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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Optical Mass Gauging System for Measuring Liquid Levels in a Reduced Gravity Environment

A compact and rugged fiber-coupled liquid volume sensor designed for flight on a sounding rocket platform is presented. The sensor consists of a Mach-Zehnder interferometer capable of measuring the amount of liquid contained in a tank under any gravitational conditions, including a microgravity environment, by detecting small changes in the index of refraction of the gas contained within a sensing region. By monitoring changes in the interference fringe pattern as the system undergoes a small compression provided by a piston, the ullage volume of a tank can be directly measured allowing for a determination of the liquid volume. To demonstrate the technique, data are acquired using two tanks containing different volumes of liquid, which are representative of the levels of liquid in a tank at different time periods during a mission. The two tanks are independently exposed to the measurement apparatus, allowing for a determination of the liquid level in each. In a controlled, laboratory test of the unit, the system demonstrated a capability of measuring a liquid level in an individual tank of 10.53 mL with a 2% error. The overall random uncertainty for the flight system is higher than that one test, at +/- 1.5 mL.

Sullenberger, Ryan M.↗
NASA NTRS2010

Combined Aircraft and Satellite-Derived Storm Electric Current and Lightning Rates Measurements and Implications for the Global Electric Circuit

Using rotating vane electric field mills and Gerdien capacitors, we measured the electric field profile and conductivity during 850 overflights of electrified shower clouds and thunderstorms spanning regions including the Southeastern United States, the Western Atlantic Ocean, the Gulf of Mexico, Central America and adjacent oceans, Central Brazil, and the South Pacific. The overflights include storms over land and ocean, with and without lightning, and with positive and negative fields above the storms. The measurements were made with the NASA ER-2 and the Altus-II high altitude aircrafts. Peak electric fields, with lightning transients removed, ranged from -1.0 kV/m to 16 kV/m, with a mean value of 0.9 kV/m. The median peak field was 0.29 kV/m. Integrating our electric field and conductivity data, we determined total conduction currents and flash rates for each overpass. With knowledge of the storm location (land or ocean) and type (with or without lightning), we determine the mean currents by location and type. The mean current for ocean storms with lightning is 1.6 A while the mean current for land storms with lightning is 1.0 A. The mean current for oceanic storms without lightning (i.e., electrified shower clouds) is 0.39 A and the mean current for land storms without lightning is 0.13 A. Thus, on average, land storms with or without lightning have about half the mean current as their corresponding oceanic storm counterparts. Over three-quarters (78%) of the land storms had detectable lightning, while less than half (43%) of the oceanic storms had lightning. We did not find any significant regional or latitudinal based patterns in our total conduction currents. By combining the aircraft derived storm currents and flash rates with diurnal lightning statistics derived from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) low Earth orbiting satellites, we reproduce the diurnal variation in the global electric circuit (i.e., the Carnegie curve) to within 4% for all but two short periods of time. This excellent agreement with the Carnegie curve was obtained without any tuning or adjustment of the satellite or aircraft data. Given our data and assumptions, mean contributions to the global electric circuit are 0.7 kA (ocean) and 1.1 kA (land) from lightning-producing storms, and 0.22 kA (ocean) and 0.04 (land) from electrified shower clouds, resulting in a mean total conduction current estimate for the global electric circuit of 2.0 kA. Breaking the results down into mean storm counts reveals 1100 for land storms with lightning, 530 for ocean storms without lightning, 390 for ocean storms with lightning, and 330 for land storms without lightning.

Mach, Douglas M.↗
NASA NTRS2012

Wireless Damage Location Sensing System

A wireless damage location sensing system uses a geometric-patterned wireless sensor that resonates in the presence of a time-varying magnetic field to generate a harmonic response that will experience a change when the sensor experiences a change in its geometric pattern. The sensing system also includes a magnetic field response recorder for wirelessly transmitting the time-varying magnetic field and for wirelessly detecting the harmonic response. The sensing system compares the actual harmonic response to a plurality of predetermined harmonic responses. Each predetermined harmonic response is associated with a severing of the sensor at a corresponding known location thereof so that a match between the actual harmonic response and one of the predetermined harmonic responses defines the known location of the severing that is associated therewith.

Woodard, Stanley E.↗
NASA NTRS2012

Forest Canopy Cover and Height from MISR in Topographically Complex Southwestern US Landscape Assessed with High Quality Reference Data

This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern USforests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are available. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery usingthe CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50, 75, and 100 energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns ofMISR 672 nm band surface reflectance (mean RMSE 0.011, mean R2 0.82, N 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid.The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.

