The energy-efficient transmission schemes are crucial to realize the Energy Harvesting (EH)-based Device-to-Device (D2D) communications. Multicast, one of the D2D modes, can serve as an effective approach to address the unreliable energy supply of EH-D2D communications and can further improve energy efficiency through cooperation among multiple users, but it has been rarely explored. To achieve the robust and energy-efficient performance for EH-D2D Multicast communications (EH-D2MD), we first design two cooperative transmission schemes: multi-cluster head content cooperation and single-cluster head energy cooperation by integrating the features of D2MD mode, efficient energy management method and wireless power transfer technology. To investigate the effectiveness and adaptability of the two cooperative schemes, we formulate a long-term average energy-efficient utility problem, which allocate the cluster heads, cooperative time and transmission power simultaneously and adaptively. We then propose an Online Convex Approximation (OCA) algorithm that combines the Lyapunov and convex approximation methods to address the non-convex Mixed Integer NonLinear Programming (MINLP) property of the modeled problem. With OCA, we can convert the long-term non-convex MINLP problem into a real-time convex MINLP problem, and obtain an optimal solution for this problem. Results reveal that the achieved energy efficiency of two proposed schemes is at least 10 times higher than that of no cooperation method, and improves at least 50% and up to 4 times compared to the single-slot cooperative algorithms.

Traditional laboratory methods for measuring soil heavy metal pollution are slow and costly. Hyperspectral remote sensing provides a more efficient alternative, but current prediction models often rely on manual parameter adjustment and lack clear interpretability. Although various optimization algorithms have been adopted in previous studies, few investigations have systematically compared their performance across different models and spectral preprocessing scenarios. This study aims to establish a reliable and interpretable framework for estimating soil cadmium (Cd) concentrations. A total of 113 soil samples were collected from an industrial-agricultural mixed region in Deyang, Sichuan, China. Forty-eight combinations involving four spectral preprocessing methods, three prediction models, and four hyperparameter optimization algorithms were evaluated. The Multi-Layer Perceptron (MLP) model optimized by the Grey Wolf Optimizer (GWO) was determined as the optimal combination. The SHapley Additive exPlanations (SHAP) approach was used to quantify the contribution of individual spectral bands and identify key wavelengths for Cd prediction. Results indicate that the combination of first-order derivative (FD) preprocessing and GWO-optimized MLP yields the best performance, with a coefficient of determination (R2) of 0.862. The 536.9 nm wavelength was recognized as the most influential band for Cd estimation. This study improves model accuracy and interpretability by constructing a complete workflow including spectral preprocessing, feature selection, hyperparameter optimization, and model explanation. The systematic comparison of optimization strategies provides practical references for the selection of appropriate algorithms in soil heavy metal hyperspectral inversion, supporting efficient soil environmental monitoring and management.

Wireless communication in cavity environment faces significant challenges in channel estimation due to complex fading characteristics and multipath effects unique to these environments. The $\kappa-\mu$ distribution is commonly used to model such fading, but accurate parameter estimation remains a challenge, especially under resource constraints, due to the complexity of the fading process. In this paper, we propose a novel energyefficient channel parameter estimation framework that integrates adaptive pilot design. The framework optimizes power allocation to enhance energy efficiency while maintaining high estimation accuracy. Specifically, the approach employs nonuniform pilot scheduling based on cavity perception, combined with a deep learning architecture that captures complex spatiotemporal correlations. This reduces energy consumption while ensuring precise estimation. Extensive simulations demonstrate that, compared to traditional methods and uniformly spaced pilots, the proposed approach improves estimation accuracy by approximately 30 % and reduces energy consumption by about 20 %. These results validate the effectiveness of the proposed framework in energy-constrained wireless communication systems.

Improving the downlink throughput of multi-UAV air-to-ground mmWave systems under rapid motion and channel uncertainty is a critical challenge. In this letter, UAV states are estimated and the resulting position uncertainty is propagated to the channel statistics through closed-form Jacobians. We propose an alternating optimization mechanism, where a surrogate sum-rate metric is used for inner-layer precoder design and a Riemannian trust-region solver is employed for outer-layer BS reconfiguration. Simulation results demonstrate downlink sum-rate improvements over non-optimized and Euclidean optimization baselines in dynamic multi-UAV mmWave deployments.

