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At least 1,117 records · Page 62

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NASA NTRS2021

Semi-Centennial of Landsat Observations & Pending Landsat 9 Launch

The first Landsat was placed in orbit on 23 July 1972, followed by a series of missions that have provided nearly continuous, two-satellite 8-day repeat image coverage of the Earth's land areas for the last half-century. These observations have substantially enhanced our understanding of the Earth's terrestrial dynamics, both as a major element of the Earth's physical system, the primary home of humans, and the major source of resources that support them. The history of Landsat is complex, reflective of the human systems that sustain it. Despite the conflicted perspectives surrounding the continuation of the program, Landsat has survived based on worldwide recognition of its critical contributions to understanding land dynamics, management of natural resources and Earth system science. Launch of Landsat 9 is anticipated in Fall 2021, and current planning for the next generation, Landsat Next is well underway. The community of Landsat data users is looking forward to another 50 years of the Landsat program.

Samuel N. Goward↗

NASA NTRS2020

On Perspectives of Generating Quasi-Fock State via Resonant Self-Phase-Modulation

Optical resonators with cubic nonlinearity were among the ?rst simplest physical systems proposed for generation of squeezed states of light as well as for veri?cation of quantum nondemolition measurement concept. These conventional squeezed states are characterized with Gaussian statistics and positive Wigner function and are frequently treated as semiclassical. In contrast, it is known that the quantum states resulting from the higher order self-phase modulation are characterized with negative Wigner function. Such states can be transformed to an imperfect Fock state via classical biasing. Using these known features, we have developed an experiment strategy for observation and characterization of the nonclassical state in a system based on a high-Q nonlinear optical microcavity. We discuss technical limitations for the state observation associated with the losses in the system and technical noise of the classical optical sources involved in the measurement.

Bilenko, I. A.↗

NASA NTRSDate not supplied

Swarm Mentality: Toward Automatic Swarm State Awareness with Runtime Verification

Cyber-Physical Systems (CPSs) already exhibit impressive performance in all areas of human life, and swarms of CPSs promise to increase their capabilities even further. However, to effectively utilize CPS swarms their complexity of operation has to scale sub-linearly with the number of swarm members. Presenting the swarm to an operator as a single entity almost eliminates the additional per-member overhead entirely. To operate a swarm as one entity, and/or to increase the swarm’s autonomy, the operator and the swarm members need to reason and communicate at the same level of abstraction, i.e. the swarm needs a sense of “self.” Therefore, we require the ability to specify whole swarm properties yet monitor them at the member level. We examine one architecture for achieving this awareness by: 1) Defining a taxonomy for comparing techniques that synthesize this belief-state 2) Propose use of the Runtime Verification formal method to fill this role 3) Present preliminary designs for extending and embedding such a system in the Distributed Spacecraft Autonomy architecture to generate per-member monitors from swarm level specification.

Runtime Verification↗

NASA NTRSDate not supplied

Self-consistent Quantum Iteratively Sparsified Hamiltonian Algorithm (SQuISH)

Due to coherence time limitations, reducing the resources required to run quantum algorithms and simulate physical systems on a quantum computer is crucial. With regards to Hamiltonian simulation, a significant effort has focused on building efficient algorithms using various factorizations and truncations, typically derived from the Hamiltonian alone. We introduce a new paradigm for improving Hamiltonian simulation and reducing the cost of ground state problems based on ideas recently developed for classical chemistry simulations. The key idea is that one can find efficient ways to reduce resources needed by quantum algorithms by making use of two key pieces of information: the Hamiltonian operator and an approximate ground state wavefunction. We refer to our algorithm as the self-consistent quantum iteratively sparsified Hamiltonian (SQuISH). By performing our scheme iteratively, one can drive SQuISH to create an accurate wavefunction using a truncated, resource-efficient Hamiltonian. By utilizing this more compact Hamiltonian, our algorithm provides an approach to reduce the gate complexity of ground state calculations on quantum hardware. As proof of principle, we implement SQuISH using configuration interaction for small molecules and coupled cluster for larger systems. Through our combination of approaches, we demonstrate how it performs on a range of systems, the largest of which would require more than 200 qubits to run on quantum hardware.

