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Low-profile, wideband, and electromagnetic induction (EMI) sensor-compatible antenna for ground penetrating impulse radar applications is an indispensable need for humanitarian demining operations. We present an ultra wideband (UWB), small-size AMC-backed fat bow-tie antenna for such detection systems. Fat bow-tie has longitudinal slots along its main polarization axis so that Eddy currents due to the EMI sensor are minimized on the antenna. Unlike discrete element or tapered reactive/resistive loading, high-impedance surface material is placed inside the cavity to increase the bandwidth and to decrease the ringing caused by the cavity. The proposed antenna has a directional radiation pattern and operates between 700 MHz and 3.2 GHz. [ABSTRACT FROM AUTHOR]

Ultra-wideband antennas that operate at low frequencies usually have a large geometry, which makes them very limited in practical engineering application environment where space is often constrained. In this paper, a novel low-frequency low-profile ultra-wideband magneto-electric (ME) dipole antenna is designed for the application in ground penetrating radar (GPR) to detect deeply buried targets. The antenna is designed at the operation frequency of 230 MHz, so that the objects that are buried as deep as 1.5 m can be detected. Compared to the traditional low-frequency _antennas, it has a profile as low as 740 mm $ \times $ × 140 mm $ \times $ × 150 mm ($0.49\lambda \times 0.09\lambda \times 0.10\lambda $ 0.49 λ × 0.09 λ × 0.10 λ). Based on the simulation analysis and validation measurements, the proposed ME dipole antenna has a wide impedance bandwidth for ${S_{11}} \lt - 10{\rm{dB}}$ S 11 < − 10 dB from 135 MHz to 382 MHz even when environmental influence is considered. The antenna is further validated with a GPR experiment, where a pipeline with a diameter of about 200 mm at 1200 mm beneath the road surface is successfully detected by an impulse GPR system where the proposed antennas are integrated. [ABSTRACT FROM AUTHOR]

This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41 m, while the error of ARMA method in depth range of 0.1-1.0 m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range. [ABSTRACT FROM AUTHOR]

A semi-hydroponic system was developed to assess canola (Brassica napus L.) root architectural traits. Four cultivars were grown under controlled conditions in germination paper rolls immersed in half-strength Hoagland's solution. Eight parameters, including total root length, total root surface area, average root diameter, tip number, total primary root length, total lateral root length, total tertiary root length, and basal link length, were analyzed using the WinRHIZO software after 7, 14, and 21 days. The results suggested that 14 days in the semi-hydroponic system were optimal for accurate root trait assessment, as clear differences were observed while maintaining ease of handling and scanning. [ABSTRACT FROM AUTHOR]

The demand for nondestructive testing techniques (NDTs) that can be implemented continuously and cost-effectively for large-scale study purposes is increasing. Ground penetrating radar (GPR) as an NDT method permits the estimation of pavement materials characteristics without disrupting the serviceability of the system. This research used typical pavement materials for constructing load-bearing layers (base and subbase) for the GPR laboratory tests. A 2 GHz GPR antenna was applied to execute the tests by changing three essential variables of the material: water content, compaction and clay content. Machine learning and interactive methods were innovatively utilised to model the collected data. As a result, a multivariate non-linear empirical function is proposed by the interactive procedure. Furthermore, the machine learning modelling (SVM method) with R2 of 0.91 indicates promising results to evaluate the pavement layers' properties. Machine learning can enhance the speed and accuracy of analysis when faced with multi-variables and extensive data. [ABSTRACT FROM AUTHOR]

Microservice architecture is a new technology for deploying large-scale applications and services in the cloud. But multivariate time series data with anomalies are increasingly generated in the cloud. Effectively diagnosing the runtime system anomalies is necessary to ensure the quality of service of microservice systems. Typical anomaly detection methods are effective in data quality and computing reliability of cloud computing. However, they all focus on one-class anomaly detection, which may not perform on practical microservice frameworks with diverse types of anomalies. Furthermore, locating the root cause of anomalies to eliminate after detection is essential. To address these issues, we propose an effective parallel convolutional anomaly multi-classification model (PCAC) based on an attention mechanism for fault diagnosis in microservice system. We first construct a parallel convolutional structure that allows subnetworks to extract features independently. Then, channel and spatial attention mechanisms are applied in the parallel convolutional layers to mitigate the loss of feature representation. Finally, causal inference based on the anomalous graph is used to locate the fault in the microservice system. The experimental results clearly show that the proposed model achieves the highest F1 scores on six public microservice datasets, improved by 37.9% in average macro-F1 and 4.4% in average micro-F1 scores respectively, outperforming eight state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Mars' polar ice deposits are thought to preserve a record of climate throughout their evolution. In addition to the large north polar layered deposits (NPLD) at Mars' north pole, smaller ice deposits are preserved in craters nearby. These outlying deposits were potentially formed by the same mechanisms that drive NPLD formation, or may represent more local mechanisms. Distinguishing between these possibilities would help elucidate the spatial homogeneity of Martian climate processes. Here, we analyzed SHARAD radar depth profiles from 34 locations across the NPLD and 5 locations within the Korolev crater ice deposit using Fourier transform analysis and dynamic time warping to quantitatively assess the similarity between the internal layered stratigraphy of the two deposits. We identify broad stratigraphic similarities between the Korolev deposit and the NPLD, suggesting they likely formed due to the same climate forcing mechanism, with local variability also observed across the NPLD. Plain Language Summary: Mars has two large ice caps at its poles, which combined contain a similar volume of ice to Greenland on Earth. Near these large ice caps are craters that are also filled with ice, which may or may not have formed due to the same mechanisms that formed the large neighboring ice cap. Ground penetrating radar observations of ice on Mars can allow us to see layers of ice and dust that are present throughout the interior of these deposits, and represent climate events that have taken place during the deposit's formation and evolution. We analyze radar observations from two deposits near Mars' north pole and quantitatively show that these deposits have a similar pattern of layering. These results could indicate that they have shared a similar climate history, therefore implying the importance of regional‐scale climate processes on Mars in addition to local processes for forming these ice deposits. Key Points: We use Fourier transform analysis and dynamic time warping to assess the similarities between two ice deposits near Mars' north poleWe identified a periodic signal with an average wavelength of ∼45 m in radar observations of the ice mound in Korolev crater and the NPLDWe identify similar broad climate forcing for both Korolev and NPLD ice, with local variability across the NPLD also observed [ABSTRACT FROM AUTHOR]

