## 5 Conclusions and Prospects  
This study presents an enhanced ENF region classification model, UniTS-SinSpec, which effectively leverages frequency domain information and periodic features. Through rigorous experimentation, including ablation studies and hyperparameter tuning, the model achieved a classification accuracy of 97.47%. The ablation experiments highlighted the distinct contributions of the sinusoidal activation function and spectral attention mechanism, with the combined model delivering the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & & **Model Name** & **Average** \\
**Category** & & & **Accuracy** \\ \hline \multirow{8}{*}{Machine learning} & \(\bullet\) & Power-line data-based grid identification using signal processing (2016) [32]. & 88.23\% \\  & \(\bullet\) & Location identification using power and audio data based on temporal variations in electric network frequency and its harmonics (2018) [10]. & 88.67\% \\  & \(\bullet\) & Power grid estimation using electric network frequency signals (2019) [11]. & 88.23\% \\  & \(\bullet\) & Spectrogram analysis in a multiple classifier fusion framework for power grid classification using the electric network frequency (2024) [14]. & 96\% \\ \hline \multirow{8}{*}{Deep learning} & \(\bullet\) & LSTM\_TSC. & 32.54\% \\  & \(\bullet\) & THF\_TSC. & 57.86\% \\  & \(\bullet\) & UniTS. & 91.49\% \\ \cline{1-1}  & \(\bullet\) & UniTS\_SAF. & 94.43\% \\ \cline{1-1}  & \(\bullet\) & UniTS\_SAM. & 94.11\% \\ \cline{1-1}  & \(\bullet\) & UniTS-SinSpec. & 96.24\% \\ \cline{1-1}  & \(\bullet\) & UniTS-SinSpec (HPO via Optuna). & 97.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model performance comparison. ENF classification has numerous important applications across various fields. In forensic analysis, ENF signals are used for time and location verifications in audio and video recordings, aiding in the authentication of multimedia content. In power grid monitoring, ENF signals are employed for power-quality monitoring and fault detection, identifying anomalies in grid stability. In audio watermarking and copyright protection, ENF serves as a unique identifier to verify the authenticity of audio content. Furthermore, ENF signals can be used for device and location tracking, determining the position of devices or individuals through signal variations, and for device identification. Additionally, ENF signals support audio-video synchronization, enabling the alignment of asynchronous media content. Despite the significant contributions of this study, there are several limitations. The dataset used primarily originates from the OSF platform, which may limit the diversity and coverage of the data. The model also relies on high-quality, synchronized ENF data. Future research should focus on collecting data from various regions and time periods with different nominal frequencies to improve the model's generalization and robustness. Moreover, the periodic nature of the sinusoidal activation function may lead to local minima during training, increasing the risk of suboptimal convergence, as highlighted in the referenced paper. Future studies should explore ways to optimize this activation function to mitigate such risks and enhance training efficiency. Lastly, the model's performance in handling short-term grid anomalies, as well as its computational efficiency when processing large datasets, is an area that warrants further exploration in future research. Original draft, Y.L. ; review and editing, T.L. ; methodology, G.Z. ; data curation, K.Z. ; validation, S.P. All authors have read and agreed to the published version of the manuscript. This research was funded and supported by the Fundamental Research Funds for the Central Universities (Grant No. 2023 KF01 ZK08) and the People's Public Security University of China Top-notch innovative talents training funds, which supports graduate research innovation projects (Grant No. 2023 yjsky010). Not applicable.