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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Development of a Prototype Real-Time Automated Filter for Operational Deep Space Navigation

Operational deep space navigation has in the past, and is currently, performed using systems whose architecture was designed to accommodate tape data transfers and computing environments with a tiny fraction of the current capability. Additionally, this 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 flow has been developed. This system will allow for more rapid response as well as much reduced staffing to support mission navigation operations.

deep↗
NASA NTRS1972

System/360 Computer Assisted Network Scheduling (CANS) System

Computer assisted scheduling techniques that produce conflict-free and efficient schedules have been developed and implemented to meet needs of the Manned Space Flight Network. CANS system provides effective management of resources in complex scheduling environment. System is automated resource scheduling, controlling, planning, information storage and retrieval tool.

Brewer, A. C.↗
NASA NTRS2004

Prototype Test Results for the Single Photon Detection SLR2000 Satellite Laser Ranging System

NASA's aging Satellite Laser Ranging (SLR) network is scheduled to be replaced over the next few years with a fully automated single photon detection system. A prototype of this new system, called SLR2000, is currently undergoing field trials at the Goddard Space Flight Center in Greenbelt, Maryland to evaluate photon counting techniques and determine system hardware, software, and control algorithm performance levels and limitations. Newly developed diode pumped microchip lasers and quadrant microchannel plate-based photomultiplier tubes have enabled the development of this high repetition rate single photon detection SLR system. The SLR2000 receiver threshold is set at the single photoelectron (pe) level but tracks satellites with an average signal level typically much less than 1 pe. The 2 kHz laser fire rate aids in satellite acquisition and tracking and will enable closed loop tracking by accumulating single photon count statistics in a quadrant detector and using this information to correct for pointing errors. Laser transmitter beamwidths of 10 arcseconds (FWHM) or less are currently being used to maintain an adequate signal level for tracking while the receiver field of view (FOV) has been opened to 40 arcseconds to accommodate point ahead/look behind angular offsets. In the near future, the laser transmitter point ahead will be controlled by a pair of Risley prisms. This will allow the telescope to point behind and enable closure of the receiver FOV to roughly match the transmitter beam divergence. Bandpass filters (BPF) are removed for night tracking operations while 0.2 nm or 1 nm filters are used during daylight operation. Both day and night laser tracking of Low Earth Orbit (LEO) satellites has been achieved with a laser transmitter energy of only 65 microjoules per pulse. Satellite tracking is presently limited to LEO satellites until the brassboard laser transmitter can be upgraded or replaced. Simultaneous tracks have also been observed with NASA s SLR standard, MOBLAS 7, for the purposes of data comparison and identification of biases. Work continues to optimize the receive optics; upgrade or replace the laser transmitter; calibrate the quadrant detector, the point ahead Risley prisms, and event timer verniers; and test normal point generation with SLR2000 data. This paper will report on the satellite tracking results to date, issues yet to be resolved, and future plans for the SLR2000 system.

Zagwodzki, Thomas W.↗
NASA NTRS2017

Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

Monsoon↗
NASA NTRSDate not supplied

Implementation of Machine Learning Methods for Crater-Based Navigation

Terrain Relative Navigation methods require surface feature detectors to gain information from images used to improve on-board state estimates. This paper presents the development of a crater detection method based on Machine Learning that can extract data from optical images with different crater shapes and sizes, under varying lighting conditions. This work includes an automated capability for generating labeled training data and iterative testing of the neural network-based crater detector. Preliminary results are included to quantify the detector’s accuracy compared to a known crater catalog, given a set of real lunar images from the Lunar Reconnaissance Orbiter.

Sofia G Catalan↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗
NASA NTRS1993

Knowledge engineering for temporal dependency networks as operations procedures

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.