To address low detection accuracy and speed due to the multisolvability of the ground‐penetrating radar signal, we proposed a novel centralized feature pyramid‐YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra‐layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground‐penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three‐dimensional imaging. Our model is suitable for object detection in ground‐penetrating radar images, thereby providing technical support for road damage and underground pipeline detection. [ABSTRACT FROM AUTHOR]

This work proposes the development of a software module for studying historical and cultural heritage objects using remote and non-invasive research data. The module aims to integrate modern technologies such as image processing, data analysis, and visualization to provide access to heritage information for researchers, conservators, and the general public. Utilizing non-invasive data, such as geophysical surveys, enables the collection of information about heritage objects without causing damage. The module facilitates the analysis and visualization of this data as well as the creation of interactive object models, thereby promoting further research, preservation, and popularization of cultural heritage. The module consists of three blocks: defining areas for ground-based research using interferograms; constructing a comprehensive 3D model based on ground and underground research data; and refining the boundaries of historical-cultural heritage objects and establishing protective zones. The program is developed in the object-oriented programming language VisualBasic with additional modules. This developed module could become a significant tool for studying and preserving historical and cultural objects in the modern world. [ABSTRACT FROM AUTHOR]

Aiming at the requirement of radar wave absorption in automotive radar tests for both the 24 GHz and 77 GHz vehicle radar bands, two ultra-thin dual-band circuit analog absorber designs based on frequency-selective conductive film have been proposed. They are obtained by first optimising the equivalent circuit parameters to achieve the desired reflection response, then determining the physical parameters (geometrical size and square resistance) of unit cells from the equivalent circuit parameters, and finally fine-tuning these physical parameters. Both designs employ unit cell based on the nested structure of conductive square ring and a conductive square patch, which has the advantages of simple structure, ease of processing, high design freedom, and good polarisation and angular stability. The 1st design employs different square resistance values for the conductive ring and patch, while the 2nd design employs a single square resistance value for both conductive shapes. Simulation results show that, for each of the two designs, the lower and upper 70% absorption or −10 dB reflection bands simultaneously cover the 24 GHz and 77 GHz vehicle radar bands, respectively, with good angular stability. The dual-band coverage keeps stable until the elevation incident angle reaches 20° and 50° for the 1st and 2nd designs, respectively. A realistic sample is processed regarding the 2nd design and its dual-band absorption performance has been verified by experimental results, which have good agreement with the simulated counterparts. [ABSTRACT FROM AUTHOR]

