ZTE (China)

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

<strong class="journal-contentHeaderColor">Abstract.</strong> Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

Abstract. Gas fluxes passing through an eddy-covariance (EC) system's measurement volume reflect the outgassing rate of these molecules from an upwind area known as the "flux footprint". While sources/sinks of these molecules may be uniform over a flat field, their spatial contribution to the measured fluxes is not. Thus, understanding the contribution to measured fluxes and the spatial quantification of sources/sinks from the measured fluxes requires footprint analysis. Such analysis yields flux footprint characteristics, which commonly include upwind maximum footprint location, upwind fetch containing certain percentages of measured flux (70%, 80%, 90%), and the percent of flux from a user-defined upwind fetch of interest. These characteristics are included in the datasets of flux networks such as ChinaFlux, AmeriFlux, and FluxNet. Ideally, the characteristics are calculated in real-time and on-site by EC systems in the field, but this has often not been the case due to the calculations being computationally challenging. For field applications, this study develops the equations and algorithms for these characteristics from analytical crosswind-integrated flux footprint equations. The development shows that in-field computation is made feasible by the following means: using time-efficient algorithms, taking advantage of the nondimensional nature of the footprint equations of Kljun et al. (2015), implementing practical limits on numerical integration, and developing a differential-based estimation of boundary layer height for each EC interval. Accuracy of in field calculations is maintained by the selection of footprint equations based on boundary-layer conditions and considerations of integration methods and computation techniques. This computational approach may also be applied to footprint analyses over complex terrain, nonuniform sources/sinks, or in cases where other footprint equations are used. The most popular application of footprint analysis is to optimize the EC sensor height for maximization of measured fluxes from an area of interest. This optimization using the nondimensional footprint equations is discussed, which leads to a practical methodology. This work serves as a technical reference for users or developers of EasyFlux programs, widely used in Campbell Scientific EC systems globally.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

A broadband 1-bit reconfigurable transmit-array (TA) featuring independent dual-polarized beam-scanning capability is reported at Ka-band. Bandwidth enhanced transmissive elements based on the current reversed mechanism are developed, which are optimized based on an equivalent circuit model. The elements are further miniaturized and aligned orthogonally in an end-to-middle scheme for dual-polarized operation. A 20 × 20-cell dual-polarized reconfigurable TA is fabricated and measured. The prototype exhibits a peak gain of 24.8 dBi, a 3-dB gain bandwidth of 18%, as well as an aperture efficiency of 26%. Moreover, it supports sum beams scanning up to 60° and difference beams steering up to 50°. The demonstrated dual-polarized reconfigurable TA can be a promising candidate for millimeter-wave communication and radar systems.

Intelligent reflecting surfaces (IRSs) operating in the terahertz (THz) band have gained considerable interest due to their high spectral bandwidth. With large-scale IRS deployment, transceivers are likely to be in the near-field region, where the near-field beam split effect becomes a major challenge for wideband IRS beamforming. This effect causes beam deviation from the intended location, leading to significant gain loss and waste in spectrum resource. While delay-based IRS has emerged as a potential solution, current beamforming schemes generally assume unbounded range time delays (TDs). In this paper, we first investigate the near-filed beam split issue at the IRS. Then, we extend the piecewise far-field model to the IRS, based on which, a double-layer delta-delay (DLDD) IRS beamforming scheme is proposed. Specifically, we employ an element-grouping strategy and the TD imposed on each sub-surface of IRS is achieved by a series of TD modules. This method significantly reduces the required range of TDs. In addition, a sub-optimal joint beamforming scheme is proposed to address the double-stage beam split at the base station and the IRS. Numerical results show that the proposed schemes can effectively mitigate the near-field beam split and achieve near-optimal performance.

The proliferation of location-based services has facilitated the generation of extensive semantic trajectories for moving objects. In this paper, we investigate the problem of semantic trajectory similarity search, a fundamental task of trajectory analysis. The objective is to identify “similar” trajectories to a query trajectory by considering both spatial proximity and semantic similarity. Existing studies predominantly utilize keywords-based discrete semantic modeling, which fails to capture broader semantics, such as text descriptions. Moreover, these studies typically develop search algorithms on a single machine, resulting in limited processing capability To this end, we propose an effective framework to perform distributed semantic trajectory similarity searches. We introduce a novel method for representing semantic trajectories by employing both geographical sequences and semantic sequences. This approach enhances spatial-semantic awareness in trajectory modeling. Next, we implement the query framework in Spark and propose a carefully designed two-phase, computation-aware partitioning architecture. This is the first architecture to consider both semantic and spatial aspects of trajectories, as well as inter-trajectory distances, to guide the partitioning process. It enables efficient pruning of most partitions in the cluster when processing queries. Within each local partition, we construct an ST-HNSW index to further accelerate queries. Our framework supports three widely used trajectory distance measures: DTW, LCSS, and EDR. Extensive experiments on two real and one synthetic datasets demonstrate the significant advantages of our design, achieving efficiency improvements of up to 1 to 3 orders of magnitude over baseline and alternative approaches.