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NASA NTRSDate not supplied

Preserving and Curating the Moon: Adventures in Lunar Core Processing

The lunar crust is the most easily accessible part of the Moon to both remote sensing and sample analyses and provides an archive of information about planetary formation, crustal evolution, and contains a wealth of information about the origin of the Earth-Moon system [e.g., 1-5]. The Apollo mission returned 382 kg of rocks, soil and core samples. Studies of these lunar samples are crucial for our understanding of the Moon’s formation and geological evolution, and for the past 50 years these returned samples have provided the foundation for lunar science [5]. The returned samples are stored and cared for in the lunar curation facility at NASA’s Johnson Space Center. This facility is comprised of a large suite of clean rooms, sample vaults for pristine and return samples, thin section labs, core and saw rooms, storage and working areas, and ancillary labs all designed to minimize contamination from the environment and other samples. Some of the returned samples were intentionally set aside and left unopened. Recently, the Apollo Next Generation Sample Analysis (ANGSA) initiative was designed to examine these pristine samples so the next generation of lunar scientists can further our insight into the Moon’s history. Here, we present the meticulous process that involves preparing for, and ultimately opening, one of the unopened core samples: Apollo 17 drive tube 73002,0,which was collected on the Moon from a landslide deposit near Lara Crater by astronauts Gene Cernan and Jack Schmitt. In order to open, examine, and curate 73002,0withminimalpotential contamination, great care had to be taken prior to opening its container. Beginning18 months before extrusion of the sample, all core processing equipment was pulled out of storage, identified, sorted, cleaned, and purged with nitrogen gas. However, limited institutional memory has made this step challenging as most of the former core processors from the Apollo area have retired or passed away. Twelvemonths prior to extrusion, table-top rehearsals were initiated to identify equipment and learn how it fits together and operates. Five months before extruding the real core, preparations further evolved to include the extrusion and dissection of a lunar core simulant. In addition, a mock-up glovebox was designed and built to allow for a more realistic practice environment. One month prior to extrusion, the actual core cabinet was prepared for use, which included fitting it with lights, a webcam, and power. The tool and equipment cleaning procedure was also modified to include increased cleanliness and sterility requirements. While still sealed, the core was CT scanned at the University of Texas at Austin to maximize its scientific return. Days before the extrusion, witness plates and foil were deployed inside the core cabinet to monitor potential particle and organic contamination within the cabinet. On Nov. 5th, 2019, core sample 73002,0 was successfully opened and extruded(Fig.1). Dissection of 73002,0 began immediately afterwards and is still under way. Processing this sample will help us prepare for future sampling missions and core extrusions and will enable new scientific discoveries about the Moon.

C H Krysher↗
DOE OSTI2024

The Effective Fragment Molecular Orbital Method: Achieving High Scalability and Accuracy for Large Systems

The effective fragment molecular orbital (EFMO) method has been developed to predict the total energy of a very large molecular system accurately (with respect to the underlying quantum mechanical method) and efficiently by taking advantage of the locality of strong chemical interactions and employing a two-level hierarchical parallelism. The accuracy of the EFMO method is partly attributed to the accurate and robust intermolecular interaction prediction between distant fragments, in particular, the many-body polarization and dispersion effects, which require the generation of static and dynamic polarizability tensors by solving the coupled perturbed Hartree–Fock (CPHF) and time-dependent HF (TDHF) equations, respectively. Solving the CPHF and TDHF equations is the main EFMO computational bottleneck due to the inefficient (serial) and I/O-intensive implementation of the CPHF and TDHF solvers. In this work, the efficiency and scalability of the EFMO method are significantly improved with a new CPU memory-based implementation for solving the CPHF and TDHF equations that are parallelized by either message passing interface (MPI) or hybrid MPI/OpenMP. Here, the accuracy of the EFMO method is demonstrated for both covalently bonded systems and noncovalently bound molecular clusters by systematically examining the effects of basis sets and a key distance-related cutoff parameter, R cut . R cut determines whether a fragment pair (dimer) is treated by the chosen ab initio method or calculated using the effective fragment potential (EFP) method (separated dimers). Decreasing the value of Rcut increases the number of separated (EFP) dimers, thereby decreasing the computational effort. It is demonstrated that excellent accuracy (<1 kcal/mol error per fragment) can be achieved when using a sufficiently large basis set with diffuse functions coupled with a small R cut value. With the new parallel implementation, the total EFMO wall time is substantially reduced, especially with a high number of MPI ranks. Given a sufficient workload, nearly ideal strong scaling is achieved for the CPHF and TDHF parts of the calculation. For the first time, EFMO calculations with the inclusion of long-range polarization and dispersion interactions on a hydrated mesoporous silica nanoparticle with explicit water solvent molecules (more than 15k atoms) are achieved on a massively parallel supercomputer using nearly 1000 physical nodes. In addition, EFMO calculations on the carbinolamine formation step of an amine-catalyzed aldol reaction at the nanoscale with explicit solvent effects are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗
NASA NTRS1996