Large rivers are characterized by large water discharges, high suspended sediment fluxes and low slope, and typically display multiple channels that are separated by large complex bars. The most common channel style found in these rivers is characterized by the alternating presence of single and multiple channel threads. Mid‐channel compound bars separate a main deeper channel from shallower secondary channels, leading to low‐order braiding and low to moderate sinuosity. Despite the importance of this type of river for global terrestrial sediment transport and the interpretation of ancient fluvial successions, integrated depositional models for large multi‐channel rivers are still not fully developed. This paper interprets the channel morphodynamics and depositional architecture of such large rivers by investigating the distinctive features of their laterally‐accreting mid‐channel bars in the Solimões–Amazonas River. This is achieved by examination of temporal series of satellite images, quantification of bedforms using multibeam echosounding surveys, analysis of shallow seismic and ground penetrating radar surveys of selected areas, and on‐site field observations and sample collections. Such mid‐channel bars produce directional variability in planform scroll bar accretion of up to 180°. As these bars – and their associated channels – evolve, they become progressively shallower, due to the bifurcation of the two channels and the resultant partitioning of discharge that leads to a gradual reduction in transport capacity within the outer channel. The preserved successions of this process in the alluvial plain are characterized by fining‐upward trends, with larger cross‐strata sets at mid‐depths and internal erosional surfaces marking distinct cycles of bar development with potentially differing palaeocurrent trends. The new depositional models proposed for these mid‐channel bars and channels constitute a basis for the recognition of large multi‐channel river deposits in the ancient rock record. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) is a high-resolution technique to estimate the parameters of dielectric permittivity (ϵ) and electrical conductivity (σ) and identify the structure of the subsurface for Ground Penetrating Radar (GPR) application. However, permittivity and conductivity parameters can be coupled in bi-parameter GPR inversion. This coupling effect leads to the crosstalk in the FWI result. To solve this problem, we propose a novel approach to use the multi-scale FWI with the hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers that simultaneously invert the ϵ and the σ parameters, which improve the inversion accuracy and reduce the crosstalk effect. The multi-scale strategy uses the Wiener filtering to process the GPR data in different frequency ranges. Then, the low frequencies signal updates the bottom part and subsequently increases the frequencies to invert for the shallow areas. The Tikhonov regularization stabilizes the reconstruction of the smoothly varying background part. In contrast, Total Variation (TV) regularization can recover the large contrasts associated with the LNAPL model. The new Tikhonov-TV (TT) regularization can mitigate the crosstalk caused by the parameter coupling effect. Numerical tests with typical GPR models demonstrate that the proposed multi-scale TT-FWI strategy can effectively eliminate the crosstalk and improve the reconstruction accuracy when the model parameters have a different structure. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) technology is used for nondestructive testing of roads. It relies on the processing and analysis of electromagnetic waves reflected due to the differences in dielectric properties encountered by electromagnetic pulses during propagation for road quality assessment and detects distresses. The moisture content of the asphalt mixture in asphalt pavement undergoes dynamic changes, affecting the pavement's relative humidity (RH). This alteration in RH has a direct impact on the dielectric properties of the asphalt mixture, thus influencing the accuracy of GPR road detection. Therefore, it is essential to quantify the relationship between RH and the dielectric properties of asphalt mixtures. This study utilized electromagnetic field theory to establish the correlation between air dielectric constant and RH. Dielectric constant tests were performed on commonly used single-phase component materials (limestone, 70# asphalt, SBS-modified asphalt) of asphalt mixtures and different types (asphalt type, asphalt-aggregate ratio, nominal maximum aggregate size) of asphalt mixtures under varying RH. The study quantified the impact of RH on the dielectric properties of asphalt mixtures and developed a CRIM optimization dielectric model that incorporates the RH effect. The research results showed that the dielectric constants of air, limestone, asphalt, and asphalt mixtures increase with the increase in RH. Specifically, the dielectric constants of air and asphalt mixtures exhibit a linear relationship with RH. Moreover, the increase in the dielectric constant of 70# asphalt with RH is greater than that of SBS-modified asphalt. Under identical conditions, reducing the impact of RH on the dielectric properties of asphalt mixtures can be achieved by increasing the asphalt-aggregate ratio, decreasing the nominal maximum particle size of the aggregates, or utilizing SBS-modified asphalt. The CRIM optimization model yielded an overall average relative error of 3.33% for calculating the dielectric constants of different types of asphalt mixtures under the influence of RH, representing at least a 1.67% improvement compared to classical models and the improved model proposed from existing literature. The research outcomes provide significant guidance for enriching and developing the fundamental theory as well as detection evaluation technology of GPR. [ABSTRACT FROM AUTHOR]

The JUpiter ICy moons Explorer (JUICE) of ESA was launched on 14 April 2023 and will arrive at Jupiter and its moons in July 2031. In this review article, we describe how JUICE will investigate the interior of the three icy Galilean moons, Ganymede, Callisto and Europa, during its Jupiter orbital tour and the final orbital phase around Ganymede. Detailed geophysical observations about the interior of the moons can only be performed from close distances to the moons, and best estimates of signatures of the interior, such as an induced magnetic field, tides and rotation variations, and radar reflections, will be obtained during flybys of the moons with altitudes of about 1000 km or less and during the Ganymede orbital phase at an average altitude of 490 km. The 9-month long orbital phase around Ganymede, the first of its kind around another moon than our Moon, will allow an unprecedented and detailed insight into the moon's interior, from the central regions where a magnetic field is generated to the internal ocean and outer ice shell. Multiple flybys of Callisto will clarify the differences in evolution compared to Ganymede and will provide key constraints on the origin and evolution of the Jupiter system. JUICE will visit Europa only during two close flybys and the geophysical investigations will focus on selected areas of the ice shell. A prime goal of JUICE is the characterisation of the ice shell and ocean of the Galilean moons, and we here specifically emphasise the synergistic aspects of the different geophysical investigations, showing how different instruments will work together to probe the hydrosphere. We also describe how synergies between JUICE instruments will contribute to the assessment of the deep interior of the moons, their internal differentiation, dynamics and evolution. In situ measurements and remote sensing observations will support the geophysical instruments to achieve these goals, but will also, together with subsurface radar sounding, provide information about tectonics, potential plumes, and the composition of the surface, which will help understanding the composition of the interior, the structure of the ice shell, and exchange processes between ocean, ice and surface. Accurate tracking of the JUICE spacecraft all along the mission will strongly improve our knowledge of the changing orbital motions of the moons and will provide additional insight into the dissipative processes in the Jupiter system. Finally, we present an overview of how the geophysical investigations will be performed and describe the operational synergies and challenges. [ABSTRACT FROM AUTHOR]

