## 2 Materials and Methods
### Data Preparation and Processing  
#### 2.4.1 Experiment Data Preparation  
In this study, the laser intensity data obtained from the study conducted by [PERSON] et al. were digitized using computer-aided software. The extracted data were saved in CSV format and imported into Matlab R2021b for further processing. To ensure consistency and integrity, the Piecewise Cubic Hermite Interpolation (PCHIP) method was applied during the reconstruction of missing data points and the generation of standardized datasets of equal length for each graph. This method preserves the monotonicity between data points, providing a more natural transition while minimizing errors that may arise from linear interpolation [21]. At the final stage of data processing, the datasets comprising angle, distance, and laser intensity values were standardized to contain 85 equally spaced data points for each parameter. This standardization ensured that laser intensity data collected at different distances and angles could be analyzed in a consistent and comparable format. In this study, laser intensity values were examined for four distinct distances: 2.5 m, 5 m, 10 m, and 30 m. At each distance, the influence of surface color on laser intensity was analyzed across incidence angles ranging from 0\({}^{\circ}\) to 70\({}^{\circ}\). Figures 3 and 4 illustrate the impact of color and surface characteristics on laser intensity values at these four distances. The data indicate that laser intensity exhibits a complex relationship with surface reflectivity, angle, and distance. While, theoretically, laser power is expected to decrease inversely with the square of the distance, experimental findings did not fully align with this expectation. These discrepancies were attributed to factors such as the optical gain  
Figure 4: Representation of laser intensity values at distances of 10 and 30 m, depending on different colored surfaces and angle changes between 0–70\({}^{\circ}\). Figure 3: Intensity values at distances of 2.5 and 5 m for different colored surfaces and angle changes between 0–70\({}^{\circ}\). settings of the LIDAR sensor, the multi-channel structure, the divergence effect of laser light, and specular reflection [20]. At short distances, the SW-Gloss surface exhibited the highest laser intensity values, which can be attributed to the homogeneous reflection of laser light by glossy surfaces. In contrast, metallic surfaces such as TCSRM-Gloss displayed higher intensity values at low angles due to specular reflection; however, these values declined rapidly as the angle increased. Conversely, homogeneous reflective surfaces such as SW-Gloss demonstrated more stable laser intensity values despite angular variations. This finding suggests that, while metallic surfaces offer a sensor-oriented advantage at low angles, this effect diminishes as the angle increases. As distance increases, the divergence effect causes laser light to spread over a larger area. However, the multi-channel structure of the LIDAR sensor can compensate for this effect on measurements. For instance, at a distance of 30 m, the intensity values of the SMRTG-Gloss surface approached those of SW-Gloss, which can be explained by the laser light being collected from a broader area on uniformly reflective surfaces. Specular reflection decreases with increasing distance, leading to significant changes in laser intensity values. Notably, the high intensity values observed at low angles on metallic surfaces diminish with increasing distance, and the scattering of laser light in multiple directions accelerates this reduction. Conversely, glossy surfaces provide homogeneous reflection, resulting in more stable laser intensity values regardless of distance. These findings underscore the critical role of design and calibration processes in enhancing LIDAR sensor reliability and understanding their sensitivity to environmental conditions.