## 4 Application to Data
### Testing the Methodology on a Reference Pulse: The 92238 Case  
Figure 2 shows the signals for ELMs (from the beryllium and the diamond UV diagnostics) and pellets (pellet monitor signal) during the pulse current flat top phase (left pair of plots), and in a small-time window (for 48.0 s \(<\) t \(<\) 48.1 s, right pair of plots). It can \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pulse** & **I [MA]** & **B\({}_{t}\) (T)** & **n\({}_{e}\)d1 [10\({}^{19}\) m\({}^{-2}\)]** & **T\({}_{e}\) [keV]** & **P\({}_{tot}\) [MW] (\% NBI)** & **Triggering Efficiency (\%)** \\ \hline
96713 & 3.5 & 3.3 & 20.0 & 8.0 & 37.1 (85.2\%) & 69.88\% \\ \hline
96994 & 3.0 & 2.8 & 17.2 & 8.2 & 31.3 (90.6\%) & 69.07\% \\ \hline
97824 & 3.0 & 2.8 & 23.6 & 5.0 & 29.5 (98.8\%) & 69.00\% \\ \hline
97825 & 3.0 & 2.8 & 21.3 & 5.5 & 29.8 (98.0\%) & 72.73\% \\ \hline
97835 & 3.8 & 3.6 & 20.0 & 7.5 & 34.1 (87.5\%) & 48.08\% \\ \hline
98004 & 3.8 & 3.6 & 21.0 & 6.5 & 34.2 (94.8\%) & 51.54\% \\ \hline
98005 & 4.0 & 3.6 & 25 & 7.0 & 32.8 (98.1\%) & 45.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pulses analysed with the SCC clustering method. be observed that ELMs appear as large and fast pulses on the Be signal and can be easily detected (especially type-I ELMs). In the case of the UV diamond, since the sensor line of sight was not optimised for looking at the edge of the plasma, the signal might be affected also by different radiation phenomena unrelated to the ELM. In order to minimise the influence of such spurious signals, a moving median subtraction has been applied to the UV diamond time trace. The Pellet monitor signal is characterised by narrow peaks indicating the crossing of Pellets. Figure 6 shows the ELMs and pellets events detected using the peak detection algorithm on the ELM and pellet signals, respectively. Considering the time window from 47.5 s to 50.5 s, 114 ELMs and 59 pellets have been detected, involving an average measured frequency of 38.0 Hz and 19.7 Hz, respectively. The SPCC profiles calculated for each ELM are shown in Figure 7a using the beryllium line (left) and the diamond UV (right). By using the k-means, the profiles are clustered into two main groups. Then, the average SPCC profile of the two groups is calculated (Figure 7b) and labelled according to the physical expectations for each cluster. In fact, in the case of natural ELMs, the profile is expected to be flat with a small average value on, while for triggered ELMs, the SPCC should be peaked and have a higher amplitude feature, as shown. In Figure 7c, all the ELM SPCC profiles are represented with their class. A clear separation is observed for time delays from 0.0 ms to 1.5 ms, while for higher time delays, sometimes high cross-correlation is observed for both classes, due to a fortuitous presence of a pellet before the considered ELM. For consistency, the approximate probability of finding a spurious peak for a time delay from 2 ms to 10 ms was calculated for natural ELMs in the following way. Figure 6: Pulse 92238. ELM signals with detected ELMs (**top**) and pellet monitor signals with detected Pellets (**bottom**) using the peak detection algorithm. For a pellet frequency \(f_{pelle}\) and a time delay range \(\Delta t_{delay}\), the number of pellets expected in the range is \(\Delta t_{delay}f_{pelle}\); therefore, the expected number of spurious SPCC peaks is \(\Delta t_{delay}f_{pelle}N_{ELM,natural}\). In this pulse, the pellet frequency is around 20 Hz, the delay time window is 10 ms and the number of natural ELMs is 70. Therefore, the expected number of spurious SPCC peaks is 11.2 \(\pm\) 6.1 (95% CI), in line with the value found through the analysis (8). In similar ways, one may calculate the number of expected false positives (i.e., the number of natural ELMs classified as triggered). The analysis clearly shows that triggered ELMs have a SPCC peak between 0 ms and 1 ms.