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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Space simulation in the next 20 years

This is a brief overview of logically expected advancements in the area of space simulation over the next twenty years. Current NASA Space Shuttle simulations will be upgraded to support more complex payload and on-orbit tasks. This includes the ability to integrate various remote ground facilities with a real time space mission simulator and an expanded use of efficient part-task simulations. Software compatibilities between simulators will increase and each simulator will have a more combined training and engineering role. Software development processors will be increasingly internetted to an integrated data processing system.

Knorre, M. J.↗
NASA NTRS1986

Remote pivot decoupler pylon: Wing/store flutter suppressor

A device for suspending a store from an aerodynamic support surface, such an an aircraft wing, and more specifically, for improving upon singlet pivot decoupler pylons by reducing both frequency of active store, alignment, and alignment system space and power requirements. Two links suspend a lower pylon/rack section and releasable attached store from an upper pylon section mounted under the wing. The links allow the lower pylon section to rotate in pitch about a remote pivot point. A leaf spring connected between the lower section and electrical alignment system servomechanism provides pitch alignment of the lower section/store combination. The servomechanism utilizes an electric servomotor to drive the gear train and reversibly move the leaf spring, thereby maintaining the pitch attitude of the store within acceptable limits. The damper strokes when the lower section rotates to damp large oscillations of store.

Hassler, J. M., Jr.↗
NASA NTRS1993

NASA high performance computing and communications program

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project.

Holcomb, Lee↗
NASA NTRS1994

NASA High Performance Computing and Communications program

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 1(X)-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientists' abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project, exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects, as well as summaries of early accomplishments and the significance, status, and plans for individual research and development programs within each project. Areas of emphasis include benchmarking, testbeds, software and simulation methods.

Holcomb, Lee↗
NASA NTRS1995

Searching for patterns in remote sensing image databases using neural networks

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

Paola, Justin D.↗
NASA NTRS2013

Human Centered Hardware Modeling and Collaboration

In order to collaborate engineering designs among NASA Centers and customers, to in clude hardware and human activities from multiple remote locations, live human-centered modeling and collaboration across several sites has been successfully facilitated by Kennedy Space Center. The focus of this paper includes innovative a pproaches to engineering design analyses and training, along with research being conducted to apply new technologies for tracking, immersing, and evaluating humans as well as rocket, vehic le, component, or faci lity hardware utilizing high resolution cameras, motion tracking, ergonomic analysis, biomedical monitoring, wor k instruction integration, head-mounted displays, and other innovative human-system integration modeling, simulation, and collaboration applications.

Stambolian Damon↗
NASA NTRSDate not supplied

Evaluation of Performance, Fatigue and Workload During Real-time, Reactive Telerobotic Mission Control Operations

Real-time, reactive telerobotic mission control operations require personnel to actively operate and manage remotely controlled vehicles or robots in real time. Due to the physical separation of the vehicle from the operator, such operations present many additional factors that can influence fatigue (i.e., degraded mental performance) and workload (i.e., mental and physical cost of task requirements), making it difficult to assess how long an individual can safely and accurately conduct operations. The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission involves remotely controlling a lunar vehicle from an Earth-based mission control station. METHODS: In order to determine how long personnel could successfully maintain alertness and performance while operating a rover, we studied seven trained operators (i.e., 5 drivers, 2 real-time scientists) in a simulated mission control environment. We further assessed workload in order to determine whether perceived workload levels would change relative to time on task and by time of day. Operators completed two five-hour simulations in a randomized order, beginning at noon and at midnight. Performance was evaluated every 30 minutes using the psychomotor vigilance task (PVT), Karolinska Sleepiness Scale (KSS), and NASA Task Load Index (NASA-TLX). RESULTS: On average, participants rated themselves as sleepier (5.06 ± 2.28) on the night drives compared to the day drives (3.12 ± 1.44; P < .0001). Workload was rated higher during the day drives (37.93 ± 20.09) compared to night (32.09 ± 21.74; P < .01). Lastly, participants had no change in average reaction time between the two drives. CONCLUSIONS: From the analysis, we observed performance began to degrade after approximately three hours of driving. Our findings suggest that rotating drivers at least every three hours would be prudent to allow for breaks, and to minimize performance degradation, particularly during night shifts.