Over the past years, remote sensing, radar, and imaging applications have all made use of ultra-wideband (UWB) technology. This study undertakes an extensive analysis of tree-shaped monopole antennas tailored for UWB systems. The intended antenna has an incomplete ground plane and a circular radiating patch. To increase bandwidth, two ears have been added to the circular structure. Possessing a dielectric constant of 4.3. The antenna substrate consists of FR-4 material with a dielectric constant of 4.3. To achieve optimal impedance matching for UWB systems, the antenna is fed via a coplanar waveguide (CPW). Design antenna is a simple structure, small size, easy design, and simple integration with the substrate with dimensions of 54 mm ×36 mm ×1.6 mm. All simulation results presented in this article were generated using computer simulation technology (CST) software. He monopole antenna exhibits an impressive impedance bandwidth of 9.6 GHz (146.68%), spanning from 1.99 GHz to 11.56 GHz. Furthermore, the simulated UWB circular monopole antenna exhibits omnidirectional radiation characteristics, boasting a peak gain of 8 dB, and a directivity of 8.2 dBi at the frequency of 5 GHz, and a remarkable radiation efficiency of 97%. With these attributes, the suggested monopole UWB antenna shows significant potential for ground penetrating radar (GPR) applications. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent analysis of GPR data and overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model was constructed for regressing the grouting thickness based on GPR waveforms. A full-scale model test and corresponding numerical simulations were carried out to collect GPR data at 400 and 900 MHz, with known backfill grouting thickness. The model test helps address the limitation of not knowing the grout body condition in actual field detection. The data were then used to create machine learning datasets. The method of feature selection was proposed based on the analysis of feature importance and the electromagnetic (EM) propagation law in mediums. The research shows that: (1) the CatBoost & BO-TPE model exhibited outstanding performance in both experimental and numerical data, achieving R2 values of 0.9760, 0.8971, 0.8808, and 0.5437 for numerical data and test data at 400 and 900 MHz. It outperformed extreme gradient boosting (XGBoost) and random forest (RF) in terms of performance in the backfill grouting thickness regression; (2) compared with the full-waveform GPR data, the feature selection method proposed in this paper can promote the performance of the model. The selected features within the 5-30 ns of the A-scan can yield the best performance for the model; (3) compared to GPR data at 900 MHz, GPR data at 400 MHz exhibited better performance in the CatBoost & BO-TPE model. This indicates that the results of the machine learning model can provide feedback for the selection of GPR parameters; (4) the application results of the trained CatBoost & BO-TPE model in engineering are in line with the patterns observed through traditional processing methods, yet they demonstrate a more quantitative and objective nature compared to the traditional method. [ABSTRACT FROM AUTHOR]

This document explores the multi-dimensionality of root traits in plants and the challenges in understanding them. It emphasizes the importance of considering various factors such as root biomass, physiological traits, and exudation in assessing root foraging capacity. The document also suggests the need to study community-level patterns and include other dimensions like root depth and chemical defense. Additionally, it highlights the significance of studying the relationship between root and leaf traits. The research was supported by the National Natural Science Foundation of China. [Extracted from the article]

In this work, a novel cavity backed leaf-shaped bowtie antenna is designed using Characteristic Mode Analysis (CMA). An analytical expression is proposed to arrive at the leaf shape of the bowtie antenna using optimization process. The developed antenna is backed by a square-cavity to get unidirectional radiation pattern with high gain and good front-to-back-ratio characteristics. CMA technique is used in the design and optimization of the cavity structure and generalized design equation is proposed to design the cavity for getting good impedance bandwidth and broadside radiation characteristics in the Desired Band of Interest (DBI). The developed antenna resonates from 0.8 to 4.3 GHz with stable and unidirectional radiation characteristics giving maximum gain and FBR of 11.5 dBi and 15.2 dB respectively, making it suitable for Ground Penetrating Radar (GPR) applications. [ABSTRACT FROM AUTHOR]

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics. [ABSTRACT FROM AUTHOR]

Many automatic classification methods published can identify the main hidden distress types of highways, but they cannot meet the precise needs of operation and maintenance. The classification of interlayer distresses based on ground penetrating radar (GPR) images is very important to improve maintenance efficiency and reduce cost. However, among models of different complexities, which models are suitable for the interlayer distress data needs further verification. Firstly, to cover enough of the variable range of distress samples, the interlayer distress dataset collected containing 32,038 samples was subcategorized into three types: interlayer debonding, interlayer water seepage, and interlayer loosening. Secondly, residual networks (ResNets) that render easier to build shallower or deeper networks (ResNet-4, ResNet-6, ResNet-8, ResNet-10, ResNet-14, ResNet-18, ResNet-34, and ResNet-50) and five classical network models (DenseNet-121, EfficientNet B0, SqueezeNet1_0, MobileNet V2, and VGG-19) were evaluated by training and validation loss, test accuracy, and model complexity. The experimental results show that all models have high test accuracy with little difference, but ResNet-4, ResNet-6, SqueezeNet1_0, and ResNet-8 exhibit no overfitting which means they have good generalization performance. [ABSTRACT FROM AUTHOR]

We conducted large-depth Ground-Penetrating Radar investigations of the seismogenic Casamicciola fault system at the volcanic island of Ischia, with the aim of constraining the source characteristics of this active and capable fault system. On 21 August 2017, a shallow (hypocentral depth of 1.2 km), moderate (Md = 4.0) earthquake hit the island, causing severe damage and two fatalities. This was the first damaging earthquake recorded on the volcanic island of Ischia from the beginning of the instrumental era. Our survey was performed using the Loza low-frequency (15–25 MHz) GPR system calibrated by TDEM results. The data highlighted variations in the electromagnetic signal due to the presence of contacts, i.e., faults down to a depth larger than 100 m below the surface. These signal variations match with the position of the synthetic and antithetic active fault system bordering the Casamicciola Holocene graben. Our study highlights the importance of employing large-depth Ground-Penetrating Radar geophysical techniques for investigating active fault systems not only in their shallower parts, but also down to a few hundred meters' depth, providing a contribution to the knowledge of seismic hazard studies on the island of Ischia and elsewhere. [ABSTRACT FROM AUTHOR]

