Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data Digitization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Universal Fourier Attack for Time Series

A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Long-Term Information Management (LTIM) of Safeguards Data at Repositories: Phase II

One of the challenges of implementing safeguards for geological repositories will be the long-term preservation of safeguards-related data for 100 years or more. While most countries considering the construction and operation of such facilities agree that safeguards information should be preserved, there are gaps with respect to standardized requirements, guidelines, timescales, and approaches. This study analyzes those gaps and explores research to clarify stakeholder needs, identify current policies, approaches, best practices and international standards, and explores existing safeguards information management infrastructure. The study also attempts to clarify what a safeguards data classification system might look like, how long data should be retained, and how information should be exchanged between stakeholders at different phases of a repository’s life cycle. The analysis produced a variety of recommendations on what information to preserve, how to preserve it, where to store it, retention options and how to exchange information in the long term. Key findings include the use of the globally recognized international records management standard, ISO15489, for guidance on the development of information management systems, and the development of a Key Information File (KIF). The KIF could be used to identify only the most relevant, high-level safeguards information and the history of decision making about the repository. The study also suggests implementing on-site and off-site records storage in digital and physical form; developing a safeguards data classification system; long-term records retention with periodic reviews every 5 to 10 years during each phase of the repository life cycle; and establishing transition procedures well in advance so that data shepherds and records officers can transfer information with incoming facility managers effectively and efficiently. These and other recommendations are further analyzed in this study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Building Towards Discovery: Preparing for New Physics at the LHC [Final papers]

Particle physics today is at a crossroads. The Standard Model (SM) has survived four decades of experimental scrutiny and spurred multiple landmark discoveries, culminating with the 2012 discovery of the Higgs boson at the Large Hadron Collider (LHC). Yet we know that this model is incomplete. We live in a universe dominated by matter and energy unaccounted for by the SM– and the Higgs boson itself is improbably light. It falls to the current generation of high-energy physicists to go beyond the model and enter a new era of discovery. Andeen is leading a new research group at the University of Texas at Austin (UT-Austin) that is endeavoring to reveal novel physical phenomena through the discovery of new particles with the ATLAS detector at the LHC. A key element of our experience thus far has been collaborating closely with theorists to identify both interesting new physics models. In addition we work closely with electrical engineers in detector development. Our approach is therefore unique in its integration of theory, analysis and instrumentation, and is encapsulated in a three-pronged strategy. First is the essential task of analyzing our rapidly expanding dataset. I am directing my group in searches for vector-like quarks (VLQs). Should they exist, these hypothetical particles would indicate physics scenarios beyond the SM. Experimental constraints suggest VLQs decay preferentially to a third generation quark and a Higgs, W or Z boson. Today at 13 TeV, both multi-lepton (electron and muon) and jet substructure signatures are sensitive to identifying the VLQ decay products. The large Run 2 dataset (collected through 2018) of up to 150 fb -1 will allow us to simultaneously use multi-lepton signatures with jet-substructure in searches for new physics. UT-Austin is well positioned to be a leader in this area. Prof. P. Onyisi’s postdoctoral researchers and graduate students are searching for ttH signatures in three- and four-lepton channels and we benefit from their experience. We are also aided by the availability of the Texas Advanced Computing Center, which provides us with easy access to several high performance computing (HPC) systems. My previous experience in VLQ searches propels the work today. I have published both the first investigation into the single production of top partners (signatures of composite Higgs Models) of any LHC experiment 1 as well as one of the first analyses utilizing jet-substructure techniques to search for the single production of vector-like quarks. 2 Adding to this is my group’s work with the Liquid Argon (LAr) calorimeter, the primary instrument we use in detecting electrons. Second, my group is contributing to the operations of the LAr calorimeter while preparing to install and commission new electronic hardware (the Phase 1 upgrade) to this crucial component of the ATLAS detector. I have played a leading role in the development of a critical, radiation-hard, high-speed Analog-to-Digital Convertor (ADC) that is essential inselecting data collected by the LAr calorimeter. 3 This upgrade will improve the e!ciency with which we can select events in our detector. Postdoctoral researcher N. Nikiforou (based at CERN May 2016 onward) and graduate students (one based at CERN from the start of 2018 onward, with one additional graduate students from summer 2018) are growing our already deep experience in the operation of the LAr calorimeter to prepare for installation and commissioning of the new trigger readout in 2018-2019. My group will then be positioned to be the first to exploit the potential of the improved triggers in our multi-lepton searches for new physics. Third, a suite of preparations for a major detector upgrade, scheduled to be completed in 2024, are underway. My group is building hardware that will improve the electronic readout of the ATLAS detector. I am partnering with a group in the Electrical and Computer Engineering (ECE) Department at UT-Austin (Prof. N. Sun) to employ his work at the cutting edge of ADC research. We will support an ECE grad student for the layout of this design, and collaborate with engineering groups at Nevis Laboratory and the Electrical Engineering Department (Prof. P. Kinget) at Columbia University to integrate our ADC with gain selection circuits. We will provide this critical component of the ASIC at the challenging boundary of the analog and digital signals in the LAr calorimeter readout chain. Furthermore UT-Austin will lead the testing of this device during development and production (approximately 55,000 four-channel chips). This effort compliments my work as the deliverables manager for the LAr front-end ASICs for the US ATLAS HL-LHC project and strengthens the US commitment to that upgrade.In so doing, my group at the UT-Austin will be at the forefront of efforts to probe the structure of the universe to an unmatched degree both at the current LHC and with its future upgrades. Through both the ongoing analysis of data at the ATLAS detector and improvements to the detector itself, an answer to what lies beyond the SM may come within our grasp. My background in instrumentation and analysis allows my group to establish a singular research effort on the ATLAS experiment. In addition to answering fundamental questions about the subatomic world, the technologies developed could advance the broader scientific community and result in valuable spin-offs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Exploring Milkyway Halo Substructures with Large-Area Sky Surveys