Hierarchical and Parallelizable Direct Volume Rendering for Irregular and Multiple Grids

A general volume rendering technique is described that efficiently produces images of excellent quality from data defined over irregular grids having a wide variety of formats. Rendering is done in software, eliminating the need for special graphics hardware, as well as any artifacts associated with graphics hardware. Images of volumes with about one million cells can be produced in one to several minutes on a workstation with a 150 MHz processor. A significant advantage of this method for applications such as computational fluid dynamics is that it can process multiple intersecting grids. Such grids present problems for most current volume rendering techniques. Also, the wide range of cell sizes (by a factor of 10,000 or more), which is typical of such applications, does not present difficulties, as it does for many techniques. A spatial hierarchical organization makes it possible to access data from a restricted region efficiently. The tree has greater depth in regions of greater detail, determined by the number of cells in the region. It also makes it possible to render useful 'preview' images very quickly (about one second for one-million-cell grids) by displaying each region associated with a tree node as one cell. Previews show enough detail to navigate effectively in very large data sets. The algorithmic techniques include use of a kappa-d tree, with prefix-order partitioning of triangles, to reduce the number of primitives that must be processed for one rendering, coarse-grain parallelism for a shared-memory MIMD architecture, a new perspective transformation that achieves greater numerical accuracy, and a scanline algorithm with depth sorting and a new clipping technique.

Wilhelms, Jane↗
NASA NTRS2001

Antibody responses to bacteriophage phi X-174 in human subjects exposed to the antarctic winter-over model of spaceflight

BACKGROUND: It has been proposed that exposure to long-term spaceflight conditions (stress, isolation, sleep disruption, containment, microbial contamination, and solar radiation) or to ground-based models of spaceflight will alter human immune responses, but specific antibody responses have not been fully evaluated. OBJECTIVE: We sought to determine whether exposure to the 8-month Antarctic winter-over model of spaceflight would alter human antibody responses. METHODS: During the 1999 Australian National Antarctic Research Expeditions, 11 adult study subjects at Casey, Antarctica, and 7 control subjects at Macquarie Island, sub-Antarctica, received primary and secondary immunizations with the T cell-dependent neoantigen bacteriophage phi X-174. Periodic plasma samples were analyzed for specific antibody function. RESULTS: All of the subjects from Casey, Antarctica, cleared bacteriophage phi X-174 normally by 1 week after primary immunization, and all had normal primary and secondary antibody responses, including immunologic memory amplification and switch from IgM to IgG antibody production. One subject showed a high normal pattern, and one subject had a low normal pattern. The control subjects from Macquarie Island also had normal immune responses to bacteriophage phi X-174. CONCLUSIONS: These data do not support the hypothesis that de novo specific antibody responses of subjects become defective during the conditions of the Antarctic winter-over. Because the Antarctic winter-over model of spaceflight lacks the important factors of microgravity and solar radiation, caution must be used in interpreting these data to anticipate normal antibody responses in long-term spaceflight.

