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NASA NTRS1992

Acquisition and use of Orlando, Florida and Continental Airbus radar flight test data

Westinghouse is developing a lookdown pulse Doppler radar for production as the sensor and processor of a forward looking hazardous windshear detection and avoidance system. A data collection prototype of that product was ready for flight testing in Orlando to encounter low level windshear in corroboration with the FAA-Terminal Doppler Weather Radar (TDWR). Airborne real-time processing and display of the hazard factor were demonstrated with TDWR facilitated intercepts and penetrations of over 80 microbursts in a three day period, including microbursts with hazard factors in excess of .16 (with 500 ft. PIREP altitude loss) and the hazard factor display at 6 n.mi. of a visually transparent ('dry') microburst with TDWR corroborated outflow reflectivities of +5 dBz. Range gated Doppler spectrum data was recorded for subsequent development and refinement of hazard factor detection and urban clutter rejection algorithms. Following Orlando, the data collection radar was supplemental type certified for in revenue service on a Continental Airlines Airbus in an automatic and non-interferring basis with its ARINC 708 radar to allow Westinghouse to confirm its understanding of commercial aircraft installation, interface realities, and urban airport clutter. A number of software upgrades, all of which were verified at the Receiver-Transmitter-Processor (RTP) hardware bench with Orlando microburst data to produce desired advanced warning hazard factor detection, included some preliminary loads with automatic (sliding window average hazard factor) detection and annunciation recording. The current (14-APR-92) configured software is free from false and/or nuisance alerts (CAUTIONS, WARNINGS, etc.) for all take-off and landing approaches, under 2500 ft. altitude to weight-on-wheels, into all encountered airports, including Newark (NJ), LAX, Denver, Houston, Cleveland, etc. Using the Orlando data collected on hazardous microbursts, Westinghouse has developed a lookdown pulse Doppler radar product with signal and data processing algorithms which detect realistic microburst hazards and has demonstrated those algorithms produce no false alerts (or nuisance alerts) in urban airport ground moving vehicle (GMTI) and/or clutter environments.

Eide, Michael C.↗

NASA NTRS2016

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

passive microwave↗

DOE OSTI2021

Design-Space Data: Informing Common Design Decisions with Pre-Simulated Data

Design Space Exploration (DSE) analysis techniques represent a data-centric approach to integrating performance analysis in early design phases when there is the greatest potential to cheaply improve the energy efficiency of a building. We focus on a novel extension of DSE called Universal Design Space Exploration (UDSE), which leverages massive databases of pre-simulated analysis that represent all possible outcomes of common analysis workflows. These databases, called Design Spaces, become “universal” when a single pre-simulated design space can be re-applied to future unknown projects. Unlike current simulation methods, which require a design to exist before it can be analyzed and often take minutes or hours to simulate, UDSE leverages pre-simulation to deliver rapid and relevant insight as new designs are conceptualized. The data underpinning UDSE enables advanced statistical and Artificial Intelligence methods, allowing UDSE to deliver a greater understanding of the larger problem being explored, rather than simply delivering analysis of several pre-conceived design options. We believe that UDSE can provide instantaneous, relevant analysis for all building design projects at negligible cost. This paper has two main goals, to develop a relevant Universal Design Space that showcases the potential of UDSE and to release this data freely to industry and academia; thereby lowering the barrier to entry to digital literacy in statistics, ML and AI within the architecture, engineering, construction (AEC) industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗

DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗

NASA NTRS1996

AIRSAR Data for Geological and Geomorphological Mapping in the Great Sandy Desert and Pilbara Regions of Western Australia

Enhancements of AIRSAR data have demonstrated the benefits of synthetic aperture radar (SAR) for revealing an additional and mich higher level of information about the composition of the terrain than enhancements f either SPOT-PAN or Landsat TM data. With appropriate image processing techniques, surface and near surface geological structures, hydrological systems (both current and ancient) and landform features, have been evidenced in a diverse range of landscapes. In the Great Sandy Desert region where spectral variability is minimal, radar's sensitivity to the micromorphology of sparse exposures of subcrop and lag gravels has provided a new insight into the region's geological framework, its landforms, and their evolution. In the Pilbara region, advanced processing of AIRSAR data to unmix the backscatter between and within the three frequencies of data has highlighted subsurface extensions of greenstone lithologies below sand cover and morphological evidence of past flow conditions under former climate regimes. On the basis of these observations, it is recommend that radar remote sensing technology involving the use of high resolution, polarimetric data be seriously considered as a viable tool for exploration in erosional and depositional environments located within Australia's mineral and oil-prospective provinces.

Tapley, Ian J.↗

DOE OSTI2020

Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint

With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.

building energy data↗

NASA NTRSDate not supplied

Data & Reasoning Fabric: Minimum Viable Product

A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of advanced air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. DRF activities will identify, test and - as needed - research and develop critical core technologies. In order to deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end users.

