bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacterial Treatment of Recycled Concrete Aggregate
Authors: Rowell Austen , Ghebrab Tewodros , Jeter Randall
Published: September 2023

Microbial treatment of recycled concrete aggregate (RCA) may improve the quality of the aggregate, and enhance its use in the production of structural concrete and promote the recycling of concrete waste. The mortar phase of the RCA is responsible for the poor performance of the aggregate. Treating the old adhered mortar or removing it from the natural aggregate (NA) is an option to make RCA beneficial for the production of quality recycled aggregate concrete (RAC). Removing the adhered mortar from recycled concrete aggregate using silicate-solubilizing bacteria was investigated. The bacteria could synthesize the silicates in the calcium silicate hydrate phase of the cement paste leading to the breakdown of the old adhered mortar. Four SSB strains were tested for survivability and activity in an alkaline medium to simulate the concrete environment. The Serratia marcescens bacterial strain, which survived the environment, was inoculated into screw-cap glass vials containing recycled concrete aggregate fragments and glucose-enhanced nutrient broth and then incubated for 14 days. Partial removal of the old adhered mortar was observed based on the weight lost from the RCA. The S. marcescens bacterial strain could survive the alkaline concrete environment and solubilize the silicates present in cement paste resulting in the removal of the old adhered mortar.
Circularity Outlines in the Construction and Demolition Waste Management: A Literature Review
Authors: Gherman Izabella-Eva , Lakatos Elena-Simina , Clinci Sorin Dan , Lungu Florin , Constandoiu Vladut Vasile , Cioca Lucian Ionel , Rada Elena Cristina
Published: September 2023

This study provides a comprehensive view of the research field of construction and demolition waste (CDW) management in the circular economy based on a literature review. The increased intensity of interest is due to the need to create frameworks, mechanisms, and tools for the process of mind-shifting towards circularity. Research topics, researched life cycle stages, strategies for CDW management, sustainability assessment, building stock quantification, assessment tools and forecast methods, materials with CDW content, waste treatment solutions, and the barriers and drivers for efficient waste management in the construction industry are identified as the main concerns in the analyzed research field. The results show that a major concern in the academic field directs research to the path of innovative strategy elaboration, identifying the enablers and barriers in CDW management, computational tool creation for design and assessment, building stock modeling, and circular building material development. The environmental approach prevails, leaving economic and social assessments in CDW management uncovered. Although stakeholders’ involvement is stressed in most cases, strategies for awareness-raising and education for a sustainable circular activity in the field are lacking. The circularity of CDW management being a multifaceted and multi-disciplinary complex challenge, it is approached on different levels. This study introduces the novelty of structuring the trends of existing knowledge in a holistic view, identifying the research directions, dimensions, specific aspects, and instruments.
A Comedian in the Pulpit: Empowering the Use of Humor in Preaching
Authors: Bostic Philmont Devon
Published: September 2023

Each week, the preacher mounts the pulpit with many tools to deliver an impactful sermon. One element of preaching that the black church should embrace is humor. Humor and preaching may appear strange bedfellows, but humor is embedded in the art of black preaching. This study explores humor within the confines of the African Methodist Episcopal Zion Church.
Journey or Destination? Rethinking Pilgrimage in the Western Tradition
Authors: Bailey Anne E.
Published: September 2023

Pilgrimage is undergoing a revival in western Europe, mainly as newly established or revitalised pilgrim routes, such as the Camino de Santiago in northern Spain. These trails have helped to foster the widespread idea that pilgrimage is essentially a journey: a spiritual or “meaningful” journey undertaken slowly, and preferably on foot, in the medieval tradition. The purpose of this article is to problematise this journey-oriented understanding of pilgrimage in Christian and post-Christian societies and to suggest that the importance given to the pilgrimage journey by many scholars, and by wider society, is more a product of modern Western values and post-Reformation culture than a reflection of historical and current-day religious practices. Drawing on evidence from a range of contemporary sources, it shows that many medieval pilgrims understood pilgrimage as a destination-based activity as is still the case at numerous Roman Catholic shrines today.
Augustine and Confucian Virtues: Mencius and Augustine on the Proper Motivations for Moral Conduct
Authors: He Teng , Hosle Paul K.
Published: September 2023

