## 2 Materials and Methods
### Modeling Method
#### 2.2.2 SE Attention Mechanism and Residual Convolutional Network
Detailed analysis attributes this degradation to two primary factors: (i) the inherent parameter redundancy of very deep networks results in a mismatch between model complexity and dataset size, thereby inducing severe overfitting; and (ii) over-parameterized models exhibit gradient instability during back-propagation, substantially complicating training. In response, a streamlined shallow residual architecture is proposed. As illustrated in Figure 3 (where C1, C2, and C3 denote different channel dimensions), the network consists of only two residual blocks, striking a judicious balance between model capacity and computational efficiency. Empirical evidence demonstrates that relative to deeper residual networks, this shallow design preserves the feature-extraction capability while markedly reducing  
Figure 2: SE attention mechanism. complexity, consequently shortening the per-iteration training time and facilitating rapid model updates. This work innovatively integrates the squeeze-and-excitation (SE) channel-attention mechanism into the residual network for two principal reasons:  
Heterogeneous channel importance. Conventional convolutions treat all channels equally, however, their contributions to the target task vary substantially; some channels even convey redundant or noisy information. The SE mechanism adaptively learns channel-specific weights, suppressing less informative channels and amplifying pivotal ones, thereby improving feature utilization. Explicit modeling of inter-channel dependencies. While residual networks mitigate gradient vanishing via skip connections, they do not explicitly model relationships among channels. By employing nonlinear mapping to capture such dependencies, the SE attention further augments representational power. The formal mathematical definitions and computational procedures of this module are provided in Equations (9)-(13). \[\text{F}_{l}=\text{BN}(\text{ReLU}(\text{Conv1D}(\text{H}_{l},W_{l}^{(1)}))) \tag{9}\]  
\[\text{F}_{l}=\text{BN}(\text{ReLU}(\text{Conv1D}(\text{F}_{l},W_{l}^{(2)}))) \tag{10}\]  
\[\text{S}_{l}=\text{ReLU}(W_{l}^{(f\angle)}\text{Sigmoid}(W_{l}^{(f\angle 1)}\text{-GAP}(\text{F}_{l}))) \tag{11}\]  
\[s=\sigma_{\text{ReLU}}(W_{2}\sigma_{\text{Sigmoid}}(W_{1}z+b_{1})+b_{2}) \tag{4}\]0  
\[s=\sigma_{\text{ReLU}}(W_{2}\sigma_{\text{Sigmoid}}(W_{1}z+b_{1})+b_{2}) \tag{4}\]1  
\[s=\sigma_{\text{ReLU}}(W_{2}\sigma_{\text{Sigmoid}}(W_{1}z+b_{1})+b_{2}) \tag{4}\]2 Residual convolutional network structure. Among them, \(\text{BN}(),\text{ReLU}(),\text{Sigmoid}()\) represent batch normalization and two activation functions, respectively; \(\text{Conv1D}()\) represents one-dimensional convolution; \(W_{l}^{(1)},W_{l}^{(2)}\) respectively represent the different weight parameters of the two convolution operations; \(W_{l}^{(f\_1)},W_{l}^{(f\_2)}\) are respectively the two weight parameters of the SE attention mechanism in the process of calculating channel weighting; \(\text{GAP}()\) represents global average pooling (which is the symbolic expression of Formula (3)). \(S_{l}\) represents the weight calculated by the channel attention mechanism, and \(\otimes\) represents the multiplication of each channel. Moreover, incorporating the SE channel-attention mechanism adds only a negligible number of parameters and incurs a minimal computational overhead, imparting an inherently lightweight nature that helps maintain training efficiency. In the proposed design, the SE module is inserted at the end of the main branch of each residual block, immediately before the residual summation; performing channel-wise recalibration prior to feature fusion yields a more discriminative combined representation. Because the shortcut branch primarily serves as an unobstructed gradient-flow pathway to alleviate vanishing gradients, no SE module is applied to this branch.