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An integrated data compression system is proposed to provide adaptive multimode data compression for an advanced multi-instrument spacecraft payload system that has various source data.
The Kennedy Space Center is transforming the PRACA bath automatic data processing (ADP) system of today into a fully integrated data base with on-line update and retrieval capabilities. The present manual system of reporting (Datafax, mail, and telephone) to the off-site design and engineering organizations is to be replaced by direct access to the most current information as it accrues at KSC or VAFB. Two major goals of the Intercenter PRACA are to provide a single data depository for both launch sites and to fully integrate the problem data with engineering data as well as other relevant information. The resulting ADP system can provide a closed loop system for problem reporting, corrective action and recurrence control that should serve the engineering community as well as reliability and quality assurance at the launch sites, KSC and VAFB, and at the design centers, JSC and MSFC.
On-line digital integrator for physiological data
Contractor data requirements for integration of scientific experiments aboard Voyager spacecraft
Descriptive global scale diagnoses of the First Global Atmospheric Research Experiment SOP-1 analyses were made and compared against controlled, real data integrations of the Goddard Laboratory of Atmospheric Science (GLAS) general circulation model (GCM) as well as other data sets. The effects of critical latitudes were studied; the influence of tropical wind data and latent heating upon the GLAS GCM was diagnosed; planetary wave structure on various time scales from the diurnal to the monthly was studied; and the GLAS analyses were compared with other analyses. Short term controlled GLAS GCM integrations show that: (1) the inclusion of tropical wind data in real data integrations has an important influence in the mid-latitude prediction in both hemispheres; and (2) the tropical divergent wind reacts almost immediately to alteration of the tropical latent heating. The presence or absence of zonally averaged easterlies depends strongly upon the presence of tropical latent heating.
The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.
Our first requirement was to create the specialized tools necessary to precisely co-locate at highest spatial resolution the rapidly growing MOC/NA and MOLA data sets in the polar regions. This meant promptly creating a very accurate Mars Polar GIs system (based on the commercial ArcView product) along with various other supporting routines. Later we extended its use to the THEMIS/VIS data. That customized GIS and integrated data system has enabled all of our polar research to date. We focused initially on a preliminary geological reconnaissance in the Ultimi Lobe region of the South Polar Layered Deposits, which also included the intended MPL landing site about which much remote sensing data and interpretations had been accumulated before the failed landing attempt on December 3, 1999. The most interesting findings from that effort were discoveries about the layering, faulting and contact relationships exposed along the bounding scarp. Those results were published in Icarus. We have had a priority interest in the cratering record of the polar layered deposits, starting with the first post-Viking survey based on MOLA shaded relief maps and crossing profiles. Those results, based primarily on craters < 1 km diameter, were published in JGR and showed that a portion of the SPLD exhibited a large crater population consistent with an exposure age of 30 million years or more. Those craters also exhibited remarkably low Depth to Diameter ratios, which Asmin Pathare, then at UCLA, subsequently interpreted as due to slow viscous flow in the ice-rich material composing the PLD. Our cratering study also recognized that the craters of less than 1 km diameter were many order of magnitudes too few compared to what the crater production function of the larger craters requires.
Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.
While traditional geologic mapping includes the examination of structural relationships between rock units in the field, more advanced technology now enables us to simultaneously collect and combine analytical datasets with field observations. Information about tectonomagmatic processes can be gleaned from these combined data products. Historically, construction of multi-layered field maps that include sample data has been accomplished serially (first map and collect samples, analyze samples, combine data, and finally, readjust maps and conclusions about geologic history based on combined data sets). New instruments that can be used in the field, such as a handheld xray fluorescence (XRF) unit, are now available. Targeted use of such instruments enables geologists to collect preliminary geochemical data while in the field so that they can optimize scientific data return from each field traverse. Our study tests the application of this technology and projects the benefits gained by real-time geochemical data in the field. The integrated data set produces a richer geologic map and facilitates a stronger contextual picture for field geologists when collecting field observations and samples for future laboratory work. Real-time geochemical data on samples also provide valuable insight regarding sampling decisions by the field geologist
The state of the art of Big Earth Data Analytics can be expected to evolve rapidly in the coming years. The forces driving evolution come from both growth in the data and advancement in the field of data analytics. In the data area, advances in sensor instrumentation and platform miniaturization are increasing both data resolution and coverage, resulting in enormous growth in data Volume. Increases in temporal resolution in particular also generate demands for higher data Velocity. At the same time, the proliferation of instruments and the platforms on which they reside is increasing the Variety of datasets. The Variety increase in turn leads to questions about the Veracity of the data. In the algorithm area, powerful machine learning methods are coming to the fore, particularly Deep Neural Networks. These are powerful at detecting interesting features in the data, integrating many different measurements (i.e., data fusion), and classification problems. However, they are still challenging when seeking explanations of how natural or socio-economic phenomena work using Earth Observations. Thus, classical analysis techniques will remain relevant when the emphasis is on forming or testing explanations, as well as to support interactive data exploration.
