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DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗

DOE OSTI2026

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗

DOE OSTI2026

2020 Can Do Colorado E-Bike Mini Pilot Program Study

### The Colorado Energy Office conducted a mini pilot program study as part of the Can Do Colorado initiative, providing e-bikes to 13 low-income participants. The program aimed to encourage energy-efficient transportation during the COVID-19 pandemic as transit services were reduced and people were concerned about exposure. The insights garnered from this small-scale pilot study informed the design of a full-scale, 2-year pilot in locations across Colorado. For more information about the mini pilot program, see NLR's [Preliminary Results Report](https://www.nlr.gov/docs/fy21osti/79657.pdf). Micromobility options such as e-bikes offer a solution for improving energy efficiency for short-distance trips, especially in urban areas. Pedal-assist e-bikes use an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the survey. #### Survey Methodology Participants in the program received a Momentum LaFree E+ e-bike (Class 1) and accessories at no cost and manually submitted travel data and feedback for 3 months using the CanBikeCo App. The smartphone app, developed in partnership with NLR, used a customized version of the [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include a total of 13 participants. This dataset contains 3 months of end-to-end, multimodal travel data manually submitted via smartphone app by 13 low-income essential workers in the greater Denver area. The data includes distance, mode (e.g., e-bike, car, transit), trip purpose, and demographic information.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

2021–2022 Can Do Colorado E-Bike Full-Scale Pilot Program Study

In 2021–2022, the Colorado Energy Office conducted a full-scale pilot program study on e-bike usage as part of the Can Do Colorado initiative, providing e-bikes to low-income participants across the state. A [2020 mini pilot program study](https://www.nlr.gov/transportation/secure-transportation-data/tsdc-2020-can-do-colorado-e-bike-pilot-program.html) informed the full-scale study. Both studies used pedal-assist e-bikes, which feature an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the study in partnership with local organizations in Adams and Broomfield counties (Smart Commute Metro North), Boulder (Community Cycles), Durango (Four Corners Office for Resource Efficiency), Fort Collins (City of Fort Collins), Pueblo (Pueblo County), and Vail (Town of Vail). #### Survey Methodology Program participants received an e-bike and accessories at no cost and manually submitted travel data and feedback via the CanBikeCO smartphone app. Developed in partnership with NLR, the app used a customized version of the open-source [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include 170 participants. The six datasets contain up to 18 months of partially automated travel diaries, combining sensed and surveyed travel behavior data—patterns of multimodal, end-to-end, individual human mobility—as well as demographic information from participants. The number of e-bike trips and e-bike miles traveled per location are 1,560 and 4,179 for Adams and Broomfield counties; 8,481 and 27,000 for Boulder; 2,815 and 6,307 for Durango; 3,483 and 7,080 for Fort Collins; 4,022 and 14,887 for Pueblo, and 1,206 and 3,3361 for Vail.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Edge at the Pier: EPCAPE Software-Defined Sensing Field Campaign Report

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was aimed to enhance the understanding of cloud and aerosol properties in the region surrounding La Jolla, California. To address challenges in data collection and processing from various instruments, an edge computing device known as Waggle Sage Node (WSN) was deployed at the Ellen Browning Scripps Memorial Pier. WSN is a distributed-sensing platform designed to collect and analyze environmental data at the edge. Sage is a multi-agency-supported project that designs and builds a new kind of national-scale reusable cyberinfrastructure to enable artificial intelligence (AI) at the edge based on the Waggle platform. Sponsors include the U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR), DOE National Nuclear Security Administration (NNSA), DOE Biological and Environmental Research (BER) through DOE Artificial Intelligence for Earth System Predictability (AI4ESP), Argonne Laboratory-Directed Research and Development (LDRD). Sage (https://sagecontinuum.org/) is funded as a National Science Foundation Mid-Scale Research Infrastructure (MSRI) project (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935984). This robust, multi-architecture edge computing platform facilitated environmental monitoring during the campaign. This report details the scientific objectives, deployment process, and key results of integrating Waggle into the EPCAPE field campaign.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Improving Thermal Management Strategies for Data Centers: A Physical Testbed Incorporating Small Modular Reactor and Microreactor Technology

This study aims to accelerate the demonstration of various thermal management systems for data centers using nuclear-generated heat to enhance energy and grid reliability. Utilizing mobile containerized and stationary test beds at INL's High Performance Computing (HPC) facility, this project integrates with various nuclear-related energy systems testing facilities. Key components include immersion cooling apparatus, absorption chillers, and adjustable thermal management simulators. Tasks involve acquiring necessary hardware, sensors, and cooling apparatus, engaging with data center industry stakeholders, and providing a testing platform for algorithms, models, tools, and software. The objective is to expedite the deployment of nuclear-powered data centers, thereby improving energy reliability and affordability.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2024

SolarAPP+ Performance Review (2023 Data)