fatigue↗
NASA NTRS2021

Flood Monitoring and Crop Damage Assessment

In recent years, the occurrence and impact of inland and coastal flood events have become more frequent and damaging, especially within agricultural fields, due to the global climate change and consistent sea level rise. Monitoring and measuring the magnitude of flood events in a timely manner and assessing the subsequent crop damages accurately are precursors in minimizing detrimental consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are capable of monitoring flood events precisely but limited within the smaller spatial extent, time-consuming, and costly. In recent decades, advancement in airborne- and satellite-based remote sensing technologies offering products at a daily global spatial extent with various spectral resolution helps address the shortcomings of the traditional in situ approaches in flood monitoring. Furthermore, the methods such as classification and band ratioing using remote sensing products are simple and effective in assessing flood-induced agricultural damages. The combination of remote sensing products and geographic information systems along with the current development in web mapping, users now can get near real-time flood monitoring and crop damage assessments, albeit dependent upon the quality of available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield across Nebraska and Missouri, and the overall accuracy in estimating corn yield was about 90%. The method implemented in this localized case study could be extended at a larger geographical scale.

Flood monitoring↗
NASA NTRS2011

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

crowdsourcing↗
DOE OSTI2021

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0–1 M) and nitric acid (0.1–10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1985

ACCESS (Assembly Concept for Construction of Erectable Space Structure) - A Shuttle flight experiment

ACCESS is a planned Shuttle flight experiment to assess the potential of an on-orbit construction concept designed for efficient manual assembly of a space truss. The experiment, which is scheduled for launch November 27, 1985, on the Space Transportation System (STS) flight 61-B, uses two astronauts secured in fixed foot restraints located in the Shuttle cargo bay to assemble a 45-foot long aluminum truss beam from 93 tubular struts and 33 nodal joints. Neutral buoyancy simulations of the flight experiment indicate the truss can be assembled in less than thirty minutes. Structural assembly, structural repair, flexible cable attachment and manual manipulation of the truss is also planned for the experiment using an astronaut secured in the Manipulator Foot Restraint attached to the Remote Manipulator System arm. Flight assembly data will be generated for correlation of the neutral buoyancy ground test data. This paper describes the ACCESS flight experiment and presents results of the neutral buoyancy development and training tests.

Heard, W. L., Jr.↗
NASA NTRS2005

Improvements in Virtual Sensors: Using Spatial Information to Estimate Remote Sensing Spectra

Various instruments are used to create images of the Earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high resolution spectral measurements may be too costly to perform on a large sample and therefore lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. In past work [1], we addressed this problem using Virtual Sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. We demonstrated this method by using models trained on the high spectral resolution Terra MODIS instrument to estimate what the equivalent of the MODIS 1.6 micron channel would be for the NOAA AVHRR2 instrument. The scientific motivation for the simulation of the 1.6 micron channel is to improve the ability of the AVHRR2 sensor to detect clouds over snow and ice. This work contains preliminary experiments demonstrating that the use of spatial information can improve our ability to estimate these spectra.

Oza, Nikunj C.↗
NASA NTRS2009

NASA Applied Sciences' DEVELOP Program Fosters the Next Generation of Earth Remote Sensing Scientists

Satellite remote sensing technology and the science associated with the evaluation of the resulting data are constantly evolving. To meet the growing needs related to this industry, a team of personnel that understands the fundamental science as well as the scientific applications related to remote sensing is essential. Therefore, the workforce that will excel in this field requires individuals who not only have a strong academic background, but who also have practical hands-on experience with remotely sensed data, and have developed knowledge of its real-world applications. NASA's DEVELOP Program has played an integral role in fulfilling this need. DEVELOP is a NASA Science Mission Directorate Applied Sciences training and development program that extends the benefits of NASA Earth science research and technology to society.

