bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of C-S-H Seed Prepared by Wet Grinding on the Properties of Cement Containing Large Amounts of Silica Fume
Authors: Wang Shiheng , Zhao Peng , Tian Yaogang , Liu Jianan
Published: September 2024

This study aimed to utilize the hydration characteristics of cement through wet grinding techniques to efficiently and conveniently prepare a stable C-S-H seed suspension, providing key parameters and a scientific basis for their large-scale production, which ensures the stability of the C-S-H suspension during production, transportation, and application. This preparation aimed to mitigate the adverse effects of high-volume silica fume on the early mechanical properties of high-performance cement concrete. The properties of C-S-H seed were characterized in detail by SEM, XRD, and TD. In the concrete performance test, silica fume was used to replace part of the cement, and different contents of C-S-H seed were added to test its effect on the compressive strength of concrete, with XRD and SEM used to analyze the performance differences. The results show that the particle size and hydration degree of cement no longer developed after 90 min of wet grinding. Polycarboxylate ether (PCE) superplasticizer can increase the fluidity of the crystal C-S-H seed suspension when the content exceeds 1.5%. When the content of PCE exceeded 2%, the C-S-H seed suspension precipitated. Adding 5% C-S-H seed can increase the compressive strength of cement concrete by 10% under the condition of reducing the amount of cement and increasing the amount of silica fume. And Ca(OH)2 (CH) was produced by cement hydration consumed by silica fumes to generate C-S-H gel, by which the concrete became denser with more strength. However, when the amount of C-S-H seed exceeded 7%, the compressive strength of the concrete decreased.
Strategic Use of Vegetable Oil for Mass Production of 5-Hydroxyvalerate-Containing Polyhydroxyalkanoate from δ-Valerolactone by Engineered Cupriavidus necator
Authors: Oh Suk-Jin , Shin Yuni , Oh Jinok , Kim Suwon , Lee Yeda , Choi Suhye , Lim Gaeun , Joo Jeong-Chan , Jeon Jong-Min , Yoon Jeong-Jun , Bhatia Shashi Kant , Ahn Jungoh , Kim Hee-Taek , Yang Yung-Hun
Published: September 2024

Although efforts have been undertaken to produce polyhydroxyalkanoates (PHA) with various monomers, the low yield of PHAs because of complex metabolic pathways and inhibitory substrates remains a major hurdle in their analyses and applications. Therefore, we investigated the feasibility of mass production of PHAs containing 5-hydroxyvalerate (5HV) using δ-valerolactone (DVL) without any pretreatment along with the addition of plant oil to achieve enough biomass. We identified that PhaCBP-M-CPF4, a PHA synthase, was capable of incorporating 5HV monomers and that C. necator PHB−4 harboring phaCBP-M-CPF4 synthesized poly(3HB-co-3HHx-co-5HV) in the presence of bean oil and DVL. In fed-batch fermentation, the supply of bean oil resulted in the synthesis of 49 g/L of poly(3HB-co-3.7 mol% 3HHx-co-5.3 mol%5HV) from 66 g/L of biomass. Thermophysical studies showed that 3HHx was effective in increasing the elongation, whereas 5HV was effective in decreasing the melting point. The contact angles of poly(3HB-co-3HHx-co-5HV) and poly(3HB-co-3HHx) were 109 and 98°, respectively. In addition, the analysis of microbial degradation confirmed that poly(3HB-co-3HHx-co-5HV) degraded more slowly (82% over 7 days) compared to poly(3HB-co-3HHx) (100% over 5 days). Overall, the oil-based fermentation strategy helped produce more PHA, and the mass production of novel PHAs could provide more opportunities to study polymer properties.
Modelling Across Multiple Scales to Design Biopolymer Membranes for Sustainable Gas Separations: 2-Multiscale Approach
Authors: Papchenko Kseniya , Ricci Eleonora , De Angelis Maria Grazia
Published: September 2024

