# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 6 Experimental Validation  
We conducted a series of classification experiments using the previously described dataset. We used the \(F1\) score as the primary evaluation metric, as suggested by [PERSON] et al. [32]. The F1 score is a harmonic mean of precision and recall, calculated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\] \[Recall=\frac{TP}{TP+FN}\] (8) \[F1=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{9}\]  
where \(FP\), \(FN\), \(TP\), and \(TN\) stand for true positives, false positives, false negatives, and true negatives, respectively. We designed our experiments to demonstrate the effectiveness of our method in high-speed environments. We considered three different conditions as follows:  
_Random occlusions_ are common in real-world scenarios, such as vehicle scanning, where objects such as streetlamps or pedestrians can obstruct the scanning process. We assumed that these occlusions are relatively small in size, and our model needs to be robust to local data loss. Our data sequences include combined scattering angles for each sampling point, which provides our model with the information it needs to locate occluded regions. We used random subsampling ranging from 5% to 10%, to emulate occlusion scenarios. This procedure increased the size of our dataset by more than a factor of 10. The data were then split into training, validation, and test sets at a 7:3:1 ratio. Our model achieved an \(F1\) score of over 95%. While this score is commendable, it is important to note that our dataset is not very large. A score in this range indicates that the model is capable of performing the task with high fidelity, but higher scores may indicate overfitting. _High-speed sparse data._ As discussed previously, data collected at a turntable speed of 10\({}^{\circ}\)/s correspond to a speed of approximately 10 km/h. A turntable speed of 20\({}^{\circ}\)/s corresponds to approximately 20 km/h, which is similar to the speed of slow-moving vehicles. While these speeds may seem moderate, they represent the upper safe limit under the load conditions of our turntable. To explore higher-speed scenarios, we used approximation techniques. High-speed data are very rare, so using low-speed data to  
Figure 4: Network architecture diagram. train a recognition model can improve data quantity and quality while simplifying data acquisition challenges. To validate our method, we conducted experiments using our acquired and constructed datasets. We referred to data at 10\({}^{\circ}\)/s as \"low-speed data\" and data at 20\({}^{\circ}\)/s as \"high-speed data\". We designed a zero-shot experiment in which a model trained on low-speed data was tested directly on high-speed data. Specifically, we subsampled the low-speed data to emulate high-speed conditions, trained a model on this modified data, and evaluated its performance on the original high-speed data. This is a zero-shot experiment because the model was not exposed to high-speed data during training. The results, shown in Table 2, reveal that the model retained some effectiveness even in this challenging scenario. We then increased the speeds in our emulated scenarios, as shown in Table 3, and adjusted the sequence length accordingly. We explored speeds of 40 km/h, 60 km/h, and 80 km/h, which correspond to typical urban vehicle cruising speeds, maximum urban vehicle speeds, and UAV (unmanned aerial vehicles) flight speeds [33], respectively. Remarkably, even under challenging conditions, such as the UAV scenario with only 10 scattering data points per surface, our method maintained an \(F1\) score above 70%. This demonstrates the effectiveness of our method in recognizing surface properties at various speeds, particularly at typical urban cruising speeds. _Narrow-angle window observation_. We considered scenarios where collecting scattering data across a wide angular range is not feasible. In real-world settings, this may occur when building-obstructed wall surfaces are only accessible through a narrow angular window.