## 3 Results and Discussions
### Acoustic Emission vs. Equilibrium Moisture Content
(**a**) Group 1, S5; (**b**) Group 2, S5; (**c**) Group 1, S6; and (**d**) Group 2, S6. Figure 15 reports the cumulative count values (\(\Sigma\Delta\)C vs. \(\Delta\)EMC) as line graphs during the whole drying process. The sample analysis shows the same behavior, confirming that the parameters of amplitude and count are influenced in the same way by the EMC in the material. Analyses of dried, natural and saturated specimens reported in the literature [28] showed similar behaviors, with cumulative counts higher in dry samples and smaller in wet samples, confirming the ability to detect acoustic events. Figure 16 shows \(\Sigma\Delta\)E vs. \(\Delta\)EMC. First, sample S081 in Group 1 (for both sensors) showed very low cumulative energy values, almost imperceptible in the graph in the figure, meaning that sample S081 already released the climate-induced stress that may produce AE events. Many samples in both groups (detected by both sensors) showed a plateau, which corresponded to critical points in which energy was not detected. This means that the saturation energy level was reached and no further energy gain occurred because no further drying process happened to samplings, although they were at different equilibrium moisture content concentrations. A plateau can occur for various reasons: for example, due to the presence of any macro or microcrack in the sample that acts as an open joint in reducing the accumulated stress without emitting new AE events, or because the samples were so damaged by climate-induced stresses that they could no longer emit acoustic events when the rest of the sample fractured (e.g., through increase in ductility of the material with increase of other forms of energy different from elastic energy). Thus, each \(\Sigma\Delta\)E vs. \(\Delta\)EMC curve in Figure 16 was linearly interpolated in order to calculate the coefficient of energy attenuation (\(\beta\)) according to Equation (7). Figure 17 shows the coefficient of the attenuation energy rate (\(\beta\)) among the samples. It is worth noticing that, when \(\beta\) is low, the attenuation energy rate (i.e., the energy loss) is at its minimum as the sample is almost dry and, in such a case, the energy detection is optimal; conversely, when \(\beta\) is high, the energy loss is high, as the sample is almost wet. Such a result is in accordance with the effect of moisture content on the propagation of acoustic emission signals in _Pinus massoniana_, as described by [PERSON] et al. [15]. Moreover, this  
attenuation rate coefficient estimation allows to confirm [8] when the achieved equilibrium moisture content within the sample is homogeneous and low (i.e., when the difference from the initial state is maxima). In such a case, the overall energy of acoustic emission increases, as a consequence of the high strength of dry wood. The samples traced as outliers from the detection of AE parameters (i.e., S081, S142, S172, and S173) demonstrate that an orthotropic and hygroscopic material, such as wood, can have different results depending on the moisture content gradient because of energy attenuation, but also on the internal micro or macroscopic characteristics (presence of knots, open micro cracks, etc.). To differentiate the type of fracture that occurs within the two groups, a punctual visual re-inspection of all the DIC frames was conducted. The categorization of the type of fracture was based on the shape of crack propagation which could depend on the EMC. Group 1 was characterized by samples as having a linear, smooth fracture propagation. On the other hand, Group 2 was characterized as having more pronounced \"zig-zag\"-like fractures. Higher EMC levels corresponded to more rounded fractures in both groups of specimens. The differences in the fractures are greatly related to the directionality of the carving, which tended to influence the way in which the moisture content gradient behaved within the wooden beam.