The site of LuneryRosieres la-Terre-des-Sablons (Lunery, Cher, France) comprises early evidence of human occupation in mid-latitudes in Western Europe. It demonstrates hominin presence in the Loire River Basin during the Early Pleistocene at the transition between an interglacial stage and the beginning of the following glacial stage. Three archaeological levels sandwiched and associated with two diamicton levels deposited on the downcutting river floor indicate repeated temporary occupations. Lithic material yields evidence of simple and more complex core technologies on local Jurassic siliceous rocks and Oligocene millstone. Hominins availed of natural stone morphologies to produce flakes with limited preparation. Some cores show centripetal management and a partially prepared striking platform. The mean ESR age of 1175 ka ± 98 ka obtained on fluvial sediments overlying the archaeological levels could correspond to the transition between marine isotopic stages (MIS) 37 and 36, during the normal Cobb Mountain subchron, and in particular at the beginning of MIS 36. The Lunery site shows that hominins were capable of adapting to early glacial environmental conditions and adopting appropriate strategies for settling in mid-latitude zones. These areas cannot be considered as inhospitable at that time as Lunery lies at some distance from the forming ice cap. [ABSTRACT FROM AUTHOR]

Estimates of radar attenuation in the shallow Martian subsurface are retrieved from RIMFAX soundings along the Perseverance rover traverse. Specifically, analyzed data is from the Hawksbill Gap area during the rover's first drives onto the Jezero Western Fan Front. The centroid frequency‐shift method is employed to quantify attenuation in terms of the constant‐Q approximation. Results are then compared with the amplitude decay method, which—in order to calculate attenuation—requires propagation velocities retrieved from radargram analysis. By verifying that results from two separate analyses are consistent, we ensure that quantified radar properties are well constrained. First estimate of constant‐Q is 78.8 ± 11.6. For a subsurface propagation velocity of 0.113 m/ns, that equals an attenuation of −2.1 ± 0.4 dB/m at the RIMFAX 675 MHz center frequency. Results are consistent with dry sedimentary rocks, and are distinguishable from the magmatic lithologies on Jezero Crater Floor. Plain Language Summary: This study presents first estimates of radar attenuation at the Jezero Western Fan Front. Measurements were made with the RIMFAX payload instrument on the Mars 2020 Perseverance rover mission, acquired along the rover drive path. Results indicate low signal losses in the subsurface that are consistent with dry sedimentary rocks, as observed on the surface by other payload instruments. Maximum imaging depths increase compared to imaging over magmatic lithologies on Jezero Crater Floor. By using separate methods of analysis (the centroid frequency‐shift method and the amplitude decay method), we reliably quantify attenuation and maximum penetration depths at the Western Fan Front, and observe differences to the Crater Floor lithologies. Key Points: The first estimate of radar attenuation at the Jezero Western Fan Front is on average −2.1 dB/mFor an average propagation velocity of 0.113 m/ns, returned power and time‐frequency analyses yield similar resultsRadar properties are consistent with dry sedimentary rocks and are distinguishable from the magmatic lithologies on Jezero Crater Floor [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a non-destructive exploration technology that utilizes high-frequency ultra-wideband signals to detect the distribution of subsurface objects and media. Benefiting from the advantages of non-destructiveness, high efficiency, and high resolution, GPR has been widely applied in underground defect detection task of urban roads. Illustrating the radar echo wave of subsurface structure, GPR B-scan images are a main means to detect underground disaster; however, compared to natural images, automatic interpretation of GPR B-scan ones is a more challenging task because of same objects with different spectra, different objects with same spectrum as well as heavy noise pollution. Aiming to improve the accuracy of subsurface disasters detection methods, a disasters classification network, i.e., MA-ResNeXt, based on ResNetXt50 is proposed by combining multi-dimensional attention mechanism, atrous space pyramid pool and multi-scale feature extraction structure. The proposed network is trained and tested on real GPR B-scan images of three common subsurface disasters, e.g., void, cavity underneath pavement (CUP) and loosely infilled void (LIV). The comparison results show that classification accuracy of the proposed network approaches 98.2%, and illustrate that the network can effectively realize accurate classification of underground disasters. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated. [ABSTRACT FROM AUTHOR]

The GPR method was used to determine the depth and extent of the subsurface archaeological features at the archaeological site of Borsippa. The extensions of the archaeological walls were identified through the creation of three-dimensional maps. The LMX200 device conducted the survey using a 250 MHZ antenna. sixty parallel profiles were collected and processed by GPR Slice software. Several reflections of possible archaeological walls buried at different depths and extensions of 2, 4, 6, 11, and 20 m were identified. The 250 MHz antenna gave clear reflections of the archaeological walls, with a penetration depth of up to 4 meters. The time slice map identified a buried archaeological structure at 1-1.1 m deep, consisting of many rooms with thick walls up to 2-3 m. [ABSTRACT FROM AUTHOR]