Fayyad, Kristina E.↗
NASA NTRS1998

Autonomous Satellite Command and Control Through the World Wide Web

The Automated Space System Experimental Testbed (ASSET) system is a simple yet comprehensive real-world operations network being developed. Phase 3 of the ASSET Project was January-December 1997 and is the subject of this report. This phase permitted SSDL and its project partners to expand the ASSET system in a variety of ways. These added capabilities included the advancement of ground station capabilities, the adaptation of spacecraft on-board software, and the expansion of capabilities of the ASSET management algorithms. Specific goals of Phase 3 were: (1) Extend Web-based goal-level commanding for both the payload PI and the spacecraft engineer. (2) Support prioritized handling of multiple (PIs) Principle Investigators as well as associated payload experimenters. (3) Expand the number and types of experiments supported by the ASSET system and its associated spacecraft. (4) Implement more advanced resource management, modeling and fault management capabilities that integrate the space and ground segments of the space system hardware. (5) Implement a beacon monitoring test. (6) Implement an experimental blackboard controller for space system management. (7) Further define typical ground station developments required for Internet-based remote control and for full system automation of the PI-to-spacecraft link. Each of those goals are examined. Significant sections of this report were also published as a conference paper. Several publications produced in support of this grant are included as attachments. Titles include: 1) Experimental Initiatives in Space System Operations; 2) The ASSET Client Interface: Balancing High Level Specification with Low Level Control; 3) Specifying Spacecraft Operations At The Product/Service Level; 4) The Design of a Highly Configurable, Reusable Operating System for Testbed Satellites; 5) Automated Health Operations For The Sapphire Spacecraft; 6) Engineering Data Summaries for Space Missions; and 7) Experiments In Automated Health Assessment And Notification For The Sapphire Microsatellite.

Cantwell, Brian↗
NASA NTRS1996

Automated Operations for Galileo Communications

Following the deployment failure of Galileo's high gain antenna, the downlink had to be redesigned so as to effectively use the low gain antenna. The downlink was redesigned to maximize the data return and increase the reliability which required the reconfiguration of the onboard software and the deep space network. The revised downlink features: data compression; antenna arraying; the recoding and reprocessing of telemetry; suppressed carrier tracking, and error-correction coding. The deep space network Galileo telemetry (DGT) subsystem was developed and deployed at three sites in Australia, Spain and the U.S. The DGT was designed as an automated system that continuously monitors and adjusts its parameters and environment in response to either pre-loaded sequences or changes in the internal status.

Statman, Joseph I.↗
NASA NTRS2021

Predicting the Functional State of Protein Kinases Using Interpretable Graph Neural Networks

Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.

Ashwin Ravichandran↗
NASA NTRS1987

MPAD MCC workstation system: ADDAM and EEVE

The flight control consoles in mission control at NASA's Johnson Space Center have evolved in sophistication to the level where expert systems are now being incorporated into them as flight controller assistants. The evolution of the gateway mode, designed to obtain and redistribute numerous kinds of data provided by Mission Control computers over a laser-optical network to enable rapid prototyping development of the above application expert systems is described. This automated distribution and management system serves as an effective buffering system for assuring the necessarily separate requirements of the operational and developmental environments.

Kozick, Bill, Jr.↗
NASA NTRS1990

The Parkes front-end controller and noise-adding radiometer

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.

Brunzie, T. J.↗
NASA NTRS1993

Scene segmentation of natural images using texture measures and back-propagation

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Sridhar, Banavar↗
NASA NTRS2011

NASA Tech Briefs, July 2011

Topics covered include: 1) Collaborative Clustering for Sensor Networks; 2) Teleoperated Marsupial Mobile Sensor Platform Pair for Telepresence Insertion Into Challenging Structures; 3) Automated Verification of Spatial Resolution in Remotely Sensed Imagery; 4) Electrical Connector Mechanical Seating Sensor; 5) In Situ Aerosol Detector; 6) Multi-Parameter Aerosol Scattering Sensor; 7) MOSFET Switching Circuit Protects Shape Memory Alloy Actuators; 8) Optimized FPGA Implementation of Multi-Rate FIR Filters Through Thread Decomposition; 9) Circuit for Communication Over Power Lines; 10) High-Efficiency Ka-Band Waveguide Two-Way Asymmetric Power Combiner; 11) 10-100 Gbps Offload NIC for WAN, NLR, and Grid Computing; 12) Pulsed Laser System to Simulate Effects of Cosmic Rays in Semiconductor Devices; 13) Flight Planning in the Cloud; 14) MPS Editor; 15) Object-Oriented Multi Disciplinary Design, Analysis, and Optimization Tool; 16) Cryogenic-Compatible Winchester Connector Mount and Retaining System for Composite Tubes; 17) Development of Position-Sensitive Magnetic Calorimeters for X-Ray Astronomy; 18) Planar Rotary Piezoelectric Motor Using Ultrasonic Horns; 19) Self-Rupturing Hermetic Valve; 20) Explosive Bolt Dual-Initiated from One Side; 21) Dampers for Stationary Labyrinth Seals; 22) Two-Arm Flexible Thermal Strap; 23) Carbon Dioxide Removal via Passive Thermal Approaches; 24) Polymer Electrolyte-Based Ambient Temperature Oxygen Microsensors for Environmental Monitoring; 25) Pressure Shell Approach to Integrated Environmental Protection; 26) Image Quality Indicator for Infrared Inspections; 27) Micro-Slit Collimators for X-Ray/Gamma-Ray Imaging; 28) Scatterometer-Calibrated Stability Verification Method; 29) Test Port for Fiber-Optic-Coupled Laser Altimeter; 30) Phase Retrieval System for Assessing Diamond Turning and Optical Surface Defects; 31) Laser Oscillator Incorporating a Wedged Polarization Rotator and a Porro Prism as Cavity Mirror; 32) Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions; 33) Dynamic Loads Generation for Multi-Point Vibration Excitation Problems; 34) Optimal Control via Self-Generated Stochasticity; 35) Space-Time Localization of Plasma Turbulence Using Multiple Spacecraft Radio Links; 36) Surface Contact Model for Comets and Asteroids; 37) Dust Mitigation Vehicle; 38) Optical Coating Performance for Heat Reflectors of the JWST-ISIM Electronic Component; 39) SpaceCube Demonstration Platform; 40) Aperture Mask for Unambiguous Parity Determination in Long Wavelength Imagers; 41) Spaceflight Ka-Band High-Rate Radiation-Hard Modulator; 42) Enabling Disabled Persons to Gain Access to Digital Media; 43) Cytometer on a Chip; 44) Principles, Techniques, and Applications of Tissue Microfluidics; and 45) Two-Stage Winch for Kites and Tethered Balloons or Blimps.

