Search Results (99)

Time domain reflectometry (TDR) has been validated for monitoring water level evolution and riverbed scouring in the laboratory. Previous studies have also validated the feasibility of field-based single hydrological parameter monitoring using TDR. However, the current research focuses on developing separated TDR sensing systems, and integrated measurements of multiple hydrological parameters from a single reflected waveform have not been reported. This study presents an improved helical probe sensor specifically designed for implementation in geologically hard soils, together with an improved data interpreting methodology to simultaneously determine water surface level, bed elevation, and suspended sediment concentration from a single reflection signal. Experimental comparisons were conducted in the laboratory to evaluate the measuring performance between the traditional dual-needle probe and the novel spiral probe under the same scouring conditions. The experiments confirmed the reliability and superior performance of spiral probe in accurately capturing multiple hydrological parameters. The measurement errors for the spiral probe across multiple hydrological parameters were all within ±10%, and the accuracy further improved with increased probe embedding depth in the sand medium. Across all tested parameters, the spiral probe showed enhanced measurement precision with a particularly significant improvement in suspended sediment concentration detection. Full article

This paper introduces a novel end-to-end fault diagnosis framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Time-Domain Reflectometry (TDR) for the detection, characterization, and localization of wiring faults. The method is designed to operate directly on TDR signals, requiring no manual feature extraction or preprocessing. A forward model is used to simulate TDR responses across various fault scenarios and topologies, serving as the basis for supervised learning. The proposed BiLSTM-based model is trained and validated on common wiring network topologies, demonstrating high diagnostic performance. Experimental results show a diagnostic accuracy of 98.97% and a macro-average sensitivity exceeding 98%, outperforming conventional machine learning techniques. In addition to technical performance, the proposed approach supports sustainable and predictive maintenance strategies by reducing manual inspection efforts and enabling real-time automated diagnostics. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article

To further comprehend kinetic processes in the unsaturated zone, a series of soil column experiments was conducted to simulate downward and upward water movement under variable saturation conditions. High-accuracy spatial and temporal measurements were carried out using the time domain reflectometry—TDR—and Ground-Penetrating Radar—GPR—geophysical methods. Several custom spatial TDR sensors were constructed and used alongside point-measuring TDR sensors, which served as reference points for the calibration of the custom spatial waveguides. The experimental results validated the ability of the custom-made spatial sensors, and the TDR technique in general, to capture water movement and soil moisture changes with high precision during varying wetting processes and demonstrated the complementarity, the limitations, and the potential of the GPR method under the same conditions. The study proved that the combination of the aforementioned measuring technologies provides a better understanding of the kinetic processes that occur in variably saturated conditions. Full article

Biochar has garnered considerable attention for its potential to improve soil properties due to its unique characteristics. However, the precise measurement of soil water content using electromagnetic sensors becomes challenging after biochar is incorporated. This study investigated the impact of biochar on soil water content measurement by adding biochar of varying dosages and particle sizes to a typical loess, under both room and subzero temperature conditions by using time domain reflectometry (TDR) and frequency domain reflectometry (FDR) techniques. The results demonstrate that biochar amendment significantly influenced the measurement accuracy of both TDR and FDR. A clear dosage-dependent relationship was observed, with measurement errors exhibiting progressive escalation as biochar addition rates increased. At room temperature, the root mean square error (RMSE) values for loess were remarkably low (TDR: 0.029; FDR: 0.093). In contrast, the 9% coarse-grained biochar-amended soil (BAS-9%C) showed substantially elevated RMSE values (TDR: 0.2006; FDR: 0.1468). Furthermore, comparative analysis revealed that particle size significantly affected measurement precision, with coarse-grained biochar demonstrating more pronounced interference effects than fine-grained biochar at equivalent application rates. At subzero temperatures, BAS-6%C exhibited significantly higher RMSE values (TDR: 0.1753; FDR: 0.2022) compared to BAS-6%F (TDR: 0.079; FDR: 0.1872). A dielectric mixing model was established for calculating the dielectric constant of BAS. In addition, calibration equations for accurately determining the water content of biochar-amended loess under both room and subzero temperature conditions were established. Furthermore, the mechanisms by which biochar influenced the performance of the TDR and FDR sensors are comprehensively discussed. These findings can provide valuable theoretical foundation and practical guidance for future soil improvement with biochar and accurate water content measurement in BAS. Full article

