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At least 145 records · Page 8

Remote sensing of global snowpack energy and mass balance: In-situ measurements on the snow of interior and Arctic Alaska

Observations led to a study of the physical properties of snow and the processes which operate on it. These observations included microwave brightness temperatures in interior Alaska which revealed: (1) up to three times more variability from one cell (1/2 degree latitude x 1/2 degree longitude) to the next in winter than in summer (5 to 15 K in winter and about 5 K in summer); (2) the overall range of temperature from week to week is about seven times greater in winter than in summer; (3) the microwave brightness temperature is about 25 K less than air temperature during summer but 35 to 60 K less during winter; and (4) the presence of snow cover appears to contribute to increasing the difference between air temperature and brightness temperature. The role of irregular substrate under the snow in enhancing convection has been studied with particular attention to variations in snow cover on water surfaces and in forested regions. LANDSAT imagery has been obtained to prepare a classification of ground surface types of the area. The extreme conditions of the 1988 to 1989 winter are discussed with respect to comparing the microwave data sets from 1985, and before, up to the present. The use of the Mt. Wrangell area as aerial photogrammetric controls for glacier measurements is given attention.

Benson, Carl S.↗

Convective Weather Forecast Accuracy Analysis at Center and Sector Levels

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60- minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.

Wang, Yao↗

2023 Cleanroom Monitoring for OSIRIS-REx

OSIRIS-REx is an asteroid sample return mission that delivered asteroid regolith from Bennu to Earth on 9/24/2023 [1]. Cleanroom monitoring of OSIRIS-REx Curation facilities has been ongoing since 2021 when construction of the OSIRIS-REx lab at Johnson Space Center (JSC) was completed [2]. The monitoring continues inside of the OSIRIS-REx lab in Houston. Monitoring was also conducted on a temporary cleanroom constructed inside a hangar at the Utah Test and Training Range (UTTR) to initially receive the Sample Return Capsule (SRC) upon its landing there. The following reports on cleanroom monitoring that has taken place in the months leading up to as well as after OSIRIS-REx return. Monitoring techniques used include particle counts, deployment of Si wafer witness plates (Balazs, Inc.) and Al foil witness plates, gas sampling (Balazs, Inc.) and microbiological monitoring. We collected particle counts at both the lab at JSC as well as the temporary cleanroom at UTTR. Particle counts were taken monthly from six locations in the lab at JSC. Particle counts were taken from seven locations at UTTR in July, August, and September. The UTTR cleanroom particle counts were taken daily in the five days leading to OSIRIS-REx return as well as consistently the day of return (every 2 minutes and 15 seconds 7:21 am- 6:27 pm) from one location inside the cleanroom. The particle counts at UTTR consistently stayed well within the ranges of its required ISO 7 equivalent classification (352,000 maximum particles/ m3 ≥ 0.5 μm). Most analysis showed that the particle counts were far below ISO 7 requirements (highest measured count was 61,484 particles/ m3 ≥ 0.5 um). but the elevated instances correlated to exterior hangar doors being open. Particle counts were reduced when keeping exterior hangar doors closed. The particle counts at JSC have stayed consistently within their ISO 5 equivalent classification (3,520 maximum particles/ m3 ≥ 0.5 um). In most instances, the counts are much better except in one instance where the count measured 13,381 particles/ m3 ≥ 0.5 um. The high spike in particle counts in the JSC lab correlated with the air handlers being shut down temporarily the previous day. The particle counts taken in the same location quickly reduced and measured 0 when taken a month later with the air handlers on. We deployed 8-inch semiconductor polished Si wafer witness plates for 24-hour periods inside of the OSIRIS-REx Curation lab at JSC (in July, August, September, and November 2023) and the cleanroom at UTTR (July and September 2023) to test for possible organic and inorganic contaminants. We also deployed these wafers inside of the OSIRIS-REx Touch-and-Go sample Acquisition Mechanism (TAGSAM) glovebox (where Bennu sample processing occurs) and desiccator (Bennu sample storage) at JSC in August 2023. These samples were all sent to Balazs for analysis via Thermal Desorption Gas Chromatography Mass Spectroscopy (TD-GC-MS) to quantify organic compounds and vapor phase decomposition inductively coupled plasma mass spectrometry (VPD-ICP-MS) to quantify inorganic contaminants. We collected air samples using an adsorbent tube connected to a pump for six hours (100 mL/minute) inside of both the JSC lab and the UTTR cleanroom. These samples were taken at JSC in July, August, September, November, and December and at UTTR in July and September. Additionally, a sample was taken just outside of the TAGSAM glovebox airlock door in July. These samples were sent to Balazs for analysis of volatile organics in air. Overall, the OSIRIS-REx lab at JSC and cleanroom at UTTR yielded very low organic and inorganic contaminants (similar results to Genesis lab at JSC which is ISO 4 equivalent) except for boron, which is attributed to the borosilicate glass in the fan filter units. We collected monthly surface and air samples inside the JSC lab to monitor potential microbial contamination. We report the recovery rate for each sampling event, which is defined as the number of samples exhibiting bacterial or fungal growth divided by the total number of samples collected. Since the lab was commissioned in 2021 the median recovery rate is 29%. This recovery rate is consistent with other ISO 5 equivalent labs used to curate astromaterials collections. Increases in recovery rate correspond to construction and/or extra activity in the lab. However, increases in recovery rate do not reliably correlate to increases in particle counts, which highlights the need for a dedicated microbial monitoring program for biologically sensitive collections. We sampled the temporary clean room at UTTR in July, August, and September of 2023 to monitor potential microbial contamination. The recovery rate decreased from 80% in August to 29% in September. However, in all instances, the diversity of bacteria and fungi was higher in the UTTR cleanroom than in the JSC lab. We routinely collected more than twenty different organisms from the UTTR cleanroom. In the JSC lab the median diversity is 2 organisms. Based on these results we hypothesize that microbes from UTTR could be transported back to JSC with the Bennu samples. To mitigate this risk, in September of 2023, we implemented additional cleaning procedures in the JSC lab to reduce the bioburden on surfaces that could come into direct contact with hardware used to process Bennu samples. Since adopting these additional cleaning measures, the median recovery rate has decreased to 14%. We did not observe an increase in fungal or bacterial diversity in the lab in the October sampling. We will continue to monitor this trend closely for the next several months. Supported by NASA under Award NNH09ZDA007O and Contract NNM10AA11C.

