bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Macroscopic Mechanical Behavior of Magnetorheological Elastomers under Shear Deformation Using Microscale Representative Volume Element Approach
Authors: Abdollahi Ilda , Sedaghati Ramin
Published: May 2024

Magnetorheological elastomers (MREs) are a class of smart materials with rubber-like qualities, demonstrating revertible magnetic field-dependent viscoelastic properties, which makes them an ideal candidate for development of the next generation of adaptive vibration absorbers. This research study aims at the development of a finite element model using microscale representative volume element (RVE) approach to predict the field-dependent shear behavior of MREs. MREs with different elastomeric matrices, including silicone rubber Ecoflex 30 and Ecoflex 50, and carbonyl iron particles (CIPs) have been considered as magnetic particles. The stress–strain characteristic of the pure silicon rubbers was evaluated experimentally to formulate the nonlinear Ogden strain energy function to describe hyper-elastic behavior of the rubbery matrix. The obtained mechanical and magnetic properties of the matrix and inclusions were integrated into COMSOL Multiphysics to develop the RVE for the MREs, in 2D and 3D configurations, with CIP volume fraction varying from 5% to 40%. Periodic boundary condition (PBC) was imposed on the RVE boundaries, while undergoing shear deformation subjected to magnetic flux densities of 0–0.4 T. Comparing the results from 2D and 3D modeling of isotropic MRE-RVE with the experimental results from the literature suggests that the 3D MRE-RVE can be effectively used to accurately predict the influence of varying factors including matrix type, volume fraction of magnetic particles, and applied magnetic field on the mechanical behavior of MREs.
Polyurethane-Encapsulated Biomass Films Based on MXene@Loofah Sponge for Piezoresistive Pressure Sensor Applications
Authors: Jia Qihan , Liu Shuai , Wang Haibo
Published: May 2024

Multifunctional wearable electronic sensors exhibit significant potential for applications in health management, motion tracking, intelligent healthcare, etc. In this study, we developed a novel assembly method for a polymeric silver nanowire (Ag NW)/transition metal carbide/nitride (MXene) @Loofah device using a facile solution dip-coating technique. During the pretreatment phase, the loofah was conditioned with polydiallyldimethylammonium chloride (PDAC), promoting the self-assembly of MXene layers and bolstering device stability. Then, the Ag NWs/MXene@Loofah was packaged with polyurethane to form a piezoresistive pressure sensor, which demonstrated superior pressure-sensing capabilities and was adept at registering movements of human joints and even subtle pulses. The design strategy presents a novel and rational approach to developing efficient pressure sensors.
Lysine-Triggered Polymeric Hydrogels with Self-Adhesion, Stretchability, and Supportive Properties
Authors: Juan Chieh-Yun , Zhang You-Sheng , Cheng Jen-Kun , Chen Yu-Hsu , Lin Hsin-Chieh , Yeh Mei-Yu
Published: May 2024

Hydrogels, recognized for their flexibility and diverse characteristics, are extensively used in medical fields such as wearable sensors and soft robotics. However, many hydrogel sensors derived from biomaterials lack mechanical strength and fatigue resistance, emphasizing the necessity for enhanced formulations. In this work, we utilized acrylamide and polyacrylamide as the primary polymer network, incorporated chemically modified poly(ethylene glycol) (DF-PEG) as a physical crosslinker, and introduced varying amounts of methacrylated lysine (LysMA) to prepare a series of hydrogels. This formulation was labeled as poly(acrylamide)-DF-PEG-LysMA, abbreviated as pADLx, with x denoting the weight/volume percentage of LysMA. We observed that when the hydrogel contained 2.5% w/v LysMA (pADL2.5), compared to hydrogels without LysMA (pADL0), its stress increased by 642 ± 76%, strain increased by 1790 ± 95%, and toughness increased by 2037 ± 320%. Our speculation regarding the enhanced mechanical performance of the pADL2.5 hydrogel revolves around the synergistic effects arising from the co-polymerization of LysMA with acrylamide and the formation of multiple intermolecular hydrogen bonds within the network structures. Moreover, the acid, amine, and amide groups present in the LysMA molecules have proven to be instrumental contributors to the self-adhesion capability of the hydrogel. The validation of the pADL2.5 hydrogel’s exceptional mechanical properties through rigorous tensile tests further underscores its suitability for use in strain sensors. The outstanding stretchability, adhesive strength, and fatigue resistance demonstrated by this hydrogel affirm its potential as a key component in the development of robust and reliable strain sensors that fulfill practical requirements.
Polyvinylalcohol Composite Filled with Carbon Dots Produced by Laser Ablation in Liquids
Authors: Cutroneo Mariapompea , Silipigni Letteria , Malinsky Petr , Slepicka Petr , Franco Domenico , Torrisi Lorenzo
Published: May 2024

