## 4 Experimental Results Analysis and Discussion
### Impact of VAT Parameters  
To evaluate the sensitivity of VAT parameters, we conducted a comprehensive grid search analysis of perturbation magnitude (\(\varepsilon\)) and VAT weight (\(\lambda\)) in the 10-class BDS signal dataset under the 40-shot scenario. Figure 7 presents a heat map illustrating the results of a grid search conducted to evaluate the sensitivity of \(\varepsilon\) and \(\lambda\).  
The experimental results indicate that under the condition of fixed \(\lambda\), the recognition accuracy shows a trend of first increasing and then slowly decreasing with the increase of \(\varepsilon\). Among them, \(\varepsilon=4.0\) reaches the peak across different \(\lambda\) settings, indicating that moderately enhancing the perturbation is beneficial to improving the robustness of the model. It is worth noting that a larger disturbance intensity (\(\varepsilon=\{3.0,4.0\}\)) is generally superior to a smaller intensity (\(\varepsilon=\{1.0,2.0\}\)), but an appropriate \(\lambda\) needs to be set to achieve the best performance. This aligns with the theoretical of VAT, when \(\varepsilon\) is too small (\(\varepsilon=1.0,2.0\)), the perturbation fails to sufficiently explore the decision boundary, limiting its regularization effect. Conversely, overly large perturbations (\(\varepsilon=5.0\)) distort the input signal representation beyond the manifold of valid samples, leading to performance degradation. The optimal range, observed at \(\varepsilon=3.0,4.0\), achieves a balance between boundary exploration and feature integrity. This is consistent with VAT theory, where moderate perturbations enforce local smoothness without destroying the discriminative structure.  
The VAT loss is weighted by the coefficient \(\lambda\), which controls the influence of the virtual adversarial regularization term relative to the supervised loss. A smaller value of \(\lambda\) (e.g., 0.1) fails to sufficiently emphasize the VAT regularization, potentially leading to overfitting in data-scarce settings. On the other hand, excessively large values (e.g., \(\lambda\geq 0.4\)) may overly constrain the model, interfering with supervised learning and resulting in underfitting. The best performance is observed with \(\lambda=0.3\), which achieves an  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Variant** & **Top-1 Acc (\%)** \\ \hline (A) ResNet Only & 73.93\% \\ (B) Hybrid Encoder Only & 80.93\% \\ \hline (C) ResNet + SSL & 86.00\% \\ (D) Hybrid Encoder + SSL & 89.67\% \\ \hline (E) ResNet + SSL + VAT & 92.87\% \\ (F) Proposed & 97.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification accuracy of various ablation variants.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Variant** & **Silhouette Coefficient** & **Separation Ratio** \\ \hline (A) ResNet Only & 0.2440 & 2.2968 \\ (B) Hybrid Encoder Only & 0.3281 & 2.1159 \\ (C) ResNet + SSL & 0.3559 & 2.4461 \\ (D) Hybrid Encoder + SSL & 0.5109 & 2.6426 \\ (E) ResNet + SSL + VAT & 0.6008 & 3.4065 \\ (F) Proposed & 0.8859 & 11.3587 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of silhouette coefficient and separation ratio for various ablation variants.  
optimal trade-off between maintaining decision boundary smoothness and preserving discriminative learning capacity.  
In summary, the optimal configuration (\(\varepsilon=4.0\), \(\lambda=0.3\)) aligns with the theoretical design of VAT, providing both sufficient boundary exploration and stability.