## 4 Results and Discussion
### Experimental Results
However, the picoHF is very responsive around 600 kHz and detects high frequencies. Far from the source, the sensors detect an equivalent information lower than 400 kHz. Figure 10 shows the evolution of several descriptors extracted from the signals recorded by the two type of sensors during the same mechanical test. These results show a strong difference on the waveforms, frequency spectra and descriptors with different sensors. The waveform and frequency spectra signatures have different shapes and magnitudes. This means that in this case, with this kind of sensors, there is practically no chance of source identification from frequency spectra or amplitude values of AE signals after long wave travel. Figure 9: FFT of the experimental signals recorded by the sensor nano30 and picoHF for the same fiber break (**a**) near the source and (**b**) far from the source (e = 2.8 mm and z = 0.9 mm). Validation of The Model: Comparison Between Experimental Results and Finite Element Simulation Results  
In this section, in order to validate the model before expanding this numerical study to different sensors and specimens, we compare the experimental signals with the simulated signals, which are derived from calculated surface signals using sensor effect according to Equation (5). This comparison is carried out for fiber breaks signals at different positions, recorded at sensor C1 position for the two types of sensors (nano30 and picoHF). Figures 11 and 12 show the experimental and the numerical results with the two sensors excited by a fiber break at positions P1 and P5 for the specimen with thickness e = 2.8 mm and e = 1.7 mm, respectively. Figure 13 shows the comparison in the time domain for waveform due to fiber breaks and obtained with the sensor nano30 at two positions of the sensor: near the source (position P1) and far from it (position P5) while Figure 14 shows the Smoothed Pseudo  
Figure 10: Descriptors of the experimental signals recorded by the nano30 and picoHF sensors for the same test (e = 2.8 mm and z = 0.9 mm or e = 1.7 mm and z = 0.25 mm): (**a**) Amplitude (**b**) Frequency centroid and (**c**) Cut-off frequency. Wigner-Ville Distribution of the simulated signals. There are some minor differences in the time and frequency domain responses. Nevertheless, in general the numerical and experimental results agree with each other. The correlation coefficients of experimental and simulated waveforms are calculated to determine the similarity in time. In addition, visual comparison is made in frequency domain. The correlation coefficient shows that the measured and the computed waveforms have high similarity, around 0.70. The simulated and experimental results agree well in spite of minor differences, attributed to the influence of coupling effects [53; 54] we neglected in the calculation. The results show that the higher frequencies attenuate considerably with propagation. Moreover, it is demonstrated that the features values calculated from the simulated signals match well with those obtained with the experimental results (Figure 15). Figure 15 shows the evolution of several descriptors extracted from the simulated waveforms recorded by sensors nano30 and picoHF for the same fiber breaks (at position P2). For the simulated data, the fiber break is located at position P2 and the sensor moves along the gauge length, from the epicentre of the source to the end of the gauge length. For experimental data, the sensor is located at position C1 and the fiber breaks are distributed over the gauge length. Although the data are not equivalent, we can compare trends in evolutions. We can notice that the simulated data show the same evolution as the experimental data. Overall, a good agreement is observed between the experimental results and the numerical simulations, allowing us to validate the FEM model. Accordingly, we can conclude that the numerical approach is able to describe AE signal propagation and detection due to fiber break.