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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Feasibility of Applying a Human Bio-signals Monitoring to Minimize Insider Threats

With the on-going global war on terror, a potential terrorist attack on a Nuclear Power Plant (NPP) is getting a lot of attention. In particular, the potential threat of an insider is a major concern as it could lead to the loss of a nationally critical asset and thus deserves serious consideration. The current approach to preventing an insider threat is the Insider Mitigation Program (IMP) based on periodic self-assessment or peer and manager review of specific employees. These are potentially biased and subjective assessments and are unreliable. Also, as the approach is reactive-based, identifying a problem employee could be too late to protect NPP assets (i.e., confidential information and nuclear materials). Thus, a new approach for detecting and predicting an insider is necessary. We propose a proactive approach based on using bio-signals as an alternative. To address the vulnerability of current insider detection systems, our studies investigated the possibility of insider detection and prediction by using biosignals (electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR)). But one main question remained in the previous studies was: Is the method using key EEG indicators specific enough to discern the difference between a normal worker's motivation level change and being ready to commit a crime? This study examined the answer to the question.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Chemo-chromic Pd-V{sub 2}O{sub 5} Sensors for Passive Hydrogen Detection in Nuclear Containments - 18268

The ability to detect and monitor hydrogen gas efficiently in process and storage facilities, handling nuclear material, is crucial to ensuring their safety. The accumulation of hydrogen gas, above the lower flammable limit (LFL), in a nuclear waste containment is a concern since it creates the potential for a hydrogen-air explosion to occur, which could lead to a loss of containment and result in the uncontrolled release of radioactive material into the surrounding environment. The events that took place at Fukushima Daiichi Nuclear Power Plant highlighted the vulnerability of conventional hydrogen detection to extreme events, where power may be lost. In the present work, chemo-chromic hydrogen sensors have been fabricated, using transition metal oxide thin films, to provide eye-readable detection systems that would be resilient to plant power failure. Vanadium oxide (V{sub 2}O{sub 5}) films were prepared on quartz glass substrates by sol-gel deposition and sensitized with a palladium (Pd) catalyst, deposited by electron beam evaporation. When exposed to hydrogen, the Pd catalyst dissociates H{sub 2} to H atoms, which diffuse into the V{sup (V)}{sub 2}O{sub 5} forming a hydrogen-vanadium metal bronze, H{sub 2}V{sup (III)}{sub 2}O{sub 5}, resulting in a noticeable colour change from orange to dark green. To assess their viability for nuclear safety applications, these sensors have been irradiated to total doses between 5 and 250 kGy using a Co-60 gamma isotope irradiator. The results suggest that gamma irradiation, at the levels examined, has an effect on the initial colour of the V{sub 2}O{sub 5} and Pd-V{sub 2}O{sub 5} thin films with decreased transmittance above 540 nm. The orange starting colour darkened and developed a green tone, with the degree of colour change depending on the applied total dose. Changes in surface morphology and characteristics have been examined by using Scanning Electron Microscopy (SEM) and Raman spectroscopy. High level (250 kGy) gamma radiation exposure begins to produce surface degradation on V{sub 2}O{sub 5} thin films; however this behaviour is not observed for films that are also coated with palladium. Chemo-chromic properties of both un-irradiated and irradiated Pd-V{sub 2}O{sub 5} thin films were determined by examining their optical transmittance, using UV-vis spectroscopy, under exposure to a 4% H{sub 2}-N{sub 2} gas mixture. Exposure to gamma radiation has been found to have negligible effect upon colour change behaviour after 30 minutes exposure to hydrogen gas. The results suggest that the thin film V{sub 2}O{sub 5} sensitised with Pd is a plausible technique for application in the monitoring of hydrogen gas in low-level gamma radiation environments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

DNA metabarcoding reveals consumption of diverse community of amphibians by invasive wild pigs (Sus scrofa) in the southeastern United States

Abstract Invasive wild pigs ( Sus scrofa ) are one of the most widespread, destructive vertebrate species globally. Their success can largely be attributed to their generalist diets, which are dominated by plant material but also include diverse animal taxa. Wild pigs are demonstrated nest predators of ground-nesting birds and reptiles, and likely pose a threat to amphibians given their extensive overlap in wetland use. DNA metabarcoding of fecal samples from 222 adult wild pigs culled monthly from 2017 to 2018 revealed a diverse diet dominated by plant material, with 166 plant genera from 56 families and 18 vertebrate species identified. Diet composition varied seasonally with availability for plants and was consistent between sexes. Amphibians were the most frequent vertebrate group consumed and represented the majority of vertebrate species detected, suggesting amphibians are potentially vulnerable to predation by wild pigs in our study region. Mammal, reptile, and bird species were also detected in pig diets, but infrequently. Our results highlight the need for research on the impacts of wild pigs on amphibians to better inform management and conservation of imperiled species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Effects of Jacobian Matrix Regularization on the Detectability of Adversarial Samples

