Obtaining Real Production Data through On-Component Printed SIR Patterns
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Hartley, Daniel [Kansas City National Security Cam↗
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Technology Assessment of High Capacity Data Storage Systems: Can We Avoid a Data Survivability Crisis
The density of digital storage media in our information-intensive society increases by a factor of four every three years, while the rate at which this data can be migrated to viable long-term storage has been increasing by a factor of only four every nine years. Meanwhile, older data stored on increasingly obsolete media, are at considerable risk. When the systems for which the media were designed are no longer serviced by their manufacturers (many of whom are out of business), the data will no longer be accessible. In some cases, older media suffer from a physical breakdown of components - tapes simply lose their magnetic properties after a long time in storage. The scale of the crisis is compatible to that facing the Social Security System. Greater financial and intellectual resources to the development and refinement of new storage media and migration technologies in order to preserve as much data as possible.
Halem, M.↗
ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels
System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.
97 MATHEMATICS AND COMPUTING↗
Hyper Spectral Anomaly Detection
Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.
97 - MATHEMATICS AND COMPUTING↗
Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid
Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.
Mathieu, Reynold↗
Classifying Aircraft using Velocity Data with Support Vector Machines and Likelihood Ratio Tests
Timely classification of aircraft is important for small unmanned aerial system (sUAS) technologies, such as onboard collision avoidance systems, and aerial perimeter security for prisons and sports venues. This work uses velocity-based metrics to classify multi-rotor sUAS, fixed wings UAS, and general aviation planes using two classification methods: Support Vector Machines (SVM), and Likelihood Ratio (LR) tests. We found that a 96% classification accuracy is achieved when either classifier is trained using average speed derived from flight controller data or radar data and tested with one second of radar data. Further, we show that LR tests perform similarly to SVM for single metric classification. In addition, we present two novel metrics for classifying aircraft: log variance of absolute change in speed, and log variance of relative change in speed. Finally, we discuss challenges associated with training classifiers with flight controller data but testing on radar data.
Logan T Dihel↗
Development of x-ray imaging technique for liquid screening at airport
X-ray imaging technology is a viable option to recognize flammable liquids for the purposes of aviation security. In this study, an X-ray imaging technology was developed whereby, the image viewing system was built with the use of a digital camera coupled with a gadolinium oxysulfide (GOS) fluorescent screen. The camera was equipped with a software for remote control setting of the camera via a USB cable which allows the images to be captured. The image was analysed to determine the average grey level using a software designed by Microsoft Visual Basic 6.0. The data was obtained for various densities of liquid thickness of 4.5 cm, 6.0 cm and 7.5 cm respectively for X-ray energies ranging from 70 to 200 kVp. In order to verify the reliability of the constructed calibration data, the system was tested with a few types of unknown liquids. The developed system could be conveniently employed for security screening in order to discriminate between a threat and an innocuous liquid.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Control and Non-Payload Communications (CNPC) Prototype Radio - Generation 2 Security Architecture Lab Test Report
NASA Glenn Research Center, in cooperation with Rockwell Collins, is working to develop a prototype Control and Non-Payload Communications (CNPC) radio platform as part of NASA Integrated Systems Research Program's (ISRP) Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. A primary focus of the project is to work with the FAA and industry standards bodies to build and demonstrate a safe, secure, and efficient CNPC architecture that can be used by industry to evaluate the feasibility of deploying a system using these technologies in an operational capacity. GRC has been working in conjunction with these groups to assess threats, identify security requirements, and to develop a system of standards-based security controls that can be applied to the current GRC prototype CNPC architecture as a demonstration platform. The security controls were integrated into a lab test bed mock-up of the Mobile IPv6 architecture currently being used for NASA flight testing, and a series of network tests were conducted to evaluate the security overhead of the controls compared to the baseline CNPC link without any security. The aim of testing was to evaluate the performance impact of the additional security control overhead when added to the Mobile IPv6 architecture in various modes of operation. The statistics collected included packet captures at points along the path to gauge packet size as the sample data traversed the CNPC network, round trip latency, jitter, and throughput. The effort involved a series of tests of the baseline link, a link with Robust Header Compression (ROHC) and without security controls, a link with security controls and without ROHC, and finally a link with both ROHC and security controls enabled. The effort demonstrated that ROHC is both desirable and necessary to offset the additional expected overhead of applying security controls to the CNPC link.
Communication Networks↗
Hydrodynamic Characteristics of Coolant Tracts in High-Temperature Gas-Cooled Nuclear Reactor
A validation of the hydrodynamic characteristics of the coolant loop of a high-temperature gas-cooled reactor at low power in a power-generating facility using a turbo-machine conversion in a Brayton cycle is given. The validation is accomplished using diagnostics of models of a coolant loop, numerical modeling of the hydrodynamics, heat-and-mass transfer in models with more accurate three-dimensional CFD codes. Relations for ensuring tangential uniformity of the coolant velocity distribution near the core shell and at the entry into the distributing manifold are obtained. A distributing manifold geometry securing a uniform coolant flow distribution over core cooling tracts at low hydraulic resistance is proposed. Data are obtained on the velocity and temperature distributions in fuel pins spaced by a wire winding.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Molecular Communication over Gas Stream Channels using Portable Mass Spectrometry
The synthetic generation/coding and transmission of olfactory information over a gas stream or an odor network is a new and unexplored field. Application areas vary from the entertainment or advertisement industry to security and telemedicine. However, current technological limitations frustrate the accurate reproduction of decoded and transmitted olfactory data. This study describes the development, testing, and characterization of a novel odor emitter (OE) that is used to investigate the generation-encoding of gaseous standards with odorous characteristics with a regulatable way, for scent transmission purposes. The calibration and the responses of a developed OE were examined using a portable quadrupole mass spectrometer (MS). Experiments were undertaken for a range of volatile organic compounds (VOCs) at different temperatures and flow rates. Individual compounds and mixtures were tested to investigate periodic and dynamic transmission characteristics within two different size tubular containers for distances up to 3 m. Olfactory information transmission is demonstrated using MS as the main molecular sensor for odor detection and monitoring and for the first time spatial encryption of olfactory information is shown. .
