Search Results (242)

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

This study investigates wavelet-based compression of smart-metering data transmitted through a software-defined radio chain implemented in LabVIEW with QPSK modulation and USRP platforms. The objective is to reduce the transmitted payload while preserving the fidelity of the reconstructed electrical load profile. The work combines a mathematical formulation of the DWT-based compression and reconstruction process, a controlled scenario evaluation, and an experimental validation on an SDR testbed. The scenario analysis shows that the compression–reconstruction trade-off is best achieved in an intermediate operating region, where excessive coefficient removal increases reconstruction error despite higher nominal reduction. In the laboratory SDR campaign, Haar wavelet order 1 at the LabVIEW coefficient-retention setting 59 was selected as the most balanced representative configuration, achieving a 60.2% unit-based compression ratio, 10.61% relative error, $\mathrm{RMSE}=31.86$ and $\mathrm{SNR}=16.98\mathrm{dB}$. This selection refers to the physical SDR implementation and should not be confused with the public-dataset validation, where bior4.4 level 8 with 40% retained coefficients provided the best offline compression–reconstruction trade-off. Under the tested USRP/LabVIEW configuration, the 5 GHz setup showed shorter channel occupation time than the 915 MHz setup, with lower measured coverage in the same laboratory campaign. The additional validation using the public UCI Individual Household Electric Power Consumption dataset confirmed that DWT compression can preserve load-profile structure under substantial coefficient reduction. Overall, the results indicate that wavelet compression is technically feasible for smart-metering transmission over SDR links when the wavelet family, order, coefficient-retention setting, and radio-link operating conditions are jointly considered. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

Efficient spectrum sensing remains a main challenge for Cognitive Radio Networks (CRNs), especially in a wireless environment where methods like energy detection have high uncertainty. This work proposes an entropy-based spectrum-sensing system enhanced with machine-learning algorithms and implemented on a Software-Defined Radio (SDR) platform for real scenario testing. Entropy measures, such as Shannon and Rényi entropies, are used as discriminative features to distinguish occupied and idle frequency bands and release the channel if needed. Machine learning classifiers have achieved good results. In this research, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and Random Forests (RFs) are used with data captured via a GNU Radio and the Universal Software Radio Peripheral (USRP)-based SDR testbed. The experimental results demonstrate a probability of detection (P_d) above 0.9 and a false alarm rate (P_fa) below 0.1, indicating a substantial improvement over the classical energy detector of more than 20% for some signal-to-noise ratio (SNR) values. The integration of entropy metrics with machine learning (ML) models enables a dynamic detection in variable spectral environments, providing a practical framework for CRNs. Full article

With the intensification of global climate change and rapid urbanization, urban flooding poses an increasing threat to urban safety and sustainable development. Flood susceptibility mapping (FSM) serves as a practical approach for recognizing areas that may be vulnerable to flooding and is therefore essential for flood mitigation and urban planning. In this study, an interpretable ensemble machine-learning framework for urban FSM was developed using social media data. First, the spatial locations of flood events were extracted from social media posts and news reports to construct a flood inventory. Subsequently, a non-flood sample selection strategy, termed Similarity- and Diversity-Based Representative Sampling (SDRS), was proposed to ensure both sample similarity and diversity. Based on these samples, a heterogeneous bagging-based ensemble machine learning model was established for flood susceptibility assessment. To enhance model interpretability, the GeoShapley method was introduced to quantify the contributions of key conditioning factors and reveal their directional effects. The findings indicated that the proposed SDRS strategy delivered the best performance, yielding an AUC of 0.893 and a test-set precision of 0.859. The resulting susceptibility map exhibited a clear south-to-north decreasing gradient, with High- and Very-high-susceptibility zones accounting for approximately 26% of the study area (1897.23 km²). The interpretability analysis further indicated that the Nighttime Light Index (NLI), Impervious Surface Percentage (ISP), and population density were among the most strongly associated positive factors in the model, with a Global Spatial Share of 7.18%. These findings demonstrate that the proposed framework can reliably recognize areas vulnerable to flooding and offer a scientific basis for urban flood management in Guangzhou. Full article