Chopping, Mark↗
NASA NTRS2019

Oculometric Assessment of Mild Neural Impairment

For over 100 years, neurologists have used eye movements to identify neural impairment, disease, or injury. Prior to the age of modern imaging, qualitative assessment of eye movements was a critical, routine component of diagnosis and remains today a routine law-enforcement tool for detecting impaired driving due to drugs or alcohol. We will describe the application of a simple 5-minute oculomotor tracking task coupled with a broad range of quantitative analyses of high-resolution oculomotor measurements for the sensitive detection of sub-clinical neural impairment and for the potential differentiation of various causes. Specifically, we will show that there are distinct patterns of impairment across our set of oculometric parameters observed with brain trauma, sleep and circadian disruption, and alcohol consumption. Such differences could form the basis of a self-administered medical monitoring or diagnostic support tool.

Stone, Lee↗
NASA NTRSDate not supplied

VSLAM and Vision-based Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft have many challenges in landing accurately and safely in urban, suburban, and rural environments. Localization in large and open rural environments could utilize GPS, but AAM aircraft in urban environments will encounter GPS degradation. Another challenge involves flight operation time, i.e., flying during the day or at night. There are different guidelines, landmarks, and landing light configurations at runways, heliports, and vertiports for daytime and nighttime applications. Tailoring feature detection methods for AAM approach and landing during the day and night pose different issues and challenges. It is easier to detect edges, lines, and other runway markers during the day than at night. Conversely, it is easier to see landing light configurations and patterns at nighttime than daytime. Consequently, utilizing the same feature detector for daytime and nighttime operations may not be feasible. This paper focuses on a vision-based precision approach and landing (PAL) by comparing ORB SLAM 2, a Vision Simultaneous Localization and Mapping (VSLAM) algorithm, and a novel EKF that combines onboard IMU measurements with coplanar pose from orthography and scaling with iterations (COPOSIT). Conducting unmanned aerial system (UAS) flight tests at NASA Armstrong Flight Research Center (AFRC) with landmarks and fiducials distributed around the landing zone provides a simulated AAM approach and landing data to test vision-based PAL methods to provide Alternative Position, Navigation, and Timing (APNT) solutions for AAM PAL applications. The novel vision-based PAL EKF with IMU and COPOSIT provides accurate state estimation when distributed landmarks and fiducials are in the field of view.

distributed sensing↗
NASA NTRS2021

A Layered Debris Disk around M Star TWA 7 in Scattered Light

We have obtained Hubble Space Telescope (HST) coronagraphic observations of the circumstellar disk around M star TWA 7 using the Space Telescope Imaging Spectrograph (STIS) instrument in visible light. Together with archival observations, including HST/NICMOS using the F160W filter and Very Large Telescope/SPHERE at the H-band in polarized light, we investigate the system in scattered light. By studying this nearly face-on system using geometric disk models and Henyey–Greenstein phase functions, we report a new discovery of a tertiary ring and a clump. We identify a layered architecture: three rings, a spiral, and an ≈150 au 2 elliptical clump. The most extended ring peaks at 28 au, and the other components are on its outskirts. Our point-source detection-limit calculations demonstrate the necessity of disk modeling in imaging fainter planets. Morphologically, we witness a clockwise spiral motion, and the motion pattern is consistent with both solid body motion and local Keplerian motion; we also observe underdensity regions for the secondary ring that might result from mean-motion resonance or moving shadows: both call for re-observations to determine their nature. Comparing multi-instrument observations, we obtain blue STIS-NICMOS color, a STIS-SPHERE radial distribution peak difference for the tertiary ring, and a high SPHERE-NICMOS polarization fraction; these aspects indicate that TWA 7 could retain small dust particles. By viewing the debris disk around M star TWA 7 at a nearly face-on vantage point, our study allows for the understanding of such disks in scattered light in both system architecture and dust property.

