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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2025

Engineering Out Industry 4.0 Cyber Risk Presentation for EnCyCriS

The increasing complexity and business requirements of operational technology (OT) devices is beginning to break the normal segmentation between information technology (IT) and OT networks. The introduction of industry 4.0 devices such as industrial internet of things (IIoT) and other intelligent industrial devices (IID), virtualized OT systems, OT cloud integration, and artificial intelligence (AI)-driven industrial control systems (ICS) has challenged traditional IT/OT cybersecurity strategies. Industry 4.0 devices are analyzed through the lens of well-regarded models such as the PERA model and confidentiality, integrity, and availability (CIA) security objectives, showing the division between what is needed and traditional cybersecurity countermeasures. In this paper, the practice of Cyber-Informed Engineering (CIE) is proposed to bridge the gap between IT/OT security, enhance the practice of cybersecurity in this modern age, and reduce the impacts of consequential events in OT.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Game Theory in Cybersecurity: A Review of Applications, Models, and Challenges

There are no aspects of society left unchanged by the Internet. From commerce to communication, from entertainment to engineering, every industry and sphere of influence exists in a new state thanks to the advent of the information age. This convenience, rarely controverted, comes with capricious actors, who wish to gain access to these systems for myriad reasons, be it financial, political, or mere intellectual gain. This paper presents information that can help readers to better understand the fundamentals of cybersecurity from a game theory perspective, such as coalitional games, Colonel Blotto systems, or how to find Nash Equilibrium. This paper also reviews how game theory can enhance understanding of cybersecurity through cooperative and non-cooperative methods, such as information sharing, defense modeling, and attacker analysis. The authors use the analysis to serve as a bridge for those in either field who need a way to best comprehend the methodologies of their contrasting fields.

Egan, Matthew↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2025

Engineering Out Industry 4.0 Cyber Risk

The increasing complexity and business requirements of operational technology (OT) devices is beginning to break the normal segmentation between information technology (IT) and OT networks. The introduction of industry 4.0 devices such as industrial internet of things (IIoT) and other intelligent industrial devices (IID), virtualized OT systems, OT cloud integration, and artificial intelligence (AI)-driven industrial control systems (ICS) has challenged traditional IT/OT cybersecurity strategies. Industry 4.0 devices are analyzed through the lens of well-regarded models such as the PERA model and confidentiality, integrity, and availability (CIA) security objectives, showing the division between what is needed and traditional cybersecurity countermeasures. In this paper, the practice of Cyber-Informed Engineering (CIE) is proposed to bridge the gap between IT/OT security, enhance the practice of cybersecurity in this modern age, and reduce the impacts of consequential events in OT.

42 - ENGINEERING↗
DOE OSTI2025

Fusing Edge Computing with Transport Security by Leveraging the Controller Area Network Transport Security Tracking and Reporting (C-STAR) Unit

Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗
DOE OSTI2026

Digital-Threat Bias and Psychological Distance: Barriers to Foundational Digital-Security Improvement

Modern life is held together by a web of digital dependencies that enable and provide delivery of critical services and functions—think the provision of utilities such as electricity and water, as well as our dependency on digital services for social and economic services (internet, communication, etc.). As this dependency grows, the complexity related to the delivery of these critical services increases as well. As complexity increases, the understanding of the risk and impact associated with potential disruption, degradation, or destruction—due to either malicious or non-malicious events of those digitally enabled functions—decreases. One potential explanation for the difficulty to 1) understand the risks faced and 2) address them appropriately and effectively is the abstractness and psychological distance assigned to “digital threat.” The complexity of these digitally enabled services creates a perceived complicatedness; as a result, digital threats are treated differently than similarly devasting (but more easily understood) kinetic or physical threats. How we categorize these threats also matters. Acts of cyber-enabled sabotage to critical infrastructure need to be defined as irregular warfare. By inadequately defining the threat, we compound the problem. Acknowledging this digital-threat bias is foundational to improving the ability to protect critical infrastructure. Using construal-level theory and psychological-distance concepts provides an intriguing starting point to address these issues, to reframe the challenges faced, and pursue more effective critical infrastructure security and defense policy.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

A Privacy First Path Analysis using Clickstream Data

In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.

Frequency and Monetary (RFM) analysis↗
DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗
DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗
DOE OSTI2026

Legacy Devices and Interoperability: Developing a Scientific Strategy for Inclusion and Representation of Older Devices

Legacy devices across grid, transportation, and building systems present complex modernization challenges that extend far beyond simple equipment obsolescence. Pulling from presentations and break-out group discussions from this workshop on legacy devices across all three sectors, this report examines the interconnected factors driving replacement decisions, documents successful modernization initiatives from recent years, and provides strategic guidance for coordinating infrastructure transformation across these critical sectors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