## 4 Experimental Results Analysis and Discussion
### Ablation Experiment  
As shown in Table 7, we conducted ablation experiments in the 10-class BDS signal dataset under the 40-shot scenario to evaluate the effectiveness of each component in the framework we proposed. All experiments use the same parameter settings described in Section 4.2. Model (A) adopts the RFFs extractor modified based on the ResNet-50. This model is trained in a supervised learning manner without going through any self-supervised pretraining stage, and uses cross-entropy loss for end-to-end optimization. Model (B) uses the hybrid encoder proposed in this paper instead of ResNet and adopts the same training strategy as Model (A). Models (C) and (E) use the same encoder as Model (A), adopt the self-supervised training strategy, and introduce contrastive learning. Model (E) further introduces the VAT-based fine-tuning method proposed in this paper. Model (D) uses the same encoder as Model (B), adopts a self-supervised training strategy, and introduces contrastive learning. Model (F) is the method proposed in this paper. 1. Effectiveness Analysis of Hybrid Encoder: The hybrid encoder leverages ResNet to extract local signal features and Transformer to capture global dependencies via self-attention. This combination leads to more expressive and generalizable RFF representations for SEI. To validate the effectiveness of the proposed hybrid encoder architecture, we compare pairs of models trained under identical strategies but with different encoders. Specifically, comparing (A) and (B), the accuracy improves from 73.93% to 80.93%, indicating that the hybrid encoder provides a more expressive representation than the vanilla ResNet. Similarly, under the SSL setting, (C) achieves 86.00%, while (D) achieves 89.67%, again demonstrating consistent performance gains from the hybrid encoder. Finally, under the full framework including both SSL and VAT, (E) achieves 92.87%, whereas (F) reaches 97.20%, further highlighting the contribution of the hybrid encoder to overall performance improvement. 2. Effectiveness Analysis of SSL: The SSL framework guides the model to learn invariant and discriminative features by maximizing agreement between augmented views of the same signal. This contrastive objective encourages the encoder to enhance RFF extraction without relying on labels. To evaluate the impact of the SSL framework, we compare models with and without SSL while keeping the encoder fixed. For  
Figure 5: Classification accuracy using different data augmentation combinations during the SSL phase. the ResNet-based models, (A) achieves 73.93%, whereas (C) improves to 86.00%, indicating that the introduction of the SSL framework achieved a significant gain of nearly 10%. A similar trend is observed with the hybrid encoder: (B) achieves 80.93%, while (D) achieves 89.67%. These results confirm that the self-supervised framework effectively improves feature representations by leveraging unlabeled data, especially when labeled samples are limited. 3. Effectiveness Analysis of VAT: VAT improves model robustness by introducing small adversarial perturbations, encouraging the classifier to maintain consistent predictions around labeled samples. This regularization smooths decision boundaries and prevents overfitting, which is crucial for SEI tasks under limited labeled data. To investigate the contribution of VAT, we compare models trained with and without VAT under the SSL setting. When using the ResNet encoder, (C) achieves 86.00%, while (E) improves to 92.87%. Similarly, with the hybrid encoder, performance increases from 89.67% in (D) to 97.20% in (F). These results demonstrate that VAT effectively enhances the model's robustness and generalization, particularly when combined with the hybrid encoder and SSL components. To further investigate the discriminative capability of the learned representations, we performed t-SNE visualization on the feature embeddings of all models. Figure 6 presents the scatter plots of features from 10 selected classes in the BDS signal datasets, extracted by different models.