## 5 Simulation Experiments and Analysis
### Performance Analysis  
The cache hit ratio is an essential metric for assessing the efficiency of a caching policy and is defined as the percentage of requests that can be satisfied by the LEO-SMC-ICN-IoT caching system. Therefore, the cache hit ratio is denoted by:  
\[Hit_{CARF}=\sum_{t=1}^{T}\frac{1}{\left|\mathit{Req}(t)\right|}\cdot R(\mathit{ Req}(t),\sum_{t=1}^{T}s(i,t)), \tag{15}\]  
where \(T\) is the total time, \(R(\mathit{Req}(t),\sum_{t=1}^{T}s(i,t))\) is the number of requests that hit the satellite node at time \(t\), and \(\left|\mathit{Req}(t)\right|\) represents the total number of requests at time \(t\). The content retrieval delay is measured in terms of the average number of hops, which is calculated as:  
\[\mathit{Hop}_{CARF}=\sum_{t=1}^{T}\frac{1}{\left|\mathit{Req}(t)\right|}\cdot \mathit{Hop}(\mathit{Req}(t),\sum_{t=1}^{\left|\right|}s(i,t)), \tag{16}\]  
where \(\mathit{Hop}(\mathit{Req}(t),\sum_{t=1}^{\left|\right|}s(i,t))=\left\{\mathit{ hop}_{1}(t),\mathit{hop}_{2}(t),\ldots,\mathit{hop}_{n}(t),\ldots,\mathit{ hop}_{N}(t)\right\}\) is the number of hops used to satisfy each request at time \(t\). If \(\mathit{req}_{n}(t)\) can be met from the satellite node \(i\), \(\mathit{hop}_{n}(t)\) is equivalent to the amount of hops from the satellite node \(i\) to the user; otherwise, \(\mathit{hop}_{n}(t)\) equals the amount of hops from the content producer to the user. We analyze the cache capacity and Zipf distribution on the performance of the LEO-SMC-ICN-IoT. And the request rate of users is assumed to be 100 packets of interest per second. The cache performance such as the average number of hops, average hit rate, and throughput by varying the cache size is shown in Figure 14. The cache size is the ratio caching capacity of the satellite node to the total contents in the LEO-SMC-ICN-IoT. Figure 13 shows that the hit rate and the throughput using the five caching policies gradually increase with the cache size increasing. And the average number of hops gradually decreases. This result occurs because the amount of content that can be cached in the satellite nodes increases at the time \(t+\Delta t\) with the cache size increasing. These results lead to an increased probability. A comparative analysis shows that CARF performs better than the other four caching strategies in three evaluation metrics, including average hop count, hit rate, and throughput. This is because the LCE, prob 0.5, and LCD cache content are redundant, which results in the cached content being less diverse. In addition, because \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Value** & **Description** & **Value** \\ \hline Constellation & Iridium & ISL bandwidth & 25 Mbps [60] \\ Number of satellite nodes & 66 & Star-ground link bandwidth & 1.5 Mbps [60] \\ Number of GS & 2 & Simulation time & 200 s \\ Number of contents & 2000 & Replacing policy & Least Recent Used (LRU) [61] \\ Packed size & 500 byte [48] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter Settings. OPP uses the relationship between the popularity of the content and the location of satellite nodes, the cache performance is better than the LCE, LCD, and prob 0.5. Considering the relationship between the popularity of the content and the location of satellite nodes, the CARF policy further improves the utilization of cache space and service efficiency by learning from satellite nodes' location and content using RF. The Zipf distribution parameter \(\gamma\) mainly reflects the degree of concentration of user preferences. We vary \(\gamma\) to investigate the impact of cache performance, and the trend of different cache performance is shown in Figure 14. As the \(\gamma\) increases, the average hit rate and throughput of the five caching policies gradually increase, and the average number of hops gradually decreases. The reason is that with the \(\gamma\) increasing, there is an increasing request for hot content in the LEO-SMC-ICN-IoT network.