In this essay, we analyze Mencius’s ethics through the lenses of Augustine’s critique of pagan virtue and its tendency to self-love. In the first part of this essay, we outline the basic conceptual framework of Augustine’s theory of virtue and the brunt of his criticism of the pagan virtue tradition. In the latter part, we explore how Mencius manages to avoid the Augustinian charge against the pagans that they render virtue subservient to honor, and how he largely agrees with Augustine on what place public performance of virtuous deeds should have. At the same time, Mencius’s emphasis on loving virtue for its own sake at times slides into expressions of taking delight in one’s own virtue, a subtler form of self-love that Augustine identifies especially in the Stoic philosophy. While Mencius gives space to the role of Heaven in his ethic, he lacks the theocentric pathos of Augustine, which includes, inter alia, an acknowledgment of our human frailty and deep dependence on God’s grace. Although Mencius’s ethical ideas ultimately correspond rather to those that Augustine associates with pagan virtue, Mencius represents at least a “higher” form that finds meaningful common ground with Augustine on several important issues.
Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection
Authors: Wang Nan , Shi Yuetian , Li Haiwei , Zhang Geng , Li Siyuan , Liu Xuebin
Published: September 2023

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.
DSRSS-Net: Improved-Resolution Snow Cover Mapping from FY-4A Satellite Images Using the Dual-Branch Super-Resolution Semantic Segmentation Network
Authors: Kan Xi , Lu Zhengsong , Zhang Yonghong , Zhu Linglong , Sian Kenny Thiam Choy Lim Kam , Wang Jiangeng , Liu Xu , Zhou Zhou , Cao Haixiao
Published: September 2023

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.
The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning
Authors: Liu Lili , Gao Zhan , Luo Pingping , Duan Weili , Hu Maochuan , Mohd Arif Zainol Mohd Remy Rozainy , Zawawi Mohd Hafiz
Published: September 2023

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.
A Hybrid 3D–2D Feature Hierarchy CNN with Focal Loss for Hyperspectral Image Classification
Authors: Wen Xiaoyan , Yu Xiaodong , Wang Yufan , Yang Cuiping , Sun Yu
Published: September 2023

Hyperspectral image (HSI) classification has been extensively applied for analyzing remotely sensed images. HSI data consist of multiple bands that provide abundant spatial information. Convolutional neural networks (CNNs) have emerged as powerful deep learning methods for processing visual data. In recent work, CNNs have shown impressive results in HSI classification. In this paper, we propose a hierarchical neural network architecture called feature extraction with hybrid spectral CNN (FE-HybridSN) to extract superior spectral–spatial features. FE-HybridSN effectively captures more spectral–spatial information while reducing computational complexity. Considering the prevalent issue of class imbalance in experimental datasets (IP, UP, SV) and real-world hyperspectral datasets, we apply the focal loss to mitigate these problems. The focal loss reconstructs the loss function and facilitates effective achievement of the aforementioned goals. We propose a framework (FEHN-FL) that combines FE-HybridSN and the focal loss for HSI classification and then conduct extensive HSI classification experiments using three remote sensing datasets: Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SV). Using cross-entropy loss as a baseline, we assess the hyperspectral classification performance of various backbone networks and examine the influence of different spatial sizes on classification accuracy. After incorporating focal loss as our loss function, we not only compare the classification performance of the FE-HybridSN backbone network under different loss functions but also evaluate their convergence rates during training. The proposed classification framework demonstrates satisfactory performance compared to state-of-the-art end-to-end deep-learning-based methods, such as 2D-CNN, 3D-CNN, etc.
LD-SLAM: A Robust and Accurate GNSS-Aided Multi-Map Method for Long-Distance Visual SLAM
Authors: Li Dongdong , Zhang Fangbing , Feng Jiaxiao , Wang Zhijun , Fan Jinghui , Li Ye , Li Jing , Yang Tao
Published: September 2023

