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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Receiver Signal to Noise Ratios for IPDA Lidars Using Sine-wave and Pulsed Laser Modulation and Direct Detections

Integrated path differential absorption (IPDA) lidar can be used to remotely measure the column density of gases in the path to a scattering target [1]. The total column gas molecular density can be derived from the ratio of the laser echo signal power with the laser wavelength on the gas absorption line (on-line) to that off the line (off-line). 80th coherent detection and direct detection IPDA lidar have been used successfully in the past in horizontal path and airborne remote sensing measurements. However, for space based measurements, the signal propagation losses are often orders of magnitude higher and it is important to use the most efficient laser modulation and detection technique to minimize the average laser power and the electrical power from the spacecraft. This paper gives an analysis the receiver signal to noise ratio (SNR) of several laser modulation and detection techniques versus the average received laser power under similar operation environments. Coherent detection [2] can give the best receiver performance when the local oscillator laser is relatively strong and the heterodyne mixing losses are negligible. Coherent detection has a high signal gain and a very narrow bandwidth for the background light and detector dark noise. However, coherent detection must maintain a high degree of coherence between the local oscillator laser and the received signal in both temporal and spatial modes. This often results in a high system complexity and low overall measurement efficiency. For measurements through atmosphere the coherence diameter of the received signal also limits the useful size of the receiver telescope. Direct detection IPDA lidars are simpler to build and have fewer constraints on the transmitter and receiver components. They can use much larger size 'photon-bucket' type telescopes to reduce the demands on the laser transmitter. Here we consider the two most widely used direct detection IPDA lidar techniques. The first technique uses two CW seeder lasers, one on-line and one offline that are intensity modulated by two different frequency sine-waves signals before being amplified by a common laser amplifier. The receiver uses narrowband amplitude demodulation, or lock-in, Signal processing at the given laser modulation frequencies [3,4]. The laser transmitter operates in a quasi CW mode with the peak power equal to twice the average power. The on-line and off-line lasers can be transmitted at the same time without interference. Another direct detection technique uses a low duty cycle pulsed laser modulation [5,6] with the laser wavelengths alternating between on-line and off-line on successive pulses. The receiver uses time resolved detection and can also provide simultaneous target range measurement. With a lower laser duty cycle it requires a much higher peak laser power for the same average power.

Sun, Xiaoli↗
NASA NTRS2012

Comparison Between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports

Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-micrometer infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (less than 200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage.

Dworak, Richard↗
NASA NTRS2013

Wise Detections of Known QSOS at Redshifts Greater Than Six

We present WISE All-Sky mid-infrared (IR) survey detections of 55 % (17/31) of the known QSOs at z greater than 6 from a range of surveys: the SDSS, the CFHT-LS, FIRST, Spitzer and UK1DSS. The WISE catalog thus provides a substantial increase in tiie quantity of IR data available for these sources: 17 are detected in the WISE Wl (3.4 micrometer) band, 16 in W2 (4.6 micrometers), 3 in W3 (12 micrometers) and 0 in W4 (22micrometers). This is particularly important with Spitzer in its warm-mission phase and no faint follow-up capability at wavelengths longwards of 5 micrometers until the launch of JWST. WISE thus provides a useful tool for understanding QSOs found in forthcoming large-area optical/IR sky surveys, using PanSTARRS, SkyMapper, VISTA, DES and LSST. The rest-UV properties of the WISE-detected and the WISE-non-detected samples differ: the detections have brighter i/z-band magnitudes and redder rest-UV colors. This suggests thai a more aggressive hunt for very-high-redshift QSOs, by combining WISE Wl and W2 data with red observed optical colors could be effective at least, for a subset of dusty candidate QSOs. Stacking the WISE images of the WISE-non-detected QSOs indicates that they are on average significantly fainter than the WISE-detccted examples, and are thus not narrowly missing detection in the WISE catalog. The WISE-catalog detection of three of our sample in the W3 band indicates that their mid-ID flux can be detected individually, although there is no stacked W3 detection of sources detected in Wl but not. W3. Stacking analyses of WISE data for large AGN samples will be a useful tool, and high-redshifl. QSOs of all types will be easy targets for JWST.

