## 4 Experimental Results Analysis and Discussion
### Exploring the Impact of Different Data Augmentation Strategies on SSL  
To investigate the impact of different data augmentation strategies on the effectiveness of self-supervised pretraining, we conducted a series of experiments using various augmentation combinations. All experiments use the same parameter settings described in Section 4.2. The experimental results are shown in Figure 5.  
The results demonstrate that single augmentations, such as Noise, Scale, or Shift, generally provide moderate improvements as the number of labeled samples increases. Among them, the Scale and Shift augmentations outperform Noise, achieving 96.93% and 92.53% accuracy, respectively, at 40 labeled samples per class, compared to 75.87% for Noise. This suggests that geometric transformations (Scale and Shift) are more beneficial for representation learning than random Noise alone. The improvement trend shown by pairwise enhancement combinations, including Noise + Scale, Noise + Shift, and Scale + Shift, is not very stable, and these combinations only outperform single enhancements under specific sample sizes. The proposed enhancement strategy (Noise + Scale + Shift) consistently outperforms all other configurations in all sample sizes. It achieves the highest accuracy rate of 97.20% when there are 40 samples in each class. More importantly, it demonstrates a significant leading advantage in low-resource scenarios. The above experimental results demonstrate the effectiveness of the data augmentation proposed in this study in the SSL phase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **\{10\}** & **\{15\}** & **\{20\}** & **\{25\}** & **\{30\}** & **\{35\}** & **\{40\}** \\ \hline Baseline & 36.58\% & 43.71\% & 58.81\% & 60.25\% & 68.44\% & 79.40\% & 81.23\% \\ MAML & 39.97\% & 61.09\% & 71.98\% & 72.06\% & 76.37\% & 80.98\% & 82.01\% \\ SimCLR & 48.03\% & 61.72\% & 70.72\% & 75.25\% & 80.52\% & 83.95\% & 86.06\% \\ SA2 SEI & 43.15\% & 50.37\% & 68.85\% & 79.30\% & 83.18\% & 88.51\% & 90.89\% \\ Proposed & 53.05\% & 65.28\% & 89.48\% & 94.29\% & 95.97\% & 97.08\% & 97.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: F1-score comparison of various methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **\{10\}** & **\{15\}** & **\{20\}** & **\{25\}** & **\{30\}** & **\{35\}** & **\{40\}** \\ \hline Baseline & 39.85\% & 47.52\% & 59.15\% & 59.80\% & 67.05\% & 78.12\% & 79.87\% \\ MAML & 35.62\% & 54.30\% & 67.25\% & 70.88\% & 75.33\% & 79.95\% & 81.40\% \\ SimCLR & 48.75\% & 56.40\% & 69.75\% & 74.05\% & 79.25\% & 82.75\% & 84.85\% \\ SA2 SEI & 42.05\% & 49.45\% & 67.25\% & 77.95\% & 81.90\% & 87.40\% & 89.95\% \\ Proposed & 53.65\% & 63.75\% & 88.05\% & 93.15\% & 94.85\% & 96.00\% & 96.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recall comparison of various methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **\{10\}** & **\{15\}** & **\{20\}** & **\{25\}** & **\{30\}** & **\{35\}** & **\{40\}** \\ \hline Baseline & 38.61\% & 44.31\% & 60.41\% & 60.64\% & 68.11\% & 80.08\% & 81.04\% \\ MAML & 41.51\% & 61.30\% & 72.49\% & 72.73\% & 76.52\% & 81.20\% & 82.64\% \\ SimCLR & 48.97\% & 62.65\% & 71.20\% & 75.43\% & 80.76\% & 84.27\% & 86.83\% \\ SA2 SEI & 43.07\% & 50.91\% & 68.41\% & 78.82\% & 83.29\% & 89.04\% & 91.12\% \\ Proposed & 54.20\% & 67.08\% & 91.15\% & 94.47\% & 96.13\% & 97.27\% & 97.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision comparison of various methods.