UAM↗

NASA NTRS1990

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

In April 1985, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on Space Station Freedom. This material was documented in the initial report (NASA Technical Memorandum 87566). The progress made by Levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology are described. Emphasis was placed upon the Space Station Freedom Program responses to specific recommendations made in ATAC Progress Report 9, the Flight Telerobotic Servicer, the Advanced Development Program, and the Data Management System. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom.

Source record↗

NASA NTRS1990

Bearingless rotor aeromechanical stability measurements and correlations using nonlinear aerodynamics

The aeromechanical stability of a 1/8th Froude scaled bearingless rotor model was investigated experimentally in a wind tunnel. Both shaft-fixed and shaft-free conditions were examined to study the aeroelastic stability of a bearingless rotor without the incorporation of auxiliary dampers. This wind tunnel investigation generated a set of stability data for four different advance ratios, and a wide range of collective pitch settings. Theoretical analysis was performed using the newly developed University of Maryland Advanced Rotorcraft Code (UMARC). For analysis, the blade is modeled as an elastic beam undergoing flap bending, lag bending, elastic twist, and axial deformation. Blade response is calculated using a finite element method in time. Nonlinear aerodynamic effects are included by using a semiempirical stall modeling. The linearized periodic rotor perturbation equations in the nonrotating frame are solved for stability roots using Floquet transition matrix theory, as well as constant coefficient approximation. The predicted results are compared with the experimental data.

Wang, James M.↗

NASA NTRS1992

Applying an 8-bit multispectral color-composite image simulation technique to operational real-time AVHRR data

The Geophysics Directorate's 8-bit multispectral color-composite image display technique has been installed on the Satellite Data Processing System at the German Military Geophysical Office in Traben-Trarbach, Germany. The technique simulates 24-bit full-color composites on 8-bit color workstations, combining image data from the NOAA multispectral Advanced Very High Resolution Radiometer. The real-time application of this technique to operational satellite data is discussed.

Klaes, K. D.↗

NASA NTRS1995

Toward Soil Spatial Information Systems (SSIS) for global modeling and ecosystem management

The general objective is to conduct research to contribute toward the realization of a world soils and terrain (SOTER) database, which can stand alone or be incorporated into a more complete and comprehensive natural resources digital information system. The following specific objectives are focussed on: (1) to conduct research related to (a) translation and correlation of different soil classification systems to the SOTER database legend and (b) the inferfacing of disparate data sets in support of the SOTER Project; (2) to examine the potential use of AVHRR (Advanced Very High Resolution Radiometer) data for delineating meaningful soils and terrain boundaries for small scale soil survey (range of scale: 1:250,000 to 1:1,000,000) and terrestrial ecosystem assessment and monitoring; and (3) to determine the potential use of high dimensional spectral data (220 reflectance bands with 10 m spatial resolution) for delineating meaningful soils boundaries and conditions for the purpose of detailed soil survey and land management.

Baumgardner, Marion F.↗

NASA NTRS2021

Comprehensive Comparison of Airborne and Spaceborne SAR and LiDAR Estimates of Forest Structure in the Tallest Mangrove Forest on Earth

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quéréet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions.

Atticus E.L. Stovall↗

DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Mapping analysis and planning system for the John F. Kennedy Space Center

Environmental management, impact assessment, research and monitoring are multidisciplinary activities which are ideally suited to incorporate a multi-media approach to environmental problem solving. Geographic information systems (GIS), simulation models, neural networks and expert-system software are some of the advancing technologies being used for data management, query, analysis and display. At the 140,000 acre John F. Kennedy Space Center, the Advanced Software Technology group has been supporting development and implementation of a program that integrates these and other rapidly evolving hardware and software capabilities into a comprehensive Mapping, Analysis and Planning System (MAPS) based in a workstation/local are network environment. An expert-system shell is being developed to link the various databases to guide users through the numerous stages of a facility siting and environmental assessment. The expert-system shell approach is appealing for its ease of data access by management-level decision makers while maintaining the involvement of the data specialists. This, as well as increased efficiency and accuracy in data analysis and report preparation, can benefit any organization involved in natural resources management.

Hall, C. R.↗

DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗

NASA NTRS1983

Monitoring global vegetation using NOAA-7 AVHRR data

The NOAA-7 polar orbiting, sun-synchronous, operational satellite carries the 5 channel Advanced Very High Resolution Radiometer (AVHRR). Data are acquired globally at a resolution of 4 km on a daily basis. This data provides the means of frequently monitoring global vegetation on continental scales. Techniques for compositing and cloud screening a vegetation index for Africa are presented. The sample data set covers 9 days beginning August 16, 1983 and is compared with a semi-operational vegetation index product produced by NOAA.

Gatlin, J. A.↗

DOE OSTI2024

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

Johnson, Andrew E.↗