Galaxies: Evolution↗
NASA NTRS2014

AE (Acoustic Emission) for Flip-Chip CGA/FCBGA Defect Detection

C-mode scanning acoustic microscopy (C-SAM) is a nondestructive inspection technique that uses ultrasound to show the internal feature of a specimen. A very high or ultra-high-frequency ultrasound passes through a specimen to produce a visible acoustic microimage (AMI) of its inner features. As ultrasound travels into a specimen, the wave is absorbed, scattered or reflected. The response is highly sensitive to the elastic properties of the materials and is especially sensitive to air gaps. This specific characteristic makes AMI the preferred method for finding "air gaps" such as delamination, cracks, voids, and porosity. C-SAM analysis, which is a type of AMI, was widely used in the past for evaluation of plastic microelectronic circuits, especially for detecting delamination of direct die bonding. With the introduction of the flip-chip die attachment in a package; its use has been expanded to nondestructive characterization of the flip-chip solder bumps and underfill. Figure 1.1 compares visual and C-SAM inspection approaches for defect detection, especially for solder joint interconnections and hidden defects. C-SAM is specifically useful for package features like internal cracks and delamination. C-SAM not only allows for the visualization of the interior features, it has the ability to produce images on layer-by-layer basis. Visual inspection; however, is only superior to C-SAM for the exposed features including solder dewetting, microcracks, and contamination. Ideally, a combination of various inspection techniques - visual, optical and SEM microscopy, C-SAM, and X-ray - need to be performed in order to assure quality at part, package, and system levels. This reports presents evaluations performed on various advanced packages/assemblies, especially the flip-chip die version of ball grid array/column grid array (BGA/CGA) using C-SAM equipment. Both external and internal equipment was used for evaluation. The outside facility provided images of the key features that could be detected using the most advanced C-SAM equipment with a skilled operator. Investigation continued using in-house equipment with its limitations. For comparison, representative X-rays of the assemblies were also gathered to show key defect detection features of these non-destructive techniques. Key images gathered and compared are: Compared the images of 2D X-ray and C-SAM for a plastic LGA assembly showing features that could be detected by either NDE technique. For this specific case, X-ray was a clear winner. Evaluated flip-chip CGA and FCBGA assemblies with and without heat sink by C-SAM. Only the FCCGA package that had no heat sink could be fully analyzed for underfill and bump quality. Cross-sectional microscopy did not revealed peripheral delamination features detected by C-SAM. Analyzed a number of fine pitch PBGA assemblies by C-SAM. Even though the internal features of the package assemblies could be detected, C-SAM was unable to detect solder joint failure at either the package or board level. Twenty times touch ups by solder iron with 700degF tip temperature, each with about 5 second duration, did not induce defects to be detected by C-SAM images. Other techniques need to be considered to induce known defects for characterization. Given NASA's emphasis on the use of microelectronic packages and assemblies and quality assurance on workmanship defect detection, understanding key features of various inspection systems that detect defects in the early stages of package and assembly is critical to developing approaches that will minimize future failures. Additional specific, tailored non-destructive inspection approaches could enable low-risk insertion of these advanced electronic packages having hidden and fine features.

Ghaffarian, Reza↗
NASA NTRS2021

Modular Composite Damage Detection System (MCDDS)

The 2020 CIF funded Modular Composite Damage Detection System (MCDDS) aims to advance the technology of damage detection in composites in the areas of thin, flexible damage detection panel construction, encapsulation techniques in composite matrixes, innovative integration and interfacing techniques with flexible embedded sensory panels, repair methods of composite materials, and improved packaging of the MCDDS. Custom thin, flexible polyimide printed circuit boards (PCBs) were designed and procured for fabrication of test specimens. A custom interface PCB was designed and fabricated to monitor the newly developed damage detection test specimens. Test specimens were fabricated using the novel flexible circuitry that were either fully or partially encapsulated. The fully encapsulated specimens were used to demonstrate embedded damage detection concepts and the partially encapsulated specimens were used to demonstrate surface-mounted (laminate) damage detection concepts. The new composite damage detection test specimens were tested and evaluated to determine if the novel damage detection sensory panels could withstand low-cycle threshold fatigue loading per a modified version of American Society for Testing and Materials (ASTM) D 7264 for up to 50 cycles without void formation. The data for multiple sensory panels indicates that the newly fabricated sensory panels are robust and do indeed withstand three-point bend flexural loading for more than 50 cycles. Additionally, testing was performed to determine if damage could be detected in damage detection test specimens subjected to failure using a 3-point bend test per a modified version of ASTM D 7264. Again, multiple sensory panels (both fully and partially encapsulated) showed the ability to detect damage to specimens subjected to failure per a modified version of ASTM D 7264. Additionally, a novel concept for tracking and monitoring of cracking (and potentially delamination) in composites was designed and developed that utilizes painted-on conductors.

