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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Autonomous Sciencecraft Experiment (ASE) Test Operations in 2003

NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASA s New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. ASE is scheduled to fly on the US Air Force Research Laboratory (AFRL) Techsat-21 constellation in 2006. Tech- Sat-21 consists of three satellites flying in a variable-geometry formation in Earth orbit. Each satellite is equipped with X-band Synthetic Aperture Radar, yielding high spatial resolution images (approx. 3 m) of the Earth s surface. The constellation will fly at an altitude of 550 km, in a 35.4 inclination circular orbit, yielding exact repeat-track observations every 13 days. Prior to full deployment, elements of the versatile ASE spacecraft command and control software, image formation software and science processing software will be utilized and tested on two very different platforms in 2003: AirSAR and EO-1 (described below). Advantages of Autonomous Operations: ASE will demonstrate advanced autonomous science data acquisition, processing, and product downlink prioritization, as well as autonomous spacecraft command and control, and fault detection. The advantages of spacecraft autonomy are to future missions include: (a) making the best use of reduced downlink; (b) the overcoming of communication delays through decisionmaking in situ, enabling fast reaction to dynamic events; (c) an increase of science content per byte of returned data; and (d) an avoidance of return of null (no-change/no feature) datasets: if there is no change detectable between two scenes of the same target, there is no need to return the second dataset.

Chien, S.↗
NASA NTRSDate not supplied

NASA POWER: Providing Present and Future Climate Services Based on NASA Data for the Energy, Agricultural, and Sustainable Buildings Communities

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the renewable energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been successfully used by decision makers to support actions that address climate change. For example, POWER data has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing climate services to provide time series of the projected future evolution of key quantities that interest our users, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We demonstrate the potential of the new climate services by presenting here a range of possible future changes in these quantities at different NASA centers across the continental United States. These data services are based on downscaled climate model data from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set. We highlight the important insights that new climate services can provide. Our climate services will help our user communities quantify the impacts of climate change to support their key decisions in planning for the future, both inside and outside the Federal Government, especially for decisions in renewable energy and in building heating and cooling.

Bradley Hegyi↗
NASA NTRSDate not supplied

High Performance Access to Archival Data Stored in HDF4 and HDF5 on Cloud Object Stores Without Reformatting the Files

Cloud computing offers numerous advantages for users of extensive Earth science data collections. These benefits encompass direct online access to data files and granules from any location, scalable access supporting parallel computing workflows, and flexible computing tools enabling innovative experimentation with processing techniques. However, older archival file formats designed for distinct computing systems hinder efficient access to decade-long time-series data when compared to data stored in modern cloud-optimized formats like Web Object Stores (WOS), exemplified by Amazon Web Services’ Simple Storage Service (S3). We describe DMR++ (Dataset Metadata Response plus plus), a technology facilitating efficient access to HDF5 (Hierarchical Data Format, version 5) and HDF4 files stored on WOS systems without requiring data reformatting. DMR++ achieves performance comparable to technologies like Zarr while preserving the original file structure, a substantial benefit considering the vast quantity of archival files held by organizations such as NASA. Moreover, DMR++ typically outperforms cloud-optimized versions of HDF5. Essentially an XML (Extensible Markup Language) document usually stored alongside the described data, DMR++ can also be generated on-the-fly but is generally created during data staging to the WOS. Archival files that use HDF4/5 often store large arrays of numerical data. The data in these files is often compressed, typically reducing their size by a factor of four or more. To achieve efficient access to portions of those arrays, they are 'chunked' into smaller sub-arrays, each individually compressed. The chunk size is a compromise, where spinning disks can efficiently access data in smaller chunks while S3 favors larger chunks. A simple optimization of aggregating smaller chunks that are stored adjacently, transferring them in a single access and then individually decompressing them will improve performance. NASA data pose an additional challenge: special Application Programmer Interface (API) libraries are often needed to compute some variables. These libraries are incompatible with WOS environments. Our solution involves storing computed values in the DMR++ document or a companion file, making them accessible like other variables and eliminating the need for specialized APIs. We outline specific optimizations for both satellite grid and swath data stored in HDF4-EOS2 (Earth Observing System).

