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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

ISDN at NASA Lewis Research Center

An expository investigation of the potential impact of the Integrated Services Digital Network (ISDN) at NASA Lewis Research Center is described. To properly frame the subject, the paper contains a detailed survey of the components of Narrowband ISDN. The principles and objectives are presented as decreed by the Consultative Committee for International Telephone and Telegraph (CCITT). The various channel types are delineated and their associated service combinations are described. The subscriber-access network functions are explained pictorially via the ISDN reference configuration. A section on switching techniques is presented to enable the reader to understand the emergence of the concept of fast packet switching. This new technology is designed to operate over the high bandwidth, low error rate transmission media that characterizes the LeRC environment. A brief introduction to the next generation of networks is covered with sections on Broadband ISDM (B-ISDN), Asynchronous Transfer Mode (ATM), and Synchronous Optical Networks (SONET). Applications at LeRC are presented, first in terms of targets of opportunity, then in light of compatibility constraints. In-place pilot projects and testing are described that demonstrate actual usage at LeRC.

Bakes, Catherine Murphy↗
NASA NTRS2011

Biochip microsystem for bioinformatics recognition and analysis

A system with applications in pattern recognition, or classification, of DNA assay samples. Because DNA reference and sample material in wells of an assay may be caused to fluoresce depending upon dye added to the material, the resulting light may be imaged onto an embodiment comprising an array of photodetectors and an adaptive neural network, with applications to DNA analysis. Other embodiments are described and claimed.

Lue, Jaw-Chyng↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗
NASA NTRS1973

Mesoscale history of a small patch of clear air turbulence.

Sequential analyses of data from a network of eight special rawinsonde stations 100 km apart demonstrate the important effects of mesoscale disturbances in a 2.5-hr episode of light-to-moderate clear air turbulence (CAT). The most significant feature was the development of two turbulent fields below and above a 2-km region of a wind minimum which passed over Wallops Island, Va., near 1530 GMT on Feb. 4, 1970. The small scale of these features precludes their detection in the standard 12-hr observation sequence. The rawinsonde network utilized, which incorporates fine-scale details in both space and time, is adequate to provide a very useful current CAT depiction and an excellent source for the preparation of a short-range forecast of CAT.

Boucher, R. J.↗
NASA NTRS1991

Photonics

Optoelectronic materials and devices are examined. Optoelectronic devices, which generate, detect, modulate, or switch electromagnetic radiation are being developed for a variety of space applications. The program includes spatial light modulators, solid state lasers, optoelectronic integrated circuits, nonlinear optical materials and devices, fiber optics, and optical networking photovoltaic technology and optical processing.

Source record↗
NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗
NASA NTRS2005

Temporal Dynamic Controllability Revisited

An important issue for temporal planners is the ability to handle temporal uncertainty. We revisit the question of how to determine whether a given set of temporal requirements are feasible in the light of uncertain durations of some processes. In particular, we consider how best to determine whether a network is Dynamically Controllable, i.e., whether a dynamic strategy exists for executing the network that is guaranteed to satisfy the requirements. Previous work has shown the existence of a pseudo-polynomial algorithm for testing Dynamic Controllability. Here, we greatly simplify the previous framework, and present a true polynomial algorithm with a cutoff based only on the number of nodes.

Morris, Paul H.↗
NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗
NASA NTRS2024

StarNAV: An Architecture for Autonomous Spacecraft Navigation by the Relativistic Perturbation of Starlight

Exploration missions to the outer Solar System (e.g., Neptune, Kuiper belt objects) or to the interstellar medium present several challenges for conventional spacecraft designs. One of the greatest challenges is a means for navigation, as Earth-based tracking with the Deep Space Network (DSN) becomes less desirable due to high cost, decreased performance at long ranges, and long light-time delays. Indeed, light time delays at Neptune are over four hours (one way), making control of spacecraft during critical events nearly impossible—and this problem only becomes worse as we move to the Kuiper belt or to interstellar space. The need for autonomous spacecraft navigation is well established and is prominently featured in the 2015 NASA Technology Roadmap (e.g., TA 5.4.2.6 & 5.4.2.8). This has led to investment in various technologies to accomplish this task, with the majority of recent work focusing on optical navigation (OPNAV; TA 5.4.4.1) [1], X-ray pulsar navigation (XNAV; TA 5.6.1) [2], or DSN one-way ranging with the Deep Space Atomic Clock (DSAC; TA 5.4.1.1) [3]. This NIAC study suggests a new (and completely different) way of autonomously navigating a spacecraft anywhere in the Solar System or beyond. We call this new technique StarNAV.

