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We give an example to illustrate the disparity of the temporal autocorrelation functions of the sea clutter and target by analyzing the recorded datasets.
By using the fixed matrix coefficients produced by the MDA method, we computed the transient projection of neural ensemble responses throughout the recorded datasets with a 10-msec sliding window [17], [28], [30].
This framework uses Voronoi diagrams to compute cell-to-cell contact areas, defines a feature-based spatio-temporal model for intercalation events and fits an anatomical coordinate system to the recorded datasets.
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The recorded dataset has five sets denoted as A to E. Each contains 100 single-channel EEG signals, and each one having 4097 samples.
The electronically recorded dataset was translated from Chinese to English by the lead researcher for further analysis, as described in the following section.
In addition, we can extract the elastic properties of the Earth below the sea bottom from the recorded acoustic pressure datasets.
The algorithm is tested by means of the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room.
Time consumption of the two methods was recorded for datasets with different feature sizes (from 100 to 5000 features).
The dataset recorded from the field included true gradients of environmental changes.
Seventy-three percent of the datasets recorded all samples correctly.
Finally, the data description is provided along with the relevant papers that utilized the dataset recorded during this experiment.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com