## 4 Experimental Results  
During the implementation phase, we set the simulation in such a way that the hash output is uniformly distributed between miners and stakes. The difficulty was adjusted to \(\alpha\) = 0.01, the stacker power was set to S = [80, 40, 20, 15, 10, 5, 5, 5, 5] and the miner power was set to M = [32, 16, 8, 6, 4, 2, 2, 2, 2, 2]. Numbers have already been set to show the linearity of the exponential rise in computational power. To begin with, the block time was set to 20 s in \(t\), with a duration of 90 days per entire chain. During the simulation, a total of 385,479 blocks were generated, out of which 192,688 were stake blocks and 192,791 were mining blocks. As shown in Figure 6, the rewards were proportional to computing power (stake/mining), which was considered a fair outcome. The target block time was 20 s, resulting in a stake block time of 40 s and a mining block time of 40 s with an average rate of Ps\(\in\)S _s/ds_, _Pm\(\in\)M \(m/dm\)_ and Ps\(\in\)S _s/ds_ + Pm\(\in\)M _m/dm_. Figure 6: Experimental results of (**a**) stake power vs. block rewards distribution and (**b**) hash power vs. block rewards distribution. During the experiment, which we ran on the Google Colab platform, we determined that the initial simulation would consume not more than 200 MB of RAM, as shown in Table 4.  
According to the simulation results, the attacker side that dominates the network with more than 51% computation power cannot easily launch the attack because the fork mechanism has a split rule between PoW and PoS implementations. Hence, to take over this network and launch an attack, the attacker needs to command over 100% of the system, which is impossible on a consensus blockchain node. Figure 7 shows that the chain power is demonstrated over the block time generation and distribution, and Figure 8 shows the proposed hybrid model computational power over PoW and PoS block time distributions. We began the simulation experiment and based on previous data, we set the computational power for mining to 76, which is the same block size. The results are shown in Table 6  
Mining power and actively minting coins may also be used to calculate the attack's cost. They may be used as a rough estimate of the cost of mining power because they are closely related to miner earnings (fees and coin base incentives). The active stake can be determined because the amount of Satoshi released every second is inversely proportional to the stake difficulty. By dividing the amount of Satoshi issued into equal parts for each PoS block, we can estimate the total amount of Satoshi currently being mined. To compute the income per block required to sustain the attack cost, these measures may be used to determine an attack-cost objective (e.g., a particular number of Bitcoins or a certain percentage of the total number of Bitcoins mined to date). In turn, this information can be used to dynamically alter the block size and ensure that the block income continues to support the set attack cost goal. Therefore, mining earnings will be increasingly predictable, a certain degree of security will be maintained, and costs will be reduced. \begin{table}
\begin{tabular}{c c} \hline \hline
**Miners** & **Computational Power Percentage** \\ \hline Miner 0 & 23\% \\ Miner 1 & 21.24\% \\ Miner 2 & 17.36\% \\ Miner 3 & 12.87\% \\ Miner 4 & 8.56\% \\ Miner 5 & 4.2\% \\ Miner 6 & 4.3\% \\ Miner 7 & 4.26\% \\ Miner 8 & 4.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Miners’ computational power before implementing the proposed hybrid mechanism. Figure 8: Experimental results of (**a**) computational power vs. PoS block time distribution and (**b**) computational power vs. PoW block time distribution. \begin{table}
\begin{tabular}{c c} \hline \hline
**Miners** & **Computational Power Percentage** \\ \hline Miner 0 & 15.19\% \\ Miner 1 & 11.18\% \\ Miner 2 & 7.94\% \\ Miner 3 & 6\% \\ Miner 4 & 4\% \\ Miner 5 & 2\% \\ Miner 6 & 2\% \\ Miner 7 & 2\% \\ Miner 8 & 2\% \\ Total & 52.31 \\ \hline \hline \end{tabular}
\end{table}