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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Computer programs: Information retrieval and data analysis, a compilation

The items presented in this compilation are divided into two sections. Section one treats of computer usage devoted to the retrieval of information that affords the user rapid entry into voluminous collections of data on a selective basis. Section two is a more generalized collection of computer options for the user who needs to take such data and reduce it to an analytical study within a specific discipline. These programs, routines, and subroutines should prove useful to users who do not have access to more sophisticated and expensive computer software.

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NASA NTRS1975

A data storage, retrieval and analysis system for endocrine research

This retrieval system builds, updates, retrieves, and performs basic statistical analyses on blood, urine, and diet parameters for the M071 and M073 Skylab and Apollo experiments. This system permits data entry from cards to build an indexed sequential file. Programs are easily modified for specialized analyses.

Newton, L. E.↗
NASA NTRS2012

Content Platforms Meet Data Storage, Retrieval Needs

Earth is under a constant barrage of information from space. Whether from satellites orbiting our planet, spacecraft circling Mars, or probes streaking toward the far reaches of the Solar System, NASA collects massive amounts of data from its spacefaring missions each day. NASA s Earth Observing System (EOS) satellites, for example, provide daily imagery and measurements of Earth s atmosphere, oceans, vegetation, and more. The Earth Observing System Data and Information System (EOSDIS) collects all of that science data and processes, archives, and distributes it to researchers around the globe; EOSDIS recently reached a total archive volume of 4.5 petabytes. Try to store that amount of information in your standard, four-drawer file cabinet, and you would need 90 million to get the job done. To manage the flood of information, NASA has explored technologies to efficiently collect, archive, and provide access to EOS data for scientists today and for years to come. One such technology is now providing similar capabilities to businesses and organizations worldwide.

Source record↗
NASA NTRS2017

Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature Datasets

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https:ismn.geo.tuwien.ac.at) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

SMOS↗
NASA NTRS1994

Automated mainframe data collection in a network environment

The progress and direction of the computer industry have resulted in widespread use of dissimilar and incompatible mainframe data systems. Data collection from these multiple systems is a labor intensive task. In the past, data collection had been restricted to the efforts of personnel specially trained on each system. Information is one of the most important resources an organizations has. Any improvement in an organization's ability to access and manage that information provides a competitive advantage. This problem of data collection is compounded at NASA sites by multi-center and contractor operations. The Centralized Automated Data Retrieval System (CADRS) is designed to provide a common interface that would permit data access, query, and retrieval from multiple contractor and NASA systems. The methods developed for CADRS have a strong commercial potential in that they would be applicable for any industry that needs inter-department, inter-company, or inter-agency data communications. The widespread use of multi-system data networks, that combine older legacy systems with newer decentralized networks, has made data retrieval a critical problem for information dependent industries. Implementing the technology discussed in this paper would reduce operational expense and improve data collection on these composite data systems.

Gross, David L.↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2025

Sensos Smart Label Performance Summary as Observed by Oak Ridge National Laboratory

The Oak Ridge National Laboratory (ORNL) team performed an evaluation of the Sensos Smart Label Gen 2.0, as shown in Figure 1, for package tracking. A long-distance round-trip shipment between Oak Ridge, Tennessee, and Seattle, Washington, was completed to assess the device’s performance in location tracking, environment sensing capabilities, alerting features, threshold options, battery life, and real-time and historical data retrieval from the “Sync” data dashboard provided by Sensos. The evaluation was conducted to gain a general understanding of the capabilities of the device. Furthermore, due to time and resource constraints, ORNL did not conduct exhaustive testing to confirm reliability, availability, or effectiveness of alerting and tracking features. On equipment arrangement, Sensos (sensos.ai) graciously agreed to provide a Sensos Smart Label Gen 2.0 device to ORNL, at no cost, for testing and evaluation purposes. ORNL conducted assessments along with other commercial off-the-shelf (COTS) tracking devices. As a courtesy, ORNL will provide Sensos with this report summarizing the observations and findings specific to the Sensos label based on the tests performed.

42 ENGINEERING↗
NASA NTRS1971

Computer program and user documentation medical data tape retrieval system

This volume provides several levels of documentation for the program module of the NASA medical directorate mini-computer storage and retrieval system. A biomedical information system overview describes some of the reasons for the development of the mini-computer storage and retrieval system. It briefly outlines all of the program modules which constitute the system.

Anderson, J.↗
NASA NTRS1976

Investigations with satellite data temperature retrievals

A method is presented for using satellite measurements to interpolate vertical temperature soundings between radiosonde stations. The method finds a set of coefficients, which when multiplied by corresponding measured radiance quantities, yield zero temperature error at a radiosonde station. This derived set of coefficients is then applied to satellite radiance measurements at places between radiosonde stations. The computations show, for example, that the average absolute error in the layer 1000-800 mbs is only 0.3K when the corresponding 'minimum-information' method error was 2.9K. The method may be most applicable to measurements from geostationary satellites, but should also be applicable to measurements from polar orbiting satellites under certain conditions.

Fritz, S.↗
NASA NTRS1980

Rocket observations of solar radiation during the eclipse of 26 February 1979

Three Nike Tomahawk rockets were launched in Red Lake, Ontario, one previous to, and two during, the total eclipse of the Sun, for the purpose of studying the atmosphere and its interaction with solar radiation. The method and preliminary results of three experiments that were used to measure solar radiation in the X-ray, Lyman-alpha, and visible parts of the spectrum are described. The instrumentation designed for this investigation is discussed as well as post-flight data processing techniques. The retrieved data were processed to some extent to verify that a valid representation of the solar radiation was obtained. The Lyman-alpha experiment yielded very good results, and preliminary data are included. The visible radiation experiment served as support for the other experiments and also functioned well. Due to a high level of energetic particles during the eclipse, the X-ray data were contaminated and are not presented. However, satellite observations indicate no appreciable level of X-rays from the Sun during the time of the eclipse.

Bliss, H. M.↗
NASA NTRS2006

A-Train Data Depot: Integrating, Visualizing, and Extracting Cloudsat, CALIPSO, MODIS, and AIRS Atmospheric Measurements Along the A-Train Tracks

The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.

Kempler, Steven↗
NASA NTRS2006

Network device interface for digitally interfacing data channels to a controller a via network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.

Ellerbrock, Philip J.↗
NASA NTRS1990

Assessing the impact of different satellite retrieval methods on forecast available potential energy

The effects of the inclusion of satellite temperature retrieval data, and of different satellite retrieval methods, on forecasts made with the NASA Goddard Laboratory for Atmospheres (GLA) fourth-order model were investigated using, as the parameter, the available potential energy (APE) in its isentropic form. Calculation of the APE were used to study the differences in the forecast sets both globally and in the Northern Hemisphere during 72-h forecast period. The analysis data sets used for the forecasts included one containing the NESDIS TIROS-N retrievals, the GLA retrievals using the physical inversion method, and a third, which did not contain satellite data, used as a control; two data sets, with and without satellite data, were used for verification. For all three data sets, the Northern Hemisphere values for the total APE showed an increase throughout the forecast period, mostly due to an increase in the zonal component, in contrast to the verification sets, which showed a steady level of total APE.

Whittaker, Linda M.↗