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DOE OSTI2021

An Active Detection Scheme for Sensor Spoofing in Grid-tied PV Systems

In this paper an active detection scheme for sensor spoofing (manipulated externally via a cyber attack) in grid-tied PV systems is discussed. The core of the proposed active detection scheme is to introduce a private (secret) watermarking signal into the control inputs of the DC-DC converter and DC-AC inverter stages to detect any malicious spoofing (manipulation) of voltage/current sensor measurements controlling both the DC-DC converter maximum power point tracking (MPPT) stage and the DC-AC inverter of the grid-tied PV system. Several types of possible spoofing mechanisms (attack models) are discussed. The proposed sensor spoofing attack detector system consists of injecting a small magnitude of digital watermarking signal (DWS) and conduct three statistical watermark tests on the reported sensor measurements to determine if a) the proposed system is healthy and operating as expected b) if sensor signals were spoofed (manipulated) externally or c) if a particular sensor is malfunctioning due to a faulty hardware. It is shown via extensive simulations that the proposed DWS approach is robust in detecting malicious external manipulation of sensors controlling the grid tied PV system. A testing platform is currently under development and the experimental results will be discussed in the conference presentation.

Ibrahim, Hasan↗

DOE OSTI2022

Designing an Intrusion Proof Adjustable Speed Drive System Controlling a Critical Process

In this paper, an intrusion proof adjustable speed drive system controlling a critical process in an industrial control system is detailed. In such a system, should the motor speed sensor signal data be compromised and/or altered via a cyber-attack, the system can potentially be unregulated, over speed and/or malfunction thereby disrupting the critical process. The proposed active detection scheme detailed in this paper introduces a private (secret) random signal termed as "watermark" into the inverter control signal that determines the PWM gating signals of the DC-AC inverter powering the motor. The watermarking signal introduced into the PWM modulation for the DC-AC inverter is shown to propagates its unique signature, which appears in all sensors signals at the inverter output such as voltage/current/speed used to control the motor. Now employing the measured data (from sensors), two statistical variance tests are conducted to identify anomalies if any in the presence of the watermarking signal. It is shown when an intrusion occurs to manipulate the sensor data to disturb and/or destabilize the process, the proposed tests immediately display a high value indicating a compromise in sensor data. It is shown that the proposed system is capable of immediate detection of a sophisticated attacks such as record/reply attack in which the actual speed sensor is disconnected and a prerecorded speed signal from the past of the same magnitude is played back to the controller. Several types of cyber-attacks such as speed reduction/increase including vibration have been tested. Extensive simulation results verify the proposed concepts. Experimental results will be discussed in the conference presentation.

Alotaibi, Faris↗

DOE OSTI2022

LCSL-II Cryomodule Testing at Fermilab

Cold powered testing of all LCLS-II production cryomodules at Fermilab is complete as of February 2021. A total of twenty-five tests on both 1.3 GHz and 3.9 GHz cryomodules were conducted over a nearly five year time span beginning in the summer of 2016. During the course of this campaign cutting-edge results for cavity Q₀ and gradient in continuous wave operation were achieved. A summary of all test results will be presented, with a comparison to established acceptance criteria, as well as overall test stand statistics and lessons learned.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study

Background The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. Methods This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t -tests, Pearson correlation analyses, and mediation effect analysis. Results The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058–0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185–0.327). Conclusion The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Chai, Yulin↗

DOE OSTI2023

Development of a High Min-Entropy Quantum Random Number Generator Based on Amplified Spontaneous Emission

We present the theory, architecture, and performance characteristics of a quantum random number generator (QRNG) which operates in a PCI express form factor-compatible plug-and-play design. The QRNG relies on a thermal light source (in this case, amplified spontaneous emission), which exhibits photon bunching according to the Bose–Einstein (BE) statistics. We demonstrate that 98.7% of the unprocessed random bit stream min-entropy is traceable to the BE (quantum) signal. The classical component is then removed using a non-reuse shift-XOR protocol, and the final random numbers are generated at a 200 Mbps rate and shown to pass the statistical randomness test suites FIPS 140-2, Alphabit, SmallCrush, DIEHARD, and Rabbit of the TestU01 library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1968

High performance structures

Materials selection, structural geometry, proof testing and statistical screening, prestressing, and system energy as tools for designing optimum trusses and other high performance structures

Barnett, R. L.↗

NASA NTRS1970

Optimum structural design based on reliability analysis

Proof-load test improves statistical confidence in the estimate of reliability, numerical examples indicate a definite advantage of the proof-load approach in terms of savings in structural weight. The cost of establishing the statistical distribution of strength of the structural material is also introduced into the cost formulation

Heer, E.↗

NASA NTRS1975

Effects of exposure time during flight maneuvers on passenger subjective comfort rating

The effects were investigated of length of exposure time to a flight maneuver environment on subjective passenger evaluation of ride comfort. Four statistical analysis tests were performed on ride comfort ratings obtained during one two-hour test flight wherein eleven test subjects were exposed to two identical programmed sequences of twenty four flight segments which covered a wide range of maneuver conditions. The results of the analysis indicate that, for over ninety five percent of the segments, there is no significant change in the test subjects comfort ratings of identical segments spaced one hour apart. These results are in contrast to those found in previous studies involving a vibration environment, rather than flight maneuver environment, where increased exposure-time was found to cause a degradation of ride comfort ratings.

