Search Results (75)

A modified near-field total least-squares-estimating signal parameter via rotational invariance techniques (NF-TLS-ESPRIT) algorithm based on a two-dimensional (2-D) scattering-center (SC) model is proposed for accurate estimation of the positions and radar cross section (RCS) of point-scattering targets under limited-aperture near-field measurements. Conventional two-dimensional ESPRIT relies on a plane-wave observation model and requires the scattering-center number to be specified in advance, which may lead to position bias, model-order sensitivity, and unstable amplitude estimation when it is directly applied to finite-distance measurements. To address these problems, the proposed algorithm introduces a CLEAN-assisted dominant scattering-center number estimation procedure, a near-field coordinate compensation relation derived from spherical-wave geometry, and a multi-angle least-squares amplitude-estimation scheme for coherent far-field RCS reconstruction. Unlike conventional near-field to far-field transformation (NFFFT) approaches that focus mainly on field transformation, the proposed method also provides a corrected scattering-center representation for RCS reconstruction under limited angular apertures. Systematic simulations under different noise levels, angular apertures, relative bandwidths, model-order settings, scattering-center spacings, and dynamic ranges show that the proposed method improves scattering-center localization and RCS reconstruction accuracy in the tested limited-aperture cases. Measured multi-sphere experiments using directly measured reference RCS further verify the feasibility of the method for targets that can be represented by a finite number of dominant scattering centers. Full article

To address the complex pressure-response mechanisms and difficulties in quantitatively characterizing dynamic reservoir properties during CO₂ geological storage in tight oil reservoirs, this work develops a dual-region composite seepage model coupling reservoir heterogeneity and CO₂-induced fluid property variation. The reservoir is divided into a near-well CO₂-stimulated zone and a far-field unstimulated zone. Combining the Laplace transform and the Stehfest368 numerical inversion method, we derive the analytical solutions of the bottom-hole pressure (BHP) and its derivative, and we establish a complete transient BHP monitoring and parameter inversion framework. The pressure-derivative curves are divided into five typical flow stages: wellbore storage, skin transition, inner-region radial flow, inter-region transition and outer-region radial flow. The key parameters, including wellbore storage coefficient, skin factor, mobility ratio, storativity ratio and CO₂ swept radius, can be accurately inverted via the BHP data analysis, which quantitatively characterizes flow capacity evolution, stimulated region scale and fluid flow patterns after CO₂ injection. The field application on two production wells in H138 block verifies the reliability of the proposed method. Further numerical simulation validation, measurement error sensitivity analysis and cross-verification of reservoir parameters are supplemented to prove the robustness and the applicability of the model. This study provides solid theoretical and technical support for on-site pressure monitoring, storage performance evaluation and operation optimization of CO₂ geological storage in tight reservoirs, and it also offers a reference for long-term storage security and storage capacity assessment. Full article

This study develops a mathematical model for non-Darcian flow toward a fully penetrating pumping well in a confined aquifer, explicitly incorporating both well skin effects and non-Darcian flow behavior, which are conventionally neglected in classical models. A two-zone radial flow model consisting of a skin zone and a background region is established to account for hydraulic property differences caused by well construction. The Izbash equation is adopted to describe the nonlinear velocity-gradient relationship, and the transformed differential quadrature method (TDQM) is employed for the efficient solution. By combining the Laplace transform, differential quadrature discretization, and Stehfest numerical inversion, transient drawdown responses are obtained. The model is verified against benchmark solutions and field pumping-test data, showing good agreement. Parametric analyses reveal that (1) increasing the non-Darcian parameter intensifies near-well flow resistance, producing a steeper and more localized drawdown cone; (2) a larger skin factor (improved near-well hydraulic conductivity) significantly reduces drawdown and enhances well efficiency; and (3) increasing skin thickness primarily improves near-field hydraulic response with limited far-field influence. The proposed framework provides an effective tool for analyzing nonlinear pumping behavior in radially heterogeneous aquifers. Full article