The majority of materials used for membrane-based separation of gas mixtures are non-renewable and non-biodegradable, and the assessment of alternative bio-based polymers requires expensive and time-consuming experimental campaigns. This effort can be reduced by adopting suitable modelling approaches. In this series of works, we propose various modelling approaches to assess the CO2/CH4 separation performance of eight different copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV) using a limited amount of experimental data for model calibration. In part 1, we adopted a fully atomistic approach based on Molecular Dynamics (MD), while, in this work, we propose a multiscale methodology where a molecular description of the polymers is bridged to a macroscopic prediction of its gas sorption behaviour. PHBV structures were simulated using MD to obtain pressure–volume–temperature data, which were used to parametrise the Sanchez–Lacombe Equation of State. This, in turn, allows for the evaluation of the CO2 and CH4 solubility in the copolymers at various pressures and compositions with little computational effort, enabling the estimate of the sorption-based selectivity. The gas separation performance obtained with this multiscale technique was compared to results obtained with a fully atomistic model and experimental data. The solubility–selectivity for the CO2/CH4 mixture is in reasonable agreement between the two models and the experimental data. The multiscale method presented is a time-efficient alternative to fully atomistic methods and detailed experimental campaigns and can accelerate the introduction of renewable materials in different applications.
The European Muslim Crisis and the Post-October 7 Escalation
Authors: Amin Hira , Hyökki Linda , Salma Umme
Published: September 2024

Israel’s war on Gaza following 7 October 2023 has given birth to several political and social changes in European nations. According to the United Nations Report of the Special Rapporteur, Israel has used this moment to “distort” international humanitarian law principles “in an attempt to legitimize genocidal violence against the Palestinian people.” In the European context, this has led to European Muslims and non-Muslims, including organizations, institutions, as well as individual academics, politicians, and activists mobilizing and voicing their condemnation and demand their governments to do more towards peaceful and equitable solutions. However, this has been met with a strong reaction from European governing bodies. This paper situates this reaction within wider discourses on the European Muslim crisis. It begins with a systematic literature review on the so-called European Muslim crisis, followed by case studies on the United Kingdom and Germany on their respective changes to policies impacting Muslims in the post-October 7 contexa Regarding the literature review, this paper illustrates how this concept has three distinct, yet intersecting meanings: the crisis of European identity; the crisis of foundational ideologies of Europe; and an internal Muslim crisis that often leads to radicalization. Through the British and German case studies, this paper illustrates that October 7 has reinforced and strengthened the shift towards values-based citizenship and integration. This paper argues that through branding pro-Palestine protesters and organizations as extremists in the British context, and adding questions related to antisemitism and Israel in the citizenship tests in the German context, the Israel/Palestine issue has now become yet another yardstick to demarcate the European, civilized “us” vs. the Muslim “other.” In doing so, October 7 has escalated elements already present within the wider discourses of the European Muslim crisis.
Intertwined Critical Realms: Caste, Babas, Deras, and Social Capital Formation in Punjab (India)
Authors: Ram Ronki
Published: September 2024

Deras are generally perceived as an alternative socio-religious space frequented mostly by lower castes and economically weaker sections of society. They promise to make a significant difference to the lives of such vulnerable sections of society by lending them much needed spiritual, moral, and social support. Within the sprawling premises of Deras, downtrodden find a welcoming ‘counter-public’ enriched with social capital, which offers them an egalitarian domain free from the afflictions of caste discriminations, social exclusion, and subtle indignities often faced by them within the mainstream religious spheres. The growth of Deras thus may be seen as an index of subaltern socio-cultural and syncretic religious realms generating a rich haul of social capital.
Blurred Boundaries and Contested Authorities in Two Archenfield Parishes in the Later Middle Ages
Authors: Thomson David
Published: September 2024

This article’s primary aim is to present a highly localised body of evidence from two neighbouring small communities, Garway and Pencoyd, in the Archenfield area of the Welsh March. In Garway’s case, two documents from either end of the long fifteenth century show the local community condemning its parish priest, siding first with one authority and then another, and stressing its Welsh lineage. In Pencoyd, the remarkable survival of the local priest’s manuscripts shows him to have been a diligent pastor and teacher, in tune with diocesan expectations and connected to networks of learning on both sides of the border. Such a local study illuminates evidence of great interest in its own right, but also helps open up the possibility of a wider narrative that attends more fully to local experience in its variety and to the agency of local people.
Application of Direct and Indirect Methodologies for Beach Litter Detection in Coastal Environments
Authors: Sozio Angelo , Scarrica Vincenzo Mariano , Rizzo Angela , Aucelli Pietro Patrizio Ciro , Barracane Giovanni , Dimuccio Luca Antonio , Ferreira Rui , La Salandra Marco , Staiano Antonino , Tarantino Maria Pia , Scicchitano Giovanni
Published: September 2024

