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DOE OSTI2024

Evaluated Nuclear Data Library, with ENDL2009.5-direct (2009.5 Rev.)

LLNL’s Nuclear Data and Theory Group have created a 2009.5 revised release of the Evaluated Nuclear Data Library (ENDL2009.5). This library is designed to support LLNL’s current and future nuclear data needs and will be employed in nuclear reactor, nuclear security and stockpile stewardship simulations with ASC codes. The ENDL2009 database was the most complete nuclear database for Monte Carlo and deterministic transport of neutrons and charged particles. It was assembled with strong support from the ASC PEM and Attribution programs, leveraged with support from Campaign 4 and the DOE/Office of Science’s US Nuclear Data Program. This document lists the revisions and fixes made in a new release called ENDL2009.5, by comparing with the existing data in the previous releases ENDL2009.3 and ENDL2009.4. In addition to the legacy library ENDL2009.5 from ENDL-format files generated by Fete, an ENDL2009.5-direct library is also released, in which ENDF6-formatted sources are used wherever possible to avoid possible translation errors from Fete.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Semi-Automated Approach for Curating a Glossary of Key Terms for Open-Source Data Queries

In FY20, the Savannah River National Laboratory (SRNL) was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development (NA-22) to build a machine learning based modeling pipeline that could extract proliferation events of interest from open text-based data sources. As a test case, the research team targeted the identification/fusion of events and indicators that fissile core fabrication would be executed at the Savannah River Site prior to its official announcement in May of 2018. The demonstration prototype proved successful by applying natural language processing and graph theoretical techniques to identify contextual shifts in key words and phrases that acted as indicators that pit production would be carried out at the Savannah River Site up to two years prior to the official announcement.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

2011.5 Revision of the Evaluated Nuclear Data Library: ENDL2011.5 (legacy and GNDS), and ENDL2011.5-direct

LLNL’s Nuclear Data and Theory Group has created the 2011.5 revised release of the Evaluated Nuclear Data Library (ENDL2011.5). ENDL2011.5 is designed to support LLNL’s current and future nuclear data needs and will be employed in nuclear reactor, nuclear security and stockpile stewardship simulations with ASC codes. This database is currently the most complete nuclear database for Monte Carlo and deterministic transport of neutrons and charged particles. This library was assembled with strong support from the ASC PEM and Attribution programs, leveraged with support from Campaign 4 and the DOE/Office of Science’s US Nuclear Data Program. This document lists the revisions made in ENDL2011.5 compared with the data existing in the original ENDL2011.2 and ENDL2011.3 releases. These changes are made in parallel with some similar revisions for ENDL2009.5. We also describe ENDL2011.5-direct that directly uses ENDF sources wherever possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Securing against errors in an error correcting code (ECC) implemented in an automotive system

In general, data is susceptible to errors caused by faults in hardware (i.e. permanent faults), such as faults in the functioning of memory and/or communication channels. To detect errors in data caused by hardware faults, the error correcting code (ECC) was introduced, which essentially provides a sort of redundancy to the data that can be used to validate that the data is free from errors caused by hardware faults. In some cases, the ECC can also be used to correct errors in the data caused by hardware faults. However, the ECC itself is also susceptible to errors, including specifically errors caused by faults in the ECC logic. A method, computer readable medium, and system are thus provided for securing against errors in an ECC.

Saxena, Nirmal Raj↗
DOE OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2022

The Dual-Axis Radiographic Hydrodynamic Test Facility Capability Expansion (DCX) Strategy

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility is a vital and important part of the Nation’s nuclear security enterprise. With more than two decades of operations, DARHT has a storied history. Conceived in the 1970s, constructed in the 1990s, and operational since 2000, DARHT has advanced from open-air hydrodynamic experiments (hydros) to foam-confined hydros, to vessel-confined hydros, and in 2022, the 75th hydro was successfully completed. Radiography has advanced from a single-axis, single-pulse system to a dual-axis, multi-pulse capability to variable fields of view (VFV) on both accelerators. The culmination of these experiences, accomplishments, and advancements has brought us to a very important question: What do the next two decades at DARHT look like? The world is not the same place it was in the 1990s when construction at DARHT was in progress. Evolving threats, an expanding mission, and technology changes necessitate adaptation. To adapt, the aging facility, accelerators, vessels, and detector systems require improvements to ensure DARHT remains the Nation’s hydrodynamic data foundation for stockpile certification, safety, surety, and global security threats.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2013

