Citrus peel, an abundant yet underutilized biomass resource, is rich in bioactive flavonoids. This study systematically investigated the effects of three types of dual-frequency ultrasonic coupling parameters (frequency difference Delta f, power allocation ratio /i, phase difference Delta p) on flavonoids extraction from citrus peel. Results demonstrated that small frequency differences in both low + low and high + high frequency couplings contributed to enhanced flavonoids yield, while low + high frequency combinations inhibited extraction efficiency even with slight frequency differences. The influences of power distribution ratio and phase difference varied with the frequency coupling mode. Under the optimized conditions (70 mL/dL ethanol solution, solvent to material ratio 25:1 mL/g, particle size 0.385 mm, and 40 degrees C), the maximum flavonoids yield of 128.63 +/- 2.27 mg/g was obtained at Delta f = 0 kHz (20 + 20 kHz), /i = 1, Delta p = 0. Nevertheless, low + high frequency coupling resulted in significantly lower yield, with only 28.74 +/- 0.93 mg/g achieved at Delta f = 160 kHz (20 + 180 kHz) and Delta p = pi/2. Furthermore, two kinetic models (two-site and Peleg's) were validated for predicting flavonoid yield over time, and they exhibited higher accuracy with lower mean percentage errors (MPE) of 0.143%-0.928% and 0.506%-2.461%, respectively. This study revealed the synergistic mechanism of dual-frequency ultrasonic parameters, which may support the efficient and green extraction of flavonoids from citrus peel.

Deep neural networks have demonstrated significant potential in direction of arrival (DOA) estimation. However, some existing architectures, especially convolution-based ones, mainly emphasize local feature extraction and may not sufficiently capture long-range dependencies in array observations. To better model such nonlocal correlations, this paper presents a Cascaded Multi-Head Attention Transformer (CMA-Former) for grid-based DOA estimation. The sample covariance matrix is first converted into a compact token sequence using its upper-triangular off-diagonal entries. For each entry, the real part, the imaginary part, and the sine and cosine of its phase are stacked as input features, providing a periodic phase encoding that avoids the discontinuity inherent in raw phase values. A stack of customized Transformer encoders, each equipped with cascaded multi-head attention modules whose head count increases progressively, is then employed to capture sensor-pair correlations across multiple representation subspaces and scales. Finally, a classification token together with a classification head produces confidence scores over a predefined angular grid. Simulation results show that CMA-Former achieves an RMSE lower than or comparable to that of the deep-learning baselines considered. Moreover, it attains a higher estimation success rate for closely spaced sources, indicating an improved capability to resolve adjacent targets. At high SNR, the performance of all grid-based methods is bounded by the off-grid error floor imposed by the fixed angular grid. In addition, a hardware experiment using a cascaded mmWave radar platform further demonstrates the feasibility of applying CMA-Former to real radar measurements without retraining. The source code is publicly available at https://github.com/Syyyt/CMA-Former-official.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems commonly operate in hybrid-field scenarios, where near-field spherical waves and far-field plane waves coexist. However, the spatial non-stationarity caused by the large array aperture of XL-MIMO and the grid mismatch induced by discrete dictionaries make hybrid-field channel estimation more challenging. To address these challenges, this letter proposes a residual-based adaptive-subarray-assisted parameter-refinement hybrid-field simultaneous orthogonal matching pursuit (ASPR-HF-SOMP) channel estimation method. Based on the multiple-measurement-vector (MMV) sparse recovery framework, the proposed method exploits the common physical path support among subcarriers for wideband joint estimation. Meanwhile, the adaptive subarray partitioning module selects a suitable local array configuration to cope with spatial non-stationarity. Furthermore, local continuous parameter refinement is introduced to optimize path parameters in the continuous domain, thereby alleviating the grid mismatch induced by discrete dictionaries. Simulation results show that the proposed method achieves accurate wideband hybrid-field channel estimation with improved normalized mean-square error (NMSE) performance.

This letter proposes a dynamic validation-based simultaneous weighted orthogonal matching pursuit (SWOMP) algorithm for accurate channel estimation using training-based millimeter-wave (mmWave) multiple input multiple output (MIMO) channels. The proposed method enhances traditional SWOMP by introducing a dynamic path validation mechanism, which adaptively selects the most significant paths based on their contribution to minimizing the residual norm. Leveraging training channel data, the algorithm dynamically optimizes the sparsity level, ensuring robust performance in frequency-selective and varying channel conditions. Simulation results show that the proposed algorithm outperforms existing SWOMP variants and other compressed sensing-based methods in estimation accuracy and robustness across diverse SNR levels.

