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ShareScore release 0.7.1
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2 results for “Radio spectrograms”
Image-based Classification of Intense Radio Bursts from Spectrograms: An Application to Saturn Kilometric Radiation
<p>A catalogue of 4874 of the Low Frequency Extensions (LFEs) of Saturn Kilometric Radiation (SKR) detected by Cassini/RPWS from the beginning of 2004 until mission end in 2017. The LFEs presented in this catalogue were identified using a modified U-Net architecture that applied semantic segmentation to spectrogram images in order to extract the exact frequency-time coordinates of the LFE. The files consist of a .json file with the coordinates of each LFE in Time Frequency Catalogue (TFCat) format (Cecconi et. al. 2023). We also include a .csv file with the start and stop times of each LFE in the form of python datetime timestamps, with the average predicted probability per LFE as an accompanying column. </p>
BRAMS Radio Spectrograms and Spectrogram Samples for Automatic Detection of Meteor Echoes
<p>The files in this dataset are based of radio recordings taped by BRAMS (Belgian RAdio Meteor Stations), the Belgian meteor detection network.</p> <p>Included in the dataset are the original BRAMS radio recordings (stored as .wav audio files), the spectrogram data for each radio recording (stored as .csv files) and the meteor and non-meteor samples extracted from the radio spectrograms (stored as .csv files).</p> <p>It should be noted that the the spectrogram data was sampled using a sliding window of size 30x20 pixels and the samples extracted in this manner were further processed by calculating the vertical average of each column in the 30x20 matrixes. The result of this sampling procedure is a set of data vectors containing the average power of the signal found in the original 30x20 spectrogram sample.</p>
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