Utilizing unmanned aerial vehicles (UAVs) for flexible and efficient mobile data dissemination offers a promising solution for infrastructure-less internet of things applications. This paper investigates a UAV-enabled data dissemination system in which a multiantenna UAV is deployed to disseminate data to ground terminals (GTs). Under the constraints of UAV speed, communication outage probability (OP), UAV transmission power, and task data size, we aim to minimize the system mission completion time (MCT) by optimizing the UAV trajectory, UAV-GT transmission scheduling, and bandwidth allocation, while considering the imperfect channel state information (CSI). To address this challenging problem, we introduce matched-filter precoding and orthogonal frequency division multiplexing (OFDM) techniques and propose a two-stage optimization algorithm. Initially, an offline optimization algorithm is developed to determine the optimum UAV trajectory and the MCT. Subsequently, an online optimization algorithm is proposed to enhance the subchannel and transmission power allocation. Finally, the numerical results confirm the effectiveness of the proposed scheme, demonstrating a substantial improvement in the time efficiency of UAV data dissemination.

Traditional assessment methods are inadequate for accurately tracing the sources of soil heavy metal pollution and implementing targeted risk management. To establish a scientific and efficient system for accurate soil pollution control, this study integrates the Positive Matrix Factorization (PMF) model with spatial autocorrelation models to analyze pollution sources and spatial differentiation patterns of heavy metals in soil. Based on source-oriented approaches, it further conducts human health risk assessments to precisely identify priority pollutants and high-risk exposed populations. In this study, the average concentrations of Cd and Pb reached 0.33 and 140.45 mg·kg−1, respectively, with an over-limit rate (Exceeding the background value of soil in Sichuan Province) of more than 80

The electromagnetic effective degrees of freedom (EM EDOF) provide an insightful measure for characterizing the performance of multiple-input-multiple-output (MIMO) wireless communication systems. The dyadic Green’s function (DGF) is widely used as a benchmark tool for modeling ideal EM propagation due to its analytically tractable closed-form solution. This paper proposes a scatterer-parameterized EM channel model based on the DGF to characterize the EM EDOF in MIMO systems. The proposed EM model is first validated by comparison with results obtained using the method of moments (MoM). Then, the impact of distinct polarizations on the EM EDOF is investigated, highlighting the advantage of full polarization over transverse polarization in a scattering environment. Finally, the EM EDOF is analyzed through a sensitivity analysis of EM model parameters and further evaluated under varying scatterer-antenna distances. Numerical results show that near-field spatial modulation induced by scatterers increases the EM EDOF, with the outer scatterer regions contributing more prominently, while the effect weakens and converges to the free-space value in the far field. The proposed model offers an efficient tool for analyzing and optimizing MIMO capacity and spatial DOF.

Some suburbs urgently need to investigate soil heavy metal contamination to ensure a clean environment. Compared to traditional monitoring methods, XRF and VIS-NIR spectroscopy offer advantages such as rapid, non-destructive, cost-effective, and environmentally friendly analysis. In this study, we developed multiple estimation models for Cd and As content, evaluated the impact of different spectral preprocessing methods on model accuracy, and analyzed the distribution characteristics and for the feature wavelengths selected by competitive adaptive reweighted sampling (CARS). We compared the accuracy of estimation models constructed using partial least squares regression (PLSR) and backpropagation neural networks (BPNN), and elaborated on the advantages of spectral concatenation (SC), outer product analysis (OPA), and Granger-Ramanathan averaging (GRA) fusion strategies. The results demonstrated that among single-spectrum estimation models, the highest accuracy was achieved using XRF and VIS-NIR transformed by second derivative (SD) preprocessing. XRF spectra exhibited a larger number of feature wavelengths with uniform distribution, while VIS-NIR feature wavelengths were concentrated in the 450-1000 nm range. PLSR models outperformed BPNN models in terms of accuracy. Among fused-spectrum estimation models, the accuracy ranking was OPA > SC > GRA, with the OPA model combined with Pearson correlation coefficient (PCC) dimensionality reduction achieving the highest accuracy (R2 = 0.9920, RPD = 11.2020 for Cd estimation; R2 = 0.9852, RPD = 8.2134 for As estimation). These findings establish a technical framework for estimating soil heavy metal content based on XRF and VIS-NIR spectroscopy, and offer a novel monitoring approach for agricultural soils in industrial-urban-rural transition zones.

