## 2 Materials and Methods  
### Dataset  
Acoustic signals have been widely used as a mean to closely monitor damages occurring over time in different systems [13; 21]. During the mechanical testing in our research, an AE monitoring equipment was used to collect all AE generated during the internal pressurization test of the tubular samples, as described in [10; 11]. Physical Acoustics NANO-30 AE sensor with a 125-750 kHz range and a 20 dB preamplifier, the Micro-II Digital AE System, seen in Figure 2, recorded the acoustic events during the internal pressure test. Pressure and strain data were also recorded. As for the SiC composite sample tubes, they are SiGA TM composite samples made of nuclear grade fiber, pyrolytic carbon interface coating and CVI SiC matrix that were manufactured and provided by General Atomics. Each acoustic signal can be represented either as a sound-wave of 30/72 values or as 15 expert-designed features. In contrast to the raw representation the acoustic emission signal with 30/72 values, the selected features listed in Table 1 were calculated by the software operating the burst test. Illustrations of an acoustic emission, along with its occurrence on the strain v. stress plot are displayed in Figures 3 and 4. Figure 2: Experimental setup of acoustic emission (AE) equipment used to collect the acoustic events. From the nine independent degradation tests, 40,140 acoustic events along with their corresponding data were recorded. After removing all AE signals recorded after the tube's rupture, a total of 39,839 samples remained in our dataset. We defined the RUS value of a given AE signal emitted from a test tube as the difference between the pressure value from that tube's breaking point (maximum pressure) and that AE's signal pressure value, which can be defined as in Equation (1):  
\[y_{(k,j)}=\max_{j}\Bigl{(}p_{(k,j)}\Bigr{)}-p_{(k,j)} \tag{1}\]  
where \(y\) is the RUS value, \(p\) is the pressure value, \(k=1,\ 2,\ldots,9\) is the tube's index, \(i,j=1,2,\ldots,\ n_{k}\) stand for the acoustic signal's index within that tube and \(n_{k}\) is the total number of acoustic signals emitted from that tube. Notably, the RUS value for an AE is simply the pressure residual value beyond \begin{table}
\begin{tabular}{c c} \hline
**Feature ID** & **Definition** \\ \hline Feature 1 & Rise-Time \\ Feature 2 & Counts \\ Feature 3 & Energy \\ Feature 4 & Duration \\ Feature 5 & Amplitude \\ Feature 6 & Average Frequency \\ Feature 7 & Root Mean square (RMS) \\ Feature 8 & Average Signal Level (ASL) \\ Feature 9 & Counts to Peak \\ Feature 10 & Reverberation Frequency \\ Feature 11 & Initiation Frequency \\ Feature 12 & Signal Strength \\ Feature 13 & Absolute-Energy \\ Feature 14 & Frequency Centroid \\ Feature 15 & Peak Frequency \\ \hline \end{tabular}
\end{table} 
Table 1: Definition of acoustic emission signal features. Figure 4: Strain vs. internal pressure (Ksi) plot from tube #1. In blue: all AE signals within the tube. In red: AE signal numbered 3280. In green: AE from breaking instance numbered 3286. Figure 3: Visualization of the AE signal numbered 3280 emitted from tube #1.  
that AE instance. Figure 5 displays the side-by-side box-and-whisker plots of RUS distribution of the nine separately tested tubes of materials. Additionally, Table 2 lists the number of acoustic events recorded per tube. While all the tubes were evaluated in a similar experimental setting, a different number of events was recorded and dissimilar degradation behaviors were observed among all the tests. To build more robust models and prevent data leakage in our model performance evaluation, we split the original dataset nine times to obtain nine newly separated sets of data, as demonstrated in Figure 6. In each newly created data-set, the testing set consists of all acoustic emission signals belonging to a given ruptured tube and the training set consists of the acoustic emission signals from all the other tubes.