## 1 Introduction
However, the distance between cloud servers and terminal IoT equipment is relatively long and inevitably brings high latency, which may be unbearable for some latency-sensitive IoT applications. In addition, the growth in data volume brought about by the development of the Internet has also posed new challenges to the capabilities of the communication network, which has brought more significant pressure to the cloud server form [22; 23]. Recently, edge computing has provided a solution to the above dilemma. Edge computing migrates services from remote cloud to network edge closer to users. Therefore, applying the edge-computing paradigm to IoT can achieve shorter communication distances and faster services [24; 25; 26]. Edge servers are closer to users than cloud servers, so edge computing is expected to solve the high-latency challenge brought by cloud-based service provision [27; 28]. Satellite Edge Computing (SEC) is proposed as a new promising computing platform. It uses LEO satellites as edge servers, and it has a lot of technical and theoretical support for its feasibility [5; 10; 29; 30]. For example, in [19], the author proposes a multi-purpose satellite, iSat, that demonstrates the feasibility of configuring computing and storage resources on the LEO satellite. Moreover, the latency from terrestrial stations to visible LEO satellites can be reduced to 1-4 ms [30], which is quite friendly for latency-sensitive IoT applications. Based on the above discussion, we imagine a way for the IoT to help the economic sustainability of LEO satellite constellations: that is, IoT products rely on LEO satellites to provide services. In this mode, users pay for IoT services, and IoT product providers pay for LEO satellite service providers; the economic sustainability of LEO satellite constellation construction will be guaranteed. However, there is a problem that must be considered, which is that both edge servers on the terrestrial and satellites serving as edge serversare resource-limited, with limited computing capabilities, making it difficult to handle complex tasks. Many IoT applications use Deep Neural Networks (DNNs), especially Convolutional Neural Networks (CNNs) to provide Artificial Intelligence (AI) services, such as image classification, object detection, text processing, etc. [31; 32; 33]. Complex CNN faces significant demands for computing resources, and it generally cannot be executed by an edge server independently. Addressing the challenges, edge-distributed inference is a popular solution, and how to implement distributed inference in the edge environment to shorten the inference latency is a research hotspot. As far as we know, this article is the first work to use edge-distributed computing in the SEC scenario. However, in the traditional edge computing scenario, there are many research studies on distributed inference, which can provide reference for our research [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. To shorten the inference latency, a lot of schemes of edge-distributed inference have been proposed in recent years. In these works, using model compression techniques, such as model pruning, to achieve distributed inference disrupts the structure of the original model and can have unpredictable impacts on the accuracy of model inference [34; 35; 36]. Building a new deep model suitable for distributed inference requires retraining the model, increasing resource and time costs [44]. In contrast, the method of directly performing distributed partitioning and redeployment on the original model has been widely welcomed, as it does not change the parameters and structure of the original model nor does it require retraining [37; 38; 39; 40; 41; 42; 43]. Motivation. We have noticed that most existing works on distributed partitioning CNN models have not considered how to trade off communication latency and computing latency, but it is important to shorten the inference latency, and how to make the trade-off is tricky. In Section 2.3, we will give a more detailed introduction to the trade-off. Contributions.