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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
DOE OSTI2025

Postearthquake Damage Mapping via Remote Sensing: Lessons From the 2023 Türkiye Disaster

This review addresses the urgent need for scalable, accurate, and reproducible remote sensing solutions following the February 2023 Türkiye earthquakes. It synthesizes the contributions of five peer-reviewed studies published in the IEEE JSTARS Special Issue on postearthquake damage and risk assessment. These studies cover areas such as damage classification with deep learning, fusion of multisource remote sensing data, creation of benchmark datasets, detailed damage mapping, and analysis of geophysical signals using outgoing longwave radiation. The article summarizes the methodological approaches and the practical relevance of the reviewed studies for detecting, evaluating, and quantifying damage, and outlines key challenges, including model generalization, class ambiguity, and data integration. It also discusses emerging trends, including explainable artificial intelligence, multimodal data fusion, and open-data platforms. This synthesis provides a foundation for building robust, interpretable, and real-time disaster response systems and aims to guide future research in earthquake-related Earth observation and rapid damage assessment.

Taskin, Gulsen [Istanbul Technical University] (OR↗
DOE OSTI2023

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Abstract Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model, ecosys . Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient ( r ) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, and r around 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Environmental Sciences & Ecology↗
DOE OSTI2014

Robust regression on noisy data for fusion scaling laws

We introduce the method of geodesic least squares (GLS) regression for estimating fusion scaling laws. Based on straightforward principles, the method is easily implemented, yet it clearly outperforms established regression techniques, particularly in cases of significant uncertainty on both the response and predictor variables. We apply GLS for estimating the scaling of the L-H power threshold, resulting in estimates for ITER that are somewhat higher than predicted earlier.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data for Protoplast Fusion as a Strategy to Increase Ploidy in Rhodotorula toruloides for Strain Development

Rhodotorula toruloides is a red oleaginous yeast with growing commercial interest because of its hardiness and exceptional lipid production capacity. Because it is a basidiomycete yeast with a complex life cycle, many of the classical breeding methods used with ascomycetes are unavailable for strain improvement. However, we have been able to construct polyploid yeast by fusing protoplasts of parents with the same mating type. Fusing of Y-6985 (A2) and Y-48190 (A2), which had been transformed with complementary antibiotic markers, led to the recovery of two diploids and one triploid. The stability of the fusion yeasts was tested by plating them on non-selective medium after several growth cycles under antibiotics and then testing five colonies per strain for nuclear DNA contents using flow cytometry and standard cell cycle analysis: the triploid and one diploid were stable. Fusants inherited their mitochondria from a single parent, which was demonstrated using restriction fragment length polymorphism (RFLP) of mitochondrial DNA. The phenotypic properties of the parents and fusants were compared in glucose fed-batch bioreactor studies and cellulosic sugar batch cultures. The final lipid titers for the fed-batch cultures were 24.9–39.7 g/L with Y-6985 and the diploid and triploid performing the best and worst, respectively. The fusants demonstrated intermediate hardiness for growth on hydrolysate prepared with dilute-acid pretreated switchgrass and were outperformed by Y-48190. Unlike one of the haploid parents, the fusants grew in 70% v/v concentrated hydrolysate. However, they did not grow as fast as the other haploid. In this study, a modernized protoplast fusion method is resurrected a useful tool for strain development in this yeast, which is complementary with other available methods.

FOS: Biological sciences↗
DOE OSTI2025

Trident Smyth: Near Real Time Sensor Fusion and Track Stitching Application

Sandia’s Sensor Data Fusion application is a NGA sponsored project that fuses tracks from different sensors to provide more accuracy than any single track. The application serves as the unclassified architecture for data ingestion, processing, and near real-time sensor fusion.

