In the orbital environment, spacecraft face challenges such as scarce fault samples, varying operating conditions, and a strong reliance on accurate models and labeled data in traditional diagnostic methods. This paper systematically reviews transfer learning techniques for spacecraft fault diagnosis, highlights their recent advancements, and outlines future research trends. Transfer learning strategies are categorized into four types: instance-based, feature-based, model-based, and domain-adaptive. The principles,advantages, limitations, and representative applications of each strategy are analyzed, along with key enabling techniques such as importance weighting, adaptive batch normalization, parameter fine-tuning, and adversarial training. The review shows that transfer learning effectively mitigates issues of data insufficiency and distribution shift by enabling knowledge transfer from source to target domains. In particular, multi-source domain adaptation and adversarial domain adaptation significantly improve cross-condition diagnostic performance by enhancing model generalization and robustness. It is concluded that transfer learning provides a promising framework for intelligent spacecraft fault diagnosis. Future research should focus on source-free domain adaptation, multi-modal data fusion, semi-supervised transfer learning, and model interpretability, aiming to support practical deployment in real-world aerospace missions.

This paper provides an overview of the current status and future trends in remote sensing satellite data transmission technology. The article begins by introducing the developmental history of remote sensing satellite data transmission. It then further elaborates on the technologies involved in remote sensing satellite data transmission, detailing key aspects such as data compression,data encryption, data framing, data encoding, data modulation, and data transmission. Subsequently the paper analyzes the challenges faced by remote sensing satellite data transmission, such as bandwidth limitations, signal processing, real-time requirements, and data handling. It also summarizes strategies to address these challenges, such as adopting laser-based data transmission, more complex modulation schemes, and multi-source fusion with computational transmission. Looking ahead, the paper envisions future trends, including inter-satellite laser relay transmission, on-board computational transmission, and the application of computational constellations for intelligent in-orbit fusion in remote sensing satellites. Research indicates that remote sensing satellite data transmission technology will evolve towards higher transmission rates, higher reliability, more convenient data processing, and more intelligent mission workflows.

Existing deep learning-based modulation recognition methods are difficult to adapt to HF channels with significant time-varying characteristics, limiting their application in modulation recognition for High Frequency (HF) signals. In addition, in non-cooperative HF communication scenarios, the training dataset is often difficult to cover all possible modulation types, so that open-set modulation recognition also has important practical significance. This paper proposes a multi-modal feature fusion-based open-set modulation recognition method by combining communication domain knowledge and open-set recognition techniques,which effectively reduces the impact of time-varying HF channels and unknown modulation types on recognition performance. The proposed method first utilizes communication domain knowledge to obtain multimodal features that are robust to channel variations,and then extracts discriminative deep feature representations through multimodal feature fusion and deep feature learning to effectively identify known and unknown modulation types. In addition, the method also generates dummy samples through manifold mixing strategy to assist network training, which can enhance the network's ability to identify unknown types. Experimental results indicate that the proposed method outperforms existing open-set modulation recognition methods. When the channel conditions of training and testing signals are the same, the proposed method improves by over 3% in open-set recognition performance. When the channel condition of testing signals is drastically changed, that is, the channel conditions of the training and testing signals are different,the proposed method improves by over 8% compared to existing method, which exhibits strong robustness to channel variations.

In the field of aerospace tracking, telemetry, and command (TT&C) communications, the capability for precise telemetry, telecommand, and data transmission is a critical technology ensuring reliable spacecraft operations. As the complexity of space missions continues to escalate and the TT&C communication environment becomes increasingly demanding, higher requirements are imposed on the reliability of communication links in TT&C systems. Polar codes, as a short-frame burst coding scheme with high reliability, low complexity, and superior coding gain, yet exhibit high sensitivity to carrier frequency offset errors. Addressing the dual high demands for reliability and synchronization accuracy in TT&C systems, this paper proposes a Polar code-aided frequency offset estimation (PCAFOE) algorithm. Compared with the traditional carrier synchronization algorithm, PCAFOE algorithm is demonstrated with higher estimation accuracy, which is able to effectively improve the carrier synchronization performance of TT&C communication systems, and provides effective technical support for next-generation aerospace TT&C systems.

A novel channel estimation algorithm based on energy ratio confidence with dynamic dual-threshold decision is proposed to address the issues of missed path detection and performance degradation in existing channel estimation algorithms for dual-antenna space-time block coding (STBC) communication systems within unmanned aerial vehicle (UAV) swarm networking scenarios. These issues arise due to fixed thresholds and insufficient noise handling within the channel response length. Significantly, the proposed algorithm does not require prior knowledge of the exact channel impulse response length. Simulation results demonstrate that the proposed algorithm achieves a performance gain of nearly 2 dB at a bit error rate (BER) on the order of 1E-5 compared to the conventional threshold-based Fourier Transform-Least Squares algorithm. Furthermore, the algorithm effectively suppresses the noise in the estimated channel impulse response, leading to significantly enhanced channel estimation performance for UAV swarm networking.

