## 3 Results and Discussions
### Acoustic Emission vs. Equilibrium Moisture Content  
Figure 13 represents the EMC modelled using the regression in Equation (5), selected for the analysis. In this graph a sub-set of data corresponding to about 6% EMC is detectable (red box), followed by data spread to a final value of 16% EMC. This behavior can be explained by the fact that the first slices respond quickly to external conditions, reaching thermodynamic equilibrium earlier than 160 days; in the central part (green box) of the graph, data are highly scattered (7% < EMC < 13%); in the final part (EMC > 13%), data are almost linearly distributed (blue box), meaning that slices were less affected by the indoor hygrothermal conditions, even after 160 days. This model is the simplest multilinear regression that independently explains the variation of EMC in the radial and longitudinal directions, and shows a high \(\overline{R}^{2}\) value (0.89) with a direct linear relationship with x\({}_{1}\) (radial direction) and x\({}_{2}\) (longitudinal direc  
Figure 11: RH\({}_{\text{response}}\) (%) trend along the two main directions. (**a**) RH\({}_{\text{response}}\) along longitudinal direction from slice S1 (10 mm depth) to slice S25 (490 mm depth) over time. (**b**) Longitudinal axis and slices of the beam. (**c**) RH\({}_{\text{response}}\) along radial direction from surface (10 mm) to the path (100 mm) of the beam over time. (**d**) Radial axis of the beam. Figure 12: Final EMC values after 160 days. (**a**) EMC in longitudinal direction, as a function of the slices along the main axis of the beam (S1–10 mm; S2–30 mm; S3–50 mm... S25–490 mm). (**b**) EMC in radial direction, as a function of the radial depth within a single slice. tion). The intercept is 1.99 and the coefficients associated with x\({}_{1}\) and x\({}_{2}\) are, respectively, 8.71 \(\times\) 10\({}^{-2}\) and 1.06 \(\times\) 10\({}^{-2}\). The AE parameters of amplitude, counts, and energy can demonstrate how the wood behaves when subjected to its surrounding environment, providing information on the state of conservation, and interaction processes with the material itself until the equilibrium is achieved. Figures 14-16 show the effect of drying (\(\Delta\)EMC on the x-axis) along the sample on the AE parameters recorded during fracture by both S5 and S6 for Group 1 and Group 2 (on the y-axis). \(\Delta\)EMC was calculated from the initial and final EMC values after 160 days of dryness, from the path to the outermost layers. Figure 14 illustrates the cumulative amplitude (\(\Delta\)AA, dB) emitted by samples S081, S091, S101, and S111 (Group 1), where the lower \(\Sigma\Delta\)AA values (orange-colored) correspond to easily detectable events along the sample subjected to a complete drying process; otherwise, samples S123, S142, and S153 (blue-colored) show a higher cumulative amplitude in connection with events that are difficult to detect and caused by an incomplete drying process along the sample. Then, sample S173 appears to be an outlier, as it has a similar pattern of amplitude values as the first samples, described by slices 8 to 11 (S081, S091, S101, S111), although with an \(\Delta\)EMC span comparable to samples S123, S142, and S153. Concerning Group 2, the same behavior of amplitude was detected by both sensors. In this case, the samples colored with the lighter colors, i.e., S093, S122, and S152, show the most reliable sensing of amplitude values. Sample S172 is the one that presents difficulties in amplitude detection because of its wet state. Figure 13: The selected linear multiple regression model (Equation (5)). On the abscissa, the EMC as calculated by Giordano, on the ordinate, the modelled EMC (unit %). The three boxes highlight areas that differ from each other. The red box indicates a first set of data with EMC = 6%; the green box highlights a central dataset with 7% < EMC < 13%; the blue box indicates a quasi-linear dataset with EMC > 13%. Figure 15: \(\Sigma\)AC vs. \(\Delta\)EMC (%).