Christopher Forney↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

IEC 61850 electrical substations↗
DOE OSTI2019

Network Detection of Radiation Sources Using Localization-Based Approaches

Radiation source detection is a critical problem in homeland security-related applications. Deploying a network of detectors is expected to provide improved detection due to the combined, albeit dispersed, capture area of multiple detectors. Recently, localization-based detection algorithms provided performance gains beyond the simple “aggregated” area as a result of localization being enabled by the networked detectors. We introduce the following three localization-based detection approaches: 1) source-attractor radiation detection (SRD); 2) triangulation-based radiation source detection (TriRSD); and 3) the ratio of square distance-based radiation source detection (ROSD-RSD). We use canonical datasets from Domestic Nuclear Detection Office's intelligence radiation sensors systems tests to assess the performance of these methods. Extensive results demonstrate that SRD outperforms TriRSD and ROSD-RSD, and other existing detection algorithms based on the sequential probability ratio test and maximum likelihood estimation in terms of both false alarm and detection rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗
DOE OSTI2015

SU-E-T-363: Error Detection Comparison of EPID and MLC Log File Based IMRT QA Systems

Purpose: In this study we will compare the ability of three QA methods (Delta4, MU-EPID, Dynalog QA) to detect specific errors. Methods: A Varian Novalis Tx with a HD120 MLC and aS1000 Electronic Portal Imaging Device (EPID) was used in our study. Multi-leaf collimator (MLC) errors, gantry angle and dose errors were introduced into 5 volumetric arc therapy (VMAT) plans. 3D dose distributions calculated with data from the EPID and Dynalog QA methods were compared with the planned dose distribution. The gamma passing percentages as well as percentage error of planning target volume (PTV) dose were used for passing determination. Baselines for gamma passing percentages and PTV dose were established by measuring the original plan 5 times consecutively. Standard passing thresholds as well as thresholds derived from receiver operator characteristic (ROC) analysis and 2 standard deviation (SD) criteria were used. Results: When applying the standard 95% pass rate at 3%/3mm gamma analysis 14, 21 and 8 of 30 errors were detected by the Delta4, MU-EPID and Dynalog QA methods respectively. Thresholds set at 2 SD from our base line measurements resulted in the detection of 18, 9 and 14 of 30 errors for the Delta4, MU-EPID and Dynalog QA methods respectively. When using D2 of the PTV as a metric the Dynalog QA detected 20 of 30 errors while the EPID method detected 14 of 30 errors. Using D98 of the PTV, Dynalog QA detected 13 of 30 while the EPID detected 3 of 30 errors. Conclusion: Although MU-EPID detected the most errors at the standard 95% cutoff it also produced the most false detections in the baseline data. The Dynalog QA was the most effective when the ROC adjusted passing threshold was used. D2 was more effective as a metric for detecting errors than D98.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Detection of Special Nuclear Material in Cargo using Continuous Neutron Interrogation and Tension Metastable Fluid Detectors

This paper presents results using a novel technique for detecting special nuclear materials (SNMs) based on interrogation with a continuous beam of 2.45 MeV neutrons coupled with high intrinsic efficiency tensioned metastable fluid detectors (TMFDs) operated in threshold rejection mode. While passive interrogation may be utilized for detecting SNMs such as {sup 239}Pu from their strong spontaneous fission neutron signatures, this is not so for highly-enriched uranium (HEU), the timely detection of which has been identified as a grand challenge by the U.S. Domestic Nuclear Detection Office (DNDO). Active (neutron and/or photon based) interrogation represents the only viable means for detecting HEU either shielded or unshielded within cargo containers. Active interrogation by neutrons or photons leads to unequivocal induced fission based signatures in terms of neutron energy and multiplicity. The main challenge under such circumstances pertains to being able to discern the relatively weak emission signatures from the intense interrogation pulses of neutrons and/or photons. Purdue University together with Sagamore Adams Laboratories, LLC (SAL) has been developing the TMFD sensor system that promises to provide high neutron detection efficiency for fast and thermal neutrons while remaining gamma-beta blind. As described in these references, the TMFD sensor system works on the principle of transient cavitation detection events (CDEs) caused by ionizing particles such as neutrons, alphas and fission fragments in fluids that have been placed in various states of tensioned (sub-zero) pressure states of fluid metastability. Such CDEs result in recordable signatures - both electronically as for conventional detectors, but also in the form of audio-visual signals. One key advantage that the TMFD holds over conventional neutron detectors is that it has proven gamma-beta 'blindness' and indeed, confirmed to possess GARRn = 1.0 to γ photons (tested in up to 10{sup 11} γ/s field from a 3 Ci {sup 137}Cs source). Such an enablement allows for detection without saturation from background, interrogating and fission created γ photons. Another key attribute is up to 60 % intrinsic fission spectrum neutron detection efficiency which, furthermore, is amenable to tailoring for threshold based rejection. That is, to be able to detect neutrons at or above a desired energy with good efficiency, while remaining relatively blind to neutrons below the chosen threshold. This research is the subject of our ongoing DNDO project at Purdue University, the results of which form the basis of this paper. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Effects of Phasor Measurement Uncertainty on Power Line Outage Detection

