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At least 271 records · Page 15

Precursor Analysis Report: Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016

The Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016 Precursor Analysis Report leverages publicly available information about the December 2016 cyber attack against the Ukrainian Ukrenergo electric transmission utility and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. Industroyer is a modular malware framework designed to deploy several Industrial Control System (ICS) protocol-specific attack payloads to disrupt electricity distribution. Adversaries deployed Industroyer within the target network on a Microsoft Windows endpoint capable of directly manipulating or communicating with ICS. Industroyer abuses the functionality of a targeted ICS’s legitimate control system to achieve its intended impact. Adversaries likely first gained access to Ukrenergo enterprise networks in early 2016 after a successful spearphishing campaign against organizations in the electric power sector. Adversaries then began capturing credentials beginning on 1 December 2016. This allowed access to the ICS environment at the Pivnichna electric transmission substation outside Kyiv through a device dual-homed on the Information Technology (IT) and ICS networks. Adversaries conducted discovery, targeting, and access to this device using information and previously captured credentials from compromised enterprise IT machines. Finally, the adversaries deployed and launched the Industroyer malware just before midnight on 17 December. By midnight, Ukrenergo had lost control of a targeted substation, resulting in electric power outages for over an hour in the city of Kyiv and the Kyiv region. Researchers and analysts identified 31 unique techniques (used in a sequence of 33 steps) utilized during the attack with a total of 846 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Twenty-nine of the identified techniques used during the Industroyer cyber attack were precursors to the triggering event. Analysis identified 548 observables associated with these precursor techniques, 353 of which were assessed to have an increased likelihood of being perceived in the 300 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Precursor Analysis Report: Cyber Attack on Thyssenkrupp Blast Furnace 2014

The Cyber Attack on Thyssenkrupp Blast Furnace 2014 Precursor Analysis Report leverages publicly available information about the Thyssenkrupp Steel Mill cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In December 2014, the German Government’s Federal Office for Information Security (BSI) released a report detailing a cyber attack on a German steel mill that occurred earlier that year, though exact dates and details of the attack were not revealed. While the report did not specify the name of the company, multiple sources identified the victim as one of Europe’s largest steel manufacturers, Thyssenkrupp AG. Further, Thyssenkrupp announced on 16 May of that year that Europe’s largest blast furnace, “Schwelgern 2,” located at its facility in Duisburg, Germany, would be offline for several weeks for repairs and upgrades, suggesting Schwelgern 2 was likely the target of the attack. The attack began in early 2014, when adversaries infiltrated the victim steel mill’s Information Technology (IT) network via a spearphishing campaign, then worked their way into the Operational Technology (OT) environment, where they executed software that caused denial of service, denial of control, and eventually a loss of control. This led to the blast furnace shutting down without proper safety procedures, resulting in catastrophic physical damage. No lives were lost in the incident, but ThyssenKrupp suffered $4 million in damage to the blast furnace and an additional $6 million in lost revenue. The adversaries required specialized knowledge and expertise in steel production, which enabled them to compromise a variety of internal systems and components across both IT and OT networks. The attack also demonstrated detailed knowledge of the industrial control systems (ICS) and production processes being used. This combination resulted in one of the earliest known publicly reported cybersecurity incidents resulting in physical damage to ICS equipment. Researchers and analysts identified 19 unique techniques (used in a sequence of 20 steps) utilized during the attack with a total of 454 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Fifteen of the identified techniques used during the Thyssenkrupp cyber attack were precursors to the triggering event. Analysis identified 369 observables associated with these precursor techniques, 316 of which were assessed to have an increased likelihood of being perceived in the 120 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗

PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE

This dataset is an open-source repository of spectral data measured at Pacific Northwest National Laboratory (PNNL). This database provides quantitative values for the complex index of refraction for seven polycyclic aromatic hydrocarbon (PAH) solids. A list of the chemicals is available in the readme file. These spectra consist of the optical constants, i.e., the real, n(ν), and imaginary, k(ν), refractive indices, over the spectral range from 7,800 to 400 cm-1 (1.28 – 25 μm). The conditions under which the individual data were acquired are described in the associated metadata files, and the user is strongly encouraged to read and understand this information to ensure the data are used appropriately for your application. Recommended Citation for Dataset Jessica M Salcido, Jeremy D. Erickson, Ashley M. Bradley, Russell G. Tonkyn, Timothy J. Johnson and Tanya L. Myers. 2026. PNNL INFRARED REFRACTIVE INDEX (n/k) DATASET FOR SEVEN PAH SOLIDS AT ROOM TEMPERATURE. [Data Set] PNNL DataHub. INSERT DOI License Information This work is marked with CC0 1.0: https://creativecommons.org/publicdomain/zero/1.0/. The authors do request that you appropriately cite the dataset when referencing or using the dataset.