Carbon dots (CDs), owing to their excellent photoluminescent features, have been extensively studied for physics preparation methods and for biomedical and optoelectronic device applications. The assessment of the applicability of CDs in the production of luminescent polymeric composites used in LEDs, displays, sensors, and wearable devices is being pursued. The present study reports on an original, environmentally friendly, and low-cost route for the production of carbon dots with an average size of 4 nm by laser ablation in liquid. Jointly, to prove the significance of the study for a wide range of applications, a free-standing flexible polyvinyl alcohol (PVA) composite containing photoluminescent carbon dots was manufactured. CDs were prepared using targets of porose charcoal with a density of 0.271 g/cm3 placed in phosphate-buffered saline (PBS) liquid solution and irradiated for 30 min by pulsed IR diode laser. The optical properties of the obtained suspension containing carbon dots were studied with UV-ViS and FTIR spectroscopies. The photoluminescence of the produced carbon dots was confirmed by the emission peak at 480 nm in the luminescence spectrum. A narrow luminescence band with a full width at half-maximum (FWHM) of less than 40 nm could be an asset in spectral emission analysis in different applications. Atomic force microscopy confirms the feasibility of manufacturing CDs in clean and biocompatible environments, paving the way for an easier and faster production route, crucial for their wider applicability.
Postural Orthostatic Tachycardia Syndrome (POTS) as an Adverse Event to the Human Papilloma Virus (HPV) Vaccine and Its Relationship with Ehlers–Danlos Syndrome (EDS)
Authors: Schipperijn Nicole , Wijesinghe Megan , Romo Aisa , Brooks Benjamin
Published: May 2024

Gardasil 4, a human papilloma virus vaccine, has been shown to protect against various cancers, including cervical cancer. Common side effects include injection site pain, fever, headaches, and muscle aches. In some individuals, the severe side effect of postural orthostatic tachycardia syndrome (POTS) has been reported. POTS is characterized by the abnormal response of lightheadedness, blurry vision, and dizziness while transitioning to an upright posture. POTS predominately affects women, with more than eighty-five (85) percent of POTS patients being female. POTS, on average, takes five years and eleven months to receive diagnosis. Additionally, a strong association between POTS and Ehlers–Danlos Syndrome Type III (EDS) exists. Eighty (80) percent of patients with EDS have POTS. This severe side effect indicates that providers need to be aware of this strong association of HPV vaccinations and POTS. In this report, we will present a case of a young women with a past medical history significant for EDS type III who was diagnosed with POTS after receiving Gardasil 4 vaccination. This case demonstrates the need for physicians to be aware of the association of POTS with EDS type III and HPV vaccination. Physician awareness of the associations, signs, and symptoms of POTS and earlier testing at the first presentation of signs and symptoms will limit the negative impact on patient’s quality of life.
An Advanced Quality Assessment and Monitoring of ESA Sentinel-1 SAR Products via the CyCLOPS Infrastructure in the Southeastern Mediterranean Region
Authors: Kakoullis Dimitris , Fotiou Kyriaki , Ibarrola Subiza Nerea , Brcic Ramon , Eineder Michael , Danezis Chris
Published: May 2024