The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts

We describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z approx. 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude MB = −20 at z approx. 1 via approx.90 nights of observation on the Keck telescope. The survey covers an area of 2.8 Sq. deg divided into four separate fields observed to a limiting apparent magnitude of R(sub AB) = 24.1. Objects with z approx. < 0.7 are readily identifiable using BRI photometry and rejected in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted approx. 2.5 times more efficiently than in a purely magnitude-limited sample. Approximately 60% of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets that fail to yield secure redshifts are blue objects that lie beyond z approx. 1.45, where the [O ii] 3727 Ang. doublet lies in the infrared. The DEIMOS 1200 line mm(exp −1) grating used for the survey delivers high spectral resolution (R approx. 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate observations. Sky subtraction is essentially photon-limited even under bright OH sky lines; we describe the strategies that permitted this, based on high image stability, accurate wavelength solutions, and powerful B-spline modeling methods. We also investigate the impact of targets that appear to be single objects in ground-based targeting imaging but prove to be composite in Hubble Space Telescope data; they constitute several percent of targets at z approx. 1, approaching approx. 5%-10% at z > 1.5. Summary data are given that demonstrate the superiority of DEEP2 over other deep high-precision redshift surveys at z approx. 1 in terms of redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far.

Galaxy↗
DOE OSTI2021

Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Grid Technology as a Cyberinfrastructure for Delivering High-End Services to the Earth and Space Science Community

Grid technology consists of middleware that permits distributed computations, data and sensors to be seamlessly integrated into a secure, single-sign-on processing environment. In &is environment, a user has to identify and authenticate himself once to the grid middleware, and then can utilize any of the distributed resources to which he has been,panted access. Grid technology allows resources that exist in enterprises that are under different administrative control to be securely integrated into a single processing environment The grid community has adopted commercial web services technology as a means for implementing persistent, re-usable grid services that sit on top of the basic distributed processing environment that grids provide. These grid services can then form building blocks for even more complex grid services. Each grid service is characterized using the Web Service Description Language, which provides a description of the interface and how other applications can access it. The emerging Semantic grid work seeks to associates sufficient semantic information with each grid service such that applications wii1 he able to automatically select, compose and if necessary substitute available equivalent services in order to assemble collections of services that are most appropriate for a particular application. Grid technology has been used to provide limited support to various Earth and space science applications. Looking to the future, this emerging grid service technology can provide a cyberinfrastructures for both the Earth and space science communities. Groups within these communities could transform those applications that have community-wide applicability into persistent grid services that are made widely available to their respective communities. In concert with grid-enabled data archives, users could easily create complex workflows that extract desired data from one or more archives and process it though an appropriate set of widely distributed grid services discovered using semantic grid technology. As required, high-end computational resources could be drawn from available grid resource pools. Using grid technology, this confluence of data, services and computational resources could easily be harnessed to transform data from many different sources into a desired product that is delivered to a user's workstation or to a web portal though which it could be accessed by its intended audience.

Hinke, Thomas H.↗
DOE OSTI2022

The Dual-Axis Radiographic Hydrodynamic Test Facility Capability eXpansion (DCX) Strategy

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility is a vital and important part of the Nation’s nuclear security enterprise. The Department of Energy/National Nuclear Security Administration (DOE/ NNSA) Stockpile Stewardship Management Plan (SSMP) identifies DARHT as a weapons mission critical facility along with the need to modernize DARHT to support weapons modernization efforts. With more than two decades of operations, DARHT has a storied history. Conceived in the 1970s, constructed in the 1990s, and operational since 2000, DARHT has advanced from open-air hydrodynamic experiments (hydros) to foam-confined hydros, to vessel-confined hydros, and in 2022, the 75 th hydro was successfully completed. Radiography has advanced from a single-axis, single-pulse system to a dual-axis, multi-pulse capability to variable fields of view (VFV) on both accelerators. The culmination of these experiences, accomplishments, and advancements has brought us to a very important question: What do the next two decades at DARHT look like? The world is not the same place it was in the 1990s when construction at DARHT was in progress. Evolving threats, an expanding mission, and technology changes necessitate adaptation. The 2018 Nuclear Posture Review (NPR) states that the nuclear weapons infrastructure has suffered the effects of age and underfunding with no margin for further delay in recapitalizing the physical infrastructure. To adapt, the aging facility, accelerators, vessels, and detector systems require improvements to ensure DARHT remains the Nation’s hydrodynamic data foundation for stockpile certification, safety, surety, and global security threats.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Artificial Intelligence for Enhancing Multiscale Analysis: Buildings Focus