Salcido, Jessica Marie Ortola↗

Data for "A study of resistive peeling-ballooning modes across spherical tokamaks"

The data in this repository correspond to the figures of the paper "A study of resistive peeling-ballooning modes across spherical tokamaks" published in the journal Plasma Physics and Controlled Fusion. The figures are created based on simulations with the M3D-C1 and NIMROD codes using Python post-processing tools.

edge localized modes↗

Gold-et-al_2024_EarthsFuture

Results from Gold et al. submitted to Earth's Future. All code to reproduce the experiment and make the figures can be found here: https://github.com/davidfgold/Gold-etal_2024_EarthsFuture For a detailed guide to data in this repository, see the README.txt file. Shortage output from .xdd files generated by StateMod was compressed into .parquet files. Reservoir output from .xre files can be found in the "Reservoir" directory. We adopted the CDSS naming convention: cm = Upper Colorado River Basin gm = Gunnison River Basin ym = Yampa River Basin wm = White River Basin sj = Southwest Basin

Climate Change↗

Data for Unraveling the Drivers of Water Shortage across Spatial Scales and Sectors in Colorado's West Slope River Basins

Results from Sunkara et al. submitted to Earth's Future. All code to reproduce the experiment and make the figures can be found here: https://github.com/ssaiveena/Sunkara-etal_2025_WestSlope For a detailed guide to data in this repository, see the README.txt file. Shortage output from .xdd files generated by StateMod was compressed into .parquet files in the Uncertain_scenarios folder. Reservoir output from .xre files can be found in the "Reservoir" directory within the Uncertain_scenarios folder. We adopted the CDSS naming convention: cm = Upper Colorado River Basin gm = Gunnison River Basin ym = Yampa River Basin wm = White River Basin sj = Southwest Basin

Climate Change↗

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗

Soil water content, matric potential, carbon dioxide and oxygen concentrations, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of soil sensor data (temperature, water content, bulk electrical conductivity, porewater dissolved oxygen and porewater dissolved carbon dioxide) in a vertical profile from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package includes: (1) soil temperature, volumetric water content and electrical conductivity at 40, 60 and 82.5 cm depth; (2) soil matric potential at 40, 60, 79 and 100 cm depth; (3) soil CO2 concentrations at 40, 60 and 82.5 cm depth; and (4) soil oxygen concentrations at 60, 82.5, 100, 135, 170 and 182 cm depth. Both the carbon dioxide and oxygen sensors are optical sensors that can measure the partial pressure of oxygen in both saturated and unsaturated conditions. Unfortunately, soil CO2 in the profile is unexpectedly high and above the sensor calibration range (0-25,000 ppm). In addition, soil CO2 sensors failed within a year of deployment, so we only report CO2 data from 2019-2020.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 4 "*data.csv" files, one for each calendar year in the dataset. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding for each measured parameter. Because sensors have been added over time, not every sensor has data dating back to Oct 2018. Note that there is a data gap over winter 2019-2020 due to a power outage. While this repository currently only contains data through December 2021, the dataset will be updated as additional years are collected and processed.

54 ENVIRONMENTAL SCIENCES↗

An infrared, Raman, and X-ray database of battery interphase components

Further improvements to lithium-ion and emerging battery technologies can be enabled by an improved understanding of the chemistry and working mechanisms of interphases that form at electrochemically active battery interfaces. However, it is difficult to collect and interpret spectra of interphases for several reasons, including the presence of a variety of compounds. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten compounds that have been identified as interphase constituents in lithium-ion or emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres provided by custom sample chambers. The data library presented in this work (and online repository) simplifies access to reference data that is otherwise either diffusely spread throughout the literature or non-existent, and provides energy storage researchers streamlined access to vital interphase-relevant data that can accelerate battery research efforts.

25 ENERGY STORAGE↗

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories

Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.

DOM↗