In this study, different approaches for detecting of beach litter (BL) items in coastal environments are applied: the direct in situ survey, an indirect image analysis based on the manual visual screening approach, and two different automatic segmentation and classification tools. One is a Mask-RCNN based-algorithm, already used in a previous work, but specifically improved in this study for multi-class analysis. Test cases were carried out at the Torre Guaceto Marine Protected Area (Apulia Region, southern Italy), using a novel dataset from images acquired in different coastal environments by tailored photogrammetric Unmanned Aerial Vehicle (UAV) surveys. The analysis of the overall methodologies used in this study highlights the potential exhibited by the two machine learning (ML) techniques (Mask-RCCN-based and SVM algorithms), but they still show some limitations concerning direct methodologies. The results of the analysis show that the Mask-RCNN-based algorithm requires further improvements and a consistent increase in the number of training elements, while the SVM algorithm shows limitations related to pixel-based classification. Furthermore, the outcomes of this research highlight the high suitability of ML tools for assessing BL pollution and contributing to coastal conservation efforts.
Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
Authors: Wu Kaili , Xu Ziqiang , Luo Yuhan , Li Qidi , Yu Kai , Si Fuqi
Published: September 2024

The Environmental Trace Gases Monitoring Instrument (EMI) series are second-generation Chinese spectrometers on board the GaoFen-5 (GF-5) and DaQi-1 (DQ-1) satellites. In this study, a comparative analysis of EMI series data was conducted to determine the daily trend of ozone concentration changes owing to different transit times and to improve the overall quality and reliability of EMI series datasets. The daily EMI total ozone column (TOC) obtained using the Differential Optical Absorption Spectroscopy (DOAS) method were compared to vertical column density (VCD) gathered by the TROPOspheric Monitoring Instrument (TROPOMI). The results from October to November 2023 indicated a fine correlation (R = 0.98) between the daily EMI series data and a fine correlation (R ≥ 0.95) and spatial distribution closely resembling that of the TROPOMI TOCs. Furthermore, the EMI series data fusion results were highly correlated with TROPOMI TOCs (R = 0.99). Since the EMI series instruments had two different overpass times and the volume of available data at same pixel was increased by approximately three-fold, the temporal and spatial resolution was improved a lot. The results indicated that, compared to a single sensor, the EMI series DOAS TOCs generated more accurate and stable global TOC results and also enabled looking at the changes in the intraday TOCs. These outcomes highlight the potential of the EMI instruments for reliably monitoring the ozone variations in polar regions.
Fine-Grained High-Resolution Remote Sensing Image Change Detection by SAM-UNet Change Detection Model
Authors: Zhao Xueqiang , Wu Zheng , Chen Yangbo , Zhou Wei , Wei Mingan
Published: September 2024

Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from different time periods. Current research faces challenges, including the accurate extraction of change features and the handling of complex and varied image contexts. To address these issues, this study proposes an innovative model named the Segment Anything Model-UNet Change Detection Model (SCDM), which incorporates the proposed center expansion and reduction method (CERM), Segment Anything Model (SAM), UNet, and fine-grained loss function. The global feature map of the environment is extracted, the difference measurement features are extracted, and then the global feature map and the difference measurement features are fused. Finally, a global decoder is constructed to predict the changes of the same region in different periods. Detailed ablation experiments and comparative experiments are conducted on the WHU-CD and LEVIR-CD public datasets to evaluate the performance of the proposed method. At the same time, validation on more complex DTX datasets for scenarios is supplemented. The experimental results demonstrate that compared to traditional fixed-size partitioning methods, the CERM proposed in this study significantly improves the accuracy of SOTA models, including ChangeFormer, ChangerEx, Tiny-CD, BIT, DTCDSCN, and STANet. Additionally, compared with other methods, the SCDM demonstrates superior performance and generalization, showcasing its effectiveness in overcoming the limitations of existing methods.
Optical Characterization of Coastal Waters with Atmospheric Correction Errors: Insights from SGLI and AERONET-OC
Authors: Higa Hiroto , Muto Masataka , Salem Salem Ibrahim , Kobayashi Hiroshi , Ishizaka Joji , Ogata Kazunori , Toratani Mitsuhiro , Takahashi Kuniaki , Maupin Fabrice , Victori Stephane
Published: September 2024

