## 6 Conclusions  
This study used computer vision techniques and unsupervised clustering algorithms to process and analyze VLF waveform data collected by the CSES satellite's EFD. First, the waveform data were transformed into an 8 s spectrogram using the short-time Fourier transform. The spectrogram was then processed by grayscale conversion, vertical edge feature enhancement, and binarization. The preprocessed data were columnwise dimensionality reduced and fed into an unsupervised K-means++ clustering model for training and prediction, enabling the identification of vertical lines on the spectrogram. Any vertical line on the spectrogram identified as exceeding 60 lines is considered the recognized VLPT result. It realized automatic recognition and labeling of VLPT disturbances as well as localization of the spatiotemporal positions of pulse signals. Pulse interference signals can potentially disrupt satellite communication or observation tasks. By promptly identifying and locating the source of interference and implementing appropriate interventions, the normal operation of satellites can be ensured. Additionally, in applications such as satellite communication and radar systems, analyzing the spectrogram of pulse signals allows for the study of signal propagation effects in the atmosphere, ionosphere, or other media. This analysis further enables the optimization of signal transmission and reception performance. In conclusion, the results of this study provide new methods and approaches for automatic recognition and localization of VLPTs. This provides the basis for electromagnetic wave disturbance monitoring, space weather research, earthquake prediction, and related applications. It will contribute to enhancing our understanding of environmental changes and natural phenomena and provide more reliable data support for the development and application of relevant industries. Future work will focus on the following areas:  
1. Further study of spectral distribution, duration, power characteristics, etc., of interference signals to enhance the ability to identify different types of interference sources. By establishing a library of interference signals, modeling and classifying signals generated by different interference sources can improve the accuracy of identification. 2. Research and apply more efficient data preprocessing and noise reduction techniques to improve the quality of VLF waveform data. For example, filtering methods are used to optimize signal to noise ratio. Conceptualization, methodology, writing--original draft, and writing--review and editing, Y.H. ; methodology, Y.L. and J.Y. ; funding acquisition, Y.L. and Y.L. ; data curation, J.H. ; supervision, X.S. ; editing, L.M. and Y.Z. ; investigation and resources, Z.L., Y.W. and X.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Hunan Province, China (No. 2023 JJ50066), Teacher Research Fund Project (No. 20150109), Fundamental Research Funds for the Central Universities (No. ZY20215143), NSFC project (No. 42104159), Investigation of the Lithosphere Atmosphere Ionosphere Coupling (LAIC) Mechanism before the Natural Hazards, and Open Project Fund of Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology (No. FZ224104), and The 14 th Five-Year Plan of Educational and Scientific Research (Lifelong Education Research Base Fundamental Theory Area) in Hunan Province (No. XJK22 ZDJD58). Publicly available datasets were analyzed in this study. The CSES electric field data can be found here: (www.leos.ac.cn, accessed on 1 September 2022). The authors acknowledge the International Space Science Institute (ISSI in Bern, Switzerland and ISSI-B) in Beijing, China) for supporting International Team 23-583 lead by [PERSON] and [PERSON]. This work used the data from the CSES mission, a project funded by the China National Space Administration (CNSA) and the China Earthquake Administration (CEA).