# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 3 Surface Sample Preparation  
Data are paramount in the realm of neural networks. Aiming for effective performance on real-world surfaces with unknown roughness, it is important to improve the generalization capabilities of limited set of surface samples with different levels of roughness. This requires our samples to embody typical characteristic properties. Due to the difficulties in obtaining enough data for our investigation, we chose metal surfaces for our primary samples, due to their excellent reflective properties and a diverse range of hardness. We applied different processes to forge surfaces with different levels of roughness, as shown in Figure 2b.  
To create smoother surfaces resembling scratches and minor textural variations on office desks or brushed finishes, we used small-diameter rectangular and cylindrical chisels on harder brass materials to create two distinct surfaces. Surface-1 features scratches and minor abrasions, while Surface-2 predominantly features scratches with shallow grooves. To create rougher surfaces with inherent natural textures, such as wood grain and genuine leather patterns, we used conical and medium-diameter rectangular chisels on materials of moderate hardness, specifically copper. Surface-3 is composed of dense conical pits, emulating certain grid-like textures. Surface-4, derived from Surface-3, incorporates deeper grooves to mimic deep leather textures. Given that rougher surfaces often exhibit more pronounced relief structures, we used larger-diameter conical and rectangular chisels on softer aluminum sheets to produce three additional surfaces. Surface-5 is an enhanced version of Surface-4. Surfaces-6 and -7, in contrast to the previous methods of vertical carving, employ lateral force application to create substantial depressions and peaks in the structure. All these surface samples possess a large size, measuring 100 mm \(\times\) 100 mm. Within this area, the central region of 80 mm \(\times\) 80 mm exhibited the roughness characteristics. We used a grid counting method to ensure maximum uniformity in the local roughness within this region. Such a large surface area endows the dataset with a richer diversity of data featuring similar numerical roughness but varied structures.  
For accurate characterization of the surface samples, we conducted a comprehensive three-dimensional (3D) laser scanning (SCANECH IREAL2E) using high-precision techniques to reconstruct all the surfaces. We utilized an industrial-grade handheld 3D laser scanner with a scanning precision of 0.02 mm and a volumetric accuracy of 0.03 mm/m. Each metal surface underwent scanning to produce cloud with at least two million points, effectively representing the roughness of various local areas on the surface. We used the standard deviation of the surface height variation (RMS) as a quantitative measure of surface roughness [28]. The ensuing results, detailed in Figure A1 of the Appendix A, reveal that our samples of rough surfaces manifest several characteristic traits: (1) Minimal overlap of roughness between different surfaces, which is essential for subsequent classification tasks, yielding classification results that can be uniquely mapped back to a specific range of roughness. (2) Variability in roughness across different areas within a single surface. This enriches the dataset and enhances the model's ability to generalize. (3) Existence of regions with comparable levels of roughness yet differing in shapes and quantities. This further contributes to the overall data richness.