The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRSDate not supplied

Spaceborne Passive-Microwave Hail Detection: Global Climatologies, Validation, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and vulnerable to nonmeteorological biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global passive-microwave climatologies of severe hail using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We find that this retrieval, which leverages a signature in the Minimum 19-GHz polarization corrected temperature (PCT) combined with the 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly, and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D. Bang↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Ground-Truth Prediction to Accelerate Soft-Error Impact Analysis for Iterative Methods

Understanding the impact of soft errors on applications can be expensive. Often, it requires an extensive error injection campaign involving numerous runs of the full application in the presence of errors. In this paper, we present a novel approach to arriving at the ground truth--the true impact of an error on the final output--for iterative methods by observing a small number of iterations to learn deviations between normal and error-impacted execution. We develop a machine learning based predictor for three iterative methods to generate ground-truth results without running them to completion for every error injected. We demonstrate that this approach achieves greater accuracy than alternative prediction strategies, including three existing soft error detection strategies. We demonstrate the effectiveness of the ground truth prediction model in evaluating vulnerability and the effectiveness of soft error detection strategies in the context of iterative methods.

iterative methods, failure prediction, Soft error ↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2017

Pattern Discovery in Internet Background Radiation

Internet Background Radiation (IBR) is observed in empty network address spaces. No traffic should arrive there, but it does in overwhelming quantities, gathering evidences of attacks, malwares and misconfigurations. The study of IBR helps to detect spreading network problems, common vulnerabilities and attack trends. However, network traffic data evolves quickly and is of high volume and diversity, i.e., an outstanding big data challenge. When used to assist network security, it also requires the online classification of dynamic streaming data. In this paper, we introduce an AGgregation & Mode (AGM) vector to represent network traffic. The AGM format characterizes IP hosts by extracting aggregated and mode values of IP header fields, and without inspecting payloads. We performed clustering and statistical analysis to explore six months of IBR from 2012 with the AGM mapping. The discovered patterns allow building a classification of IBR, which identifies phenomena that have been actively polluting the Internet for years. The AGM representation is light and tailored for monitoring and pattern discovery. We show that AGM vectors are suitable to analyze large volumes of network traffic: they capture permanent operations, such as long term scanning, as well as bursty events from targeted attacks and short term incidents.

Iglesias, Felix↗
DOE OSTI2020

Mitigation of External Exposure of Energy Delivery System (MEEDS).

MEEDS is a cybersecurity solution that empowers under resourced owners and operators of critical energy infrastructure to rapidly identify and detect the publicly exposed and vulnerable operational technology (OT) and other critical Industrial Control and Energy Delivery Systems.

Mylrea, Michael↗
NASA NTRS2010

Automatic Review of Abstract State Machines by Meta Property Verification

A model review is a validation technique aimed at determining if a model is of sufficient quality and allows defects to be identified early in the system development, reducing the cost of fixing them. In this paper we propose a technique to perform automatic review of Abstract State Machine (ASM) formal specifications. We first detect a family of typical vulnerabilities and defects a developer can introduce during the modeling activity using the ASMs and we express such faults as the violation of meta-properties that guarantee certain quality attributes of the specification. These meta-properties are then mapped to temporal logic formulas and model checked for their violation. As a proof of concept, we also report the result of applying this ASM review process to several specifications.

Arcaini, Paolo↗
NASA NTRS1998

Verification and Quantification of Single Event Effects on High Speed SRAM in Terrestrial Environments

As integrated circuits become more sensitive to charged particles and neutrons, anomalous performance due to single event effects (SEE) is a concern and requires experimental verification and quantification. The Center for Applied Radiation Research (CARR) at Prairie View A&M University has developed experiments as a participant in the NASA ER-2 Flight Program, the APEX balloon flight program and the Student Launch Program. Other high altitude and ground level experiments of interest to DoD and commercial applications are being developed. The experiment characterizes the SEE behavior of high speed and high density SRAM's. The system includes a PC-104 computer unit, an optical drive for storage, a test board with the components under test, and a latchup detection and reset unit. The test program will continuously monitor the stored checkerboard data pattern in the SW and record errors. Since both the computer and the optical drive contain integrated circuits, they are also vulnerable to radiation effects. A latchup detection unit with discrete components will monitor the test program and reset the system when necessary. The first results will be obtained from the NASA ER-2 flights, which are now planned to take place in early 1998 from Dryden Research Center in California. The series of flights, at altitudes up to 70,000 feet, and a variety of flight profiles should yield a distribution of conditions for correlating SEES. SEE measurements will be performed from the time of aircraft power-up on the ground throughout the flight regime until systems power-off after landing.