Non-NASA Center↗
DOE OSTI2023

Spin Solar Cell Phenomenon on a Single-Molecule Magnet (SMM) Impacted CoFeB-Based Magnetic Tunnel Junctions

The single-molecule magnet (SMM) is demonstrated here to transform conventional magnetic tunnel junctions (MTJs), a memory device used in present-day computers, into solar cells. For the first time, we demonstrated an electronic spin-dependent solar cell effect on an SMM-transformed MTJ under illumination from unpolarized white light. We patterned cross-junction-shaped devices to form a CoFeB/MgO/CoFeB-based MTJ. The MgO barrier thickness at the intersection between the two exposed junction edges was less than the SMM extent, which enabled the SMM molecules to serve as channels to conduct spin-dependent transport. The SMM channels yielded a region of long-range magnetic ordering around these engineered molecular junctions. Our SMM possessed a hexanuclear [Mn 6 (μ 3 -O) 2 (H 2 N-sao) 6 (6-atha) 2 (EtOH) 6 ] [H 2 N-saoH = salicylamidoxime, 6-atha = 6-acetylthiohexanoate] complex and thioesters end groups to form bonds with metal films. SMM-doped MTJs were shown to exhibit a solar cell effect and yielded ≈80 mV open-circuit voltage and ≈10 mA/cm 2 saturation current density under illumination from one sun equivalent radiation dose. A room-temperature Kelvin Probe AFM (KPAFM) study provided direct evidence that the SMM transformed the electronic properties of the MTJ’s electrodes over a lateral area in excess of several thousand times larger in extent than the area spanned by the molecular junctions themselves. As a result, the decisive factor in observing this spin-photovoltaic effect is the formation of SMM spin channels between the two different ferromagnetic electrodes, which in turn is able to catalyze the long-range transformation in each electrode around the junction area.

14 SOLAR ENERGY↗
NASA NTRS1994

Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs

This paper presents a methodology for debugging the performance of message-passing programs on both tightly coupled and loosely coupled distributed-memory machines. The AIMS (Automated Instrumentation and Monitoring System) toolkit, a suite of software tools for measurement and analysis of performance, is introduced and its application illustrated using several benchmark programs drawn from the field of computational fluid dynamics. AIMS includes (i) Xinstrument, a powerful source-code instrumentor, which supports both Fortran77 and C as well as a number of different message-passing libraries including Intel's NX Thinking Machines' CMMD, and PVM; (ii) Monitor, a library of timestamping and trace -collection routines that run on supercomputers (such as Intel's iPSC/860, Delta, and Paragon and Thinking Machines' CM5) as well as on networks of workstations (including Convex Cluster and SparcStations connected by a LAN); (iii) Visualization Kernel, a trace-animation facility that supports source-code clickback, simultaneous visualization of computation and communication patterns, as well as analysis of data movements; (iv) Statistics Kernel, an advanced profiling facility, that associates a variety of performance data with various syntactic components of a parallel program; (v) Index Kernel, a diagnostic tool that helps pinpoint performance bottlenecks through the use of abstract indices; (vi) Modeling Kernel, a facility for automated modeling of message-passing programs that supports both simulation -based and analytical approaches to performance prediction and scalability analysis; (vii) Intrusion Compensator, a utility for recovering true performance from observed performance by removing the overheads of monitoring and their effects on the communication pattern of the program; and (viii) Compatibility Tools, that convert AIMS-generated traces into formats used by other performance-visualization tools, such as ParaGraph, Pablo, and certain AVS/Explorer modules.

Yan, Jerry C.↗
NASA NTRSDate not supplied

Conditions of the Toll-Like Receptors System of the Human Innate Immunity Cells During the Long-Term Isolation