Scale effects and evaluation models are crucial to the accuracy of landscape ecological risk evaluation. However, most studies conduct these evaluations at a single scale or with a single model, ignoring potential scale effects and changes in landscape patterns. To address this, we took the Leshan City in Sichuan Province of China as a study case. We determined that the optimal spatial granularity for the study area is 150 m by analyzing the sensitivities of eight landscape pattern indices such as landscape fragmentation, landscape spreading, and Shannon’s diversity at different spatial granularities, and employing the inflection point identification method. Building on this, we constructed a landscape pattern index model (ERI model) and a landscape pattern index model coupled with the ecological process of soil erosion (SI-ERI model) by incorporating the natural geographic factors of the study area. We used the ERI and SI-ERI models to evaluate the landscape ecological risk of Leshan City across multiple scales, including ecological, administrative, and sample scales. After conducting overlay and spatial autocorrelation analyses of the multi-scale evaluation results, we determined that the administrative scale is optimal for evaluating landscape ecological risk in the study area. At this scale, we verified the accuracy and reliability of the two models’ evaluation results against the actual ecological environment in typical areas within the study area. The findings indicated that the SI-ERI model provided more precise and accurate spatial characterization, effectively reflecting the actual landscape ecological risk of Leshan City. According to the SI-ERI model’s evaluation results at the administrative scale, Leshan City’s overall risk level is relatively low, with good ecological environmental quality. Low-risk areas constitute 56.16

False Data Injection Attacks (FDIA) have emerged as a critical threat to smart grid security by injecting sophisticated false data, which can lead to severe operational disruptions. Existing statistical analysis-based detection methods face a trade-off between detection accuracy and false positive rate, as they fail to fully capture the spatiotemporal features of power system measurement data. To address this, we propose a FDIA detection method by uti-lizing the properties of Similar Month, Joint Transformation and Discrete Wasserstein Distance (sMJT-dWD). First, the month similarity is calculated through a spatiotemporal rank correlation analysis of multi-node load sequences, which helps eliminate the influence of spatiotemporal variations on power grid dynamics extraction. Next, a joint transformation algorithm enhances the discriminative features of measurement variations by reconstructing the spatial distributions of these variations. Finally, to quantify distributional deviations, we apply discrete wasserstein distance to guarantee robustness of FDIA detection. Extensive experiments on the IEEE 14-bus system validate the effectiveness of the proposed (sMJT-dWD) method. The results indicate that the proposed method attains a 99.99% detection rate for 10% and 5% FDIA, and 99.9% for 1 % FDIA, with a false positive rate below 0.1 %.

This paper introduces a novel prediction method for spatio-temporal non-stationary channels between unmanned aerial vehicles (UAVs) and ground control vehicles, essential for the fast and accurate acquisition of channel state information (CSI) to support UAV applications in ultra-reliable and low-latency communication (URLLC). Specifically, an empirical mode decomposition (EMD)-empowered spatio-temporal attention neural network is proposed, referred to as EMD-STANN. The STANN sub-module within EMD-STANN is designed to capture the spatial correlation and temporal dependence of CSI. Furthermore, the EMD component is employed to handle the non-stationary and nonlinear dynamic characteristics of the UAV-to-ground control vehicle (U2V) channel, thereby enhancing the feature extraction and refinement capabilities of the STANN and improving the accuracy of CSI prediction. Additionally, we conducted a validation of the proposed EMD-STANN model across multiple datasets. The results indicated that EMD-STANN is capable of effectively adapting to diverse channel conditions and accurately predicting channel states. Compared to existing methods, EMD-STANN exhibited superior predictive performance, as indicated by its reduced root mean square error (RMSE) and mean absolute error (MAE) metrics. Specifically, EMD-STANN achieved a reduction of 24.66

To improve management and refined control of soil contamination, the positive matrix factorization (PMF) model was integrated with the global and local Moran's index (Moran's I) to quantitatively identify the sources of heavy metal contamination. Furthermore, an assessment of soil health was conducted, taking into account heavy metal concentrations, soil fertility, and buffering capacity. A new methodology was proposed, integrating pollution source contribution analysis with soil health evaluation to systematically classify and delineate risk zones for soil heavy metal contamination. A total of 209 soil samples were collected from the rice-growing areas in Deyang region. The research findings on heavy metal pollution in this region indicated that Cd and Pb (1.81 mg/kg and 136.11 mg/kg, respectively) are the primary control elements. These values are 4.17 and 4.28 times the background values in the study area, respectively. Five source factors were identified through the PMF model, and combined with the Moran's I, it was indicated that Cd mainly originated from industrial activities (13.06 %), while Pb mainly came from road traffic (accounting for 22.96 %). The results of the soil health index implied that approximately 40 % of the area in the study region exhibits unhealthy soil conditions. The integrated grading and zoning model, incorporating the interaction of pollution source contributions and soil health, demonstrated that roughly 25 % of the study area is at medium-to-high risk. The areas prioritized for control are predominantly distributed within a circular zone with a radius of 750 m, centered around the industrial enterprises along the Shiting River. The findings present a viable strategy for mitigating soil heavy metal contamination at point sources, thereby diminishing total remediation expenses.