Rachel Comstock Funk↗

Change in land use in the Phoenix (1:250,000) Quadrangle, Arizona between 1970 and 1973: ERTS as an aid in a nationwide program for mapping general land use

Changes in land use between 1970 and 1973 in the Phoenix (1:250,000 scale) Quadrangle in Arizona have been mapped using only the images from ERTS-1, tending to verify the utility of a standard land use classification system proposed for use with ERTS images. Types of changes detected have been: (1) new residential development of former cropland and rangeland; (2) new cropland from the desert; and (3) new reservoir fill-up. The seasonal changing of vegetation patterns in ERTS has complemented air photos in delimiting the boundaries of some land use types. ERTS images, in combination with other sources of information, can assist in mapping the generalized land use of the fifty states by the standard 1:250,000 quadrangles. Several states are already working cooperatively in this type of mapping.

Place, J. L.↗

Current Status of Multi-Angle Implementation of Atmospheric Correction (MAIAC) Algorithm

A new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based rather than pixel-based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over all land surface types with the exception of snow. MAIAC has an internal Cloud Mask, a dynamic land-water-snow classification and a surface change mask which allows it to flexibly choose processing path over different surfaces. A distinct feature of MAIAC is a high 1 km resolution of aerosol retrievals including optical thickness and fine mode fraction, which is required in different applications including the air quality analysis. An overview of the algorithm, results of AERONET validation, and examples of comparison with MODIS Collection 5 aerosol product, including Deep Blue algorithm, will be presented for different parts of the world including continental USA, Persian Gulf region and India.

Lyapustin, A.↗

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Weinman, James A.↗

Tests of Several Model Nacelle-Propeller Arrangements in Front of a Wing

An investigation was conducted in the N.A.C.A. 20-foot wind tunnel to determine the drag, the propulsive and net efficiencies, and the cooling characteristics of severa1 scale-model arrangements of air-cooled radial-engine nacelles and present-day propellers in front of an 18- percent-thick, 5- by 15-foot airfoil. This report deals with an investigation of wing-nacelle arrangements simulating the geometric proportions of airplanes in the 40,000- to 70,000- pound weight classification and having the nacelles located in the vicinity of the optimum location determined from the earlier tests.

McHugh, James G.↗

Accelerated Over-The-Air Neural Receiver Training Using Self-Contrastive Learning

Self-contrastive learning (SCL), a self-supervised learning method, has been shown to improve image and signal classifier accuracies and reduce the training time for neural communications receivers. In particular, prior work has shown that SCL applied as a pre-training step can improve simulated performance of OFDM in 3GPP TDL channel models by reducing the training time of the downstream classification task (demodulation and demapping). In this work a practical implementation demonstrating SCL pre-training using software defined radios (SDRs) is proposed.

