Coherent XUV sources, which may operate at MHz repetition rate, could find applications in high-precision spectroscopy and for spatio-time-resolved measurements of collective electron dynamics on nanostructured surfaces. We theoretically investigate utilizing the enhanced plasmonic fields in an ordered array of gold nanoparticles for the generation of high-harmonic, extreme-ultraviolet (XUV) radiation. By optimization of the chirp of ultrashort laser pulses incident on the array, our simulations indicate a potential route towards the temporal shaping of the plasmonic near-field and, in turn, the generation of single attosecond pulses. The inherent effects of inhomogeneity of the local fields on the high-harmonic generation are analyzed and discussed. While taking the inhomogeneity into account does not affect the optimal chirp for the generation of a single attosecond pulse, the cut-off energy of the high-harmonic spectrum is enhanced by about a factor of two.

This article presents a high-precision obstacle detection algorithm using 3D mechanical LiDAR to meet railway safety requirements. To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm × 15 cm × 15 cm in the range of ±25 m; at the same time, for obstacles of 10 cm × 10 cm × 10 cm, an STDR of over 80% was achieved within a range of ±20 m. This research provides a possible solution and approach for railway security via obstacle detection.

The superhydrophobic surface can change the friction property of the material, reduce the adhesion of the friction interface, and produce a certain slip, thereby reducing the friction coefficient. The laser has high energy, high density, and is especially suitable for the surface treatment of materials. The laser surface texturing is a good way to construct superhydrophobic surfaces. The experiment uses a nanosecond pulse laser to construct the groove texture on the nickel surface. The contact area between the air and the droplets retained on the rough surface is increased, effectively preventing the water droplets from entering the gully of the surface microstructure, reducing the water droplets and the solid surface. The contact area ultimately makes the surface exhibit excellent superhydrophobicity. A superhydrophobic nickel surface having an apparent contact angle of water (ACAW) of 160° and a sliding angle (SA) of less than 10° was prepared. The MM-W1B vertical universal friction and wear tester was used to test the groove texture samples with different depths. The surface texture can capture the wear debris generated by the wear and store the lubricant, which is beneficial to the formation of fluid dynamic pressure lubrication and improve the load. The friction coefficient is reduced from 0.65 of the unprocessed surfaces to 0.25 after the texturing, and the friction performance is greatly improved.

In response to the growing demand for railway obstacle monitoring, lidar technology has emerged as an up-and-coming solution. In this study, we developed a mechanical 3D lidar system and meticulously calibrated the point cloud transformation to monitor specific areas precisely. Based on this foundation, we have devised a novel set of algorithms for obstacle detection within point clouds. These algorithms encompass three key steps: (a) the segmentation of ground point clouds and extraction of track point clouds using our RS-Lo-RANSAC (region select Lo-RANSAC) algorithm; (b) the registration of the BP (background point cloud) and FP (foreground point cloud) via an improved Robust ICP algorithm; and (c) obstacle recognition based on the VFOR (voxel-based feature obstacle recognition) algorithm from the fused point clouds. This set of algorithms has demonstrated robustness and operational efficiency in our experiments on a dataset obtained from an experimental field. Notably, it enables monitoring obstacles with dimensions of 15 cm × 15 cm × 15 cm. Overall, our study showcases the immense potential of lidar technology in railway obstacle monitoring, presenting a promising solution to enhance safety in this field.