Intelligent reflecting surfaces (IRS) enable radar sensing in blocked environments by reconfiguring propagation and creating virtual apertures, which is crucial for non-line-of-sight (NLoS) localization. This work addresses high-accuracy direction-of-arrival (DoA) estimation in semi-passive IRS-assisted sensing. We introduce a virtual-domain lifting that vectorizes the received echoes and induces a structured atomic set, leading to an atomic norm minimization (ANM) formulation. The ANM estimator is formulated as a semidefinite program (SDP) via convex relaxation, and we develop an accelerated proximal gradient (APG) solver that leverages the problem structure and avoids interior-point steps, resulting in substantial computational savings. Compared with spatial-domain and grid-based approaches, the transformed-domain estimator delivers an optimal accuracy-complexity tradeoff. It achieves gridless (ANM-level) high resolution while reducing runtime. Extensive simulations across array sizes, transmit power, and IRS configurations confirm accuracy, robustness to off-grid mismatch, and scalability, demonstrating the practicality of the proposed method for IRS-enabled NLoS sensing in complex environments.

This paper proposes a robust hierarchical channel estimation algorithm for massive multiple-input multiple-output (MIMO) systems, specifically addressing the challenges posed by near-field spatial non-stationary channels. In near-field communication scenarios, the channel characteristics exhibit significant spatial variations due to the proximity between the transmitter and receiver, resulting in non-uniform sparsity patterns that hinder traditional estimation methods. To enhance estimation efficiency and accuracy, we propose an approach that integrates a pre-selection strategy with a multi-level dynamic thresholding mechanism. The proposed algorithm operates in two stages. In the first stage, a pre-selection process effectively reduces the number of candidate atoms, improving the computational efficiency of sparse adaptive estimation. In the second stage, a dynamic multi-level thresholding scheme is introduced, where the noise reconstruction parameter is adaptively adjusted based on the instantaneous signal-to-noise ratio (SNR), ensuring robustness across varying SNR conditions. Simulation results demonstrate that the proposed method outperforms existing algorithms in terms of mean square error (MSE) and reconstruction success probability while maintaining computational complexity comparable to conventional approaches. Given its superior performance and efficiency, the proposed algorithm is well-suited for deployment in near-field MIMO systems, making it a promising solution for next-generation wireless networks.

Small-sized and high-precision velocity receiving sensors offer significant potential for direction-of-arrival (DOA) estimation in multiple-input multiple-output (MIMO) radar systems. This paper addresses the challenge of twodimensional (2D) angle estimation in a conformal MIMO radar architecture that employs a scalar transmitting array and a velocity-based receiving array. To this end, we propose a novel coarse-to-refined estimation strategy for 2D-DOA estimation. In the first stage, a coarse estimate is obtained by leveraging an enhanced rotational invariance technique that exploits the velocity diversity inherent in the receiving sensors. In the second stage, the initial estimates are refined using the spatial diversity of the full array configuration. Unlike existing methods, the proposed strategy supports arbitrary array geometries, provides closed-form solutions, and inherently resolves the pairing problem between azimuth and elevation angles. Extensive simulations validate the effectiveness and robustness of the proposed algorithm, achieving a favorable trade-off between complexity and accuracy compared to state-of-the-art methods.

Federated learning enables training across multiple entities while ensuring data security and the effectiveness of knowledge dissemination. Despite its benefits, it remains susceptible to privacy breaches by both external and internal adversaries, who may exploit data or model parameters to glean sensitive participant information or disrupt the training process, thus compromising participant privacy and security. This paper proposes a novel methodology, Adversarial Samples for Defense Inference Attack (ASDIA), aimed at dual protection of data privacy and model robustness within federated learning through adversarial samples and gradient reconstruction. ASDIA includes gradient processing approach before uploading: initially identifying privacy-sensitive gradient, followed by the injection of well-calibrated noise to these gradients. This method not only obfuscates the adversary's classification demarcations but also aids in model performance recovery, all the while maintaining computational efficiency. ASDIA reduces the efficacy of attacks to near-random guessing levels and shows better balance between the model utility and privacy protection compared to the most advanced defense strategies. Additionally, regarding model performance, ASDIA proves its merit across diverse datasets under overfitting and non-overfitting scenarios.

