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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1976

SSL: A software specification language

SSL (Software Specification Language) is a new formalism for the definition of specifications for software systems. The language provides a linear format for the representation of the information normally displayed in a two-dimensional module inter-dependency diagram. In comparing SSL to FORTRAN or ALGOL, it is found to be largely complementary to the algorithmic (procedural) languages. SSL is capable of representing explicitly module interconnections and global data flow, information which is deeply imbedded in the algorithmic languages. On the other hand, SSL is not designed to depict the control flow within modules. The SSL level of software design explicitly depicts intermodule data flow as a functional specification.

Austin, S. L.↗
NASA NTRS1988

The measurement of angular differential cross sections at the SSL Atomic Scattering Facility

The design of the SSL Atomic Scattering Facility (ASF) located at the NASA/Marshall Space Flight Center as well as some of the initial experiments to be performed with it, are covered. The goal is to develop an apparatus capable of measuring angular differential cross sections (ADCS) for the scattering of 2 to 14 eV atomic oxygen from various gaseous targets. At present little is known about atomic oxygen scattering with kinetic energies of a few eV. This apparatus is designed to increase the understanding of collisions in this energy region. Atomic oxygen scattering processes are of vital interest to NASA because the space shuttle as well as other low earth orbit satellites will be subjected to a flux of 5 eV atomic oxygen on the ram surfaces while in orbit. The primary experiments will involve the measurements of ADCS for atomic oxygen scattering from gaseous targets (in particular, molecular nitrogen). These, as well as the related initial experiments involving thermal He scattering from N2 and O2 targets will be described.

Kvale, Thomas J.↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2025

Resimulation-based self-supervised learning for pretraining physics foundation models

Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Observation of the spiral spin liquid in a triangular-lattice material

The spiral spin liquid (SSL) is a highly degenerate state characterized by a continuous contour or surface in reciprocal space spanned by a spiral propagation vector. Although the SSL state has been predicted in a number of various theoretical models, very few materials are so far experimentally identified to host such a state. Via combined single-crystal wide-angle and small-angle neutron scattering, we report observation of the SSL in the quasi-two-dimensional delafossite-like AgCrSe 2 . We show that it is a very close realization of the ideal Heisenberg J 1 –J 2 –J 3 frustrated model on the triangular lattice. By supplementing our experimental results with microscopic spin-dynamics simulations, we demonstrate how such exotic magnetic states are driven by thermal fluctuations and exchange frustration.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observation of Unprecedented Fractional Magnetization Plateaus in a New Shastry-Sutherland Ising Compound

Geometrically frustrated magnetic systems, such as those based on the Shastry-Sutherland lattice (SSL), offer a rich playground for exploring unconventional magnetic states. The delicate balance between competing interactions in these systems leads to the emergence of novel phases. We present the characterization of Er 2 ⁢Be 2⁢ GeO 7 , an SSL compound with Er 3+ ions forming orthogonal dimers separated by nonmagnetic layers whose structure is invariant under the 𝑃⁢$\bar{4}21$⁢𝑚 space group. Neutron scattering reveals an antiferromagnetic dimer structure at zero field, typical of Ising spins on that lattice and consistent with the anisotropic magnetization observed. However, magnetization measurements exhibit fractional plateaus at 1/4 and 1/2 of saturation, in contrast to the expected 1/3 plateau of the SSL Ising model. By comparing the energy of candidate states with ground-state lower bounds we show that this behavior requires spatially anisotropic interactions, leading to an anisotropic Shastry-Sutherland Ising model symmetric under the 𝐶⁢𝑚⁢𝑚⁢2 space group. This anisotropy is consistent with the small orthorhombic distortion observed with single-crystal neutron diffraction. The other properties, including thermodynamics, which have been investigated theoretically using tensor networks, point to small residual interactions, potentially due to further couplings and quantum fluctuations. This study highlights Er 2 ⁢Be 2 ⁢GeO 7 as a promising platform for investigating exotic magnetic phenomena.

