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At least 1,027 records · Page 57

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗

Corrective Action Decision Document/Corrective Action Plan for Corrective Action Units 101 and 102: Central and Western Pahute Mesa Nevada National Security Site, Nevada, Revision 2

This revision was prepared to document changes to the data-collection activities. Specifically, the drilling and completion of the wells have been revised, and enhancements to the surface geophysics measurements have been included. Corrective action is needed because underground radioactive contamination exists in these areas resulting from historic underground nuclear testing. The PM CAUs are located in the northwestern portion of the Nevada National Security Site and comprise 82 corrective action sites. A total of 85 underground nuclear detonations took place within the area covered by the PM CAUs between 1965 and 1992 and resulted in the release of radionuclides in the subsurface in the vicinity of the test cavities.

54 ENVIRONMENTAL SCIENCES↗

Incentivizing Cyber Security Investment in the Power Sector Using An Extended Cyber Insurance Framework

Collaboration between the DHS Cybersecurity and Infrastructure Security Agency (CISA) and public- sector partners has revealed that a dearth of cyber- incident data combined with the unpredictability of cyber attacks have contributed to a shortfall in first-party cyber insurance protection in the critical infrastructure community. This research explores the foundations of insurance theory and adopts behavioral manipulation methods to incentivize cyber-security investment. We validate the model by applying power industry performance data from 2013-2015 to assess risk facing the industry. Results show that the model can successfully discriminate between individual power companies as well as geographic regions on the basis of risk and can recommend cyber risk- management strategies tailored to individual risk profiles. Here, the adoption of this framework could invite more market participation, which will create a more robust cyber- incident reporting environment, contributing directly to the DHS goal of creating a national cyber- incident data repository.

97 MATHEMATICS AND COMPUTING↗

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm(exp 3) cm(exp -3)), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

R Abolafia-Rosenzweig↗

User and Task Analysis of the Flight Surgeon Console at the Mission Control Center of the NASA Johnson Space Center

Astronauts in a space station are to some extent like patients in an intensive care unit (ICU). Medical support of a mission crew will require acquisition, transmission, distribution, integration, and archiving of significant amounts of data. These data are acquired by disparate systems and will require timely, reliable, and secure distribution to different communities for the execution of various tasks of space missions. The goal of the Comprehensive Medical Information System (CMIS) Project at Johnson Space Center Flight Medical Clinic is to integrate data from all Medical Operations sources, including the reference information sources and the electronic medical records of astronauts. A first step toward the full CMIS implementation is to integrate and organize the reference information sources and the electronic medical record with the Flight Surgeons console. In order to investigate this integration, we need to understand the usability problems of the Flight Surgeon's console in particular and medical information systems in general. One way to achieve this understanding is through the use of user and task analyses whose general purpose is to ensure that only the necessary and sufficient task features that match users capacities will be included in system implementations. The goal of this summer project was to conduct user and task analyses employing cognitive engineering techniques to analyze the task of the Flight Surgeons and Biomedical Engineers (BMEs) while they worked on Console. The techniques employed were user interviews, observations and a questionnaire to collect data for which a hierarchical task analysis and an information resource assessment were performed. They are described in more detail below. Finally, based on our analyses, we make recommendations for improvements to the support structure.

Johnson, Kathy A.↗

The Wind Data Hub, also known as The Data Archive and Portal.

The Wind Data Hub, or WDH (formerly known as the Data Archive and Portal), is established by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy’s Wind Energy Technologies Office (WETO). The WDH provides secure, timely, easy, and open access to all laboratory, field, and benchmark model data produced by land-based and offshore projects funded by WETO.

17 WIND ENERGY↗

Applying Compression-Based Metrics to Seismic Data in Support of Global Nuclear Explosion Monitoring

The analysis of seismic data for evidence of possible nuclear explosion testing is a critical global security mission that relies heavily on human expertise to identify and mark seismic signals embedded in background noise. To assist analysts in making these determinations, we adapted two compression distance metrics for use with seismic data. First, we demonstrated that the Normalized Compression Distance (NCD) metric can be adapted for use with waveform data and can identify the arrival times of seismic signals. Then we tested an approximation for the NCD called Sliding Information Distance (SLID), which can be computed much faster than NCD. We assessed the accuracy of the SLID output by comparing it to both the Akaike Information Criterion (AIC) and the judgments of expert seismic analysts. Our results indicate that SLID effectively identifies arrival times and provides analysts with useful information that can aid their analysis process.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

The NASA John C. Stennis Environmental Geographic Information System

In addition to the Environmental Geographic Information System (EGIS) presentation, we will present two live demonstrations of a portion of the work being performed in support of environmental operations onsite and NASA-wide. These live demonstrations will showcase the NASA EGIS database through working versions of two software packages available from Environmental Systems Research Institute, Inc. (ESRI, Inc.): ArcIMS 3.0 and either ArcView 3.2a or ArcGIS 8.0.2. Using a standard web browser, the ArcIMS demo will allow users to access a project file containing several data layers found in the EGIS database. ArcIMS is configured so that a single computer can be used as the data server and as the user interface, which allows for maximum Internet security because the computer being used will not actually be connected to the World Wide Web. Further, being independent of the Internet, the demo will run at an increased speed. This demo will include several data layers that are specific to Stennis Space Center. The EGIS database demo is a representative portion of the entire EGIS project sent to NASA Headquarters last year. This demo contains data files that are readily available at various government agency Web sites for download. Although these files contain roads, rails, and other infrastructure details, they are generalized and at a small enough scale that they provide only a general idea of each NASA center's surroundings rather than specific details of the area.