The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.
PyDDA is an expandable framework that integrates data from weather radars and forecasting models using SciPy’s optimization package to create meteorological fields.
The application of laser anemometry to the measurement of turbomachinery flow fields is reviewed. Choices of optical configuration, seed particle generation, and seed injection techniques are discussed. The modification of experimental facilities to gain optical access is considered. The efficiency of data acquisition schemes is analyzed and issues related to data integrity and error estimation are addressed. Data reduction and analysis techniques for extracting and understanding the flow physics from laser anemometer measurements are presented.
Real-time sensor validation improves process monitoring and control system dependability by ensuring data integrity through automated detection of sensor data failures. The NASA Lewis Research Center, Expert Microsystems, and Intelligent Software Associates have developed an innovative sensor validation system that can automatically detect automated sensor failures in real-time for all types of mission-critical systems. This system consists of a sensor validation network development system and a real-time kernel. The network development system provides tools that enable systems engineers to automatically generate software that can be embedded within an application. The sensor validation methodology captured by these tools can be scaled to validate any number of sensors, and permits users to specify system sensitivity. The resulting software reliably detects all types of sensor data failures.
The black-hole X-ray binary transient GRO J1655-40 underwent an outburst beginning in early 2005. We present the results of our multi-wavelength observational campaign to study the early outburst spectral and temporal evolution, which combines data from X-ray (RXTE, INTEGRAL), radio (VLA) and optical (ROTSE, SMARTS) instruments. During the reported period the source left quiescence and went through four major accreting black hole states: low-hard, hard intermediate, soft intermediate and high-soft. We investigated dipping behavior in the RXTE band and compare our results to the 1996-1997 case, when the source was predominantly in the high-soft state, finding significant differences. We consider the evolution of the low frequency quasi-periodic oscillations and find that the frequency strongly correlates with the spectral characteristics, before shutting off prior to the transition to the high-soft state. We model the broad-band high-energy spectrum in the context of empirical models, as well as more physically motivated thermal and bulk-motion Comptonization and Compton reflection models. RXTE and INTEGRAL data together support a statistically significant high energy cut-off in the energy spectrum at approximately equal to 100 - 200 keV during the low-hard state. The RXTE data alone also show it very significantly during the transition, but cannot see one in the high-soft state spectra. We consider radio, optical and X-ray connections in the context of possible synchrotron and synchrotron self-Compton origins of X-ray emission in low-hard and intermediate states. In this outburst of GRO J1655-40, the radio flux does not rise strongly with the X-ray flux.
This report assesses the capacity of incorporating NASA#s Aquarius SSS (sea surface salinity) data into the SMAST (School of Marine Science and Technology) DSS for Fisheries Science. This data will enhance the SMAST DSS by providing SSS over a large area. Aquarius is a focused satellite mission designed to measure global SSS. SSS mapping is limited because conventional in situ SSS sampling is too sparse to give a large-scale view of the salinity variability. Aquarius will resolve missing physical processes that link the water cycle, the climate, and the ocean. The SMAST Fisheries program provides a DSS for fisheries science. It collects fisheries and environmental data, integrates them into a suite of data assimilation ocean models, and provides hindcasts, nowcasts, and forecasts for fisheries research, fisheries management, and the fishery industry. Currently, SMAST is using SSS data from the National Oceanic and Atmospheric Administration#s National Data Buoy Center. The SMAST DSS would be enhanced with SSS data from the Aquarius mission.
The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.
The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.