Results for “secure data” — Page 92

DOE OSTI2025

Quantum adversarial learning for kernel methods

We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defense strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Sampling-based Uncertainty Quantification in Deconvolution of X-ray Radiographs

In imaging applications that focus on quantitative analysis{such as X-ray radiography in the security sciences--it is necessary to be able to reliably estimate the uncertainties in the processing algorithms applied to the image data, and deconvolving the system blur out of the image is usually an essential step. In this work we solve the deconvolution problem within a Bayesian framework for edge-enhancing reconstruction with uncertainty quantification. The likelihood is a normal approximation to the Poisson likelihood, and the prior is generated from a classical total variation regularized Poisson deconvolution. Samples from the corresponding posterior distribution are computed using a Markov chain Monte Carlo approach, giving a pointwise measure of uncertainty in the final, deconvolved signal. We demonstrate the results on real data used to calibrate a high-energy X-ray source and show that this approach gives reconstructions as good as classical regularization methods, while mitigating many of their drawbacks.

Deconvolution, Inverse Problems, Markov Chain Mont↗

DOE OSTI2018

Measuring Distribution Performance? Benchmarking Warrants Your Attention

Identifying, designing, and measuring performance metrics is critical to securing customer value, but can be a difficult task. This article examines the use of benchmarks based on publicly available performance data to set challenging, yet fair, metrics and targets.

2017 customer value ranking↗

DOE OSTI2024

Measuring the robustness of predictive probability for early stopping in two-group comparisons

We report physical experiments are often expensive and time-consuming. Test engineers must certify the compatibility of aircraft and their weapon systems before they can be deployed in the field, but the testing required is time consuming, expensive, and resource limited. Adopting Bayesian adaptive designs is a promising way to borrow from the successes seen in the clinical trials domain. The use of predictive probability (PP) to stop testing early and make faster decisions is particularly appealing given the aforementioned constraints. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. Although PP has been thoroughly studied for binary data, there is less work with continuous data, where many reliability studies are interested in certifying the specification limits of components. A simulation study evaluating the robustness of this approach indicates early stopping based on PP is reasonably robust to minor assumption violations, especially when only a few interim analyses are conducted. The simulation study also compares PP to conditional power, showing its relative strengths and weaknesses. A post-hoc analysis exploring whether release requirements of a weapon system from an aircraft are within specification with desired reliability resulted in stopping the experiment early and saving 33% of the experimental runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A Survey on the Expanding Scope and Interdisciplinary Opportunities for Processing-in-Memory Techniques

Processing-in-Memory (PIM) is emerging as a practical path to overcome the limitations of traditional von Neumann architectures. At its core, PIM systems implement computing primitives such as logic operations and multiply-accumulate acceleration through compute-in-memory, near-memory processing, or hybrid designs. The role of memory cells varies widely across technologies, acting as inputs, outputs, or analog accumulators through bit-lines and sense amplifiers. This diversity creates trade-offs in precision, bandwidth, latency, and programmability, making it difficult to build a unified understanding on the progress of the field. In this survey, we organize recent advances of PIM into three areas. First, we discuss the progress on the architectural optimizations of PIM and its integration with both DRAM and emerging non-volatile memories. Second, we examine how PIM is being used to accelerate key computing domains, including generative AI workloads and high-performance kernels, along with new approaches. Third, we highlight the growing adoption of PIM in computational sciences, where it is being applied to solve interdisciplinary problems such as genome analysis, mRNA quantification, mass spectrometry, quantum circuit simulation, wave modeling, and secure computation. Finally, we synthesize the major challenges that continue to slow PIM adoption, including manufacturing constraints, power delivery, thermal reliability, data consistency, runtime and memory-management coordination, and the difficulty of building portable software abstractions without sacrificing commercial viability. This work provides an updated, structured perspective on PIM’s potential across computing and computational sciences and the barriers that must be solved for it to reach its full impact.