This study identifies the characteristics of water regions with negative normalized water-leaving radiance (nLw(λ)) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative nLw(λ) values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative nLw(λ) values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of nLw(λ) and the single-scattering albedo (ω(λ)) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI nLw(λ), indicate the occurrence of negative values in SGLI nLw(λ) in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by ω(λ) at 443 nm alone. Additionally, a comparison of in situ nLw(λ) measurements in Tokyo Bay and the Ariake Sea with nLw(λ) values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative nLw(λ) values are more likely to occur. These regions also tend to have lower nLw(λ)  values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative nLw(λ) values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative nLw(λ)  values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances.
Perspectives and Challenges in Bolide Infrasound Processing and Interpretation: A Focused Review with Case Studies
Authors: Silber Elizabeth A.
Published: September 2024

Infrasound sensing plays a critical role in the detection and analysis of bolides, offering passive, cost-effective global monitoring capabilities. Key objectives include determining the timing, location, and yield of these events. Achieving these goals requires a robust approach to detect, analyze, and interpret rapidly moving elevated sources such as bolides (also re-entry). In light of advancements in infrasonic methodologies, there is a need for a comprehensive overview of the characteristics that distinguish bolides from other infrasound sources and methodologies for bolide infrasound analysis. This paper provides a focused review of key considerations and presents a unified framework to enhance infrasound processing approaches specifically tailored for bolides. Three representative case studies are presented to demonstrate the practical application of infrasound processing methodologies and deriving source parameters while exploring challenges associated with bolide-generated infrasound. These case studies underscore the effectiveness of infrasound in determining source parameters and highlight interpretative challenges, such as variations in signal period measurements across different studies. Future research should place emphasis on improving geolocation and yield accuracy. This can be achieved through rigorous and systematic analyses of large, statistically significant samples of such events, aiming to resolve interpretative inconsistencies and explore the causes for variability in signal periods and back azimuths. The topic described here is also relevant to space exploration involving planetary bodies with atmospheres, such as Venus, Mars, and Titan.
Robust Underwater Direction-of-Arrival Estimation Method Using Acoustic Sensor Array under Unknown Swing Deviation Elements
Authors: Wang Weidong , Ma Linya , Shi Wentao , Ali Wasiq
Published: September 2024

This paper presents a strategy called the alternating iterative minimization method (AIMM), aimed at enhancing the precision of direction of arrival (DOA) estimation when utilizing an acoustic vector sensor array (AVSA) with unknown swing deviation elements (SDEs). The AVSA model with unknown SDEs is formulated by incorporating the swing deviation parameter. Later, to estimate the swing deviation matrix (SDM) and the sparse signal power by using the alternating iteration method, the auxiliary cost functions with respect to SDM and the sparse signal power are formulated based on the regularized weighted least squares (RWLS) and regularized covariance matrix fitting (RCMF) criteria. Furthermore, their analytical expressions have also been quantified. In order to mitigate the effect of unknown SDEs on the accuracy of DOA estimation, any sub-time segment (STS) in the dataset is selected as the reference to convert the received data of different STS into the reference STS using the estimated SDM. The simulation and experimental outcomes conclusively represent the effectiveness of the suggested TSIM approach using AVSA in handling unknown SDEs.
A Multi-Scale Feature Fusion Based Lightweight Vehicle Target Detection Network on Aerial Optical Images
Authors: Yu Chengrui , Jiang Xiaonan , Wu Fanlu , Fu Yao , Pei Junyan , Zhang Yu , Li Xiangzhi , Fu Tianjiao
Published: September 2024