A survey was conducted to investigate buried archaeological remains at Tingambato, a pre‐Tarascan classic‐epiclassic archaeological site located in the north‐central part of the State of Michoacán in western Mexico, using ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT). The aim of this study was to detect the foundations (ancient buried walls) and cavities (tombs), define the geometry of the foundations and correlate construction style and depth with relative chronological buried structures. The survey was carried out on two grids of 15 m × 37 m (Zona Verde) and 10.25 m × 36.5 m (Ballgame court), using a 200 MHz antenna for GPR and Schlumberger‐Wenner arrays for ERT. GPR 3D acquisition was carried out along parallel lines spaced 0.25 m apart in a single direction. In the first area, considering the geometric shape found at a depth of 1.35 m, we can assume the existence of a buried structure, probably wall remains. In the second area, a very diffractive zone coincides with a resistive anomaly (> 2000 ohm·m). In order to refine the GPR results, synthetic modelling and a comparison with real traces were carried out. The 1D GPR modelling allows us to precise the presence of a cavity with a rectangular cross section. Since the type of volcaniclastic avalanche deposits of the subsoil do not allow the formation of cavities of that size, we infer that it may be the remains of a tomb or an anthropogenic cavity. [ABSTRACT FROM AUTHOR]

In recent years, CO2 flooding has become an important technical measure for oil and gas field enterprises to further improve oil and gas recovery and achieve the goal of "dual carbon". It is also one of the concrete application forms of CCUS. Numerical simulation based on CO2-EOR plays an indispensable role in the study of the mechanism of CO2 flooding and buried percolation, allowing for technical indicators to be selected and EOR/EGR prediction to be improved for reservoir engineers. This paper discusses the numerical simulation techniques related to CO2 flooding and storage, including mathematical models and solving algorithms. A multiphase and multicomponent mathematical model is developed to describe the flow mechanism of hydrocarbon components–CO2–water underground and to simulate the phase diagram of the components. The two-phase P-T flash calculation with SSI (+DEM) and the Newton method is adopted to obtain the gas–liquid phase equilibrium parameters. The extreme value judgment of the TPD function is used to form the phase stability test and miscibility identification model. A tailor-made multistage preconditioner is built to solve the linear equation of the strong-coupled, multiphase, multicomponent reservoir simulation, which includes the variables of pressure, saturation, and composition. The multistage preconditioner improves the computational efficiency significantly. A numerical simulation of CO2 injection in a carbonate reservoir in the Middle East shows that it is effective for researching the recovery factor and storage quantity of CO2 flooding based on the above numerical simulation techniques. [ABSTRACT FROM AUTHOR]

The quality of asphalt pavement (HMA) is often assessed by its dielectric permittivity, e.g., using ground-penetrating radar technology. The mostly employed relationships were developed for dry HMA pavement as follows: Complex Refractive Index Model (CRIM), Bottcher, Rayleigh, and Al-Qadi–Lahouar–Leng models (so-called ALL model), while the relationships for the estimation of wet HMA properties are empiric and not consistent with those for dry HMA mixtures. The article presents the results of the development and analysis of novel relationships between the values of dielectric permittivity and the mechanical characteristics of wet asphalt. It is shown that the new relationships are the general consistent cases of the expressions previously developed for dry HMA. The new expressions for the Rayleigh and ALL models are shown providing close estimates for the value of moisture content in the range of porosity and degree of water saturation: 0–0.12 and 0–1, respectively, and in the wide range of aggregate specific gravity (2.5–2.9), the binder content (0.04–0.08), the dielectric permittivity of aggregate (6–11), and the maximum specific gravity (2.5–2.9). Since the ALL model is an implicit function, in which the dielectric permittivity variable cannot be extracted, the direct problem (to calculate the dielectric constant value from density/specific gravity and porosity value) can be solved numerically while the inverse problem can be solved algebraically. The newly developed expression for the Rayleigh model (for wet asphalt) can be used for the estimation of dielectric permittivity based on expected values of porosity and density, and vice versa, and hence it is simpler for using. [ABSTRACT FROM AUTHOR]

This study aims to develop a damage-detection algorithm based on the electromagnetic wave properties inside a reinforced concrete structure. The proposed method involves employing two algorithms based on data measured using ground-penetrating radar—a common electromagnetic wave method in civil engineering. The possible defect area was identified based on the energy dissipated by the damage in the frequency-wavenumber domain, with the damage localized using the calculated relative permittivity of the measurements. The proposed method was verified through a finite difference time-domain-based numerical analysis and a testing slab with artificial damage. As a result of verification, the proposed method quickly identified the presence of damage inside the concrete, especially for honeycomb-like defects located at the top of the rebar. This study has practical significance in scanning structures over a large area more quickly than other non-destructive testing methods, such as ultrasonic methods. [ABSTRACT FROM AUTHOR]

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna. [ABSTRACT FROM AUTHOR]

A common practice used by the Germans and collaborators in World War II, as part of the Holocaust, was to use existing Jewish cemeteries as places for mass burial. Research was completed at the Old Jewish Cemetery in Riga, Latvia, the Livas Jewish Cemetery in Liepaja, Latvia, and the Zaliakalnis Jewish Cemetery in Kaunas, Lithuania. The Old Jewish Cemetery in Riga was adjacent to the Riga Ghetto and was used to bury individuals murdered in the ghetto. In Kaunas, an area of the Zaliakalnis Jewish Cemetery is devoid of grave stones, and literature sources and testimony indicate that this area was used for the mass burial of Jews from the Kaunas Ghetto and other mass killings. In Liepaja, the local Jewish Heritage Foundation believes that there are mass graves within the Livas Cemetery. Methodologies for this research include the use of a pulseEkko Pro 500-megahertz ground-penetrating radar (GPR) system. Electrical resistivity tomography (ERT) data were collected through a linear array of electrodes coupled to a direct current (DC) resistivity transmitter and receiver. Analysis of aerial photography and satellite images was also employed at each location. ERT and GPR data indicate three separate trench anomalies in the Old Jewish Cemetery in Riga. The presence of these anomalies corroborates Holocaust survivor testimony that bodies were buried in mass graves in that area. In the Zaliakalnis Jewish Cemetery in Kaunas, ERT and GPR data indicate an anomaly in the western part of the cemetery, and ERT data further indicate two other possible mass graves. In Liepaja, preliminary GPR analysis indicates an anomaly in a cleared section of the cemetery. Based on the presence of geophysical anomalies in all three cemeteries, which correlate with literature sources and Holocaust survivor testimony, there is a high probability that mass graves are present at each site. Future research directions include expanding the search areas in each cemetery, additional literature and testimony-based research, and the addition of other geophysical methodologies. [ABSTRACT FROM AUTHOR]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

