# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Detection Algorithm
The top to bottom panels correspond to the X, Y, and Z components, respectively. Figure 5: Spectrograms of EFD-ELF observations (X, Y, and Z components from top to bottom in the top three panels) and IPSD\({}_{\rm t}\) (bottom panel). long-term trend (due to satellite's motion along its trajectory) and a high-pass filter with a cutoff frequency of \(f_{co}\) = 60 Hz to suppress low-frequency disturbances, which primarily affect frequencies below \(f_{co}\) (as previously discussed). This process was implemented using the built-in \"detrend\" and \"highpass\" functions in MATLAB R2024a. As for Figures 6 and 7 showing waveforms (left panels) and spectrograms (right panels) computed around \(t_{a}\) and \(t_{b}\), respectively, despite the preprocessing, in both cases, waveforms and spectrograms exhibit significant noise levels characterized by beats at approximately 1.5 kHz. In addition, two continuous bands at \(\simeq\)1 kHz and \(\simeq\)2.2 kHz are observed (particularly in the X component). As explained in [44; 45], the 1 kHz signal corresponds to plasmaspheric hits, while the \(\simeq\)2.2 kHz signal could be the second harmonic of the same signal. In the case of the largest SCM IPSD peak corresponding to the blue dashed line at \(t_{a}\) whose electric field waveforms and spectrograms are shown in Figure 6, the characteristic whistler signature is still clearly discernible in the Y and Z components (SNR\(\sim\)10\({}^{2}\), central and bottom right panels), while it is partially concealed by the noise in the X component (SNR\(\sim\)1, top right panel). Figure 7 shows electric field waveforms (left panels) and spectrograms (right panels) corresponding to the representative minor peaks identified at \(t_{b}\) (red dashed line in Figure 2). In this case, the whistler signature is almost completely obscured by noise (SNR below 10\({}^{-1}\)). A comparison between Figure 3 and Figure 6 showing the waveforms and spectrograms computed around \(t_{a}\) for magnetic and electric observations, respectively, reveals the well-known lack of synchronization between SCM and EFD, which is due to differences in hardware and software implementations between the two instruments [46; 47]. These findings highlight significant challenges associated with utilizing electric field data for whistler studies. In practice, only the most prominent events are discernible in electric field observations, and they are partially concealed by the noise. Given the primary objective of this study to develop a computationally efficient whistler detection technique for the top-side ionosphere, the final stages of the WhISPER algorithm were optimized specifically for SCM observations. Indeed, as demonstrated in Figures 5-7, EFD data exhibit higher noise levels (SNR is reduce by a factor of \(\sim\)10\({}^{2}\)) and require extensive preprocessing  
Figure 6: Electric field waveforms (left panels) and spectrograms (right panels) within a \(\simeq\) 2 s window centered on \(t_{a}\) (blue dashed line, corresponding to the largest SCM IPSD peak in Figure 2). The top to bottom panels correspond to the X, Y, and Z components, respectively. to reveal clear whistler signatures, which significantly impact computational efficiency. The development of techniques for EFD data cleansing and the implementation of a precise synchronization mechanism between EFD and SCM for a joint magnetic and electric field analysis, essential to determine all the characteristics of detected the whistler waves, will be addressed in a forthcoming paper (hereafter Part 2).