During long duration missions onboard the International Space Station (ISS), alterations in T cell function and plasma cytokine profiles have been observed. Recent studies involving pattern recognition receptors (PRRs) have established that adaptation of innate immunity to long duration space flight occurs through changes in signal PRRs, Toll-like receptors (TLRs). Despite the fact that fundamental research of PRR molecular biology is far from complete, there are an increasing number of arguments confirming the important role of this system in regulating the initial stages of the adaptive immune response, as well as in the formation of autoimmune and allergic diseases. Long duration isolation is widely used by NASA as an analog for space flight with previous missions including several Antarctic bases, an undersea research station (NEEMO), and other isolation chambers. ‘SIRIUS’ is a multi-compartment pressurized facility at the IMBP in Moscow, Russia, in which subjects will spend 8 months executing tasks similar to those performed by astronauts in space. Prolonged isolation studies like SIRIUS replicate many of the influences expected to occur during long duration spaceflight missions including isolation, stress, confinement, and living in remote conditions. The immune study to be performed on the upcoming SIRIUS mission is being led by Dr. Sergey Ponomarev at the Institute of Biomedical Problems in Moscow, Russia. The NASA JSC Immunology lab will provide analysis of immune cell function, serum cytokines and stress hormones, and salivary cortisol, cytokines, and viruses. This study will be conducted with SIRIUS subjects under the auspices of the parent program. The goal of the study is to evaluate the effect of the 8-month isolation mission on components of human innate immunity at the molecular and cellular level. Successful implementation and completion of this project will provide a unique assortment of new data, significantly expanding the current understanding of the molecular and cellular changes in TLRs during long-term isolation. We hypothesize that mission-like isolation in the Russian NEK facility, consisting of circadian misalignment, isolation, stress, and ‘station lifestyle’, will result in a pattern of immune dysregulation similar to that observed in astronauts onboard ISS. This pattern of alterations can be summarized as phenotypic changes (altered distribution of peripheral cytotoxic, central memory, CD8+ T cell subsets), reductions in T cell function, alterations in plasma cytokine profiles (increased inflammatory and certain chemokines), and reductions in mitogen stimulated cytokine profiles (spans Th1, Th2, Th17). We further hypothesize that these immune changes will correlate with the reactivation of latent herpesviruses (which persists during orbital flight), to be used as measurable adverse clinical manifestation or with other observed symptomology. The SIRIUS-21 mission began in November of 2021 and successfully concluded with the crew emerging in July of 2022. Currently, the logistics of shipping the samples from Russia to the United States is being organized.

Cody L Gutierrez↗
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
NASA NTRS2011

Dynamical Evolution of Meteoroid Streams, Developments Over the Last 30 Years

As soon as reliable methods for observationally determining the heliocentric orbits of meteoroids and hence the mean orbit of a meteoroid stream in the 1950s and 60s, astronomers strived to investigate the evolution of the orbit under the effects of gravitational perturbations from the planets. At first, the limitations in the capabilities of computers, both in terms of speed and memory, placed severe restrictions on what was possible to do. As a consequence, secular perturbation methods, where the perturbations are averaged over one orbit became the norm. The most popular of these is the Halphen- Goryachev method which was used extensively until the early 1980s. The main disadvantage of these methods lies in the fact that close encounter can be missed, however they remain useful for performing very long-term integrations. Direct integration methods determine the effects of the perturbing forces at many points on an orbit. This give a better picture of the orbital evolution of an individual meteoroid, but many meteoroids have to be integrated in order to obtain a realistic picture of the evolution of a meteoroid stream. The notion of generating a family of hypothetical meteoroids to represent a stream and directly integrate the motion of each was probably first used by Williams Murray & Hughes (1979), to investigate the Quadrantids. Because of computing limitations, only 10 test meteoroids were used. Only two years later, Hughes et. al. (1981) had increased the number of particles 20-fold to 200 while after a further year, Fox Williams and Hughes used 500 000 test meteoroids to model the Geminid stream. With such a number of meteoroids it was possible for the first time to produce a realistic cross-section of the stream on the ecliptic. From that point on there has been a continued increase in the number of meteoroids, the length of time over which integration is carried out and the frequency with which results can be plotted so that it is now possible to produce moving images of the stream. As a consequence, over recent years, emphasis has moved to considering stream formation and the role fragmentation plays in this.

