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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2011

NID Copper Sample Analysis

The current focal point of the nuclear physics program at PNNL is the MAJORANA DEMONSTRATOR, and the follow-on Tonne-Scale experiment, a large array of ultra-low background high-purity germanium detectors, enriched in 76Ge, designed to search for zero-neutrino double-beta decay (0νββ). This experiment requires the use of germanium isotopically enriched in 76Ge. The MAJORANA DEMONSTRATOR is a DOE and NSF funded project with a major science impact. The DEMONSTRATOR will utilize 76Ge from Russia, but for the Tonne-Scale experiment it is hoped that an alternate technology, possibly one under development at Nonlinear Ion Dynamics (NID), will be a viable, US-based, lower-cost source of separated material. Samples of separated material from NID require analysis to determine the isotopic distribution and impurities. DOE is funding NID through an SBIR grant for development of their separation technology for application to the Tonne-Scale experiment. The Environmental Molecular Sciences facility (EMSL), a DOE user facility at PNNL, has the required mass spectroscopy instruments for making isotopic measurements that are essential to the quality assurance for the MAJORANA DEMONSTRATOR and for the development of the future separation technology required for the Tonne-Scale experiment. A sample of isotopically separated copper was provided by NID to PNNL in January 2011 for isotopic analysis as a test of the NID technology. The results of that analysis are reported here. A second sample of isotopically separated copper was provided by NID to PNNL in August 2011 for isotopic analysis as a test of the NID technology. The results of that analysis are also reported here.

copper isotopics↗

DOE OSTI2014

HC-NIDS: signatures and simulations for detecting cyber-attacks aiming to cause damage against cyber-physical energy systems

Physical device safety is typically implemented locally using embedded controllers, while operations safety is primarily performed in control centers. Safe operations can be enhanced by correct design of device-level control algorithms, and protocols, procedures and operator training at the control-room level, but all can fail. Moreover, these elements exchange data and issue commands via vulnerable communication layers. In order to secure these gaps and enhance operational safety, we believe monitoring of command sequences must be combined with an awareness of physical device limitations and automata models that capture safety mechanisms. One way of doing this is by leveraging specification-based intrusion detection to monitor for physical constraint violations. The method can also verify that physical infrastructure state is consistent with monitoring information and control commands exchanged between field devices and control centers. This additional security layer enhances protection from both outsider attacks and insider mistakes. We implemented specification-based SCADA command analyzers using physical constraint algorithms directly in the Bro framework and Broccoli APIs for three separate scenarios: a water heater, an automated distribution system, and an over-current protection scheme. To accomplish this, we added low-level analyzers capable of examining control system-specific protocol packets for both Modbus TCP and DNP3, and also higher-level analyzers able to interpret device command and data streams within the context of each device's physical capabilities and present operational state. Thus the software that we are making available includes the Bro/Broccoli scripts for these three scenarios, as well as simulators, written in C, of those scenarios that generate sample traffic that is monitored by the Bro/Broccoli scripts. In addition, we have also implemented systems to directly pull cyber-physical information from the OSIsoft PI historian system. We have included the Python scripts used to perform that monitoring.

Source record↗

DOE OSTI2013

Cyber-Security and Networking, NIDS Front-End for Load Balancing at 100 Gbps

Explore the source record for details and available documents.

100Gbps↗

DOE OSTI2014

HC-NIDS: signatures and simulations for detecting cyber-attacks aiming to cause damage against cyber-physical energy systems

Source record↗

DOE OSTI2019

Negative Ion Drift Time Projection Chamber Development for High-Resolution Tracking