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.
Modeling Climate Characteristics of Qinghai Lake Ice in 1979–2017 by a Quasi-Steady Model
Authors: Tang Hong , Zhao Yixin , Wen Lijuan , Leppäranta Matti , Niu Ruijia , Fu Xiang
Published: May 2024

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons.
Study on the Expansion Potential of Artificial Oases in Xinjiang by Coupling Geomorphic Features and Hierarchical Clustering
Authors: Song Keyu , Cheng Weiming , Wang Baixue , Xu Hua , Wang Ruibo , Zhang Yutong
Published: May 2024

The study of the expansion potential of artificial oases based on remote sensing data is of great significance for the rational allocation of water resources and urban planning in arid areas. Based on the spatio-temporal relationship between morphogenetic landform types and the development of artificial oases in Xinjiang, this study explored the development pattern of artificial oases in the past 30 years by using trend analysis and centroid migration analysis, constructing a series of landform–artificial oasis change indices, and investigating the suitability of different landforms for the development of artificial oases based on geomorphological location by adopting a hierarchical clustering method. The following conclusions are drawn: (1) From 1990 to 2020, the area of artificial oases in the whole territory continued to increase, with significant expansion to the south from 2005 to 2010. (2) Six categories of landform types for artificial oasis development were created based on the clustering results. Of these, 7.39% and 6.15% of the area’s geomorphological types belonged to the first and second suitability classes, respectively. (3) The optimal scale for analyzing the suitability of landforms for the development of artificial oases over the past 30 years in the whole area was 8 km, which could explain more than 96% of the changes in the growth of artificial oases. The distribution of landforms of first- and second-class suitability within the 8 km buffer zone of an artificial oasis in the year 2020 was 10.55% and 9.90%, respectively, and landforms of first-class suitability were mainly concentrated in the near plain side of the urban agglomerations located on the northern and southern slopes of the Tianshan Mountains, and the urban agglomerations at the southern edge of Altai Mountains. This study quantified the potential of different geomorphological types for the development of artificial oases and provided a basis for site selection in future artificial oasis planning and urban construction.
A Machine-Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures
Authors: Agapiou Athos , Gravanis Elias
Published: May 2024

The detection of subsurface archaeological remains using a range of remote sensing methods poses several challenges. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work, we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near-infrared parts of the spectrum were taken in a controlled environment in Cyprus during 2011–2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, in particular in the neighborhood of 570 nm, and (2) the red edge part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis, we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors’ knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.
Influence of Inter-System Biases on Combined Single-Frequency BDS-2 and BDS-3 Pseudorange Positioning of Different Types of Receivers
Authors: Ma Zeyu , Cui Jianhui , Liu Zhimin , Su Xing , Xiang Yan , Xu Yan , Deng Chenlong , Hui Mengtang , Li Qing
Published: May 2024