Vehicle detection with optical remote sensing images has become widely applied in recent years. However, the following challenges have remained unsolved during remote sensing vehicle target detection. These challenges include the dense and arbitrary angles at which vehicles are distributed and which make it difficult to detect them; the extensive model parameter (Param) that blocks real-time detection; the large differences between larger vehicles in terms of their features, which lead to a reduced detection precision; and the way in which the distribution in vehicle datasets is unbalanced and thus not conducive to training. First, this paper constructs a small dataset of vehicles, MiVehicle. This dataset includes 3000 corresponding infrared and visible image pairs, offering a more balanced distribution. In the infrared part of the dataset, the proportions of different vehicle types are as follows: cars, 48%; buses, 19%; trucks, 15%; freight, cars 10%; and vans, 8%. Second, we choose the rotated box mechanism for detection with the model and we build a new vehicle detector, ML-Det, with a novel multi-scale feature fusion triple cross-criss FPN (TCFPN), which can effectively capture the vehicle features in three different positions with an mAP improvement of 1.97%. Moreover, we propose LKC–INVO, which allows involution to couple the structure of multiple large kernel convolutions, resulting in an mAP increase of 2.86%. We also introduce a novel C2F_ContextGuided module with global context perception, which enhances the perception ability of the model in the global scope and minimizes model Params. Eventually, we propose an assemble–disperse attention module to aggregate local features so as to improve the performance. Overall, ML-Det achieved a 3.22% improvement in accuracy while keeping Params almost unchanged. In the self-built small MiVehicle dataset, we achieved 70.44% on visible images and 79.12% on infrared images with 20.1 GFLOPS, 78.8 FPS, and 7.91 M. Additionally, we trained and tested our model on the following public datasets: UAS-AOD and DOTA. ML-Det was found to be ahead of many other advanced target detection algorithms.
Exploration of the Urbanization Process and Its Impact on Vegetation in 125 Resource-Based Cities in China and Comparison with Other Cities
Authors: Han Jiazheng , Sajadi Payam , Hu Zhenqi , Zhou Kaiping , Li Shijin , Feng Zhanjie , Pilla Francesco
Published: September 2024

Resource-based cities (RBCs) in China are at a historic juncture in their transformative development. Observing and assessing the role of the resource curse in urban expansion and greening is crucial for the sustainable development of these cities. This study proposes a new framework to extract urban boundary data from 2000 to 2020 in China. Utilizing these data, we analyzed differences in urban expansion intensity and urban vegetation cover between 125 RBCs and 223 non-RBCs. We found the following: (1) While urban areas in China experienced steady growth from 2000 to 2020, the urban area expansion rates of RBCs lagged behind those non-RBCs located in the same geographical areas except in South China, with the lowest annual expansion rate of 1.18% occurring in the Northeast. (2) Although the existing urban areas in some cities show a greening trend, both existing and new urban areas in China are predominantly characterized overall by browning. Surprisingly, RBCs exhibit a stronger greening trend than non-RBCs, particularly in Northwestern China. (3) There is a nuanced interplay and coexistence between resource dependency and urban expansion, with a specific negative correlation when resource dependency is very high or very low. This study provides a novel method and approach for urban boundary delineation. It offers new insights into the developmental characteristics of RBCs, enriching the theoretical framework of resource curse research and supporting the green development of RBCs with robust data.
Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data
Authors: Li Yisa , Lu Dengsheng , Lu Yagang , Li Guiying
Published: September 2024

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.
Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis
Authors: Lin Kaixiong , Jiapaer Guli , Yu Tao , Zhang Liancheng , Liang Hongwu , Chen Bojian , Ju Tongwei
Published: September 2024

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.
Correction: Ocholla et al. Livestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning Techniques. Remote Sens. 2024, 16, 2929
Authors: Ocholla Ian A. , Pellikka Petri , Karanja Faith , Vuorinne Ilja , Väisänen Tuomas , Boitt Mark , Heiskanen Janne
Published: September 2024

The authors would like to make the following corrections to their published paper [...]
Early Diagnosis of Bronchopulmonary Dysplasia with E-Nose: A Pilot Study in Preterm Infants
Authors: Tenero Laura , Piazza Michele , Sandri Marco , Ferrante Giuliana , Giacomello Elisabetta , Ficial Benjamim , Zaffanello Marco , Biban Paolo , Piacentini Giorgio
Published: September 2024

Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of “no-BPD” vs. “BPD”. Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and p = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms.
Gelatin-Coated High-Sensitivity Microwave Sensor for Humidity-Sensing Applications
Authors: Yeo Junho , Kwon Younghwan
Published: September 2024

