# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 6 Conclusions and Outlook  
This paper proposes the QP-DETR method, a DETR-based automatic detection model for the detection of the QP emissions observed by the CSES-01 satellite. Our model utilizes high-sampling-rate ELF frequency band electromagnetic field data and employs deep learning techniques to achieve the automated detection of QP waves. The research primarily focused on efficiently and accurately extracting target signals from large volumes of satellite-collected electromagnetic field data. The feasibility and effectiveness of the QP-DETR model were verified through testing on both the original significant sample set and a new unscreened dataset. Additionally, the incorporation of proton cyclotron frequency constraints in the detection process successfully mitigated false alarm issues associated with QP events. This lightweight and fast detection model is suitable for deployment in satellite equipment and greatly enhances the possibility of onboard detection.  
To effectively identify QP emissions, the presented detection model requires an extensive collection of training samples. In future work, we plan to iterate the training process using CSES-01 orbital observations while optimizing the model. Furthermore, we aim to explore the integration of meta-learning methods to achieve the more comprehensive and accurate detection of these QP waves. Additionally, we intend to conduct further research based on the identified ionospheric QP events using the QP-DETR method. By applying quantitative analysis techniques such as correlation analysis and the wave growth rate method, we seek to investigate the underlying mechanisms involving ULF waves and the modulation of ELF/VLF electromagnetic waves by high-energy electrons. These investigations will provide a detailed understanding of the generation of ionospheric QP emissions.  
[PERSON] performed the data process and wrote the original manuscript version. [PERSON] put forward the main idea and led the whole analysis process and the manuscript writing of this work. [PERSON], [PERSON], [PERSON], and [PERSON] provided consultation on the idea and the manuscript writing of the work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Key Research and Development Program of China: 2023 YFE0117300, the National Natural Science Foundation of China: 41874174, the APSCO Earthquake Research Project: Phase II and Dragon 5 cooperation 2020-2024: 59236.  
**Data Availability Statement:** The ZH-I satellite data can be downloaded from the Internet at [[https://www.leos.ac.cn](https://www.leos.ac.cn)]([https://www.leos.ac.cn](https://www.leos.ac.cn)) via the Data Service term, where scientists can access the data download service by registering online and filling in the application form following the data policy (accessed on 30 July 2023).  
**Acknowledgments:** We acknowledge the CSES-01 scientific mission center for providing the data.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.