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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Towards Inner Magnetosphere Particle and Field Models

We report on the initial stages of an effort to construct comprehensive empirical models of the plasma and fields in the inner magnetosphere. The models are based not only on archival data (e.g., SCATHA) but also on current spacecraft mission data (e.g., POLAR). In this component of the effort, we incorporate the recently archived data provided by the SCATRA (Spacecraft Charging AT High Altitude) satellite. The SCAT14A satellite was in a near-geostationary orbit and was in operation for more than a decade. In this paper, we focus on the SCATRA plasma and magnetic field data from approximately the first two years of operation. The time-series data are binned according to spatial location and geomagnetic activity. Examples of statistical, empirical models from this initial effort are presented; even the simply-constructed preliminary models reveal such subtle features as the previously reported warm (greater than 100 eV) plasma density enhancement near the magnetic equator.

Spence, H. E.↗
NASA NTRS2012

NASA Tech Briefs, June 2012

Topics covered include: iGlobe Interactive Visualization and Analysis of Spatial Data; Broad-Bandwidth FPGA-Based Digital Polyphase Spectrometer; Small Aircraft Data Distribution System; Earth Science Datacasting v2.0; Algorithm for Compressing Time-Series Data; Onboard Science and Applications Algorithm for Hyperspectral Data Reduction; Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data; Security Data Warehouse Application; Integrated Laser Characterization, Data Acquisition, and Command and Control Test System; Radiation-Hard SpaceWire/Gigabit Ethernet-Compatible Transponder; Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager; High-Voltage, Low-Power BNC Feedthrough Terminator; SpaceCube Mini; Dichroic Filter for Separating W-Band and Ka-Band; Active Mirror Predictive and Requirement Verification Software (AMP-ReVS); Navigation/Prop Software Suite; Personal Computer Transport Analysis Program; Pressure Ratio to Thermal Environments; Probabilistic Fatigue Damage Program (FATIG); ASCENT Program; JPL Genesis and Rapid Intensification Processes (GRIP) Portal; Data::Downloader; Fault Tolerance Middleware for a Multi-Core System; DspaceOgreTerrain 3D Terrain Visualization Tool; Trick Simulation Environment 07; Geometric Reasoning for Automated Planning; Water Detection Based on Color Variation; Single-Layer, All-Metal Patch Antenna Element with Wide Bandwidth; Scanning Laser Infrared Molecular Spectrometer (SLIMS); Next-Generation Microshutter Arrays for Large-Format Imaging and Spectroscopy; Detection of Carbon Monoxide Using Polymer-Composite Films with a Porphyrin-Functionalized Polypyrrole; Enhanced-Adhesion Multiwalled Carbon Nanotubes on Titanium Substrates for Stray Light Control; Three-Dimensional Porous Particles Composed of Curved, Two-Dimensional, Nano-Sized Layers for Li-Ion Batteries 23 Ultra-Lightweight; and Ultra-Lightweight Nanocomposite Foams and Sandwich Structures for Space Structure Applications.

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NASA NTRS2003

Accelerating Large Data Analysis By Exploiting Regularities

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Moran, Patrick J.↗
NASA NTRS1996

Compilation and Analysis of 20 and 30 GHz Rain Fade Events at the ACTS NASA Ground Station: Statistics and Model Assessment

The purpose of the propagation studies within the ACTS Project Office is to acquire 20 and 30 GHz rain fade statistics using the ACTS beacon links received at the NGS (NASA Ground Station) in Cleveland. Other than the raw, statistically unprocessed rain fade events that occur in real time, relevant rain fade statistics derived from such events are the cumulative rain fade statistics as well as fade duration statistics (beyond given fade thresholds) over monthly and yearly time intervals. Concurrent with the data logging exercise, monthly maximum rainfall levels recorded at the US Weather Service at Hopkins Airport are appended to the database to facilitate comparison of observed fade statistics with those predicted by the ACTS Rain Attenuation Model. Also, the raw fade data will be in a format, complete with documentation, for use by other investigators who require realistic fade event evolution in time for simulation purposes or further analysis for comparisons with other rain fade prediction models, etc. The raw time series data from the 20 and 30 GHz beacon signals is purged of non relevant data intervals where no rain fading has occurred. All other data intervals which contain rain fade events are archived with the accompanying time stamps. The definition of just what constitutes a rain fade event will be discussed later. The archived data serves two purposes. First, all rain fade event data is recombined into a contiguous data series every month and every year; this will represent an uninterrupted record of the actual (i.e., not statistically processed) temporal evolution of rain fade at 20 and 30 GHz at the location of the NGS. The second purpose of the data in such a format is to enable a statistical analysis of prevailing propagation parameters such as cumulative distributions of attenuation on a monthly and yearly basis as well as fade duration probabilities below given fade thresholds, also on a monthly and yearly basis. In addition, various subsidiary statistics such as attenuation rate probabilities are derived. The purged raw rain fade data as well as the results of the analyzed data will be made available for use by parties in the private sector upon their request. The process which will be followed in this dissemination is outlined in this paper.