The steep and unstable terrain found on debris-covered glaciers, rock glaciers, talus slopes, moraines and other proglacial features often make terrestrial ground-penetrating radar (GPR) surveys unsafe or cost-prohibitive. To address these challenges, this research introduces a novel approach for studying buried ice using multi-low-frequency drone-based GPR. Monostatic antennas of 50, 100, and 200 MHz were flown along a transect spanning a debris-covered glacier and an ice–debris complex at Shár Shaw Tagà (Grizzly Creek) in southwest Yukon, Canada. The drone-based results were compared to manual GPR at two locations along the transect. The two manual segments were conducted using the same radar system in a bi-static mode and included common mid-point (CMP) surveys. Overall, the drone-based radar successfully identified buried ice and enabled estimation of ice body thickness. Notably, CMP results confirmed layer characteristics and enabled depths to be measured across the entire drone-based transect. Discrimination of detail across a range of depths was made possible by comparing the three low frequencies, highlighting the possibility of using this method for future investigations of debris thickness in addition to quantifying buried ice. This study confirms the effectiveness of drone-based GPR combined with manual CMP for surveying ice beneath previously inaccessible terrain. [ABSTRACT FROM AUTHOR]

The Koksu River valley is located in the Pamir‐Alay mountain range and contains 25 glaciers larger than 1 km2 and numerous smaller glaciers. The largest glacier in the catchment is Abramov Glacier with a current surface area of 22.55 km2 (in 2022), which was extensively monitored between 1965 and 1999, and resumed in 2011. The long and detailed mass balance time series provide, among other information, benchmark climate variables for the Pamir‐Alay range. We report 10 new cosmogenic 10Be exposure dates of glacial moraines directly deposited by Abramov Glacier to extend the glacial history of the valley. Six boulders indicate that the Local Last Glacial Maximum occurred at 17.1±1.0 ka. Four boulders suggest a Little Ice Age (LIA) glacial advance around AD 1750. Secular glacier mass balance reconstructions suggest a progressively negative mass balance since the LIA advance. The decrease in mass balance accelerated in the last quarter of the 20th century. Results from repeated ground penetrating radar (GPR) measurements suggest that Abramov Glacier lost about 403 million m3 of ice volume between 1986 and 2018. Based on the reconstruction of the glacier surface, the corresponding equilibrium line altitude, which is closely correlated with the mass balance, increased by about 70 to 80 m during this period. Our results also suggest that Abramov Glacier has become increasingly out of equilibrium with the climate over the last two decades. This is supported by repeated GPR measurements of the tongue area, which indicate a dramatic decrease in glacier area and ice volume over the period 1986–2018. [ABSTRACT FROM AUTHOR]

We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (western Svalbard, Brøggerhalvøya Peninsula, Norway) as part of the project ICEtoFLUX. The aim of the investigation is to characterize the role of groundwater flow through the active layer as well as through and/or below the permafrost. The data set is composed of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. Overall, 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). The resistivity models revealed deep resistive structures, probably related to the heterogeneous permafrost, which are often interrupted by electrically conductive regions that may relate to aquifers and/or faults. The interpretation of these data can support the identification of the active layer, the occurrence of spatial variation in soil conditions at depth, and the presence of groundwater flow through the permafrost. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate change studies of the Ny-Ålesund area. The data set is of major relevance because there are few geophysical data published about the Ny-Ålesund area. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, and geomorphology, etc. The geophysical data are provided in a free repository and can be accessed at 10.5281/zenodo.10260056 (Pace et al., 2023). [ABSTRACT FROM AUTHOR]

Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kgm-3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval. [ABSTRACT FROM AUTHOR]

This paper focuses on the study and development of an efficient numerical method designed to simulate the radar cross section (RCS) of objects buried in lossy ground and illuminated by a plane wave. The primary objective aligns with the overarching challenge of detecting buried targets in the ground using an airborne radar system. In this scenario, a source antenna illuminates a considered 3D domain, and sensors receive the scattered field from the targets. To enable accurate and efficient simulations, the proposed tool utilizes a Cartesian/unstructured mesh and employs hybrid method that combines two finite volume solvers. In the contents of the paper, we first present a strategy for obtaining Cartesian/unstructured meshes. Subsequently, we study the hybridization of two specific finite volume schemes. Additionaly, a ground and a Near‐ to Far‐field model are introduced for buried targets. To validate and showcase the advantages of our hybrid approach, practical examples are presented. Finally, the strategy designed for handling meshes composed of multiple Cartesian and unstructured zones is detailed. [ABSTRACT FROM AUTHOR]

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs. [ABSTRACT FROM AUTHOR]

