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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

End-To-End Decentralized Transmission Line Protection in IBR-Dominated Weak Grids Using Interpretable Data-Driven Methods

Traditional transmission line protection relies on predictable synchronous-based fault signatures, which frequently fail under the non-standard, current-limited fault characteristics of Inverter-Based Resources (IBRs). This study investigates how to achieve secure, communication-free fault isolation in IBR-dominated weak grids without relying on opaque, computationally heavy "black-box" machine learning algorithms. To address this, we propose a novel, standalone, and inherently interpretable data-driven protection framework. Unlike centralized methods requiring multi-terminal communication, this decentralized approach relies solely on local measurements using a hierarchical linear-kernel Support Vector Machine (SVM). The methodology decomposes the protection task into four sequential stages that mimic traditional protection elements: fault detection and fault direction identification, fault type classification, zone classification, and location estimation. This multi-stage architecture allows for specialized feature engineering at each stage, combining high computational efficiency with logic traceability. The framework's end-to-end performance was validated via C-code and PSCAD/EMTDC co-simulation, utilizing a real-world utility network and an OEM black-box IBR model. The proposed relay achieves 97.2% overall accuracy and provides a reliable trip decision within a 2.5-cycle window. The results confirm 100% accuracy in fundamental fault detection, reliable zone selectivity across low to moderate fault resistances, and robust security against non-fault transients, proving its immediate viability for integration into commercial numerical relays.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
NASA NTRS2002

The Development of a Data Archive and Analysis Tools for WIRE

Contents include the following: mission description and system identification; environment description; system architectural description; system security requirements; organization and resources; and ditscap plan. Appendix A, acronym list. Appendix B, definitions. Appendix C, references.

Buzasi, D.↗
NASA NTRS2009

MSLICE Science Activity Planner for the Mars Science Laboratory Mission

MSLICE (Mars Science Laboratory InterfaCE) is the tool used by scientists and engineers on the Mars Science Laboratory rover mission to visualize the data returned by the rover and collaboratively plan its activities. It enables users to efficiently and effectively search all mission data to find applicable products (e.g., images, targets, activity plans, sequences, etc.), view and plan the traverse of the rover in HiRISE (High Resolution Imaging Science Experiment) images, visualize data acquired by the rover, and develop, model, and validate the activities the rover will perform. MSLICE enables users to securely contribute to the mission s activity planning process from their home institutions using off-the-shelf laptop computers. This software has made use of several plug-ins (software components) developed for previous missions [e.g., Mars Exploration Rover (MER), Phoenix Mars Lander (PHX)] and other technology tasks. It has a simple, intuitive, and powerful search capability. For any given mission, there is a huge amount of data and associated metadata that is generated. To help users sort through this information, MSLICE s search interface is provided in a similar fashion as major Internet search engines. With regard to the HiRISE visualization of the rover s traverse, this view is a map of the mission that allows scientists to easily gauge where the rover has been and where it is likely to go. The map also provides the ability to correct or adjust the known position of the rover through the overlaying of images acquired from the rover on top of the HiRISE image. A user can then correct the rover s position by collocating the visible features in the overlays with the same features in the underlying HiRISE image. MSLICE users can also rapidly search all mission data for images that contain a point specified by the user in another image or panoramic mosaic. MSLICE allows the creation of targets, which provides a way for scientists to collaboratively name features on the surface of Mars. These targets can also be used to convey instrument-pointing information to the activity plan. The software allows users to develop a plan of what they would like the rover to accomplish for a given time period. When developing the plan, the user can input constraints between activities or groups of activities. MSLICE will enforce said constraints and ensure that all mission flight rules are satisfied.

Powell, Mark W.↗
NASA NTRS2007

Distributed Operations Planning

Maestro software provides a secure and distributed mission planning system for long-term missions in general, and the Mars Exploration Rover Mission (MER) specifically. Maestro, the successor to the Science Activity Planner, has a heavy emphasis on portability and distributed operations, and requires no data replication or expensive hardware, instead relying on a set of services functioning on JPL institutional servers. Maestro works on most current computers with network connections, including laptops. When browsing down-link data from a spacecraft, Maestro functions similarly to being on a Web browser. After authenticating the user, it connects to a database server to query an index of data products. It then contacts a Web server to download and display the actual data products. The software also includes collaboration support based upon a highly reliable messaging system. Modifications made to targets in one instance are quickly and securely transmitted to other instances of Maestro. The back end that has been developed for Maestro could benefit many future missions by reducing the cost of centralized operations system architecture.

Fox, Jason↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
NASA NTRS1998

Technology Assessment of High Capacity Data Storage Systems: Can We Avoid a Data Survivability Crisis

The density of digital storage media in our information-intensive society increases by a factor of four every three years, while the rate at which this data can be migrated to viable long-term storage has been increasing by a factor of only four every nine years. Meanwhile, older data stored on increasingly obsolete media, are at considerable risk. When the systems for which the media were designed are no longer serviced by their manufacturers (many of whom are out of business), the data will no longer be accessible. In some cases, older media suffer from a physical breakdown of components - tapes simply lose their magnetic properties after a long time in storage. The scale of the crisis is compatible to that facing the Social Security System. Greater financial and intellectual resources to the development and refinement of new storage media and migration technologies in order to preserve as much data as possible.

