Many scientific experiments are conducted in space; therefore, it is critical to understand the microgravity environment of a space laboratory. The first Chinese cargo ship, Tianzhou-1 (TZ-1), entered space on 20 April, 2017 and later joined with the Tiangong-2 (TG-2) Chinese space laboratory. TZ-1 carried a high-precision electrostatic suspension accelerometer system (ES-ACC) for measuring the microgravity acceleration on the spacecraft and a microgravity-active vibration system (MAIS), which contained flexible quartz accelerometers (Q-ACC). The ES-ACC was able to provide a reduced-disturbance environment for the MAIS. The purpose of these two instruments was to validate novel technologies and as an opportunity to record the microgravity acceleration of TZ-1 and TG-2 in detail during spacecraft operation in different flight modes, with or without vibration isolation. The acceleration data were analyzed comprehensively in a time–frequency–amplitude spectrogram. Some periodical disturbances with orbital period and irregular signals related to certain in-orbit events were observed. After reducing those disturbances, the microgravity levels on TZ-1 and TG-2 could be resolved to better than 10⁻⁶ m/s² in the root mean square in the frequency of 0.01–10 Hz. These accurate measurements aboard the Chinese space laboratory will provide valuable information to optimize working conditions for scientific experiments.

The space motion control is an important issue on space robot, rendezvous and docking, small satellite formation, and some on-orbit services. The motion control needs robust object detection and high-precision object localization. Among many sensing systems such as laser radar, inertia sensors, and GPS navigation, vision-based navigation is more adaptive to noncontact applications in the close distance and in high-dynamic environment. In this work, a vision-based system serving for a free-floating robot inside the spacecraft is introduced, and the method to measure space body 6-DOF position-attitude is presented. At first, the deep-learning method is applied for robust object detection in the complex background, and after the object is navigated at the close distance, the reference marker is used for more precise matching and edge detection. After the accurate coordinates are gotten in the image sequence, the object space position and attitude are calculated by the geometry method and used for fine control. The experimental results show that the recognition method based on deep-learning at a distance and marker matching in close range effectively eliminates the false target recognition and improves the precision of positioning at the same time. The testing result shows the recognition accuracy rate is 99.8% and the localization precision is far less than 1% in 1.5 meters. The high-speed camera and embedded electronic platform driven by GPU are applied for accelerating the image processing speed so that the system works at best by 70 frames per second. The contribution of this work is to introduce the deep-learning method for precision motion control and in the meanwhile ensure both the robustness and real time of the system. It aims at making such vision-based system more practicable in the real-space applications.

Electrostatic levitation is an important method of studying material properties. Without using a container, a physical object is levitated between electrostatic plates and melted to the liquid state using a laser. Then, measurements are made via fast cooling or oscillation. Control technology is critical to the electrostatic levitation system. Uncertainty regarding the sample charge during the start-up and laser-melting periods often causes disturbances or causes levitation to fail. In this paper, we design a two-step adaptive control strategy with charge estimation and feed-forward control. This method can better adapt to charge uncertainty during the initial stage. In addition, we propose an innovative new method of superimposing oscillation signals via software to measure the material surface tension and viscosity. Unlike the traditional method, this approach does not require extra hardware resources and is flexible with regard to regulating the frequency and amplitude. A control system model with an accurate electric field model is established and used to simulate control progress in order to illustrate the advantage of our control method. Experiments based on a high-speed vision-servo system also validate the effectiveness of the adaptive and oscillation control strategies.

Rydberg-atom receivers have developed rapidly with increasing sensitivity. However, studies on their application in low-frequency electric fields remain limited. In this work, we demonstrate low-frequency communication using an electrode-embedded atom cell and a whip antenna without the need for a low-noise amplifier (LNA). Three modulations—binary phase-shift keying (BPSK), on–off keying (OOK), and two-frequency shift keying (2FSK)—were employed for communication using a Rydberg-atom receiver operating near 100 kHz. The signal-to-noise ratio (SNR) of the modulated low-frequency signal received by Rydberg atoms was measured at various emission voltages. Additionally, we demonstrated the in-phase and quadrature (IQ) constellation diagram, error vector magnitude (EVM), and eye diagram of the demodulated signal at different symbol rates. The EVM values were measured to be 8.8% at a symbol rate of 2 kbps, 9.4% at 4 kbps, and 13.7% at 8 kbps. The high-fidelity digital color image transmission achieved a peak signal-to-noise ratio (PSNR) of 70 dB. Our results demonstrate the feasibility of a Rydberg-atom receiver for low-frequency communication applications.

In recent years, the Rydberg-atom sensor has attracted significant attention due to its high sensitivity, wide dynamic range, and miniaturization capabilities. However, noise from laser power fluctuations, frequency drift, and environment is introduced alongside the intrinsic quantum noise of Rydberg atoms, significantly degrading system performance for microwave sensing. This study tries to reduce the noise impact by combining the Rydberg-atom sensor with the convolutional neural network (CNN)-transformer model. The CNN module captures local temporal features of the signals, while the transformer module extracts global correlations within these features. We apply it to the following classification tasks of atomic microwave sensing: multifrequency microwave recognition, broadband spectrum monitoring, and automatic modulation recognition. We demonstrate that the CNN-transformer model achieves high accuracy across the three tasks and can effectively reduce the noise impact, which holds significant potential for applications in radar detection and wireless communication.