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Room temperature linear arrays (up to 160 detectors in array) from silicon metal- oxide-semiconductor field-effect transistors (Si-MOSFETs) have been designed for sub- THz (radiation frequency 140 GHz) close to real-time direct detection operation scanner to be used for detection and recognition of hidden objects. For this scanner, the optical system with aspherical lenses has been designed and manufactured. To estimate the quality of optical system and its resolution, the system modulation transfer function was applied. The scanner can perform real-time imaging with the spatial resolution better than 5 mm at the radiation frequency 140 GHz and contrast 0.5 for the moving object speed up to 200 mm/s and the depth of field 20 mm. The average dynamic range of real time imaging system with 160-detector linear array is close to 35 dB, when the sources with the output radiation power of 23 mW (IMPATT diodes) are used (scan speed 200 mm/s). For the system with 32-detector array, the dynamic range was about 48 dB and for the single-detector system with raster scanning 80 dB with lock-in amplifier. However, in the latter case for obtaining the image with the sizes 20×40 mm and step of 1 mm, the average scanning time close to 15 min is needed. Convolutional neural network was exploited for automatic detection and recognition of hidden items.

Soil moisture analysis is widely used in numerous practical cases, from weather forecasts to precise agriculture. Recently, availability of moisture data increased due to the rapid development of satellite image processing. However, satellite retrievals mostly provide low-resolution surface data. In this study, we attempt to retrieve surface soil moisture on the field scale using a decomposition algorithm. Furthermore, we add a mathematical model based on Richards equation to evaluate soil moisture in the root zone. To combine the results of both models, we employ a nudging data assimilation technique. Also, a dynamical variation of the method is proposed which makes it more adaptive to the soil type and provides improvement to modeling results. Two types of numerical experiments are conducted. Simulation results show reasonably good convergence with the measurements. The model performs with average correlation of 0.58 on the whole root zone, reaching 0.85 on top soil layers.

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This paper deals with a nonlinear soil moisture transport problem, solved with addition of satellite observed soil moisture. The satellite data are assimilated into the model using Newtonian nudging method.  Evaluation is done by the triple collocation method, which involves three independent data sources: model results, ground stations and ERA5 climatic data. The results testify that model results are nearly as accurate as the ground station measurements.