Clarifying the source distribution and source-oriented human health risks of heavy metals (HMs) in the soil-dustfall-crop system is beneficial for accurately identifying the priority control factors and restoration areas. In a typical mining area in Southwest China, a total of 284 samples of atmospheric fallout (59), soil (123), and crops (102) were collected, and the concentration values of seven heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were determined. The positive matrix factorization model (PMF) and Moran’s index were employed to identify and allocate the sources spatially, and the human health risks were apportioned via the source-oriented health risk assessment. The pollution of heavy metals (As, Cd, Cu, Pb, and Zn) in the soil is the most serious, with their concentrations being 5 times, 22 times, 6 times, 5 times and 5 times the background values of the study area respectively. The concentration values of heavy metals in crops are higher than those in other regions, posing a potential threat to human health. The main sources of heavy metal pollution in the soil were industrial distribution (33.40

Toxic metal pollution threatens agricultural soil, food safety, and human health. Layered double hydroxides (LDHs) effectively remove heavy metal cations, though cadmium (Cd) and lead (Pb) fixation mechanisms are not fully understood. This study developed a MgFe hydrotalcite and attapulgite clay (ATP/LDHs) composite for remediating Cd and Pb contaminated soil. Optimal preparation parameters were determined by adjusting metal cation types and charge density. ATP/LDHs showed excellent adsorption for Cd2* and Pb2*, achieving 98.35 % for Cd2* and over 99 % for Pb2*. XRD, FTIR, XPS, XRF, and SEM-EDS confirmed that LDHs were loaded on the ATP surface. In aqueous environments, adsorption of Cd2* and Pb2* ions by this material resulted in the formation of precipitates such as Cd(OH)2 and Pbs(COs)2(OH)2, minor quantities of Pbs(COs)2(OH)2 were also present in the soil. Adsorption processes were controlled by chemical adsorption, fitting pseudo-second-order kinetic models and Langmuir, Temkin, and D-R isotherm models. Pot experiments and soil column leaching experiments further validated ATP/LDHs' remediation effectiveness and long-term stability in real soils, significantly reducing the bioavailability of Cd and Pb and their accumulation in plants, effectively immobilizing Cd and Pb even under simulated acid rain conditions. This study provides a green, efficient novel remediation material and practical strategy for soil contaminated with Cd and Pb.

The acquisition of real-time and accurate channel state information (CSI) is the foundation for achieving ultra-reliable and low-latency communication (URLLC). However, in high-speed mobile application scenarios, the wireless channel between the transmitter and receiver exhibits rapid changes and non-stationary characteristics. This makes obtaining CSI increasingly difficult, and conventional data-driven prediction models are difficult to adapt. Therefore, this paper proposes a new model-driven prediction framework to tackle the challenge. Specifically, we propose a new prediction framework based on Bayesian optimisation and long short-term memory (LSTM). This method continuously acquires and updates CSI data in real time, evaluates prediction accuracy, and optimises the model based on these evaluations. This iterative process allows the model to adapt to changes, leading to more accurate CSI predictions. To demonstrate the effectiveness of our forecasting framework, we designed a UAV operational context and conducted experiments on the UAV control channels. In comparing various data-driven models, including LSTM, gated recurrent unit (GRU), TCN, and recurrent neural network (RNN), the simulation outcomes revealed that our approach performed better on dynamic non-stationary UAV control channels. Our method, evaluated by mean squared error (MSE), shows superior performance over other prediction methods, reducing errors by 3.86% on average.

