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At least 937 records · Page 52

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗

Synthesis and Properties of Nanostructured Exotic Superconductors (Final Report)

Superconductors are materials that conduct electricity with no resistance, i.e. no energy dissipation. In future electrically activated nanodevices, the use of superconducting interconnects is highly desirable, since it circumvents the damaging heat produced by energy dissipation in normal nano-conductors whose high resistance is inversely proportional to its cross-section area. Furthermore, the wave functions of the charge carriers - Cooper pairs will be strongly modified in a superconductor when they are squeezed into a small volume comparable to the coherence length or penetration depth. Thus, superconducting nanostructures provide unique platforms to investigate and discover novel superconducting phenomena in confined geometries. The objective of this DOE funded project was the development of novel approaches to synthesizing a new class of superconducting nanostructures such as nanowires and nanoribbons that are stable in atmosphere, enabling the exploration of superconducting properties and potential applications of individual nanostructures. We also pursued the modification of properties of the fabricated nanostructures by introducing artificial defects through nanoengineering. We developed a conversion approach comprised of two-steps to synthesize NbSe 2 and NbN nanowires/ribbons that were not achievable using other routes: first, we synthesized one-dimensional (1D) nanostructures of NbSe 3 and second, we converted these into desired 1D superconducting nanostructures of NbSe 2 and NbN by adjusting the composition of NbSe 3 nanostructure precursors while maintaining their shapes. Novel phenomena such as resistance oscillations with increasing magnetic fields were observed in both NbSe 2 and NbN nanowires/ribbons. By replacing the catalytic microparticles used in conventional whisker growth with the same kind of nanoparticles that are available now commercially, we improved whisker technologies for nanofabrication of high-T c superconductor nanowires/ribbons: we succeeded in the growth of Bi 2 Sr 2 CaCu 2 O 8+x (BSCCO-2212) and YBa 2 Cu 3 O 7-x (YBCO) nanowires and nanoribbons by annealing glassy precursors with alumina nanoparticles as growth sites. The high-quality whiskers enabled us to observe the ‘matching effect’ that represents itself as dips in the field dependence of the magnetoresistance and the influence of the periodic defects on the melting behavior of vortex matter. We also observed N-shaped V-I characteristics similar to those observed in semiconductor and plasma devices for the first time. Each nanofabrication technique has its advantages and drawbacks. For example, electron-beam lithography (EBL), in combination thin film deposition, has been widely used to fabricate nanowires (nanobridge) with well-defined cross-sections. However, electron-beam resist usually contaminates the sample up to a few hundred nanometers and hence degrades the quality of the entire nanowires if their widths are only a few hundred nanometers. We found that e-beam resist can degrade the superconducting critical temperature of a Nb nanowire from 9 K down to below 4 K if its width is less than 200 nm. We investigated the suitability of the various nanofabrication methods for achieving high-quality samples with desired properties. For example, we developed a novel way to achieve high quality Nb nanostrips with transverse dimensions of 150 nm or smaller. In the new process a superconducting bridge a few micrometers wide is fabricated with EBL. Subsequently, focused-ion-beam (FIB) is used to mill nano- 2 grooves to isolate an uncontaminated area of the microbridge to form a nanostrip. The high-quality superconducting Nb nanostrips with well-defined rectangular cross-sections fabricated using this new technique have enabled experimental confirmation of the theoretically predicted confinement-induced anisotropy effect on the magnetoresistance and critical field. We also found that FIB milling is more suitable than EBL in producing a pinning potential array with desired barrier between them into a MoGe film, which enabled us to realize the predicted vortex ices for the first time. By utilizing a filtration membrane as the substrate, we developed a new method to achieve networks of superconducting MoGe nanowires with transverse dimensions smaller than 10 nm. We observed superconducting confinement effect such as thermally activated phase slips and Little-Parks effect in these new samples. This research significantly advanced our knowledge on confined superconductors, in both their synthesis and physical properties. Though the new fabrication methods developed in this project aimed to achieve samples for studying new phenomena in confined superconductors, they can be utilized to obtain nanostructures of other functional materials. For example, we utilized the method developed to produce superconducting nanowire networks for the fabrication of networks of ultrasmall palladium (Pd) nanowires, which take advantage of single Pd nanowires while eliminating their nanofabrication obstacles, resulting in high-performance hydrogen sensors that are much more cost effective than those based on single Pd nanowires fabricated with lithography methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab project

Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.