Diana Chamaki↗

NASA NTRS2022

Deciphering the Birth Region, Formation, and Evolution of Ambient and Transient Solar Wind Using Heavy Ion Observations

This paper outlines key scientific topics that are important for the development of solar system physics and how observations of heavy ion composition can address them. The key objectives include, 1) understanding the Sun’s chemical composition by identifying specific mechanisms driving elemental variation in the corona. 2) Disentangling the solar wind birthplace and drivers of release by determining the relative contributions of active regions (ARs), quiet Sun, and coronal hole plasma to the solar wind. 3) Determining the principal mechanisms driving solar wind evolution from the Sun by identifying the importance and interplay of reconnection, waves, and/or turbulence in driving the extended acceleration and heating of solar wind and transient plasma. The paper recommends complementary heavy ion measurements that can be traced from the Sun to the heliosphere to properly connect and study these regions to address these topics. The careful determination of heavy ion and elemental composition of several particle populations, matched at the Sun and in the heliosphere, will permit for a comprehensive examination of fractionation processes, wave-particle interactions, coronal heating, and solar wind release and energization that are key to understanding how the Sun forms and influences the heliosphere.

chemical composition↗

NASA NTRSDate not supplied

Designing Experiments for SpinSat, A Novel Variable-Gravity-and-Radiation Platform for Deep-Space Science

Conducting experiments to measure the effects of deep-space radiation and reduced gravity on biological and physical systems remains challenging. The result is a substantial knowledge gap that poses risks to our ability to sustain life and conduct critical operations in deep space. The SpinSat spacecraft platform is designed to bridge such gaps by providing low-cost, reliable, and frequent access to deep space. A disk-shaped rotating satellite that can provide artificial gravity and exposure to space radiation simultaneously, SpinSat is designed to accommodate payloads in a CubeSat form factor (with at least 48 “U” volume), providing power, communications, and a benign thermal environment. It is orbit-agnostic, enabling access to a variety of radiation environments (Van Allen belts, deep space, cis-lunar); and can be equipped with shielding to mimic planetary radiation environments, for both short- and long-term experiments. Because of its versatility and prioritization of late loading for biological payloads, it is well suited to host a wide range of ranging from human tissues and organoids to microorganisms, plants, chemistry, and regolith. Here, we present examples of potential experiment concepts for SpinSat, and discuss the details of how experimental designs could interact with the platform. Potential SpinSat studies have diverse applications, including fundamental radiation biology and DNA repair; cancer biology and countermeasure development; space agriculture; bioproduction of nutrients and pharmaceuticals; understanding regolith dynamics in low gravity; prebiotic chemistry and panspermia. We will further highlight ideas for SpinSat-compatible experimental hardware, existing and in development, and experiment-relevant details on SpinSat capabilities including artificial gravity, potential radiation environments, data, and power. This presentation will aim to provide investigators with the high-level technical information necessary to inspire experiments for SpinSat. We also seek to stimulate conversation and to gain community input on accommodations needs to help guide the evolving design of this platform.

experiment design↗

DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗

DOE OSTI2021

Computing Free Energies with Fluctuation Relations on Quantum Computers

One of the most promising applications for quantum computers is the dynamic simulation of quantum materials. Current hardware, however, sets stringent limitations on how long such simulations can run before decoherence begins to corrupt results. The Jarzynski equality, a fluctuation theorem that allows for the computation of equilibrium free energy differences from an ensemble of short, non-equilibrium dynamics simulations, can make use of such short-time simulations on quantum computers. Here, we present a quantum algorithm based on the Jarzynski equality for computing free energies of quantum materials. We demonstrate our algorithm using the transverse field Ising model on both a quantum simulator and real quantum hardware. As the free energy is a central thermodynamic property that allows one to compute virtually any equilibrium property of a physical system, the ability to perform this algorithm for larger quantum systems in the future has implications for a wide range of applications including the construction of phase diagrams, prediction of transport properties and reaction constants, and computer-aided drug design.