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling
Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.
54 ENVIRONMENTAL SCIENCES↗
Social-ecological interactions in a disaster context: Puerto Rican farmer households’ food security after Hurricane Maria
Islands are uniquely vulnerable to extreme weather events and food insecurity, and have additional response challenges due to their limited landmasses and economies, isolation, colonial legacies, and high dependence of food imports. Domestic farmers have a key role in producing food for island communities like Puerto Rico, which can safeguard food security when food importation may be challenging. Nevertheless, in the context of disaster, farmers themselves may be vulnerable to food insecurity and unable to contribute to domestic markets. This paper examines Puerto Rican farmers households’ food security in the aftermath of 2017’s Hurricane Maria using a social-ecological lens. Survey data from 405 farmers gathered eight months after Maria, coupled with biophysical data from the hurricane’s impacts (winds, rains, and landslides), were analyzed. Overall, 69% of farmers experienced at least one month of food insecurity in the aftermath of Hurricane Maria, and 38% reported persistent food insecurity (three months or more). A multinomial logistic regression suggests that biophysical impacts, but especially social factors, such as age and constraint access to external sources of support, are linked with persistent food insecurity. This suggests that the biophysical impacts of the hurricane interact with existing infrastructure and social resources to affect farmer vulnerability and the food environment in different ways. Thus, strengthening adaptive capacity in multiple domains can help farmers and vulnerable populations better navigate the disruptions faced during disasters to alleviate food insecurity.
54 ENVIRONMENTAL SCIENCES↗
Efficient Management of Certificate Revocation Lists in Smart Grid Advanced Metering Infrastructure
Advanced Metering Infrastructure (AMI) forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication within an AMI needs to be secure, key management becomes an issue due to overhead and limited resources. While using public-keys eliminate some of the overhead of key management, there is still challenges regarding certificates that store and certify the publickeys. In particular, distribution and storage of certificate revocation list (CRL) is major a challenge due to cost of distribution and storage in AMI networks which typically consist of wireless multi-hop networks. Motivated by the need of keeping the CRL distribution and storage cost effective and scalable, in this paper, we present a distributed CRL management model utilizing the idea of distributed hash trees (DHTs) from peer-to-peer (P2P) networks. The basic idea is to share the burden of storage of CRLs among all the smart meters by exploiting the meshing capability of the smart meters among each other. Thus, using DHTs not only reduces the space requirements for CRLs but also makes the CRL updates more convenient. We implemented this structure on ns-3 using IEEE 802.11s mesh standard as a model for AMI and demonstrated its superior performance with respect to traditional methods of CRL management through extensive simulations.
25 ENERGY STORAGE↗
Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery
The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.
Khadka, Rajiv [Idaho National Laboratory (INL), Id↗
Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera
Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.
Mavian, Alex (ORCID:0000000279448830)↗
FY2017 Analysis Annual Progress Report
During fiscal year 2017, the U.S. Department of Energy Vehicle Technologies Office (VTO) funded analysis projects supportive of VTO’s goals to pursue early stage research in vehicle and mobility system technologies to reduce petroleum dependence, increase energy reliability and security, improve transportation affordability, and promote economic growth. VTO analysis projects result in a foundation of data, analytical models, and applied analyses that provide insights into critical transportation energy problems and assist in research investment prioritization and portfolio planning.
33 ADVANCED PROPULSION SYSTEMS↗
FY2018 Analysis Annual Progress Report
During fiscal year 2018 (FY 2018), the U.S. Department of Energy Vehicle Technologies Office (VTO) funded analysis projects supportive of VTO’s goals to pursue early stage research in vehicle and mobility system technologies to improve energy efficiency, increase energy reliability and security, improve transportation affordability, and promote economic growth. VTO analysis projects result in a foundation of data, analytical models, and applied analyses that provide insights into critical transportation energy problems and assist in research investment prioritization and portfolio planning.
33 ADVANCED PROPULSION SYSTEMS↗
Free-field Ground Motion Induced by Underground Explosions at Aqueduct Mesa with Predictions for Physical Experiment One (PE1)
We fit standard power-law models for ground acceleration and particle velocity using scaled ground motion observations from underground nuclear explosions at Aqueduct Mesa on the Nevada National Security Site. The models are then validated using small-scale high-explosive (HE) test data from nearby Rainier Mesa. The comparison between model predictions and observations is good and can be made more favorable if the assumed yield for the HE tests are doubled. Further validation is made with a numerical experiment that additionally shows a transition in ground motion attenuation to elastic propagation (1 = r). An extended model is provided to incorporate the long-range transition in attenuation, which results in increased ground motions at farther range. We then make predictions for the field experiment, Physical Experiment One (PE1), to take place in P-tunnel on Aqueduct Mesa, and suggest that the models could be used in planning various operations for PE1.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