## 3 Results and Discussion
The same figure and the intensity variation along line 73 (Figure 9, top) indicate that there was alternating heating and cooling of certain parts, manifested by changes in intensity of certain pixels due to heat diffusion in the defect area, immediately after the cooling process began. Figures 5 and 6 show a non-uniform level of gray within the zone of the defect with the largest radius in column B (Procedure B, parameter \(k\) = 6, 9, 12 and 15) and column C (Procedure B, parameter \(k\) = 12, 15, 18, 21 and 24). Therefore, this effect is attributed to lateral diffusion. Its existence is apparent and depends on the circumference and depth of the defect at certain frequencies. For example, the effect is not visible in Figure 6 at \(k\) = 24, in the case of the defect with the largest radius in column B. It should be kept in mind that the sampling frequency is then low enough, so the effect that results from the transient process in the form of lateral diffusion is not apparent. Figures 12-14 show the development of the thermal process in the defect areas along vertical A and B. The curves represent the change in the mathematical expectation of pixel intensity within squares whose dimensions correspond to the defect diameters (\(16\times 16\) and \(8\times 8\) pixels for \(\emptyset\) = 15 and 8 mm, and 6x6 pixels for \(\emptyset\) = 5 and 3 mm), relative to the mathematical expectation of pixel intensity in Sa (Figure 2). The centers of these areas coincide with the centers of the corresponding defects. The diameter of the cylindrical defects along vertical A (\(\emptyset\) = 5 mm) was selected to test the nature of the transition process, which takes place after irradiation of the test sample, when the diameter was comparable to the depth. The curve maxima shown in Figures 12 and 13 occurred as expected, meaning that their value declined with increasing defect depth. However, local minima were noted immediately after the temperature rose due to irradiation and became increasingly pronounced when the defect depth was comparable to the diameter (curves b and c in Figure 12 and curves in Figure 13). This phenomenon attested to the existence of lateral heat diffusion towards the defect-free area of the test sample. Then a reverse diffusion process began, or the heat started to spread again, toward the central area above the defects, and they were re-heated. This phenomenon was not interesting only in terms of physics. It allowed greater insight into the reason why detection was more difficult when the diameter was small (\(\emptyset\) = 3 mm defect was not detected along verticals D and E). Figure 3:2 Segments of consecutive frames (broken lines) selected for intensity variation analysis. Figure 14 depicts the effect of reducing the diameter of the defects at the same depth. The results shown relate to the defects along vertical B. The highest maximum value, as expected, was noted in the case of the largest-diameter defect (curve a). Keeping in mind that the depth of all the defects along vertical B was the same and that diameter \(\emptyset\) = 15 mm was the largest, it was reasonable to expect that the transition process would be the slowest in that case and for this maximum to occur last, or after the maxima of curves b and c relating to defect diameters \(\emptyset\) = 8 mm and \(\emptyset\) = 3 mm. Figure 3:3 Change in mathematical expectation of pixel intensity in \(\emptyset\) = 5 mm defect area along vertical A at depths: (a) \(h\) = 3.7 mm, (b) \(h\) = 3.4 mm and (c) \(h\) = 2.7 mm. Figure 3:4 Change in mathematical expectation of pixel intensity in \(\emptyset\) = 5 mm defect area along vertical A at depths: (a) \(h\) = 3.4 mm, (b) \(h\) = 3.2 mm and (c) \(h\) = 2.7 mm.