State Radio Regulation Of China

Abstract Pulsar timing of double neutron star (DNS) systems is one of the best methodologies to study the neutron star mass distribution. Here we report the discovery of a DNS system PSR J0641+0448 in the Five-hundred-meter Aperture Spherical radio Telescope (FAST) Galactic Plane Pulsar Snapshot survey. This pulsar has a 25.7 ms spin period and moves in a 3.73 day eccentric orbit with an eccentricity of 0.145. Using FAST observations, we obtained its phase-connected timing solution with periastron advance and Shapiro delay detected. Using χ 2 analysis based on the DDGR model, we constrain the pulsar mass to 1.31 9 − 0.035 + 0.021 M ⊙ , and the companion mass to 1.26 9 − 0.016 + 0.022 M ⊙ with a 68.3% confidence level. The low companion mass and mild orbital eccentricity are consistent with the correlation between neutron masses and orbital eccentricities.

Obtaining an antenna’s back-up structure (BUS) temperature field is an essential prerequisite for analyzing its thermal deformation. Thermodynamic simulation can obtain the structure’s thermal distribution, but it has low computational accuracy. There is a problem with cumbersome wiring and difficult maintenance of the temperature measurement system. This study developed an improved RIME-XGBoost model to realize the temperature prediction of the BUS of the Nanshan 26-m Radio Telescope (NSRT). The proposed model successfully predicts the NSRT’s BUS temperature distribution based solely on environmental sensing (ambient temperature, angle of solar radiation, antenna’s orientation, etc.). The relative prediction accuracy between the predicted and actual BUS temperature is 97.15%, and the predictive error is less than 0.897 K (root mean square error, RMSE). This research result provides an alternative method for the real-time reconstruction of the structure’s thermal distribution in large-aperture radio telescopes.

This letter investigates a movable antenna (MA)-aided two-way relay (TWR) system, in which multiple MAs are deployed at the relay to assist bidirectional communication. We aim to maximize the minimum signal-to-noise ratio (SNR) between the two terminals by jointly optimizing the MA positions and the beamforming matrix of the relay. To shed light, we first derive the closed-form upper bounds on the SNRs. Then, an efficient two-loop iterative algorithm is proposed to tackle the non-convex optimization problem, which employs particle swarm optimization (PSO) in the outer loop for antenna position optimization and utilizes maximum ratio combining/transmission in the inner loop for beamforming matrix design. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of the MA-aided TWR system over conventional systems with fixed-position antennas (FPAs).

The advent of deep learning has propelled significant advancements in object detection, thereby enhancing the intelligence of underwater autonomous driving systems. In this paper, we explore the cutting-edge applications of autonomous driving technology in the field of underwater exploration, addressing the pivotal role of target detection in navigating and executing tasks within challenging marine environments. In this study, the object detection capability of such systems is enhanced by integrating deep learning and multisensor fusion technology, especially by combining high-precision sensor data with multitask learning models to achieve efficient and robust detection. Our study has three principal contributions. First, we introduce a novel light perception detection system that combines monocular camera technology with 4D radar. It enriches environmental perception by weaving in radar signals and significantly enhances the accuracy and stability of target detection. Second, we have developed a dual-modal detection framework, named Radar-Picture Detection, which utilizes a parallel sequence prediction method. This approach prioritizes radar signal processing, aiding in the improvement of target detection accuracy in intricate underwater environments. Third, we conducted a comprehensive evaluation of our model's performance using the FloW Dataset, which is specifically curated for identifying floating waste in inland waters through unmanned vessel footage. We not only propel forward the field of target detection for underwater autonomous systems but also establish new avenues and a solid foundation for deploying deep learning and multisensor fusion technology in marine environmental perception. Insights and methodologies from this study are poised to spearhead further developments in autonomous marine exploration, enhancing safety, efficiency, and our understanding of underwater environments.

While beam hopping (BH) has revolutionized satel-lite communication by addressing non-uniform traffic demands and minimizing beam interference, existing research on multi-satellite BH scenarios often overlooks energy efficiency (EE) optimization. This can lead to increased operational costs and reduced satellite lifespans due to higher onboard energy consumption. In this paper, we propose a novel energy-efficient beam hopping strategy in the coexistence scenario of geosynchronous orbit (GSO) and non-geosynchronous orbit (NGSO) satellite systems. Specifically, the proposed strategy can both balance traffic load among NGSO satellites and avoid interference to GSO ground stations. A joint multi-satellite BH optimization problem is formulated to optimize a trade-off function between quality of service (QoS) and EE for the NGSO system. Then, the original problem is divided into the satellite-cell assignment, BH pattern design, and beam power allocation sub-problems and solved sequentially. The numerical results demonstrate the superiority of the proposed strategy in enhancing the QoS and EE of the NGSO system while effectively mitigating interference with the GSO system.

The space-air-ground-sea integrated network is expected to provide seamless data services in 6G. As an important enabler, non-terrestrial network (NTN) should be carefully designed so that a satisfactory quality of experience service can be provided for ground users and maritime users. In this paper, a sparse channel estimation aided federated learning (FL) assisted NTN is proposed for 6G. Firstly, a sparse channel estimation scheme is proposed for providing accurate channel estimation results both in uplink model parameter transmission and in downlink model parameter transmission for FL training. Then, the system performance of the FL assisted NTN is evaluated in terms of spectral efficiency and energy efficiency. Finally, simulation results are provided for evaluating the attainable system performance of the proposed scheme, showing that the proposed FL assisted NTN is capable of attaining a limited level of co-channel interference performance. Furthermore, a better system performance can be attained by the proposed sparse channel estimation aided FL assisted NTN system, in comparison to the erroneous counterparts.