Chai, Chengping↗

Snapshot multispectral imaging using a diffractive optical network

Abstract Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 λ m , where λ m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

36 MATERIALS SCIENCE↗

Detecting Sensors and Inferring their Relations at Level-0 in Industrial Cyber-Physical Systems

While there exist tools and techniques to discover, identify, map, and analyze cyber physical components at higher levels of the cyber space, there is a lack of capabilities to reach down to the sensors at the bottom-most levels, such as levels 0 and 1 of the Purdue Enterprise Reference Model for cyber-physical systems (CPS). Conventional information technology (IT)-based tools reach as far as the network-side of programmable logic controllers, but are inadequate to access and analyze the physical side of the CPS infrastructure that directly interfaces with the actual physical processes and systems. In this paper, we present our research and development efforts aimed at addressing this gap, by building a system called Deep-cyberia (Deep Cyber-Physical System Interrogation and Analysis) that incorporates algorithms and interfaces aimed at uncovering sensors and computing estimates of correlations among them.

Perumalla, Kalyan↗

Multifunctional Silk Fibroin Hydrogels with Strong Adhesion for Tissue Sealing and Wearable Electronic Sensors

Multifunctional hydrogels with excellent adhesion, biodegradability, and conductivity are essential for overcoming the obstacles of postoperative secondary injury, flexible sensing instability, and so on. Herein, we develop a multifunctional silk fibroin (SF) hydrogel modified with poly(acrylic acid). Owing to the stable chemical cross-linking network and the abundant carboxylic acid groups of the SF network, the SF hydrogel exhibits a high tensile strength of 74.34 kPa due to sufficient cohesion and interfacial interactions. Additionally, the tensile strain reaches a maximum of 414.6%, the compressive strength is 0.9 MPa, and the shear adhesive strength for pig skin tissues is as high as 64 kPa. Compared with most hydrogels, our multifunctional SF hydrogel with a low swelling ratio provides excellent adhesion, biodegradation, and conductivity, which shows advantages in terms of invasive tissue sealing. Furthermore, the use of self-adhesive SF hydrogels as conductive hydrogels in flexible sensors also benefits the collection of physiological electricity and human motion signals in the field of wearable and implantable electronic devices.

adhesive↗

Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction

In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends model's predictions with noisy observations to correct initial state and/or model parameters. We apply this approach on a projection based reduced order model (ROM) of the one-dimensional viscous Burgers equation with a square wave defining a moving shock, and the two-dimensional vorticity transport equation formulating a decay of Kraichnan turbulence. We investigate the capability of the approach to approximate an optimal value for eddy viscosity with different measurement configurations. Specifically, we show that our approach can sufficiently assimilate information either through full field or sparse noisy measurements to estimate eddy viscosity closure to cure standard Galerkin reduced order model (GROM) predictions. Therefore, our approach provides a modular framework to correct forecasting error from a sparse observational network on a latent space. We highlight that the proposed GROM-FSM framework is promising for emerging digital twin applications, where real-time sensor measurements can be used to update and optimize surrogate model's parameters.

42 ENGINEERING↗

A Co-Registered In-Situ and Ex-Situ Tensile Properties Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-11)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from five Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L builds containing 6,299 SS-J3 individually tracked tensile coupons. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, room-temperature static tensile test results, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗

Comparison of Ring-Buffer-Based Packet Capture Solutions

Traditional packet-capture solutions using commodity hardware incur a large amount of overhead as packets are copied multiple times by the operating system. This overhead slows sensor systems to a point where they are unable to keep up with high bandwidth traffic, resulting in dropped packets. Incomplete packet capture files hinder network monitoring and incident response efforts. While costly commercial hardware exists to capture high bandwidth traffic, several software-based approaches exist to improve packet capture performance using commodity hardware.

