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HY-2C is the third satellite in China's ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (TB) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR's antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated TB at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3. [ABSTRACT FROM AUTHOR]

The Calibration Microwave Radiometer (CMR) is a three-band radiometer deployed on the HY-2C satellite in a near-Earth orbit, and since it launched, there are few studies presented on the performance of CMR to date. Therefore, this paper focuses on providing an assessment of HY-2C CMR brightness temperature and wet troposphere correction (WTC). CMR works at 18.7 GHz, 23.8 GHz and 37 GHz in a nadir-viewing direction, aligned with the HY-2C radar altimeter. The wet troposphere path delay of the radar altimeter signal caused by water vapour and cloud liquid water content can be monitored and corrected by CMR. In this paper, guided by the concept of antenna pattern correction algorithm and a purely statistical method, we directly establish the function between the CMR antenna temperature and the model-derived WTC calculated by the European Centre from Medium-Range Weather Forecasting (ECMWF) Reanalysis data, which can obtain the brightness temperature and the WTC of CMR simultaneously. Firstly, the algorithm principle of CMR to establish the function between the antenna temperature and the model-derived WTC is introduced, and then the brightness temperature of CMR is evaluated using reference brightness temperatures of the Advanced Microwave Radiometer 2 (AMR-2) on Jason-3 satellite at crossover points. Furthermore, the performance of the CMR WTC is validated in three ways: (1) directly comparing with the colocated WTC measured by Jason-3 AMR-2, (2) directly comparing with model-derived WTC from ECMWF, which allows a rapid check at a global scale, (3) comparing the standard deviation of the Sea Surface Height (SSH) difference at crossover points using different WTC retrieval methods. The linear fit with Jason-3 brightness temperature and WTC in all non-precipitation conditions demonstrated a good agreement with Jason-3. In addition, the WTC of CMR has an obvious decrease in the standard deviation of the SSH difference compared with model-derived WTC, indicating the CMR can significantly improve the accuracy of the HY-2C SSH measurements. All the assessments indicate that the CMR performances are satisfying the expectations and fulfilling the mission requirements. [ABSTRACT FROM AUTHOR]

Straight bevel gears are widely used in mining equipment, ships, heavy industrial equipment, and other fields due to their high capacity and robust transmission. Accurate measurements are essential in order to determine the quality of bevel gears. We propose a method for measuring the accuracy of the top surface profile of the straight bevel gear teeth based on binocular visual technology, computer graphics, error theory, and statistical calculations. In our method, multiple measurement circles are established at equal intervals from the small end of the top surface of the gear tooth to the large end, and the coordinates of the intersection points of these circles with the tooth top edge lines of the gear teeth are extracted. The coordinates of these intersections are fitted to the top surface of the tooth based on NURBS surface theory. The surface profile error between the fitted top surface of the tooth and the designed surface is measured and determined based on the product use requirements, and if this is less than a given threshold, the product is acceptable. With a module of 5 and an eight-level precision, such as the straight bevel gear, the minimum surface profile error measured was −0.0026 mm. These results demonstrate that our method can be used to measure surface profile errors in the straight bevel gears, which will broaden the field of in-depth measurements for the straight bevel gears. [ABSTRACT FROM AUTHOR]

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures. [ABSTRACT FROM AUTHOR]

With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE. [ABSTRACT FROM AUTHOR]