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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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At least 289 records · Page 16

AmeriFlux US-NYd NYSM - Red Hook

This is the AmeriFlux version of the carbon flux data for the site US-NYd NYSM - Red Hook. Site Description - Located in an grass/orchard setting. It is in an open area next to the school fields with some trees nearby. Obstructions within 100m include small trees. The soil type is Haven loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYe NYSM - Ontario

This is the AmeriFlux version of the carbon flux data for the site US-NYe NYSM - Ontario. Site Description - Located in an orchard setting. It is in an open grassy area near vineyard/orchard, small pond to the south. Obstructions within 100m include orchard/vineyard, small pond. The soil type is loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYf NYSM - Burt

This is the AmeriFlux version of the carbon flux data for the site US-NYf NYSM - Burt. Site Description - Located in a vineyard/crop field setting. It is in an opening in a vineyard with a crop field to the north. Obstructions within 100m include vineyards. The soil type is Niagara silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYg NYSM - Chazy

This is the AmeriFlux version of the carbon flux data for the site US-NYg NYSM - Chazy. Site Description - Located in a crop field setting. It is small open area in an overgrown field. Obstructions within 100m include high vegetation. The soil type is Malone gravelly loam, very stony. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYh NYSM - Owego

This is the AmeriFlux version of the carbon flux data for the site US-NYh NYSM - Owego. Site Description - Located in a grassy field setting. It is surrounded by a flat, open field with some trees and a pond to the north. Obstructions within 100m include small trees, small pond, pavement. The soil type is Volusia channery silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYj NYSM - Queens

This is the AmeriFlux version of the carbon flux data for the site US-NYj NYSM - Queens. Site Description - Located in an urban setting. It is on a city rooftop. Obstructions within 100m include buildings, pavement. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYk NYSM - Staten Island

This is the AmeriFlux version of the carbon flux data for the site US-NYk NYSM - Staten Island. Site Description - Located in an urban setting. It is on a rooftop. Obstructions within 100m include tall buildings. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Profiler site and a Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYl NYSM - Southold

This is the AmeriFlux version of the carbon flux data for the site US-NYl NYSM - Southold. Site Description - Located in a vineyard setting. It is a flat, open area surrounded by vineyards. Obstructions within 100m include vineyard, trees. The soil type is Haven loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYm NYSM - Belleville

This is the AmeriFlux version of the carbon flux data for the site US-NYm NYSM - Belleville. Site Description - Located in a grass/crop field setting. It is a in a flat, open field with high vegetation to the northeast, paved road to the east, and a small treeline to the north. Obstructions within 100m include trees, paved road, high crops. The soil type is Collamer silt loam, bedrock substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYn NYSM - Brooklyn

This is the AmeriFlux version of the carbon flux data for the site US-NYn NYSM - Brooklyn. Site Description - Located in an urban setting. It is on a city rooftop. Obstructions within 100m include buildings, pavement. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYo NYSM - Penn Yan

This is the AmeriFlux version of the carbon flux data for the site US-NYo NYSM - Penn Yan. Site Description - Located in a crop field setting. It is very flat and open, at the edge of a crop field. Obstructions within 100m include high crops. The soil type is Honeoye silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗

AmeriFlux US-NYp NYSM - Fredonia

This is the AmeriFlux version of the carbon flux data for the site US-NYp NYSM - Fredonia. Site Description - Located in a vineyard setting. It is in a small open area surrounded by vineyards. Obstructions within 100m include vineyards, trees. The soil type is Niagara silt loam, loamy substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗

Center for Alternate Synchronization and Timing (CAST) PTP Network Monitoring Report

The Oak Ridge National Laboratory Center for Alternative Synchronization and Timing (CAST) performs research, development, testing, and evaluation of alternative terrestrial-based timing and synchronization infrastructure for the US power grid and other critical infrastructures. Alternative timing options reduce reliance on GPS and enhance the overall resilience of critical infrastructures. CAST infrastructure uses Precision Time Protocol (PTP) as the primary conduit for delivery of synchronization packets. CAST deploys PTP over long terrestrial links to synchronize a multitude of remote boundary clocks and downstream power grid components with the authoritative grand master clocks. Network traffic issues can severely degrade PTP accuracy. This report focuses on examining network traffic anomalies and their effects on PTP operation as well as the potential implications to CAST’s high-precision remote synchronization operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Multi-resonant switched capacitor power converter architecture

A switched-capacitor (SC) network in an SC converter is controlled to operate at varying resonant modes to achieve high conversion ratio efficiency, at a low circuit component count. These power converters are suited to numerous application areas including improving energy efficiency of data centers. A family of resonant switched capacitor (SC) converters with multiple operating phases are presented “Multi-Resonant SC Converters”. Described in detail are an 8-to-1 Multi-Resonant-Doubler (MRD) converter and a 6-to-1 Cascaded Series-Parallel (CaSP). The topology of these converters make them amenable to combining like units in parallel toward reaching higher power levels.

Ye, Zichao↗