## 3 Results
### Swarm Electron Density Results  
In this section, we applied the WSC algorithm to the electron density (_Ne_) data recorded by the three main satellites of the _Swarm_ constellation from 2 December 2013 until 1 December 2021, i.e., the first 8 years of available data. Most of the results of _Ne_ are provided in the Supplementary Materials, as they have not been found to be statistically significant. Figure S6 (in the Supplementary Materials) provides the results of WSC applied to 8 years of _Swarm Ne_ data correlated with M5.5+ shallow earthquakes using the above four methods to associate the anomalies and earthquakes. The results do not pass the statistical criteria defined in [29]: \(d\geq 1.5\) and \(n\geq 4.0\). Furthermore, the analyses with the minimization of log (\(\Delta\)T R) and the maximum earthquake magnitude (methods 2 and 3) find a post-seismic bin as the maximum concentration. In fact, the analysis in the long symmetrical time from 500 days before until 500 days after the earthquakes with the same data (Figure S7 in the Supplementary Materials) shows low or null significance, and, overall, the number of anomalies after the earthquake (15,618) is greater than those before the earthquake (15,504); their difference being equal to 114 anomalies, which is lower than the square root of the anomalies, which is equal to 125 and could be considered as an approximation of their uncertainty, as further discussed in Section 4.4. In the same way that we detected an increase in the anticipation time of the anomalies with the earthquake magnitude in the magnetic field data (Figure 2), we now apply the same approach to 8 years of the _Swarm Ne_ data, as shown in Figure S8 in the Supplementary Materials. In this case, we tried to make a fit through the maximum concentrations of anomalies detected by the WSC algorithm in the first row (i.e., closer to the earthquake epicenter) of different magnitude ranges following the empirical Rikitake law. We note that the magnitude 7.5+ earthquakes analysis shows the absolute maximum concentration on the second row, i.e., between 380 km and 750 km from the epicenter. As such, a magnitude range is the one with fewer earthquakes (26 in total); this effect could be due to an insufficient number of cases to construct good statistics. Furthermore, it is important to note that these analyses did not pass the statistical evaluation criteria, and some of them even show a concentration of the same intensity as the one expected for a random simulation (\(d\cong 1\)) and a negative \(n\), indicating a real largest concentration lower than the random one. Even if the concentration of anomalies is not significant, the maximum concentration in the row closer to the epicenter for magnitudes \(\geq\)6 seems to follow the Rikitake law, as can be seen in the fit shown in Figure S8A, which provides the following coefficients: \(a=-3.5\pm 2.1\) and \(b=0.83\pm 0.27\). The maximum concentration in the magnitude band \(5.5\leq\) M \(\leq 5.9\) is found 250 days before the earthquake, but the concentration 10 days before the earthquake is also high but lower than the maximum and, in the fit, has been used as the objective maximum extracted by the code. So, we can suppose that there may be some anomalies related to the earthquake, but the \"anomaly definition\" employed is too weak to extract all possible seismo-induced phenomena, consequently including many anomalies not related to earthquakes. To improve the anomaly definition, we introduce the analysis of frequency content in the investigated window with the same approach as that one used for the magnetic field data. As can be seen in Figure 4, the significance of the results is improved for most of the analyses, passing the statistical criteria (\(d\geq 1.5\) and \(n\geq 4.0\)).