Over the last two decades, our understanding of the Milky Way has been improved thanks to large data sets arising from large-area digital sky surveys. The stellar halo is now known to be inhabited by a variety of spatial and kinematic stellar substructures, including stellar streams and stellar clouds, all of which are predicted by hierarchical Lambda Cold Dark Matter models of galaxy formation. In this dissertation, we first present the analysis of spectroscopic observations of individual stars from the two candidate structures discovered using an M-giant catalog from the Two Micron All-Sky Survey. The follow-up observations show that one of the candidates is a genuine structure which might be associated with the Galactic Anticenter Stellar Structure, while the other one is a false detection due to the systematic photometric errors in the survey or dust extinction in low Galactic latitudes. We then presented the discovery of an excess of main sequence turn-off stars in the direction of the constellations of Eridanus and Phoenix from the first-year data of the Dark Energy Survey (DES) – a five-year, 5,000 deg2 optical imaging survey in the Southern Hemisphere. The Eridanus-Phoenix (EriPhe) overdensity is centered around l ~ 285° and b ~ -60° and the Poisson significance of the detection is at least 9σ. The EriPhe overdensity has a cloud-like morphology and the extent is at least ~ 4 kpc by ~ 3 kpc in projection, with a heliocentric distance of about d ~ 16 kpc. The EriPhe overdensity is morphologically similar to the previously-discovered Virgo overdensity and Hercules-Aquila cloud. These three overdensities lie along a polar plane separated by ~ 120° and may share a common origin. In addition to the scientific discoveries, we also present the work to improve the photometric calibration in DES using auxiliary calibration systems, since the photometric errors can cause false detection in first the halo substructure. We present a detailed description of the two auxiliary calibration systems built at Texas A&M University. We then discuss how the auxiliary systems in DES can be used to improve the photometric calibration of the systematic chromatic errors – source color-dependent systematic errors that are caused by variations in the wavelength dependence of the atmospheric transmission and the instrumental throughput.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Identifying crack tip position and stress intensity factors from displacement data

Fracture prognosis and characterization efforts require knowledge of crack tip position and the Stress Intensity Factors (SIFs) acting in the vicinity of the crack. Here, in this work, we present an efficient numerical approach to infer both of these characteristics under a consistent theoretical framework from noisy, unstructured displacement data. The novel approach utilizes the separability of the asymptotic linear elastic fracture mechanics fields to expedite the search for crack tip position and is particularly useful for noisy displacement data. The manuscript begins with an assessment of the importance of accurately locating crack tip position when quantifying the SIFs from displacement data. Next, the proposed separability approach for quickly inferring crack tip position is introduced. Comparing to the widely used displacement correlation approach, the performance of the separability approach is assessed. Cases involving both noisy data and systematic deviation from the asymptotic linear elastic fracture mechanics model are considered, e.g. inelastic material behavior and finite geometries. An open source python implementation of the proposed approach is available for use by those doing field and laboratory work involving digital image correlation and simulations, e.g. finite element, discrete element, molecular dynamics and peridynamics, where the crack tip position is not explicitly defined.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: anisotropic clustering analysis in configuration space