NIAC Phase I↗
NASA NTRS2011

Contact Graph Routing

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology of scheduled communication contacts in a network based on the DTN (Delay-Tolerant Networking) architecture. It is designed to enable dynamic selection of data transmission routes in a space network based on DTN. This dynamic responsiveness in route computation should be significantly more effective and less expensive than static routing, increasing total data return while at the same time reducing mission operations cost and risk. The basic strategy of CGR is to take advantage of the fact that, since flight mission communication operations are planned in detail, the communication routes between any pair of bundle agents in a population of nodes that have all been informed of one another's plans can be inferred from those plans rather than discovered via dialogue (which is impractical over long one-way-light-time space links). Messages that convey this planning information are used to construct contact graphs (time-varying models of network connectivity) from which CGR automatically computes efficient routes for bundles. Automatic route selection increases the flexibility and resilience of the space network, simplifying cross-support and reducing mission management costs. Note that there are no routing tables in Contact Graph Routing. The best route for a bundle destined for a given node may routinely be different from the best route for a different bundle destined for the same node, depending on bundle priority, bundle expiration time, and changes in the current lengths of transmission queues for neighboring nodes; routes must be computed individually for each bundle, from the Bundle Protocol agent's current network connectivity model for the bundle s destination node (the contact graph). Clearly this places a premium on optimizing the implementation of the route computation algorithm. The scalability of CGR to very large networks remains a research topic. The information carried by CGR contact plan messages is useful not only for dynamic route computation, but also for the implementation of rate control, congestion forecasting, transmission episode initiation and termination, timeout interval computation, and retransmission timer suspension and resumption.

Burleigh, Scott C.↗
NASA NTRS1986

The Venus Balloon Project

On June 11 and 15, 1985, two instrumental balloons were released from the Soviet VEGA 1 and VEGA 2 spacecraft and deployed in the atmosphere of Venus. The VEGA probes flew by the planet on their way to a rendezvous with comet Halley in March 1986. Drifting with the wind at altitudes of 54 km, the balloons traveled one-third of the way around the planet during their 46-hour lifetimes. Sensors on-board the gondolas made periodic measurements of pressure, temperature, vertical wind velocity, cloud particle density, ambient light level, and frequency of lightning. The data were transmitted to Earth and received at the Deep Space Network (DSN) 64-m stations and at several large antennas in the USSR. Approximately 95 percent of the telemetry data were successfully decoded at the DSN complexes and in the Soviet Union, and were provided to the international science team for analysis. Very Long Baseline Interferometry (VLBI) data were acquired by 20 radio observatories around the world for the purpose of monitoring the Venus winds. The DSN 64-m subnet was part of a 15-station VLBI network organized by the Centre National d'Etudes Spatiales (CNES) of France. In addition, five antennas of the Soviet network participated. VLBI data from the CNES network are currently being processed at the Jet Propulsion Laboratory.

Stelzried, C. T.↗
NASA NTRS1987

The status of the international Halley watch

More than 1000 professional astronomers worldwide actually observed Halley's comet from the ground. Preliminary logs from the observers indicate that 20-40 Gbytes of data were acquired in eight professional disciplines and as much as 5 Gbytes in the amateur network. The latter will be used to fill in gaps in the Archive and to provide a visual light curve. In addition roughly 400 Mbytes of data were taken on Comet Giacobini-Zinner. Data will be accepted for archiving until early 1989. The permanent archive will consist of a set of CD-ROMs and a set of books, publication of both to be completed by mid-1990. Data from the space missions will be included but only on the CDs. From every indication, the ground based effort and the space missions complimented each other beautifully, both directly in the solution of spacecraft navigation problems and indirectly in the solution of scientific problems. The major remaining concern is that scientists submit their data to the Archive before the 1989 deadline.

Newburn, Ray L., Jr.↗
NASA NTRS2012

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

Malakar, Nabin K.↗
NASA NTRS2022

Soil Respiration Phenology Improves Modeled Phase of Terrestrial Net Ecosystem Exchange in Northern Hemisphere

In the northern hemisphere, terrestrial ecosystems transition from net sources of CO2 to the atmosphere in winter to net ecosystem carbon sinks during spring. The timing (or phase) of this transition, determined by the balance between ecosystem respiration (RECO) and primary production, is key to estimating the amplitude of the terrestrial carbon sink. We diagnose an apparent phase bias in the RECO and net ecosystem exchange (NEE) seasonal cycles estimated by the Terrestrial Carbon Flux (TCF) model framework and investigate its link to soil respiration mechanisms. Satellite observations of vegetation canopy conditions, surface meteorology, and soil moisture from the NASA SMAP Level 4 Soil Moisture product are used to model a daily carbon budget for a global network of eddy covariance flux towers. Proposed modifications to TCF include: the inhibition of foliar respiration in the light (the Kok effect); a seasonally varying litterfall phenology; an O2 diffusion limitation on heterotrophic respiration (RH); and a vertically resolved soil decomposition model. We find that RECO phase bias can result from bias in RECO magnitude and that mechanisms which reduce northern spring RECO, like substrate and O2 diffusion limitations, can mitigate the phase bias. A vertically resolved soil decomposition model mitigates this bias by temporally segmenting and lagging RH. Applying these model enhancements at Continuous Soil Respiration (COSORE) sites verifies their improvement of RECO and NEE skill compared to in situ observations (up to ∆RMSE = −0.76 g C m−2 d −1 35 ). Ultimately, these mechanisms can improve prior estimates of NEE for atmospheric inversion studies.