Whether urban park supply effectively responds to residents’ actual use remains a critical issue for public service provision, residents’ health and well-being, and spatial equity in high-density cities. Conventional assessments based on static population data may fail to capture dynamic patterns of human activity, potentially obscuring mismatches between service provision and real demand. This study integrates mobile phone signaling data into a supply–demand assessment framework to evaluate urban park systems from a dynamic population perspective. The framework is applied to Shenzhen as a representative high-density megacity. Park supply is measured by service capacity, coverage, and accessibility, while demand is derived from observed visitation behavior. A Supply–Demand Ratio (SDR) index, combined with Getis-Ord Gi* analysis, is employed to identify spatial patterns of mismatch. The results reveal substantial supply–demand imbalances that are not captured by traditional static indicators, with approximately 30.9% of communities identified as significant cold spots. High-density central areas exhibit a persistent deficit in park services despite relatively high coverage levels, whereas peripheral areas with abundant ecological resources show relative surpluses. These patterns are closely associated with urban functional structure, population mobility, and jobs–housing separation. By uncovering the divergence between nominal accessibility and actual use, this study highlights the limitations of place-based planning approaches and underscores the need for a people-centered perspective. The findings point to the importance of shifting from “opportunity equity” to “outcome equity” in evaluating and improving urban public service provision to foster sustainable urban development. Full article

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments. Full article

OTFS, proposed for next-generation wireless communication systems such as 6G mobile networks, incorporates ISAC into DD-domain multiplexing, enabling simple detection of distance, velocity, and movement direction. This paper presents a SDR implementation of OTFS in an ARoF setup with wireless RF transmission. The main goal of this study is to validate and evaluate the implemented OTFS with static objects and to explore the quality of velocity and direction estimation in sensing scenarios involving moving objects. For the BER measurements, experiments were performed using a static object while varying the SDR transmitter power and introducing additional CFO. Experimental validation shows a minimum BER ≤ 5 × 10−⁷ with 0 errors per 2 × 10⁶ bits. Data transmission at fractional Doppler yielded a BER ≈ 0.09, which is attributed to the use of a LMMSE channel estimator, that is not optimal for channels with fractional Doppler. Estimation of the velocity of a mobile object with an absolute velocity of $\left|v\right|=0.15$ m/s yielded a RMSE = 0.0839 m/s. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

The spatiotemporal evolution of ecosystem services and the elucidation of their driving mechanisms constitute a central scientific issue in territorial spatial optimization and regional sustainable development. Taking Gansu Province, a core area of the ecological security barrier in northwestern China, as the study area, this study integrates land-use, natural geographic, and socioeconomic data from 2000 to 2020. Using a land-use transfer matrix, the InVEST model, the Geographical Detector, and the PLUS model, we constructed a comprehensive analytical framework that combines historical evolution analysis, spatial differentiation identification, and multi-scenario simulation and prediction. The framework was used to systematically reveal the spatiotemporal dynamics of four core ecosystem services, namely carbon storage (CS), water yield (WY), habitat quality (HQ), and soil retention service (SDR), and to analyze their natural and socioeconomic driving mechanisms, while also simulating land-use change and ecosystem-service responses under the natural development, ecological protection, and urban expansion scenarios in 2030. The results show that, from 2000 to 2020, land use in Gansu Province was dominated by grassland (average proportion: 33.34%) and unused land (average proportion: 41.35%). Urban land expanded from 660.52 km² to 2227.36 km², with its share increasing from 0.15% to 0.50%, mainly through the conversion of cropland and grassland. Ecosystem services exhibited marked spatial differentiation: CS increased from east to west; WY showed an increasing pattern from northwest to southeast; HQ was lower in the central and southeastern regions and higher in the western and southern regions; and SDR was dominated by low-value areas in the northwest (average proportion: 84.81%). Driving-mechanism analysis indicated that slope was the core natural factor affecting CS, HQ, and SDR (q = 0.18–0.45), while mean annual precipitation dominated the variation in WY (q = 0.31–0.35). The influence of socioeconomic factors such as GDP increased gradually over time, showing an evolutionary trend from natural dominance to coordinated natural–socioeconomic regulation. Multi-scenario simulation further showed that, under the ecological protection scenario, grassland area increased significantly (+0.60%), the proportions of medium-value CS zones and high-value WY zones increased, and ecosystem services were optimized overall; under the urban expansion scenario, cropland and urban land expanded (+0.87% and +0.23%, respectively), imposing potential pressure on part of the ecosystem-service functions. These findings provide a scientific basis for optimizing territorial spatial planning, strengthening the ecological security barrier, and promoting regional sustainable development in Gansu Province. The methodological framework also offers a broadly applicable reference for ecologically sensitive arid and semi-arid regions in northwestern China. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