Brown, V. J.↗

NASA NTRS1976

Joint aircraft loading/structure response statistics of time to service crack initiation

A reliability analysis for predicting the statistical distribution of time to fatigue crack initiation for aircraft structures in service is presented. The present analysis utilizes the statistical data of the specimen fatigue tests, the full-scale structure tests, and the statistical dispersion of aircraft service loads. The statistical distribution of the time to fatigue crack initiation of the full-scale structure under laboratory loading spectrum is assumed to be Weibull. The service loads for gust turbulences are modeled as Poisson processes for transport-type aircraft, while the maneuver loads are modeled as compound Poisson processes for fighter and training aircraft. It is found that the statistical distribution of time to fatigue crack initiation for aircraft structures in service is not Weibull and that the prediction on the basis of the Weibull distribution is unconservative, in particular in the early service time.

Yang, J.-N.↗

NASA NTRS1977

Optimal spatial sampling techniques for ground truth data in microwave remote sensing of soil moisture

The paper examines optimal sampling techniques for obtaining accurate spatial averages of soil moisture, at various depths and for cell sizes in the range 2.5-40 acres, with a minimum number of samples. Both simple random sampling and stratified sampling procedures are used to reach a set of recommended sample sizes for each depth and for each cell size. Major conclusions from statistical sampling test results are that (1) the number of samples required decreases with increasing depth; (2) when the total number of samples cannot be prespecified or the moisture in only one single layer is of interest, then a simple random sample procedure should be used which is based on the observed mean and SD for data from a single field; (3) when the total number of samples can be prespecified and the objective is to measure the soil moisture profile with depth, then stratified random sampling based on optimal allocation should be used; and (4) decreasing the sensor resolution cell size leads to fairly large decreases in samples sizes with stratified sampling procedures, whereas only a moderate decrease is obtained in simple random sampling procedures.

Rao, R. G. S.↗

NASA NTRS1982

Geometrical aspects of the tribological properties of graphite fiber reinforced polymide composites

A latin square statistical experimental test design was used to evaluate the effect of temperature, load and sliding speed on the tribological properties of graphite fiber reinforced polymide (GFRPI) composite specimens. Hemispherically tipped composite riders were slid against 440 C HT stainless steel disks. comparisons were made to previous studies in which hemispherically tipped 440 C HT stainless steel riders were slid against GFRPI composite disks and to studies in which GFRPI was used as a liner in plain spherical bearings. The results indicate that sliding surface geometry is especially important, in that different geometries can give completely different friction and wear results. Load, temperature, and sliding distance were found to influence the friction and wear results but sliding speed was found to have little effect. Experiments on GFRPI riders with 10 weight percent additions of graphite fluoride showed that this addition has no effect on friction and wear.

Fusaro, R. L.↗

NASA NTRS1982

Adaptive identification for the dynamics of large space structures

A new approach for identifying the dynamics of large space structures using noisy measurements is presented. In this approach, a modal system description is avoided and, hence, the a priori specification of model order. Instead, the system's order is recursively determined on-line, along with its parameters, using recursive lattice filters which are widely used in adaptive signal processing. The order determination is carried out based on statistical hypothesis testing theory. The approach is illustrated for the problem of identifying the structural dynamic characteristics of a free-free beam. Results on the effectiveness and accuracy of the identification scheme with respect to noise levels, sampling rates, and data window sizes are presented

Sundararajan, N.↗

NASA NTRS1982

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Hoffer, R. M.↗

NASA NTRS1983

Longitudinal flying qualities criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Stengel, R. F.↗

NASA NTRS1984

The 20 x 20 high speed microwave switches

Tests were conducted at NASA Lewis Research Center to characterize the proof-of-concept matrix switches built under NASA contract by Ford Aerospace and Aeronautics Corporation at Palo Alto, California, and the General Electric Company at Valley Forge, Pennsylvania. The contract requirements and goals are tabulated along with the results of the NASA tests. Characteristics examined are bandwidth, insertion loss, ripple, switching speed, isolation, standing wave ratio (input and output), deviation from linear phase, noise figure, reconfiguration rates, spurious responses, gain compression, and third order intermodulation distortion. A brief description of the testing method and a statistical analysis of the test results for each of the switches are provided.

Saunders, A.↗

NASA NTRS1983

Short-term climate variability and atmospheric teleconnections from satellite-observed outgoing longwave radiation. I Simultaneous relationships. II - Lagged correlations

Attention is given to the low-frequency variability of outgoing longwave radiation (OLR) fluctuations, their possible correlations over different parts of the globe, and their relationships with teleconnections obtained from other meteorological parameters, for example, geopotential and temperature fields. Simultaneous relationships with respect to the Southern Oscillation (Namais, 1978; Barnett, 1981) signal and the reference OLR fluctuation over the equatorial central Pacific are investigated. Emphasis is placed on the relative importance of the Southern Oscillation (SO) signal over preferred regions. Using lag cross-correlation statistics, possible lagged relationships between the tropics and midlatitudes and their relationships with the SO are then investigated. Only features that are consistent with present knowledge of the dynamics of the system are emphasized. Certain features which may not meet rigorous statistical significance tests but yet are either expected a priori from independent observations or are predicted from dynamical theories are also explored.

Lau, K.-M.↗