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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

R&D100 Finalist: Neuromorphic Cyber Microscope

The Neuromorphic Cyber Microscope provides security analysts with unprecedented visibility of their network, computer and storage assets. This processor is the world's first practical implementation of neuromorphic technology to a major computer science mission. Working with Lewis Rhodes Labs, engineers at Sandia National Laboratories have created a device that is orders of magnitude faster at analyzing data to identify cyber-attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sensitivity Studies, Gap Analysis, and Benchmark Experiment Optimization for Reactor Physics and Criticality Safety Applications

Many new reactor designs, such as advanced reactors and micro reactors, have materials that lack nuclear data validation. This is also true for many other applications in criticality safety and global security. Both differential and integral experiments are needed to validate cross-section data. Without this, a user cannot have confidence in the predicted results of a radiation-transport code. This work describes an approach called ARCHIMEDES (Application Relevant Critical/Subcritical HEU/Pu-based Integral Measurements for Enhancing Data and Evaluating Sensitivities) to design new criticality experiments that have similar k eff cross-section sensitivities to an application of interest. This process involves simulations to generate cross-section sensitivities to a parameter of interest (such as k eff ), a gap analysis to determine which existing benchmarks are most similar to the application, and an experiment optimization. Recently, there has been a great deal of interest in the reactor physics community on advanced reactors, micro reactors, and accelerator driven systems (ADS). This work will apply the described method to specific examples in this area. The focus of this work will be on the sensitivity study and gap analysis, while future work will include experiment design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
DOE OSTI2025

Harnessing the Power of AI: Status and Expansion of Current Domestic Transport Security Through Flexible Embedded Hardware

As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗
DOE OSTI2015

Enrichment Meter Dataset from High-Resolution Gamma Spectroscopy Measurements of U3O8 Enrichment Standards and UF6 Cylinder Wall Equivalents

The Enrichment Meter Principle (EMP) is the basis for a commonly used standard test method for the non-destructive assay of 235 U enrichment in bulk compounds [1]. The technique involves determining the net count rate in the direct 186 keV peak using medium or high energy gamma-ray spectrometry in a fixed geometry. With suitable correction for wall attenuation, compound type, rate loss (live time), and peaked background (if significant), the atom fraction of 235 U may be obtained from the counting rate from a linear relationship through the origin. The widespread use of this method for field verification of enrichment [2,3] together with the fact that the response function rests on fundamental physics considerations (i.e., is not represented by a convenient but arbitrary form) makes it an interesting example of uncertainty quantification, one in which one can expect a valid measurement model can be applied. When applied using NaI(Tl) and region of interest analysis, the technique is susceptible to both interference error and bias [2-4]. When implemented using high-resolution gamma-ray spectroscopy, the spectrum interpretation is considerable simplified and more robust [5]. However, a practical challenge to studying the uncertainty budget of the EMP method (for example, to test linearity, extract wall corrections and so forth using modern methods) is the availability of quality experimental data that can be referenced and shared. To fill this gap, the research team undertook an experimental campaign [6]. A measurement campaign was conducted to produce high-resolution gamma spectroscopy enrichment meter data comparable to UF 6 cylinder measurements. The purpose of this report is to provide both an introduction to and quality assurance (QA) of the raw data produced. This report is intended for the analyst or researcher who uses the raw data. Unfortunately, the raw data (i.e., the spectra files) are too voluminous to include in this report but can be requested from Steven Croft of the Safeguards & Security Technology Group (scroft@ornl.gov 865-241-2834). The complete processed data are tabulated in Appendix A. The analysis techniques used to produce the QA data presented in this report [e.g., three regions-ofinterest (ROI) peak extraction and batch analysis processes] are not the most sophisticated techniques available; analysts are encouraged to reanalyze the raw data using more sensitive techniques and to improve upon the results presented here. With that being said, the analysis techniques used here are more than adequate to present and inspect the quality of the data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Enhancing Network Visibility and Security through Tensor Analysis