Gas Time Projection Chambers (TPCs) are used extensively in High Energy Physics. Modern micro-pattern gaseous detectors (MPGDs) have made it feasible to construct large-volume TPCs with high readout granularity, so that ionization can be imaged with spatial resolution of order 100 μm or better. A challenge with large TPCs is preserving the track geometry over large drift distances. The reduced diffusion offered by negative ion drift (NID) has long been considered a possible means to scaling up TPCs at reduced cost, but NID has up to now required toxic and often flammable multi-component gas mixtures. A number of recent breakthroughs in the field have, however, led to a resurgence of interest in NID gas TPCs: First, it was found that multiple charge carriers in NID can be utilized to measure the absolute position of primary ionization in the drift direction, yielding a powerful new handle on backgrounds, at zero additional cost. Second, work by one of the PIs has revealed SF6 gas to be a non-toxic, non-flammable, NID gas with suitable minority charge carriers present. Third, preliminary work by the PIs suggests that multiple MPGD technologies work well with SF6. This opens the door to a new generation of large, high-resolution, low-background, low-cost gas TPCs. In this work we will investigate MPGD charge multiplication and charge readout in NID TPCs with SF6 gas. The objectives are to quantify the improvement over electron drift TPCs, and to identify the optimal MPGD types, operational parameters, electronics, and vacuum technologies for future low-background experiments. We will measure low-level detector performance parameters such as gain, gain resolution, and spatial resolution, as well as the resulting higher-level performance parameters such nuclear recoil identification efficiency, gamma-ray background rejection, track angle resolution and recoil energy resolution. The proposed work also includes investigation of acrylic vacuum vessels and collaboration on dedicated charge readout chips, both required for next-generation NID MPGD TPCs. MGPD NID TPCs may prove transformative for applications which require both high readout granularity and large detector volumes, such as the reconstruction of nuclear recoils in the context of large-scale directional dark matter searches, measurements of coherent neutrino-nucleus scattering, and directional neutron detection. While the proposed work is focused on nuclear recoils, the lessons learned will be widely applicable to gas TPCs in HEP, and hence have broad impact.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Experimental Validation of a Command and Control Traffic Detection Model

Network intrusion detection systems (NIDS) are commonly used to detect malware communications, including command-and-control (C2) traffic from botnets. NIDS performance assessments have been studied for decades, but mathematical modeling has rarely been used to explore NIDS performance. This paper details a mathematical model that describes a NIDS performing packet inspection and its detection of malware's C2 traffic. Here, the paper further describes an emulation testbed and a set of cyber experiments that used the testbed to validate the model. These experiments included a commonly used NIDS (Snort) and traffic with contents from a pervasive malware (Emotet). Results are presented for two scenarios: a nominal scenario and a “stressed” scenario in which the NIDS cannot process all incoming packets. Model and experiment results match well, with model estimates mostly falling within 95 % confidence intervals on the experiment means. Model results were produced 70-3000 times faster than the experimental results. Consequently, the model's predictive capability could potentially be used to support decisions about NIDS configuration and effectiveness that require high confidence results, quantification of uncertainty, and exploration of large parameter spaces. Furthermore, the experiments provide an example for how emulation testbeds can be used to validate cyber models that include stochastic variability.

mathematical model↗

DOE OSTI2015

Anisotropic CMB distortions from non-Gaussian isocurvature perturbations

We calculate the CMB μ-distortion, (μ), and the angular power spectrum of its cross-correlation with the temperature anisotropy, (μT), in the presence of the non-Gaussian neutrino isocurvature density (NID) mode. While the pure Gaussian NID perturbations give merely subdominant contributions to (μ) and do not create (μT), we show large (μT) can be realized in case where, especially, the NID perturbations S(x) are proportional to the square of a Gaussian field g(x), i.e. S(x)∝ g{sup 2}(x). Such Gaussian-squared perturbations contribute to not only the power spectrum, but also the bispectrum of CMB anisotropies. The constraints from the power spectrum is given by P{sub SS}(k{sub 0})∼P{sub g}{sup 2}(k{sub 0})∼<10{sup −10} at k{sub 0}=0.05 Mpc{sup −1}. We also forecast constraints from the CMB temperature and E-mode polarisation bispectra, and show that P{sub g}(k{sub 0})∼<10{sup −5} would be allowed from the Planck data. We find that (μ) and |l(l+1)C{sup μT}{sub l}| can respectively be as large as 10{sup −9} and 10{sup −14} with uncorrelated scale-invariant NID perturbations for P{sub g}(k{sub 0})=10{sup −5}. When the spectrum of the Gaussian field is blue-tilted (with spectral index n{sub g}≅1.5), (μT) can be enhanced by an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗

DOE OSTI2020

A Novel Architecture for Attack-Resilient Wide-Area Protection and Control System in Smart Grid

