Vacancy defects are intrinsically present in the as-grown MoSe ₂ , which may greatly affect its gas sensing performance.

Defect detection on textured surfaces remains a challenging task due to the wide range of textures and defects. Current unsupervised learning-based texture defect detection methods based on texture background reconstruction cannot detect texture defects with high precision because it is difficult to guarantee a high-precision reconstruction of the texture background while suppressing the defect foreground. In this study, we propose a novel semantic information decomposition network (SIDN) for accurate texture defect segmentation. The SIDN is trained on artificial defective images produced by a defect generation module (DGM). First, the SIDN uses a feature extraction module (FEM) to extract latent features with both texture semantic information and defect semantic information. Then, a novel feature separation extraction module (FSEM) for decomposing the texture semantic information and defect semantic information from the feature map generated by the FEM is proposed, preventing the coupling of the texture and defect semantic information from affecting the final segmentation accuracy. Next, a novel global semantic relation module (GSRM) is proposed to determine the relevance of the global semantic information to comprehensively consider the context and improve the feature representation. Finally, a segmentation module (SM) that directly segments the textures and defects instead of reconstructing the texture background is proposed. The final detection result is obtained by calculating a weighted average of the texture and defect segmentation results. The extensive experimental tests with the most popular and most challenging texture defect dataset demonstrate that the SIDN achieves accurate segmentation of various texture defects without using real defect samples.

Because of the flexibility band structure, the nanotubes based on the (001) two-dimensional monolayer of β-P are expected to be a promising candidate for electronic and optical applications. By density functional theory calculations, it could be investigated the structural stability of single-wall armchair and zigzag blue phosphorus nanotubes. The formation energy, structure parameter, Young’s modulus, radial Poisson’s ratio, band gap and static electronic polarizabilities for the two types of nanotubes are computed and analyzed as functions of the tube radius and axial strain. The properties of armchair and zigzag nanotubes are almost the same, and isotropy is observed for radius up to 13 Å. Furthermore, the band gaps are sensitive to the effects of axial strain.

The pose measurement and motion estimation of long-distance space CubeSats is a difficult problem for autonomous rendezvous and track maintenance. In this work, an approach is proposed for the pose estimation of CubeSat based on the micro-Doppler effect by a laser radar system. Parameters such as spin rate and attitude angle can be estimated using data extracted from the returned signal. By utilizing threshold processing, edge extraction of time-frequency (TF) images can be achieved effectively. When the target size is unknown, the double edge ratio method (DERM) is suggested to obtain the attitude angle. The simulation results show that when the noise is not too high, the spin rate and the attitude angle can be acquired precisely. Our experiment indicates that the error for the estimation of spin and pose by our approach fulfills the demand in practice.

Accurately classifying and identifying non-cooperative targets is paramount for modern space missions. This paper proposes an efficient method for classifying and recognizing non-cooperative targets using deep learning, based on the principles of the micro-Doppler effect and laser coherence detection. The theoretical simulations and experimental verification demonstrate that the accuracy of target classification for different targets can reach 100% after just one round of training. Furthermore, after 10 rounds of training, the accuracy of target recognition for different attitude angles can stabilize at 100%.