Continuous, robust, and precise localization is pivotal in enabling the autonomous operation of robots and aircraft in intricate environments, particularly in the absence of GNSS (global navigation satellite system) signals. However, commonly employed approaches, such as visual odometry and inertial navigation systems, encounter hindrances in achieving effective navigation and positioning due to issues of error accumulation. Additionally, the challenge of managing extensive map creation and exploration arises when deploying these systems on unmanned aerial vehicle terminals. This study introduces an innovative system capable of conducting long-range and multi-map visual SLAM (simultaneous localization and mapping) using monocular cameras equipped with pinhole and fisheye lens models. We formulate a graph optimization model integrating GNSS data and graphical information through multi-sensor fusion navigation and positioning technology. We propose partitioning SLAM maps based on map health status to augment accuracy and resilience in large-scale map generation. We introduce a multi-map matching and fusion algorithm leveraging geographical positioning and visual data to address excessive discrete mapping, leading to resource wastage and reduced map-switching efficiency. Furthermore, a multi-map-based visual SLAM online localization algorithm is presented, adeptly managing and coordinating distinct geographical maps in different temporal and spatial domains. We employ a quadcopter to establish a testing system and generate an aerial image dataset spanning several kilometers. Our experiments exhibit the framework’s noteworthy robustness and accuracy in long-distance navigation. For instance, our GNSS-assisted multi-map SLAM achieves an average accuracy of 1.5 m within a 20 km range during unmanned aerial vehicle flights.
Regional Models for Sentinel-2/MSI Imagery of Chlorophyll a and TSS, Obtained for Oligotrophic Issyk-Kul Lake Using High-Resolution LIF LiDAR Data
Authors: Pelevin Vadim , Koltsova Ekaterina , Molkov Aleksandr , Fedorov Sergei , Alymkulov Salmor , Konovalov Boris , Alymkulova Mairam , Jumaliev Kubanychbek
Published: September 2023

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range.
Co-Seismic Landslides Triggered by the 2014 Mw 6.2 Ludian Earthquake, Yunnan, China: Spatial Distribution, Directional Effect, and Controlling Factors
Authors: Duan Yuying , Luo Jing , Pei Xiangjun , Liu Zhuo
Published: September 2023

The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m2. These landslides covered a total area of 8.43 km2 and were identified through a comprehensive interpretation of high-resolution satellite images taken before and after the earthquake. It is noteworthy that the co-seismic landslides do not exhibit a linear concentration along the seismogenic fault; instead, they predominantly extend along major river systems with an NE–SW trend. Moreover, the southwest-facing slopes have the highest landslide area ratio of 1.41. To evaluate the susceptibility of the Ludian earthquake-triggered landslides, we performed a random forest model that considered topographic factors (elevation, slope, aspect, distance to rivers), geological factors (lithology), and seismic factors (ground motion parameters, epicentral distance, distance to the seismogenic fault). Our analysis revealed that the distance to rivers and elevation were the primary factors influencing the spatial distribution of the Ludian earthquake-triggered landslides. When we considered the directional variation in ground motion parameters, the AUC of the model slightly decreased. However, incorporating this variation led to a significant reduction in the proportion of areas classified as “high” and “very high” landslide susceptibility. Moreover, SEDd emerged as the most effective ground motion parameter for interpreting the distribution of the co-seismic landslides when compared to PGAd, PGVd, and Iad.
Applying High-Resolution Satellite and UAS Imagery for Detecting Coldwater Inputs in Temperate Streams of the Iowa Driftless Region
Authors: Mishra Niti B. , Siepker Michael J. , Simmons Greg
Published: September 2023

Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate change. Coldwater streams receive cooler groundwater inputs and, as a result, typically remain ice-free during the winter. Based on this empirical thermal evidence, we examined the potential of very high-resolution (VHR) satellite and uncrewed aerial system (UAS) imagery to (i) detect coldwater streams using semi-automatic classification versus visual interpretation approaches, (ii) examine the physical factors that contribute to inaccuracies in detecting coldwater habitats, and (iii) use the results to identify inaccuracies in existing thermal stream classification datasets and recommend coverage updates. Due to complex site conditions, semi-automated classification was time consuming and produced low mapping accuracy, while visual interpretation produced better results. VHR imagery detected only the highest quality coldwater streams while lower quality streams that still met the thermal and biological criteria to be classified as coldwater remained undetected. Complex stream and site variables (narrow stream width, canopy cover, terrain shadow, stream covered by ice and drifting snow), image quality (spatial resolution, solar elevation angle), and environmental conditions (ambient temperature prior to image acquisition) make coldwater detection challenging; however, UAS imagery is uniquely suited for mapping very narrow streams and can bridge the gap between field data and satellite imagery. Field-collected water temperatures and stream habitat and fish community inventories may be necessary to overcome these challenges and allow validation of remote sensing results. We detected >30 km of coldwater streams that are currently misclassified as warmwater. Overall, visual interpretation of VHR imagery it is a relatively quick and inexpensive approach to detect the location and extent of coldwater stream resources and could be used to develop field monitoring programs to confirm location and extent of coldwater aquatic resources.
Unraveling Regional Patterns of Sea Level Acceleration over the China Seas
Authors: Qu Ying , Jevrejeva Svetlana , Wang Shijin
Published: September 2023

Accelerated sea level rise is placing coastal communities in a vulnerable position; however, the processes underlying sea level acceleration in China remain uncertain. In this study, we examine the sea level acceleration and its contributors over the China Seas. We calculate acceleration along the Chinese coast using satellite altimetry and tide gauge records. During the satellite altimetry era, sea level acceleration from tide gauge records varies across all stations, reaching up to 0.30 ± 0.20 mm/yr2, while satellite altimetry could underestimate/overestimate the sea level acceleration in most locations. Acceleration near the coast, except in the Bohai Sea, is mainly driven by changes in the mass component. In contrast, for the open ocean, changes in steric sea level are the main contributor to sea level acceleration. The evolution of spatial acceleration patterns over the China Seas reveals that the ENSO and PDO variabilities dominate the changing patterns of sea level acceleration in the open ocean, including the Philippine Sea through steric sea level, and changes in most coastal locations are due to the non-steric component.
A Novel Method Based on GPU for Real-Time Anomaly Detection in Airborne Push-Broom Hyperspectral Sensors
Authors: Xue Tianru , Wang Chongru , Xie Hui , Wang Yueming
Published: September 2023

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%.
Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms
Authors: Alvites Cesar , Maesano Mauro , Molina-Valero Juan Alberto , Lasserre Bruno , Marchetti Marco , Santopuoli Giovanni
Published: September 2023

Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.
Application of Ground-Penetrating Radar with the Logging Data Constraint in the Detection of Fractured Rock Mass in Dazu Rock Carvings, Chongqing, China
Authors: Yuan Wenxing , Liu Sixin , Zhao Qiancheng , Deng Li , Lu Qi , Pan Lei , Li Zhilian
Published: September 2023

Geologic interpretation results from conventional ground-penetrating radar (GPR) detection methods tend to have a certain degree of uncertainty. In order to improve the reliability of ground-penetrating radars in the detection of rock mass fissures in grottoes, this study proposes a ground-penetrating radar detection method with the logging data constraint, which is applied to detect the fractured rock mass in the Baodingshan Scenic Area of Dazu Rock Carvings, Chongqing, China. First, conventional logging and borehole televiewer data were compared and verified, yielding detailed lithological and wellbore fissure information. Next, electromagnetic wave velocity was calibrated using GPR profile and the depth of the stratigraphic interface determined by borehole data. Utilizing this calibrated velocity, we are able to accurately calculate the depth values of anomalies in GPR interpretation profiles. Subsequently, we compared the preliminary GPR interpretation profile with the borehole televiewer images. After eliminating false anomalies caused by interference, we obtained more reliable location information for detection targets such as fissures, fracture zones, and weak interlayers. The results of fissure detection in the Dazu Rock Carvings indicate that the detection results of ground-penetrating radar are verified and supplemented under the constraints of stratigraphic and well-wall fissure information obtained by logging. This effectively mitigates the influence of multiplicity and false anomalies of GPR detection on interpretation results. GPR with the logging data constraint enhances the accuracy of the fissure detection results, providing novel technical means for the protection and restoration of grotto relics.
Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding
Authors: Wang Xin , Bai Ximin , Li Guang , Sun Liwei , Ye Hailong , Tong Tao
Published: September 2023