Williams, I. P.↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
NASA NTRS1997

Development and Operation of a Material Identification and Discrimination Imaging Spectroradiometer

Many imaging applications require quantitative determination of a scene's spectral radiance. This paper describes a new system capable of real-time spectroradiometric imagery. Operating at a full-spectrum update rate of 30Hz, this imager is capable of collecting a 30 point spectrum from each of three imaging heads: the first operates from 400 nm to 950 nm, with a 2% bandwidth; the second operates from 1.5 micro-m to 5.5 micro-m with a 1.5% bandwidth; the third operates from 5 micro-m to 12 micro-m, also at a 1.5% bandwidth. Standard image format is 256 x 256, with 512 x 512 possible in the VIS/NIR head. Spectra of up to 256 points are available at proportionately lower frame rates. In order to make such a tremendous amount of data more manageable, internal processing electronics perform four important operations on the spectral imagery data in real-time. First, all data in the spatial/spectral cube of data is spectro-radiometrically calibrated as it is collected. Second, to allow the imager to simulate sensors with arbitrary spectral response, any set of three spectral response functions may be loaded into the imager including delta functions to allow single wavelength viewing; the instrument then evaluates the integral of the product of the scene spectral radiances and the response function. Third, more powerful exploitation of the gathered spectral radiances can be effected by application of various spectral-matched filtering algorithms to identify pixels whose relative spectral radiance distribution matches a sought-after spectral radiance distribution, allowing materials-based identification and discrimination. Fourth, the instrument allows determination of spectral reflectance, surface temperature, and spectral emissivity, also in real-time. The spectral imaging technique used in the instrument allows tailoring of the frame rate and/or the spectral bandwidth to suit the scene radiance levels, i.e., frame rate can be reduced, or bandwidth increased to improve SNR when viewing low radiance scenes. The unique challenges of design and calibration are described. Pixel readout rates of 160 MHz, for full frame readout rates of 1000 Hz (512 x 512 image) present the first challenge; processing rates of nearly 600 million integer operations per second for sensor emulation, or over 2 billion per second for matched filtering, present the second. Spatial and spectral calibration of 66,536 pixels (262,144 for the 512 x 512 version) and up to 1,000 spectral positions mandate novel decoupling methods to keep the required calibration memory to a reasonable size. Large radiometric dynamic range also requires care to maintain precision operation with minimum memory size.

Dombrowski, Mark↗
NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project

This report constitutes the final report for NASA Contract NASW-5054. This project processed Clementine I high resolution images of the Moon, mosaicked these images together, and created a 22-disk set of compact disk read-only memory (CD-ROM) volumes. The mosaics were produced through semi-automated registration and calibration of the high resolution (HiRes) camera's data against the geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic produced by the US Geological Survey (USGS). The HiRes mosaics were compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution nadir-looking observations. The images were spatially warped using the sinusoidal equal-area projection at a scale of 20 m/pixel for sub-polar mosaics (below 80 deg. latitude) and using the stereographic projection at a scale of 30 m/pixel for polar mosaics. Only images with emission angles less than approximately 50 were used. Images from non-mapping cross-track slews, which tended to have large SPICE errors, were generally omitted. The locations of the resulting image population were found to be offset from the UV/Vis basemap by up to 13 km (0.4 deg.). Geometric control was taken from the 100 m/pixel global and 150 m/pixel polar USGS Clementine Basemap Mosaics compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Radiometric calibration was achieved by removing the image nonuniformity dominated by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap, that approximately transform the 8-bit HiRes data to photometric units. The sub-polar mosaics are divided into tiles that cover approximately 1.75 deg. of latitude and span the longitude range of the mosaicked frames. Images from a given orbit are map projected using the orbit's nominal central latitude. Polar mosaics are tiled into squares 2250 pixels on a side, which spans approximately 2.2 deg. Two mosaics are provided for each pole: one corresponding to data acquired while periapsis was in the south, the other while periapsis was in the north. The CD-ROMs also contain ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files.

Source record↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
NASA NTRS2006

