In this paper, we begin by describing the Starlink constellation’s configuration plan, treating each satellite as a network node, naming and numbering the various nodes, and then classifying the laser interstellar links (LISLs) according to their orbital alignment and whether they are permanently visible. Whereupon, the method for calculating the respective theoretical interstellar distances required for establishing LISLs under two distinct orbital scenarios, co-orbital and hetero-orbital are analyzed, and the optimal phasing factors are proposed by solving an optimization algorithm for the shortest distance. The OneWeb and Starlink constellations, as well as the constellation states of various phasing factors are simulated, respectively. The Starlink constellation with F = 11 obtains the best coverage between 60° north and south latitudes predicated upon the analysis of the constellation N Asset Coverage. Following that, the first phase of the Starlink constellation deployment was modeled in order to analyze and count the number of permanent LISLs in orbit, adjacent to, and nearby. Subsequently, the characteristics of azimuthal, elevation, and range (AER) were subsequently analyzed to ascertain their variation law and to compile the number of permanent and temporary LISLs that could be established at various inter-distance ranges. Finally, predicated on the simulation results, the optimal LISLs connection strategy for the Starlink constellation is evaluated and a static topology for the constellation is constructed.

The massive mega constellation of satellites will have a significant impact on global space safety. With Starlink as an example, this paper is aimed at assessing the risk of in-orbit collision, analyzing the probability of collision in orbit in its natural operating state, and forecasting the probability of secondary collision between the collision-generated short-term debris cloud and satellites in the same orbit. The mass, size, velocity, and direction of space debris in a particular orbit of Starlink satellite are calculated based on the MASTER-8 model, and the shape characteristics of the Starlink satellite are added to the model to determine the probability of a Starlink satellite colliding with space debris in that orbit. A modified spacecraft impact disintegration model then is used to calculate the collision results and estimate the collision threat level of the short-term debris cloud formed by the Starlink satellite after its destruction to satellites in the same orbit. The results indicate that the collision probability of Starlink satellite in orbit natural operation exceeds the red warning threshold 10^-4 that the satellite disintegration after the first collision will generate 14088 pieces of debris over 1cm, of which 4092 debris are potentially dangerous to other spacecraft, and that the collision probability to a satellite in the same orbit exceeds the red warning threshold of 10^-4 within 30 minutes, implying that collision avoidance needs to be improved.

The extent of the impact of mega constellations on the low-orbiting geospace environment, which has not yet been assessed in more concrete quantitative terms, is an extremely important issue to consider as mega constellations are built. Satellite safety and lifetime can clearly represent the situation of space targets, and thus can reflect the impact of mega constellations on geospace security. Three target satellites with different characteristics were selected and the Accepted Collision Probability Level (ACPL) was calculated to obtain the impact of Starlink on satellite mission lifetime. Upon considering Starlink without early avoidance control, the lifetimes of the three target satellites were shortened by 56.21%, 99.09%, and 99.82%, respectively. After 10 revolutions of early avoidance control, two were shortened to 92.166% and 91.99%, while the lifetime of JILIN-01 was extended by 155.44%. After joining Starlink, the total risk became larger; even if the target satellite avoided control far more frequently than before joining Starlink, it will face a worse geospace environment. Adopting the most aggressive orbit avoidance control cannot avoid the deterioration of the geospace environment from the perspective of satellite lifetime, which is an irreversible and deteriorating process.

As one of the most widespread and important types of terrestrial vegetation in the world, grasslands play an irreplaceable role in global climate change. The grasslands of Inner Mongolia, represented by the Xilin Gol League, are typical of Eurasian grasslands and have an important ecological status in the world. In this paper, taking the grassland of Xilin Gol League as the research object, based on the machine learning method, we mainly carry out two aspects of work: the prediction of grassland soil health and evaluation of grassland sustainable development. To address the issue of predicting soil health in grasslands, we focus on an important indicator in grasslands: soil moisture. By analyzing the characteristics of soil moisture time series values and related influencing factors, based on a NAR neural network model, three important factors of soil moisture were predicted: soil evaporation data, average air temperature, and precipitation. Subsequently, the corresponding soil moisture calculation model was trained using regression models based on hyperparameter optimization, and the final predicted soil moisture values were obtained for different months and depths in 2023 and 2024. To evaluate the sustainability of grassland development, we developed a model for the degree of grassland desertification based on the kernel principal component analysis, focusing on three dimensions: environmental factors, surface factors, and human factors. Based on this, a quantitative definition of soil denudation is given by analyzing the main influencing factors of grassland soil degradation. At the same time, a prediction model for the evaluation of soil slumping was established based on a fuzzy comprehensive evaluation matrix, and the evaluation weights of each major factor were given and analyzed. Based on the above research, this paper suggests a reasonable grazing strategy for the grassland areas of the Xilin Gol League: when the grazing intensity is medium and the total number of grazing days is [85, 104] days in a year, the degree of soil slumping and soil desertification in the pastures is minimized. The research results of this paper are useful for the future maintenance and management of the grasslands of Xilin Gol League and other similar areas.

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 $m_V$, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services.