Phasor measurement unit (PMU) technology provides an effective tool to enhance the wide-area monitoring systems (WAMSs) in power grids. Although extensive studies have been conducted to develop several PMU applications in power systems (e.g., state estimation, oscillation detection and control, voltage stability analysis, and line outage detection), the uncertainty aspects of PMUs have not been adequately investigated. This paper focuses on quantifying the impact of PMU uncertainty on power line outage detection and identification, in which a limited number of PMUs installed at a subset of buses are utilized to detect and identify the line outage events. Specifically, the line outage detection problem is formulated as a multi-hypothesis test, and a general Bayesian criterion is used for the detection procedure, in which the PMU uncertainty is analytically characterized. We further apply the minimum detection error criterion for the multi-hypothesis test and derive the expected detection error probability in terms of PMU uncertainty. The framework proposed provides fundamental guidance for quantifying the effects of PMU uncertainty on power line outage detection. Case studies are provided to validate our analysis and show how PMU uncertainty influences power line outage detection.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2026

Utilizing Time Reversal Ultrasonics to Detect the Removal of Nuclear Materials from Geological Repositories (FY26 Mid-Year)

Detecting unauthorized nuclear material removal from storage environments, such as geological repositories, is a critical safeguards task essential to ensuring the integrity and non-diversion of nuclear materials. However, this process is fraught with significant technical challenges. Storage configurations often involve tightly packed nuclear material containers or obstructed environments, making detection of removal events exceedingly difficult. Optical surveillance cameras, which are commonly used for monitoring, suffer from substantial limitations, including restricted coverage, reliance on line-of-sight measurements, and vulnerability to environmental conditions in certain storage scenarios. As the global inventory of monitored nuclear materials increases and storage configurations become more complex— such as deep geological repositories, inaccessible storage vaults, and tightly packed containers—there is an urgent need for innovative detection technologies that can reliably identify unauthorized diversion events in these challenging environments. The challenge of detecting nuclear material removal in complex storage environments is both significant and urgent. Preventing unauthorized access, diversion, or tampering with nuclear materials is a cornerstone of global nuclear safeguards and nonproliferation efforts. Current detection methods are increasingly inadequate as storage configurations become more intricate and inaccessible. The limitations of existing technologies—such as their inability to detect changes behind obstructions, reliance on costly and labor-intensive processes, and vulnerability to environmental conditions—pose risks to the effectiveness of safeguards systems. Addressing this challenge is critical to maintaining international trust in nuclear safeguards frameworks and ensuring compliance with nonproliferation agreements. Our project builds on the proven concept of TRU technology that can address this unmet need. TRU has demonstrated exceptional spatial sensitivity and change detection capabilities in complex non-line-ofsight environments, making it uniquely suited for detecting unauthorized nuclear material removal in challenging storage configurations. Unlike optical methods, TRU is not limited by line-of-sight constraints or environmental conditions, enabling reliable detection of subtle alterations even behind obstructions. By leveraging TRU’s ability to identify removal or tampering events, we aim to develop a robust detection system that enhances safeguards in geological repositories, storage vaults, and other complex environments.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1995

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Ahumada, A. J., Jr.↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. In order to collect information on the complete global precipitation cycle and to understand the energy budget in terms of precipitation, uniform global estimates of both liquid and frozen precipitation must be collected. Active observations of falling snow are somewhat easier to estimate since the radar will detect the precipitation particles and one only needs to know surface temperature to determine if it is liquid rain or snow. The challenges of estimating falling snow from passive spaceborne observations still exist though progress is being made. While these challenges are still being addressed, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Important information to assess falling snow retrievals includes knowing thresholds of detection for active and passive sensors, various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (~2.5 km) cloud tops having an ice water content (Iwe) at the surface of 0.25 g m-3 and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The analysis relies on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Results are presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz (Skofronick-Jackson, et al. submitted to IEEE TGRS, April 2012). The notable results show: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with ~0.05 g m-3 detected at the surface, or an ~0.5-1 mm hr-l melted snow rate (equivalent to 0.5-2 cm hr-l solid fluffy snowflake rate).

Jackson, Gail↗