Accurate snow depth (SD) data are essential for understanding and simulating hydrological processes, particularly in regions with uneven snow distribution and complex terrain, such as the Third Pole (TP). However, existing SD products often suffer from low spatial resolution, introducing uncertainties in regional snowpack and runoff modeling. In this study, we developed an integrated downscaling framework that combines deep learning (DL) with physical constraints to improve the resolution and accuracy of SD data for the TP region. First, a residual network incorporating multifactor spatial-terrain relationships is used to generate preliminary 500-m DL-downscaled SD data. Subsequently, by incorporating both snow cover fraction (SCF) data and snow depletion curve (SDC) as dual physical constraints, the new algorithm significantly improves the estimation accuracy in shallow snow areas. The proposed algorithm significantly enhances SD estimation accuracy, reducing the root-mean-square error (RMSE) from 1.99 to 0.75 cm, which represents a 62% improvement over the purely DL-based downscaling algorithm (RMSE = 1.02 cm). Notably, the improved algorithm demonstrates enhanced capability in terrain feature representation while simultaneously minimizing misclassification in snow-free areas and reducing overestimation in shallow snow regions. The results demonstrate progressive improvement in SD estimation accuracy across diverse geographical environments through stepwise validation of each downscaling step, confirming that integrating DL-based downscaling with physical constraints yields complementary advantages. Although the new algorithm significantly improves the accuracy of SD data, it is still underestimated in complex terrain areas and areas with SD exceeding 7 cm. Future research needs to deeply integrate physical constraint methods, comprehensively consider physical processes such as snow density, water content changes, and energy balance, and establish a more complete downscaling model.

Passive microwave remote sensing has witnessed unprecedented progress in soil moisture (SM) estimation over the decades. However, it is challenging to estimate SM accurately due to the insufficient understanding of microwave emission mechanisms. A ground-based radiometry experiment is undertaken over an agricultural field, toward the reexamination and improvement of the microwave emission models and retrieval algorithms of SM and vegetation water content (VWC). This article reports the preliminary analysis of the experimental data and the calibration of the$\tau $–$\omega $model against the collected measurements. First, the collected multifrequency dual-polarized brightness temperature (TB) reflects the temporal variation of surface SM and a significantly negative correlation between them is observed, with the coefficient of determination ($R^{2}$) and slope (S) of a linear fitting line ranging from 0.036 to 0.367 and from −16.7 to −81.5, respectively. Surface roughness and VWC impact the relationship between TB and SM, with variable$R^{2}$and S observed. Second, the calibrated parameters have improved the model performance, with$R^{2}$greater than 0.65 and root mean standard error (RMSE) less than 4.7 K at all frequencies and polarizations. The parameter values are frequency- and polarization-dependent, and the best performance of the model simulation is observed at V-polarization of L- and Ku-bands, with$R^{2} =0.80$and RMSE =4.69 K at the L-band and$R^{2} =0.74$and RMSE =2.9 K at the Ku-band. Overall, the experiment has provided valuable datasets for calibrating forward models and the calibrated model will facilitate the improvement of surface parameters (e.g., SM and VWC) retrieval.

The conventional transmitted reference (TR) technique in ultra wide-band (UWB) communications has provided a springboard for a lot of studies, and a TR pulse cluster (TRPC) structure was proved to have robust performance. Most of TR and TRPC related studies hitherto are based on the assumption that the delay lines (DLs) in their schemes are ideal. However, the group delay ripple (GDR) of nonideal DLs triggered by practical imperfect factors at implementation causes an unexpected distortion in both TR and TRPC UWB systems, and there are few studies so far. In this article, we first analyze the impact of the GDR of nonideal DLs on the performance of TR and TRPC UWB systems. Then a new design solution for both TR and TRPC transceivers is proposed, where the GDR information of the nonideal DL at the receiver side is introduced into transmitter side. The performance of the new design solution is analyzed and simulation results prove the robustness and efficiency of this new design method in reducing the impaction caused by nonideal DLs. Moreover, the proposed design solution can also greatly relieve the high design requirement of DLs in both the transmitter and receiver side and therefore offer valuable insight for the practical transceiver design in both TR and TRPC related UWB systems.

