## 1 Introduction  
The services in low-Earth-orbit (LEO) satellite networks have diversified with the continuous expansion of these networks and the increase in the associated number of users. The demand for network resources to guarantee network transmission in concurrent, multiservice scenarios has experienced exponential growth [1]. However, the available network resources on the satellite, including bandwidth, computing power, and storage, are severely limited by the harsh space environment, resulting in a bottleneck in network service performance. The varying demands for resources among different services further increase the complexity of network resource management and scheduling [2]. Therefore, it is necessary to utilize network resource allocation technology, in conjunction with the resource demands of services, to jointly optimize the allocation of multi-dimensional network resources and enhance the service capability of LEO satellite networks. Network resource allocation methods primarily encompass static pre-allocation of resources and dynamic adaptive scheduling of resources. Due to the fixed position of terrestrial network nodes and the stability of links, the method of allocating resources on demand is commonly employed to achieve dynamic resource scheduling, while static pre-allocation is used for special services [3]. However, the topology of satellite networks undergoes dynamic changes, characterized by large inter-satellite distances and poor link stability. As such, the transmission of operational or management commands is subject to large latency and latency deviation, leading to inadequate timeliness in dynamic resource allocation processes, including resource status awareness, decision-making, and the dispatch of commands [4]. The intelligent and dynamic online decision-making approach for terrestrial networks has become ineffective due to the limited computing capabilities on satellites and the complexity of time-varying scenarios. Static resource allocation and resource reservation mechanisms are therefore frequently adopted in satellite networks, helping to guarantee the stable transmission of network services. The efficient real-time awareness of the resource status of the satellite [5] and the precise prediction of changes in resource status are important prerequisites for network resource allocation. However, the application of terrestrial network awareness and prediction methodologies to satellite networks encounters challenges such as inadequate awareness latency and imprecise resource state predictions, owing to the dynamic characteristics of the network topology and the unpredictability of services. On the one hand, the instability of inter-satellite and satellite-ground links, coupled with the significant distance involved, results in propagation delay, transmission delay, and onboard processing delay at each node as the awareness data traverse multiple nodes within the network [6]. The cumulative effect of these delays leads to a certain degree of awareness latency when the awareness data are relayed back to the ground, meaning that the awareness data received on the ground exhibit lag. On the other hand, satellite network resources exhibit complex temporal and spatial distributions, with resource status characterized by nonlinearity and discontinuity. Furthermore, the operations within the network demonstrate significant randomness, leading to considerable uncertainty in resource variations. The insufficient availability of data to guarantee the robustness and generalization capabilities of prediction models poses challenges in accurately forecasting the resource status. Consequently, developing an efficient network resource awareness method, augmented with precise and agile prediction techniques, is necessary to facilitate accurate and flexible resource allocation. There have been numerous studies on resource allocation based on satellite network resource awareness and the prediction of network resource status. However, research on these two aspects is often conducted in isolation.