Due to increasing human engineering activities in cold regions, the precise measurement of frozen soil’s physical property parameters has become particularly important. Traditional measurements of thermal conductivity and unfrozen water content of frozen soil are usually tested separately, leading to errors in accurately understanding the dynamic variation law of permafrost’s hydrothermal parameters in the near-phase transition zone. To address this, a multi-sensor fusion technology–thermo time domain reflectometry (thermo-TDR) sensor was designed and optimized for measuring the unfrozen water content and thermal conductivity of frozen soil. Three-dimensional thermal and electromagnetic numerical models were developed to analyze and validate the design parameters of the proposed sensor. Furthermore, a corresponding validation experiment was carried out to confirm the usability and accuracy of the designed sensor. The results show that (1) under the optimized probe parameters, the deviation between the theoretical thermal conductivity and the numerical preset value is 2.94%, verifying the accuracy of the sensor in thermal physical testing. (2) With a 10 mm probe spacing design, the test area of the thermo-TDR significantly increased, and the skin effect coefficient reached 25.54%, satisfying the electromagnetic design requirements of the TDR method. (3) The designed thermo-TDR sensor realizes the simultaneous measurement of unfrozen water and thermal conductivity of frozen soil, and the experimental results present a good consistency with that of the nuclear magnetic resonance (NMR) and transient planar heat source methods. (4) Additionally, due to the drastic changes in the soil’s physical properties due to the probe’s heating process, testing errors of the thermo-TDR sensor will significantly increase in the near-phase transition range, especially in the range of −2~−1 °C. Full article

Coal mining disrupts soil structure and causes water loss, thereby affecting the ecological environment of mining areas. Rapid, accurate, and non-destructive detection of surface soil moisture is crucial for advancing ecological restoration in these regions. This study focuses on the mined and unmined areas of the Yushuquan coal mine, located on the southern slope of the Tianshan Mountains in Xinjiang, China. The soil volumetric water content (SVWC) was measured using time-domain reflectometry (TDR), while the shallow subsurface soil was investigated using ground-penetrating radar (GPR). Various features were extracted from GPR signals in both the time- and frequency-domains, and their relationships with SVWC were analyzed. Multiple features were selected and optimized to determine the optimal feature combination for building a multi-feature backpropagation neural network model for soil volumetric water content prediction (Muti-BP-SVWC). The performance of this model was compared with two single-feature-based methods for SVWC prediction: the average envelope amplitude (AEA) method and the frequency shift method. The application results of the Muti-BP-SVWC model in different regions demonstrated significant improvements in accuracy and stability compared to the AEA method and the frequency shift method. In the mined area validation set, the model achieved an determination coefficient (R²) of 0.77 and the root mean square error (RMSE) of 0.0091 cm³/cm³, while in the unmined area validation set, the R² of 0.84 and an RMSE of 0.0059 cm³/cm³. These results indicate that incorporating multiple features into the BP neural network can better capture the complex relationship between GPR signals and SVWC. This approach effectively inverts the shallow subsurface soil moisture in mining areas and provides valuable guidance for ecological restoration in these regions. Full article

The identification and resolution of faults, along with the proactive maintenance of wiring networks, are essential for ensuring the reliable, safe, and energy-efficient operation of industrial systems. Research in this domain advances fault detection and prevention, thereby enhancing overall safety, reliability, efficiency, and cost-effectiveness. Time-domain reflectometry (TDR) responses are extensively utilized for this purpose; however, their inherent nonlinearity and complexity pose significant challenges in interpretation. We propose an innovative solution to this problem that is aimed at diagnosing the state of the wiring network: integrating TDR responses with twin support vector machines (TWSVMs) by utilizing kernel functions. The effectiveness and feasibility of the TDR and TWSVM-based fault diagnosis methodology are substantiated through its application to two prevalent wiring network configurations, demonstrating superior performance compared to other fault diagnosis techniques. Full article

Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain reflectometry (TDR) technique is a geophysical method that allows, in a time-varying electric field, the determination of dielectric permittivity and electrical conductivity for a wide class of porous materials. Measuring the volumetric water content in soils is the most frequent application of TDR in soil science and soil hydrology. TDR has grown in popularity over the last 40 years because it is a practical and non-destructive technique that provides laboratory and field-scale measurements. However, a significant limitation of this technique is the relatively high cost of TDR devices, despite the availability of a range of commercial systems with varying prices. This paper aimed to design and implement a low-cost, compact TDR device tailored for classical hydrological applications. A series of laboratory experiments were carried out on soils of different textures to calibrate and validate the proposed measuring system. The results show that the device can be used to obtain predictions for monitoring soil water status with acceptable accuracy (R² = 0.95). Full article

Slope failures caused by changes in soil moisture content have become a growing global concern, resulting in significant loss of life and economic damage. To ensure the stability of slopes, it is necessary to accurately monitor the moisture content and understand the complex interactions between soil, water, and slope behavior. This paper provides a comprehensive overview of advanced soil moisture detection techniques for unsaturated soil slopes, including point-scale measurements and geophysical methods. It first introduces the fundamental concepts of the soil–water characteristic curve (SWCC) and its influence on the shear strength and stability of unsaturated soil slopes. It then delves into the working principles and applications of various point-scale measurement techniques, such as time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and neutron probe methods. Additionally, this paper explores the use of geophysiDear Editor: The author has checked that the name and affiliation are accuratecal methods, including ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and electromagnetic induction (EMI), for the non-invasive assessment of soil moisture conditions and slope stability monitoring. This review highlights the advantages of integrating multiple geophysical techniques, combined with traditional geotechnical and hydrological measurements, to obtain a more comprehensive understanding of the subsurface conditions and their influence on slope stability. Several case studies are presented to demonstrate the successful application of this integrated approach in various slope monitoring scenarios. The continued advancement in these areas will contribute to the development of more accurate, reliable, and widely adopted solutions for the assessment and management of slope stability risks. Full article

This study explores the application of materials used in 3D printing to manufacture the housings of non-invasive sensors employed in measurements using a TDR (Time Domain Reflectometry) meter. The research investigates whether sensors designed with 3D printing technology can serve as viable alternatives to conventional invasive and non-invasive sensors. This study focuses on innovative approaches to designing humidity sensors, utilizing Fused Deposition Modeling (FDM) technology to create housings for non-invasive sensors compatible with TDR devices. The paper discusses the use of 3D modeling technology in sensor design, with particular emphasis on materials used in 3D printing, notably polylactic acid (PLA). Environmental factors, such as moisture in building materials, are characterized, and the need for dedicated sensor designs is highlighted. The software utilized in the 3D modeling and printing processes is also described. The Materials and Methods Section provides a detailed account of the construction process for the non-invasive sensor housing and the preparation for moisture measurement in silicate materials using the designed sensor. A prototype sensor was successfully fabricated through 3D printing. Using the designed sensor, measurements were conducted on silicate samples soaked in aqueous solutions with water absorption levels ranging from 0% to 10%. Experimental validation involved testing silicate samples with the prototype sensor to evaluate its effectiveness. The electrical permittivity of the material was calculated, and the root-mean-square error (RMSE) was determined using classical computational methods and machine learning techniques. The RMSE obtained using the classical method was 0.70. The results obtained were further analyzed using machine learning models, including Gaussian Process Regression (GPR) and Support Vector Machine (SVM). The GPR model achieved an RMSE of 0.15, while the SVM model yielded an RMSE of 0.25. These findings confirm the sensor’s effectiveness and its potential for further research and practical applications. Full article