The Solar Automated Permit Processing Plus (SolarAPP+) platform is an online portal to facilitate and expedite rooftop solar photovoltaic (PV) and battery storage permitting processes. SolarAPP+ allows PV contractors to upload system specifications, have that information automatically reviewed for code compliance, and receive instant approval for code-compliant systems, reducing authority having jurisdiction (AHJ) staff time needed for review. SolarAPP+ also provides inspection checklists to verify installation practices and adherence to approved designs. SolarAPP+ is available to AHJs at no cost. This report is part of an ongoing series of reviews of SolarAPP+ performance. Consistent with previous performance reviews, we summarize SolarAPP+ adoption trends to date and compare various metrics for PV systems permitted through SolarAPP+ versus systems permitted through traditional AHJ permitting processes. As of the end of 2023, the National Renewable Energy Laboratory (NREL) had contacted over 1,700 AHJs with significant solar permitting volume regarding SolarAPP+. Of those, 793 AHJs had expressed interest in the platform as of the end of 2023. 161 AHJs had begun piloting the platform and 97 of these had publicly launched the platform by the end of 2023. In 2023, 668 installers submitted 18,906 permits through the SolarAPP+ platform, including 4,834 permits submitted as part of a solar plus storage program. SolarAPP+ permits accounted for around 43% of all permits issued in participating AHJs. We compare permitting timelines through SolarAPP+ to traditional AHJ permitting processes to assess the platform's performance. Consistent with previous SolarAPP+ performance reviews, we find that permitting timelines are significantly shorter for SolarAPP+ projects. Based on median timelines, a typical SolarAPP+ project is permitted and inspected 14.5 business days sooner than traditional projects. We estimate that automatic SolarAPP+ permitting saved around 7,200 hours of AHJ staff time in 2023. Finally, we estimate that SolarAPP+ eliminated over 150,000 business days in permitting-related delays in 2023.

14 SOLAR ENERGY↗

DOE OSTI2026

SolarAPP+ Performance Review (2024 Data)

The Solar Automated Permit Processing Plus (SolarAPP+) platform is an online portal to facilitate and expedite rooftop solar photovoltaic (PV) and battery storage permitting processes. SolarAPP+ allows PV contractors to upload system specifications, have that information automatically reviewed for code compliance, and receive instant approval for code-compliant systems, reducing authority having jurisdiction (AHJ) staff time needed for review. SolarAPP+ also provides inspection checklists to verify installation practices and adherence to approved designs. This report is part of an ongoing series of reviews of SolarAPP+ performance. Consistent with previous performance reviews, we summarize SolarAPP+ adoption trends to date and compare various metrics for PV systems permitted through SolarAPP+ versus systems permitted through traditional AHJ permitting processes. As of the end of 2024, 799 AHJs had expressed interest in the platform, with 264 fully adopting (215) or piloting (49) the platform. In 2024, 861 installers submitted 37,393 permits through the SolarAPP+ platform, including 27,375 permits for PV+storage systems. SolarAPP+ permits accounted for around 43% of all permits issued in all participating AHJs, and more than 60% of all permits in several participating AHJs. We compare permitting timelines through SolarAPP+ to traditional AHJ permitting processes to assess the platform's performance. Consistent with previous SolarAPP+ performance reviews, we find that permitting timelines are significantly shorter for SolarAPP+ projects. Based on median timelines, a typical SolarAPP+ project is permitted and inspected 12 business days sooner than traditional projects. We estimate that automatic SolarAPP+ permitting saved around 18,400 hours of AHJ staff time in 2024. Finally, we estimate that SolarAPP+ eliminated over 100,000 business days in permitting-related delays in 2024.

14 SOLAR ENERGY↗

DOE OSTI2025

NGEE Arctic Tram: Radiation and Environmental Measurements over Low- and High-Centered Polygon Vegetation Communities, Barrow, Alaska, 2014-2017

These data were generated from an observational platform (Tram), an automated cart carrying a suite of radiation and remote sensing instruments running on 68 meters of elevated track 1 to 1.5 meters above the surface of the vegetation communities of low- and high-centered polygon ground. The Tram is in the footprint of the NGEE Arctic/AmeriFlux eddy covariance flux tower (US-NGB) at the Barrow Environmental Observatory, Barrow, Alaska, and operated before, during, and after the growing seasons of 2014-2017. Dataset includes one user guide (*.pdf) with final and raw *.csv data 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 OSTI2025

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗

DOE OSTI2026

Dataset for "A primer on forest structure measurement with lidar for ecologists"

This repository includes data and code accompanying the case study included in the manuscript "A primer on forest structure measurement with lidar for ecologists" (submitted to Ecosphere). We compiled lidar datasets from multiple platforms in a common area to: 1. Demonstrate how differences in sensor characteristics influence density and resolution of lidar data. 2. Provide open-source, co-located datasets for users to further inspect differences in lidar data. 3. Provide example code to perform basic lidar analysis. This case study is meant to allow readers to get hands-on experience with real-world data from different platforms. This case study is not meant to be a rigorous comparison of derived ecological metrics among all sensors; such comparisons can be found throughout other publications referenced throughout the main manuscript. Code includes basic functions in R commonly used to visualize and manipulate lidar data accessible with a normal laptop computer; more sophisticated algorithms for advanced users are also referenced throughout the main manuscript. Terrestrial laser scanning (TLS), mobile laser scanning (MLS), UAS laser scanning (ULS), airborne laser scanning (ALS), and spaceborne laser scanning (SLS) data were collected within the Smithsonian Environmental Research Center (SERC) forest dynamics plot in Maryland, USA. TLS, MLS, and ALS data were collected within 1 month of the 2021 growing season; ULS data were collected in November 2020 (“leaf-off” data) and July 2022 (“leaf-on” data).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