Wide-area protection and control (WAPAC) systems are widely applied in the energy management system (EMS) that rely on a wide-area communication network to maintain system stability, security, and reliability. As technology and grid infrastructure evolve to develop more advanced WAPAC applications, however, so do the attack surfaces in the grid infrastructure. This paper presents an attack-resilient system (ARS) for the WAPAC cybersecurity by seamlessly integrating the network intrusion detection system (NIDS) with intrusion mitigation and prevention system (IMPS). In particular, the proposed NIDS utilizes signature and behavior-based rules to detect attack reconnaissance, communication failure, and data integrity attacks. Further, the proposed IMPS applies state transition-based mitigation and prevention strategies to quickly restore the normal grid operation after cyberattacks. As a proof of concept, we validate the proposed generic architecture of ARS by performing experimental case study for wide-area protection scheme (WAPS), one of the critical WAPAC applications, and evaluate the proposed NIDS and IMPS components of ARS in a cyber-physical testbed environment. Our experimental results reveal a promising performance in detecting and mitigating different classes of cyberattacks while supporting an alert visualization dashboard to provide an accurate situational awareness in real-time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams

This article outlines a semi-autonomous approach for using a fusion of light detection and ranging (LiDAR) and optical remote sensing data to identify and measure small impoundments (SIs) and their dams. Quantifying such water bodies as hydrologic network features is critical for ecosystem and species conservation, emergency management, and water resource planning; however, such features are incompletely mapped at national and state levels. By merging an airborne LiDAR-derived point cloud with a normalized water index using airborne optical imagery we demonstrate an improvement upon single-source methods for identifying these water bodies; classification accuracies increased over 10% by using this multi-source fusion method. Furthermore, the method presented here illustrates a cost-effective pathway to improve the National Inventory of Dams (NID) and includes a framework for estimating dam heights, with results showing strong correlations between derived dam heights and those recorded in the NID (r=.78). With the steady increase in available LiDAR coverage, the 87,000+ dams in the NID could be updated using this technique, a method which could also be expanded for global inventories of SIs and dams.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Access to residual carrier concentration in ZnO nanowires by calibrated scanning spreading resistance microscopy

Scanning spreading resistance microscopy (SSRM) was performed on non-intentionally doped (nid) ZnO nanowires (NWs) grown by metal-organic chemical vapor deposition in order to measure their residual carrier concentration. For this purpose, an SSRM calibration profile has been developed on homoepitaxial ZnO:Ga multilayer staircase structures grown by molecular beam epitaxy. The Ga density measured by SIMS varies in the 1.7 × 10{sup 17 }cm{sup −3} to 3 × 10{sup 20 }cm{sup −3} range. From measurements on such Ga doped multi-layers, a monotonic decrease in SSRM resistance with increasing Ga density was established, indicating SSRM being a well-adapted technique for two dimensional dopant/carrier profiling on ZnO at nanoscale. Finally, relevant SSRM signal contrasts were detected on nid ZnO NWs, and the residual carrier concentration is estimated in the 1–3 × 10{sup 18 }cm{sup −3} range, in agreement with the result from four-probe measurements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), v4

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2025) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2025) – Power plants that are listed in the 2025 U.S. Hydropower Development Pipeline Data or were listed in previous versions of the dataset These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) – EPA SuRGE sampling locations Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗

DOE OSTI2013

Efficient Aho-Corasick String Matching on Emerging Multicore Architectures

String matching algorithms are critical to several scientific fields. Beside text processing and databases, emerging applications such as DNA protein sequence analysis, data mining, information security software, antivirus, ma- chine learning, all exploit string matching algorithms [3]. All these applica- tions usually process large quantity of textual data, require high performance and/or predictable execution times. Among all the string matching algorithms, one of the most studied, especially for text processing and security applica- tions, is the Aho-Corasick algorithm. 1 2 Book title goes here Aho-Corasick is an exact, multi-pattern string matching algorithm which performs the search in a time linearly proportional to the length of the input text independently from pattern set size. However, depending on the imple- mentation, when the number of patterns increase, the memory occupation may raise drastically. In turn, this can lead to significant variability in the performance, due to the memory access times and the caching effects. This is a significant concern for many mission critical applications and modern high performance architectures. For example, security applications such as Network Intrusion Detection Systems (NIDS), must be able to scan network traffic against very large dictionaries in real time. Modern Ethernet links reach up to 10 Gbps, and malicious threats are already well over 1 million, and expo- nentially growing [28]. When performing the search, a NIDS should not slow down the network, or let network packets pass unchecked. Nevertheless, on the current state-of-the-art cache based processors, there may be a large per- formance variability when dealing with big dictionaries and inputs that have different frequencies of matching patterns. In particular, when few patterns are matched and they are all in the cache, the procedure is fast. Instead, when they are not in the cache, often because many patterns are matched and the caches are continuously thrashed, they should be retrieved from the system memory and the procedure is slowed down by the increased latency. Efficient implementations of string matching algorithms have been the fo- cus of several works, targeting Field Programmable Gate Arrays [4, 25, 15, 5], highly multi-threaded solutions like the Cray XMT [34], multicore proces- sors [19] or heterogeneous processors like the Cell Broadband Engine [35, 22]. Recently, several researchers have also started to investigate the use Graphic Processing Units (GPUs) for string matching algorithms in security applica- tions [20, 10, 32, 33]. Most of these approaches mainly focus on reaching high peak performance, or try to optimize the memory occupation, rather than looking at performance stability. However, hardware solutions supports only small dictionary sizes due to lack of memory and are difficult to customize, while platforms such as the Cell/B.E. are very complex to program.