A robust procedure for compensating for inaccuracies caused by 3-D positioning errors in the measurement of the near-field (NF) data required by the non-redundant (NR) spherical near-to-far-field (NtFF) transformations for long antennas is presented in this article. These errors may arise from hardware defects and positioners’ controlling inaccuracies, which may cause the probe to deviate from the intended spherical scan surface and prevent it from reaching the NR sampling points required by either of the two NR representations for long antennas. To account for these errors, the method proceeds through two steps. The first step, called spherical wave correction, compensates for the phase shifts due to radial displacements from the intended scanning sphere. As a result of this correction, the NF samples belong to the intended scanning sphere, but at points different from those required by the adopted NR representation, thus impairing the subsequent NF reconstruction via the optimal sampling interpolation (OSI) algorithm. Such an algorithm enables one to efficiently build the iterative scheme used in the second step, which makes it possible to effectively retrieve the NF samples at the prescribed NR positions. Test results are shown to numerically validate the capability of the developed two-step compensation technique to correct even significant and pessimistic 3-D positioning errors affecting the collection of the NF data. Full article

Millimeter-wave (mmWave) radar provides a privacy-preserving and illumination-robust sensing modality for contactless gesture recognition. However, sparse radar point clouds degrade substantially as sensing distance increases: the number of valid detections decreases, echo intensity attenuates, and Doppler-related motion cues become less reliable. Such range-induced degradation leads to a distribution shift between near-range training samples and far-field test samples, making it difficult for models trained at short distances to generalize to unseen longer distances. Existing point-cloud gesture recognition methods usually treat radar detections as generic sparse point sequences and rarely model distance-related point loss, echo attenuation, and physical-attribute unreliability explicitly. This work introduces RPT-Mamba, a range-aware physical token Mamba network for sparse mmWave radar point cloud sequences. RPT-Mamba constructs physical point tokens from spatial coordinates, Doppler velocity, echo intensity, point-level range, and sample-level range information. During training, a range-aware stochastic degradation strategy adaptively removes points and masks dynamic attributes according to the estimated sensing distance, while a context-guided attribute reconstruction objective recovers masked Doppler and intensity attributes from spatial and frame-level context. A bidirectional Mamba temporal encoder then models long-range gesture dynamics over frame tokens. On the public mTransSee dataset, RPT-Mamba achieves 92.09% accuracy and 92.04% Macro-F1 under the random split protocol, and 85.34% accuracy and 84.77% Macro-F1 under a challenging near-to-far protocol, exceeding point-cloud, radar-gesture, Transformer, and Mamba baselines. Full article

Current terahertz (THz) metasurfaces are often constrained by fixed operational states, lacking the flexibility to switch dynamically between transmission and reflection modes. To address this limitation, we propose a tunable coded metasurface based on the photo-adjustable conductivity of silicon, enabling seamless mode switching and versatile wavefront manipulation. By leveraging the photo-induced dielectric-to-metallic transition, the device functions as a high-efficiency transmission-type polarization converter under zero pump fluence, transforming incident X-polarized waves into Y-polarized waves across a broad frequency range of 0.85–1.5 THz, with a polarization conversion ratio (PCR) exceeding 99%. Upon excitation by 800 nm near-infrared laser pulses, the metasurface transitions to reflection mode, where it simultaneously achieves linear polarization conversion and generates dual-channel orbital angular momentum (OAM) beams through a phase-coding strategy integrated with Fourier convolution. Furthermore, by employing the Gerchberg–Saxton (GS) algorithm to optimize the phase profile, holographic reconstruction is realized in the far field. This design integrates diverse manipulation capabilities into a single, dynamically controllable platform, offering a promising technological approach for THz information processing and integrated photonic systems. Full article

