# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 3 Data Compression and Decompression
### Compression and Decompression Model
Currently, the model is primarily trained using profile data, but due to the limitations of the data type, its application scope is somewhat restricted. To broaden the model's application, we plan to introduce other types of Argo float data in the future, such as hydrophone data, to enhance the model's generalization ability and applicability. Furthermore, the current model is based on a complex deep learning architecture, resulting in high computational complexity, which may lead to inference speed bottlenecks. Therefore, we will consider using optimization tools such as TensorRT to accelerate the model, significantly improving the inference efficiency for data compression tasks. Additionally, techniques like quantization and pruning will be employed to further optimize inference speed and reduce power consumption. Furthermore, to ensure the efficiency and reliability of data transmission between the Jetson Nano and the main control board, the communication protocol needs to be improved in the future, to enhance the data transmission and verification mechanisms. This would help ensure data integrity and accuracy, thus improving the overall system performance and reliability. Conceptualization, S.G.; methodology, W.Z. ; software, W.Z. ; validation, J.X., H.L. and Y.Z. ; formal analysis, W.Z. ; investigation, Y.Z. and Y.Y. ; resources, J.X. and H.L. ; data curation, J.X., W.Z. and Y.Z. ; writing--original draft preparation, W.Z. ; writing--review and editing, S.G.; visualization, J.X. ; supervision, J.X. ; project administration, J.X. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the China Postdoctoral Science Foundation (No. 2024T170637), the young elite scientist sponsorship program (No. YESS20230630), the \"Qiming Plan\" for Cultivating Leading Talents in Science and Technology Innovation at Tianjin University (No. 2023 XQM0018), and the State Key Laboratory of Precision Measuring Technology and Instruments (No. pilb2305). Not applicable. The data used to support the findings of this study are available from the corresponding author upon request. **Acknowledgments:** We thank the members of the Ocean Group of Tianjin University and Shandong University of Science and Technology for their contributions to this buoy research. Buoy hardware resources were provided by Tianjin University. **Conflicts of Interest:** The authors declare that they have no conflicts of interest to report regarding the present study. ## References

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