Source record↗
NASA NTRS1995

Automated Consultation for the Diagnosis of Interplanetary Telecommunications

SHARP (Spacecraft Health Automated Reasoning Program) is a knowledge-based system for the diagnosis of problems in NASA's Deep Space Network (DSN) telecommunications system. This system provides the means of communication between a spacecraft and operations personnel at Jet Propulsion Laboratory. SHARP analyzes problems that occur in both the on-board spacecraft telecom subsystem, and the DSN.

SHARP spacecraft health automated reasoning progra↗
NASA NTRS2005

An Advanced Orbiting Systems Approach to Quality of Service in Space-Based Intelligent Communication Networks

As humans and robotic technologies are deployed in future constellation systems, differing traffic services will arise, e.g., realtime and non-realtime. In order to provide a quality of service framework that would allow humans and robotic technologies to interoperate over a wide and dynamic range of interactions, a method of classifying data as realtime or non-realtime is needed. In our paper, we present an approach that leverages the Consultative Committee for Space Data Systems (CCSDS) Advanced Orbiting Systems (AOS) data link protocol. Specifically, we redefine the AOS Transfer Frame Replay Flag in order to provide an automated store-and-forward approach on a per-service basis for use in the next-generation Interplanetary Network. In addition to addressing the problem of intermittent connectivity and associated services, we propose a follow-on methodology for prioritizing data through further modification of the AOS Transfer Frame.

Riha, Andrew P.↗
NASA NTRS2004

Secure, Mobile, Wireless Network Technology Designed, Developed, and Demonstrated

The inability to seamlessly disseminate data securely over a high-integrity, wireless broadband network has been identified as a primary technical barrier to providing an order-of-magnitude increase in aviation capacity and safety. Secure, autonomous communications to and from aircraft will enable advanced, automated, data-intensive air traffic management concepts, increase National Air Space (NAS) capacity, and potentially reduce the overall cost of air travel operations. For the first time ever, secure, mobile, network technology was designed, developed, and demonstrated with state-ofthe- art protocols and applications by a diverse, cooperative Government-industry team led by the NASA Glenn Research Center. This revolutionary technology solution will make fundamentally new airplane system capabilities possible by enabling secure, seamless network connections from platforms in motion (e.g., cars, ships, aircraft, and satellites) to existing terrestrial systems without the need for manual reconfiguration. Called Mobile Router, the new technology autonomously connects and configures networks as they traverse from one operating theater to another. The Mobile Router demonstration aboard the Neah Bay, a U.S. Coast Guard vessel stationed in Cleveland, Ohio, accomplished secure, seamless interoperability of mobile network systems across multiple domains without manual system reconfiguration. The Neah Bay was chosen because of its low cost and communications mission similarity to low-Earth-orbiting satellite platforms. This technology was successfully advanced from technology readiness level (TRL) 2 (concept and/or application formation) to TRL 6 (system model or prototype demonstration in a relevant environment). The secure, seamless interoperability offered by the Mobile Router and encryption device will enable several new, vehicle-specific and systemwide technologies to perform such things as remote, autonomous aircraft performance monitoring and early detection and mitigation of potential equipment malfunctions. As an additional benefit, team advancements were incorporated into open standards, ensuring technology transfer. Low-cost, commercial products incorporating the new technology are already available. Furthermore, these products are fully interoperable with legacy network technology equipment currently being used throughout the world.

Ivancic, William D.↗