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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.

Markham, B. L.↗
NASA NTRS1992

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

Djorgovski, Stanislav↗
NASA NTRS1993

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Djorgovski, George↗
NASA NTRS1999

Data Reduction Methods Applied to the Fastrac Engine

The Fastrac rocket engine is currently being developed for the X-34 technology demonstrator vehicle. The engine performance model must be calibrated to support accurate performance prediction. Data reduction is the process of estimating hardware characteristics from available test data, and is essential for effective performance model calibration and prediction. A new data reduction procedure was developed, implemented, and tested using data from Fastrac engine tests. The procedure selects hardware and test measurements to use in the reduction process based on examination of the model influence matrix condition number. Predicted hardware characteristics are recovered from the solution of a quadratic programming problem. Computational tests indicate that the new procedure provides a significant improvement in test data reduction capability. Enhancements include improved test data utilization and time history data reduction capability. The new method is generically applicable to other systems.

Santi, L. Michael↗
NASA NTRSDate not supplied

Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Collaborative open science practices are changing the way research is conducted. These changes affect how scientists work together on data, code and information. Data systems enhance open science by offering forward thinking technological solutions, such as providing data and computation on the cloud, to enable collaboration, sharing and analysis. In this paper, we present our vision for a conceptual data system on the cloud that enables open science. We also present our work on the Multi-Mission Algorithm and Analysis Platform (MAAP)which has served as a pathfinder data system for this conceptual approach.

Kaylin Bugbee↗
NASA NTRSDate not supplied

Expanding Biological Repository Data Available for Sharing and Knowledge Discovery

Biology has developed next-generation data science and alternative analytical approaches with methodologies which require principal investigator (PI) experimental assay data be re-used. This new approach involves mining multiple datasets at once from various hierarchical organizations of biological complexity, while concurrently evaluating how experimental factors affect endpoints of standard assays. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make findable, accessible, interoperable, and reusable (FAIR) all non-human space-relevant biological data. These data include mission metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery, and subject-experienced telemetry (radiation, temperature, humidity, acoustics, vibrations). ALSDA has transformed to bring current biological repository data and all future collected data into this new scientific data mining reality. It has integrated into the ‘NASA Open Science’ group of projects to facilitate a suite of new tools and workflows to improve data accessibility and reusability by implementing data management plans, automating data submission agreements, and adopting the single-point-of-entry data submission portal, originally developed by NASA GeneLab. These systems required ALSDA to develop science assay configurations for the submission portal, capturing essential assay parameters according to established norms in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. ALSDA datasets are curated to maintain rich metadata, accuracy of datasets, data transparency, provenance, and additionally ensure data are machine-readable (e.g., R and Python languages). ALSDA integration with GeneLab and its analysis portals enable higher-order physiological-level datasets be mined in conjunction with -omics datasets. As ALSDA physiological-level datasets are published (micro-computed tomography, histology, intraocular pressure, hormonal assays, immunostaining, ultrasonography), the merging of hierarchical organizations of biological complexity from spaceflight will enable new knowledge discovery approaches.

Ryan T Scott↗
NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing and Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

Biology↗
NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing And Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

life science↗
NASA NTRS2013

Method for Enhancing a Three Dimensional Image from a Plurality of Frames of Flash LIDAR Data

A method for enhancing a three dimensional image from frames of flash LIDAR data includes generating a first distance R(sub i) from a first detector i to a first point on a surface S(sub i). After defining a map with a mesh theta having cells k, a first array S(k), a second array M(k), and a third array D(k) are initialized. The first array corresponds to the surface, the second array corresponds to the elevation map, and the third array D(k) receives an output for the DEM. The surface is projected onto the mesh theta, so that a second distance R(sub k) from a second point on the mesh theta to the detector can be found. From this, a height may be calculated, which permits the generation of a digital elevation map. Also, using sequential frames of flash LIDAR data, vehicle control is possible using an offset between successive frames.

Vanek, Michael D.↗
NASA NTRS2004

Solar Variability and the Near-Earth Environment: Mining Enhanced Low Dose Rate Sensitivity Data From the Microelectronics and Photonics Test Bed Space Experiment

This effort is a detailed analysis of existing microelectronics and photonics test bed satellite data from one experiment, the bipolar test board, looking to improve our understanding of the enhanced low dose rate sensitivity (ELDRS) phenomenon. Over the past several years, extensive total dose irradiations of bipolar devices have demonstrated that many of these devices exhibited ELDRS. In sensitive bipolar transistors, ELDRS produced enhanced degradation of base current, resulting in enhanced gain degradation at dose rates <0.1 rd(Si)/s compared to similar transistors irradiated at dose rates >1 rd(Si)/s. This Technical Publication provides updated information about the test devices, the in-flight experiment, and both flight-and ground-based observations. Flight data are presented for the past 5 yr of the mission. These data are compared to ground-based data taken on devices from the same date code lots. Information about temperature fluctuations, power shutdowns, and other variables encountered during the space flight are documented.

Turflinger, T.↗
NASA NTRS1985

Computer aided processing using laser measurements

The challenge exists of processing the STS and its cargo through KSC facilities in the most timely and cost effective manner possible. To do this a 3-D computer graphics data base was established into which was entered the STS, payloads, and KSC facilities. The facility drawing data are enhanced by laser theodolite measurements into an as-built configuration. Elements of the data base were combined to study orbiter/facility interfaces payload/facility access problems and design/arrangement of various GSE to support processing requirements. With timely analysis/design utilizing the 3-D computer graphics system, costly delays can be avoided. Better methodology can be analyzed to determine procedures for cost avoidance.