We explore the cosmological implications of anisotropic clustering measurements of the quasar sample from Data Release 14 (DR14) of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS) in configuration space. The similar to 147000 quasar sample observed by eBOSS offers a direct tracer of the density field and bridges the gap of previous baryon acoustic oscillation measurements between redshift 0.8 < z < 2.2. By analysing the two-point correlation function characterized by clustering wedges xi(wi)(s) and multipolesi xi(l)(S), we measure the angular diameter distance, Hubble parameter, and cosmic structure growth rate. We define a systematic error budget for our measurements based on the analysis of N-body simulations and mock catalogues. Based on the DR14 large-scale structure quasar sample at the effective redshift z(eff) = 1.52, we find the growth rate of cosmic structure f sigma(8)(z(eff)) = 0.396 +/- 0.079, and the geometric parameters Dv(z)/r(d) = 26.47 +/- 1.23, and F-AP(Z) = 2.53 +/- 0.22, where the uncertainties include both statistical and systematic errors. These values are in excellent agreement with the best-fitting standard A cold dark matter model to the latest cosmic microwave background data from Planck.

cosmology↗
DOE OSTI2016

The mid-infrared luminosity evolution and luminosity function of quasars with WISE and SDSS

We determine the 22 μm luminosity evolution and luminosity function for quasars from a data set of over 20,000 objects obtained by combining flux-limited Sloan Digital Sky Survey optical and Wide field Infrared Survey Explorer mid-infrared data. We apply methods developed in previous works to access the intrinsic population distributions non-parametrically, taking into account the truncations and correlations inherent in the data. We find that the population of quasars exhibits positive luminosity evolution with redshift in the mid-infrared, but with considerably less mid-infrared evolution than in the optical or radio bands. With the luminosity evolutions accounted for, we determine the density evolution and local mid-infrared luminosity function. The latter displays a sharp flattening at local luminosities below ∼10{sup 31} erg s{sup −1} Hz{sup −1}, which has been reported previously at 15 μm for AGN classified as both type 1 and type 2. We calculate the integrated total emission from quasars at 22 μm and find it to be a small fraction of both the cosmic infrared background light and the integrated emission from all sources at this wavelength.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

NGEE Arctic TIR and Digital Photos, Drained Thaw Lake Basin, Barrow, Alaska, July 2015

Forward-looking infrared (FLIR) thermal infrared (TIR), digital camera photos, and plot notes across the Barrow, Alaska DTLB_2 site. Data were collected together with measurements of canopy spectral reflectance (see associated metadata record (NGEE Arctic HR1024i Canopy Spectral Reflectance, Drained Thaw Lake Basin, Barrow, Alaska, July 2015 https://doi.org/10.5440/1472053). Data contained within this archive include exported FLIR images (analyzed with FLIR-Tools), digital photos, TIR report, and sample notes. Further TIR image analysis can be conducted in FLIR-Tools. The dataset contains JPG images, PDF, Excel, and CSV files. 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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Sandia Particle Holography Processor v. 1.0

HoloSand is a suite of MATLAB® functions for measurement of particle properties from digital holograms. Functions to read and display digital holograms are included along with scripts to extract three-dimensional particle positions, sizes, and velocities. This software is a research tool used to process experimental data. A user of this software would first record digital holograms of a particle field and then use this software to measure the three-dimensional particle information encoded in the hologram. Applications range from basic laboratory fluids research to applied particle measurements. The software has no known military applications. The software solves the diffraction integral equation to numerically refocus digital holograms and then applies the hybrid and cross-correlation methods described in the references to extract three-dimensional particle information. User input is required to read-in the hologram and select optimum settings for image segmentation.

Guildenbecher, Daniel↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management

The operation of large US Department of Energy (DOE) research facilities, like the DIII-D National Fusion Facility, results in the collection of complex multi-dimensional scientific datasets, both experimental and model-generated. In the future, it is envisioned that integrated data analysis coupled with large-scale high performance computing (HPC) simulations will be used to improve experimental planning and operation. Practically, massive data sets from these simulations provide the physics basis for generation of both reduced semi-analytic and machine-learning-based models. Storage of both HPC simulation datasets (generated from US DOE leadership computing facilities) and experimental datasets presents significant challenges. In this paper, we present a vision for a DOE-wide data management workflow that integrates US DOE fusion facilities with leadership computing facilities. Data persistence and long-term availability beyond the length of allocated projects is essential, particularly for verification and recalibration of artificial intelligence and machine learning (AI/ML) models. Because these data sets are often generated and shared among hundreds of users across multiple leadership computing facility centers, they would benefit from cross-platform accessibility, persistent identifiers (e.g. DOI, or digital object identifier), and provenance tracking. Here, the ability to handle different data access patterns suggests that a combination of low cost, high latency (e.g. for storing ML training sets) and high cost, low latency systems (e.g. for real-time, integrated machine control feedback) may be needed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advanced Manufacturing Basic Entity Relationships Ontology

