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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

NASA Deep Space Network operations organization

The organization of the NASA Deep Space Network (DSN), a network of tracking station control and data handling facilities, is briefly reviewed. It has been designed, constructed, maintained, and operated by the Jet Propulsion Laboratory at California Institute of Technology in support of NASA lunar and interplanetary flight programs. Some important technological and organizational advances made by DSN since the early development of spacecraft tracking in the 1950s are considered.

Chafin, R. L.↗
NASA NTRS2014

Networked Operations of Hybrid Radio Optical Communications Satellites

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.

Delay Tolerant Networking↗
NASA NTRS1993

Toward an embedded training tool for Deep Space Network operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor. The cognitive model was developed in Soar for the DSN's Link Monitor and Control (LMC) system; it led to several insights about learning in the task domain that were used to design an intelligent tutor called REACT that implements a method called 'impasse-driven tutoring'. REACT is one component of the LMC training system, which also includes a communications link simulator and a graphical user interface. A pilot study of the LMC training system indicates that REACT shows promise as an effective way for helping operators to quickly acquire expert skills.

Hill, Randall W., Jr.↗
NASA NTRS1993

Toward an Embedded Training Tool for Deep Space Network Operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor.

Johnson, W. Lewis↗
NASA NTRS1980

Navigation network operational considerations

The development of the philosphy necessary for the initial operations costing of the three candidate navigation network designs is described. The effect of the need to minimize costs upon the operations and maintenance concepts, the simplification of operations by adopting the very long base interferometry principle as a basis for the scenarios, and the estimation of the annual load factor based upon an assumed mission set are detailed.

Hird, E.↗
NASA NTRS2002

Statistical porcess control in Deep Space Network operation

This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).

Deep Space Network operations statistical process ↗
NASA NTRS1994

Attempt of automated space network operations at ETS-VI experimental data relay system

National Space Development Agency of Japan (NASDA) is to perform experimental operations to acquire necessary technology for the future inter-satellite communications configured with a data relay satellite. This paper intends to overview functions of the experimental ground system which NASDA has developed for the Engineering Test Satellite VI (ETS-VI) Data Relay and Tracking Experiment, and to introduce Space Network System Operations Procedure (SNSOP) method with an example of Ka-band Single Access (KSA) acquisition sequence. To reduce operational load, SNSOP is developed with the concept of automated control and monitor of both ground terminal and data relay satellite. To perform acquisition and tracking operations fluently, the information exchange with user spacecraft controllers is automated by SNSOP functions.

Ishihara, Kiyoomi↗
NASA NTRS1995

Reengineering Deep Space Network Operations

Eight additional antennas are being added to NASA's Deep Space Network (DSN) at the same time that the budget is being decreased. Therefore, the DSN is reengineering its processes to operate more efficiently.

reengineering↗
NASA NTRS1984

GSFC network operations with Tracking and Data Relay Satellites

The Tracking and Data Relay Satellite System (TDRSS) Network (TN) has been developed to provide services to all NASA User spacecraft in near-earth orbits. Three inter-relating entities will provide these services. The TN has been transformed from a network continuously changing to meet User specific requirements to a network which is flexible to meet future needs without significant changes in operational concepts. Attention is given to the evolution of the TN network, the TN capabilities-space segment, forward link services, tracking services, return link services, the three basic capabilities, single access services, multiple access services, simulation services, the White Sands Ground Terminal, the NASA communications network, and the network control center.

Spearing, R.↗
NASA NTRS2014

Nodes - Network & Operation Demonstration Satellite

Nodes is a technology demonstration mission that will launch from the International Space Station (ISS) in early 2015 and will demonstrate new network capabilities critical to the operation of swarms of multiple spacecraft. Nodes continues the legacy of the PhoneSat series of small satellites that first introduced and successfully implemented the use of Android Smartphone technology to perform many of the spacecraft functions previously accomplished through custom technology development efforts. The Nodes mission consists of two 1.5-unit (1.5U) nanosatellites each weighing approximately 2 kilograms (4 pounds) and measuring 10 centimeters by 10 centimeters by 15 centimeters. The Nodes spacecraft are derived from the hardware and software developed for the EDSN (Edison Demonstration of Smallsat Networks) mission (a swarm of eight spacecraft). Each Node utilizes the Android operating system with EDSN-specific software programmed to perform command and data handling tasks that allow the satellites to 1) relay ground commands through one satellite to the second satellite, 2) collect and relay science data from each satellite to the ground station, and 3) autonomously determine which of the two satellites is best suited to control the space network and relay data to the ground (“Captain”) and notify the ground system and second satellite (“Lieutenant”) of the result.

Network and Operations↗
NASA NTRS1999

Mars Network Operations Concept

NASA has initiated at Jet Propulsion Laboratory the design of a Communications and Navigation Network at Mars.

NASA Jet Propulsion Laboratory JPL Satellites Mars↗
NASA NTRS1988

Knowledge-based network operations

An expert system for enhancing the operability of the ground communication element of the Jet Propulsion Laboratory's Deep Space Network is described. The system performs network fault management, configuration management, and performance management in real time. Extracted management information serves as input to the expert system and is used to update a management information data base. The monitor and control activities involve dividing software for each processor into layers which are each modeled as a finite state machine.

Wu, Chuan-Lin↗
NASA NTRS1972

STDN network operations procedure for Apollo range instrumentation aircraft, revision 1

The Apollo range instrumentation aircraft (ARIA) fleet which consists of four EC-135N aircraft used for Apollo communication support is discussed. The ARIA aircraft are used to provide coverage of lunar missions, earth orbit missions, command module/service module separation to spacecraft landing, and assist in recovery operations. Descriptions of ARIA aircraft, capabilities, and instrumentation are included.

Vette, A. R.↗
NASA NTRS1974

Spaceflight tracking and data network operational reliability assessment for Skylab

Data on the spaceflight communications equipment status during the Skylab mission were subjected to an operational reliability assessment. Reliability models were revised to reflect pertinent equipment changes accomplished prior to the beginning of the Skylab missions. Appropriate adjustments were made to fit the data to the models. The availabilities are based on the failure events resulting in the stations inability to support a function of functions and the MTBF's are based on all events including 'can support' and 'cannot support'. Data were received from eleven land-based stations and one ship.

Seneca, V. I.↗
NASA NTRS1974

Spaceflight tracking and data network operational reliability computer output for MTBF and availability. Appendix V to CSC-1-395

Tables of data are provided to show the availability of Skylab data to selected ground stations during the phases of Skylab preflight, Skylab unmanned condition, and Skylab manned condition. The mean time between failure (MTBF) of the same Skylab functions is tabulated for the selected ground stations. All reliability data are based on a 90 percent confidence interval.

Seneca, V. I.↗