Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Multiparameter collaborative optimization of the vibrating screen based on the behavior of oil sunflower seed penetrating screen holes

The behavior of oil sunflower seeds penetrating screen holes is an important factor that affects the screening performance of oil sunflower seeds.In this study,a double-deck reverse-motion vibrating screening device for oil sunflower seed screening was designed.The force condition and motion law of the oil sunflower seeds on the screen surface were analyzed.This study compared the effect of particle filling amount of discrete element model of oil sunflower seeds on the simulation effects.The screening process was numerically simulated using the coupled Discrete Element Method and Multibody Dynamics(DEM-MBD)technique with the screening percentage of oil sunflower seeds as the index.The influence of the operating parameters of the vibrating screen on the screening effect was analyzed using a multiparameter collaborative optimization scheme.The results of this study can provide a reference for the numerical simulation of crop screening behavior and the development of screening devices.

Domain wall state diagram for SrTiO_(3)/BaTiO_(3) superlattice structures

The domain wall structure of ferroelectric/paraelectric superlattices can be much more complex due to the influence of the superlattice stacking structure,the in-plane strain induced by the substrate and environmental temperature.In this study,we employed a phase field model to investigate the domain wall state of the SrTiO_(3)/BaTiO_(3) superlattice structure.The domain wall thickness for the SrTiO_(3)/BaTiO_(3) layer was measured using a hyperbolic function.Based on the simulation results,here,we show a domain wall state diagram to distinguish the hard and soft domain states.The polarization profiles across hard/soft domain walls were illustrated and analyzed.Our simulation results offer a useful concept for the control of the domain wall state in the ferroelectric superlattice.

Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing

The noncontact blade tip timing(BTT)measurement has been an attractive technology for blade health monitoring(BHM).However,the severe undersampled BTT signal causes a significant challenge for blade vibration parameter identification and fault feature extraction.This study proposes a novel method based on the minimum variance distortionless response(MVDR)of the direction of arrival(DoA)estimation for blade natural frequency estimation from the non-uniformly undersampled BTT signals.First,based on the similarity between the general data acquisition model for BTT and the antenna array model in DoA estimation,the circumferentially arranged probes on the casing can be regarded as a non-uniform linear array.Thus,BTT signal reconstruction is converted into the DoA estimation problem of the non-uniform linear array signal.Second,MVDR is employed to address the severe undersampling issue and recover the BTT undersampled signal.In particular,spatial smoothing is innovatively utilized to enhance the estimation of covariance matrix of the BTT signal to avoid ill-condition or singularity,while improving efficiency and robustness.Lastly,numerical simulation and experimental testing are employed to verify the validity of the proposed method.Monte Carlo simulation results suggest that the proposed method behaves better than conventional methods,especially under a lower signal-to-noise ratio condition.Experimental results indicate that the proposed method can effectively overcome the severe undersampling problem of BTT signal induced by physical limitations,and has a strong potential in the field of BHM.