In this paper, the humidity-sensing characteristics of gelatin were compared with those of poly(vinyl alcohol) (PVA) at L-band (1 ~ 2 GHz) microwave frequencies. A capacitive microwave sensor based on a defected ground structure with a modified interdigital capacitor (DGS-MIDC) in a microstrip transmission line operating at 1.5 GHz without any coating was used. Gelatin is a natural polymer based on protein sourced from animal collagen, whereas PVA is a high-sensitivity hydrophilic polymer that is widely used for humidity sensors and has a good film-forming property. Two DGS-MIDC-based microwave sensors coated with type A gelatin and PVA, respectively, with a thickness of 0.02 mm were fabricated. The percent relative frequency shift (PRFS) and percent relative magnitude shift (PRMS) based on the changes in the resonant frequency and magnitude level of the transmission coefficient for the microwave sensor were used to compare the humidity-sensing characteristics. The relative humidity (RH) was varied from 50% to 80% with a step of 10% at a fixed temperature of around 25 °C using a low-reflective temperature and humidity chamber manufactured with Styrofoam. The experiment’s results show that the capacitive humidity sensitivity of the gelatin-coated microwave sensor in terms of the PRFS and PRMS was higher compared to that of the PVA-coated one. In particular, the sensitivity of the gelatin-coated microwave sensor at a low RH from 50% to 60% was much greater compared to that of the PVA-coated one. In addition, the relative permittivity of the fabricated microwave sensors coated with PVA and gelatin was extracted by using the measured PRFS and the equation was derived by curve-fitting the simulated results. The change in the extracted relative permittivity for the gelatin-coated microwave sensor was larger than that of the PVA-coated one for varying the RH.
An Electronic “Tongue” Based on Multimode Multidirectional Acoustic Plate Wave Propagation
Authors: Ageykin Nikita , Anisimkin Vladimir , Smirnov Andrey , Fionov Alexander , Li Peng , Qian Zhenghua , Ma Tingfeng , Awasthi Kamlendra , Kuznetsova Iren
Published: September 2024

This paper theoretically and experimentally demonstrates the possibility of detecting the five basic tastes (salt, sweet, sour, umami, and bitter) using a variety of higher-order acoustic waves propagating in piezoelectric plates. Aqueous solutions of sodium chloride (NaCl), glucose (C6H12O6), citric acid (C6H8O7), monosodium glutamate (C5H8NO4Na), and sagebrush were used as chemicals for the simulation of each taste. These liquids differed from each other in terms of their physical properties such as density, viscosity, electrical conductivity, and permittivity. As a total acoustic response to the simultaneous action of all liquid parameters on all acoustic modes in a given frequency range, a change in the propagation losses (ΔS12) of the specified wave compared with distilled water was used. Based on experimental measurements, the corresponding orientation histograms of the ΔS12 were plotted for different types of acoustic waves. It was found that these histograms for different substances are individual and differ in shape, area, and position of their extremes. Theoretically, it has been shown that the influence of different liquids on different acoustic modes is due to both the electrical and mechanical properties of the liquids themselves and the mechanical polarization of the corresponding modes. Despite the fact that the mechanical properties of the used liquids are close to each other, the attenuation of different modes in their presence is not only due to the difference in their electrical parameters. The proposed approach to creating a multi-parametric multimode acoustic electronic tongue and obtaining a set of histograms for typical liquids will allow for the development of devices for the operational analysis of food, medicines, gasoline, aircraft fuel, and other liquid substances without the need for detailed chemical analysis.
Tag-Array-Based UHF Passive RFID Tag Attitude Identification of Tracking Methods
Authors: Wang Honggang , Li Sicheng , Zhou Yurun , Wang Yongli , Pan Ruoyu , Pang Shengli
Published: September 2024

Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the spatial freedom and eliminates phase ambiguity by leveraging the phase difference information between the two tags. Additionally, we delve into the issue of the phase shift caused by coupling interference between the two tags. To effectively compensate for this coupling effect, a series of experiments were conducted to thoroughly examine the specific impact of coupling effects between tags, and based on these findings, a coupling model between tags was established. This model was then integrated into the original phase model to correct for the effects of phase shift, significantly improving the sensing accuracy. Furthermore, we considered the influence of the object rotation angle on phase changes to construct an accurate object attitude recognition and tracking model. To reduce random errors during phase measurement, we employed a polynomial regression method to fit the measured tag phase information, further enhancing the precision of the sensing model. Compared to traditional positioning modes, the dual-tag array strategy essentially increases the number of virtual antennas available for positioning, providing the system with more refined directional discrimination capabilities. The experimental results demonstrated that incorporating the effects of inter-tag coupling interference and rotation angle into the phase model significantly improved the recognition accuracy for both object localization and attitude angle determination. Specifically, the average error of object positioning was reduced to 12.3 cm, while the average error of attitude angle recognition was reduced to 8.28°, making the method suitable for various practical application scenarios requiring attitude recognition.
Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion
Authors: Aissaoui Rachid , De Lutiis Amaury , Feghoul Aiman , Chénier Félix
Published: September 2024