Non Radiation Hardened Microprocessors in Spaced Based Remote Sensing Systems

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission is a comprehensive suite of active and passive sensors including a 20Hz 230mj Nd:YAG lidar, a visible wavelength Earth-looking camera and an imaging infrared radiometer. CALIPSO flies in formation with the Earth Observing System Post-Meridian (EOS PM) train, provides continuous, near-simultaneous measurements and is a planned 3 year mission. CALIPSO was launched into a 98 degree sun synchronous Earth orbit in April of 2006 to study clouds and aerosols and acquires over 5 gigabytes of data every 24 hours. The ground track of one CALIPSO orbit as well as high and low intensity South Atlantic Anomaly outlines is shown. CALIPSO passes through the SAA several times each day. Spaced based remote sensing systems that include multiple instruments and/or instruments such as lidar generate large volumes of data and require robust real-time hardware and software mechanisms and high throughput processors. Due to onboard storage restrictions and telemetry downlink limitations these systems must pre-process and reduce the data before sending it to the ground. This onboard processing and realtime requirement load may mean that newer more powerful processors are needed even though acceptable radiation-hardened versions have not yet been released. CALIPSO's single board computer payload controller processor is actually a set of four (4) voting non-radiation hardened COTS Power PC 603r's built on a single width VME card by General Dynamics Advanced Information Systems (GDAIS). Significant radiation concerns for CALIPSO and other Low Earth Orbit (LEO) satellites include the South Atlantic Anomaly (SAA), the north and south poles and strong solar events. Over much of South America and extending into the South Atlantic Ocean the Van Allen radiation belts dip to just 200-800km and spacecraft entering this area are subjected to high energy protons and experience higher than normal Single Event Upset (SEU) and Single Event Latch-up (SEL) rates. Although less significant, spacecraft flying in the area around the poles experience similar upsets. Finally, powerful solar proton events in the range of 10MeV/10pfu to 100MeV/1pfu as are forecasted and tracked by NOAA's Space Environment Center in Colorado can result in Single Event Upset (SEU), Single Event Latch-up (SEL) and permanent failures such as Single Event Gate Rupture (SEGR) in some technologies. (Galactic Cosmic Rays (GCRs) are another source, especially for gate rupture) CALIPSO mitigates common radiation concerns in its data handling through the use of redundant processors, radiation-hardened Application Specific Integrated Circuits (ASIC), hardware-based Error Detection and Correction (EDAC), processor and memory scrubbing, redundant boot code and mirrored files. After presenting a system overview this paper will expand on each of these strategies. Where applicable, related on-orbit data collected since the CALIPSO initial boot on May 4, 2006 will be noted.

Decoursey, Robert J.↗
DOE OSTI2024

Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evidence for metal sources, fluid-mixing processes, and S isotope recycling within the feeder zone of an Irish type Zn-Pb deposit

The origin and evolution of fluids in Irish-type Zn-Pb deposits remains debated, particularly regarding the mobility of metals such as Cu and Ni, sources of sulphur, and the role of fluid mixing and replacement. The Lisheen Zn-Pb deposit, Ireland, offers a well-defined natural laboratory to investigate these questions. While most studies have focused on the Waulsortian Limestone Formation, the primary sulphide host, less is known about mineralisation in underlying units, such as the Lisduff Oolite Member (LOM). The LOM displays enrichment in Cu and Ni and displays intense replacement textures compared to other hosts at Lisheen, making it an ideal target for studying metal mobility and sulphur recycling in carbonate-hosted systems. Through characterising and studying LOM-hosted sulphides, valuable insights into mineralisation processes, especially related to Cu-Ni metals, can be defined. This study integrates petrography, EMPA, and in situ sulphur isotope (δ 34 S) analysis to investigate sulphide paragenesis, mineral chemistry, and fluid evolution across LOM ore zones. Results reveal a multistage mineralising system involving extensive replacement of early pyrite (Py0, δ 34 S = −28.4 to −21.9 ‰) by sphalerite and galena, with zoned pyrite (Py1) enriched in As-Cu-Ni-Tl. The δ 34 S values and trace element trends indicate mixing between hydrothermal and bacteriogenic sulphur-rich fluids, with evidence for sulphur recycling during replacement. Pyrite textures and compositions capture this evolving fluid regime, with trace element enrichment linked to paragenetic stage. The steel ore region, adjacent to major fault intersections, records intense hydrothermal fluid interaction, hosting Ni- and As-rich phases such as nickeline, gersdorffite, and arsenopyrite. These findings highlight the importance of structural controls and fluid mixing in metal transport and deposition, positioning the LOM as a key stratigraphic unit for understanding ore-forming processes in Irish-type systems. These results have implications for targeting similar carbonate-hosted systems globally, especially where deeper or structurally complex ore zones remain underexplored.

58 - GEOSCIENCES↗