Yadav, Lalit [Duke University, Durham, NC (United ↗
NASA NTRS1992

Tracking performance and cycle slipping in the all-digital symbol synchronizer loop of the block 5 receiver

Computer simulated noise performance of the symbol synchronizer loop (SSL) in the Block 5 receiver is compared with the theoretical noise performance. Good agreement is seen at the higher loop SNR's (SNR(sub L)'s), with gradual degradation as the SNR(sub L) is decreased. For the different cases simulated, cycle slipping is observed (within the simulation time of 10(exp 4) seconds) at SNR(sub L)'s below different thresholds, ranging from 6 to 8.5 dB, comparable to that of a classical phase-locked loop. An important point, however, is that to achieve the desired loop SNR above the seemingly low threshold to avoid cycle slipping, a large data-to-loop-noise power ratio, P(sub D)/(N(sub 0)B(sub L)), is necessary (at least 13 dB larger than the desired SNR(sub L) in the optimum case and larger otherwise). This is due to the large squaring loss (greater than or equal to 13 dB) inherent in the SSL. For the special case of symbol rates approximately equaling the loop update rate, a more accurate equivalent model accounting for an extra loop update period delay (characteristic of the SSL phase detector design) is derived. This model results in a more accurate estimation of the noise-equivalent bandwidth of the loop.

Aung, M.↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Particle trajectory representation learning with masked point modeling

Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.

Young, Samuel [Stanford Univ., CA (United States)]↗
NASA NTRS1983

Software Specification Language

SSL translator aids in developing and checking software systems. Goal of SSL is to provide form of verification and consistency checking early in design phase. Serves as formal document to communicate software architecure to detailed designers.

Buckles, B. P.↗
NASA NTRS1988

Cost effective alternative to low irradiance measurements

Martin Marietta's Space Simulation Laboratory (SSL) has a Thermal Environment Simulator (TES) with 56 individually controlled heater zones. The TES has a temperature range of approximately minus 129 C to plus 149 C. Because of the ability of TES to provide complex irradiance distributions, it is necessary to be able to measure a wide range of irradiance levels. SSL currently uses ambient temperature controlled radiometers with the capacity to measure sink irradiance levels of approximately 42.6 mw/sq cm, sink temperature equals 21 C and up. These radiometers could not be used to accurately measure the lower irradiance levels of the TES. Therefore, it was necessary to obtain a radiometer or develop techniques which could be used to measure lower irradiance levels.

Oleary, Scott T.↗
NASA NTRS1992

Design evolution of a telerobotic servicer through neutral buoyancy simulation

This paper briefly recounts the history of telerobotic vehicles in the Space Systems Laboratory. Based on this overview, the design of two vehicles are examined in depth, and directly compared. The Beam Assembly Teleoperator was the first true telerobot designed be the Space Systems Laboratory, and has been used extensively for six years. Ranger is the newest telerobot of the SSL, and is currently under development for operational testing in the summer of 1992. The primary focus of this paper is to compare and contrast the design details of these two telerobots, and to examine in some detail the evolution of design knowledge on some telerobots gained over the years of neutral buoyancy testing in the SSL.

Akin, David L.↗
DOE OSTI2025

Tunable broadband luminescence in lead-free hybrid copper halides

Metal halides are an important class of optoelectronic materials combining exceptional optical and electronic properties. An inherent advantage of metal halides is their solution synthesis and processability, which render them as low-cost and environmentally friendly materials for a range of applications from photovoltaics and photodetection to solid-state lighting (SSL). Here, in this study, we synthesized three previously unreported lead-free organic–inorganic hybrid copper halides: (OA) 4 CuX 5 (X = Br, I; OA + = C 8 H 17 NH 3+ , n-octylammonium cation) and (HA) 2 CuI 3 (HA + = C 6 H 13 NH 3 + , n-hexylammonium cation), all of which exhibit broadband emissions arising from self-trapped excitons (STEs). Among these compounds, (OA) 4 CuI 5 demonstrates tunable dual-band white-light emission with a high color rendering index value of 91 at room temperature. Temperature-dependent photoluminescence measurements and first-principles calculations reveal distinct behaviors between the two emission states in (OA) 4 CuI 5 . These findings highlight the potential of copper halide compounds for optoelectronic applications, particularly in the development of environmentally friendly solid-state lighting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
DOE OSTI2025

VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability

The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Why Tunable? A Look at Schools Using Tunable Lighting

Between 2016-2019, Pacific Northwest National Laboratory (PNNL), documented two early pilot installations of tunable lighting in schools, supported by U.S. Department of Energy (DOE) Solid-State Lighting (SSL) program. These pilot installations were opportunities for the schools to become more familiar with the technology, save energy, and support teachers and students. Tunable lighting continues to be of interest, particularly in classrooms for students with disabilities. This case study looks at tunable lighting systems installed in classrooms at eight school districts across the U.S. since 2015, the earliest known installation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