Debris disks↗
DOE OSTI2016

Ppb-level mid-infrared ethane detection based on three measurement schemes using a 3.34-μm continuous-wave interband cascade laser

A ppb-level mid-infrared ethane (C 2 H 6 ) sensor was developed using a continuous-wave, thermoelectrically cooled, distributed feedback interband cascade laser emitting at 3.34 μm and a miniature dense patterned multipass gas cell with a 54.6-m optical path length. The performance of the sensor was investigated using two different techniques based on the tunable interband cascade laser: direct absorption spectroscopy (DAS) and second-harmonic wavelength modulation spectroscopy (2f-WMS). Three measurement schemes, DAS, WMS and quasi-simultaneous DAS and WMS, were realized based on the same optical sensor core. A detection limit of ~7.92 ppbv with a precision of ±30 ppbv for the separate DAS scheme with an averaging time of 1 s and a detection limit of ~1.19 ppbv with a precision of about ±4 ppbv for the separate WMS scheme with a 4-s averaging time were achieved. An Allan–Werle variance analysis indicated that the precisions can be further improved to 777 pptv @ 166 s for the separate DAS scheme and 269 pptv @ 108 s for the WMS scheme, respectively. For the quasi-simultaneous DAS and WMS scheme, both the 2f signal and the direct absorption signal were simultaneously extracted using a LabVIEW platform, and four C 2 H 6 samples (0, 30, 60 and 90 ppbv with nitrogen as the balance gas) were used as the target gases to assess the sensor performance. A detailed comparison of the three measurement schemes is reported. Here, atmospheric C 2 H 6 measurements on the Rice University campus and a field test at a compressed natural gas station in Houston, TX, were conducted to evaluate the performance of the sensor system as a robust and reliable field-deployable sensor system.

47 OTHER INSTRUMENTATION↗
NASA NTRS1989

Detecting and diagnosing SSME faults using an autoassociative neural network topology

An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.

Ali, M.↗
DOE OSTI2021

A Distinct Atmospheric Mode for California Precipitation

The hydrologic cycle in California is strongly influenced by wet-season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet-season precipitation variability over both northern and southern California. This mode, named as the “California precipitation mode” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM's strong association with California wet-season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Antibody profiling sensitivity through increased reporter antibody layering

A method for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an embodiment of the invention, the analyte is a drug, such as marijuana, Cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method comprises attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to the antigens in the array to form immune complexes; washing away antibodies that do form immune complexes; and detecting the immune complexes, to form an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to the subject's identity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Global teleconnections influencing large-scale drought in the United States using SVDI

Understanding recent large-scale drought patterns and the mechanisms producing extreme drought events is vital for future drought forecasts and understanding future drought risks. Increasingly, vapor pressure deficit (VPD) has been used as an important measure of evaporative demand and proxy for drought detection. In this study, VPD is used to calculate the new Standardized VPD Drought Index (SVDI) with NASA North American Land Data Assimilation System (NLDAS) data. Previous studies have shown that SVDI accurately identifies the timing and magnitude short-term droughts in the United States (U.S). In the present study, SVDI is now used to identify large-scale drought patterns between 1980 and 2021 and drought variability driven by selected global teleconnections originating in the Pacific and Atlantic Oceans. Spatial drought characteristics were extracted from SVDI using empirical orthogonal function (EOF) analysis. Then a k-means clustering algorithm was applied to both EOF principal components and primary teleconnections, including the El Nino-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) to identify drought events driven by the Pacific Ocean. Results show that the SVDI is useful in evaluating large-scale drought variability in the U.S. related to global teleconnections, and that mechanisms influencing summer drought patterns in the Western and Southwestern U.S. are driven by a tropical-extratropical interactions originating in the equatorial Pacific Ocean related to ENSO dynamics with interdecadal variability modulated by PDO. The large-scale droughts in the Central and Southern U.S., like those in 2011 and 2012, on the other hand, are driven by the North Pacific Ocean warm pool during a strong negative PDO, which subsequently influenced variability in the Bermuda-Azores High in the Atlantic Ocean. In summer 2011, the Bermuda-Azores High weakened, reducing the onshore winds and moisture transport along the eastern Gulf of Mexico and contributing to ongoing drought in the region. The Northern Pacific and Atlantic Ocean sea surface temperatures (SSTs) have increased between 1980 and 2021. In conclusion, as SSTs continue to rise in the Northern Pacific Ocean, one consequence of the coupled North Pacific warm pool and atmospheric dynamics, is to increase summer drought variability over a large region in the southern and midwestern U.S. under global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Planck 2015 results: XVIII. Background geometry and topology of the Universe

