# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
###### Abstract  
This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with a multilayer perceptron (BiLSTM-MHSA-MLP). We constructed an Argo buoy data compression system using the main buoy control board, Jetson nano development board, and the BeiDou-3 satellite transparent transmission module. By processing input sequences bidirectionally, BiLSTM enhances the understanding of the temporal relationships within profile data, whereas the MHSA processes the outputs of the BiLSTM layer in parallel to obtain richer representations. Building on this preliminary probability prediction model, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced to achieve block compression during training, dynamically updating the model and optimizing symbol probability distributions for more accurate predictions. Experiments conducted on multiple 4000 m single-batch profile datasets from both the PC and Jetson nano platforms demonstrate that this method achieves a lower compression ratio, shorter compression time, and greater specificity. This approach significantly reduces the communication time between Argo buoys and satellites, laying a foundation for the future integration of Jetson Nano into Argo buoys for real-time data compression. Argo buoy; lossless data compression; bidirectional long short-term memory network; multi-head self-attention +
Footnote †: journal: Marine Science and Engineering  
0000-0001-9399-3333-3333-33333-33333-33333-33333-3333-3333-3333-3333-3333-33333-33333-3333-33333-33333-3333-33333-33333-33333-333333-33 Ocean buoys generally use satellite communications to transmit data and report their positions to ground-based platforms. With the successful launch of the last BeiDou-3 satellite in June 2020, the BeiDou-3 satellite was completed. Its communication range can cover the entire world and greatly improve the communication capabilities of China and its surrounding areas [6]. Therefore, BeiDou-3 satellites play an important role in the data transmission and communication of Argo buoys. The Argo buoys upload the collected single batch of profile data to the satellite via the BeiDou-3 transparent transmission module at 228 bytes at an interval of 65 s, and then the satellite transmits the collected data to the ground-based platform. However, the buoy data storage capacity is limited, and satellite communications are generally charged by volume. Therefore, it is necessary to compress the profile data via a lossless data compression algorithm based on the characteristics of the data before they are uploaded to the satellite, which is very important for improving transmission efficiency and reducing satellite communication costs. Due to the high accuracy requirements of deep-sea profile data, this study focuses exclusively on lossless compression methods, which ensure that the data remains completely identical before and after compression and decompression. Current compression algorithms are mainly divided into dictionary coding and entropy coding [7]. At present, the mainstream dictionary coding algorithm is the Lempel-Ziv (LZ) [8; 9] series algorithm. Although these algorithms can remove redundancy in data, their compression efficiency is low, and the implementation of encoding and decoding is relatively complex, especially due to the need to maintain a dynamic dictionary. [PERSON] [10] applied the improved Lempel-Ziv-Welch (LZW) compression algorithm to the sonar buoy signal acquisition system, achieving a compression ratio between 54% and 60%. Compared to the conventional LZW compression algorithm, it demonstrated a significant reduction in compression ratio. [PERSON] et al. [11] proposed the prediction by partial matching (PPM) compression algorithm, which is a lossless compression algorithm based on context modeling.