The BeiDou Navigation Satellite System (BDS) has developed rapidly, and the combination of BDS Phase II (BDS-2) and BDS Phase III (BDS-3) has attracted wide attention. It is found that there are code ISBs between BDS-2 and BDS-3, which may have a certain impact on the BDS-2 and BDS-3 combined positioning. This paper focuses on the performance of BDS-2/BDS-3 combined B1I single-frequency pseudorange positioning and investigates the positioning performance with and without code ISBs correction for different types of receivers, include geodetic GNSS receivers and low-cost receivers. The results show the following: (1) For geodetic GNSS receivers, the code ISBs of each receiver is about −0.3 m to −0.8 m, and the position deviation is reduced by 7% after correcting code ISBs. The code ISBs in the baseline with homogeneous receivers has a little influence on the positioning result, which can be ignored. The code ISBs in the baseline with heterogeneous receivers is about −0.5 m, and the position deviation is reduced by 4% after correcting code ISBs. (2) The code ISBs in the low-cost receivers are significantly larger than those in the geodetic GNSS receivers, and the impact on the positioning performance of the low-cost receivers is significantly greater than that on the geodetic GNSS receivers. After correcting the code ISBs, the position deviation of low-cost receivers can be reduced by around 12% for both undifferenced and differenced modes. (3) For low-cost receivers, correcting the code ISBs can increase the number of epochs successfully solved, which effectively improves the low-cost navigation and positioning performance. (4) The carrier-phase-smoothing method can effectively reduce the distribution dispersion of code ISBs and make the estimation of ISBs more accurate. The STD values of estimated code ISBs in geodetic GNSS receivers are reduced by about 40% after carrier-phase smoothing, while the corresponding values are reduced by about 7% in low-cost receivers due to their poor carrier-phase observation quality.
Diurnal Asymmetry Effects of Photovoltaic Power Plants on Land Surface Temperature in Gobi Deserts
Authors: Wang Xubang , Zhou Qianru , Zhang Yong , Liu Xiang , Liu Jianquan , Chen Shengyun , Wang Xinxin , Wu Jihua
Published: May 2024

The global expansion of photovoltaic (PV) power plants, especially in ecologically fragile regions like the Gobi Desert, highlights the suitability of such areas for large-scale PV development. The most direct impact of PV development in the Gobi Desert is temperature change that results from the land-use-induced albedo changes; however, the detailed and systemic understanding of the effects of PV expansion on land surface temperature remains limited. This study focuses on the 16 largest PV plants in the Chinese Gobi Desert, utilizing remote sensing data to assess their effects on land surface temperature. Our result showed a cooling effect during the daytime (−0.69 ± 0.10 °C), but a warming effect during the nighttime (0.23 ± 0.05 °C); the overall effect on the daily mean was a cooling effect (−0.22 ± 0.05 °C). Seasonal variations were observed, with the most significant cooling effect in autumn and the weakest in summer. The PV area was the most significant factor which influenced the temperature variation across PV plants. Our findings enrich our understanding of the environmental effects arising from the construction of PV plants and provide vital information for the design and management of increasingly renewable electricity systems globally.
On the Initial Phase of the Ongoing Unrest at Campi Flegrei and Its Relation with Subsidence at Vesuvio (Italy)
Authors: Amoruso Antonella , Gualandi Adriano , Crescentini Luca
Published: May 2024

The densely inhabited area of Naples (Italy), between the Campi Flegrei and Vesuvio volcanoes, is one of the most hazardous regions in the world. After two decades of sustained subsidence, Campi Flegrei has been experiencing an accelerating uplift since 2005. The uplift is currently associated with unusual seismicity and increased degassing. To try to identify the cause of the shift from subsidence to uplift and explore any connection between Campi Flegrei and Vesuvio, we analysed the ground displacement time series of the two volcanoes from 1993 to 2010, obtained from ERS/ENVISAT Synthetic Aperture Radar imagery. To distinguish between the various sources of deformation, we used simple scatter plots and a blind source separation technique called variational Bayesian independent component analysis (vbICA). We obtained consistent results using both approaches. Specifically, with vbICA, we identified two significant independent components (ICs). IC1 describes the subsidence that occurred at Campi Flegrei prior to 2000, including the mini-uplifts of 2000 and 2005, and part of the post-2005 uplift. The expansion and contraction of two volumes beneath Campi Flegrei satisfy IC1: a sill-shaped volume at a depth of approximately 3 km and a small volume at a depth of 1–2 km, respectively. The two sources of deformation reproduce the large-scale deformation in the Campi Flegrei area and the local deformation in the Solfatara area, respectively. In the Campi Flegrei area, IC2 exhibits primarily uplift, which is concentrated in the eastern part of the caldera. The deformation pattern is complex and difficult to interpret. If we model it using simple spheroidal deformation sources, the pattern suggests that two volumes at depths of approximately 9 and 8 km are experiencing opposite activity, namely contraction (beneath the southwestern part of the caldera) and expansion (beneath the central part of the caldera). In the Vesuvio area, IC2 is consistent with the deformation induced by the contraction of a volume at a depth of around 9 km. The contraction beneath Vesuvio is smaller in magnitude than the expansion/contraction beneath Campi Flegrei. The correlation observed after 2002 between uplift at Campi Flegrei and subsidence at Vesuvio suggests the transfer of magma and/or magmatic fluids between the two plumbing systems at 8–9 km depth. This implies that part of the ongoing unrest at Campi Flegrei may have been promoted by mass transfer from below Vesuvio.
Retrieval of At-Surface Upwelling Radiance and Albedo by Parameterizing Cloud Scattering and Transmittance over Rugged Terrain
Authors: Jia Junru , Menenti Massimo , Jia Li , Chen Qiting , Xu Anlun
Published: May 2024

