Due to the lack of the efficient and accurate detection method, the axial crack of pipeline seriously threatens the safe operation of oil pipeline, and therefore the application of micromagnetic nondestructive testing technology in the field of pipeline axial crack detection is of practical significance. To study the characterization of axial crack by micromagnetic signal, a numerical model of micromagnetic signal to detect the axial crack of pipeline is established based on the micromagnetic theory in this article. The characteristics of micromagnetic detection signals of cracks with different sizes and directions are calculated. The propagation law of micromagnetic detection signal is analyzed, and the experimental study of X70 pipeline is carried out to verify the correctness of theoretical model and simulation. The results show that at each tip of the axial crack, the axial component of the micromagnetic detection signal has a peak and a valley, and the radial component has an extreme value. The amplitude of the micromagnetic internal detection signal at the axial crack linearly increases as the depth or length of the crack increases. The propagation law of micromagnetic signal conforms to the falling exponential function, specifically, the smaller the crack depth, the faster the signal decays and eventually stabilizes. The micromagnetic detection signal of the probes to detect the crack having a certain angle with the axial direction is arranged with equal displacement spacing.

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Driven by the rapidly growing demand for information security, covert wireless communication conceived as one of the essential technology has attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations that challenge deploying in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS) assisted covert communication system (CCS) with a friendly UAV, where the UAV generates artificial noise (AN) to interfere with the monitors. Furthermore, we model the power of AN emitted by UAV as an uncertainty model, with which the closed-form detection error probability (DEP) on the covert wireless communication for the monitor is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, and an iterative algorithm is designed to solve the optimization problem to obtain the optimal covert rate by using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units, the trajectory, and the transmit power of the UAV are jointly optimized.

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