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Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf's dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study. [ABSTRACT FROM AUTHOR]

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.

Taking the mining of 201 working face of Wangjiata Coal Mine as the engineering background, the application of 3D laser scanning in the deformation monitoring and supporting of tunnel was studied. Under complex conditions in the mine, the effective scanning distance of the scanner is greatly reduced, which seriously affects the overall stitching accuracy. Therefore, this paper proposes a method for cloud registration of single test stations in two periods to obtain the deformation of the roof and floor of the roadway. The results show that: the cloud registration method at a single measurement site has millimeter accuracy and meets the requirements of underground tunnel deformation monitoring; in the monitoring area, the average subsidence of the roof is 14.70 mm, and the floor is 0.283 mm, and the deformation of tunnel shows non-uniform asymmetric characteristics; the maximum moving amount of the top and bottom plates and the thickness of the coal seam under the current support scheme are far less than the requirements of the specification. Actual investigation shows that after the mining of the 201 and adjacent 202 working faces, the tunnel has not collapsed, the support is too strong and the observation results are consistent with the actual situation. The mining support strength can be appropriately weakened.

This study mainly presents the method for monitoring the surface dynamic subsidence basin (SDSB) caused by underground coal mining and obtaining parameters of mining subsidence (PMS) in the short term by using an unmanned aerial vehicle (UAV) Photogrammetry Technology. The basic ideas and methods are proposed; that is, the two-stage surface digital elevation model (DEM) is obtained in the short term by UAV; The SDSB is obtained through two phases of DEM subtraction; Based on the dynamic inversion method established in this study, the PMS was obtained. The UAV method was used to monitor the Wangjiata coal mine in Inner Mongolia of China three times in three months to obtain the three phases of DEM; we obtained the two phases SDSB by the three phases of DEM subtraction. The accuracy of DEM and SDSB were 118 mm and 121 mm respectively, Although the accuracy cannot fulfill the requirements of mining subsidence, the PMS was obtained by dynamic inversion method of full subsidence basin fitting, which has better resistance to errors; hence, the obtained PMS are reliable. Based on the engineering application, this study concludes that as a new technology, UAV photogrammetry technology can obtain the SDSB of coal mining areas in a short period with reliable PMS. Meanwhile, it has the advantages of low cost, flexible maneuverability, and so on, and overcomes the shortages of traditional observation stations, which has long observation time and high labor intensity, needs to bury fixed measuring points and easily lose them, and just has a small amount of “spot-like” observation data that cannot reflect the deformation characteristics of the whole subsidence basin. Furthermore, it is feasible to apply this method to mining subsidence monitoring and parameter inversion, and it has a great application prospect for the mining area purposes.

The development of strong and ductile alloys for application in cryogenic temperatures has long been sought after. In this work, we have developed a face-centered cubic Ni10Co56.5V33.5 multi-principal element alloy (MPEA) that exhibits a balanced combination of high strength and good ductility at 77 K, based on the considerations of large local lattice distortion (LLD) and low stacking fault energy. The small-grained Ni10Co56.5V33.5 MPEA exhibits a yield strength of 1400 MPa and an ultimate tensile strength of 1890 MPa, while preserving a good ductility of 23%. Moreover, precession electron diffraction and transmission electron microscopy revealed multiple deformation mechanisms, including wavy dislocations, atypically severely twisted dislocation bands, hierarchical stacking faults, and deformation twins, which are implicated in the alloy's outstanding mechanical performance. These insights offer a strategic guide for the design of strong and ductile alloys, particularly for utilization in extreme environments. [ABSTRACT FROM AUTHOR]

Electroreduction of CO2 to C2+ products provides a promising strategy for reaching the goal of carbon neutrality. However, achieving high selectivity of C2+ products at high current density remains a challenge. In this work, we designed and prepared a multi‐sites catalyst, in which Pd was atomically dispersed in Cu (Pd−Cu). It was found that the Pd−Cu catalyst had excellent performance for producing C2+ products from CO2 electroreduction. The Faradaic efficiency (FE) of C2+ products could be maintained at approximately 80.8 %, even at a high current density of 0.8 A cm−2 for at least 20 hours. In addition, the FE of C2+ products was above 70 % at 1.4 A cm−2. Experiments and density functional theory (DFT) calculations revealed that the catalyst had three distinct catalytic sites. These three active sites allowed for efficient conversion of CO2, water dissociation, and CO conversion, ultimately leading to high yields of C2+ products. [ABSTRACT FROM AUTHOR]

Cu is a promising electrocatalyst for the CO2 reduction reaction (CO2RR) to produce high‐value C2+ products. Due to the fierce competition of the hydrogen evolution reaction, the slow diffusion of CO2, and the high energy barrier of the C−C coupling reaction, it is still challenging to achieve high activity and high selectivity to produce multi‐carbon products on copper‐based electrocatalysts. In this work, we synthesized Cu/CeO2 catalysts with varying amounts of Cu doping, aiming at effectively converting CO2 into C2+ products through electroreduction. At a copper doping level of 9.77 wt%, the catalyst exhibited a current density of 16.8 mA cm−2 using a standard H‐type cell, achieving a C2+ faradaic efficiency (FE) of 78.3 %. Through additional experiments and material characterization, we confirmed that controlling the Cu loading on the surface of CeO2 is an effective way to regulate the ratio of Cu+ to Cu0 active sites and the number of oxygen vacancies. Furthermore, the strong electron interaction between Ce4+−O2−−Cu+ structure can stabilize Cu+ species and enhance the overall stability of the catalyst. This strategy enhances the selectivity towards C2+ products and effectively suppresses the competing hydrogen evolution reaction. [ABSTRACT FROM AUTHOR]

[Objective] With the increased implementation of cryptographic algorithms in cryptographic devices as hardware circuits, low-cost physical attacks against these devices have gained importance. Among them, fault attacks have low cost and high efficiency and have gradually become one of the most effective means of current integrated circuit security attacks. However, in real-world high-frequency and high-noise environments, attackers typically require extensive data support and high computing costs, thus reducing the attack efficiency. [Methods] Given the large data requirements, high computational complexity, and poor portability, this study considers the AES-128 algorithm implemented based on microcontrol units as the research object. It combines the data dependence of fault probability and researches the attack methods in fault attacks. [Results & Conclusions] The main research work is as follows: (1) To address the significant demand for template attack data and the low portability of the method, a fault template attack based on fault probability is proposed. Fault probability traces are established, points of interest are selected, the hamming weight of the matching template is inversely calculated, and finally, the intersection is determined for key recovery. The method applies to all substitution-permutation network cipher algorithms, thereby improving the universality of the attack method. Experiments show that this method shows superior performance over traditional template attack methods and related improved methods, such as the key advantage template attack against the AES-128 algorithm and amplified template attacks using the hamming weight model. (2) A mutual information analysis based on fault probability is proposed to address the significant demand for mutual information analysis data and low attack efficiency. Calculating the mutual information between the measured and simulated leakage values to recover the key requires detailed knowledge of the algorithm and a few specific assumptions consistent with the situation practically faced by the attackers. The experimental results demonstrate the superior performance of this method over traditional mutual information analysis methods and related improved methods such as neural mutual information analysis methods. (3) Correlation analysis requires enormous data and has high computational complexity; thus, a fault probability correlation analysis based on secondary filtering is proposed. Random plaintext and sampling points are selected using the difference summary, which reduces the number of calculation iterations. The experimental results show that the method outperforms traditional correlation analysis, multiple sieve correlation power analysis, and related improved methods such as block-oriented correlation power analysis. [ABSTRACT FROM AUTHOR]