Barker, Steven Andrew↗

Continuously-operable flow stream processing system and method

A continuously-operable flow stream processing system and method has two or more fluid processing media arranged in a fluid conduit network to provide alternate flow paths for a flow stream to be processed (e.g. filtered). Valves and other flow control devices, e.g. controller module and sensors, are used to redirect the flow stream from one or more service-needed fluid processing media to service-ready fluid processing media without taking the system offline to remove, repair, replace, clean in situ, or otherwise service the service-needed fluid processing media, so that the system may operate continuously to process the flow stream without stoppage. Redirection into alternative flow paths and media may be automatically implemented based on a predetermined threshold or trigger condition, such as may be detected by sensors directly or indirectly monitoring media condition.

42 ENGINEERING↗

Real-Time Boundary-Layer Profiling at the Southern Great Plains Field Campaign Report

The National Research Council (NRC 2009, 2010) has argued that the nation needs to establish a boundary-layer profiling network across the conterminous United States (CONUS). These observations would benefit a wide range of applications including short-term severe weather forecasting by providing better initial conditions for weather prediction models (Wulfmeyer et al. 2015). While active remote sensors like Raman lidars (e.g., Turner et al. 2016) and differential absorption lidars (e.g., Spuler et al. 2015) are able to provide high-temporal-and-vertical-resolution profiles of water vapor and temperature from the ground, these systems are not yet available commercially. Passive remote-sensing systems, such as multi-channel microwave radiometers and infrared spectrometers, are commercially available and can provide retrieved profiles of temperature and humidity (e.g., Löhnert et al. 2009; Blumberg et al. 2015). These passive thermodynamic profiling systems are certainly a candidate for a possible boundary-layer network. Radio acoustic sounding systems (RASS), coupled with either a radar or sodar, can provide partial profiles of virtual temperature and are another option. However, the RASS signal is audible and considered by many to be annoying, and strong winds blow the acoustic signal away from the radar and greatly hamper its maximum vertical range. Three basic ground-based remote sensing technologies are used for wind profiling. All of these are active remote sensors that take advantage of a Doppler-shifted signal along different radial directions to determine the wind speed and direction. The three approaches are sodar (which uses pulses of sonic energy), radar (pulses of microwave energy), or lidar (pulses of laser energy). All three methods use either plane-parallel-indicator (PPI; constant elevation) scans that are analyzed with a velocity-azimuth display (VAD) technique or Doppler beam-swinging observing strategies, which are then analyzed to provide the horizontal wind speed and direction profiles as a function of range.

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Synoptic Sites Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Correlating structural changes in thermoresponsive hydrogels to the optical response of embedded plasmonic nanoparticles

Stimuli-responsive microgels, composed of small beads with soft, deformable polymer networks swollen through a combination of synthetic control over the polymer and its interaction with water, form a versatile platform for development of multifunctional and biocompatible sensors. The interfacial structural variation of such materials at a nanometer length scale is essential to their function, but not yet fully comprehended. Here, we take advantage of the plasmonic response of a gold nanorod embedded in a thermoresponsive microgel (AuNR@PNIPMAm) to monitor structural changes in the hydrogel directly near the nanorod surface. By direct comparison of the plasmon response against measurements of the hydrogel structure from dynamic light scattering and nuclear magnetic resonance, we find that the microgel shell of batch-polymerized AuNR@PNIPMAm exhibits a heterogeneous volume phase transition reflected by different onset temperatures for changes in the hydrodyanmic radius (R H ) and plasmon resonance, respectively. The new approach of contrasting plasmonic response (a measure of local surface hydrogel structure) with R H and relaxation times paves a new path to gain valuable insight for the design of plasmonic sensors based on stimuli-responsive hydrogels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Analyzing Data Privacy for Edge Systems

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.

Kotevska, Olivera↗

State Machine Operation of Complex Systems

SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Internship Presentation

SQMS, affiliated with Fermilab, is one of five research centers funded by the DOE dedicated to the development and deployment of advanced quantum computers and sensors. The objective of our research was to devise an experimental design for quantum transduction, with the aim of facilitating the development of a distributed quantum network to connect quantum computers over large distances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