Search Results (43)

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

This study evaluated the pollutant removal efficiency of two decentralized wastewater treatment plants (WWTPs) located in the high-altitude southern Andes of Ecuador, Acchayacu and Churuguzo, from 2015 to 2024. Acchayacu previously operated using an upflow anaerobic filter (UAF), and from 2021, it transitioned to using vertical-subsurface-flow constructed wetlands (VSSF-CWs). In contrast, Churuguzo employs surface-flow constructed wetlands (SF-CWs). These systems were assessed based on parameters such as the five-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), total phosphorus, organic nitrogen, ammonia nitrogen, total solids, fecal coliforms (TTCs), and total coliforms (TCs). The data were divided into two subperiods to account for the change in technology in Acchayacu. Statistical analysis was conducted to determine whether significant differences existed between the treatment efficiencies of these technologies, and the SF-CW was found to consistently outperform both the UAF and VSSF-CW in removing organic matter and microbial pollutants. This difference is likely attributed to the longer hydraulic retention time, lower hydraulic loading rate, and vegetation type. The findings highlight the environmental implications of treatment technology selection in WWTPs, particularly regarding the quality of receiving water bodies and their potential applications for public health, proper water resource management, and the design of decentralized systems in high-altitude regions, especially in developing countries. Full article

Air-coupled ground penetrating radar (GPR) systems are widely used for subsurface imaging in demining, geological surveys, and infrastructure assessment applications. However, strong surface reflections can introduce interference, leading to receiver saturation and reducing the clarity of subsurface features. This paper presents a novel surface reflection suppression algorithm for stepped-frequency continuous wave (SFCW) GPR systems. The proposed method estimates the surface reflection component and applies phase-compensated subtraction at the receiver site, effectively suppressing background reflections. A modular SFCW radar system was developed and tested in a laboratory setup simulating a low-altitude airborne deployment to validate the proposed approach. B-scan and time-domain analyses demonstrate significant suppression of surface reflections, improving the visibility of subsurface targets. Unlike previous static echo cancellation methods, the proposed method performs on-board pre-downconversion removal of surface clutter that compensates for varying ground distance, which is a unique contribution of this work. Full article

Constructed wetlands play a critical role in mitigating aquaculture wastewater pollution. However, the comprehensive treatment performance of aquatic plants and microorganisms under various water treatment processes remains insufficiently understood. Here, a multi-stage surface flow constructed wetland (SFCW) comprising four different aquatic plant species, along with aeration and biofiltration membrane technologies, was investigated to explore the combined effects of aquatic plants and epiphytic biofilms on wastewater removal efficiency across different vegetation periods and treatment processes. The results demonstrated that the total removal efficiency consistently exceeded 60% in both vegetation periods, effectively intercepting a range of pollutants present in aquaculture wastewater. Changes in the vegetation period influenced the performance of the SFCW, with the system’s ability to treat total nitrogen becoming more stable over time. The removal efficiency of the treatment pond planted with submerged plants was highest in July, while the pond planted with emergent plants showed an increased removal rate in November. The aeration pond played a significant role in enhancing dissolved oxygen levels, thereby improving phosphorus removal in July and nitrogen removal in November. Additionally, the α-diversity of epiphytic bacteria in the aeration and biofiltration ponds was significantly higher compared to other ponds. In terms of bacterial composition, the abundance of Firmicutes was notably higher in July, whereas Nitrospirota and Acidobacteriota exhibited a significant increase in November. Furthermore, the functional genes associated with sulfur metabolism, nitrogen fixation, and oxidative phosphorylation displayed significant temporal variations in the aeration pond, highlighting that both growth period changes and treatment processes influence the expression of functional genes within biofilms. Our findings suggest that the integration of water treatment processes in SFCWs enhances the synergistic effects between aquatic plants and microorganisms, helping to mitigate the adverse impacts of vegetation period changes and ensuring stable and efficient wastewater treatment performance. Full article

