# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 1 Introduction  
As the integration of sensing and communication technologies rapidly advances, the terahertz (THz) frequency band (0.1-10 THz) has emerged as a pivotal frontier for both domains, presents opportunities in achieving high-data-rate communication and precise environmental sensing [1, 2]. These features promise a significant advancement in capabilities for 6G integrated sensing and communication (ISAC) technology [3], autonomous vehicles [4], and vehicular communication networks (VCN) [5]. In such integrated systems, the role of environmental sensing becomes important increasingly. They require swift and precise perception of the environment to adapt in real-time environmental variations and enhance communication and/or control system stability [6, 7]. In these scenarios, accurate recognition of surface roughness emerges as a critical environmental parameter, as shown in Figure 1. The scattering characteristics of wireless channels, for example, are profoundly influenced by the texture of surrounding surfaces [8], and then the control system of autonomous vehicles must adapt to the roughness of the terrain [9]. Terahertz frequencies are inherently sensitive to surface granularity, offering a potent advantage in addressing these challenges [10]. In other words, they can decode intricate details of surrounding surfaces, yielding distinct signatures within their scattering spectra [11, 12]. This makes them invaluable for non-invasive surface texture assessments. There have been diverse strategies, such as Kirchhoff theory [13], Rayleigh roughness theory [14], the time-of-flight method [15], etc., developed for surface texture modeling and recognition, relying on numerical scattering models [16, 17]. These methods requirecomplicated modeling trajectories and are anchored in approximate and idealized postulates, which often fail to accurately capture real-world complexities. Other techniques harness the richness of THz imaging features, predominantly integrating deep learning frameworks [9; 18; 19]. While all these approaches are adept in low-speed scenarios, they are not well suited for high-speed conditions, where numerical computations become impractical and radar imagery becomes prohibitively resource intensive. In high-speed scenarios, static equipment positioning is unfeasible, and sampling must transpire dynamically. This introduces tailing effect, which means the data captured at a specific time and angle include traces from previous conditions. The magnitude of the tailing effect increases with speed, leading to directional spectral biases and attenuation of spectral details. In addition, high-speed sampling introduces additional challenges, such as random occlusions, high-speed sparse data, and narrow-angle window observations, which render conventional methodologies inadequate [20]. To overcome these challenges, we propose a deep learning-driven paradigm for surface texture recognition in high-speed scenarios in this work, which has already been demonstrated as its prowess in pragmatic terahertz applications such as imaging [21], synthetic aperture radar (SAR) [22], and hyperspectral modalities [23]. This paradigm necessitates an authentic, diverse, and concise dataset for model training, ensuring its alignment with real-world scenarios, robust generalization capabilities, and stability in complex environments. Thus, we designed a data recording platform that emulates real-world dynamics. We also fabricated several kinds of rough surface samples, characterized them by high-fidelity 3D scanning, and acquired a large amount of high-speed scattering data. To strengthen data heterogeneity, we focused on localized sample regions and further enriched them with diverse data processing and augmentation strategies to sculpt the dataset. The remainder of this article is laid out as follows: Section 2 gives a thorough description on the data collection platform, which includes how we set up our experiments and the system we used to control them.