The problem of output voltage waveform oscillation in the 28 V radiation-resistant P-channel field effect transistor at a resistive load of 10.4 Ω was studied, and it was found that the main reason was the mismatch between the device's own parasitic parameters and the peripheral circuit parameters. To avoid this phenomenon, a circuit improvement solution was proposed, that is, the series resistance in the gate electrode reduces the V_GS change rate and destroys the conditions for oscillation. At the same time, the clamp diode is added to the gate source electrode to ensure that the gate source voltage V_GS is always lower than the rated value, so as to improve the safety of the circuit operation and long-term working reliability.

The sum and difference channels of S-band single-channel measurement and control equipment exhibit phase differences, which may vary with environmental changes, transmission media, and assembly processes. Phase difference correction must be completed before the signal enters the baseband terminal for demodulation, to ensure the correct angular error signal is demodulated and stable target tracking is ultimately achieved. In practical work, the phase difference correction is basically carried out by setting up beacon machines at high positions, which can effectively solve the problem of inconsistent phase difference. However, with the expansion of the distribution area of measurement and control equipment and the shortening of state preparation time, traditional phase correction methods are no longer applicable. This article focuses on the research of S-band single-channel measurement and control equipment within a certain frequency working range. By pre-testing its phase difference and adopting methods such as cable compensation or adding phase shifters, the impact of phase difference on angular error is reduced within a certain error range at the design stage. In the later stage, during the operation of the equipment, the complex workflow of phase correction is eliminated. This article presents the principle and method of phase free calibration for S-band single channel measurement and control equipment,and verifies the effectiveness of the proposed method through experiments.

The Media Access Control (MAC) protocol is a critical component of wireless communication systems. The Statistical Priority-based Multiple Access (SPMA) protocol optimizes channel resource allocation through priority thresholds and a backoff mechanism. However, in specific scenarios, the traditional SPMA protocol exhibits shortcomings in threshold setting and backoff time calculation. This paper proposes an improved protocol that combines dynamic threshold adjustment and a multi-factor backoff mechanism. For dynamic threshold adjustment, the protocol adapts the thresholds in real-time based on the transmission success rate of data packets at each priority level, ensuring alignment with dynamic service demands. Under high-load conditions, a circuit-breaker mechanism is employed to suppress low-priority transmissions. In terms of backoff time calculation, a channel load differential factor is introduced, integrating priority level, traffic proportion, and load variation speed to construct a multi-factor fusion back-off algorithm. Simulation results demonstrate that the improved protocol significantly outperforms the traditional SPMA protocol in network throughput, transmission success rate, and latency performance. Under low-load scenarios, the transmission success rate of low-priority traffic improves by approximately 5%. In high-load scenarios, the circuit breaker mechanism suppresses low-priority transmissions, ensuring that the transmission success rate of high-priority traffic remains above 80%. At the same time, the improved protocol controls the average end-to-end delay of all priority levels within 10 ms, with the highest priority delay being less than 2 ms, effectively meeting the requirements for differentiated service quality.

Nowadays, spacecraft software is becoming increasingly complex and its functions are gradually increasing. If it can be split reasonably in different software, the project can be managed better. However, considering factors such as cost, power consumption, and wiring, it is difficult to use different processors to run different software. Now, multi-core processor is developing rapidly. One processor contains more than one core, so that different software can be run on different cores. Therefore, this paper proposes a design of a boot and monitor software based on multi-core processor which can enable 2 cores to run 2 different software. The ground tests and on-orbit experiments indicated that this scheme can correctly perform boot for different cores and accomplish refactor software for updating. The boot and monitor software based on this design runs well on the orbiting satellites.

A data compression collaborative algorithm that integrates transmission optimization and delay control was proposed to address the data dimension explosion and complexity increase caused by the large-scale grid connection of distributed photovoltaic systems. Firstly, based on the theory of decision boundary sensitivity, an optimization framework for minimizing total delay was constructed, and an enhanced inter-class boundary preservation algorithm (EIPB) was proposed to reduce the amount of transmitted data by dynamically maintaining key instances of decision boundaries. Secondly, the traditional distance instance selection method was improved by proposing an enhanced instance selection algorithm (EIS) based on K-dimensional tree (KD-Tree) spatial partitioning, which utilized nearest neighbor search acceleration technology to enhance instance selection efficiency. Then, a dynamic error allocation sector lossless compression algorithm (DEASC) was proposed, which achieved collaborative optimization of compression efficiency and fidelity through adaptive slope constraints and multi-stage entropy encoding. Experimental verification showed that the EIPB-EIS joint algorithm improved the average compression ratio to 7.8 compared to traditional methods, reduced the root mean square error of reconstruction percentage to 0.51%, and reduced transmission delay by 62.7%, effectively solving the problem of efficient transmission and accurate reconstruction of high-dimensional photovoltaic data.