Sorroche, Joseph John [Sandia National Laboratorie↗
DOE OSTI2016

Constrained partitioning of autotrophic and heterotrophic respiration reduces model uncertainties of forest ecosystem carbon fluxes but not stocks

Abstract We partitioned the soil carbon dioxide flux ( R s ) into its respective autotrophic and heterotrophic components in a mature temperate‐boreal forest (Howland Forest in Maine, USA). We combined automated chamber measurements of R s with two different partitioning methods: (1) a classic root trenching experiment and (2) a radiocarbon ( 14 C) mass balance approach. With a model‐data fusion approach, we used these data to constrain a parsimonious ecosystem model (FöBAAR), and we investigated differences in modeled C fluxes and pools under both current and future climate scenarios. The trenching experiment indicated that heterotrophic respiration accounted for 53 ± 11% of total R s . In comparison, using the 14 C method, the heterotrophic contribution was 42 ± 9%. For both current and future model runs, incorporating the partitioning data as constraints substantially reduced the uncertainties of autotrophic and heterotrophic respiration fluxes. Moreover, with best fit model parameters, the two partitioning methods yielded fundamentally different estimates of the relative contributions of autotrophic and heterotrophic respiration to total R s , especially at the annual time scale. Surprisingly, however, modeled soil C and biomass C pool size trajectories did not differ significantly between model runs based on the different methods. Instead, model differences in partitioning were compensated for by changes in C allocation, resulting in similar, but still highly uncertain, soil C pool trajectories. Our findings show that incorporating constraints on the partitioning of R s can reduce model uncertainties of fluxes but not pools, and the results are sensitive to the partitioning method used.

Carbone, Mariah S.↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗
NASA NTRS2014

Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.

ASCAT↗
DOE OSTI2019

MAVNet: Design of a Reliable Beyond Visual Line of Sight Communication System for Unmanned Vehicles

This paper details the design and system components of the Multi-Modal Autonomous Vehicle Network system (MAVNet). MAVNet was designed to provide reliable and redundant command, control and payload data distribution for unmanned vehicles operating beyond visual line of sight. Current industry solutions do not adequately solve key networking challenges, do not support network diversity and fall short of providing desirable next-generation requirements including data fusion from national air space data sources, multi-pilot and multi-agent control. The MAVNet system and the approach taken to solve these issues will be detailed in this paper.

Stinson, Brad J↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Fast and Invertible Simplicial Approximation of Magnetic‐Following Interpolation for Visualizing Fusion Plasma Simulation Data

We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.

Ren, Congrong [The Ohio State Univ., Columbus, OH ↗
NASA NTRS1991

Sensor fusion III: 3-D perception and recognition; Proceedings of the Meeting, Boston, MA, Nov. 5-8, 1990

The volume on data fusion from multiple sources discusses fusing multiple views, temporal analysis and 3D motion interpretation, sensor fusion and eye-to-hand coordination, and integration in human shape perception. Attention is given to surface reconstruction, statistical methods in sensor fusion, fusing sensor data with environmental knowledge, computational models for sensor fusion, and evaluation and selection of sensor fusion techniques. Topics addressed include the structure of a scene from two and three projections, optical flow techniques for moving target detection, tactical sensor-based exploration in a robotic environment, and the fusion of human and machine skills for remote robotic operations. Also discussed are K-nearest-neighbor concepts for sensor fusion, surface reconstruction with discontinuities, a sensor-knowledge-command fusion paradigm for man-machine systems, coordinating sensing and local navigation, and terrain map matching using multisensing techniques for applications to autonomous vehicle navigation.

Schenker, Paul S.↗
DOE OSTI2014

Visualization of the operational space of edge-localized modes through low-dimensional embedding of probability distributions

Information visualization aimed at facilitating human perception is an important tool for the interpretation of experiments on the basis of complex multidimensional data characterizing the operational space of fusion devices. This work describes a method for visualizing the operational space on a two-dimensional map and applies it to the discrimination of type I and type III edge-localized modes (ELMs) from a series of carbon-wall ELMy discharges at JET. The approach accounts for stochastic uncertainties that play an important role in fusion data sets, by modeling measurements with probability distributions in a metric space. The method is aimed at contributing to physical understanding of ELMs as well as their control. Furthermore, it is a general method that can be applied to the modeling of various other plasma phenomena as well.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