The real-time monitoring of plant water status is an important issue for digital irrigation to increase water productivity. This work focused on a comparison of three biosensors that continuously evaluate plant water status: trunk microtensiometers (MTs), trunk time-domain reflectometry (TDR), and LVDT sensors. During the summer and autumn seasons (DOY 150–300), nectarine trees were subjected to four different consecutive irrigation periods based on the soil Management Allowed Deficit (MAD) concept, namely: MAD₁₀ (light deficit); MAD₅₀ (moderate deficit); MAD₁₀₀ (severe deficit), and MAD₀ (full irrigation). Measurements of stem water potential (Ψ_stem) and leaf gas exchange were recorded on representative days. A continuous measurement of the plant water status of Ψ_trunk, MDS, and K_trunk revealed the water deficits imposed on the soil. The highest water deficit observed at the end of the MAD₁₀₀ period (Ψ_stem = −2.04 MPa and Ɵ_v = 17%) resulted in a minimum value of Ψ_trunk (−1.81 MPa). The maximum value of MDS (408 µm) was observed earlier than that of Ψ_trunk, motivated by the low sensitivity of MDS at Ψ_trunk < −1.2 MPa and Ψ_stem < −1.5 MPa due to a decrease in the tissue elasticity of the trunk when severe water deficit conditions are reached. Both Ψ_trunk and Ψ_stem were more dependent on soil water content, while MDS was more responsive to environmental changes. K_trunk was the weakest indicator for determining plant water status, although when expressed as a daily fraction of depletion (K_trunkFD), it improved, evidencing a process of hysteresis. Ψ_trunk showed the highest sensitivity, suggesting the potential use of MTs as a valuable biosensor for monitoring nectarine water status in digital agrosystems. Full article

This research review article provides a detailed examination of SMA (SubMiniature version A) connectors, which are integral components in high-frequency electronic systems. Through extensive S-parameter and time-domain reflectometry (TDR) measurements conducted on various SMA connector constructions, this study aims to evaluate the performance and impact of SMA connectors on signal integrity. Results reveal insights into the comparative performance of different SMA connector types mounted on PCB land pads, highlighting their strengths and limitations. Additionally, this paper explores the application of reference plane cut-outs for discontinuity impedance compensation, aiming to enhance the frequency response of SMA connectors. By linking measured performance parameters with relative market prices, this study offers valuable insights into the economic viability of different SMA connector types. The best and worst performing SMA connector measurements reveal an S₁₁ < −10 dB bandwidth of more than 8 GHz and 1.5 GHz and a transition impedance of 46.5 Ω and 21 Ω, respectively. Overall, this research contributes to advancing the understanding and selection of SMA connectors for RF applications in telecommunications, aerospace, medical devices, and beyond. Full article

Irrigation efficiency is important for the environment and the economy. SIMDualKc has been effectively used to calculate evaporation and transpiration separately in vineyards for different irrigation strategies, but not to analyze the impact of irrigation frequency. This study was conducted for the in-field adjustment of the soil wetted fraction (wf) with a time domain reflectometry (TDR) sensor as a function of different irrigation frequency treatments (T03, T07, and T15 with irrigation every 3, 7, and 15 days, respectively). Evaporation and transpiration values were estimated separately with the SIMDualKc model for different irrigation frequencies with the adjusted wf, comparing them with vineyard field measurements and analyzing the effect of different irrigation frequencies on vineyard yield. The wf in T15 was higher than that in T07, which in turn was higher than that in T03. SIMDualKc indicates the most unfavorable treatment to be the T07, with higher evaporation and lower transpiration than T15 and T03, while T15 had higher evaporation and lower transpiration than T03. Although stomatic conductance also indicates the most unfavorable case to be T07, no clear differences were found between T03 and T15. No statistically significant differences were obtained in yield. The results indicate that the differences observed between irrigation frequencies are not reflected in yield but do show up in water use efficiency. Full article

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N₀ parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (f_b). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m⁻² (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community. Full article