## 4 Experimental Results Analysis and Discussion
### Comparison Methods  
To verify the effectiveness of the proposed method, four comparative methods are introduced as follows. 1. FineZero: FineZero indicates that it directly trains the RFFs extractor and classifier without going through the pretraining stage, where the RFFs extractor is the hybrid encoder proposed in this paper. Specifically, there is no knowledge transfer during the training process, and the parameters of the RFFs extractor and classifier are randomly initialized. Therefore, the performance of FineZero represents the performance baseline. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Self-Supervised Training** & **Fine-Tuning** \\ \hline Python & 3.9.19 & \\ PyTorch & 1.11 & \\ Platform & NVIDIA GeForce RTX 4090 GPU & \\ Epochs & 200 & 100 \\ Training Batch Size & 256 & 32 \\ Test Batch Size & - & 32 \\ Optimizer & LARS & Adam \\ Learning Rate & \(5\times 10^{-2}\) & \(5\times 10^{-4}\) \\ Temperature (\(\tau\)) & 0.1 & - \\ VAT Weight (\(\lambda\)) & - & 0.3 \\ VAT Perturbation (\(\varepsilon\)) & - & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings of SSL-SEI. 2. MAML: MAML [39] is a model-agnostic meta-learning framework that aims to quickly adapt to new tasks with a small number of samples. The core idea is to learn a common set of initialization parameters so that the model can efficiently solve new tasks with a small number of gradient updates. 3. SimCLR: SimCLR [40] is a self-supervised contrastive learning method. SimCLR takes the augmented views of samples as positive examples and all other samples in a batch as negative examples. 4. SA2 SEI: SA2 SEI [34] is a few-shot SEI method based on self-supervised learning and adversarial enhancement. Specifically, a novel adversarial enhancing-driven self-supervised learning is used to pretrain the RFFs extractor with unlabeled auxiliary datasets, and knowledge transfer is introduced to fine-tune the extractor and classifier. Table 3 presents the performance comparison of various methods applied to the BDS signal datasets across different scenarios ({10,15,20,25,30,35,40}-shot). The experimental results show that the proposed method significantly outperforms other methods on the BDS signal datasets. As shown by the experimental results in Table 3, the proposed method consistently achieves significantly higher identification accuracy than existing mainstream approaches across all training sample settings. In the 10-shot setting, the proposed method reaches an accuracy of 54.73%, outperforming SimCLR (49.93%), SA2 SEI (43.13%), MAML (36.67%), and the baseline (40.93%). As the number of training samples increases, the performance of the proposed method improves at a much faster rate than other methods. For instance, under the 20-shot setting, it achieves 89.27% accuracy, whereas SimCLR reaches 70.93%, SA2 SEI 68.43%, MAML 68.52%, and the baseline only 60.27%. This pronounced performance gap demonstrates the effectiveness of the proposed framework in enabling the model to capture discriminative features even when labeled data are limited. Under larger sample settings ({25,30,35,40}-shot), the proposed method continues to maintain a strong lead, ultimately achieving an accuracy of 97.20% in the 40-shot setting, significantly surpassing SA2 SEI (91.08%), SimCLR (86.00%), and all other methods. In addition to accuracy, we also evaluate the performance of the model in terms of recall, precision, and F1-score, as shown in Tables 4-6. The proposed method consistently achieves the highest values on all three metrics across all shot settings. For example, in the 20-shot scenario, the proposed method achieves 88.05% recall, 91.15% precision, and 89.48% F1-score, substantially outperforming all comparison methods. This trend persists across increasing sample sizes, culminating in 40-shot scores of 96.15% recall, 97.60% precision, and 97.44% F1-score. These results further validate the robustness and effectiveness of the proposed approach from multiple evaluation perspectives.