Bassman, Lindsay↗

DOE OSTI2020

Enabling Marine Energy Integration for Ocean Observing: Functional Requirements

Marine renewable energy (i.e., wave, tidal or ocean current, thermal gradient, and salinity gradient) or MRE can revolutionize ocean observing capabilities. Five use cases representing a range of ocean observing platforms were developed based on end-user feedback. Drawing from these use cases, common needs or functional requirements were developed to help guide the integration of MRE power delivery for ocean observation structures and functions. The functional requirements were determined from descriptions of the use cases, specifications of hardware that are used or closely associated with existing ocean observation platforms, and direct feedback from ocean observation sensor/platform developers and users. Twenty types of functional requirements were identified, including those associated with power and energy use; physical system requirements; temperature and depth; electrical, acoustic, and motion interference; and reliability. Each set of functional requirements is illustrated as features of the use cases. By defining functional requirements for MRE technology integration in ocean observing platforms, and focusing on end-user requirements, barriers to developing MRE-driven ocean observation missions can be reduced.

Cavagnaro, Robert J.↗

DOE OSTI2022

m-CUBES An efficient and portable implementation of multi-dimensional integration for gpus

The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation. Multi-dimensional integration is often time-prohibitive on CPUs. Efficient implementation on many-core architectures is challenging as the workload across the integration space cannot be predicted a priori. We propose m-Cubes, a novel implementation of the well-known Vegas algorithm for execution on GPUs. Vegas transforms integration variables followed by calculation of a Monte Carlo integral estimate using adaptive partitioning of the resulting space. m-Cubes improves performance on GPUs by maintaining relatively uniform workload across the processors. As a result, our optimized Cuda implementation for Nvidia GPUs outperforms parallelization approaches proposed in past literature. We further demonstrate the efficiency of m-Cubes by evaluating a six-dimensional integral from a cosmology application, achieving significant speedup and greater precision than the CUBA library's CPU implementation of VEGAS. We also evaluate m-Cubes on a standard integrand test suite. m-Cubes outperforms the serial implementations of the Cuba and GSL libraries by orders of magnitude speedup while maintaining comparable accuracy. Our approach yields a speedup of at least 10 when compared against publicly available Monte Carlo based GPU implementations. In summary, m-Cubes can solve integrals that are prohibitively expensive using standard libraries and custom implementations. A modern C++ interface header-only implementation makes m-Cubes portable, allowing its utilization in complicated pipelines with easy to define stateful integrals. Compatibility with non-Nvidia GPUs is achieved with our initial implementation of m-Cubes using the Kokkos framework.

Sakiotis, Ioannis↗

DOE OSTI2022

CANShield: Signal-based Intrusion Detection for Controller Area Networks

Modern vehicles rely on complex cyber-physical systems made up of hundreds of electronic control units (ECUs) connected through controller area network (CAN) buses. However, the CAN bus attack surface is increasing due to advanced features in automobiles, making it prone to injection attacks. The ordinary injection attacks disrupt the typical timing properties of the CAN data stream, and the rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal level, maintaining the regular pattern/frequency of the CAN messages. Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on binary payload data. To make the vehicles robust against such intelligent attacks, we propose CANShield, a signal-based intrusion detection framework for the CAN bus that consists of three modules. A data preprocessing module handles the high-dimensional CAN data stream at the signal level and make them suitable for any machine learning model. A data analyzer module consists of multiple deep autoencoder networks, each analyzing the time series data from a different perspective. Finally, an attack detection module uses an ensemble method to make the final decision. Evaluation results on a standard signal-based dataset show the effectiveness of the CANShield in detecting five advanced attacks.

Shahriar, Md Hasan↗

DOE OSTI2022

Protecting Smart Buildings with STIG

This is a submission for the 2022 Intern Poster Session. The abstract of the poster is: Smart buildings are getting more common, and so are hackers that target them. The physical systems in our businesses and homes are now vulnerable to cyber attacks. Using STIG, an INL program that turns cybersecurity data into graphs, buildings can be better protected.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Immersive Digital Twins and Decision Making

This work focuses on developing and implementing immersive digital twins to support decision-making processes in construction automation and energy simulation analysis. The use of digital twins enables users to explore and interact with virtual replicas of physical systems, providing a unique and valuable perspective for problem-solving. In the context of construction automation, we present two case studies that integrate immersive visualization and worker-machine interaction. In the context of energy simulation analysis using digital twins, we describe our digital twin framework that combines interactive visualization with machine learning techniques to approximate computationally expensive simulations, allowing for faster and more efficient analysis of complex energy systems. Through these research endeavors, we demonstrate the potential of immersive digital twins in decision-making across various domains.

