## 1 Introduction
In other words, relevant research may solely concentrate on optimizing awareness methods or enhancing prediction accuracy while neglecting the relationship between them. For example, in the case of satellite network awareness technology, the method of [7] employs telemetry technology to perceive the network status in real time and implements dynamic adaptive routing based on the awareness data. However, it fails to take into account the issue of awareness delay during the telemetry process. The volume of awareness transmission data is enhanced by employing dynamic channel allocation, enabling the establishment of an awareness method characterized by low latency and high bandwidth [8]. However, this approach also increases the complexity and awareness overhead of the network. Research on the allocation of network resources has employed intelligent prediction models [9; 10], such as RNNs, long short-term memory (LSTM) [11], graph convolutional networks (GCNs) [12], and transfer learning [13], to forecast network traffic or resource status, subsequently making intelligent computing or dynamic allocation decisions based on the predictions. However, due to the limited onboard computing resources, these models struggle to fulfill the dynamic and intricate resource allocation demands of the network in terms of their real-time capability and deployment ability. Reference [14] predicted the computing, storage, and bandwidth resources of onboard control plane units and user plane units using an LSTM in order to improve the dynamic resource allocation capability for network service functions. The authors of [15] proposed a routing strategy based on cache resource awareness and prediction, which can reduce the end-to-end transmission delay by sensing user needs and using cache prediction results for dynamic routing planning. However, unpredictable random events and subsequent data perturbations are prone to arise within the network due to the dynamic and uncertainty of network topology, leading to a decline in the prediction accuracy of the model [16]. Additionally, the sparsity of data and the non-stationarity of time series further impact the authenticity and reliability of the data. Aiming at the problems of poor awareness latency and low resource prediction accuracy, a network resource allocation method for LEO satellite networks based on awareness-prediction joint compensation (NRA-PPJC) is proposed in this study. This method can be utilized to dynamically compensate for resource prediction results, improve the timeliness and accuracy of data, and build an efficient network resource allocation method. Satellite networks have fewer available open-source resource data than terrestrial networks, while the data gathered by terrestrial networks lack complex and time-evolving characteristics. Therefore, the variations in resource occupancy and release of satellite network resources during the arrival and transmission processes of service were simulated in this work using service arrival models. These simulations were tailored to account for differences in the service generation period, user distributions, and varying resource demands among different types of services. In-band network telemetry (INT) technology was utilized to obtain the resource status of each node. To address the issue of significant awareness latency unique to satellite networks, node resource awareness data were correlated with awareness latency to create a resource dataset that sequences data based on latencies. Using the differentiated awareness delay between nodes as the prediction step size, the LSTM method was employed to flexibly forecast the resource status for various nodes. Furthermore, an awareness-prediction joint compensation model was designed considering the impact of awareness latency between nodes on the timeliness of awareness data, as well as the varying prediction accuracies of various resources.