Dynamic link failures disrupt the connectivity and geometric symmetry of the constellation structure, thereby increasing protocol overhead and degrading the effective capacity for traffic transport. The fundamental relationship between constellation size and effective capacity under protocol overhead constraints remains unclear. To this end, we define capacity scalability as the ratio of constellation capacity under non-failure conditions to protocol overhead. Specifically, if ISL states follow a two-state discrete Markov chain and the maintenance period is $k \geq 1$, the upper bound of capacity scalability under the uniform traffic pattern is $O(1/n)$, where $n$ is the number of satellites. With perfect information about the constellation topology, the upper bound can be achieved via shortest-path routing. For any given protocol, there exists an optimal constellation deployment scale in terms of capacity scalability. When the constellation size is below this optimum scale, capacity scalability increases with constellation size, thereby improving effective capacity. Increasing the maintenance period $k$ can improve capacity scalability, but it does not change the fact that the capacity scalability converges to zero when the constellation size exceeds the optimal scale.

翻译：动态链路故障会破坏星座结构的连通性和几何对称性，从而增加协议开销并降低流量传输的有效容量。在协议开销约束下，星座规模与有效容量之间的基本关系尚不明确。为此，我们将容量可扩展性定义为无故障条件下星座容量与协议开销的比值。具体而言，若星际链路状态遵循两状态离散马尔可夫链且维护周期为$k \geq 1$，则在均匀流量模式下的容量可扩展性上界为$O(1/n)$，其中$n$为卫星数量。在拥有星座拓扑完美信息的情况下，可通过最短路径路由达到该上界。对于任意给定协议，存在一个基于容量可扩展性的最优星座部署规模。当星座规模低于该最优值时，容量可扩展性随星座规模增大而提升，从而提高有效容量。增加维护周期$k$可改善容量可扩展性，但不会改变以下事实：当星座规模超过最优值后，容量可扩展性将趋于零。