dataset1

The methodology employed for modeling Gaussian stationary pulse noise, utilizing spectral filters, follows a consistent approach. Within each cycle, the noise behavior is partitioned into three distinct regions, each characterized by specific noise power levels and spectral profiles. The piecewise spectral cyclostationary Gaussian model (PSCGM) demonstrates the ability to capture time-varying variations in variance, adding a dynamic dimension to the modeling process. By adhering to the parameters outlined in LV14, we proceed to generate a noise trace based on the PSCGM model. This trace is sampled at a rate of 400 kHz, with each trace spanning a length of 16,384 data points (equivalent to 40.96 milliseconds). The overall dataset comprises a total of 15,000 individual instances, meticulously generated to serve as valuable training data for deep learning applications.[Ref] IEEE Standard for Low-Frequency (less than 500 kHz) Narrowband Power Line Communications for Smart Grid Applications, IEEE Std 1901.2-2013, 2013EEE Standard for Low-Frequency (less than 500 kHz) NarrowbandPower Line Communications for Smart Grid Applications, IEEE Std1901.2-2013, 2013