Saudi Arabia has consistently had low insurance penetration (around 0.89% of GDP over the previous three decades), which is far lower than the worldwide average and the goals set by Vision 2030. This research examines the factors influencing insurance penetration (INSP) in Saudi Arabia from 1990 to 2024, primarily testing the demand-following hypothesis which posits that sustainable economic growth acts as a key determinant of insurance demand. The Kingdom intends to diversify its economy as part of Vision 2030 by lowering its dependence on oil, boosting the use of renewable energy, expanding financial markets, and strengthening resilience. The insurance industry is becoming more and more important for managing risk, making green investments, and allocating long-term capital. The analysis employs annual data and the Autoregressive Distributed Lag (ARDL) bounds testing methodology to investigate both short- and long-term relationships between insurance penetration and five critical variables: sustainable economic growth (SD, indicated by GDP per capita growth), financial development (FD, domestic credit to the private sector as a percentage of GDP), renewable energy consumption (REC, percentage of total final energy consumption), trade openness (TO), and urbanization (URB). The main results show that the insurance industry is very route dependent. In the long term, sustainable economic growth, financial development, and the use of renewable energy all have big beneficial effects on insurance penetration. This shows how important they are for extending the insurable base and supporting green investments. Urbanization has a little negative but statistically weak long-term impact (coefficient −0.0056, p < 0.10), while trade openness does not have any effect at all. In the near term, using renewable energy is the biggest positive driver (coefficient 0.096, p < 0.01). This shows how important insurance is in paying for and reducing the risk of energy transition. These findings are resilient to CUSUM and CUSUMSQ stability assessments. This study makes a unique contribution to the field by presenting the first single-country cointegration analysis of an oil-rich economy undergoing structural transformation, directly correlating the adoption of renewable energy with insurance demand, supported by data extending to 2024. The results show that making insurance markets work with the Saudi Green Initiative through green insurance products, mandated coverage for private finance, and digital/micro-insurance aimed at city dwellers can help Vision 2030 targets be reached faster. Policy suggestions stress the need to combine insurance with changes in the financial and renewable energy sectors in order to reach greater penetration goals (which have recently been raised to 3.6–4.5% levels) and build a more diverse, strong, and low-carbon economy. Full article

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

The atmospheric reentry of hypersonic vehicles generates a plasma sheath enveloping the vehicle surface. This fluid medium moves at velocities distinct from the vehicle body, significantly altering its electromagnetic scattering properties. This paper introduces a Multi-Frame Lorentz Transformation Finite-Difference Time-Domain (FDTD) method, which incorporates a spatially varying velocity field into the computational scheme. The proposed algorithm maintains velocity synchronization in electromagnetic field updates and employs a near-to-far-field transformation for far-zone analysis. We systematically investigate the scattering characteristics of a plasma-sheath-covered hypersonic vehicle across a range of velocities and analyze the effect of velocity on the Radar Cross-Section (RCS) under different polarization conditions. Full article

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making. Full article

Phase-retrieval problems are employed to tackle the challenge of recovering a complex signal from amplitude-only data. In phaseless spherical near-field antenna measurements, the task is to recover the complex coefficients describing the radiation behavior of the antenna under test (AUT) from amplitude near-field measurements. The coefficients refer, for example, to equivalent currents or spherical modes, and from these, the AUT’s far-field characteristic, which is usually of interest, can be obtained. In this article, the concept of a mask-based phase recovery is applied to spherical near-field antenna measurements. First, the theory of the mask approach is described with its mathematical definition. Then, several mask types based on random distributions, $\varphi$-rotations, or probes are introduced and discussed. Finally, the performances of the different masks are evaluated based on simulations with multiple AUTs and with Wirtinger flow as a phase-retrieval algorithm. The simulation results show that the mask approach can improve the reconstruction error depending on the number of masks, oversampling, and the type of mask. Full article

In this article, a novel procedure is developed to properly handle the 3-D mispositioning of the scanning probe in the near-field to far-field (NFtFF) transformations with spherical scanning for quasi-planar antennas under test, which make use of a non-redundant (NR) number of samples. It proceeds through two stages. In the former, a phase correction technique, named spherical wave correction, is applied to compensate for the phase shifts of the collected NF samples, which do not belong to the measurement sphere, due to mechanical defects of the arc, or inaccuracy of the robotic arm employed in the considered NF facility driving the probe. Once the phase shifts have been compensated, the recovered NF samples belong to the set spherical surface, but their positions differ from those prescribed by the adopted NR representation, because of an imprecise control and/or inaccuracy of the positioning system. Thus, the resulting sampling arrangement is affected by 2-D mispositioning errors. Accordingly, an iterative procedure is used in the latter step to restore the NF samples at their exact locations from those determined at the first step. Once the correct sampling arrangement has been retrieved from the 3-D mispositioned one, an optimal sampling interpolation formula is employed to obtain the massive input NF data necessary for the classical spherical NFtFF transformation technique. Numerical results, showing the precision of the NF and FF reconstructions, assessed the efficacy of the developed procedure. Full article