James Gallagher↗
NASA NTRSDate not supplied

Apollo Botany Experiments and Artemis: Discussion of Botany Experiment Records Digitized as Part of the Apollo Records Synthesis Project (ARSP)

The Artemis Program, with its objective of establishing a sustained human presence on the moon, has rekindled interest in lunar botany as an area of inquiry. Limited availability of collected lunar regolith samples for plant growth experiments underlines the need to leverage all existing historic datasets. As part of NASA’s Life Sciences Data Archive’s (LSDA) Apollo Records Synthesis Project (ARSP), a collection of historic Apollo era botany experiment research records from the former Space Life Sciences Archival Library (SLSAL) collection, these records have been digitized to improve access for data reuse. The records are related to post-mission quarantine experiments conducted to characterize any pathogenic effects of lunar regolith on exposed plant specimens and includes several possible dataset sources such as microscopy photos, laboratory notebooks, correspondence, data sheets, and gas chromatography results. This collection includes the output of investigations of taxa previously under-investigated in the context of space biology. An unusually phylogenetically diverse group of plants are represented in this dataset (including Pinaceae, Poeaceae, Solanaceae, Apiaceae and more). This presentation will discuss the contents of the collection, their historic context, the digitization of the collection, and the opportunities they may provide when placed in the larger corpus of lunar botany regolith experiments.

Archives↗
NASA NTRS2017

An Automaton Rover Enabling Long Duration In-Situ Science in Extreme Environments

The Automaton Rover for Extreme Environments (AREE) is a NASA Innovative Advanced Concept (NIAC) funded study focused on enabling long duration science on Venus by replacing vulnerable electronics with an entirely mechanical design. By utilizing high temperature alloys, the rover would survive on the surface of Venus for weeks if not months. The rover concept harvests wind energy using a turbine and stores it in a constant force spring. The mobility system would be guided by a mechanical computer and logic system, programed to carry out the mission. It would collect basic science data such as wind speed, temperature, and seismic events. Communicating the data back to Earth is the most challenging aspect of the system design with multiple options being explored in a trade: a simple electronic high temperature transponder, a retroreflector target or inscribing phonograph style records to be launched via a balloon to a high altitude drone capable of transmitting the data back to Earth. AREE is not only a new exciting in-situ rover concept, but also a paradigm shift to conducting in-situ science in extreme environments. Traditional extreme environment vehicles collect as many diverse data points as possible in the short period of time before system failure. AREE breaks that trend by exploring what can be done with only a few basic scientific measurements, but recorded over long periods of time. In addition to Venus, the concept can be useful in other extreme environments in the solar system including Mercury, Jupiter's radiation belts, the interiors of gas giants, the mantle of the Earth and volcanoes throughout the solar system.

Parness, Aaron↗
NASA NTRS2022

Supporting Responsible Machine Learning in Heliophysics

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.

Machine learning↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
DOE OSTI2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Using Large Language Models to help customers monitor global threat data

Large Language Models have proven adept at answering general knowledge questions. To make these generative AI tools useful to our mission customers for monitoring global threats, the data sciences team at Sandia is utilizing retrieval augmented generation (RAG) techniques to customize these models with local data. The local data we use consists of data such as research articles and patent abstracts that we've collected over the last several years using automated pipelines.

Herzer, John Andrew [Sandia National Laboratories ↗
NASA NTRS1978

Directory of astronomical data files

This Directory of Astronomical Data Files was prepared by the Data Task Force of the Interagency Coordination Committee for Astronomy (ICCA) in cooperation with the National Space Science Data Center (NSSDC). The purpose of the Directory is to provide a listing which will enable a user to locate stellar and extragalactic data sources keyed along with sufficient descriptive information to permit him to assess the value of the files for his use as well as the status and availability of the compilations.

Source record↗
NASA NTRS1983

Telecommunications and data acquisition systems support for the Viking 1975 mission to Mars

The background for the Viking Lander Monitor Mission (VLMM) is given, and the technical and operational aspects of the tracking and data acquisition support that the Network was called upon to provide are described. An overview of the science results obtained from the imaging, meteorological, and radio science data is also given. The intensive efforts that were made to recover the mission are described.