Tumeo, Antonino↗

DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗

DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗

DOE OSTI2014

Electrical modeling of InAs/GaSb superlattice mid-wavelength infrared pin photodiode to analyze experimental dark current characteristics

Dark current characteristics of 7 Monolayers (ML) InAs/ 4 ML GaSb SL pin photodiodes are simulated using ATLAS software. Using appropriate models and material parameters, we obtain good agreement between the simulated and the experimental dark current curves of photodiodes grown by molecular beam epitaxy. The n-type non-intentionally-doped (nid) SL samples exhibit a dependence of the lifetime with temperature following the T{sup -1/2} law, signature of Shockley-Read-Hall (SRH) Generation-Recombination current. We also studied the dependence of the dark current with the absorber doping level. It appears that the absorber doping level must not exceed a value of 2×10¹⁵cm⁻³, above this value the dark current is increasing with increased doping level. However for this doping value, a dark current as low as 5 × 10⁻⁹ A/cm², at 50 mV reverse bias at 77 K can be obtained.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

VotE-Dams: a compilation of global dams' locations and attributes (v1)

This dataset represents a compilation of two global and three USA-specific datasets of dam locations and their attributes. The major hurdle toward developing this compilation was the identification of duplicates within the source datasets, especially given the variable precision of dam location coordinates. The most immediately-useful product in this dataset is a spreadsheet (VotE-Dams_v1.csv) that documents the unique dams found across the datasets, their coordinates, and their ids within the respective source datasets. We do not reproduce the source datasets (GRaND, GOODD, GeoDAR, NID, and EHA) here, but their download locations are provided in the README files ('Overview' tab). Some of the source datasets are provided as shapefiles, which require geospatial data software to open (e.g. QGIS/ArcGIS for graphical display, geopandas for Python, rgdal for R, many others freely available). The provided README documents metadata of the source datasets and provides attribute-linking information (i.e. matches attributes among various source datasets that contain the same, or similar, information but have different names). Note that the README is provided as both .xslx and a collection of .csvs (one per tab in the .xslx file). We suggest using the .xlsx version that preserves images, formatting, and sheets. .xlsx files can be viewed using (free) Google Docs or Microsoft Excel.Finally, we provide Technical Documentation.pdf that describes the procedures used to identify unique and duplicate dams.The title of this dataset refers to our 'Veins of the Earth' (VotE) project, which seeks to provide a flexible, scale-free representation of the Earth's river networks. Dams are a critical component of VotE as they heavily influence flows throughout river networks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Negative Ion Drift TPC Development for High-Resolution Tracking --- Final Technical Report

We have measured the avalanche gain and gain resolution in prototype negative ion drift gas time projection chambers (NID TPCs). Measurements utilizing the fill gas SF6 and gain stages consisting of thick and thin gas electron multipliers (GEMs) were performed. Simulation studies comparing the sensitivity of different TPC charge readout strategies have also been performed. This work is part of the wider international CYGNUS proto-collaboration effort, where we investigate the feasibility of a large-scale nuclear recoil observatory with directional sensitivity to dark matter and neutrinos. Results to date suggest that it will be feasible to construct a cost-effective directional detector with recoil energy threshold and electron discrimination energy threshold below 10 keVee. Such a detector would be able to penetrate the so-called neutrino floor that will soon be limiting non-directional dark matter search experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