# 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
Although the compression time on the Jetson Nano is relatively longer, it is acceptable for the data transmission of the Argo buoy. The Argo buoy's main control board sends profile data to the satellite every 65 s, with each transmission consisting of 228 bytes of data. For example, in the case of 4000 m Data 1, uncompressed data requires a total of 4486 bytes to be transmitted, taking approximately 1300 s with 20 transmissions. The energy consumption per transmission is about 151 J, resulting in a total energy consumption of approximately 3020 J. After compression using the Jetson Nano platform, the data size is reduced to 540 bytes, the transmission time is shortened to 195 s, and the number of transmissions is reduced to 3. During the deep learning-based compression task, the Jetson Nano consumes an average of 8.5 W and completes the task in about 37 s, requiring 315 J of energy. With data compression, the total energy consumption for transmitting one profile is reduced to 453 J, significantly lower than the 3020 J required without compression. Overall, data compression reduces the total energy consumption used for transmitting a single batch of profile data to 768 J, saving approximately 2252 J compared to the uncompressed scenario. This not only significantly shortens the data transmission time between the Argo float and the satellite but also effectively reduces energy consumption. Although the compression algorithm proposed in this paper achieves significant results compared to the three baseline methods, it still has certain limitations. The algorithm is primarily designed for single-batch profile data from Argo floats and builds the alphabet through character matching. Specifically, the data to be compressed must conform to a specific alphabet; if the data contains characters not in the alphabet, the algorithm will fail to operate correctly and will report an error. Furthermore, the algorithm may not be applicable to other types of data, such as those collected by the Argo float's hydrophone, which limits its effectiveness in broader applications. Figure 18: Comparison of compression ratio and compression time on the PC. ## 5 Conclusions

Faced with the limited storage capacity of the Argo buoy's SD card and the fact that satellite communications are generally charged by data volume, this paper proposes a block-based lossless data compression method based on a BiLSTM-MHSA-MLP, and it transplants the algorithm from the PC to the Jetson nano development board to achieve lossless data compression. The preliminary probability prediction model in this paper is constructed mainly via the BiLSTM and the MHSA, which pay more attention to the correlations among profile data, learn the relationships and characteristics of the data in parallel, and improve the generalization ability of the probability prediction model and its ability to capture complex patterns. On this basis, MLP and block_len are introduced to realize block compression while training. The model can dynamically update the probability model during the compression process, gradually adapting to the distribution of compressed data, and thus providing more accurate probability predictions. Compared with traditional mainstream compression algorithms, the compression method in this paper has a lower compression ratio and shorter compression time, and it is more suitable for Argo buoys. In addition, the compression algorithm in this paper was transplanted to the Jetson nano development board and lossless compression was achieved. The Argo buoy first sends the collected profile data to the Jetson nano development board through the serial port for compression, and then uses the compressed profile data for satellite communication, which can greatly reduce the time and communication cost of data transmission between the buoy and the satellite. It also lays the foundation for the subsequent transplantation of the Jetson nano development board to the Argo buoy to achieve real-time data compression.