Intelligent Reflecting Surfaces (IRS) mark a major advancement in wireless communication, enabling Multiple-Input Multiple-Output (MIMO) radar to detect targets in non-line-of-sight (NLOS) conditions by reconstructing wireless channels. However, existing optimization-based methods are often too complex for practical use. This paper addresses angle estimation in arbitrary-manifold array bistatic MIMO radar and proposes a joint two-dimensional direction-of-departure (2D-DOD) and two-dimensional direction-of-arrival (2D-DOA) estimation algorithm assisted by IRS. A tensor decomposition approach is used for 2D-DOD, leveraging the multi-dimensional structure of received signals for improved accuracy and robustness. For 2D-DOA, an overcomplete dictionary matrix enables high-resolution estimation through value matching. The method supports joint NLOS angle estimation without prior knowledge of the target-IRS channel. Complexity and estimation error are analyzed, and simulations validate the method’s effectiveness and precision in IRS-assisted MIMO radar systems.

Channel estimation has attracted extensive attention for millimeter wave (mmWave) massive multiple input multiple output systems. The particle swarm optimization (PSO)-based channel estimation algorithm suffers from low convergence accuracy and falls into local extremality. This paper proposes the improved PSO (IPSO)-based channel estimation algorithm to solve these problems in the PSO-based algorithm. Firstly, a group of particles containing velocity and location information is randomly generated. Then, the proposed IPSO-based algorithm combines the hybridization technique and the extreme perturbation mechanism. Specifically, the optimization measures of updating the velocity and position information of the particle swarm through the hybridization technology can improve the convergence accuracy of the algorithm, and the introduction of the extreme perturbation mechanism expands the search area to avoid the problem of falling into the local extremum. The velocity information and the position information of the particles are updated by comparing the fitness function values. Finally, the obtained global optimal value is the required channel matrix. Theoretical studies and simulation results show that the proposed algorithm is superior to other algorithms, which is a practical channel estimation algorithm. The convergence accuracy of the proposed IPSO-based algorithm is two orders of magnitude better than that of the PSO-based algorithm, which indicates that the proposed IPSO-based algorithm has strong global search ability compared to the PSO-based algorithm. At -5, 0, 5, 10, 15, and 20 dB, the NMSE estimation accuracy of the proposed IPSO-based algorithm is 60.3

The utilization of an Electromagnetic Vector Sensor (EMVS) array presents valuable insights in wireless communications and radars by incorporating the advantages of spatial, temporal, and polarization diversities. This study focuses on addressing the problem of estimating the Two-Dimensional (2D) Direction-of-Arrival (DOA) for an arbitrarily spaced EMVS array. Firstly, the Cramer-Rao Bounds (CRB) for estimating the 2D-DOA and polarized parameters are derived, which provides design guideline for array geometry. As suggested by the CRB, a sparse planar EMVS array is a more appealing choice compared to the popular coprime EMVS array. Thereafter, a Parallel Factor (PARAFAC)-based weighted phase fitting algorithm is presented, designed to fully explore the aperture of the conformal array as well as capitalize on the tensorial structure inherent in array data. The proposed PARAFAC algorithm not only provides a closed-form solution for conformal arrays but also exhibits superior accuracy in 2D-DOA estimation compared to existing approaches. Numerical results corroborate the theoretical advantages of the proposed array geometry and PARAFAC algorithm.

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RISs) with nonorthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for sixth-generation (6G) vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association-a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as nonlinear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: 1) IoV association with BS; 2) BS power allocation; and 3) BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves significant improvements of 32% in energy efficiency and 28% in spectral efficiency compared to conventional RIS-assisted architectures, validating its potential for next-generation vehicular networks.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

This study addresses the problem of two-dimensional (2D) direction of arrival (DOA) estimation in Non-Line-of-Sight (NLOS) propagation scenarios. It specifically focuses on the reconfigurable intelligent surface (RIS) architecture, which is equipped with arbitrarily placed Electromagnetic Vector Sensors (EMVS). A novel method is introduced to utilize the multidimensional characterization of received signals. This is achieved by applying High-Order Singular Value Decomposition (HOSVD). It shows the outstanding superiority especially in low signal-to-noise ratio (SNR) and limited samples.

