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Abstract Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Slope deformation is one of the focal issues of concern during the normal operation and maintenance of the South-North Water Transfer Middle Route Project. To study the slope deformation evolution in the deep excavation section at the head of the canal, we applied 88 views of Sentinel-1A ascending image data from 2017 to 2019 and MT-InSAR(Multi-temporal InSAR) deformation monitoring technology to obtain long-time series deformation rates and cumulative deformation fields over the slope in the study area. Based on the analysis of the time-series monitoring data of the deformation field sample points, a LSTM (Long Short Term Memory Network) slope deformation predictive model was constructed to predict the slope deformation for the next 12 months at 12 sample points of the deep excavation slope. The impact of rainfall on slope deformation was investigated, and the reliability of the LSTM model was verified by using the measured data. The results show that the average annual deformation rate of the slope ranges from 10mm/a to 25mm/a, the maximum cumulative deformation is about 60 mm, and the slope of the excavated section is generally in an uplifted state. The rainfall-induced repeated uplift or subsidence of the canal slopes together with the peak deformation was closely related to the amount of rainfall during the wet season, and the longer the duration of the wet season, the more obvious the crest. Among the12 sample sites, the minimum and maximum deformation predicted using the LSTM model were 51.7 mm and 73.9 mm respectively, with the lowest correlation coefficient of 0.994 and the highest of 0.999. The maximum and minimum values of RMSE (Root Mean Square Error) were 4.4 mm and 3.6 mm respectively, indicating reliable prediction results. The results of the study can provide reference for the prevention and control of geological hazards in the South-North Water Transfer Canal.