Manning, Robert M.↗
NASA NTRS2012

Assessment of a Bidirectional Reflectance Distribution Correction of Above-Water and Satellite Water-Leaving Radiance in Coastal Waters

Water-leaving radiances, retrieved from in situ or satellite measurements, need to be corrected for the bidirectional properties of the measured light in order to standardize the data and make them comparable with each other. The current operational algorithm for the correction of bidirectional effects from the satellite ocean color data is optimized for typical oceanic waters. However, versions of bidirectional reflectance correction algorithms specifically tuned for typical coastal waters and other case 2 conditions are particularly needed to improve the overall quality of those data. In order to analyze the bidirectional reflectance distribution function (BRDF) of case 2 waters, a dataset of typical remote sensing reflectances was generated through radiative transfer simulations for a large range of viewing and illumination geometries. Based on this simulated dataset, a case 2 water focused remote sensing reflectance model is proposed to correct above-water and satellite water-leaving radiance data for bidirectional effects. The proposed model is first validated with a one year time series of in situ above-water measurements acquired by collocated multispectral and hyperspectral radiometers, which have different viewing geometries installed at the Long Island Sound Coastal Observatory (LISCO). Match-ups and intercomparisons performed on these concurrent measurements show that the proposed algorithm outperforms the algorithm currently in use at all wavelengths, with average improvement of 2.4% over the spectral range. LISCO's time series data have also been used to evaluate improvements in match-up comparisons of Moderate Resolution Imaging Spectroradiometer satellite data when the proposed BRDF correction is used in lieu of the current algorithm. It is shown that the discrepancies between coincident in-situ sea-based and satellite data decreased by 3.15% with the use of the proposed algorithm.

LISCO(LONG ISLAND SOUND COASTAL OBSERVATORY)↗
NASA NTRS1995

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗
NASA NTRS1993

Hidden Markov models for fault detection in dynamic systems

The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

Smyth, Padhraic J.↗
NASA NTRS1986

Earth observing system. Data and information system. Volume 2A: Report of the EOS Data Panel

The purpose of this report is to provide NASA with a rationale and recommendations for planning, implementing, and operating an Earth Observing System data and information system that can evolve to meet the Earth Observing System's needs in the 1990s. The Earth Observing System (Eos), defined by the Eos Science and Mission Requirements Working Group, consists of a suite of instruments in low Earth orbit acquiring measurements of the Earth's atmosphere, surface, and interior; an information system to support scientific research; and a vigorous program of scientific research, stressing study of global-scale processes that shape and influence the Earth as a system. The Eos data and information system is conceived as a complete research information system that would transcend the traditional mission data system, and include additional capabilties such as maintaining long-term, time-series data bases and providing access by Eos researchers to relevant non-Eos data. The Working Group recommends that the Eos data and information system be initiated now, with existing data, and that the system evolve into one that can meet the intensive research and data needs that will exist when Eos spacecraft are returning data in the 1990s.

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NASA NTRS2006

X-Windows PVT Widget Class

The X-Windows Process Validation Table (PVT) Widget Class ( Class is used here in the object-oriented-programming sense of the word) was devised to simplify the task of implementing network registration services for Information Sharing Protocol (ISP) graphical-user-interface (GUI) computer programs. Heretofore, ISP PVT programming tasks have required many method calls to identify, query, and interpret the connections and messages exchanged between a client and a PVT server. Normally, programmers have utilized direct access to UNIX socket libraries to implement the PVT protocol queries, necessitating the use of many lines of source code to perform frequent tasks. Now, the X-Windows PVT Widget Class encapsulates ISP client server network registration management tasks within the framework of an X Windows widget. Use of the widget framework enables an X Windows GUI program to interact with PVT services in an abstract way and in the same manner as that of other graphical widgets, making it easier to program PVT clients. Wrapping the PVT services inside the widget framework enables a programmer to treat a PVT server interface as though it were a GUI. Moreover, an alternate subclass could implement another service in a widget of the same type. This program was written by Matthew R. Barry of United Space Alliance for Johnson Space Center. For further information, contact the Johnson Technology Transfer Office at (281) 483-3809. MSC-23582 Shuttle Data Center File- Processing Tool in Java A Java-language computer program has been written to facilitate mining of data in files in the Shuttle Data Center (SDC) archives. This program can be executed on a variety of workstations or via Web-browser programs. This program is partly similar to prior C-language programs used for the same purpose, while differing from those programs in that it exploits the platform-neutrality of Java in implementing several features that are important for analysis of large sets of time-series data. The program supports regular expression queries of SDC archive files, reads the files, interleaves the time-stamped samples according to a chosen output, then transforms the results into that format. A user can choose among a variety of output file formats that are useful for diverse purposes, including plotting, Markov modeling, multivariate density estimation, and wavelet multiresolution analysis, as well as for playback of data in support of simulation and testing.