Davis, R. M.↗
NASA NTRS1973

Machine processing methods for earth observational data

A brief review of the development over the last decade of earth resource information systems is presented. Machine data preprocessing and analysis methods are surveyed and illustrated. These include preprocessing steps intended to modify geometric and radiometric aspects of earth observational image data to enhance the ability of either human interpreters or machine algorithms to extract information from the data. Illustrations of processed and analyzed images from spaceborne sensors including the Earth Resources Technology Satellite are discussed.

Landgrebe, D. A.↗
NASA NTRS1981

An experimental and computational investigation of the transport of Reynolds stress in an axisymmetric swirling boundary layer

A coordinated experimental and computational investigation of the transport and interaction of the individual Reynolds stresses within a turbulent axisymmetric swirling boundary layer flowing over a stationary cylinder is described. The cylinder is instrumented with new directional surface fence skin-friction gages developed for the experiment. The longitudinal and transverse mean velocity profiles are measured with a miniature directional pressure probe. These data provided a standard on which to compare computed results employing a variety of turbulence models. The turbulence models tested range from a simple mixing length model, through a two-equation model, on to the Reynolds stress equation models. The full Reynolds stress equation models are tested with various sets of modeling coefficients for the pressure rate-of-strain correlation. It is shown that while even the simplest model shows the general features of the data, agreement with the experimental data is enhanced through the use of the more complex models. For a better prediction of the data, however, further model improvement is still required.

Higuchi, H.↗
NASA NTRS2008

Health and Environment Linked for Information Exchange (HELIX)-Atlanta: A CDC-NASA Joint Environmental Public Health Tracking Collaborative Project

HELIX-Atlanta was developed to support current and future state and local EPHT programs to implement data linking demonstration projects which could be part of the CDC EPHT Network. HELIX-Atlanta is a pilot linking project in Atlanta for CDC to learn about the challenges the states will encounter. NASA/MSFC and the CDC are partners in linking environmental and health data to enhance public health surveillance. ~ The use of NASA technology creates value added geospatial products from existing environmental data sources to facilitate public health linkages. Proving the feasibility of the approach is the main objective

Al-Hamdan, Mohammad↗
NASA NTRS2008

Remote Sensing, Air Quality, and Public Health

HELIX-Atlanta was developed to support current and future state and local EPHT programs to implement data linking demonstratio'n projects which could be part of the EPHT Network. HELIX-Atlanta is a pilot linking project in Atlanta for CDC to learn about the challenges the states will encounter. NASA/MSFC and the CDC are partners in linking environmental and health data to enhance public health surveillance. The use of NASA technology creates value - added geospatial products from existing environmental data sources to facilitate public health linkages. Proving the feasibility of the approach is the main objective

Quattrochi, Dale A.↗
NASA NTRS2008

Environmental Public Health Survelliance for Exposure to Respiratory Health Hazards: A Joint NASA/CDC Project to Use Remote Sensing Data for Estimating Airborne Particulate Matter Over the Atlanta, Georgia Metropolitan Area

Describes the public health surveillance efforts of NASA, in a joint effort with the Center for Disease Control (CDC). NASA/MSFC and the CDC are partners in linking nvironmental and health data to enhance public health surveillance. The use of NASA technology creates value - added geospatial products from existing environmental data sources to facilitate public health linkages. The venture sought to provide remote sensing data for the 5-country Metro-Atlanta area and to integrate this environmental data with public health data into a local network, in an effort to prevent and control environmentally related health effects. Remote sensing data used environmental data (Environmental Protection Agency [EPA] Air Quality System [AQS] ground measurements and MODIS Aerosol Optical Depth [AOD]) to estimate airborne particulate matter over Atlanta, and linked this data with health data related to asthma. The study proved the feasibility of linking environmental data (MODIS particular matter estimates and AQS) with health data (asthma). Algorithms were developed for QC, bias removal, merging MODIS and AQS particulate matter data, as well as for other applications. Additionally, a Business Associate Agreement was negotiated for a health care provider to enable sharing of Protected Health Information.

Quattrochi, Dale A.↗
NASA NTRS2015

Measured Polarized Spectral Responsivity of JPSS J1 VIIRS Using the NIST T-SIRCUS

Recent pre-launch measurements performed on the Joint Polar Satellite System (JPSS) J1 Visible Infrared Imaging Radiometer Suite (VIIRS) using the National Institute of Standards and Technology (NIST) Traveling Spectral Irradiance and Radiance Responsivity Calibrations Using Uniform Sources (T-SIRCUS) monochromatic source have provided wavelength dependent polarization sensitivity for select spectral bands and viewing conditions. Measurements were made at a number of input linear polarization states (twelve in total) and initially at thirteen wavelengths across the bandpass (later expanded to seventeen for some cases). Using the source radiance information collected by an external monitor, a spectral responsivity function was constructed for each input linear polarization state. Additionally, an unpolarized spectral responsivity function was derived from these polarized measurements. An investigation of how the centroid, bandwidth, and detector responsivity vary with polarization state was weighted by two model input spectra to simulate both ground measurements as well as expected on-orbit conditions. These measurements will enhance our understanding of VIIRS polarization sensitivity, improve the design for future flight models, and provide valuable data to enhance product quality in the post-launch phase.

JPSS↗