Everything else being equal, operating at a very low Earth orbit (VLEO) gives better ground resolution compared to operating at higher altitudes. However, operating at a VLEO faces marked atmospheric drag and various other uncertain interference factors. In this paper, we describe a pioneering satellite named ELITE (Extremely Low-earth Imaging and Technology Explorer) for high-resolution imaging and solar activity observation in VLEO. ELITE is planned to launch to an altitude of 550 km (Sun-synchronous orbit), then maneuver to a VLEO within the range of 200 to 350 km, and maintain that altitude for a year in VLEO through electrical propulsion. To address the challenges of VLEOs, a multiphase altitude planning framework is proposed, covering 4 critical scenarios that account for atmospheric drag, solar activity forecasts, and the strict requirements of time delay integration imaging. This enables robust and energy-efficient altitude adjustment strategies for stable long-term operation. An atmospheric density model is also developed, integrating solar activity predictions, drag coefficient estimation, public datasets, and onboard measurements, and validated using real VLEO mission data. A risk-aware collision avoidance strategy based on a beta-distributed differential evolution algorithm is introduced to balance safety and fuel efficiency, ensuring fast convergence during different evolutionary stages. These innovations ensure that ELITE can successfully accomplish its VLEO mission objectives: a groundbreaking 0.5-m ground sampling distance in color and innovative measurements of ionospheric intensity and atomic oxygen in VLEO. Our mission planning confirms ELITE’s capability to accomplish its missions, marking an important milestone in the field of VLEO satellites.

In the field of close-proximity space operations, RGB-D cameras have been used widely. However, publicly available spacecraft datasets for pose estimation are primarily based solely on image data. There is no publicly available dataset based on RGB-D cameras that can be directly used. Therefore, this paper introduces a novel RGB-D camera based spacecraft (RDCS) dataset. Due to high acquisition costs and confidentiality issues, it is difficult to obtain real spacecraft data and their associated pose information. To address this, the space environment and camera trajectories are constructed in Blender to capture pixel-aligned RGB images and depth maps of spacecraft from various observation angles. In addition, the RDCS dataset also includes pose annotations, bounding box labels, and corresponding mask images. The process focuses on addressing the challenges of acquiring the RGB image and the depth map of the spacecraft at the same keyframe while ensuring their pixel alignment, as well as determining the corresponding pose annotations. Finally, a spacecraft pose estimation network based on the pixel-level fused feature is applied to verify the availability of the RDCS dataset.

Long-term spaceflight composite stress (LSCS) can induce detrimental impacts on the human central nervous system, leading to conditions such as depression. This study aimed to identify mechanisms of simulated LSCS-induced spaceflight depression and the protective effects of traditional Chinese medicine Baoyuan Jieyu Formula (BYJYF) on spaceflight depression rats. The present study simulated the real long-term space station environment by conducting a 6-week experiment that involved simulating microgravity, isolation, noise, circadian rhythm disruptions, and low pressure. Glutamatergic neuron firing in hippocampus of the rats was examined by patch clamp to explore the effects of simulated LSCS exposure and BYJYF intervention on neurons. Single-cell RNA sequencing was then conducted to reveal the underlying mechanisms of simulated LSCS-induced spaceflight depression and the efficiency of BYJYF intervention. Results showed that simulated LSCS caused glutamate overload, induced by an abnormally high release frequency ( P < 0.05) from glutamatergic neurons. Single-cell sequencing revealed that the possible causes of glutamate overload lied in the reduction in hippocampal stromal cells, especially astrocytes, thereby delaying glutamate clearance, which could be mitigated by BYJYF intervention by up-regulating the proportion of stromal cells. Transcriptomics revealed that the target pathway of simulated LSCS-induced spaceflight depression was the calcium signaling pathway, and BYJYF functioned by regulating genes related to long-term potentiation pathway. Taken together, we demonstrate the target pathways of simulated LSCS-induced spaceflight depression and the potential clinical application of BYJYF, opening up broader prospects for the application of traditional Chinese medicine in space medicine.

Given the limited ground infrastructure in remote areas such as suburbs and rural regions, Internet of Remote Things (IoRT) devices have been widely deployed to gather critical data and information. However, traditional data transmission methods struggle to directly transmit the data to data processing centers. This paper proposes a space–air–ground Internet of Remote Things (SAG-IoRT) network architecture, which leverages the extensive coverage and efficient communication capabilities of satellites as its core advantage. In the SAG-IoRT network, low Earth orbit (LEO) satellites play a crucial role, addressing communication challenges in remote areas through their global coverage. Unmanned aerial vehicles (UAVs) serve as flexible bridges between ground and space, rapidly transmitting data collected by IoRT devices to LEO satellites, thereby enhancing data transmission efficiency and reliability. Our research focuses on optimizing the flight trajectories and scheduling strategies of UAVs to maximize the utilization of satellite communication resources, aiming to boost system throughput and reduce UAV energy consumption. To tackle the challenges of data collection and transmission in the dynamic and uncertain SAG-IoRT network environment, we formulate the optimization problem as a Markov decision process and apply the multi-agent deep deterministic policy gradient (MADDPG) algorithm to plan optimal paths for UAVs. Experimental results show that compared to the single-agent DDPG algorithm, the MADDPG-based solution not only improves system throughput by approximately 25.6% but also reduces UAV energy consumption by around 24.9%. This achievement underscores the pivotal role of satellites in advancing the development of IoRT and enabling efficient space-based communications.