Nondestructive testing (NDT) methods are widely used in the rail industry to detect rolling contact damage (RCD) defects to ensure railway safety. With the need of monitoring millions of miles of rail tracks more effectively and efficiently that leads to improved rail safety, reliability, and optimized assets management, the research on high-speed and high-accuracy NDT methods that can obtain high signal-to-noise ratio (SNR) and high sensitivity defect signals at higher speeds is of vital importance. Magnetic flux leakage (MFL) is one of the NDT methods used for high-speed rail inspection, which is suitable for detecting surface and subsurface RCD defects on railhead, but the measured MFL signals are distorted severely by the effect of motion-induced eddy currents (MIEC). However, the fundamental understanding of the mechanism of MIEC generation, distribution, and magnitude is lacking, and the physical interaction between MIEC and MFL measurement during high-speed rail inspection has not been well studied. This article investigates the effect of MIEC on three-axis MFL signals through 3-D finite-element method (FEM) simulations. The 3-D distributions of MIEC and 2-D image signals of MFL measurements for high-speed rail MFL inspection are presented and analyzed. The simulations are conducted with an inspection speed range of 0-62.5 mi/h, and a surface hemisphere-type defect and subsurface hole-type defect are both considered. The results show that the motion between the magnetizer and the rail will generate two types of MIEC, which in turn decreases the peak values of three-axis MFL signals from the subsurface defect and affects the MFL signals spatially, especially the base values along rail transverse and vertical directions. The effect of MIEC on the three-axis MFL signals in terms of SNR, sensitivity, and asymmetry is discussed.

Nondestructive testing (NDT) methods are widely used in the rail industry to detect and characterize rolling contact fatigue (RCF) defects in railroads, which is very important for railway inspection and maintenance to prevent catastrophic accidents. Existing NDT methods, e.g., ultrasonic testing (UT), magnetic flux leakage (MFL), and eddy current testing (ECT) have been successfully applied in the rail industry, and the state-of-the-art UT method reported recently achieved a high speed of 40 mi/h with a probability of detection (POD) over 80% under 30% false alarms. However, NDT methods still suffer from a bottleneck in that a higher inspection speed causes lower detection sensitivity due to their physical limits, such as negative velocity effect and long sensing time. One of the leading challenges to the rail NDT community is to develop a high-speed high-sensitivity (HSHS) capability that can provide an improved POD of rail defects in high-speed inspection scenarios over 60 mi/h. This article proposes a horizontal U-shaped magnets-based motion-induced eddy current array (MIECA) method to detect rail surface defects with the HSHS capability. The MIECA method deploys a three-axis magnetic sensor array along the rail transverse direction at the middle of the magnets to measure the MIECA signals, which utilizes the wake effect of the diffused motion-induced eddy current (MIEC) caused by the relative high-speed motion between the magnets and the rail track. Finite-element method (FEM) simulations with a wide speed range from 0 to 62.5 mi/h are carried out to investigate the relationships between the MIEC generation, diffusion and magnitude, and the three-axis MIECA signals. The simulation results show that the higher the speed, the greater the magnitude of diffused MIEC, and the greater the peak-to-peak values of three-axis MIECA signals caused by rail surface defects, which shows a great promise for detecting rail surface defects in high-speed scenarios and is superior inspection relative to existing NDT methods in terms of inspection speed, detection sensitivity, and defect characterization capability.

A newly designed thermal/vacuum (T/V) prelaunch multiangle calibration test is proposed for Microwave Humidity and Temperature Sounder (MWHTS) onboard the Feng Yun 3D satellite. Compared with other existing prelaunch calibration tests designed at fix scan angle, this test is first proposed to analyze the measurements of MWHTS from multiscan angles. A set of comprehensive calibration method is established and used for calibration data processing. The high-quality data preprocessing methods are performed. Essential calibration coefficients such as cold bias, hot bias, and nonlinearity coefficients, and calibration results such as calibration residual error, sensitivity, and calibration accuracy of MWHTS are all obtained and analyzed in MWHTS's six observing positions. The measured sensitivity value of channel 2 is 1.5 K while the measured sensitivities of the others range from 0.18 to 0.57 K. The calibration accuracy of channel 2 is 1.5 K while the calibration accuracies of the others range from 0.32 to 0.7 K. According to the discrepancy of multiangle calibration test results, the radiation leakage and the response lag for special channels are discovered for the first time in T/V test, and the reasons of these two special phenomena are fully analyzed. Moreover, the measurements of the T/V test with fast changing instrument temperatures are analyzed and the applicability of nonlinearity coefficients obtained in T/V test for in-orbit calibration is verified. In conclusion, MWHTS meets the basic design requirements of sensitivity and calibration accuracy, the changing of the MWHTS observing positions does not impact the calibration coefficients and results, and the nonlinearity coefficients obtained in T/V test are valid when instrument temperature experiences some fast changing.

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

The roots are a vital organ for plant growth and health. The opaque surrounding environment of the roots and the complicated growth process means that in situ and non-destructive root phenotyping face great challenges, which thus spur great research interests. The existing methods for root phenotyping are either unable to provide high-precision and high accuracy in situ detection, or they change the surrounding root environment and are destructive to root growth and health. Thus,we propose and develop an ultra-wideband microwave scanning method that uses time reversal to achieve in situ root phenotyping nondestructively. To verify the method’s feasibility, we studied an electromagnetic numerical model that simulates the transmission signal of two ultra-wideband microwave antennas. The simulated signal of roots with different shapes shows the proposed system’s capability to measure the root size in the soil. Experimental validations were conducted considering three sets of measurements with different sizes, numbers and locations, and the experimental results indicate that the developed imaging system was able to differentiate root sizes and numbers with high contrast. The reconstruction from both simulations and experimental measurements provided accurate size estimation of the carrots in the soil, which indicates the system’s potential for root imaging.