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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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Design optimization of MAPS-based detectors using a data-driven fast simulation approach
A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180 nm complementary metal-oxide-semiconductor imaging process. Modifications to the sensor’s periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65 nm Tower Partners Semiconductor Co. process.
High-Brightness Self-seeded X-ray Free Electron Laser to Precisely Map Macromolecular Structure
Two datasets were uploaded. Run numbers #r0101~#r0112 were collected from self-seeded XFEL mode. Run numbers #r0079~#r0088 and #r0090 were collected from SASE mode. The run number is shown in the file name as "ue_191027_SFX_runnumber-c00.cxi.". To understand the effect of self-seeded XFEL source, we used the datasets from run numbers #r0102 to #r0112. Datasets used were randomly chosen. - SS1 is from #r0102~#r0112. - SS2 is from #r0102~#r0105 and #r0109. - SS3 is from #r0104 and #r0105. To compare self-seeded mode and SASE mode, we also used corresponding three SASE datasets labeled as SASE1, SASE2, and SASE3 from run numbers #r0079 to #r0089. Datasets used were randomly chosen. - SASE1 is from #r0079~#r0089. - SASE2 is from #r0080, #r0081, #r0085, #r0087. - SASE3 is composed of #r0079, #r0080.
Mapping Protein Dynamics at High-Resolution with Temperature-Jump X-ray Crystallography
Time-resolved temperature-jump using HEWL; please check the README file for more information about the datasets.
Parallel Lattice Subpaving Map (plsm)
Custom spatial subdivision of N-dimensional lattice region in GPU memory. An extension of the method of a hyper-octree is used to allow for arbitrary subdivision ratios in each dimension. In addition, the decision to refine (or select) a given sub-region is based on a user-provided callback, allowing for arbitrary refinement domains to be expressed either geometrically or programmatically.
COMPASS (Continuous Ordinance Mapping for Planning and Siting Systems) [SWR-25-62]
COMPASS (Formerly, INFRA-COMPASS) is an innovative software tool that harnesses the power of Large Language Models (LLMs) to automate the compilation and continued maintenance of an inventory of state and local codes and ordinances pertaining to energy infrastructure.
Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps
Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.
MAPS Wind Profile Data
This dataset includes 40km vertical wind profiles from late 2023 through 9/30/2024 for Oklahoma, Kansas, and the Northeastern US. The data are in text format. Key for interpreting data is included in this package. Additionally, a descriptor accompanies each data file.
High-throughput mapping of the phage resistance landscape in E. coli
Not Available
Mapping the immunogenic landscape of near-native HIV-1 envelope trimers in non-human primates
The induction of broad and potent immunity by vaccines is the key focus of research efforts aimed at protecting against HIV-1 infection. Soluble native-like HIV-1 envelope glycoproteins have shown promise as vaccine candidates as they can induce potent autologous neutralizing responses in rabbits and non-human primates. In this study, monoclonal antibodies were isolated and characterized from rhesus macaques immunized with the BG505 SOSIP.664 trimer to better understand vaccine-induced antibody responses. Our studies reveal a diverse landscape of antibodies recognizing immunodominant strain-specific epitopes and non-neutralizing neo-epitopes. Additionally, we isolated a subset of mAbs against an epitope cluster at the gp120-gp41 interface that recognize the highly conserved fusion peptide and the glycan at position 88 and have characteristics akin to several human-derived broadly neutralizing antibodies.
Custom NEON AOP reflectance mosaics and maps of shade masks, canopy water content
This mosaic of visible to shortwave infrared (VSWIR) data was derived from the assignable asset NEON AOP radiance data that was collected by LBNL’s Watershed Function SFA during the summer of 2018 (DOI: 10.15485/1617204). This atmospheric correction was completed to take into account site-specific terrain variability in the 334 km2 survey area centered around Crested Butte, CO. The atmospheric correction was completed using ACORN atmospheric correction software executed on 200 x 200 pixel kernels rather than line by line in order to capture local flight altitude conditions. Manual cloud delineation removed any small shaded areas that occurred within the data collection areas. Mosaics were developed based on preference for days that were in close timing proximity to the coincident ground campaign and with mosaicing criteria that minimized the angle between the sun and the sensor to retrieve the most consistent reflectance (min_phase_refl_tiled). Finally, we generated shade masks based on the geometry between the sun angle at time of flight, the ground surface, and the sensor. Here we provide the orthomosaiced estimated reflectance data (min_phase_refl_tiled.tif), canopy water content (wtrl), estimated atmospheric water vapor (wtrv), the observational data (obs) for this particular mosaic, the estimated visibility (vis), shade masks derived from the digital surface elevation model (shade) and the digital terrain model (shade_tch) added to the canopy height model (DOI: 10.15485/1617203), and a wavelength metadata file. Each image file is provided as a GeoTiff with internal tiling and LZW compression. These data can also be found on Google Earth Engine for extraction, download, and analysis of smaller extents (Google Earth Engine data is linked in external link table).
Drone flight visible images, digital elevation maps, and geotiffs, Council, Seward Peninsula, Alaska, USA, July 2017
Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along each flight path. This package includes data from four flight paths over the NGEE Arctic Council Road Site near mile marker 71 in the Seward Peninsula, Alaska in July 2017. Derived image products for each flight include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Drone flight visible images, digital elevation maps, and geotiffs, Teller, Seward Peninsula, Alaska, USA, July 2017
Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along the flight path. This package includes data from a flight path over the NGEE Arctic Teller Site, west of mile marker 27 Bob Blodgett Highway in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Drone flight visible images, digital elevation maps, and geotiffs, Kougarok, Seward Peninsula, Alaska, USA, July 2017
Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along each flight path. This package includes data from four flight paths over the NGEE Arctic Kougarok Site, west of mile marker 64 on Kuzitrin Road in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)
This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.
Using Drone-Mounted Geophysical Sensors to Map Legacy Oil and Gas Infrastructure
Conference manuscript describing NETL’s development of drone methods to quickly survey large areas for the presence of abandoned wells and pipelines, which present risk to current and future land use.
Mapping Rydberg States of H 2 with the Halfium R-Matrix Method
In this article, we use the Halfium R-matrix method to investigate the Rydberg states of the H 2 molecule up to n = 20, filling the gap above the low-lying bound states already calculated with configuration interaction packages. Moreover, we show that the use of Quantum Defect Theory scaling laws, allows for a comprehensive analysis of the regular patterns resulting from the coupling between Rydberg series and doubly excited states. The results should open the door for more efficient quasi-diabatization of the potential energy curves which is required for calculating cross sections and rate coefficients of the (e + H 2 + ) collisional processes, involved in the plasma modeling for fusion devices.