The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.

Satellite internet is blasting off right now. Nations and states are inking deals with satellite providers to fill in service gaps for their residents and keep their critical infrastructure connected. Phone makers are building satellite capabilities into their handsets. Airlines are partnering with satellite operators to keep your in-flight Netflix stream stutter-free. And the race to blast the satellites powering these networks into orbit is helping the rocket business thrive.

When the equipment is working, real-time collection of environmental sensor data for anomaly detection is one of the key links to prevent industrial process accidents and network attacks and ensure system security. However, under the environment with specific real-time requirements, the anomaly detection for environmental sensors still faces the following difficulties: (1) The complex nonlinear correlation characteristics between environmental sensor data variables lack effective expression methods, and the distribution between the data is difficult to be captured. (2) it is difficult to ensure the real-time monitoring requirements by using complex machine learning models, and the equipment cost is too high. (3) Too little sample data leads to less labeled data in supervised learning. This paper proposes an unsupervised deep learning anomaly detection system. Based on the generative adversarial network and self-attention mechanism, considering the different feature information contained in the local subsequences, it automatically learns the complex linear and nonlinear dependencies between environmental sensor variables, and uses the anomaly score calculation method combining reconstruction error and discrimination error. It can monitor the abnormal points of real sensor data with high real-time performance and can run on the intelligent satellite Internet of things system, which is suitable for the real working environment. Anomaly detection outperforms baseline methods in most cases and has good interpretability, which can be used to prevent industrial accidents and cyber-attacks for monitoring environmental sensors.

What's coming: Elon Musk and Jeff Bezos are about to lock horns once again. Last month, the US Federal Communications Commission approved the final aspects of Project Kuiper, Amazon's effort to deliver high-speed internet access from space. In May, the company will test its satellites in an effort to take on SpaceX's own venture, Starlink, and tap into a potentially very lucrative market. The catch: The key difference is that Starlink is operational, and has been for years, whereas Amazon doesn't plan to start offering Kuiper as a service until 2024, giving SpaceX a considerable head start. Also, none of the rockets Amazon has bought a ride on has yet made it to space.