Accurate and continuous estimation of surface albedo is vital for assessing and understanding land–surface–atmosphere interactions. We developed a method for estimating instantaneous all-sky at-surface shortwave upwelling radiance and albedo over the Tibetan Plateau. The method accounts for the complex interplay of topography and atmospheric interactions and aims to mitigate the occurrence of data gaps. Employing an RTLSR-kernel-driven model, we retrieved surface shortwave albedo with a 1 km resolution, incorporating direct, isotropic diffuse; circumsolar diffuse; and surrounding terrain irradiance into the all-sky solar surface irradiance. The at-surface upwelling radiance and surface shortwave albedo estimates were in satisfactory agreement with ground observations at four stations in the Tibetan Plateau, with RMSE values of 56.5 W/m2 and 0.0422, 67.6 W/m2 and 0.0545, 98.6 W/m2 and 0.0992, and 78.0 98.6 W/m2 and 0.639. This comparison indicated an improved accuracy of at-surface upwelling radiance and surface albedo and significantly reduced data gaps. Valid observations increased substantially in comparison to the MCD43A2 data product, with the new method achieving an increase ranging from 40% to 200% at the four stations. Our study demonstrates that by integrating terrain, cloud properties, and radiative transfer modeling, the accuracy and completeness of retrieved surface albedo and radiance in complex terrains can be effectively improved.
ABNet: An Aggregated Backbone Network Architecture for Fine Landcover Classification
Authors: Si Bo , Wang Zhennan , Yu Zhoulu , Wang Ke
Published: May 2024

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.
Initial Study of Adaptive Threshold Cycle Slip Detection on BDS/GPS Kinematic Precise Point Positioning during Geomagnetic Storms
Authors: Su Xing , Zeng Jiajun , Zhou Quan , Liu Zhimin , Li Qiang , Li Zhanshu , Wang Guangxing , Ma Hongyang , Cui Jianhui , Chen Xin
Published: May 2024

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth’s magnetosphere, ionosphere, and upper atmosphere affect GNSS’s positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms.
Estimation of Suspended Sediment Concentration along the Lower Brazos River Using Satellite Imagery and Machine Learning
Authors: Stull Trevor , Ahmari Habib
Published: May 2024

This article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R2 above 0.69, with the three best performing models having an R2 of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model’s ability to estimate SSCs.
Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites
Authors: Chatar Keenan , Kitamura Kentaro , Cho Mengu
Published: May 2024

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.
A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction
Authors: Feng Yuhu , Zhang Jiahuan , Li Guang , Togo Ren , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: May 2024

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction.
Environmental Surveillance through Machine Learning-Empowered Utilization of Optical Networks
Authors: Awad Hasan , Usmani Fehmida , Virgillito Emanuele , Bratovich Rudi , Proietti Roberto , Straullu Stefano , Aquilino Francesco , Pastorelli Rosanna , Curri Vittorio
Published: May 2024

