To solve the problem of the low diagnosis rate of early weak faults of rolling bearings, a novel bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and convolutional neural network (CNN)−Bidirectional Long Short-Term Memory (BiLSTM) was proposed. Based on the basic Sparrow Search Algorithm, the tent chaotic mapping, the Osprey Optimization Algorithm, and the Cauchy mutation were used to enhance the global search ability of the algorithm. To improve the accuracy of fault diagnosis, the BiLSTM layer is introduced into CNN to preserve the global and local features to the maximum extent. The experimental results show that VMD avoids the end effect problem in Empirical Mode Decomposition (EMD). The accuracy rate of the diagnosis model based on CNN-BILSTM reached 97.6667%, which was higher than that of the common diagnosis model.

The beam spreading and evolution behavior of a partially coherent, Hermite-cosine-Gaussian (HcosG) rectangular multi-Gaussian correlated Schell-model beam propagating in oceanic turbulence is studied. Analytical expressions for the cross-spectral density function, as well as the root mean square (rms) beam width and the spectral degree of coherence, are derived based on the extended Huygens-Fresnel principle. The HcosG rectangular multi-Gaussian correlated Schell-model beam exhibits a multi-lobe pattern at short propagation distances. The dependencies of the number, size, shape, and centroid of the lobes on displacement parameters, source size, order of field distribution, and displacement are investigated. As the propagation distance increases, the spectral coherence decreases, and the differences between the spectral coherence curves gradually diminish. Additionally, for HcosG rectangular multi-Gaussian correlated Schell-model beams, better propagation performance was found in oceanic turbulence with larger mean square temperature dissipation rate, smaller turbulent kinetic energy dissipation rate per unit mass of fluid, and larger relative strength of temperature and salinity fluctuation.

Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-assisted communication networks have been widely used in civil and military fields. However, the open accessibility of wireless channels brings a huge risk of privacy disclosure to UAV-based networks. This paper considers a multi-UAV-assisted covert communication system based on Wireless Powered Communication (WPC) and Clustered-Non-Orthogonal-Multiple-Access (C-NOMA), aiming to hide the transmission behavior between UAVs and legitimate ground users (LGUs). Specifically, the UAVs serve as aerial base stations to provide services to LGUs, while avoiding detection by the ground warden. In order to improve the considered covert communication performance, the average uplink covert rate of all clusters in each slot is maximized by jointly optimizing the cluster scheduling variable, subslot allocation, LGU transmit power and multi-UAV trajectory subject to covertness constraints. The original problem is a mixed integer non-convex problem, which are typically difficult to solve directly. To solve this challenge, this paper decouples it into four sub-problems and solves the sub-problems by alternating iterations until the objective function converges. The simulation results show that the proposed multi-UAV-assisted covert communication scheme can effectively improve the average uplink covert rate of all clusters compared with the benchmark schemes.

The beam spreading, spectral degree of coherence and evolution behavior of the intensity profile of partially coherent Lorentz-Gaussian elliptical multi-Gaussian correlation Schell-Model beam propagating in anisotropic atmospheric turbulence are studied. The analytical expressions of cross-spectral density function, as well as root mean square (rms) beam width, are derived based on the extended Huygens-Fresnel principle and the relationship of Lorentz distribution and Hermitian Gaussian function. With the increases of propagation distance, the elliptical beam will first evolve into Gaussian beam and then change back to elliptical beam. In anisotropic atmospheric turbulence, the influence of the inner scale of turbulence on the spectral degree of coherence and rms beam width are obviously greater than that of the outer scale. For Lorentz-Gaussian elliptical multi-Gaussian correlated Schell-Model beams, better propagation performance was found in anisotropic atmospheric turbulence with larger anisotropic factor and smaller inner scale.