bluuebunny/crossref_metadata_2025 huggingface dataset:
Service Life Prediction of Type-IV Composite CNG Cylinder under the Influence of Drivers’ Refueling Habits—A Numerical Study
Authors: Reza Kashyzadeh Kazem , Marusin Aleksandr Vyacheslavovich
Published: May 2023

The new generation presented for CNG fuel tanks of vehicles (type-IV) is made entirely of composites. The reason for that is to prevent the sudden explosion of metal tanks and to use the advantage of gas leakage in composite materials. Previous research has shown that type-IV CNG fuel tanks also have challenges such as variable wall thickness in outer shell parts, which are prone to failure under cyclic refueling loading. The optimization of this structure is on the agenda of many scholars and automakers, and in this regard, there are many standards for strength assessment. Despite reporting injury events, it seems that another parameter should be included in these calculations. In this article, the authors have attempted to numerically investigate the effect of drivers’ refueling habits on the service life of type-IV CNG fuel tanks. For this purpose, a 34-L CNG tank made of glass/epoxy composite, polyethylene, and Al-7075T6, respectively, for the outer shell parts, liner, and flanges was considered as a case study. Moreover, a real-size measurement-based finite element model validated in the corresponding author’s previous research was used. The loading history was applied as internal pressure according to the standard statement. Furthermore, considering different behavior of drivers for refueling, several loading histories with asymmetric details were applied. Eventually, the results obtained for different cases were compared to experimental data in symmetrical loading. The results showed that, based on the car’s mileage, the driver’s behavior in the refueling process can significantly reduce the service life of the tank (up to 78% of the predicted life based on the standard methodology).
Synthesis, Characterization, Properties, and Biomedical Application of Chitosan-Based Hydrogels
Authors: Ye Ruixi , Liu Siyu , Zhu Wenkai , Li Yurong , Huang Long , Zhang Guozheng , Zhang Yeshun
Published: May 2023

The prospective applications of chitosan-based hydrogels (CBHs), a category of biocompatible and biodegradable materials, in biomedical disciplines such as tissue engineering, wound healing, drug delivery, and biosensing have garnered great interest. The synthesis and characterization processes used to create CBHs play a significant role in determining their characteristics and effectiveness. The qualities of CBHs might be greatly influenced by tailoring the manufacturing method to get certain traits, including porosity, swelling, mechanical strength, and bioactivity. Additionally, characterization methods aid in gaining access to the microstructures and properties of CBHs. Herein, this review provides a comprehensive assessment of the state-of-the-art with a focus on the affiliation between particular properties and domains in biomedicine. Moreover, this review highlights the beneficial properties and wide application of stimuli-responsive CBHs. The main obstacles and prospects for the future of CBH development for biomedical applications are also covered in this review.
Experimental and Numerical Analysis of Stitched Composite Laminates Subjected to Low-Velocity Edge-on Impact and Compression after Edge-on Impact
Authors: Liu Bangxiong , Lai Jiamei , Liu Hesheng , Huang Zhichao , Liu Bin , Peng Ze , Zhang Wei
Published: May 2023

Composite laminates are susceptible to impact events during use and maintenance, affecting their safety performance. Edge-on impact is a more significant threat to laminates than central impact. In this work, the edge-on impact damage mechanism and residual strength in compression were investigated using experimental and simulation methods by considering variations in impact energy, stitching, and stitching density. The damage to the composite laminate after edge-on impact was detected in the test by visual inspection, electron microscopic observation, and X-ray computed tomography techniques. The fiber and matrix damage were determined according to the Hashin stress criterion, while the cohesive element was used to simulate the interlaminar damage. An improved Camanho nonlinear stiffness discount was proposed to describe the stiffness degradation of the material. The numerical prediction results matched well with the experimental values. The findings show that the stitching technique could improve the damage tolerance and residual strength of the laminate. It can also effectively inhibit crack expansion, and the effect increases with increasing suture density.
Adhesion of Conventional, 3D-Printed and Milled Artificial Teeth to Resin Substrates for Complete Dentures: A Narrative Review
Authors: Tzanakakis Emmanouil-George , Pandoleon Panagiotis , Sarafianou Aspasia , Kontonasaki Eleana
Published: May 2023

