically, the Licklider Transmission Protocol (LTP) is used, which is a component of the Disruption-Tolerant Networking (DTN) protocol suite that is well suited for high bandwidth-delay product links subject to disruptions. We provide an analysis of envisioned deep space mission scenarios and quantify buffering, latency and throughput performance, using a simulation in which long-term weather effects are modeled with a Gilbert -Elliot Markov chain, short-term outages occur as a Bernoulli process, and scheduled outages arising from geometric visibility or operational constraints are represented. We find that both short- and long-term effects impact throughput, but long-term weather effects dominate buffer sizing and overflow losses as well as latency performance.

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

Water vapor radiometry research and development phase

NASA Technical Reports Server (NTRS)

Resch, G. M.; Chavez, M. C.; Yamane, N. L.; Barbier, K. M.; Chandlee, R. C.

1985-01-01

This report describes the research and development phase for eight dual-channel water vapor radiometers constructed for the Crustal Dynamics Project at the Goddard Space Flight Center, Greenbelt, Maryland, and for the NASA Deep Space Network. These instruments were developed to demonstrate that the variable path delay imposed on microwave radio transmissions by atmospheric water vapor can be calibrated, particularly as this phenomenon affects very long baseline interferometry measurement systems. Water vapor radiometry technology can also be used in systems that involve moist air meteorology and propagation studies.

Free-Electron Laser (FEL) Utilization in Space Applications (Ship-Borne Pointing Accuracy, Deep-Space Communications, and Orbital Debris Tracking)

DTIC Science & Technology

2011-12-01

Network STK Satellite Tool Kit WFOV Wide-Field-of-View xv ACKNOWLEDGMENTS I would like to first and foremost thank the Lord, Jesus Christ, our...frequencies in FSK is easily visualized . Table 5.1 details the phase difference between each state as the number of represented states is increased...assist in visualizing the phase separation when adding additional phases to the system. Each of the rows from Table 5.1 is displayed in Figure 5.10

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1984-01-01

This publication provides reports on work performed for the Office of Space Tracking and Data Systems (OSTDS). It reports on the activities of the deep space network (DSN) and the Ground Communications Facility (GCF). Topics discussed on the operation of the DSN include: (1) spacecraft-ground communications; (2) station control and system technology; and (3) capabilities for new projects for systems implementation. The GCF compatibility with packets and data compression is discussed. In geodynamics, the publication reports on the application of radio interferometry at microwave frequencies for geodynamic measurements.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1983-01-01

This publication reports on developments in programs managed by JPL's office of Telecommunications and Data Acquisition (TDA). In space communications, radio navigation, radio science, and ground based radio astronomy, it reports on activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) in planning, in supporting research and technology, in implementation and in operations. In geodynamics, the publication reports on the application of radio interferometry at microwave frequencies for geodynamic measurements. This publication also reports on implementation and operations for searching the microwave spectrum.

Dreaming of Atmospheres

NASA Astrophysics Data System (ADS)

Waldmann, Ingo

2016-10-01

Radiative transfer retrievals have become the standard in modelling of exoplanetary transmission and emission spectra. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain.To address these issues, we have developed the Tau-REx (tau-retrieval of exoplanets) retrieval and the RobERt spectral recognition algorithms. Tau-REx is a bayesian atmospheric retrieval framework using Nested Sampling and cluster computing to fully map these large correlated parameter spaces. Nonetheless, data volumes can become prohibitively large and we must often select a subset of potential molecular/atomic absorbers in an atmosphere.In the era of open-source, automated and self-sufficient retrieval algorithms, such manual input should be avoided. User dependent input could, in worst case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is build to address these issues. RobERt is a deep belief neural (DBN) networks trained to accurately recognise molecular signatures for a wide range of planets, atmospheric thermal profiles and compositions. Using these deep neural networks, we work towards retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process.In this talk I will discuss how neural networks and Bayesian Nested Sampling can be used to solve highly degenerate spectral retrieval problems and what 'dreaming' neural networks can tell us about atmospheric characteristics.

NASA Near Earth Network (NEN), Deep Space Network (DSN) and Space Network (SN) Support of CubeSat Communications

NASA Technical Reports Server (NTRS)

Schaire, Scott H.; Altunc, Serhat; Bussey, George; Shaw, Harry; Horne, Bill; Schier, Jim

