## 1 Introduction
Based on these outstanding performances, it appears that SSDs are the best choice for applications requiring high-speed data processing and storage. However, despite the exceptional performance of SSDs, their installation costs remain considerably high. Although SSDs have become cheaper, a significant price difference remains between HDDs and SSDs. In addition, the capacity of an SSD is relatively limited compared with that of an HDD. Therefore, the use of HDDs remains an efficient and economical solution for AE measurements, which require a large storage space to store measurement data. In the art of storage solutions, solid-state hybrid drives (SSHDs) [23] combine the advantages of traditional HDDs and SSDs. This offers the large storage capacity of HDDs, and the faster data write speeds of SSDs. The improved performance of an SSH depends on its firmware and caching algorithms. When the firmware identifies certain data as frequently accessed, it stores that data in the NOT-AND (NAND) flash memory, achieving speeds comparable to entry-level SSDs. However, typically, measurement data are not often categorized as frequently accessed. In addition, once the NAND flash cache is full, the SSH writes data on the traditional HDD platters, resulting in write speeds similar to standard HDDs. Therefore, for AE measurements, SSHDs do not provide significant write performance improvements over HDDs. Consequently, this study focuses solely on HDDs as the storage solution. Although the sequential write rate of a standard HDD is approximately 160 MB/s, the actual write rate is generally lower when performing common tasks, particularly when dealing with text formats. Text formats are commonly employed to store sensor readings in measurements, provide accessibility through various text editor programs, and offer convenience to general computer users. However, the utility of text formats results in slow and inefficient data storage, which is attributed to high memory consumption and suboptimal write rates. Hence, text formats are not preferable for AE measurements, particularly for those employing HDDs. To address these issues, binary file formats are recommended. These formats offer significant performance benefits over text file formats. Binary file formats are designed for direct access, offering unmatched speeds compared with other file formats that require parsing and interpretation. Values stored in binary formats occupy the same space on the disk as they occupy in memory, ensuring the efficient utilization of storage resources. Furthermore, most general programming languages support encoding a number in the binary format as a series of bytes, and vice versa, simplifying the process of working with binary file formats across different programming environments. The ISO/IEC 60559:2020 [24] (IEEE 754 [25; 26]) standard is the most widely used for representing real numbers in binary form. This standard defines different binary encoding algorithms that can represent real numbers in large ranges, thereby enabling the storage of a wide range of sensor reading values. However, this may not be the most efficient method of encoding sensor reading values, as these values typically have a limited range for each measuring channel. For instance, sensor reading values are generally acquired as real numbers with a range of \(\pm\)10 V (voltage) or 4-20 mA (current), depending on the specification of the DAQ device employed. In addition, each DAQ device has a specific measurement resolution that limits the number of measurement values [27]. Using standard algorithms to store sensor reading values can lead to the wastage of storage resources. To optimize data storage, data-encoding algorithms should be specialized with respect to the characteristics of the target values. This approach ensures that the required memory is minimized. To this end, this study aimed to improve the efficiency of storing sensor reading values in tasks with exceptionally high sampling rates, such as AE measurements.