Monochromatic mode-filtering-based scattering suppression techniques have been shown to be applicable to all commonly used forms of far- and near-field antenna and RCS measurement techniques. Traditionally, the frequency-domain mode-filtering technique takes a far-field pattern, either measured directly or obtained using a suitable near-field to far-field transformation, as its starting point. The measurement is required to be conducted such that the antenna under test (AUT) is positioned offset from the origin of the measurement coordinate system. This physical offset introduces a phase taper across the AUT pattern and results in far greater interference occurring between the direct and indirect parasitically coupled spurious scattered signals. The method is very general and can be applied to all forms of near- or far-field measurements. However, for the case of a spherical near-field measurement (SNF) approach, it is somewhat cumbersome and tedious as first we must perform a probe-corrected spherical near-field to far-field transformation, which itself involves the computation of a complete set of spherical mode coefficients, and then after the displacement has been applied to the far-electric-fields, a second spherical wave expansion and summation is required to implement the mode-filtering procedure. While this data processing chain has been widely deployed and exhaustively validated, it requires passing through the asymptotic far-field, which inevitably results in additional computational effort, as well as incurring some loss of information, which can impose limitations on further near-field applications. This paper introduces an alternative, novel, rigorous algorithm that applies the displacement of the AUT directly using the vector addition theorem for spherical waves. An efficient implementation has been developed, and it is shown that the new, rigorous algorithm for the translation and filtering can be easily implemented directly within the data processing chain of any standard spherical near-field transformation algorithm, avoiding the need to first transform to the asymptotic far-field and also removing the need for a secondary spherical mode expansion and secondary spherical mode summation. While the vector addition theorem required for the spherical near-field to far-field transformation (SNFFFT) algorithm has been described in detail in the open literature, its implementation has been limited to the case of impinging waves and positive z-directed translations where the magnitude of the displacement is necessarily larger than the minimum sphere radius (MRE). In the current paper, the addition theorem will be derived in a new form that allows the translation to be applied in any desired direction, without the need for additional rotations, as well as being valid for solutions for waves transitioning through the sphere and applicable for the case where the magnitude of the translation is smaller or larger than the radius of the minimum sphere. Full article

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article

This study investigates pre-seismic ionospheric anomalies preceding the 2023 Jishishan Ms6.2 earthquake using total electron content (TEC) data derived from BDS geostationary orbit (GEO) satellites. Multi-scale analysis integrating Butterworth filtering and wavelet transforms resolved TEC disturbances into three distinct frequency regimes: (1) high-frequency perturbations (0.56–3.33 mHz) showed localized disturbances (amplitude ≤ 4 TECU, range < 300 km), potentially associated with near-field acoustic waves from crustal stress adjustments; (2) mid-frequency signals (0.28–0.56 mHz) exhibited anisotropic propagation (>1200 km) with azimuth-dependent N-shaped waveforms, consistent with the characteristics of acoustic–gravity waves (AGWs); and (3) low-frequency components (0.18–0.28 mHz) demonstrated phase reversal and power-law amplitude attenuation, suggesting possible lithosphere–atmosphere–ionosphere (LAI) coupling oscillations. The stark contrast between near-field residuals and far-field weak fluctuations highlighted the dominance of large-scale atmospheric gravity waves over localized acoustic disturbances. Geometry-based velocity inversion revealed incoherent high-frequency dynamics (5–30 min) versus anisotropic mid/low-frequency traveling ionospheric disturbance (TID) propagation (30–90 min) at 175–270 m/s, aligning with theoretical AGW behavior. During concurrent G1-class geomagnetic storm activity, spatial attenuation gradients and velocity anisotropy appear primarily consistent with seismogenic sources, providing insights for precursor discrimination and contributing to understanding multi-scale coupling in seismo-ionospheric systems. Full article