# 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
### Comparison of the Compression Effects on PC and Jetson Nano

To realize the real-time lossless data compression of the Argo buoy in the future, it is necessary to transplant the compression algorithm in this paper to the Jetson nano development board. To verify the performance of the compression algorithm on the Jetson nano development board, the 12 groups of data in Table 2 are compressed on the PC and Jetson nano, respectively, and the compression ratio and compression time are compared. The specific comparison results are shown in Figures 12 and 13. As shown in the above figures, the average compression ratios of the PC and Jetson nano are both approximately 12.11%, which are basically the same. Owing to the difference in hardware architecture between Jetson nano and PC, there is a slight difference in the size of the compressed file. The average compression time on the PC is 4.4 s, and the average compression time on Jetson nano is 36.6 s. Compared with the PC, the average compression time is increased by 8.3 times, which is within an acceptable range. The compressed file of the above Jetson nano is sent to the PC through the serial port, and decompression is completed on the PC. The file is consistent with the one before compression, indicating that the compression algorithm presented in this paper enables lossless data compression. Figure 11: Compression time comparison: (**a**) comparison between D1 and D7; (**b**) comparison between D8 and D12. Figure 12: Comparison of the compression ratio between PC and Jetson nano: (**a**) comparison between D1 and D7; (**b**) comparison between D8 and D12. Figure 13: _Cont._

### Comparison of the Compression Effects of the BiLSTM-MHSA-MLP Algorithms

To verify the effectiveness of the compression algorithm in this paper, traditional compression algorithms (Huffman, LZW, PPM [11], Bzip2 [18] and Mftpc), traditional deep learning compression algorithms (BiGRU [23], GRU_Multi [24], BiLSTM [25], LSTM_Multi [24], Transformer [27] and MHSA-MLP), and popular compressors (ZIP, ZZ, TGZ and GZ) are used to compress the five 4000 m single-batch profile datasets D1, D3, D5, D7, and D9, as shown in Table 2, and the compression ratio and compression time are calculated. First, the compression ratio is compared with those of traditional compression algorithms (Huffman, LZW, PPM, Bzip2 and Mftpc). The test results are shown in Figure 14.

As shown in Figure 14, the preliminary probability prediction model (BiLSTM-MHSA) and the probability prediction model (BiLSTM-MHSA-MLP) proposed in this paper achieve lower compression ratios compared to traditional compression algorithms. Specifically, the

Figure 14: Comparison of the compression ratio with traditional compression algorithms. Figure 13: Comparison of the compression time between PC and Jetson nano: (**a**) comparison between D1 and D7; (**b**) comparison between D8 and D12. average compression ratios of the seven compression algorithms are 13.33%, 12.13%, 43.1%, 66.56%, 43.37%, 32.8%, and 45.41%, respectively. Among them, the traditional compression algorithms all have compression times of less than 1 s, with an average compression ratio of 46.25%. Although traditional algorithms perform better in terms of compression time, their compression ratios are 34.12% higher than those of the models proposed in this paper. This suggests that while traditional algorithms generally have lower computational complexity, their compression effectiveness is somewhat limited. On the other hand, deep learning algorithms, despite having higher computational complexity, can capture long-range dependencies and high-level features in the data through more complex models, leading to better compression performance. Second, the compression algorithm in this paper is compared with traditional deep learning compression algorithms (BiGRU, GRU_Multi, BiLSTM, LSTM_Multi, Transformer, and MHSA-MLP).