digital twins↗

DOE OSTI2025

Future Opportunities for LWR Irradiations in US Test Reactors

After several years of relatively low activity in the field of Light Water Reactor (LWR) fuel development, the Department of Energy again began to engage in developing new fuel technologies and irradiation performance data prompted by the Fukushima Daichi nuclear accidents. New competencies for irradiation testing in material test reactors in the United States began to be developed at this time using the Advanced Test Reactor (ATR), High Flux Isotope Reactor (HFIR), Massachusetts Institute of Technology Reactor (MITR), and the Transient Reactor Test Facility (TREAT). Capsules for testing fuel and cladding materials in ATR and HFIR were deployed, a Pressurized Water Reactor (PWR) condition loop for testing fuel rods was established in ATR, cladding corrosion studies were performed using a water loop in MITR, and TREAT pulse testing capabilities were commissioned for fuel rods in water capsules. The more recent and unexpected closure of the Halden Boiling Water Reactor (HBWR) also prompted further investments in Loss of Coolant Accident (LOCA) testing capabilities at TREAT. New configurations of these test devices show further potential in enhanced steam condition control and other investigations are building toward a flowing water loop for testing transient to dryout conditions. The closure of HBWR also prompted a major project currently underway to construct additional water loops in ATR where a novel approach is being pursued to enable Boiling Water Reactor (BWR) conditions. A meaningful collaborative project was awarded to MITR which, amidst an unexpected major overhaul of the reactor, has expanded cladding corrosion test capabilities at MITR. New explorations have led to methods for unique experiments at HFIR including channel box irradiations. New device developments are also bridging toward future potential for instrumented capsule irradiation tests in ATR and HFIR. Finally, a new project referred to as the System Physics Advanced Reactor Critical facility (SPARC) is gaining traction towards a large zero-power reactor able to produce physics validation data for LWR fuel bundle designs with increased enrichment and enhanced absorbers for 24-month operation cycles. This paper provides a brief summary of the status of these irradiation testbed capabilities with an emphasis on current efforts toward future capabilities to obtain new data and maximize the performance potential of LWR fuel technologies.

Woolstenhulme, Nicolas [Idaho National Laboratory ↗

DOE OSTI2025

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗

DOE OSTI2025

Data Centers and Digital Assurance Workshop 2 – Prioritizing Digital Assurance Challenges, Session 2

The second session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 10, 2025, focused on prioritizing digital assurance challenges at the intersection of data centers and the electric grid. Building on the foundational concepts introduced in Workshop 1, this session deepened the application of the Threat–Vulnerability–Consequence (TVC) framework and emphasized the urgency of addressing cybersecurity, supply chain integrity, and operational reliability. Participants explored the growing convergence of digital and physical systems, the expanding attack surface due to global supply chain dependencies, and the implications of AI-driven load behavior. Real-world incidents—including the Volt Typhoon campaign and vulnerabilities in Solarman and Deye platforms—were analyzed to illustrate the risks of unpatched systems, insecure APIs, and inadequate vendor oversight. Key themes included architecture and interface weaknesses, governance gaps, and human and procedural shortcomings. The workshop also examined the evolving regulatory landscape, highlighting new federal mandates around Foreign Entity of Concern (FEOC) compliance and large-load reliability standards. Through interactive exercises, stakeholders ranked and mapped digital assurance risks from their respective perspectives—utilities, operators, and vendors—laying the groundwork for mitigation strategies and shared accountability models to be developed in Workshop 3. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Cyber-Informed Engineering Briefing for ABET

Cyber-Informed Engineering (CIE) is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system, energy or otherwise, to mitigate or even eliminate avenues for cyber-enabled attacks.?CIE concepts use design decisions and engineering controls to prioritize defense against the worst possible consequences of cyberattacks facing critical infrastructure systems and asset owners. These slides offer a deep dive into Cyber-Informed Engineering for engineering educators.

42 - ENGINEERING↗