## 4 Results
### Real sinograms: Material Classification (Model 2)  
The quality of the results obtained from the real measurements did not match those from the simulations. This difference was influenced by various factors that affect reconstruction quality but were not present during the simulation process:  
* The signal-to-noise relationship due to the signals is real and is affected by interference, electronic noise, and noise from the amplifiers.
* Small dispersers that were not considered in the simulation.  
Figure 20: Simulated reconstructions using the model that identifies material P1 and material P2. (**a**) Example of reconstruction for one embedded object. (**b**) Example of reconstruction for two embedded objects. The rows represent the two types of mortar for each example.  
* Multiple reflections due to the placement of the transducers inside the water vessel as well as their walls. These aspects produced discrepancies between the sinograms acquired empirically and those derived from the simulations, resulting in a worse reconstruction performance than that obtained with the simulations. However, since we were interested in seeing the transition of the carbonation front, we used model 2 (classification) for the detection of the internal cement (P2). Therefore, the neural network trained with simulations was employed to reconstruct the internal cement (P2) from real sinograms. Figure 21 shows the real reconstructions of the P2 for the different real cases.  
In the reconstruction of cases with a single internal element, the model performed well. However, some deformation was observed such as the flattening of objects that were theoretically circular (Figure 21, cases A and B). Despite this, the effect was not significant, and the overall shape of the object was preserved. In cases with two internal objects, the reconstruction of one object failed and artifacts appeared (Figure 21 case D). Furthermore, these artifacts were also observed in case C and were likely due to the low signal-to-noise ratio in the sinograms or offset errors in the measurement system. Such artifacts could potentially lead to the misidentification of additional internal elements in real-world scenarios where none exists. Table 5 presents the SSIM values, which correlate with the descriptive analysis provided.  
A comparison of the traditional reconstruction methods using the real measure was made and can be seen in Table 11; the reconstruction images for each method and specimen are shown in Figures 12 and 13 in Appendix A.  
The proposed model demonstrated superiority to conventional algorithms in terms of SSIM, as evidenced by the mean SSIM value of 0.714 (\(\pm 0.067\)) across the four real cases  
Figure 21: Real case reconstructions (second row) versus the original images (first row) using model 2 for internal material recognition (P2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **SSIM** \\ \hline A & 0.82 \\ B & 0.70 \\ C & 0.72 \\ D & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quality metrics for reconstruction in real cases using the trained model 2.  
for each reconstruction method, which is significantly higher than the mean SSIM value of 0.470 (\(\pm\)0.036) for the conventional algorithms.