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Time-domain backprojection algorithms are widely used in state-of-the-art synthetic aperture radar (SAR) imaging systems that are designed for applications where motion error compensation is required. These algorithms include an interpolation procedure, under which an unknown SAR range-compressed data parameter is estimated based on complex-valued SAR data samples and backprojected into a defined image plane. However, the phase of complex-valued SAR parameters estimated based on existing interpolators does not contain correct information about the range distance between the SAR imaging system and the given point of space in a defined image plane, which affects the quality of reconstructed SAR scenes. Thus, a phase-control procedure is required. This paper introduces extensions of existing linear, cubic, and sinc interpolation algorithms to interpolate complex-valued SAR data, where the phase of the interpolated SAR data value is controlled through the assigned a priori known range time that is needed for a signal to reach the given point of the defined image plane and return back. The efficiency of the extended algorithms is tested at the Nyquist rate on simulated and real data at THz frequencies and compared with existing algorithms. In comparison to the widely used nearest-neighbor interpolation algorithm, the proposed extended algorithms are beneficial from the lower computational complexity perspective, which is directly related to the offering of smaller memory requirements for SAR image reconstruction at THz frequencies.

In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme.

Accurate prediction of polar motion are crucial for various scientific fields, including astronomy, geoscience, and oceanography. The temporal resolution of the modeling data currently utilized in polar motion prediction research is 1 day. This paper proposes to use multiple interpolation methods to interpolate the polar motion observation data to obtain interpolation data with a resolution of 6 hr, and conducts 480 groups of ultra‐short‐term experiments based on the combined prediction model of least‐squares extrapolation of harmonic and autoregressive modeling. Experimental results demonstrate that: (a) the forecasting approach proposed in this paper, which utilizes 6‐hr resolution data, significantly enhances prediction accuracy of polar motion; (b) compared with the forecasting scheme without interpolation, the proposed optimal forecasting scheme in this study achieves average improvement rates of 42.27% and 46.94% in the X and Y directions, respectively; (c) the effectiveness of the proposed scheme in this paper was validated through comparison with IERS Bulletin A and the forecast results from the second Earth Orientation Parameter Prediction Comparison Campaign. Key Points In contrast to conventional approaches, this study utilizes high‐resolution interpolation data to predict variations in polar motion The forecasting results generated by the RBFNN model demonstrate improved prediction accuracy

Air pollution is one of the severe environmental issues in Chongqing. Many measures made by the government for improving air quality have been put into use these past few years, while the influence of these measures remains unknown. This study analyzed the changes in the air quality of the urban area of Chongqing between 2015 and 2021 using a complete in situ observation dataset that all missing data were filled by the interpolation of a low-rank tensor completion model with truncate nuclear norm minimization (LRTC-TNN). The results include: (1) the LRTC-TNN model robustly performs to reconstruct missing data of pollutant concentrations with an R2 of 0.93 and an RMSE of 7.78; (2) the air quality index (AQI) decreases by 15.96%, and the total polluted days decrease by 21.05% from 2015 to 2021, showing an obvious promotion in air quality; and (3) the changing air quality is attributed to decreasing concentrations of PM2.5 (34.10%), PM10 (25.03%), and NO2 (5.53%) from 2015 to 2021, whereas an increasing concentration of O3 (10.49%) is observed. The processing method for missing data, intact AQI datasets, and analysis of changes are beneficial to policy-making for environmental improvement and fill the gap in the field of data interpolation for air quality datasets in mountainous areas.

With the rapid advancement of information technology, the Internet, as the core infrastructure for global information exchange, faces increasingly severe security challenges. However, traditional network traffic detection methods typically focus solely on the local features of traffic, failing to comprehensively consider the global relationships between traffic flows. This limitation results in poor detection performance against multi-flow coordinated attacks. Additionally, the inherent imbalance in real-world network traffic data significantly hampers the performance of most models in practical scenarios. To address these issues, this paper proposes a network traffic detection method based on data interpolation and contrastive learning (TICL). The method employs data interpolation techniques to generate negative samples, effectively mitigating the data imbalance problem in real-world scenarios. Furthermore, to enhance the model’s generalization capability, contrastive learning is introduced to capture the differences between positive and negative samples, thereby improving detection performance. Experimental results on two publicly available real-world datasets demonstrate that TICL significantly outperforms existing intrusion detection methods in large-scale data scenarios, showcasing its strong potential for practical applications.

(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling.

In this article we present a new adaptive algorithm for solving 2D interpolation problems of large scattered data sets through the radial basis function partition of unity method. Unlike other time-consuming schemes this adaptive method is able to efficiently deal with scattered data points with highly varying density in the domain. This target is obtained by decomposing the underlying domain in subdomains of variable size so as to guarantee a suitable number of points within each of them. The localization of such points is done by means of an efficient search procedure that depends on a partition of the domain in square cells. For each subdomain the adaptive process identifies a predefined neighborhood consisting of one or more levels of neighboring cells, which allows us to quickly find all the subdomain points. The algorithm is further devised for an optimal selection of the local shape parameters associated with radial basis function interpolants via leave-one-out cross validation and maximum likelihood estimation techniques. Numerical experiments show good performance of this adaptive algorithm on some test examples with different data distributions. The efficacy of our interpolation scheme is also pointed out by solving real world applications.

In earthquake data interpretation process, data body precision would not be very high as the workload of interpretation or data memory. Using the interpolate method mentioned in this paper, we can finish the interpolation without increasing the workload of interpretation, increase earthquake data precision, and acquire more information from earthquake data.

Seismic data interpolation is an essential tool for providing complete seismic data when field data are incomplete due to the influence of obstacles, topography and acquisition cost. Among the existing interpolation methods, sparsity inversion-based methods are commonly used for noisy data interpolation. These methods assume that seismic data can be sparsely expressed in a transformed domain, and a sparse inversion should be solved to obtain sparse coefficients. The L1 norm is often chosen as the regular operator since it is a convex function and can measure sparsity of solutions. Nonetheless, nonconvex regularization represented by the L1/2 norm has better numerical properties than those of the L1 norm regularization, since the L1/2 norm is a closer expression of sparsity. Based on the idea of nonconvex regularization, a novel nonconvex regularization model was developed to realize seismic interpolation. The L1 norm and a nonconvex, hyperbolic tangent-based function were combined as the regularization constraint, and a revised proximal method was proposed to efficiently solve the inversion model. A revised Newton direction of the nonconvex term was used to ensure that the proposed method gave stable results. Synthetic and field data examples demonstrate that the proposed technique is especially useful for interpolating missing traces in recorded seismic data sets, and it is robust even when data are contaminated with noise.

The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha−1) and a higher confidence index (C = 0.99).