Barry, Matthew R.↗
NASA NTRS2006

Shuttle Data Center File-Processing Tool in Java

A Java-language computer program has been written to facilitate mining of data in files in the Shuttle Data Center (SDC) archives. This program can be executed on a variety of workstations or via Web-browser programs. This program is partly similar to prior C-language programs used for the same purpose, while differing from those programs in that it exploits the platform-neutrality of Java in implementing several features that are important for analysis of large sets of time-series data. The program supports regular expression queries of SDC archive files, reads the files, interleaves the time-stamped samples according to a chosen output, then transforms the results into that format. A user can choose among a variety of output file formats that are useful for diverse purposes, including plotting, Markov modeling, multivariate density estimation, and wavelet multiresolution analysis, as well as for playback of data in support of simulation and testing.

Barry, Matthew R.↗
NASA NTRS2009

Data Processing for Atmospheric Phase Interferometers

This paper presents a detailed discussion of calibration procedures used to analyze data recorded from a two-element atmospheric phase interferometer (API) deployed at Goldstone, California. In addition, we describe the data products derived from those measurements that can be used for site intercomparison and atmospheric modeling. Simulated data is used to demonstrate the effectiveness of the proposed algorithm and as a means for validating our procedure. A study of the effect of block size filtering is presented to justify our process for isolating atmospheric fluctuation phenomena from other system-induced effects (e.g., satellite motion, thermal drift). A simulated 24 hr interferometer phase data time series is analyzed to illustrate the step-by-step calibration procedure and desired data products.

Acosta, Roberto J.↗
NASA NTRS2012

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB(R) that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane s destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.

Kalcic, M. T.↗
NASA NTRS2016

Drought Impacts on Agricultural Production and Land Fallowing in California's Central Valley in 2015

The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to time-series data from Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), OLI (Operational Land Imager), and MODIS (Moderate Resolution Imaging Spectroradiometer). Our effort has been focused on development of indicators of drought impacts in the March-August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the algorithm along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015.

Valleys↗
NASA NTRS2018

Ground and Satellite Based Observation Datasets for the Lower Mekong River Basin

In ‘Satellite observations and modeling to understand the Lower Mekong River Basin streamflow variability’ [1] hydrological fluxes, meteorological variables, land cover land use maps, and soil characteristics and parameters data were compiled and processed for the Lower Mekong River Basin. In this work, daily streamflow time series data at nine gauges located at five different countries in the Mekong region (Thailand, Laos People׳s Democratic Republic (PDR), Myanmar, Cambodia, and Viet Nam) is presented. Satellite-based daily precipitation and air temperature (minimum & maximum) data is processed and provided over the entire basin as part of the dataset provided in this work. Moreover, land cover land use raster data that contains 18 classes that cover agriculture, urban, range and forests land cover land use classes for the basin is offered. In addition, a soil data that contains physical and chemical characteristics needed by physically based hydrological models to simulate the cycling of water and air is also provided.

Streamflow↗
NASA NTRS1989

Methods for trend analysis: Examples with problem/failure data

Statistics are emphasized as an important role in quality control and reliability. Consequently, Trend Analysis Techniques recommended a variety of statistical methodologies that could be applied to time series data. The major goal of the working handbook, using data from the MSFC Problem Assessment System, is to illustrate some of the techniques in the NASA standard, some different techniques, and to notice patterns of data. Techniques for trend estimation used are: regression (exponential, power, reciprocal, straight line) and Kendall's rank correlation coefficient. The important details of a statistical strategy for estimating a trend component are covered in the examples. However, careful analysis and interpretation is necessary because of small samples and frequent zero problem reports in a given time period. Further investigations to deal with these issues are being conducted.

Church, Curtis K.↗
NASA NTRS2020

Monitoring Matang's Mangroves in Peninsular Malaysia through Earth observations: A globally relevant approach

Expansion of rotational timber harvesting of mangroves is set to increase, particularly given greater recognition of the economic, societal and environmental benefits. Generic and standardized procedures for monitoring mangroves are, therefore, needed to ensure their long-term sustainable utilisation. Focusing on the Matang Mangrove Forest Reserve (MMFR), Perak State, Peninsular Malaysia, thematic and continuous environmental descriptors with defined codes or units, including life form, forest age (years), canopy cover (%), above-ground biomass (Mg/ha) and relative amounts of woody debris (%), were retrieved from time-series data from spaceborne optical and single/dual polarimetric and interferometric RADAR. These were then combined for multiple points in time to generate land cover and evidence-based change maps according to the Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and using the framework of the Earth Observation Data for Ecosystem Monitoring (EODESM). Change maps were based on a pre-defined taxonomy, with focus on clear cutting and regrowth. Uncertainties surrounding the land cover and change maps were based on those determined for the environmental descriptors used for their generation and through comparison with independent retrieval from other EO data sources. For the MMFR and also for other mangroves worldwide where harvesting is occurring or being considered, a new approach and opportunity for supporting management of mangroves is presented, which has application for future planning of mangrove resources.

Richard Lucas↗
NASA NTRS1979

Studies in astronomical time series analysis: Modeling random processes in the time domain

Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.

Scargle, J. D.↗