PMU Data Quality and Sensor Health Monitoring

Phasor Measurement Units (PMUs) play a critical role in the evolution of the electric power industry by providing high-precision, real-time monitoring of essential power system metrics. However, effectively detecting abnormalities and critical events from PMU data is a complex task, complicated by intricate temporal patterns, a scarcity of labeled data for training algo- rithms, and constraints on online computational power. In this study, we apply TranAD, an innovative algorithm that combines transformer architectures with the refinement of adversarial learning, to both synthetic and real-world PMU datasets for developing a data quality and sensor online health monitoring platform for utilities. Our findings reveal that TranAD not only provides efficient detection and localization but also enhances the detail with which abnormalities are detected, marking a a significant step forward in the field of clean data acquisition processes for power system monitoring

deep neural network, machine learning (ML)↗

DOE OSTI2024

BETTER Together

The Standard Energy Efficiency Data (SEED) and Building Efficiency Targeting Tool for Energy Retrofits (BETTER) platforms are both developed by the Department of Energy and work better together. SEED is a database to manage building characteristics and performance data from a variety of sources. BETTER provides simple energy efficiency measure analyses based on high level data about the building or portfolio of buildings. A demonstration of each platform and their integration will be provided. The inputs for BETTER are building type, floor area, location, utility data, and whether PV shall be included in the analysis. The BETTER analysis can be manually set up through the web application or data can be uploaded with a BuildingSync XML file either directly or through the API. SEED can be the source of this data and the data can be sent to BETTER through the SEED application after the BETTER API token has been entered. The benefit of utilizing SEED is that it has connections to many other sources of data such as ENERGY STAR Portfolio Manager, Audit Template, and Salesforce. Therefore, it is likely that a user of SEED will already have the required inputs for BETTER in SEED already and can create BETTER analyses across their whole portfolio in a couple mouse clicks. This is a major time savings and enables decision makers an easy path to identify buildings that should undergo more detailed audits or retrofit pathways.

ASHRAE↗

DOE OSTI2025

AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere

Materials Acceleration Platforms (MAPs) – also known as self-driving laboratories– present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery compared to the traditional trial-and-error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, particularly for films fabricated in a humid atmosphere. Here, in this work, a machine learning (ML)-guided closed-loop platform is developed with a multimodal data fusion approach to predict synthesis–property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5–55%. The efficiency of this approach is confirmed by the fast-dropping learning rate to 2% after experimentally sampling less than 1% of the possible 5,000+ combinations. The prediction of synthesis–property relations is done by optical and imaging characterizations. In situ photoluminescence characterization revealed the origin of thin film quality variation at different RH. These insights provide an avenue for controlling the MHP crystallization by fine-tuning the synthesis parameters and RH for a given chemistry, thus lifting the need for stringent atmosphere control. The MAP enables an accelerated screening and understanding of the synthesis design space, facilitating rational synthesis recipe choice for a wide range of materials.

AI-driven robot↗

DOE OSTI2024

Evaluation of Subetadex-α-methyl, a Polyanionic Cyclodextrin Scaffold, as a Medical Countermeasure against Fentanyl and Related Opioids

Subetadex-α-methyl (SBX-Me), a modified, polyanionic cyclodextrin scaffold, has been evaluated for its utilization as a medical countermeasure (MCM) to neutralize the effects of fentanyl and related opioids. Initial in vitro toxicity assays demonstrate that SBX-Me has a nontoxic profile, comparable to the FDA-approved cyclodextrin-based drug Sugammadex. Pharmacokinetic analysis showed rapid clearance of SBX-Me with an elimination half-life of ~7.4 h and little accumulation in major organs. SBX-Me was also evaluated for its ability to counteract the effects of fentanyl, carfentanil, and remifentanil in rats. Recovery times in rats exposed to sublethal fentanyl doses were found to be shorter when treated with SBX-Me after opioid exposure. The recovery times were reduced from ~35 to ~17 min for fentanyl, ~172 to ~59 min for carfentanil, and ~18 to ~12 min for remifentanil. SBX-Me increased the elimination half-life for fentanyl and remifentanil from 5.37 to 6.42 h and 8.24 to 9.74 h, respectively. These data support SBX-Me as a solid platform from which further research can be launched for the development of a MCM against the effects of fentanyl and its analogs. Furthermore, the data suggests that SBX-Me and other analogs are attractive candidates as broad spectrum opioids targeting MCMs.

Chemistry↗