The Dalian-1 Lianli satellite, a 17-kg 12U high-resolution remote sensing CubeSat, was developed to validate a series of innovative technologies, including submeter high-resolution remote sensing imaging, the high-reliability OpenHarmony real-time operating system (RTOS), and the nontoxic hydroxylamine nitrate propulsion system. Launched on 2023 May 10, aboard the Tianzhou-Ⅵ cargo spacecraft, the satellite was successfully deployed into orbit on 2024 January 18, following 253 d of in-orbit storage at the China Space Station. Most traditional micro/nanosatellites have poor reliability and real-time performance due to the lack of an RTOS. The satellite uses the free, high-reliability OpenHarmony RTOS with a small kernel for memory-limited microcontroller subsystems. The OpenHarmony RTOS was ported to 3 attitude determination subsystems—a magnetometer, a digital interface sun sensor, and an attitude measurement unit—to test its feasibility in micro/nanosatellites. The ground test and on-orbit verification focused on real-time performance and reliability. Ground tests indicated that the task switching time of the OpenHarmony RTOS averaged ≤2 μs, and the 3 subsystems continuously and stably operated for more than 1,000 h, and the stable update rate of data for all 3 subsystems had been substantially improved. During on-orbit operations, the telemetry data from the 3 subsystems were normal, demonstrating high stability and reliability and the ability to quickly respond to external events or data. These results lead to the conclusion that the real-time performance and reliability of the subsystems were substantially enhanced after being ported with the OpenHarmony RTOS. Additionally, with the assistance of the 3 subsystems, the Dalian-1 Lianli satellite successfully captured remote sensing images with a resolution better than 1 m, meeting the expected requirements. It is anticipated that in future space missions, more micro/nanosatellites will adopt subsystems ported with the OpenHarmony RTOS.

Many error sources influence the calibration experiment of 4-quadrant sun sensors, making the calibration of sun sensors cumbersome and its accuracy difficult to improve. Any continuous function on a bounded closed set can be approximated by a deep neural network. This paper uses the deep neural network model to approximate the error model. The data-driven training network is adopted to continuously modify the model parameters to fit the error compensation model and ensure that the accuracy reaches the target requirements after calibration. Considering that the deep neural network model needs a considerable amount of data, the neural network model training is divided in 2 stages. In the preliminary stage, cubic surface fitting is used to generate a dataset, which is small in size and controllable. After the completion of the initial training, the experimental data are used to fine-tune the model to achieve error compensation. The accuracy can be improved from 1° (1σ) to 0.1° (1σ) after the incident angle of the sun sensor is corrected. The error compensation model eliminates the loss of accuracy caused by the distortion of light spots at the edge of the field angle and provides a favorable condition for the expansion of the field angle of the 4-quadrant analog sun sensor.