Millimeter-wave (mmWave) and terahertz (THz) communications are susceptible to frequent link disruptions and severe performance degradation due to high directionality, significant path loss, and sensitivity to blockages. These challenges are particularly acute in highly dynamic and densely populated user environments. The issues present significant obstacles to ensuring reliability and quality of service (QoS) in future space–air–ground integrated networks. To address these challenges, this paper proposes an adaptive transmission control scheme designed for space–air–ground integrated multi-hop networks operating in the mmWave/THz bands. By analyzing the intermittent connectivity inherent in such networks, the proposed scheme incorporates an incremental factor and a backlog indicator into its congestion control mechanism. This allows for the accurate differentiation between packet losses resulting from network congestion and those caused by channel blockages, such as human body occlusion or beam misalignment. Furthermore, the scheme optimizes the initial congestion window during the slow-start phase and dynamically adapts its transmission strategy during the congestion avoidance phase according to the identified cause of packet loss. Simulation results demonstrate that the proposed method effectively mitigates throughput degradation from link blockages, improves data transmission rates in highly dynamic environments, and sustains more stable end-to-end connectivity. Our proposed scheme achieves a 35% higher throughput than TCP Hybla, 40% lower latency than TCP Veno, and maintains 99.2% link utilization under high mobility.

An examination of different distributed real-time applications operating on the blockchain platform is conducted. These applications can be broadly classified into three types: permissionless public, permissioned private, and consortium chains. In order for a decentralized network to function independently, consensus mechanisms are needed to facilitate the delivery of transactions and keep track of them in a ledger. But the fundamental idea behind Blockchain technology is the use of several consensus protocols, like Proof of Stake, Proof of Elapsed Time, etc., which requires greater processing power. In order to arrange transactions, it increases the demand for buying more computing units. Furthermore, present consortia blockchain consensus mechanisms lack a policy to collect socio-economic financial levies, including monies for charitable donations, education, and social activities. To collect socio-economic taxes, this study suggests a lightweight Plenum consensus algorithm called "BLPCA" for consortium blockchains built on Hyperledger Indy. The Byzantine Fault Tolerance concept combined with optimization is used in the suggested BLPCA systems to manage large-scale decentralized traffic of socioeconomic hierarchy. Because there are no transaction costs, BLPCA encourages government analysts to review funds using fewer resources. By including a multithreading heterogeneous technique, the BLPCA can effectively handle multi-transaction needs and execute this protocol in an industrial setting that operates in real-time. It is observed from the simulations that even in the worst-case network scenario—such as a fork—the suggested consortium blockchain does not crash a single transaction. In order to guarantee node scalability, a high propagation speed is determined. Furthermore, the BLPCA shows an excellent average time while constructing socioeconomic transaction blocks.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

Massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) systems play a vital role in high-mobility scenarios like vehicle-to-everything and high-speed railways, offering strong resilience against Doppler effects and mitigating channel degradation. However, accurate channel estimation remains a challenge, especially in dynamic environments where conventional methods like orthogonal matching pursuit (OMP) struggle. A two-dimensional regularized orthogonal matching pursuit (2D-ROMP) algorithm tailored was introduced for channel estimation in massive MIMO OTFS systems. Unlike traditional OMP-based methods, in which delay and Doppler shifts were estimated independently, the proposed algorithm combined these two dimensions into a unified delay-Doppler (DD) domain, effectively leveraging the multi-dimensional sparsity of OTFS channels. By employing regularization, the most relevant DD indices were first identified, , followed by refinement in the antenna angle domain to leverage burst sparsity. Compared to conventional OMP and three-dimensional structured OMP (3D-SOMP) methods, the proposed algorithm was demonstrated to achieve improved channel estimation accuracy and computational efficiency. Simulation results demonstrate that it outperforms existing approaches with reduced pilot overhead and enhanced robustness in dynamic environments.

Accurate channel estimation in hybrid-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems is crucial for unlocking their potential in future wireless communications. Traditional methods, such as least squares (LS) and orthogonal matching pursuit (OMP), are limited by their inability to fully exploit correlations between near-field and far-field paths. To address these limitations, this letter first explores hybrid-field sparse Bayesian learning (Hybrid SBL), which separately applies the traditional SBL framework to near-field and far-field paths, and then develops a joint sparse Bayesian learning (Joint SBL) framework. By integrating near-field and far-field dictionaries into a unified structure and leveraging a global sparse prior, Joint SBL eliminates the need for prior path information while enhancing channel estimation accuracy and robustness. Simulation results illustrate that the proposed Joint SBL significantly outperforms traditional methods, especially under challenging signal-to-noise ratio (SNR) conditions.