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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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W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing Scan in Co-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Resolution Scan in Cross Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Sensitivity Scan in Cross Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing Scan in Cross-Polarization Mode
Ka-Band ARM Scanning Cloud Radar (KASACR) Filtered Spectral Data of Cross-Wind RHI Scan in Cross-Polarization Mode
Ka-Band ARM Scanning Cloud Radar (KASACR) Filtered Spectral Data of Vertically Pointing High Resolution Scan in Co-Polarization Mode
Ka-Band ARM Scanning Cloud Radar (KASACR) Filtered Spectral Data of Vertically Pointing High Resolution Scan in Cross-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Cross-Wind RHI Scan in Co-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Cross-Wind RHI Scan in Cross-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Resolution Scan in Co-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Resolution Scan in Cross-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Sensitivity Scan in Co-Polarization Mode
W-Band ARM Scanning Cloud Radar (WSACR) Filtered Spectral Data of Vertically Pointing High Sensitivity Scan in Cross-Polarization Mode
The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.
The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.
Presentation describes project to develop cutting-edge analysis methods to more fully exploit the spectral data recorded by NaI detector systems.
In the current paper, the problem of bending vibrations of a beam in which the binding on the right end is unknown and not available for visual inspection is studied. The main objective is to study an inverse problem: find additional unknown boundary conditions by additional spectral data, i.e., the conditions of fixing the right end of the rod. In this work, unlike many other works, as such additional conditions we choose the first natural frequencies (eigenvalues) of two new problems corresponding to the problem of bending vibrations of a beam with loads of different weights at the central point.
In the field of analytical chemistry, it is the nature of the available instrumentation that makes a specific technique uniquely suited for answering a specific challenge. Often times, instruments equipped with ultrasensitive array of detectors and state-of-the-art separation components have served to highlight their invaluable role in the field. Nonetheless, it is their ultimate ability to predict the structure of an unknown material on the basis of mass spectral data that exponentially magnifies their value. Their efficacy in achieving this prediction is largely based on the size of their internal mass spectral library that conveniently can be accessed by using the software. The library can thus exhaustively correlate an unknown compound's mass spectrum to known compounds in a database and provide the analytical chemist with a probable structure for the unknown analyte. During routine analysis, it is customary that after the appropriate collection of the compound's mass spectrum, the next step involves its comparison with a compound in the instrument's National Institute of Standards and Technology (NIST) library.