The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network. With this being said, In this paper, we demonstrate the successful use of the tool in two completely diverse operational cyber security environments, namely, (1) security operations center (SOC) for the SCinet network at the SuperComputing (SC) Conference in 2016 and 2017 and (2) Reservoir Labs’ Local Area Network (LAN). In each of these environments, we produce actionable results for cyber security specialists including (but not limited to) (1) finding malicious network traffic involving internal and external attackers using port scans, SSH brute forcing, and NTP amplification attacks, (2) uncovering obfuscated network threats such as data exfiltration using DNS port and using ICMP traffic, and (3) finding network misconfiguration and performance degradation patterns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Investigating the impact of a multi-module operation environment on the task performance time of human operators – An explanatory study

The worldwide demand for Small Modular Reactors (SMRs) has surged in recent years due to their enhanced safety and versatility in supporting diverse industrial sectors. A unique feature of SMR operation is that a single human operator is responsible for managing multiple modules. Therefore, securing a sufficient amount of human performance data pertaining to this new environment is essential for the safe operation of SMRs. In this explanatory study, a series of experiments were conducted using the NuScale simulator, a representative SMR design, with student operators. A total of 12 student operators were assigned two types of off-normal events and asked to cope with them using paper-based procedures. Subsequently, their task performance times were compared with those of student operators responsible for a single unit based on the Task Complexity (TACOM) measure. Results indicate that the performance of student operators under the experimental conditions of this study degraded by a factor of 2 to 3, depending on the characteristics of the off-normal events.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Cyber-physical security framework for Photovoltaic Farms

With the evolution of PV converters, a growing number of vulnerabilities in PV farms are exposing to cyber threats. To mitigate the influence of cyber-attack on PV farms, it is necessary to study attacks' impact and propose detection methods. To meet this requirement, a cyber-physical security framework is proposed for PV farms. Data integrity attacks (DIAs) are studied on different control loops. As μPMU is gaining in popularity, a lower sampling rate of μPMU data is applied to develop a detection algorithm. We have evaluated two data-driven methods, which are support vector machine (SVM) and long short-term memory (LSTM). Lastly, the data-driven methods verify the feasibility of μPMU data in attack detection.

Attack Impact Analysis↗
DOE OSTI2025

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2025

Testbed and Experiments for Quantum-Conventional Networking

The realization of quantum networks requires the development of devices and methods unprecedented in conventional networks, and yet they critically depend on the latter for implementing foundational blocks and essential operations. We describe a testbed to support the development and testing of their functionality and performance by providing quantum and conventional data planes and devices, together with a secure conventional control plane. It incorporates a variety of entangled photon sources, qubit technologies, detector technologies, photonic components, and supporting conventional switches and workstations. It implements a novel fiber telescoping scheme that provides suites of connections using fiber spools and inground-aerial fiber loops. We briefly summarize a variety of experiments conducted over this testbed including: (i) flex-grid quantum connection experiments, (ii) quantum state and channel tomography, (iii) utilization of quantum key distribution keys to secure conventional encryption and firewall devices, (iv) comparative study of analytical capacity estimates and entanglement throughput, (v) deployed squeezing coexisting with conventional communications, and (iv) measurement of polarization time variation.

Rao, Nageswara [ORNL] (ORCID:0000000234085941)↗
DOE OSTI2019

Spatio-Temporal Characterization of Synchrophasor Data Against Spoofing Attacks in Smart Grids

“Source ID Mix” has emerged as a new type of highly deceiving attack which can alter the source information of synchrophasor data measured by multiple phasor measurement units, thereby paralyzing many wide-area measurement systems applications. To address such sophisticated cyber attacks, we have exploited the spatio-temporal characteristics of synchrophasor data for authenticating measurements’ source information. Specifically, the source authentication is performed when the measurements are subjected to three types of spoofing attacks. Some practical difficulties in applying the proposed method on real-time authentication caused by insufficient measurement data have also been solved. Experimental results with real synchrophasor measurements have validated the effectiveness of the proposed method in detecting such complicated data spoofing and improving power systems’ cyber security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Evolution and Trends of Industrial Control System Cyber Incidents since 2017