Cohan, Tyrus↗

Holographic Optical Data Storage

Although the basic idea may be traced back to the earlier X-ray diffraction studies of Sir W. L. Bragg, the holographic method as we know it was invented by D. Gabor in 1948 as a two-step lensless imaging technique to enhance the resolution of electron microscopy, for which he received the 1971 Nobel Prize in physics. The distinctive feature of holography is the recording of the object phase variations that carry the depth information, which is lost in conventional photography where only the intensity (= squared amplitude) distribution of an object is captured. Since all photosensitive media necessarily respond to the intensity incident upon them, an ingenious way had to be found to convert object phase into intensity variations, and Gabor achieved this by introducing a coherent reference wave along with the object wave during exposure. Gabor's in-line recording scheme, however, required the object in question to be largely transmissive, and could provide only marginal image quality due to unwanted terms simultaneously reconstructed along with the desired wavefront. Further handicapped by the lack of a strong coherent light source, optical holography thus seemed fated to remain just another scientific curiosity, until the field was revolutionized in the early 1960s by some major breakthroughs: the proposition and demonstration of the laser principle, the introduction of off-axis holography, and the invention of volume holography. Consequently, the remainder of that decade saw an exponential growth in research on theory, practice, and applications of holography. Today, holography not only boasts a wide variety of scientific and technical applications (e.g., holographic interferometry for strain, vibration, and flow analysis, microscopy and high-resolution imagery, imaging through distorting media, optical interconnects, holographic optical elements, optical neural networks, three-dimensional displays, data storage, etc.), but has become a prominent am advertising, and security medium as well. The evolution of holographic optical memories has followed a path not altogether different from holography itself, with several cycles of alternating interest over the past four decades. P. J. van Heerden is widely credited for being the first to elucidate the principles behind holographic data storage in a 1963 paper, predicting bit storage densities on the order of 1/lambda(sup 3) with source wavelength lambda - a fantastic capacity of nearly 1 TB/cu cm for visible light! The science and engineering of such a storage paradigm was heavily pursued thereafter, resulting in many novel hologram multiplexing techniques for dense data storage, as well as important advances in holographic recording materials. Ultimately, however, the lack of such enabling technologies as compact laser sources and high performance optical data I/O devices dampened the hopes for the development of a commercial product. After a period of relative dormancy, successful applications of holography in other arenas sparked a renewed interest in holographic data storage in the late 1980s and the early 1990s. Currently, with most of the critical optoelectronic device technologies in place and the quest for an ideal holographic recording medium intensified, holography is once again considered as one of several future data storage paradigms that may answer our constantly growing need for higher-capacity and faster-access memories.

Timucin, Dogan A.↗

Collection and Analysis of Telemetry for CyOTE Heuristics (CATCH)

The Collection and Analysis of Telemetry for CyOTE Heuristics (CATCH) provides a framework for augmenting an organization’s existing security controls with CyOTE developed analyses. CATCH collects, stores, analyzes, and creates STIX reports on anomalous data. CATCH connects the CyOTE analysis framework together with the MITRE ICS ATT&CK® patterns and highlights areas of improvement and further research. This tool is designed to enhance an organization’s security controls by providing a structured approach to collecting, storing, analyzing, and reporting anomalous data.

99 GENERAL AND MISCELLANEOUS↗

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗

Historical and Future Global Irrigation Energy Consumption by Fuel and Region

Irrigation energy use is a significant component of agricultural production costs, contributing directly to the energy and emissions intensity of crop production and ultimately to food prices. Understanding the existing structure of irrigation energy consumption help achieve food-energy-water security and environmental goals. We present a comprehensive global data set detailing country-level irrigation energy consumption, emphasizing the comparative use of electric, diesel, and emerging solar pumps. To our knowledge, no such data set exists. We draw from a literature review to develop a logistic transformed regression model to estimate the shares of fuel sources for irrigation across countries over historical years to construct a global data set of country-level irrigation energy consumption by multiple fuel sources. Additionally, we compare our estimates of irrigation energy use with agricultural energy use as reported by the International Energy Agency and other external sources. We then use this data to project future irrigation energy use with the Global Change Analysis Model, which is a multisector dynamics model, to showcase the usage of this data set. Projections under the reference scenario show a global shift in fuel types for irrigation pumping, while patterns vary across regions, with India and Pakistan leading in solar-powered irrigation growth and countries like the USA and China continuing to rely primarily on grid electricity. This data set provides a resource to understand the role of irrigation fuel choices within the broader energy sector, as well as the connected agricultural, land use, and water sectors under alternative future scenarios, enabling informed decision making toward efficient agricultural practices.