Volume scattering (VS) estimation plays a critical role in microwave emission modeling of the snowpack. However, it is challenging to obtain VS accurately for different frequencies by using the microwave emission model of layered snowpacks (MEMLS), which is one of the representative microwave emission models. This article develops a new VS method to consider frequency and exponential correlation length based on a snowfield campaign from November 2015 to April 2016 in Altay, China. Compared with the commonly used empirical and improved Born approximation (IBA) algorithms, the proposed VS algorithm exhibits better performances at both 18 and 36 GHz with a wide range of snow grain sizes. The bias of brightness temperatures at vertical polarization from the proposed algorithm against the observed brightness temperatures are 1.1 K and −0.4 K at 18 and 36 GHz, respectively; the root mean square errors (RMSEs) are 1.8 K and 2.6 K, respectively. The RMSEs decreased by 16.2 K at 18 GHz and 6.5 K at 36 GHz compared with those from the empirical methods and by 2.1 K and 22.2 K compared with those from the IBA. This work demonstrates that the VS difference between 18 and 36 GHz is larger and the dependence of VS on grain size is weaker than those represented by existing methods.

Craig interpolant generation for non-linear theory and its combination with other theories are still in infancy, although interpolation-based techniques have become popular in the verification of programs and hybrid systems where non-linear expressions are very common. In this paper, we first prove that a polynomial interpolant of the form $$h(\mathbf {x})>0$$ exists for two mutually contradictory polynomial formulas $$\phi (\mathbf {x},\mathbf {y})$$ and $$\psi (\mathbf {x},\mathbf {z})$$ , with the form $$f_1\ge 0\wedge \cdots \wedge f_n\ge 0$$ , where $$f_i$$ are polynomials in $$\mathbf {x},\mathbf {y}$$ or $$\mathbf {x},\mathbf {z}$$ , and the quadratic module generated by $$f_i$$ is Archimedean. Then, we show that synthesizing such interpolant can be reduced to solving a semi-definite programming problem ( $$\mathrm{SDP}$$ ). In addition, we propose a verification approach to assure the validity of the synthesized interpolant and consequently avoid the unsoundness caused by numerical error in $$\mathrm{SDP}$$ solving. Besides, we discuss how to generalize our approach to general semi-algebraic formulas. Finally, as an application, we demonstrate how to apply our approach to invariant generation in program verification.

Since microservice has merged as a promising architectural style with advantages in maintainability, scalability, evolvability, etc., increasing companies choose to restructure their legacy monolithic software systems as the microservice architecture. However, it is quite a challenge to properly partitioning the systems into suitable parts as microservices. Most approaches perform microservices identification from a function-splitting perspective and with sufficient legacy software artifacts. That may be not realistic in industrial practices and possibly results in generating unexpected microservices. To address this, we proposed an automated microservice identification (AMI) approach that extracts microservices from the execution and performance logs without providing documentation, models or source codes, while taking both functional and non-functional metrics into considerations. Our work firstly collects logs from the executable legacy system. Then, controller objects (COs) are identified as the key objects to converge strongly related subordinate objects (SOs). Subsequently, the relation between each pair of CO and SO is evaluated by a relation matrix from both the functional and non-functional perspective. We ultimately cluster classes(objects) into the microservices by optimizing the multi-objective of high-cohesion-low-coupling and load balance. The usefulness of the proposed approach is illustrated by applying to a case study.