Software-defined radio (SDR) has high flexibility and low cost. It conforms to the miniaturization, lightweight, and digitization trends in through-wall radar systems. Stepped frequency continuous waveform (SFCW) is commonly used in through-wall radar, which has high resolution and strong anti-interference ability. This article develops an SFCW through-wall radar system based on a dual-channel SDR platform. Without changing hardware structure and complicated accessories, a phase compensation method of solving the phase incoherence problem in a low-cost dual-channel SDR platform is proposed. In addition, this article proposes a wall clutter mitigation approach by means of singular value decomposition (SVD) and principal component analysis (PCA) framework for through-wall applications. This approach can process the wall clutter and noise efficiently, and then extract the target subspace to obtain location information. The experimental results indicate that the proposed windowing-based SVD-PCA approach is effective for the developed radar system, which can ensure the accuracy of through-wall detection. It is also superior to the traditional methods in terms of the image quality of range profiles or signal-to-noise ratio (SNR). Full article

In recent years, numerous studies highlighted the crucial role of the soil–structure interaction (SSI) in the seismic performance of basement structures. However, there remains a limited understanding of how this interaction affects buildings with basement structures under varying site conditions. Based on the three-dimensional (3D) numerical analysis method, the influence of the SSI on the seismic response of high-rise steel frame–core wall (SFCW) structures situated on shallow-box foundations were investigated in this study. To further investigate the effects of the SSI and site conditions, three types of soil profiles—soft, medium, and hard—were considered, along with a fixed-foundation model. The results were compared in terms of the maximum lateral displacement, inter-story drift ratio (IDR), acceleration amplification coefficient, and tensile damage for the SFCW structure under different site conditions, with both fixed-base and shallow-box foundation configurations. The findings highlight that the site conditions significantly affected the seismic performance of the SFCW structure, particularly in the soft soil, which increased the lateral deflection and inter-story drift. Moreover, compared with non-pulse-like ground motion, pulse-like ground motion resulted in a higher acceleration amplification coefficient and greater structural response in the SFCW structure. The RC core wall–basement slab junction was a critical region of stress concentration that exhibited a high sensitivity to the site conditions. Additionally, the maximum IDRs showed a more significant variation at incidence angles between 20 and 30 degrees, with a more pronounced effect at a seismic input intensity of 0.3 g than at 0.2 g. Full article

An enhanced algorithm, YOLOv5s-FCW, is put forward in this study to tackle the problems that exist in the current helmet detection (HD) methods. These issues include having too many parameters, a complex network, and large computation requirements, making it unsuitable for deployment on embedded and other devices. Additionally, existing algorithms struggle with detecting small targets and do not achieve high enough recognition accuracy. Firstly, the YOLOv5s backbone network is replaced by FasterNet for feature extraction (FE), which reduces the number of parameters and computational effort in the network. Secondly, a convolutional block attention module (CBAM) is added to the YOLOv5 model to improve the detection model’s ability to detect small objects such as helmets by increasing its attention to them. Finally, to enhance model convergence, the WIoU_Loss loss function is adopted instead of the GIoU_Loss loss function. As reported by the experimental results, the YOLOv5s-FCW algorithm proposed in this study has improved accuracy by 4.6% compared to the baseline algorithm. The proposed approach not only enhances detection concerning small and obscured targets but also reduces computation for the YOLOv5s model by 20%, thereby decreasing the hardware cost while maintaining a higher average accuracy regarding detection. Full article

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features. Full article

The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda. Full article

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan. Full article

A “sememe” is an indivisible minimal unit of meaning in linguistics. Manually annotating sememes in words requires a significant amount of time, so automated sememe prediction is often used to improve efficiency. Semantic fields serve as crucial mediators connecting the semantics between words. This paper proposes an unsupervised method for sememe prediction based on the common semantics between words and semantic fields. In comparison to methods based on word vectors, this approach demonstrates a superior ability to align the semantics of words and sememes. We construct various types of semantic fields through ChatGPT and design a semantic field selection strategy to adapt to different scenario requirements. Subsequently, following the order of word–sense–sememe, we decompose the process of calculating the semantic sememe similarity between semantic fields and target words. Finally, we select the word with the highest average semantic sememe similarity as the central word of the semantic field, using its semantic primes as the predicted result. On the BabelSememe dataset constructed based on the sememe knowledge base HowNet, the method of semantic field central word (SFCW) achieved the best results for both unstructured and structured sememe prediction tasks, demonstrating the effectiveness of this approach. Additionally, we conducted qualitative and quantitative analyses on the sememe structure of the central word. Full article