2015-01-01

There has been a historical trend to increase capability and drive down the Size, Weight and Power (SWAP) of satellites and that trend continues today. Small satellites, including systems conforming to the CubeSat specification, because of their low launch and development costs, are enabling new concepts and capabilities for science investigations across multiple fields of interest to NASA. NASA scientists and engineers across many of NASAs Mission Directorates and Centers are developing exciting CubeSat concepts and welcome potential partnerships for CubeSat endeavors. From a communications and tracking point of view, small satellites including CubeSats are a challenge to coordinate because of existing small spacecraft constraints, such as limited SWAP and attitude control, low power, and the potential for high numbers of operational spacecraft. The NASA Space Communications and Navigation (SCaN) Programs Near Earth Network (NEN), Deep Space Network (DSN) and the Space Network (SN) are customer driven organizations that provide comprehensive communications services for space assets including data transport between a missions orbiting satellite and its Mission Operations Center (MOC). The NASA NEN consists of multiple ground antennas. The SN consists of a constellation of geosynchronous (Earth orbiting) relay satellites, named the Tracking and Data Relay Satellite System (TDRSS). The DSN currently makes available 13 antennas at its three tracking stations located around the world for interplanetary communication. The presentation will analyze how well these space communication networks are positioned to support the emerging small satellite and CubeSat market. Recognizing the potential support, the presentation will review the basic capabilities of the NEN, DSN and SN in the context of small satellites and will present information about NEN, DSN and SN-compatible flight radios and antenna development activities at the Goddard Space Flight Center (GSFC) and across industry. The presentation will review concepts on how the SN multiple access capability could help locate CubeSats and provide a low-latency early warning system. The presentation will also present how the DSN is evolving to maximize use of its assets for interplanetary CubeSats. The critical spectrum-related topics of available and appropriate frequency bands, licensing, and coordination will be reviewed. Other key considerations, such as standardization of radio frequency interfaces and flight and ground communications hardware systems, will be addressed as such standardization may reduce the amount of time and cost required to obtain frequency authorization and perform compatibility and end-to-end testing. Examples of standardization that exist today are the NASA NEN, DSN and SN systems which have published users guides and defined frequency bands for high data rate communication, as well as conformance to CCSDS standards. The workshop session will also seek input from the workshop participants to better understand the needs of small satellite systems and to identify key development activities and operational approaches necessary to enhance communication and navigation support using NASA's NEN, DSN and SN.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Preparing for the 90s using today's communications assets

NASA Technical Reports Server (NTRS)

Posner, Edward C.

1987-01-01

Such existing NASA/U.S. facilities and spacecraft as those of the Deep Space Network, VLA, and Arecibo are presently judged capable, at modest additional investment during the next five years, to acquire unique space science data, to generate mission planning data for missions to be launched in the early 1990s, and to evaluate and demonstrate communications and navigation technology for missions of the late 1990s and beyond. The more ambitious of these efforts will contribute the continuation of space research attractiveness for students, as well as furnish an important part of their scientific training.

Ka-Band Transponder for Deep-Space Radio Science

NASA Technical Reports Server (NTRS)

Dennis, Matthew S.; Mysoor, Narayan R.; Folkner, William M.; Mendoza, Ricardo; Venkatesan, Jaikrishna

2008-01-01

A one-page document describes a Ka-band transponder being developed for use in deep-space radio science. The transponder receives in the Deep Space Network (DSN) uplink frequency band of 34.2 to 34.7 GHz, transmits in the 31.8- to 32.3 GHz DSN downlink band, and performs regenerative ranging on a DSN standard 4-MHz ranging tone subcarrier phase-modulated onto the uplink carrier signal. A primary consideration in this development is reduction in size, relative to other such transponders. The transponder design is all-analog, chosen to minimize not only the size but also the number of parts and the design time and, thus, the cost. The receiver features two stages of frequency down-conversion. The receiver locks onto the uplink carrier signal. The exciter signal for the transmitter is derived from the same source as that used to generate the first-stage local-oscillator signal. The ranging-tone subcarrier is down-converted along with the carrier to the second intermediate frequency, where the 4-MHz tone is demodulated from the composite signal and fed into a ranging-tone-tracking loop, which regenerates the tone. The regenerated tone is linearly phase-modulated onto the downlink carrier.

Combining GPS and VLBI earth-rotation data for improved universal time

NASA Technical Reports Server (NTRS)

Freedman, A. P.

1991-01-01

The Deep Space Network (DSN) routinely measures Earth orientation in support of spacecraft tracking and navigation using very long-baseline interferometry (VLBI) with the deep-space tracking antennas. The variability of the most unpredictable Earth-orientation component, Universal Time 1 (UT1), is a major factor in determining the frequency with which the DSN measurements must be made. The installation of advanced Global Positioning System (GPS) receivers at the DSN sites and elsewhere may soon permit routine measurements of UT1 variation with significantly less dependence on the deep-space tracking antennas than is currently required. GPS and VLBI data from the DSN may be combined to generate a precise UT1 series, while simultaneously reducing the time and effort the DSN must spend on platform-parameter calibrations. This combination is not straightforward, however, and a strategy for the optimal combination of these data is presented and evaluated. It appears that, with the aid of GPS, the frequency of required VLBI measurements of Earth orientation could drop from twice weekly to once per month. More stringent real-time Earth orientation requirements possible in the future would demand significant improvements in both VLBI and GPS capabilities, however.