It is unclear how high concentrations of copper affect the structure and function of roots in the process of controlling root entanglement in containers although copper pruning technology has been widely applied. One-year-old glossy privet (Ligustrum lucidum) seedlings were treated with latex carrier (T1), 100 g L− 1 Cu(OH)2 in combination with latex (T2), and a control (T0) without latex and Cu(OH)2. The roots were classified into seven classes according to diameter and six root orders by branching order. There were 6 root orders under the T2, one more than that under the T1 and T0. The length sum of the first to fourth root orders under the T2 was 52.4% and 132.0% higher than that under the T1 and T0. The T1 treatment significantly increased the length of the finest roots less than 0.50 mm in diameter, especially the roots less than 0.25 mm in diameter. No root circling was observed under the T2 treatment where the length of the tap root decreased and root volume increased. The T2 treatment significantly improved the plant height (+ 62.2% and + 24.1%), maximum net photosynthetic rate (+ 70.5% and + 63.8%), leaf area (+ 4.10% and + 37.6%), and total leaf number (+ 143.0% and + 66.9%) compared to the T0 and the T1. Latex treatment enhanced the growth of the lateral roots by increasing humidity and temperature in the containers but could not control root circling, whose improvement mechanism was different from that of copper. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The need for reconstruction of the distribution of physical properties like dielectric permittivity and electrical conductivity of shallow subsurface sedimentary architecture leads to the development of an optimum strategy of GPR data inversion. In this paper, we present finite difference frequency domain (FDFD) full waveform inversion (FWI) method to get high-resolution subsurface model using GPR data. FWI is an optimization technique which involves in search of the minima between recorded and predicted data. The inversion process includes the quasi-Newton method and simultaneous frequency sampling strategy of irregular sampling. The Hessian term in quasi-Newton algorithm is approximated using preconditioned-LBFGS consideration and the search directions are also optimized after following the Wolfe conditions. At the end of each iteration during inversion, permittivity and conductivity models were updated and became ready to be the initial model for the next iteration. The goals of this research were to develop a robust framework for sedimentary-GPR data inversion and to evaluate the efficacy of the novel grid strategy introduced by Layek and Sengupta (2021) proposed for FWI. This paper presents a comparative analysis between conventional and newly proposed technique from Layek and Sengupta (2021), supported by numerical experiments conducted through our own MATLAB programming. Numerical tests conducted on a benchmark from previously published article, established the fact that new grid formulation produces a faster converging rate and required less computation time. This approach demonstrates remarkable efficacy when applied to a comprehensive sedimentary model comprising a lossy medium. [ABSTRACT FROM AUTHOR]

In the structure of composite pavement, the formation of voids beneath concrete panels poses significant risks to structural integrity and operational safety. Ground-Penetrating Radar (GPR) detection serves as an effective method for identifying voids beneath concrete pavement panels. This paper focuses on analyzing the morphological features of GPR echo signals. Leveraging the GprMax numerical simulation software, a numerical simulation model for void conditions in concrete pavement is established by setting reasonable pavement structure parameters, signal parameters, and model space parameters. The reliability of the numerical simulation model is validated based on field data from full-scale test sites with pre-fabricated voids. Various void conditions, including different void thicknesses, sizes, shapes, and filling mediums, are analyzed. The main conclusions of the study are as follows: the correlation coefficient between measured and simulated signals is above 0.8; a noticeable distinction exists between echo signals from intact and voided structures; signals exhibit similar phase and time delays for different void thicknesses and sizes but significant differences are observed in the A-scan signal intensity, the signal intensity, and the width of the B-scan signal; the impact of void shape on GPR echo signals mainly manifests in the variation of void thickness at different measurement points; and the relationship between the dielectric properties of the void-filling medium and the surrounding environment dictates the phase and time delay characteristics of the echo signal. [ABSTRACT FROM AUTHOR]

A new algorithm is developed to automatically detect rebar locations and diameters of reinforced concrete structures using the ground penetrating radar technique. The study uses two-way travel time and biquadratic equations to formulate electromagnetic wave speed in reinforced concrete structures where hyperbolic signatures are approximated. Leveraging an established algorithm, a computer code has been developed to offer automated analysis of ground-penetrating radar data obtained from survey grids. Four reinforced concrete slabs were designed, fabricated, and tested to validate the developed evaluation approach. The proposed methodology demonstrates outstanding signal processing proficiency and reliably and effectively identifies rebar information. [ABSTRACT FROM AUTHOR]

This study focuses on monitoring the deformation of the shallow unsymmetrical section of a super-large-span tunnel portal relying on the newly built Shimentangshan Tunnel, and through numerical simulations, the construction sequence and drift ratios were optimized to address challenges related to the stability of surrounding rock and structure. The findings indicate that employing the double-side drift method results in a maximum settlement value of 107.0 mm and a maximum convergence value of 108.8 mm, leading to larger deformations. Excavating the shallow buried side first followed by the deep buried side proves beneficial for deformation control of the support structure and effectively limits damage to the surrounding rock. A drift ratio of 0.3 ensures optimal support structure security and stability. Considering both structural deformation and surrounding rock damage, a ratio between 0.25 and 0.35 for the drifts is recommended. Taking into account construction efficiency and economic benefits, a construction plan for the shallow buried unsymmetrical section at the portal of super-large-span tunnels is proposed. [ABSTRACT FROM AUTHOR]

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model. [ABSTRACT FROM AUTHOR]

Distributed tracing is cutting-edge technology used for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and the direct storage and utilization of every trace is impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as the simplification of application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine-learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)