## 3 System Model Description
### Push Model  
To address the challenge of minimizing user-perceived latency in accessing time-sensitive remote sensing data, we propose a dynamic intelligent dissemination framework that leverages real-time contextual awareness and adaptive network resource allocation. Recognizing that conventional batch-processing methodologies inherently introduce delays through periodic data aggregation and post-acquisition analysis, our strategy emphasizes proactive data delivery by continuously monitoring evolving user requirements and operational environments. By capitalizing on the temporal dynamics of satellite-derived data streams, the system performs in-transit analytics to predict user needs and pre-emptively routes relevant information across dynamically reconfigurable network topologies. This approach ensures the optimal utilization of intermittent connectivity windows while maintaining service continuity across time-slot transitions, where network configurations can be instantaneously adjusted based on prevailing conditions.  
The core innovation lies in synchronizing data generation cycles with user decision workflows through the predictive modeling of demand patterns and network state variables, thereby transforming reactive data retrieval into an anticipatory service paradigm that significantly reduces the latency without compromising data integrity or relevance. It is assumed that the network topology is fixed during each time slot, but can be changed instantaneously during time slot transitions. The set of time slots is defined as \(\tau=\{1,2,...,T\}\), and each time slot will have multiple remote sensing data randomly generated in remote sensing satellite network using \(F=\{f_{1},...,f_{F}\}\), \(f_{i}=\{id_{i},image_{i},shoottime_{i},latstart_{i},latend_{i},!onstart_{i},!onend_{i}, _{i},{payload_{i}}\), \(resolution_{i}\), \(product_{i}\}\) to represent all remote sensing data generated by the remote sensing satellite network. The data-generating satellite transmits content summary information to the cluster head node, which then performs intelligent analysis to determine the optimal caching and dissemination strategy. Instead of random caching, the cluster head dynamically selects appropriate remote sensing satellites within the cluster to store the content based on real-time demand forecasts and network conditions. Simultaneously, it employs predictive algorithms to match content relevance with user profiles, scheduling proactive pushes during low-traffic time slots to ensure the timely delivery of high-priority data while maintaining the efficient utilization of user terminal cache resources through coordinated content rotation and priority management.  
We establish a unified framework where all remote sensing data items have equal size \(C_{f}\). Cache-enabled satellites and user terminals possess storage capacities of \(C_{s}\) and \(C_{u}\), respectively, with both significantly smaller than the total data size (\(C_{s}<<F\), \(C_{u}<<F\)). This configuration allows each satellite to store \(L_{sate}=\left\lfloor\frac{C_{s}}{C_{f}}\right\rfloor\) data items, while user devices can hold \(L_{user}=\left\lfloor\frac{C_{s}}{C_{f}}\right\rfloor\) contents. When storage reaches capacity, the first-in-first-out (FIFO) policy is implemented for cache replacement. The content dissemination decision making process is centralized at the cluster head corresponding to the originating satellite's cluster. For cluster \(k\), the binary decision variable \(ps^{k,jl}_{f_{i}}=1(0<j\leq M)\) determines whether content \(f_{i}\) generated by cluster \(k\) is pushed to the \(j\)-th user at time slot \(l\), if \(l=T+1\), the content \(f_{i}\) is not pushed to the \(j\)-th user. To enforce efficient cache utilization, each content item can only be actively pushed once across all time slots, governed by the constraint  
\[\sum_{l=1}^{T+1}p_{f}^{k,j,l}=1,\ \forall k,f,j. \tag{5}\]