## 2 Dataset and Methodology
Three profiles were investigated at approximately 15:30 UT in the Northern Hemisphere, the dip equator, and the Southern Hemisphere. The ionospheric parameters from radio occultation measurements can be distorted by solar activity, geomagnetic fluctuations, ionospheric irregularities, and atmospheric gradients below an altitude of 200 km. These effects may cause anomalies in the electron density profiles, such as missing F2-layer peaks, negative values, or severe structural disturbances. To ensure reliable analysis, it is essential to rigorously assess and exclude poor-quality profiles before using radio occultation data [38; 39]. In this study, we employed the cubic spline function approach. Based on this method, we eliminated the distorted electron density profiles while retaining only the well-shaped ones, with a correlation coefficient exceeding 99%. All profiles used in the current study showed correlation coefficient >0.99 for adjacent spline sampling points from 2 to 30. To investigate the ionospheric disturbances from satellite data, we implemented two different methods: one by calculating the first-time derivative of the observed data directly, and the other by using the cubic spline fit to remove the long wavelengths and trends from the observed raw data. The first derivative was implemented by [40; 41] to highlight the disturbances caused by earthquakes, and the second and third derivatives were implemented by [14] to highlight the nuclear explosion signature in the FAC. It is worth noting that we selected 9 August 2020 and 10 August 2020, which are the quietest two days of this month, to represent the background quiet days for comparison, in addition to the hour preceding the event. The Swarm-B and COSMIC profiles of these days, according to the criteria set earlier, are shown in Figures S1-S3 in the Supplementary Materials. All profiles demonstrated minimal perturbations compared with the event day. The cubic spline fit was recommended by [42] to subtract the long-wavelength background from the observed to obtain the residual perturbations which represent the difference between the observed data and the fit. The longest wavelength of the cubic spline fit was twice the distance between adjacent spline sampling points. In this method, a cubic spline function (\(s\)) smooths the observed data by minimising the following potential function:  
\[p{\sum}w_{i}\,|\,y_{i}-s(x_{i})\,|\,^{2}+(1-p)\int\left(\frac{d^{2}s}{dx^{2}} \right)^{2}dx \tag{1}\]  
where \(p\) is the smoothing parameter, \(w_{i}\) is the weighting factor, and \(y_{i}\) represents the data of interest. In the current analysis, we implemented the MATLAB (version 2024b) spap2 function, which assumes equal weight and minimizes the \(\sum w_{i}\,|\,y_{i}-s(x_{i})\,|\,^{2}\) term. We fitted the cubic spline function twice: first to 10 data points to capture high transient variations and then to 60 data points to remove long wavelengths when feeding the residual data into the spectrum analysis. The residual, defined as the difference between the observed data and the corresponding cubic spline fit, was the target of interest. To reveal transient variations in the data, the first-time derivative was utilised as an auxiliary tool alongside the 10-data-point fitting technique. The frequency domain of the data was analysed using the wavelet transformation technique. The wavelet transform decomposes a time-series signal into its corresponding time-frequency representation using a selected base wavelet function. The wavelet coefficients were obtained by convolving the signal with the complex conjugate of the base wavelet, as expressed in Equation (2):  
\[W_{n}(s)=\sqrt{\frac{dt}{s}}\sum_{n=1}^{N}x_{n}\psi_{n}^{*}[(n-n_{0})\frac{dt}{ s}] \tag{2}\]  
where \(dt,s\), \(n\), and \(\psi_{n}^{*}\) are the sampling time interval, scales, the shifting parameter, and the complex conjugate of the base wavelet filter, respectively. As a bandpass filter, the wavelet transform effectively isolates specific frequency components over time.