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.
Mobile Robot + IoT: Project of Sustainable Technology for Sanitizing Broiler Poultry Litter
Authors: Kunz Cechinel Alan , Soares Carlos Eduardo , Pfleger Sergio Genilson , De Oliveira Leonardo Luiz Gambalonga Alves , Américo de Andrade Ederson , Damo Bertoli Claudia , De Rolt Carlos Roberto , De Pieri Edson Roberto , Plentz Patricia Della Méa , Röning Juha
Published: May 2024

The traditional aviary decontamination process involves farmers applying pesticides to the aviary’s ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O3) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot’s navigation uses a back-and-forth motion that parallels the aviary’s longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries’ standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment.
Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients
Authors: Wang Litong , Wang Likai , Wang Zhan , Gao Fei , Wu Jingyi , Tang Hong
Published: May 2024

(1) Background: This study evaluates the effectiveness of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) in improving gait in post-stroke hemiplegic patients, using wearable sensor technology for objective gait analysis. (2) Methods: A total of 72 stroke patients were randomized into control, sham stimulation, and LF-rTMS groups, with all receiving standard medical treatment. The LF-rTMS group underwent stimulation on the unaffected hemisphere for 6 weeks. Key metrics including the Fugl-Meyer Assessment Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and gait parameters were measured before and after treatment. (3) Results: The LF-rTMS group showed significant improvements in the FMA-LE, BBS, MBI, and various gait parameters compared to the control and sham groups (p < 0.05). Specifically, the FMA-LE scores improved by an average of 5 points (from 15 ± 3 to 20 ± 2), the BBS scores increased by 8 points (from 35 ± 5 to 43 ± 4), the MBI scores rose by 10 points (from 50 ± 8 to 60 ± 7), and notable enhancements in gait parameters were observed: the gait cycle time was reduced from 2.05 ± 0.51 s to 1.02 ± 0.11 s, the stride length increased from 0.56 ± 0.04 m to 0.97 ± 0.08 m, and the walking speed improved from 35.95 ± 7.14 cm/s to 75.03 ± 11.36 cm/s (all p < 0.001). No adverse events were reported. The control and sham groups exhibited improvements but were not as significant. (4) Conclusions: LF-rTMS on the unaffected hemisphere significantly enhances lower-limb function, balance, and daily living activities in subacute stroke patients, with the gait parameters showing a notable improvement. Wearable sensor technology proves effective in providing detailed, objective gait analysis, offering valuable insights for clinical applications in stroke rehabilitation.
A Study of the Elemental Profiles of Wines from the North-Eastern Coast of the Black Sea
Authors: Oganesyants Lev A. , Panasyuk Alexandr L. , Sviridov Dmitriy A. , Egorova Olesya S. , Akbulatova Dilyara R. , Ganin Mikhail Y. , Shilkin Aleksey A. , Il’in Alexandr A.
Published: May 2024

Due to the increasing consumer interest in wines with a controlled place of origin, PGI (Protected Geographical Indication) and PDO (Protected Designation of Origin), the most acute question is how to identify them. One of the most effective ways to confirm the place of origin of wine in global practice is a comprehensive study of the elemental profile using statistical analysis methods. In the period from 2020 to 2023, 152 grape samples of grapes were collected from various wineries in Crimea and Kuban. The grape must that was obtained from them was fermented in laboratory conditions. The elemental profile was determined in the prepared wines, which included 71 indicators. In the conducted work, it was revealed that wines from Crimea and Kuban differ statistically significantly in the concentration of the elements B, Ca, Cu, Mn, Na, Ni, Re, Si, Sn and U. At the same time, the contents of the elements U, Sn and Re prevail in wines from Crimea, and those of B, Ca, Cu, Mn, Na, Ni and Si prevail in wines from Kuban. At the same time, methods of univariate and multivariate statistics do not allow us to reliably classify wine samples from Crimea and Kuban by their place of origin. In order to reveal the non-linear dependence of the studied indicators in wines on the geographical place of grape growing, the method of a supervised learning Random Forest was used. After training the model on the dataset, the proportion of its correct predictions was 96%. The model used 61 parameters, among which the most important were Ni, Re, Ba, Rb, Na, U, Sb, Zn, Bi, Ag and Ti.
Gender-Based Biopsychosocial Correlates of Truancy in Physical Education: A National Survey among Adolescents in Benin
Authors: Srem-Sai Medina , Agormedah Edmond Kwesi , Hagan John Elvis , Gbordzoe Newton Isaac , Sarfo Jacob Owusu
Published: May 2024