We report that maps of cosmic microwave background (CMB) temperature and polarization from the 2015 release of Planck data provide the highestquality full-sky view of the surface of last scattering available to date. This enables us to detect possible departures from a globally isotropic cosmology. We present the first searches using CMB polarization for correlations induced by a possible non-trivial topology with a fundamental domain that intersects, or nearly intersects, the last-scattering surface (at comoving distance χ rec ), both via a direct scan for matched circular patterns at the intersections and by an optimal likelihood calculation for specific topologies. We specialize to flat spaces with cubic toroidal (T3) and slab (T1) topologies, finding that explicit searches for the latter are sensitive to other topologies with antipodal symmetry. These searches yield no detection of a compact topology with a scale below the diameter of the last-scattering surface. The limits on the radius R i of the largest sphere inscribed in the fundamental domain (at log-likelihood ratio ΔlnL > -5 relative to a simply-connected flat Planck best-fit model) are: R i > 0.97 χ rec for the T3 cubic torus; and R i > 0.56 χ rec for the T1 slab. The limit for the T3 cubic torus from the matched-circles search is numerically equivalent, R i > 0.97 χ rec at 99% confidence level from polarization data alone. We also perform a Bayesian search for an anisotropic global Bianchi VII h geometry. In the non-physical setting, where the Bianchi cosmology is decoupled from the standard cosmology, Planck temperature data favour the inclusion of a Bianchi component with a Bayes factor of at least 2.3 units of log-evidence. However, the cosmological parameters that generate this pattern are in strong disagreement with those found from CMB anisotropy data alone. Fitting the induced polarization pattern for this model to the Planck data requires an amplitude of -0.10 ± 0.04 compared to the value of + 1 if the model were to be correct. In the physically motivated setting, where the Bianchi parameters are coupled and fitted simultaneously with the standard cosmological parameters, we find no evidence for a Bianchi VII h cosmology and constrain the vorticity of such models to (ω/H) 0 < 7.6 × 10 -10 (95% CL).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

Detection Limit of Smectite by Chemin IV Laboratory Instrument: Preliminary Implications for Chemin on the Mars Science Laboratory Mission

The CheMin instrument on the Mars Science Laboratory (MSL) is an miniature X-ray diffraction (XRD) and X-ray fluorescence (XRF) instrument capable of detecting the mineralogical and elemental compositions of rocks, outcrops and soils on the surface of Mars. CheMin uses a microfocus-source Co X-ray tube, a transmission sample cell, and an energy-discriminating X-ray sensitive CCD to produce simultaneous 2-D XRD patterns and energy-dispersive X-ray histograms from powdered samples. CRISM and OMEGA have identified the presence of phyllosilicates at several locations on Mars including the four candidate MSL landing sites. The objective of this study was to conduct preliminary studies to determine the CheMin detection limit of smectite in a smectite/olivine mixed mineral system.

Archilles, Cherie↗
DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Aftershock sequence of the 2011 Virginia earthquake derived from the dense AIDA array and backprojection

After the 23 August 2011 Mineral, Virginia, earthquake, a temporary dense array (Aftershock Imaging with Dense Arrays (AIDA)) consisting of ~200 stations was deployed at 200-400 m spacing near the epicenter for 12 days. Backprojection of the data was used to automatically detect and locate aftershocks. The co-deployment of a traditional aftershock network of 36 stations at ~2-10 km spacing enables a quantitative comparison. The AIDA backprojection aftershock catalog is complete to magnitude -1.0 and includes events as small as M-1.8. For comparison, the traditional network was complete to M-0.3 for the same time period. The AIDA backprojection catalog observes the same major patterns of seismicity in the epicentral region, but additional details are illuminated. The primary zone of seismicity is not a single fault but is a tabular zone of multiple small faults, this zone has a subtle concave shape along strike and with depth, and a broader zone of new events is observed at shallow depth. In addition, a new separate, shallow cluster was detected and located to the east of the main aftershock zone. The addition of smaller events to the catalog did not change the b-value or the temporal decay constant, but illuminated spatial and temporal patterns. Both the b-value and temporal decay constant are different for 12 days than for 4 months and are different at < 3km depth than at greater depth. Very low b-value, especially at greater depth, is consistent with observed very high stress drops. Conclusively, the results indicate the benefits of dense arrays and auto-detection by backprojection for aftershock studies. And finally, the reduced detection threshold and higher spatial resolution enabled the study of earthquake mechanisms and strain transfer at an unprecedented small scale.

Beskardes, Gungor Didem↗
NASA NTRS2006

Developing Signal-Pattern-Recognition Programs

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

Shelton, Robert O.↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