Asifuzzaman, Kazi [Oak Ridge National Laboratory (↗

DOE OSTI2020

The GABLE Report: Garbled Autonomous Bots Leveraging Ethereum

Simple but mission-critical internet-based applications that require extremely high reliability and availability could potentially benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is ordinarily publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. However, might it be possible to encode an application's business logic and data for these platforms in such a way that it becomes impossible for unauthorized parties to infer any meaningful information whatsoever about the semantics of the data, and the operations being performed on that data? In this report, we describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a system concept developed at Sandia that achieves this security goal in a limited, but still useful range of circumstances. GABLE, uses simple but effective algorithms to permit secure private execution of garbled state machines (and more efficient garbled circuits) on public computing resources. We give an example working implementation for garbled state machines, written using the Python and Solidity programming languages, and outline how our methods can be extended to support a more powerful garbled universal circuit model of computation. The capability embodied by the GABLE, system has significant potential applications, a few of which we discuss in this report.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

FY22 Grid Modernization & Energy Storage Program: Accomplishments & Impacts

Sandia’s Grid Modernization and Energy Storage program works to advance a national vision of a secure, resilient, and sustainable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY22 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Much of the material in this report comes from the separate 2022 Accomplishments Report compiled by our Energy Storage subprogram team, a cornerstone of our grid research and achievements. The Grid Energy Storage Program at Sandia is focused on making energy storage cost-effective through research and development (R&D) in new battery technologies, advanced power electronics and power conversion systems, improved safety and reliability for energy storage systems, analytical tools for the valuation of energy storage, and the validation of new energy storage technologies through demonstration projects. During the 2022 fiscal year, Sandia executed R&D work supported by the U.S. Department of Energy’s (DOE) Office of Electricity – Energy Storage Program under the leadership of Dr. Imre Gyuk. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by the DOE Office of Electricity and key programs within the DOE Office of Energy Efficiency and Renewable Energy. As we indicated in our report from last year, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Electric Grid Security (EGS) FY24 Annual Report

Sandia’s Electric Grid Security program advances a national vision of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY24 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • The advancement of energy storage technologies, including creation of a national Long Duration Energy Storage Consortium; • Applications of artificial intelligence and machine learning to enhanced grid operations and planning; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 4

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024).

36 MATERIALS SCIENCE↗

DOE OSTI2016

ON THE RR LYRAE STARS IN GLOBULARS. IV. ω CENTAURI OPTICAL UBVRI PHOTOMETRY

New accurate and homogeneous optical UBVRI photometry has been obtained for variable stars in the Galactic globular cluster ω Cen (NGC 5139). We secured 8202 CCD images covering a time interval of 24 years and a sky area of 84 × 48 arcmin. The current data were complemented with data available in the literature and provided new, homogeneous pulsation parameters (mean magnitudes, luminosity amplitudes, periods) for 187 candidate ω Cen RR Lyrae (RRLs). Among them we have 101 RRc (first overtone) and 85 RRab (fundamental) variables, and a single candidate RRd (double-mode) variable. Candidate Blazhko RRLs show periods and colors that are intermediate between the RRc and RRab variables, suggesting that they are transitional objects. A comparison of the period distribution and the Bailey diagram indicates that RRLs in ω Cen show a long-period tail not present in typical Oosterhoff II (OoII) globulars. The RRLs in dwarf spheroidals and in ultra-faint dwarfs have properties between Oosterhoff intermediate and OoII clusters. Metallicity plays a key role in shaping the above evidence. These findings do not support the hypothesis that ω Cen is the core remnant of a spoiled dwarf galaxy. Using optical period–Wesenheit relations that are reddening-free and minimally dependent on metallicity we find a mean distance to ω Cen of 13.71 ± 0.08 ± 0.01 mag (semi-empirical and theoretical calibrations). Finally, we invert the I -band period–luminosity–metallicity relation to estimate individual RRLs’ metal abundances. The metallicity distribution agrees quite well with spectroscopic and photometric metallicity estimates available in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Advanced-Research-on-Integrated-Energy-Systems-Based Analysis to Support Resilient System Upgrades: Energy to Communities Energyshed In-Depth Partnership with Molokai, Hawaii