Mudgway, D. J.↗
NASA NTRS1993

Wide-field direct CCD observations supporting the Astro-1 Space Shuttle mission's Ultraviolet Imaging Telescope

Wide field direct CCD observations are being obtained to support and complement the vacuum-ultraviolet (VUV) images provided by Astro's Ultraviolet Imaging Telescope (UIT) during a Space Shuttle flight in December 1990. Because of the wide variety of projects addressed by UIT, the fields observed include (1) galactic supernova remnants such as the Cygnus Loop and globular clusters such as Omega Cen and M79; (2) the Magellanic Clouds, M33, M81, and other galaxies in the Local Group; and (3) rich clusters of galaxies, principally the Perseus cluster and Abell 1367. Ground-based observations have been obtained for virtually all of the Astro-1 UIT fields. The optical images allow identification of individual UV sources in each field and provide the long baseline in wavelength necessary for accurate analysis of UV-bright sources. To facilitate use of our optical images for analysis of UIT data and other projects, we plan to archive them, with the UIT images, at the National Space Science Data Center (NSSDC), where they will be universally accessible via anonymous FTP. The UIT, one of three telescopes comprising the Astro spacecraft, is a 38-cm f/9 Ritchey-Chretien telescope on which high quantum efficiency, solar-blind image tubes are used to record VUV images on photographic film. Five filters with passbands centered between 1250A and 2500A provide both VUV colors and a measurement of extinction via the 2200A dust feature. The resulting calibrated VUV pictures are 40 arcminutes in diameter at 2.5 arcseconds resolution. The capabilities of UIT, therefore, complement HST's WFPC: the latter has 40 times greater collecting area, while UIT's usable field has 170 times WFPC's field area.

Hintzen, Paul↗
NASA NTRS1992

Submillimeter, millimeter, and microwave spectral line catalogue, revision 3

A computer-accessible catalog of submillimeter, millimeter, and microwave spectral lines in the frequency range between 0 and 10,000 GHz (i.e., wavelengths longer than 30 micrometers) is described. The catalog can be used as a planning or as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, the lower state energy, and the quantum number assignment. This edition of the catalog has information on 206 atomic and molecular species and includes a total of 630,924 lines. The catalog was constructed by using theoretical least square fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances. Future versions of this catalog will add more atoms and molecules and update the present listings as new data appear. The catalog is available as a magnetic data tape recorded in card images, with one card image per spectral line, from the National Space Science Data Center, located at Goddard Space Flight Center.

Pickett, H. M.↗
NASA NTRS1999

Extended Duration Orbiter Medical Project

Biomedical issues have presented a challenge to flight physicians, scientists, and engineers ever since the advent of high-speed, high-altitude airplane flight in the 1940s. In 1958, preparations began for the first manned space flights of Project Mercury. The medical data and flight experience gained through Mercury's six flights and the Gemini, Apollo, and Skylab projects, as well as subsequent space flights, comprised the knowledge base that was used to develop and implement the Extended Duration Orbiter Medical Project (EDOMP). The EDOMP yielded substantial amounts of data in six areas of space biomedical research. In addition, a significant amount of hardware was developed and tested under the EDOMP. This hardware was designed to improve data gathering capabilities and maintain crew physical fitness, while minimizing the overall impact to the microgravity environment. The biomedical findings as well as the hardware development results realized from the EDOMP have been important to the continuing success of extended Space Shuttle flights and have formed the basis for medical studies of crew members living for three to five months aboard the Russian space station, Mir. EDOMP data and hardware are also being used in preparation for the construction and habitation of International Space Station. All data sets were grouped to be non-attributable to individuals, and submitted to NASA s Life Sciences Data Archive.

Sawin, Charles F.↗
NASA NTRS2001

New Data on the Topside Electron Density Distribution

The existing uncertainties about the electron density profiles in the topside ionosphere, i.e., in the height region from hmF2 to approx. 2000 km, require the search for new data sources. The ISIS and Alouette topside sounder satellites from the sixties to the eighties recorded millions of ionograms and most were not analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2001 about 350,000 ionograms have been digitized from the original 7-track analog tapes. These data are available in binary and CDF format from the anonymous ftp site of the National Space Science Data Center. A search site and browse capabilities on CDAWeb assist the scientific usage of these data. All information and access links can be found at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. This paper describes the ISIS data restoration effort and shows how the digital ionograms are automatically processed into electron density profiles from satellite orbit altitude (1400 km for ISIS-2) down to the F peak. Because of the large volume of data an automated processing algorithm is imperative. The automatic topside ionogram scaler with true height algorithm TOPIST software developed for this task is successfully scaling approx.70 % of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The automated processing of the digitized ISIS ionograms is now underway, producing a much-needed database of topside electron density profiles for ionospheric modeling covering more than one solar cycle. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.

