# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
###### Abstract
Its core idea is to use known contextual information to predict the probability distribution of the next symbol. However, it requires complex probability estimation and context modeling, so it requires high computing speed and high memory consumption. In addition, it has poor adaptability when processing data without obvious statistical regularities. [PERSON] [12] inserted phrases into the dictionary of the x3 compressor to reduce the compression ratio. Notably, x3 is a lossless optimized dictionary-based data compressor. Entropy coding methods include Huffman coding [13; 14; 15] and arithmetic coding [16; 17]. The Bzip2 [18] compression algorithm combines block sorting compression and Huffman coding techniques, where block sorting compression improves the matching efficiency of the duplicate data by sorting the input data, and Huffman coding is used for entropy coding of these matched data. The move-to-front predictive coding (Mtfpc) algorithm encodes symbols via a probability model and contextual information to achieve higher compression efficiency, making it a compression algorithm that is based on entropy coding. [PERSON] et al. [19] applied Huffman coding and the LZW algorithm to buoy communication compression, with compression ratios of 45% and 55%, respectively. The compression ratio is relatively high, and there is much room for improvement. [PERSON] et al. [20] proposed a new lossless Huffman deep compression (HDC) algorithm, which combines sliding window technology to adapt to memory, uses deep learning pruning and pooling technology to reduce the complexity of the Huffman tree, and utilizes pattern matching and weights to achieve greater compression. Huffman coding cannot reach the entropy limit, whereas arithmetic coding can represent a set of characters as floating-point numbers in the interval [0, 1] through a probability distribution, which is closest to the entropy limit [21]. Therefore, this paper uses arithmetic coding for lossless data compression. With the development of deep learning, probabilistic prediction algorithms have been rapidly applied. The essence of lossless data compression is also a probabilistic statistical model, so deep learning has achieved good application in data compression. [PERSON] et al. [22] applied principal component analysis (PCA) and a deep neural network (DNN) for image compression and achieved a good compression ratio. [PERSON] [23] introducedthe recurrent neural network (RNN) into a lossless data compression model and achieved good results. However, the traditional RNN has problems such as gradient vanishing and explosion, long-distance dependency, and difficulty in parallelization. [PERSON] et al. [24] proposed a lossless compression method based on the gated recurrent unit (GRU). Although it alleviates the vanishing and exploding gradient problems, it still faces the problems of long-distance dependency and inability to parallelize. [PERSON] et al. [25; 26] proposed a variety of lossless compression models based on long short-term memory (LSTM) variants and achieved good compression results. Among them, bidirectional long short-term memory (BiLSTM) can capture the dependency between the context in the input sequence, thereby improving the model's ability to understand the sequence. Although it alleviates the long-term dependency problem, it is still difficult to parallelize. The emergence of the attention mechanism [27] thus solved the abovementioned problem of parallelization difficulty. [PERSON] [28] used Transformer as a probabilistic prediction model and achieved good results on multiple large datasets. Although Transformer has strong representation and parallel computing capabilities, it is large and complex, has a long training cycle, has excessive performance, and has average results on smaller single-batch profile datasets. [PERSON] et al.