Validation of existing satellite soil moisture (SM) products are critical for retrieval algorithms refinement and high-quality SM datasets achievement. Based on distributed ground observations, accuracy performance of two L-band (1.41GHz) passive microwave remotely sensed SM products of SMAP enhanced level 3 (L3) (SMAP-L3) and the newly published SMOS-INRA-CESBIO (SMOS-IC) are evaluated in Heihe River Basin (HRB) over various land surface types in Northwest China. Results reveals that: both two L-band SM products underestimate over grassland and cropland land cover types in HRB, and the underestimation obviously increases with the increasing vegetation effect. Totally, the SMAP L3 product behaves better in capturing the SM variation with the averaged ubRMSE values of 0.03m3/m3and high R values above 0.6 in ascending orbits for different land cover types in HRB, while the SMOS-IC retrievals are strongly affected by the severe RFI contamination and algorithm uncertainties which need to be handled before application, especially for the descending product.

Cyber physical systems (CPS) is a tightly coupled integration and interaction between computational and physical components. In many cases, information collection in CPS is provided through a group of distributed sensors and all of them change continuously with time. Thus the sensor information is usually in the form of time series. One particularly interesting application in time series analysis is use of complex networks to represent and study behaviors of system. Complex networks has been playing an important role for analyzing complex systems as it helps understanding the topology structure of systems with different interacting units. In this paper, we proposed a reliable method for constructing complex networks from multivariate time series (MTSs) in the cases of single and multisensor based on information geometry theory, which allows the information in the time series to be extracted by analyzing the associated complex network. We first estimate covariance matrices and then a geodesic-based distance between the covariance matrices is introduced. Consequently, the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and the geodesic-based distance, respectively. The proposed method provides us with a nonlinear relationship and intrinsic geometry viewpoint to understand the MTSs and also an alternative approach to fuse, model, represent, and visualize the multisensor data in CPS. A number of experimental studies and numerical examples are presented to demonstrate the generality and the effectiveness of our approach with both synthetic and real datasets.

A new projection operator based on cylindrical algebraic decomposition (CAD) is proposed. The new operator computes the intersection of projection factor sets produced by different CAD projection orders. In other words, it computes the gcd of projection polynomials in the same variables produced by different CAD projection orders. We prove that the new operator still guarantees obtaining at least one sample point from every connected component of the highest dimension, and therefore, can be used for testing semi-definiteness of polynomials. Although the complexity of the new method is still doubly exponential, in many cases, the new operator does produce smaller projection factor sets and fewer open cells. Some examples of testing semi-definiteness of polynomials, which are difficult to be solved by existing tools, have been worked out efficiently by our program based on the new method.

In this paper, capacity and coverage performances by deploying vertical sectorization in HSPA networks are evaluated. A novel resource-scheduling scheme for vertical sectorization is proposed to improve the SINR of cell-edge users. Performances of both conventional vertical sectorization and proposed mechanism are investigated through analysis and simulations. A three dimensional propagation model is used in the system level simulator. Our results indicate in a regular HSPA network conventional vertical sectorization is able to provide up to 59.2% capacity gain at the cost of significantly increased network outage levels. On the other hand, the proposed scheme can remarkably improve network outage performance while providing maximal 21% network throughput enhancement.

Historic freeze-up and break-up dates of lake can record the regional climate variability. Observations of lake ice from different regional or country's stations are inconsistent and their number was declined in past two decades, which makes climate change analysis difficult. This paper proposes a dynamic threshold method to derive the freeze- up and break-up dates of big lakes using passive microwave brightness temperature data. Lake ice information from 35 big lakes in Northern Hemisphere is retrieved in past three decades. These freeze-up and break-up dates were validated from 18 stations' observations. The linear correlation coefficients between observations and retrievals are 0.926 for freeze-up dates and 0.936, respectively.

In [1], it is shown that the word error rate (WER) of binary codes under binary phase-shift keying (BPSK) modulation can be evaluated via the square radius distribution of decision region and a simple approximation formula of WER for turbo-like codes is presented. In this paper, we extend the method in [1] to more general cases including high-order modulations and fading channels. Moreover, an improved closed-form approximate WER formula is also proposed which can reduce the approximation error to 0.05 dB under a BPSK modulation and additive white Gaussian noise (AWGN) channel for the signal-to-noise ratio (SNR) of interesting in wireless systems.