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein–Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments.
Design and Implementation of an Ultra-Wideband Water Immersion Antenna for Underwater Ultrasonic Sensing in Microwave-Induced Thermoacoustic Tomography
Authors: Tan Feifei , Wang Haishi
Published: September 2024

Microwave-induced thermoacoustic tomography (MITAT) holds significant promise in biomedical applications. It creates images using ultrasonic sensors to detect thermoacoustic signals induced by microwaves. The key to generating thermoacoustic signals that accurately reflect the fact is to achieve sufficient and uniform microwave power absorption of the testing target, which is closely tied to the microwave illumination provided by the antenna. In this article, we introduce a novel design and implementation of an ultra-wideband water immersion antenna for an MITAT system. We analyze and compare the advantages of selecting water as the background medium. Simulations are conducted to analyze the ultra-wideband characteristics in impedance matching, axial ratio, and radiation pattern of the proposed antenna. The measured |S11| shows good agreement with the simulated results. We also simulate the microwave power absorption of tumor and brain tissue, and the uniform microwave power absorption and high contrast between the tumor and brain indicate the excellent performance of the proposed antenna in the MITAT system.
Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush
Authors: Lyu Qiang , Pei Fan , Zhao Ying-Long , Ling Jia-Wei , Zhang Neng-Hui
Published: September 2024

The development of numerous diseases, such as renal cyst, cancer, and viral infection, is closely associated with the pathological changes and defects in the cellular peripheral brush. Therefore, it is necessary to develop a potential new method to detect lesions of cellular peripheral brush. Here, a piecewise linear viscoelastic constitutive model of cell is established considering the joint contribution of the peripheral brush and intra-cellular structure. By combining the Laplace transformation and its inverse transformation, and the differential method in the temporal domain and differential quadrature method (DQM) in the spatial domain, the signal interpretation models for quasi-static and dynamic signals of microcantilever are solved. The influence mechanisms of the peripheral brush on the viscoelastic properties of cells and quasi-static/dynamic signals of microcantilever are clarified. The results not only reveal that the peripheral brush has significant effects on the complex modulus of the cell and multi-channel signals of the microcantilever, but also suggest that an alternative mapping method by collecting multi-channel signals including quasi-static and higher frequency signals with more brush indexes could be potentially used to identify cancerous cells.
Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks
Authors: Esheh Jehan , Affes Sofiene
Published: September 2024

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.
Stereo Bi-Telecentric Phase-Measuring Deflectometry
Authors: Wang Yingmo , Fang Fengzhou
Published: September 2024

Replacing the endocentric lenses in traditional Phase-Measuring Deflectometry (PMD) with bi-telecentric lenses can reduce the number of parameters to be optimized during the calibration process, which can effectively increase both measurement precision and efficiency. Consequently, the low distortion characteristics of bi-telecentric PMD contribute to improved measurement accuracy. However, the calibration of the extrinsic parameters of bi-telecentric lenses requires the help of a micro-positioning stage. Using a micro-positioning stage for the calibration of external parameters can result in an excessively cumbersome and time-consuming calibration process. Thus, this study proposes a holistic and flexible calibration solution for which only one flat mirror in three poses is needed. In order to obtain accurate measurement results, the calibration residuals are utilized to construct the inverse distortion map through bicubic Hermite interpolation in order to obtain accurate anchor positioning result. The calibrated stereo bi-telecentric PMD can achieve 3.5 μm (Peak-to-Valley value) accuracy within 100 mm (Width) × 100 mm (Height) × 200 mm (Depth) domain for various surfaces. This allows the obtaining of reliable measurement results without restricting the placement of the surface under test.