This project aims to develop multi-scale building energy data, potentially improving the representation of the U.S. buildings sector in GCAM-USA, an U.S.-focused human-energy-Earth systems model. Existing building energy datasets are typically limited to national or regional levels, which constrains the ability of models to capture fine-scale human-energy-Earth systems interactions and reduces their relevance for decision-making on issues such as energy security, resilience, and energy planning. By leveraging AI and advanced data integration methods, this work fuses multiple existing datasets to enhance the physical and geographic representation of both residential and commercial building energy use. So far, progress includes processing residential building data, designing the data structure for commercial buildings, and testing AI approaches for integrating datasets and addressing spatial-temporal gaps. This effort can not only advances GCAM-USA’s capability in modeling the buildings sector but also supports broader DOE missions, such as developing digital testbeds, enhancing grid resilience analysis, and improving building–energy system modeling at decision-relevant scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An Automated, Disruption-Tolerant Device Authentication and Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. In this paper a novel Kerberos based framework for automated, disruption-tolerant key management for control system environments is presented. Experimental tests and their results are presented to quantify expected performance overhead of this approach. In addition, Zeek sensor analytics are presented to monitor the health and security of the key management framework operation.

Edgar, Thomas W.↗
NASA NTRS2011

HEASARC - The High Energy Astrophysics Science Archive Research Center

The High Energy Astrophysics Science Archive Research Center (HEASARC) is NASA's archive for high-energy astrophysics and cosmic microwave background (CMB) data, supporting the broad science goals of NASA's Physics of the Cosmos theme. It provides vital scientific infrastructure to the community by standardizing science data formats and analysis programs, providing open access to NASA resources, and implementing powerful archive interfaces. Over the next five years the HEASARC will ingest observations from up to 12 operating missions, while serving data from these and over 30 archival missions to the community. The HEASARC archive presently contains over 37 TB of data, and will contain over 60 TB by the end of 2014. The HEASARC continues to secure major cost savings for NASA missions, providing a reusable mission-independent framework for reducing, analyzing, and archiving data. This approach was recognized in the NRC Portals to the Universe report (2007) as one of the HEASARC's great strengths. This poster describes the past and current activities of the HEASARC and our anticipated developments in coming years. These include preparations to support upcoming high energy missions (NuSTAR, Astro-H, GEMS) and ground-based and sub-orbital CMB experiments, as well as continued support of missions currently operating (Chandra, Fermi, RXTE, Suzaku, Swift, XMM-Newton and INTEGRAL). In 2012 the HEASARC (which now includes LAMBDA) will support the final nine-year WMAP data release. The HEASARC is also upgrading its archive querying and retrieval software with the new Xamin system in early release - and building on opportunities afforded by the growth of the Virtual Observatory and recent developments in virtual environments and cloud computing.

Smale, Alan P.↗
DOE OSTI2017

Pyroprocessing Monitoring Technology Development at Idaho National Laboratory

The Idaho National Laboratory (INL), with support from the U.S. Department of Energy, Office of Nuclear Energy, Nuclear Technology Research and Development program, has been developing advanced instrumentation and approaches for enhancing the effectiveness of nuclear material control and accounting for future reprocessing facilities with particular emphasis on pyroprocessing. Advanced modeling capabilities augment new instrumentation development, predict material flows, integrate process monitoring data, and assess overall system effectiveness of the safeguards and security system. In this paper we provide a brief overview of the Safeguards and Security by Design approach being pursued under the program and details of advanced process monitoring instrumentation under development at the INL, which includes a triple bubbler system for on-line monitoring of level and density of molten salt, sensors for monitoring actinide concentrations in molten salt, and thermal-couple instrumentation for near real time determination of Pu content in U/TRU ingots. Laboratory and field testing data will be presented, as well as integration of these sensors into a concept for utilization of process monitoring information in concert with traditional nuclear material accountancy data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Data-Driven Disease Forecasting

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

An Automated Disruption-Tolerant Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. This paper presents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Here, experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