The Molokai, Hawaii, Energy to Communities (E2C) Energyshed project represents a collaborative effort between the National Laboratory of the Rockies, Shake Energy Collaborative, the Molokai Clean Energy Hui, Sustainable Molokai, and Ho'ahu Energy Cooperative Molokai to advance Molokai's Community Energy Resilience Action Plan (CERAP). Supported by Hawaiian Electric Company and the Hawaii State Energy Office, the initiative aims to develop a community-defined portfolio of renewable energy solutions that enhance energy resilience while aligning with the Hawaiian Electric Integrated Grid Plan (IGP) and Molokai's energy goals. Phase 1 focused on technical analyses and community engagement to co-design feasible energy scenarios. Challenges such as grid upgrades, storage sizing, and inverter ride-through standards were addressed to align technical and operational requirements with community preferences. The project equips Molokai with actionable data and insights to implement energy initiatives while ensuring resilient and culturally informed solutions. Future efforts aim to finalize project designs, secure interconnection agreements, and deploy energy projects that reflect community priorities and technical feasibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1977

Secure communications system

System employs electronically randomized variant of quadraphase modulation and demodulation between two synchronized transceivers. System uses off-the-shelf components. It may be used with digital data, command signals, delta-modulated voice signals, digital television signals, or other data converted to digital form.

Doland, G. D.↗

NASA NTRS2001

Latency Hiding in Dynamic Partitioning and Load Balancing of Grid Computing Applications

The Information Power Grid (IPG) concept developed by NASA is aimed to provide a metacomputing platform for large-scale distributed computations, by hiding the intricacies of highly heterogeneous environment and yet maintaining adequate security. In this paper, we propose a latency-tolerant partitioning scheme that dynamically balances processor workloads on the.IPG, and minimizes data movement and runtime communication. By simulating an unsteady adaptive mesh application on a wide area network, we study the performance of our load balancer under the Globus environment. The number of IPG nodes, the number of processors per node, and the interconnected speeds are parameterized to derive conditions under which the IPG would be suitable for parallel distributed processing of such applications. Experimental results demonstrate that effective solution are achieved when the IPG nodes are connected by a high-speed asynchronous interconnection network.

Das, Sajal K.↗

NASA NTRS2016

Incorporating Yearly Derived Winter Wheat Maps Into Winter Wheat Yield Forecasting Model

Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.

MODIS↗

DOE OSTI2022

2022 GETEM Geothermal Drilling Cost Curve Update: Preprint

The Geothermal Electricity Technology Evaluation Model (GETEM) is an essential tool for the Department of Energy's (DOE) Geothermal Technology Office (GTO) to understand the performance and cost of technologies it is seeking to improve. This detailed model is used for supply curve analyses, assessing the current economic feasibility and Levelized Cost of Energy (LCOE) of hydrothermal geothermal systems and EGS, and evaluating the potential impact of advanced geothermal technologies. GETEM can be used to estimate the performance and costs of currently available U.S. geothermal power systems. It is also used to estimate the costs of technologies 5 to 20 years in the future, given the direction of potential research, development, and demonstration (RD&D) projects. The model is intended to help GTO determine which proposed RD&D programs and projects might offer the most efficient improvement when using taxpayer funding. The model requires annual updates as well as revisions to reflect the current state of the art. Drilling costs are a significant portion of total geothermal development costs. The current GETEM drilling cost inputs rely on drilling data from 2009 and require an updated analysis of more recent data to ensure they remain representative of current technologies. An updated, more accurate understanding of costs could help the geothermal industry secure project development financing and investment funding and better allow the oil and gas (O&G) industry (both operators and service companies) weigh potential geothermal market participation and customization. This report details recent drilling improvements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) and O&G, comparing drilling performance and costs with values in GETEM, particularly the baseline drilling cost curves. Though drilling performance at FORGE has improved significantly, we did not find associated cost decreases that would justify lowering the GETEM baseline cost curves as of now.

API↗