The coupling of non-stationary marine noise and complex ship-radiated signals makes high-fidelity signal recovery exceptionally difficult. Existing deep learning methods often prioritize objective metrics, such as the Scale-Invariant Signal-to-Noise Ratio (SI-SNR), but fail to maintain the integrity of narrow-band line spectral data. We propose FocuS-MN, an end-to-end framework that combines learnable resampling with Feedforward Sequential Memory Network (FSMN)-based temporal modeling for precise waveform reconstruction. The model is optimized using a two-stage training strategy to ensure stable magnitude estimation and waveform consistency. On the ShipsEar dataset, FocuS-MN shows strong generalization to unseen vessel types. At a −5 dB Signal-to-Noise Ratio (SNR), it achieves a Signal-to-Distortion Ratio (SDR) of 3.77 dB and a Segmental Signal-to-Noise Ratio (SSNR) of 3.83 dB. Power Spectral Density (PSD) analysis further confirms that FocuS-MN recovers fine-grained line spectral structures, proving its effectiveness in both noise suppression and signal fidelity. Full article

This study systematically analyzes the spatio-temporal evolution, trade-offs, synergies and driving mechanisms of five ecosystem services (ESs) in Fujian Province (carbon storage, CS; habitat quality, HQ; sediment delivery ratio, SDR; water yield, WY; food provision, FP) based on multi-source data from 2003, 2013 and 2023 by adopting the InVEST model, Spearman correlation analysis, geographically weighted regression (GWR), self-organizing maps (SOM) and geographic detectors. Results show that: (1) ESs present a spatial pattern of “high in northwest and low in southeast” in Fujian; CS, HQ and FP show an overall decline, while SDR and WY increase significantly. (2) ES trade-offs and synergies have obvious scale effects and spatial heterogeneity, with stronger relationship intensity at the county level than the grid level, and FP generally shows a trade-off relationship with other services. (3) Land use is the key driving factor for CS, FP and HQ; precipitation dominates the changes in WY and SDR; and dual-factor interactions generally enhance the explanatory power of ES changes. The findings enrich the theoretical system of multi-scale ES trade-off and synergy research under rapid urbanization and provide a scientific basis for sustainable territorial spatial planning and differentiated ecological governance in Fujian. Meanwhile, the research framework can serve as a reference for ES management in other coastal mountainous regions worldwide, contributing to the realization of regional sustainable development goals (SDGs). Full article

Global Navigation Satellite Systems (GNSS) are highly affected in equatorial regions, especially due to the formation of Equatorial Plasma Bubbles (EPBs), which cause disturbances in the ionosphere resulting in different forms of signal degradation. Despite Colombia’s privileged geographic position, its limited monitoring infrastructure hinders the detection and mitigation of these effects. This study proposes the development of a Low-Cost Scintillation Laboratory (LCSL) using a cognitive radio–based approach for real-time scintillation monitoring, aimed at improving GNSS reliability. The system was designed following a Systems Engineering methodology, defining functional architectures and constraints. A communication system model was developed to account for EPBs’ effects on GNSS signals, while cognitive radio algorithms within a Software-Defined Radio (SDR) framework enabled real-time detection, monitoring, and alert generation. To implement this approach, monitoring stations were deployed in Bogotá, Cartagena, and Santa Marta utilized low-cost GNSS receivers integrated with Machine Learning (ML) algorithms for the automatic classification of scintillation events. Additionally, the system’s accuracy was validated by comparing experimental data with historical records from the Geophysical Institute of Peru (IGP). The results demonstrated that the integration of cognitive radio and ML-based detection enhanced precision and adaptability compared to traditional methods. The network of monitoring stations effectively validated the system’s performance, providing valuable insights into equatorial ionospheric dynamics. This study contributes to the advancement of monitoring methodologies and highlights the importance of accessible infrastructure for mitigating EPB effects on GNSS, ultimately fostering more resilient navigation and communication systems. Full article

We propose a unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and jamming (U-ISJC) framework, in which a multifunctional UAV first detects the sensing target to obtain sensing information, and subsequently transmits the information to communication users via a unified beam in the presence of multiple eavesdroppers. To avoid functional conflicts, a time slot frame structure is designed for the UAV’s multifunctional capabilities, enabling communication, sensing, and jamming tasks within each timeslot. The time slot allocation factor dynamically adjusts based on the UAV’s flight trajectory for efficient UAV resource utilization. Additionally, to prevent security rate leakage caused by eavesdroppers, a jamming beam is added to serve both jamming and sensing functions. Our objective is to maximize the the worst-case total secure data transmission rate by jointly optimizing sub-time slot allocation, beamforming, and UAV trajectory. To address this problem, we propose a joint optimization algorithm that adopts the concave–convex procedure (CCCP) technique and semi-definite relaxation (SDR), under the block coordinate descent (BCD) framework. The simulation results show that compared with the baseline scheme, the proposed algorithm substantially improves the communication security rate while ensuring the quality of communication and sensing. Full article