Truancy among in-school adolescents has long been established as a major issue that negatively impacts educational attainment or goals and mental health-related issues. However, research on subjects such as physical education (PE) truancy and its correlates is somewhat limited. This study estimated the prevalence of PE and assessed the factors associated with PE truancy in Benin among in-school adolescents. The 2016 Benin’s Global School-Based Health Survey (GSHS) data from a sample of in-school adolescents (n = 2496; 13–17 years) were used for data analysis. The overall prevalence of past 30-day truancy in PE class was 15.4%, with female adolescents reporting a higher prevalence of PE truancy than their male counterparts. Stratified by gender, the regression analyses showed that males in 3rd–6th grade (aOR = 0.69, CI = 0.50–0.96) experiencing hunger (aOR = 0.51, CI = 0.32–0.81) and having suicidal ideations (aOR = 1.64, CI = 1.07–2.53) predisposed adolescents to PE truant behaviour. For female in-school adolescents experiencing hunger (aOR = 1.75, CI = 1.15–2.65), drinking alcohol (aOR = 0.62, CI = 0.44–0.87), having sedentary lifestyles (aOR = 0.62, CI = 0.40–0.96), and being physically attacked (aOR = 0.53, CI = 0.33–0.87) were significantly associated with PE truancy. However, understanding and supportive parents [aOR = 0.61, 95% CI = 0.41–0.91] was a protective factor against PE truancy. The present findings suggest the design of gender-sensitive school-based interventions to help minimise or prevent PE truancy among in-school adolescents in Benin based on the predisposing factors while emphasising the protective influences.
The More Democracy, the Better? On Whether Democracy Makes Societies Open
Authors: López Cristian
Published: May 2024

It is a common view that Popper’s defense of the open society has been a defense of Western, liberal democracies. This seems to imply that by fostering democratic institutions we are ipso facto fostering open societies. I criticize this view by arguing that in-built incentives in democratic mechanisms move us away from (or hamper) the open society. Democracy promotes voters’ ignorance, indulges voters’ irrationality, and allows voters to externalize costs. This is contrary to well-informed, rational decisions and personal responsibility that lie at the fundamentals of the open society. I suggest that it has been free-market capitalism, or free-market societies, which has moved us closer to the ideal of the open society and which best realizes open society’s values.
The Mediating Effect of Post-Traumatic Growth on the Relationship between Adverse Childhood Experiences and Psychological Distress in Adults
Authors: Caetano Sara , Pereira Henrique
Published: May 2024

Background: Research has shown that Adverse Childhood Experiences (ACEs) are prevalent and are associated with psychological distress. Some studies indicate facing these adversities can lead to post-traumatic growth. This study aims to assess the impact of ACEs on psychological distress and post-traumatic growth and to determine the mediating effect of post-traumatic growth between ACEs and psychological distress, in a sample of adults. Methods: In this study, there were 521 participants (mean = 31.32, SD = 12.28), who answered the following surveys online: a sociodemographic questionnaire, the Family ACE Questionnaire, the Kessler Psychological Distress Scale (K10) and the Post-Traumatic Growth Inventory (PTGI). Results: ACEs were positive and significant predictors of psychological distress, and the “Change in the perception of the self and life in general” factor of post-traumatic growth was the strongest predictor of lower perceived psychological distress. Post-traumatic growth did not mediate the relationship between ACEs and psychological distress. Conclusions: These findings contribute to the improvement of clinical practice and health policies and highlight the need for a more in-depth understanding of the impact of ACEs on mental health.