With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy metal concentrations and assessment of pollution risks are urgent tasks. This study selected the affected area of a non-ferrous metal smelting slag yard in Gejiu City as the research subject. Farmland soil samples were collected from this area, and the basic physicochemical properties of the soil, along with the contents of Pb and Cd, were analyzed. Models for quantitatively inverting Pb and Cd contents based on soil physicochemical parameters were established using multiple linear regression (MLR), backpropagation neural network (BPNN), and genetic algorithm-optimized BPNN (GA-BPNN). Among the three inversion models, the GA-BPNN model demonstrated the highest accuracy, with inversion precision R2 reaching 0.8980 and 0.9013 for Pb and Cd, respectively; the corresponding root mean square error (RMSE) values were 0.0001868 and 0.0001821. The inversion model established a nonlinear relationship between heavy metals and basic soil physicochemical properties, enabling further heavy metal pollution assessment based on the inversion results. This method provides a novel approach for inverting heavy metal content in soil and holds practical value for heavy metal remediation.

This paper explores high-precision joint parameter estimation for kappa-mu fading channels using a deep learning approach. The kappa-mu model provides a flexible framework for characterizing composite channel dynamics across diverse environmental scales, making it particularly suitable for small-scale metal-enclosed wireless sensor networks (WSNs) that exemplify the fine-grained connectivity of 6G ubiquitous IoT. To enable accurate performance assessment and robust transmission design, we propose a deep learning framework based on a multilayer perceptron (MLP) for precise kappa and mu estimation. This approach enables fast online inference with strong noise resilience, mitigating the drawbacks of traditional moment-based estimators and the computational burden of maximum likelihood estimation (MLE). Although deep learning-based estimators inherently introduce bias, they effectively trade off bias for variance reduction, achieving superior performance that, in some cases, surpasses the Cramer-Rao Lower Bound (CRLB) for unbiased estimators. These advancements improve transmission reliability and facilitate the efficient deployment of WSNs in highly confined 6G critical IoT environments.

Unmanned Aerial Vehicle (UAV) swarm applications, which are highly dependent on location information for task execution, encounter significant limitations when GNSS signal is denied under some harsh scenarios, such as military conflict environment. In such scenarios, unknown location information will lead to the failure of path planning, significantly impact task efficiency, and pose substantial challenges to location-based communication technologies. To address the above limitations, this paper proposes a Hierarchical Clustered Relative Localization Algorithm (DH-RLA). Based on the topological structure of nodes, the algorithm hierarchically partitions UAV nodes operating in GNSS-denied environments and adopts Multilateration Localization Algorithm (MLA) and Multi-Dimensional Scaling (MDS)-MAP algorithm for outer and inner layer localization, respectively. To ensure the accuracy and increase the speed of localization, a connectivity-enhanced anchor-assisted clustering algorithm is introduced to cluster the inner-layer nodes, enabling parallel execution of the corresponding localization algorithm within each cluster. Simulation results demonstrate that the proposed DH-RLA improves localization accuracy by 44% and 22.22% compared to MLA and MDS-MAP algorithm.

Multipath routing with its complementary heterogeneous characteristics and data backup transmission mechanism, can provide high-reliable transmission services for Unmanned aerial vehicle Ad hoc Network (UANET) in low-altitude economies. However, the application of multipath routing in UANET is currently hindered by multipath coupling, which undermines transmission reliability. To address this problem, the reliability of multipath transmission is set as the optimization objective, aiming to improve the reliability of a single path while simultaneously reducing the coupling between paths. To solve the objective, a fast-converging, reliability-enhancing multipath routing method named as SAOMDV is proposed. The method is based on the heuristic algorithm (MPGA, Multipath Genetic Algorithm), iteratively adjusting the relay node allocation strategy to identify path construction solutions with high reliability performance. Meanwhile, Viterbi algorithm is introduced to predict the optimal direction for the MPGA iteration, thereby enhancing the convergence speed of the method. Experimental results show that the proposed method achieves an average data transmission reliability of over 99.45%, with a 35%-54% improvement in convergence performance compared to GA-AOMDV, MPGA-AOMDV, and Color-Tree methods.

This study examines attapulgite-bentonite-pyrite (AB7Fe) and zeolite-bentonite-pyrite (ZB7Fe) mixtures as buffer and backfill materials for nuclide migration using soil column experiments and COMSOL modeling. The results indicated that ZB7Fe possessed higher adsorption capacities for cesium (Cs) and strontium (Sr) compared to AB7Fe. Conversely, AB7Fe demonstrated superior retardation performance, more effectively slowing nuclide migration. After 1000 years, Cs and Sr concentrations in AB7Fe surpassed those in ZB7Fe. Additionally, nuclide migration increased with higher initiation pressure, dispersion, and diffusion coefficients. This research is crucial for improving the long-term safety of nuclear waste repositories.