A data ontology defined using the W3C Web Ontology Language (OWL) format. It defines classes and attributes for objects directly implicated in manufacturing such as materials, preform geometries, and manufacturing processes and settings. It also includes classes to describe entities that are instrumental to making digital twins and performing predictive activities on manufacturing data such as designs of experiment and predictive models.

Harris, BrennanKay↗
DOE OSTI2021

Enhancing the Operational Resilience of Advanced Reactors with Digital Twins by Recurrent Neural Networks

Because of a lack of operational data and uncertainty in evaluation model for abnormal and accident scenarios, the established operating procedures can be biased in characterizing the reactor states and ensuring operational resilience. To reduce uncertainty associated with actual plant conditions, digital twin (DT) technology is suggested to support operator’s decision-making by effectively extracting and using knowledge of the current and future plant states from the knowledge base. This study first builds a knowledge base based on the characterization of issue space and the simulation tool. Next, this study discusses diagnosis and prognosis DTs for enhancing operational resilience by recovering the complete states of reactors and by predicting the future reactor behaviors. Finally, the decision-making module of the control system can determine the optimal control strategy that meets operational goals during loss-of-flow scenarios. To demonstrate and evaluate the DTs capability for supporting the operations of nuclear reactors, this study develops and assesses both the diagnosis and prognosis DTs in a nearly autonomous management and control system for an Experimental Breeder Reactor-II simulator during different loss-of-flow scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

The SDSS-IV extended baryon oscillation spectroscopic survey: Luminous red galaxy target selection

Here, we describe the algorithm used to select the luminous red galaxy (LRG) sample for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV) using photometric data from both the SDSS and the Wide-field Infrared Survey Explorer. LRG targets are required to meet a set of color selection criteria and have z-band and i-band MODEL magnitudes z < 19.95 and 19.9 < i < 21.8, respectively. Our algorithm selects roughly 50 LRG targets per square degree, the great majority of which lie in the redshift range 0.6 < z < 1.0 (median redshift 0.71). We also demonstrate that our methods are highly effective at eliminating stellar contamination and lower-redshift galaxies. We perform a number of tests using spectroscopic data from SDSS-III/BOSS ancillary programs to determine the redshift reliability of our target selection and its ability to meet the science requirements of eBOSS. The SDSS spectra are of high enough signal-to-noise ratio that at least ~89% of the target sample yields secure redshift measurements. Finally, we present tests of the uniformity and homogeneity of the sample, demonstrating that it should be clean enough for studies of the large-scale structure of the universe at higher redshifts than SDSS-III/BOSS LRGs reached.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗
DOE OSTI2019

Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO 2 and H 2 O flux data, and to study climate change impacts on the terrestrial biosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Data trustworthiness signatures for nuclear reactor dynamics simulation

With the increased reliance on digitization in industrial control systems, the need for effective monitoring techniques has risen dramatically. Specifically, there is now a growing concern about the so-called false data injection (FDI) attacks. These attacks aim to alter the raw sensors’ data to cause malicious outcomes. Any serious FDI algorithm is based on an intimate knowledge of the system and its associated physics models, which renders conventional outlier/anomaly detection techniques almost obsolete in the face of such attacks. Thus, a critical need has emerged to develop a new class of defense methods that are capable of detecting FDI attacks under the assumption that the attacker has a strong familiarity with the system and its physics modeling. This class of defense methods are denoted by model-based defenses which are premised on the assumption that the attacker, while having a good understanding of the system, does not have full privileged access to all proprietary data and historical records of operation. However, (s)he is assumed to be capable of learning system behavior using self-learning techniques during an initial lie-in-wait period. To defend against this scenario, we propose a new model-based randomized window algorithm that searches time-series data for signatures that can serve as classifiers between normal and FDI scenarios. The classifiers are based on the correlations between the dominant degrees of freedom (DOFs) and the less-dominant DOFs (expected to be very sensitive to the system details that are unknown to the attacker). For demonstration, RELAP5 models are employed to calculate representative nuclear reactor behavior during a number of transient scenarios. Finally, falsified data are injected into the RELAP5-simulated behavior, and the proposed signature-identification algorithm is employed to detect the injected data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