The industrial control systems (ICSs) that manage our critical infrastructure are increasingly converging with corporate networks and the Internet as technology and businesses prioritize digital connectivity. These connections make them more vulnerable and available to malicious cyber actors who traditionally targeted the companies’ more public-facing information technology (IT) networks. This paper will review select publicly reported cyber incidents to highlight the continued and growing threat to ICS devices and operational technology (OT) environments. It will summarize the incident and when available, will provide information on the cyber actors, the vulnerabilities they exploited, and any publications the U.S. Government (USG) provided in response. Data belonging to the Department of Homeland Security (DHS) will be used to highlight quantitative trends concerning ICS incidents. This paper builds on “History of Industrial Control System Cyber Incidents” (Hemsley & Fisher 2018), a paper that highlighted select noteworthy threats and incidents to ICS systems up to 2017. This paper will similarly review select incidents occurring after the last previously reviewed incident, Triton/HatMan, December 2017, and will note ICS incident trends including IT/OT convergence and advances in cyber-threat actors’ capabilities in observed in the examined incidents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Underground Test Area Calendar Year 2014 Annual Sampling Analysis Report Nevada National Security Site, Nevada, Revision 0

This report presents the analytical data for the 2014 fiscal year (FY) and calendar year (CY) (October 1, 2013, through December 31, 2014), and an evaluation of the data to ensure that the Sampling Plan’s objectives are met. In addition to samples collected and analyzed for the Sampling Plan, some NNSS wells are monitored by NNSA/NFO to demonstrate compliance with State-issued water discharge permits; with protection of groundwater from ongoing radiological waste disposal activities (compliance wells); and to demonstrate that the onsite drinking water supply is below SDWA maximum contaminant levels (MCLs) (public water system [PWS] wells). While not all sampled locations are required by the Sampling Plan, these samples are relevant to its objectives and are therefore presented herein for completeness purposes. Special investigations that took place in 2014 that are relevant to the Sampling Plan are also presented. This is the first annual report released to support Sampling Plan implementation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Calendar Year 2017 Underground Test Area Annual Sampling Report Nevada National Security Site, Nevada, Revision 1

This report presents the analytical data for the 2017 calendar year (CY) (January 1 through December 31, 2017) and an evaluation of the data to ensure that the Sampling Plan’s objectives are achieved. Data from samples collected in 2016 that were not reported in the UGTA CY 2016 Annual Sampling Analysis Report (DOE/EMNV, 2018b) are presented in Appendix D.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Cyber Security Concerns for Sharing Distributed Antenna Systems

Wireless access to data is a challenge for environments with poor radio frequency coverage such as nuclear power plants. An 802.11 Wi-Fi data network is often not a cost-effective solution not a cost-effective solution due to the requirement for extensive antenna outlays in large nuclear facilities and poor signal penetration due to thick concrete walls. Combining commercial Long-Term Evolution (LTE) with an existing distributed antenna system’s (DAS) resources and infrastructure is an attractive solution due to and lower cost of implementation and likely coverage increases. A 2017 technical report by EPRI has demonstrated that multiple systems such as Land Mobile Radio (LMR) and LTE can be operated using the same DAS and by sharing the leaky coaxial cabling and indoor antenna systems (Reference 1). Since the radio frequency (RF) bands used by the two wireless systems are specified to be non-overlapping, it is believed that the systems are isolated from each other. No extensive analysis has been conducted to determine whether RF signals from one system sharing antennas and coaxial cable can influence another. This paper investigates the assumption of isolation between the two wireless systems and provides recommendations for future needed work to identify specific vulnerabilities and potential mitigations.

97 MATHEMATICS AND COMPUTING↗