Global Change Analysis Model (GCAM)↗

Evaluating Recursive Blind Forecast Against API and Baseline: A Puerto Rican Case Study on Solar Irradiance for Normal and Extreme Weather

This paper leverages ongoing work in a community microgrid in Adjuntas, Puerto Rico to forecast global horizontal irradiance (GHI) and compare performance in normal and extreme weather. Given a positive correlation of 0.98 between GHI and PV power, forecasting GHI can be an effective, indirect forecast of photovoltaic (PV) power, especially in microgrids where the end-users, owners, operators, or other stakeholders are reluctant to share data for training or validation due to privacy and security concerns. A recursive one-shot (termed as "blind") forecast is, hence, formulated, wherein a gradient-boosted regression tree (GBR) is built to forecast GHI for a 7-day horizon in normal weather, and a 2-day horizon in extreme weather. To demonstrate its resilience, the architecture is trained on normal and hurricane weather GHI from 2002-2022. It is generalized on February 9-16, 2023, and on the landfall of Hurricane Nicole (Nov 4-5, 2022), respectively. Forecasts from GBR are compared against that from a satellite-based API resource and three baselines: persistence, averaging, and exponential smoothing. Results show GBR and persistence outperform sophisticated API in both types of weather for this case study.

Sundararajan, Aditya↗

Hyperspectral vegetation identification at a legacy underground nuclear explosion test site

The detection, location, and identification of suspected underground nuclear explosions (UNEs) are global security priorities that rely on integrated analysis of multiple data modalities for uncertainty reduction in event analysis. Vegetation disturbances may provide complementary signatures that can confirm or build on the observables produced by prompt sensing techniques such as seismic or radionuclide monitoring networks. For instance, the emergence of non-native species in an area may be indicative of anthropogenic activity or changes in vegetation health may reflect changes in the site conditions resulting from an underground explosion. Previously, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of visible and near-infrared wavebands over 4.3 km 2 of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. In this work, we employ various spectral detection and classification algorithms to identify and map vegetation species in an area of interest containing the legacy test site. Here, we employed a frequentist framework for fusing multiple spectral detections across various reference spectra captured at different times and sampled from multiple locations. The spatial distribution of vegetation species is compared to the location of the underground nuclear explosion. We discover a difference in species abundance within a 130 m radius of the center of the test site.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Multichannel Deconvolution of Vibrational Shock Signals: An Inverse Filtering Approach

When transporting critical systems of national security interest, out-of-the ordinary, impulsive events that can potentially be undetected and affect overall system performance are of great concern. Impulsive events that can occur are essentially pulse-like, transient signals of short duration that evolve from various phenomena. Here the event can be created by either the dropping of a test object, the system, subjecting it to a compact high-energy blow or being struck unintentionally during transit resulting in potential damage. The intensity and location of the strike can cause an inoperability condition that is unacceptable in a national security environment. Therefore, it is essential to detect, classify and localize damage of any test object subjected to an impulsive-event. This effort was targeted to evaluate the vibrational response of test objects that are subjected to “transport” shocks and roadway vibrations during shipping and handling. Any potential damage that could be inflicted during transportation must not only be detected, but also be evaluated to determine the operational readiness of a test object before and after transport. This event is a critical task that must be addressed as part of the Lawrence Livermore National Laboratory (LLNL) national security mission. The estimation of excitation signals from noisy data is termed the deconvolution problem in the signal processing literature. The deconvolution problem is based on recovering the input excitation signal from a system characterized by its impulse response sequence. Using this model of the system, an “inverse” representation or filter is developed to remove the system from the measured data and recover the input. Deconvolution techniques have existed for a long-time; however, transient deconvolution presents a few uncommon problems, since the signal has a finite-length time duration resulting in a limited amount of data containing information about the excitation process. The transient is wideband in the frequency domain relative to any measurement sensor implying that the smaller bandwidth sensor system “filters” the excitation eliminating some of its essential information for recovery. This fact, coupled with the filtering effect of the test object itself makes this excitation recovery (deconvolution) problem a challenge for signal processing.

42 ENGINEERING↗

Index of Classified Reports Collection now available Vital resource for Weapons researchers, today’s mission work

The collection is made up of classified technical reports containing weapons data dating back to the start of the nuclear security enterprise. The information is compiled into reports, slides, handbooks and literature. It originated from Los Alamos, Lawrence Livermore, and Sandia Labs, as well as from the Department of Defense and likely every other organization involved in nuclear testing, including contractors and those that are now defunct.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Schema Elements for Granta Annual Report: FY23

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE additive manufacturing (AM) database, so the objective of this project is to create and document such a schema.

36 MATERIALS SCIENCE↗