## 2 System Model  
In this section, we establish the system model for an AOS environment, which provides the foundation for subsequent algorithm design and performance evaluations. The model describes the integration of heterogeneous data sources, the classification of services based on their delay sensitivity, and the differentiated frame generation processes to meet various QoS requirements. In the AOS, various types of data are received from spacecraft payloads. As shown in Figure 1, the system input integrates six types of heterogeneous data sources. Among them, telemetry data monitor device states, while telecommand data issue control instructions with the highest operational priority. Voice data are real-time streaming media, requiring stringent low-latency transmission. Non-real-time bulk data, such as video, observation and experimental data generated from scientific tasks, allow for flexible delay tolerance. After classification, data packets are separated into two service queues. Voice, telemetry, and telecommand data, which are highly delay-sensitive, are directed into the delay-sensitive service queue and processed using the real-time framing algorithm to generate complete frames. The real-time frame generation process operates within a time-slotted framework. Each frame period collects the arrived packets and supplements idle packets if necessary. Each frame carries both the arrived packets during each time window and dynamically generated idle packets to maintain frame structure integrity under bandwidth fluctuations. In contrast, video, observation, and experimental data, which are less delay-sensitive, enter the non-delay-sensitive service queue and are processed using the high-efficiency framing algorithm. After framing, all data frames are scheduled and transmitted through the virtual channel scheduling module for subsequent transmission within the AOS network. The overall structure of the system model is illustrated in Figure 1, showing the classification of incoming data, the queuing and the framing mechanisms. We construct a dual-queue framing model to accommodate both delay-sensitive and non-delay-sensitive traffic in AOS. Two distinct traffic models are employed to more accurately reflect real-world service characteristics. First, we define two types of data: real-time data \(U_{h}=\{u_{h0},u_{h1},\ldots,u_{hH}\}\) and non-real-time data \(U_{l}=\{u_{l0},u_{l1},\ldots,u_{lL}\}\). Among them, real-time data include audio data, telemetry data, etc. Non-real-time data include video data, remote control data, observation data, experimental data, etc. After the arrival of all data packets, real-time data enter the delay-sensitive service queue, and non-real-time data enter the non-delay-sensitive service queue. On the one hand, the framing task of packets in the delay-sensitive service queue is primarily handled by the real-time framing (RTFG) module. Delay-sensitive data such as telemetry, telecommand, and voice traffic typically consist of short-duration flows with limited burstiness and minimal long-range dependence. These characteristics have been extensively validated in the literature, which supports modeling such traffic using Poisson arrival processes. We establish a packet arrival model. We assume that the packet arrival process follows a Poisson distribution with arrival rate parameter \(\lambda\). In this case, the probability of receiving exactly \(k\) packets during a time interval \(t\) is characterized by the Poisson distribution, as given by Equation (1):  
\[P(X=k)=\frac{(\lambda t)^{k}}{k!}e^{-\lambda t}\quad k=0,1,2,3,\cdots \tag{1}\]  
where \(X\) denotes the number of packet arrivals within time duration \(t\), and \(\lambda\) is the average arrival rate. Assuming that each packet has a fixed size \(l_{s}\), the number of packets which can be accommodated in an MPDU is \(n\), and the length of the MPDU is \(l_{ms}=n\times l_{s}\). Thus, incoming packets can exactly fill the MPDU data area, avoiding the issue of incomplete packet filling. Figure 1: System model.