Soil respiration↗
NASA NTRS1985

White-light and radio sounding observations of coronal transients

A concerted search for coronal transients was conducted with the 'Solwind' coronagraph during the solar occultations of the two Helios spacecraft in October/November 1979. The polarization angle and bandwidth of the linearly polarized S-band downlink signal were monitored at the three 64-m tracking stations of the NASA Deep Space Network to determine coronal Faraday rotation and spectral broadening. A one-to-one correspondence could be established between abrupt disturbances in the two signal parameters and the passage of a white-light transient through the signal ray path from spacecraft to earth. The white-light morphology and the additional information provided by the radio sounding coverage are presented for each of the five distinct events recorded. Although no specific example could be observed in sufficient detail in both white light and Faraday rotation to derive the small-scale magnetic structure, some qualitative descriptions of the orientation and rough estimates of the magnitude of the transient magnetic field could be made.

Bird, M. K.↗
NASA NTRS1990

A polar orbit for the Mars Global Network Mission

The purpose of the Global Network Mission (GNM) is to deploy simple landers on the Martian surface in late 1998. The objective is to create a globally distributed network of ground stations which will collect environmental data, perhaps for as long as several years. The GNM presents unique mission design challenges, which are addressed by the following essay. The GNM mission concept calls for two carrier spacecraft, each equipped with a number of simple landers. Some of the landers may be deployed from approach, either to reduce carrier mass prior to orbit insertion, or to reach latitudes not available from the carrier orbit. The remaining landers are deployed from orbit. One configuration for the Global Network Mission was proposed in a report from the Exploration Precursors Task Team to the Office of Space Science and Applications. This formed the basis of a previous orbit design for the GNM. This mission scenario is used as a point of reference, but results from the current study are generally applicable to a wide range of GNM mission variants. The analysis concluded that a 1/5 sol, polar orbit with a periapse altitude of 275 km offers the best circumstances for orbital deployment of the Global Network Mission landers. It allows easy polar access at nominal entry angles, and global dispersal of landing sites at lighting angles suitable for descent imaging. The polar orbit allows the option of deploying all the landers from orbit. A wait interval of 160 days after arrival is required before deployment can commence.

Knocke, Philip↗
NASA NTRS2018

MoonBEAM: A Beyond Earth-Orbit Gamma-Ray Burst Detector for Multi-Messenger Astronomy

Moon Burst Energetics All-sky Monitor (MoonBEAM) is a CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. A gamma-ray instrument in cislunar orbit will have greatly reduced sky blockage compared to instruments in low Earth orbit. Working in conjunction with another instrument in low Earth orbit, MoonBEAM can also help constrain the arrival direction of the wavefront to an annulus on the sky by utilizing the light arrival times between the different orbits. This method has been demonstrated by the Interplanetary Gamma- Ray Burst Timing Network. However, delays in data downlink for instruments outside the Tracking and Data Relay Satellite network prevent rapid follow-up observations. We present here a gamma-ray CubeSat concept in Earth-Moon L3 halo orbit that is capable of faster response and provide a timing baseline for localization improvement. Such an instrument would aid in the gravitational wave follow-up observations in other wavelengths to identify the gamma-ray burst afterglow and kilonova emission. Reducing the region of interest makes identifying afterglows much faster, allowing for rapid on-source observations and monitoring of the rise and decay times. It will also prevent source confusion between two transients and enable robust association. A gamma-ray detection could also increase the confidence of a simultaneous but marginal gravitational wave signal, extending the detection horizon. MoonBEAM is a 12U CubeSat concept of deploying gamma-ray detectors in cislunar space to increase gamma-ray burst detections and improve localization precision with the timing triangulation technique. Such an instrument would probe the extreme processes in cosmic collision of compact objects and facilitate multi-messenger time-domain astronomy to explore the end of stellar life cycles and black hole formations.

gamma-ray Cubesat↗
NASA NTRS1991

Ferroelectric Memory Capacitors For Neural Networks

Thin-film ferroelectric capacitors proposed as nonvolatile analog memory devices. Intended primarily for use as synaptic connections in electronic neural networks. Connection strengths (synaptic weights) stored as nonlinear remanent polarizations of ferroelectric films. Ferroelectric memory and interrogation capacitors combined into memory devices in vertical or lateral configurations. Photoconductive layer modulated by light provides variable resistance to alter bias signal applied to memory capacitor. Features include nondestructive readout, simplicity, and resistance to ionizing radiation. Interrogated without destroying stored analog data. Also amenable to very-large-scale integration. Allows use of ac coupling, eliminating errors caused by dc offsets in amplifier circuits of neural networks.

Thakoor, Sarita↗