Halem, M.↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Classifying Aircraft using Velocity Data with Support Vector Machines and Likelihood Ratio Tests

Timely classification of aircraft is important for small unmanned aerial system (sUAS) technologies, such as onboard collision avoidance systems, and aerial perimeter security for prisons and sports venues. This work uses velocity-based metrics to classify multi-rotor sUAS, fixed wings UAS, and general aviation planes using two classification methods: Support Vector Machines (SVM), and Likelihood Ratio (LR) tests. We found that a 96% classification accuracy is achieved when either classifier is trained using average speed derived from flight controller data or radar data and tested with one second of radar data. Further, we show that LR tests perform similarly to SVM for single metric classification. In addition, we present two novel metrics for classifying aircraft: log variance of absolute change in speed, and log variance of relative change in speed. Finally, we discuss challenges associated with training classifiers with flight controller data but testing on radar data.

Logan T Dihel↗
NASA NTRS2015

Control and Non-Payload Communications (CNPC) Prototype Radio - Generation 2 Security Architecture Lab Test Report

NASA Glenn Research Center, in cooperation with Rockwell Collins, is working to develop a prototype Control and Non-Payload Communications (CNPC) radio platform as part of NASA Integrated Systems Research Program's (ISRP) Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. A primary focus of the project is to work with the FAA and industry standards bodies to build and demonstrate a safe, secure, and efficient CNPC architecture that can be used by industry to evaluate the feasibility of deploying a system using these technologies in an operational capacity. GRC has been working in conjunction with these groups to assess threats, identify security requirements, and to develop a system of standards-based security controls that can be applied to the current GRC prototype CNPC architecture as a demonstration platform. The security controls were integrated into a lab test bed mock-up of the Mobile IPv6 architecture currently being used for NASA flight testing, and a series of network tests were conducted to evaluate the security overhead of the controls compared to the baseline CNPC link without any security. The aim of testing was to evaluate the performance impact of the additional security control overhead when added to the Mobile IPv6 architecture in various modes of operation. The statistics collected included packet captures at points along the path to gauge packet size as the sample data traversed the CNPC network, round trip latency, jitter, and throughput. The effort involved a series of tests of the baseline link, a link with Robust Header Compression (ROHC) and without security controls, a link with security controls and without ROHC, and finally a link with both ROHC and security controls enabled. The effort demonstrated that ROHC is both desirable and necessary to offset the additional expected overhead of applying security controls to the CNPC link.

Communication Networks↗
DOE OSTI2025

Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery

The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.

Khadka, Rajiv [Idaho National Laboratory (INL), Id↗
DOE OSTI2025

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Mavian, Alex (ORCID:0000000279448830)↗
DOE OSTI2025

Metadata Standards for the NSE: Core Fields

This standard presents a core set of metadata fields required for each managed digital object within the Nuclear Security Enterprise (NSE). Metadata standardization is a critical enabler for two primary objectives: 1) effectively sharing data, documents, and other digital objects between NSE sites; and 2) supporting digital engineering through the digital thread at the object level. Standardization is necessary for both schemas and vocabularies, meaning that both field standards and value standards must be specified. This document serves as a foundational field standard, recommending a core set of fields that should be uniformly required for all managed digital objects within the NSE.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1975

Automatic documentation system extension to multi-manufacturers' computers and to measure, improve, and predict software reliability. Appendix A and B

A manual which explains how to use the DOMONIC command language is presented. The manual consists of the following sections: 1. Introduction 2. What You Must Know to Use DOMONIC 3. Functions of DOMONIC 4. Entering and Manipulating Data 5. Templates and Data Definitions 6. Recipes and Document Generation 7. Initiating A Project and 8. Entering and Changing Security Controls. The first four sections must be known by all DOMONIC users. Sections five, seven and eight describe functions normally used only by project management. Section six tells how to generate documents. The manual specifies what commands to use in performing each of the functions mentioned above.

Simmons, D. B.↗
NASA NTRS1995

A multifrequency radio continuum and IRAS faint source survey of markarian galaxies

Results are presented from a multifrequency radio continumm survey of Markarian galaxies (MRKs) and are supplemented by IRAS infrared data from the Faint Source Survey. Radio data are presented for 899 MRKs observed at nu = 4.755 GHz with the National Radio Astronomy Observatory (NRAO)-Green Bank 300 foot (91 m) telescope, including nearly 88% of those objects in Markarian lists VI-XIV. In addition, 1.415 GHz measurements of 258 MRKs, over 30% of the MRKs accessible from the National Aeronomy and Ionosphere Center (NAIC)-Arecibo, are reported. Radio continuum observations of smaller numbers of MRKs were made at 10.63 GHz and at 23.1 GHz and are also presented. Infrared data from the IRAS Faint Source Survey (Ver. 2) are presented for 944 MRKs, with reasonably secure identifications extracted from the NASA/IPAC Extragalactic Database. MRKs exhibit the same canonical infrared characteristics as those reported for various other galaxy samples, that is well-known enhancement of the 25 micrometer/60 micrometer color ratio among Seyfert MRKs, and a clear tendency for MRKs with warmer 60 micrometer/100 micrometer colors to also possess cooler 12 micrometer/25 micrometer colors. In addition, non-Seyfert are found to obey the well-documented infrared/radio luminosity correlation, with the tightest correlation seen for starburst MRKs.

Bicay, M. D.↗