Huang, Xue-Qin↗
NASA NTRS2002

Electron Density Profiles of the Topside Ionosphere

The existing uncertainties about the electron density profiles in the topside ionosphere, i.e., in the height region from h,F2 to - 2000 km, require the search for new data sources. The ISIS and Alouette topside sounder satellites from the sixties to the eighties recorded millions of ionograms but most were not analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2001 about 350000 ionograms have been digitized from the original 7-track analog tapes. These data are available in binary and CDF format from the anonymous ftp site of the National Space Science Data Center. A search site and browse capabilities on CDAWeb assist the scientific usage of these data. All information and access links can be found at http://nssdc.gsfc.nasa.gov/space/isis/isis- status.htm1. This paper describes the ISIS data restoration effort and shows how the digital ionograms are automatically processed into electron density profiles from satellite orbit altitude (1400 km for ISIS-2) down to the F peak. Because of the large volume of data an automated processing algorithm is imperative. The TOPside Ionogram Scaler with True height algorithm TOPIST software developed for this task is successfully scaling - 70% of the ionograms. An < > is available to manually scale the more difficult ionograms. The automated processing of the digitized ISIS ionograms is now underway, producing a much-needed database of topside electron density profiles for ionospheric modeling covering more than one solar cycle.

Huang, Xue-Qin↗
NASA NTRS2011

Improvements in Space Geodesy Data Discovery at the CDDIS

The Crustal Dynamics Data Information System (CDDIS) supports data archiving and distribution activities for the space geodesy and geodynamics community. The main objectives of the system are to store space geodesy and geodynamics related data products in a central data bank. to maintain information about the archival of these data, and to disseminate these data and information in a timely manner to a global scientific research community. The archive consists of GNSS, laser ranging, VLBI, and DORIS data sets and products derived from these data. The CDDIS is one of NASA's Earth Observing System Data and Information System (EOSDIS) distributed data centers; EOSDIS data centers serve a diverse user community and arc tasked to provide facilities to search and access science data and products. Several activities are currently under development at the CDDIS to aid users in data discovery, both within the current community and beyond. The CDDIS is cooperating in the development of Geodetic Seamless Archive Centers (GSAC) with colleagues at UNAVCO and SIO. TIle activity will provide web services to facilitate data discovery within and across participating archives. In addition, the CDDIS is currently implementing modifications to the metadata extracted from incoming data and product files pushed to its archive. These enhancements will permit information about COOlS archive holdings to be made available through other data portals such as Earth Observing System (EOS) Clearinghouse (ECHO) and integration into the Global Geodetic Observing System (GGOS) portal.

Noll, C.↗
NASA NTRS2015

Enhancing Discovery, Search, and Access of NASA Hydrological Data by Leveraging GEOSS

An ongoing NASA-funded project has removed a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series) for selected variables of the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) and other EOSDIS (Earth Observing System Data Information System) data sets (e.g., precipitation, soil moisture). These time series (data rods) are pre-generated. Data rods Web services are accessible through the CUAHSI Hydrologic Information System (HIS) and the Goddard Earth Sciences Data and Information Services Center (GES DISC) but are not easily discoverable by users of other non-NASA data systems. The Global Earth Observation System of Systems (GEOSS) is a logical mechanism for providing access to the data rods. An ongoing GEOSS Water Services project aims to develop a distributed, global registry of water data, map, and modeling services cataloged using the standards and procedures of the Open Geospatial Consortium and the World Meteorological Organization. The ongoing data rods project has demonstrated the feasibility of leveraging the GEOSS infrastructure to help provide access to time series of model grid information or grids of information over a geographical domain for a particular time interval. A recently-begun, related NASA-funded ACCESS-GEOSS project expands on these prior efforts. Current work is focused on both improving the performance of the generation of on-the-fly (OTF) data rods and the Web interfaces from which users can easily discover, search, and access NASA data.

GEOSS↗