###### Abstract  
The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression  
exceed 560 bits in length, and a single civil communication card can only send one message per minute [4]. The success rate decreases as the message length increases, sometimes dropping as low as 50% [6; 7; 8].  
In our dataset, for instance, there are 41 fields to transmit, which would require 1312 bits when encoded in float32. The monitoring system records a data point every 10 s, with no tolerance for data loss. Although existing methods, such as utilizing multiple communication cards to increase bandwidth [9] and employing channel coding to eliminate data loss [8; 9], can be adopted, the capacity of Beidou falls significantly short of the number of data to be transmitted. Therefore, data compression is necessary to minimize its length. Furthermore, both the compression algorithm's allowable calculation time and the computing power of both the sender on the ship and the receiver in the database are relatively abundant. Hence, optimizing the compression rate becomes the primary objective in the compression problem for the real-time transmission of ship data via Beidou Navigation System.  
In this paper, we introduce our QAAPA method, a novel method combining **Q**uantization, **A**ttention-**A**veraging-based **P**redictive coding, and **A**rithmetic coding and facilitating the real-time transmission of ship data via the Beidou Navigation System. Our contributions can be summarized as follows:  
* To address the challenge of economically transmitting real-time ship status data via the Beidou system, we have developed the QAAPA Method. This method comprises a series of tailored steps designed to compress data in real-time while maintaining fixed precision.
* Unlike traditional compression methods that transmit entire datasets, our approach transmits only the residuals, resulting in a significant reduction in data size. This transmission of residuals is made possible through a critical step in our method: attention-averaging-based predictive coding. Here, we compute the difference between actual and predicted data, reconstructing it at the receiver's end. Our predictor is built on a foundation of linear regression models and Convolutional Neural Networks (CNNs), incorporating appropriate weights. This framework ensures accurate real-time predictions by leveraging historical data, enabling precise reconstruction of transmitted residuals.
* Experimental Validation: We conducted experiments using actual ship monitoring system data. Results showcase that QAAPA achieves compression rates approaching theoretical minimums estimated by commercial algorithms, surpassing traditional approaches for similar problems.  
The remainder of this paper is structured as follows: Section 2 discusses existing compression solutions, highlighting the absence of suitable methods for real-time ship status data transmission via Beidou. Section 3 details the QAAPA method tailored for Beidou-based ship data transmission. Section 4 presents experiments validating QAAPA's performance. Finally, Section 5 concludes the paper and outlines future work.