Childs, Lauren M.↗
DOE OSTI2025

Active Learning Meets Foundation Models: Fast Remote Sensing Data Annotation for Object Detection

Object detection in remote sensing demands extensive, high-quality annotations—a process that is both labor-intensive and time-consuming. In this work, we introduce a real-time active learning and semi-automated labeling framework that leverages foundation models to streamline dataset annotation for object detection in remote sensing imagery. For example, by integrating a Segment Anything Model (SAM), our approach generates mask-based bounding boxes that serve as the basis for dual sampling: (a) uncertainty estimation to pinpoint challenging samples, and (b) diversity assessment to ensure broad data coverage. Furthermore, our Dynamic Box Switching Module (DBS) addresses the well-known cold start problem for object detection models by replacing its suboptimal initial predictions with SAM-derived masks, thereby enhancing early-stage localization accuracy. Extensive evaluations on multiple remote sensing datasets plus a real-world user study, demonstrate that our framework not only reduces annotation effort, but also significantly boosts detection performance compared to traditional active learning sampling methods. The code for training and the user interface will be made available.

Burges, Marvin [ORNL] (ORCID:0000000312690769)↗
NASA NTRS1989

Jackson State University's Center for Spatial Data Research and Applications: New facilities and new paradigms

Jackson State University recently established the Center for Spatial Data Research and Applications, a Geographical Information System (GIS) and remote sensing laboratory. Taking advantage of new technologies and new directions in the spatial (geographic) sciences, JSU is building a Center of Excellence in Spatial Data Management. New opportunities for research, applications, and employment are emerging. GIS requires fundamental shifts and new demands in traditional computer science and geographic training. The Center is not merely another computer lab but is one setting the pace in a new applied frontier. GIS and its associated technologies are discussed. The Center's facilities are described. An ARC/INFO GIS runs on a Vax mainframe, with numerous workstations. Image processing packages include ELAS, LIPS, VICAR, and ERDAS. A host of hardware and software peripheral are used in support. Numerous projects are underway, such as the construction of a Gulf of Mexico environmental data base, development of AI in image processing, a land use dynamics study of metropolitan Jackson, and others. A new academic interdisciplinary program in Spatial Data Management is under development, combining courses in Geography and Computer Science. The broad range of JSU's GIS and remote sensing activities is addressed. The impacts on changing paradigms in the university and in the professional world conclude the discussion.

Davis, Bruce E.↗
DOE OSTI2025

Increased Asian Aerosols Contribute to Historical Climate Change in Antarctica

Since the 1980s, anthropogenic aerosols (AAs) have decreased in North America and Europe, while they have continued to increase in East and South Asia. Meanwhile, climate change in Antarctica has exhibited a zonally asymmetric pattern. However, few studies have linked the two. In this study, we conduct fully coupled model experiments and find that Antarctica responds more significantly to Asian AAs than to those from North America and Europe. Increased Asian AAs induce a tri‐polar anomaly pattern in Antarctica via a poleward propagating Rossby wave train. This pattern is characterized by warmer temperature and less sea ice in the Amundsen‐Bellingshausen Seas, and cooler temperature and more sea ice in regions on its both flanks. Our findings highlight the importance of remote forcing, such as AAs in the Northern Hemisphere, in driving historical changes in Antarctica through atmospheric teleconnection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multimodal Data Fusion via Entropy Minimization

The use of gradient-based data-driven models to solve a range of real-world remote sensing problems can in practice be limited by the uniformity of available data. Use of data from disparate sensor types, resolutions, and qualities typically requires compromises based on assumptions that are made prior to model training and may not necessarily be optimal given over-arching objectives. For example, while deep neural networks (NNs) are state-of-the-art in a variety of target detection problems, training them typically requires either limiting the training data to a subset over which uniformity can be enforced or training independent models which subsequently require additional score fusion. The method we introduce here seeks to leverage the benefits of both approaches by allowing correlated inputs from different data sources to co-influence preferred model solutions, while maintaining flexibility over missing and mismatching data. In this work we propose a new data fusion technique for gradient updated models based on entropy minimization and experimentally validate it on a hyperspectral target detection dataset. We demonstrate superior performance compared to currently available techniques using a range of realistic data scenarios, where available data has limited spacial overlap and resolution.

97 MATHEMATICS AND COMPUTING↗