To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual network (ResNet), and propose a method based on the residual denoising convolutional neural network (ResDnCNN) and shift-invariant sparse coding (SISC) for denoising CSEM data. Firstly, a sample library was constructed by adding simulated noises of different types and amplitudes to high-quality CSEM data collected. Then, the sample library was used for model training in the ResDnCNN, resulting in a network model specifically designed for denoising CSEM data. Subsequently, the trained model was employed to denoise the measured data, generating preliminary denoised data. Finally, the preliminary denoised data was processed using SISC to obtain the final denoised high-quality data. Comparative experiments with the ResNet, DnCNN, U-Net, and long short-term memory (LSTM) networks demonstrated the significant advantages of our proposed method. It effectively removed strong noise such as Gaussian, impulse, and square wave, resulting in an improvement of the signal-to-noise ratio by nearly 20 dB. Testing on CSEM data from Sichuan Province, China, showed that the apparent resistivity curves plotted using our method were smoother and more credible.
ARE-Net: An Improved Interactive Model for Accurate Building Extraction in High-Resolution Remote Sensing Imagery
Authors: Weng Qian , Wang Qin , Lin Yifeng , Lin Jiawen
Published: September 2023

Accurate building extraction for high-resolution remote sensing images is critical for topographic mapping, urban planning, and many other applications. Its main task is to label each pixel point as a building or non-building. Although deep-learning-based algorithms have significantly enhanced the accuracy of building extraction, fully automated methods for building extraction are limited by the requirement for a large number of annotated samples, resulting in a limited generalization ability, easy misclassification in complex remote sensing images, and higher costs due to the need for a large number of annotated samples. To address these challenges, this paper proposes an improved interactive building extraction model, ARE-Net, which adopts a deep interactive segmentation approach. In this paper, we present several key contributions. Firstly, an adaptive-radius encoding (ARE) module was designed to optimize the interaction features of clicks based on the varying shapes and distributions of buildings to provide maximum a priori information for building extraction. Secondly, a two-stage training strategy was proposed to enhance the convergence speed and efficiency of the segmentation process. Finally, some comprehensive experiments using two models of different sizes (HRNet18s+OCR and HRNet32+OCR) were conducted on the Inria and WHU building datasets. The results showed significant improvements over the current state-of-the-art method in terms of NoC90. The proposed method achieved performance enhancements of 7.98% and 13.03% with HRNet18s+OCR and 7.34% and 15.49% with HRNet32+OCR on the WHU and Inria datasets, respectively. Furthermore, the experiments demonstrated that the proposed ARE-Net method significantly reduced the annotation costs while improving the convergence speed and generalization performance.
Learning-Based Optimization of Hyperspectral Band Selection for Classification
Authors: Ayna Cemre Omer , Mdrafi Robiulhossain , Du Qian , Gurbuz Ali Cafer
Published: September 2023

Hyperspectral sensors acquire spectral responses from objects with a large number of narrow spectral bands. The large volume of data may be costly in terms of storage and computational requirements. In addition, hyperspectral data are often information-wise redundant. Band selection intends to overcome these limitations by selecting a small subset of spectral bands that provide more information or better performance for particular tasks. However, existing band selection techniques do not directly maximize the task-specific performance, but rather utilize hand-crafted metrics as a proxy to the final goal of performance improvement. In this paper, we propose a deep learning (DL) architecture composed of a constrained measurement learning network for band selection, followed by a classification network. The proposed joint DL architecture is trained in a data-driven manner to optimize the classification loss along band selection. In this way, the proposed network directly learns to select bands that enhance the classification performance. Our evaluation results with Indian Pines (IP) and the University of Pavia (UP) datasets show that the proposed constrained measurement learning-based band selection approach provides higher classification accuracy compared to the state-of-the-art supervised band selection methods for the same number of bands selected. The proposed method shows 89.08% and 97.78% overall accuracy scores for IP and UP respectively, being 1.34% and 2.19% higher than the second-best method.
Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring
Authors: Zheng Xiaohan , Wang Chao , Tang Yixian , Zhang Hong , Li Tianyang , Zou Lichuan , Guan Shaoyang
Published: September 2023

Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions.
Flood Extent and Volume Estimation Using Remote Sensing Data
Authors: Popandopulo Georgii , Illarionova Svetlana , Shadrin Dmitrii , Evteeva Ksenia , Sotiriadi Nazar , Burnaev Evgeny
Published: September 2023

Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km3. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions.
An Adaptive Bandwidth Management Algorithm for Next-Generation Vehicular Networks
Authors: Huang Chenn-Jung , Hu Kai-Wen , Cheng Hao-Wen
Published: September 2023

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology.
Generalized Concept and MATLAB Code for Modeling and Analyzing Wideband 90° Stub-Loaded Phase Shifters with Simulation and Experimental Verifications
Authors: Alnahwi Falih M. , Al-Yasir Yasir I. A. , See Chan Hwang , Abdullah Abdulkareem S. , Abd-Alhameed Raed A.
Published: September 2023

In the design of phase shifters, the modeling equations are too complicated and require some approximations to be derived correctly by hand. In response to this problem, this paper presents a generalized concept, algorithm, and MATLAB code that provide the exact modeling equations of the transmission parameters and the scattering parameters of any 90° wideband stub-loaded phase shifter. The proposed code gives the modeling equations in term of variables for any number of stubs and characteristic impedance value by utilizing the symbol-based analysis of the MATLAB code. It also illustrates the results as a function of normalized frequency relative to the center frequency fo, and can be and can be tailored to any user-defined frequency range. As a matter of comparison, a three-stub wideband 90° stub-loaded phase shifter is simulated using CST Microwave Studio and experimentally fabricated on Rogers RT5880 dielectric substrate with dimensions of 30 × 40 × 0.8 mm3. The comparison reveals the accuracy of the proposed computerized modeling with −10 dB impedance bandwidth equal to 90% (0.55fo–1.45fo), (90°∓5°) phase difference bandwidth equal to 100% (0.5fo–1.5fo), and negligible insertion loss. The novelty of this work is that the proposed code provides the exact modeling equations of the stub-loaded phase shifter for any number of stubs regardless the complexity of the mathematical derivations.
Evaluation of a Scintillating Plastic Optical Fiber Device for Measuring kV-Cone Beam Computed Tomography Dose
Authors: Popotte Christian , Letellier Romain , Paul Didier , Waltener Alexandre , Guillochon Nicolas , Munier Mélodie , Retif Paul
Published: September 2023

Background: Justification of imaging procedures such as cone beam computed tomography (CBCT) in radiotherapy makes no doubt. However, the CBCT composite dose is rarely reported or optimized, even though the repeated CBCT cumulative dose can be up to 3% of the prescription dose. This study aimed to evaluate the performance and utility of a new plastic scintillating optical fiber dosimeter for CBCT dosimetric quality assurance (QA) applications before a potential application in patient composite CBCT dosimetry. Methods: The dosimeter, made of 1 mm diameter plastic fiber, was installed under a linear accelerator treatment table and linked to photodetectors. The fiber impact on the fluence and dose delivered was respectively assessed with an electronic portal imaging device (EPID) and EBT3 Gafchromic® film. The presence of artifacts was visually evaluated on kV images. The dosimeter performances were determined for various acquisition parameters by comparison with ionization chamber values. Results: The maximum impact of the fiber on the fluence measured by the EPID was −1.2% for the 6 MV flattening filter-free beam. However, the fiber did not alter the film dose profile when measured for all the beams tested. The fiber was not visible at energies ≥ 80 kV and was merely visible on the CBCT images. When the rate of images per second or mA was changed, the maximum relative difference between the device and the ionization chamber CTDIs was <5%. Changing collimation led to a −7.2% maximum relative difference with an absolute dose difference that was insignificant (−0.3 mGy). Changing kV was associated with a −8.7% maximum relative difference, as published in the literature. Conclusions: The dosimeter may be a promising device for CBCT recurrent dosimetry quality control or dose optimization. According to these results, further developments are in progress in order to adapt the solution to the measurement of patient composite CBCT doses.
Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm
Authors: Akindele Romoke Grace , Yu Ming , Kanda Paul Shekonya , Owoola Eunice Oluwabunmi , Aribilola Ifeoluwapo
Published: September 2023

The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.