Huff, H.↗
DOE OSTI2016

S&T converging trends in dealing with disaster: A review on AI tools

Science and Technology (S&T) has been able to help mankind to solve or minimize problems when arise. Different methodologies, techniques and tools were developed or used for specific cases by researchers, engineers, scientists throughout the world, and numerous papers and articles have been written by them. Nine selected cases such as flash flood, earthquakes, workplace accident, fault in aircraft industry, seismic vulnerability, disaster mitigation and management, and early fault detection in nuclear industry have been studied. This paper looked at those cases, and their results showed nearly 60% uses artificial intelligence (AI) as a tool. This paper also did some review that will help young researchers in deciding the types of AI tools to be selected; thus proving the future trends in S&T.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Proactive Defense for Evolving Cyber Threats

There is great interest to develop proactive methods of cyber defense, in which future attack strategies are anticipated and these insights are incorporated into defense designs; however, little has been done to place this ambitious objective on a sound scientific foundation. Indeed, even fundamental issues associated with how the “arms race” between attackers and defenders actually leads to predictability in attacker activity, or how to effectively and scalably detect this predictability in the relational/temporal data streams generated by attacker/defender adaptation, haven’t been resolved. This LDRD project addressed many of these challenges and the results are briefly summarized here. We have characterized the predictability of attacker/defender coevolution and have leveraged our findings to create a framework for designing proactive defenses for large (organizational) networks. More specifically, this project applied rigorous predictability-based analytics to two central and complementary aspects of the network defense problem – attack strategies of the adversaries and vulnerabilities of the defenders’ systems – and used the results to develop a scientifically-grounded, practically-implementable methodology for designing proactive cyber defense systems. Briefly, predictive analysis of attack strategies involved first conducting predictability assessments to characterize attacker adaptation patterns in given domains, and then used these patterns to “train” adaptive defense systems capable of providing robust performance against both current and (near) future threats. The problem of identifying and prioritizing defender system vulnerabilities was addressed using statistical and machine learning to analyze a broad range of data (e.g., cyber, social media) on recently detected system vulnerabilities to “learn” classifiers that predict how likely it is that, and how soon, new vulnerabilities will be exploited. A variety of cyber threat case studies were developed and investigated throughout the project, one selected from the cyber security research community and one that is more comprehensive and of higher priority to SNL and to external national security partners. A sample of research results and application of this methodology are included in this report (as a series of peer-reviewed publications). For ease of reference the title and SAND number are included below.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Tools and Methods for Hardening Communication Security of Energy Delivery Systems

This document summarizes the research and development work the TT Government Solutions (TTGS), d.b.a. Applied Communication Sciences (ACS), team performed for the Department of Energy Cybersecurity for Energy Delivery Systems (CEDS) program. It addresses the challenges in protecting critical grid control and data communication, including the identification of vulnerabilities and deficiencies of communication protocols commonly used in energy delivery systems (e.g., ICCP, DNP3, C37.118, C12.22), as well as the development of effective means to detect and prevent the exploitation of such vulnerabilities and deficiencies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

An iterative learning and inference approach to managing dynamic cyber-vulnerabilities of complex systems

As modern systems become increasingly reliant on cyber-technologies and continue to be integrated with physical systems, managing risks from deliberate and non-deliberate sources is a significant research challenge. Unlike strictly physical systems, cyber-enabled physical systems are influenced by dynamic and evolving technologies, environments, and attack mechanisms. As a result, vulnerabilities are rapidly changing and difficult to detect and manage. While there is recent interest in the dynamic properties of performance through resilience analysis, limited research addresses the dynamic nature of cyber-system vulnerability. This paper presents a dynamic iterative learning approach to evaluate the state of health for systems as related to cyber-vulnerabilities. These time-varying system health characteristics may not be visible, but can be inferred using observable characteristics. The approach recognizes that multiple types of vulnerabilities need to be included in a holistic system health assessment. The methods are applied to the Common Vulnerability Scoring System database containing over 100,000 documented cybersecurity vulnerabilities. This work is the first to acknowledge the dynamic properties of cyber-vulnerability, while also inferring system health using observable data and hidden states of system health. This work will be of interest to managers of large-scale cyber-enabled physical systems who are seeking to prioritize system health investments.

Chatterjee, Samrat↗
DOE OSTI2018

Nuclear Power Plant Simulation and Cybersecurity

Critical infrastructure protection is vital to the day-to-day operation of any country, and those systems need to be protected to the fullest extent possible. As with what was seen in Ukraine in late 2016, with the cyber attack on the power grid, these systems are susceptible to attacks that can cause serious damage, both physically and economically. This incident shut down the power grid for 6 hours, with nearly 80,000 people affected. Cyber security can be referred to as the protection of data and systems in networks, both wired and wireless, from unauthorized access or attack. In nuclear power plants, assessments of cyber security are critical to ensuring the safe and reliable operation of the systems used. According to the Nuclear Regulatory Commission (NRC), the purpose of cyber security assessments is to detect and then eliminate or mitigate vulnerabilities in the digital system that could be exploited either from outside or inside the digital system protected area. These cyber security assessments are essentially intended to reduce cyber security risk. The NRC refers to cyber security risk as the combination of the consequence to the nuclear power plant and the susceptibility of a digital system to internal and external cyber attack.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