Cooke, Corey [ORNL] (ORCID:0000000234263672)↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic

To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as a heat and humidity feeding mechanism which can modify Arctic cloud properties. Cloud microphysical properties in the central Arctic are analysed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. The Cloudnet classification algorithm is used to characterize the clouds based on remote sensing observations and the atmospheric thermodynamic state from the observatory on board the research vessel (RV) Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapour transport (WVT) being conveyed towards RV Polarstern has been utilized as a mechanism to associate upwind sea ice conditions with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer. Only a conical sub-sector of sea ice concentration (SIC) and the lead fraction (LF) centred on the RV Polarstern location and extending up to 50 km in radius and with an azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT and selected by LF regimes. Liquid water path of low-level clouds is found to increase as a function of LF, while the ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to generally have a lower cloud base and larger thickness than decoupled clouds. Thermodynamically, for coupled cases the cloud-top temperature is warmer and accompanied by a temperature inversion at the cloud top, whereas the decoupled cases are found to be closely compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface–atmosphere processes. With this formulation, long-term observations can be analysed to enforce the statistical significance of the asymmetries. Furthermore, our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system.

54 ENVIRONMENTAL SCIENCES↗

Altitude determination and descriptive analysis of clouds on ERTS-1 multispectral photography

A simple method to determine the approximate altitude of clouds is described, with the objective of refining their classification using only marginal data from the photographs. Results of the application of this method on photographs of the Goajira Peninsula, Paraguana Peninsula and the Central Coast of Venezuela are presented. Here, the altitudes computed are used to classify clouds and to identify the genus of others without typical form. Instability of air masses through clouds vertical development, and wind direction as well as other local climatic characteristics such as moisture content, loci of condensation, area, etc. are determined using repetitive coverage for the time interval of the photography. Applications for the regional and urban planning (including airport location and flights schedule) and natural resources evaluation are suggested.

Albrizzio, C.↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

Assessment of Computer-based Geologic Mapping of Rock Units in the LANDSAT-4 Scene of Northern Death Valley, California

Geologists obtain low accuracy levels when maps derived from LANDSAT MSS data are compared with those made by conventional methods. Procedures developed for the IDIMS computer system and used to classify a subset of a TM image of the Death Valley, California - Nevada border are described. Despite the superior resolution, broader spectral coverage, and greater sensitivity inherent to the TM, the actual recorded measured accuracy was in the same narrow range (30 to 60%) recorded for MSS data from earlier LANDSATs. The supervised classification approach appears to be superior to the unsupervised approach when applied to vegetation-sparse surfaces composed of spectrally contrasting rock/soil units distributed in relatively flat to low relief terrain. As spatial resolution improves and optimal spectral bands for identifying rock materials are specified, use of classified multispectral remote sensing data from air and space when coupled with supporting field calibration and checks should become the dominant way in which geologic mapping is carried out in future decades.

Short, N. M.↗

Health Physics Innovations Developed During Cassini for Future Space Applications

The long history of space flight includes missions that used Space Nuclear Auxiliary Power devices, starting with the Transit 4A Spacecraft (1961), continuing through the Apollo, Pioneer, Viking, Voyager, Galileo, Ulysses, Mars Pathfinder, and most recently, Cassini (1997). All Major Radiological Source (MRS) missions were processed at Kennedy Space Center/Cape Canaveral Air Station (KSC/CCAS) Launch Site in full compliance with program and regulatory requirements. The cumulative experience gained supporting these past missions has led to significant innovations which will be useful for benchmarking future MRS mission ground processing. Innovations developed during ground support for the Cassini mission include official declaration of sealed-source classifications, utilization of a mobile analytical laboratory, employment of a computerized dosimetry record management system, and cross-utilization of personnel from related disciplines.

Nickell, Rodney E.↗

Machine learning for reactor power monitoring with limited labeled data

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in a transfer learning paradigm. Twenty-three supervised models were trained on labeled sequences of magnetic field and acceleration data from each of the target sites. Self-learning and transfer learning methods were applied to the top performing models to assess their classification performance with increasing amounts of labeled data. While reactor power level classification was achieved with a Matthews Correlation Coefficient of up to 0.739 ± 0.003 and 0.622 ± 0.009 with only 400 sequences per power state for the large research reactor and TRIGA target sites, respectively, self-learning and transfer learning leveraging source site data did not improve target classification performance. These findings suggest that alternative methods, such as higher sensitivity sensors, digital twins, or the use of physics-informed models, are required to enable high-performance classification in machine learning approaches to reactor monitoring with a dearth of target ground truth.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