This study designed surface flow constructed wetlands (SFCWs) with Myriophyllum aquaticum (M. aquaticum) to evaluate how different influent C/N ratios (0:1 (C₀N), 5:1 (C₅N), 10:1 (C₁₀N), and 15:1 (C₁₅N)) affect pollutant removal, greenhouse gas (GHG) emissions, and microbial communities. The results showed that effluent ammonia nitrogen (${\mathrm{N}\mathrm{H}}_4^{+}$-N), nitrate nitrogen (${\mathrm{N}\mathrm{O}}_3^{-}$-N), and total nitrogen (TN) concentrations decreased, but effluent chemical oxygen demand (COD) concentration increased with increasing influent C/N ratios. The highest removal rates of TN (73.17%) and COD (74.56%) were observed with C₅N. Regarding GHG emissions, a few changes in CO₂ fluxes were caused by the influent C/N ratio, whereas CH₄ fluxes obviously increased with the increasing influent C/N ratio. The highest N₂O emission occurred with C₀N (211.03 ± 44.38 mg-N·m⁻²·h⁻¹), decreasing significantly with higher C/N ratios. High-throughput sequencing revealed that different influent C/N ratios directly influenced the microbial distribution and composition related to CH₄ and N₂O metabolism in SFCWs. The highest abundance (46.24%) of denitrifying bacteria (DNB) was observed with C₅N, which helped to achieve efficient nitrogen removal with a simultaneous reduction in N₂O emissions. Methanogen abundance rose with higher C/N ratios, whereas methanotrophs peaked under C₅N and C₁₀N conditions. Additionally, the random forest model identified influent C/N ratio and Rhodopseudomonas as primary factors influencing CH₄ and N₂O emissions, respectively. This highlights the importance of the influent C/N ratio in regulating both pollutant removal and GHG emissions in constructed wetlands. Full article

Macrophytes have the potential to withstand pollutant-induced stress and can be used to clean contaminated water using phyto-extraction, phyto-degradation, phyto-filtration, phyto-stimulation, and phyto-volatilization technique(s). Phytoremediation through constructed wetlands (CWs) for eliminating inorganic and organic pollutants from household sewage and wastewater has attracted scientific attention. CWs are artificially engineered treatment systems that utilize natural cycles or processes involving soils, wetland vegetation, and plant and soil-associated microbial assemblages to remediate contaminated water and improve its quality. Herein, we present a detailed assessment of contaminant removal effectiveness in different CW systems, i.e., free-water surface or surface-flow constructed wetlands (FWSCWs/SFCWs), subsurface-flow constructed wetlands (SSFCWs), and hybrid constructed wetlands (HCWs). Several wetland floral species have been reported as potential phytoremediators, effectively reducing aquatic contamination through biodegrading, biotransforming, and bioaccumulating contaminants. Water hyacinth (Pontederia crassipes) is one of the most resistant macrophytes, capable of tolerating high nitrate (NO₃⁻) and phosphate (PO₄²⁻) concentrations. Other aquatic weeds also effectively alleviate biological oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), and pathogen levels and ameliorate the impact of different ionic forms of nitrogen (N), phosphorus (P), and trace elements (TEs). The review primarily focuses on using hydrophyte(s)-microbe(s) associations in different CWs as an essential phytoremediation tool for sustainable management of freshwater ecosystems, ecorestoration, and prospective resource recovery, favoring a circular bioeconomy (CBE). Full article