## 5 Conclusions  
In this paper, we propose a novel SSL-SEI framework based on self-supervised contrastive learning to address the challenge of limited labeled data in SEI. During the pre-training phase, we design tailored data augmentation strategies for satellite signals and construct a hybrid encoder that combines ResNet and Transformer modules to enhance feature learning. This enables the model to extract robust and discriminative RFFs from unlabeled satellite signals. To mitigate the overfitting risk caused by scarce labeled data, we introduce a VAT-based fine-tuning phase, where adversarial perturbations are applied \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **[10]** & **[15]** & **[20]** & **[25]** & **[30]** & **[35]** & **[40]** \\ \hline Dataset1 & 53.65\% & 63.75\% & 88.05\% & 93.15\% & 94.85\% & 96.00\% & 96.15\% \\ Dataset2 & 48.34\% & 75.95\% & 80.36\% & 82.60\% & 82.00\% & 90.47\% & 93.59\% \\ Dataset3 & 47.23\% & 69.21\% & 77.49\% & 85.64\% & 91.88\% & 94.86\% & 96.96\% \\ Dataset4 & 56.72\% & 68.80\% & 74.47\% & 84.04\% & 83.50\% & 97.17\% & 98.37\% \\ Dataset5 & 40.47\% & 49.91\% & 74.21\% & 82.77\% & 84.67\% & 95.13\% & 95.31\% \\ Dataset6 & 48.30\% & 50.42\% & 73.37\% & 91.21\% & 94.58\% & 96.71\% & 96.19\% \\ \hline \end{tabular}
\end{table} 
Table 10: Recall comparison of various datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **[10]** & **[15]** & **[20]** & **[25]** & **[30]** & **[35]** & **[40]** \\ \hline Dataset1 & 54.20\% & 67.08\% & 91.15\% & 94.47\% & 96.13\% & 97.27\% & 97.60\% \\ Dataset2 & 50.63\% & 83.15\% & 82.65\% & 83.75\% & 88.35\% & 93.91\% & 95.91\% \\ Dataset3 & 54.88\% & 72.78\% & 81.94\% & 93.32\% & 92.14\% & 95.41\% & 97.07\% \\ Dataset4 & 64.70\% & 75.11\% & 81.27\% & 88.65\% & 87.89\% & 97.31\% & 98.43\% \\ Dataset5 & 44.96\% & 53.58\% & 78.23\% & 87.98\% & 89.14\% & 95.26\% & 95.69\% \\ Dataset6 & 51.97\% & 51.76\% & 77.48\% & 91.70\% & 95.05\% & 96.75\% & 96.29\% \\ \hline \end{tabular}
\end{table} 
Table 11: Precision comparison of various datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **[10]** & **[15]** & **[20]** & **[25]** & **[30]** & **[35]** & **[40]** \\ \hline Dataset1 & 53.05\% & 65.28\% & 89.48\% & 94.29\% & 95.97\% & 97.08\% & 97.44\% \\ Dataset2 & 49.47\% & 79.47\% & 81.50\% & 83.17\% & 85.07\% & 92.13\% & 94.72\% \\ Dataset3 & 50.89\% & 70.83\% & 79.68\% & 89.39\% & 92.01\% & 95.13\% & 97.02\% \\ Dataset4 & 60.45\% & 71.97\% & 77.80\% & 86.29\% & 85.62\% & 97.24\% & 98.40\% \\ Dataset5 & 42.63\% & 51.67\% & 76.16\% & 85.28\% & 86.81\% & 95.19\% & 95.50\% \\ Dataset6 & 50.11\% & 51.10\% & 75.39\% & 91.45\% & 94.81\% & 96.73\% & 96.24\% \\ \hline \end{tabular}
\end{table} 
Table 12: F1-score comparison of various datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **[10]** & **[15]** & **[20]** & **[25]** & **[30]** & **[35]** & **[40]** \\ \hline Dataset1 & 54.73\% & 64.93\% & 89.27\% & 94.27\% & 95.93\% & 97.07\% & 97.20\% \\ Dataset2 & 51.40\% & 69.67\% & 81.33\% & 83.40\% & 85.07\% & 92.67\% & 94.73\% \\ Dataset3 & 51.93\% & 71.00\% & 79.73\% & 89.93\% & 92.07\% & 95.07\% & 97.00\% \\ Dataset4 & 60.40\% & 72.33\% & 77.67\% & 86.53\% & 85.67\% & 97.27\% & 98.40\% \\ Dataset5 & 43.33\% & 52.27\% & 76.13\% & 85.13\% & 87.07\% & 95.20\% & 95.60\% \\ Dataset6 & 51.00\% & 52.33\% & 76.27\% & 91.47\% & 94.87\% & 96.73\% & 96.27\% \\ \hline \end{tabular}
\end{table} 
Table 9: Accuracy comparison of various datasets. in the feature space to improve the robustness and generalization of the model, thereby achieving high-accuracy emitter identification. We conduct extensive experiments on the real-world datasets constructed from collected BDS signals. Results demonstrate that the proposed SSL-SEI framework achieves excellent SEI performance using only a small number of labeled samples and significantly outperforms competing methods under various low-label settings, validating its effectiveness and generalizability.