## 4 Experiments and Results  
In our study, we configured six distinct information systems for testing, utilizing two different processing architectures. The system configurations varied significantly in terms of hardware capabilities:  
1. System 1 featured an Intel Core i7-9750H CPU (2.6 GHz, 6 cores, 12 threads), 16 GB of 2667 MHz SODIMM RAM, and a 1 TB Crucial P5 Plus SSD.
2. System 2 was equipped with an Intel Core i5-10400F CPU (2.9 GHz, 6 cores, 12 threads), 16 GB of 3600 MHz DDR4 RAM, and a 512 GB Adata XPG AS40G RGB SSD.
3. System 3 involved five computers with specifications identical to System 2.
4. System 4 utilized an Intel Core i9 Extreme Edition 10980 XE CPU (3.0 GHz, 18 cores, 36 threads), 64 GB of 3000 MHz DDR4 RAM, and a 1 TB Samsung 970 EVO Plus SSD.
5. System 5 involved five computers with specifications identical to System 4.
6. System 6 used an Intel E5-2680 Xeon CPU (2.7 GHz, 8 cores, 16 threads), 128 GB of 3000 MHz DDR3 RAM, and a 12 TB WD Red SATA HDD.  
As we will see, the results are highly interesting, particularly considering that they were achieved using the six hardware configurations described above, which were available to us but are admittedly somewhat limited. In the future, it will be highly beneficial to conduct a study utilizing computational resources typical of computational clusters, data centers, and farm computing environments. This future study will serve as the foundation for enabling the proposed solution to operate in contexts characteristic of Cloud Computing and Big Data.  
Indeed, the proposed solution can operate independently and autonomously; however, given the current level of investment by public and private institutions, it is reasonable to envision a migration towards post-quantum solutions like the one presented here over medium- to long-term time horizons. The primary advantage of the solution presented in this work lies in its immediate applicability to existing cloud infrastructures. Specifically, our solution could operate as an intermediary layer between the client and one of the most widely used cloud providers, such as those mentioned in Section 2, or it could represent the foundational layer of a cloud system that interacts directly with the client.  
The analysis focused on processing files of various sizes (1 KB, 100 KB, 1 MB, 10 MB, 100 MB) and examining several statistical indicators. We measured the average number of fragments per file size, total execution time and saving time, fragment creation and its save time, and fragment size. Additionally, we defined and calculated the efficiency ratio as in Equation (15):  
\[e=\frac{a\times c}{b}\,, \tag{15}\]  
and the overhead ratio as in Equation (16)  
\[f=\frac{b-(a\times c)}{b}\,, \tag{16}\]  
where  
\(a\) is the average number of fragments by file size;  
\(b\) is the average total execution and save time by file size;  
\(c\) is the average fragment creation and save time by file size.  
These metrics provided insights into the performance and efficiency of each system configuration under varying operational loads.  
As described above, in this section, to improve the readability of the study and to emphasize the variety and methodological completeness of the tests performed, we summarize the tests in the following subsections, numbered from Sections 4.1-4.5. Specifically, in Section 4.1, we consider the average number of fragments with respect to the file size. In Section 4.2, we analyze the average total execution time and the saving time with respect to the file size. In Section 4.3, we consider the average fragment creation time and the saved time with respect to the file size. In Section 4.4, we present the results related to the average fragment size with respect to the file size, while in Section 4.5, we analyze the efficiency ratio and the overhead ratio.