Background: One type of failure in complete or partial dentures is the detachment of resin teeth from denture base resin (DBR). This common complication is also observed in the new generation of digitally fabricated dentures. The purpose of this review was to provide an update on the adhesion of artificial teeth to denture resin substrates fabricated by conventional and digital methods. Methods: A search strategy was applied to retrieve relevant studies in PubMed and Scopus. Results: Chemical (monomers, ethyl acetone, conditioning liquids, adhesive agents, etc.) and mechanical (grinding, laser, sandblasting, etc.) treatments are commonly used by technicians to improve denture teeth retention with controversial benefits. Better performance in conventional dentures is realized for certain combinations of DBR materials and denture teeth after mechanical or chemical treatment. Conclusions: The incompatibility of certain materials and lack of copolymerization are the main reasons for failure. Due to the emerging field of new techniques for denture fabrication, different materials have been developed, and further research is needed to elaborate the best combination of teeth and DBRs. Lower bond strength and suboptimal failure modes have been related to 3D-printed combinations of teeth and DBRs, while milled and conventional combinations seem to be a safer choice until further improvements in printing technologies are developed.
A Red-Light-Responsive DASA–Polymer with High Water Stability for Controlled Release
Authors: Ma Hao , Li Wan , Fan Haojun , Xiang Jun
Published: May 2023

Photoresponsive polymers hold vast potential in the realm of drug delivery. Currently, most photoresponsive polymers use ultraviolet (UV) light as the excitation source. However, the limited penetration ability of UV light within biological tissues serves as a significant hindrance to their practical applications. Given the strong penetration ability of red light in biological tissues, the design and preparation of a novel red-light-responsive polymer with high water stability, incorporating the reversible photoswitching compound and donor-acceptor Stenhouse adducts (DASA) for controlled drug release is demonstrated. In aqueous solutions, this polymer exhibits self-assembly into micellar nanovectors (~33 nm hydrodynamic diameter), facilitating the encapsulation of the hydrophobic model drug Nile red (NR) within the micellar core. Upon irradiation by a 660 nm LED light source, photons are absorbed by DASA, leading to the disruption of the hydrophilic–hydrophobic balance of the nanovector and thereby resulting in the release of NR. This newly designed nanovector incorporates red light as a responsive switch, successfully avoiding the problems of photodamage and limited penetration of UV light within biological tissues, thereby further promoting the practical applications of photoresponsive polymer nanomedicines.
Evaluating Eco-Friendly Refrigerant Alternatives for Cascade Refrigeration Systems: A Thermoeconomic Analysis
Authors: Shanmugasundar G. , Logesh Kamaraj , Čep Robert , Roy Ranendra
Published: May 2023

A simple vapor-compression refrigeration system becomes ineffective and inefficient as it consumes a huge energy supply when operating between large temperature differences. Moreover, the recent Kigali amendment has raised a concern about phasing out some hydrofluorocarbon refrigerants due to their impact on the environment. In this paper, a numerical investigation is carried out to compare the performance of a cascade refrigeration system with two environmentally friendly refrigerant combinations, namely, R170–R404A and R41–R404A. Refrigerant R170, from the hydrocarbon category, and refrigerant R41, from the hydrofluorocarbon category, are separately chosen for the low-temperature circuit due to their similar thermophysical properties. On the other hand, refrigerant R404A is selected for the high-temperature circuit. An attempt is made to replace refrigerant R41 with refrigerant R170 as a possible alternative. The condenser temperature is kept constant at 40 °C, and the evaporator temperature is varied from −60 °C to −30 °C. The mathematical model developed for the cascade refrigeration system is solved using Engineering Equation Solver (EES). The effect of evaporator temperature on different performance parameters such as the COP, exergetic efficiency, and total plant cost rate is evaluated. The predicted results show that the thermoeconomic performance of the R170–R404A-based system is marginally lower compared to that of the R41–R404A-based system. The system using refrigerant pair R170–R404A has achieved only a 2.4% lower exergetic efficiency compared to the system using R41–R404A, with an increase in the annual plant cost rate of only USD 200. As the global warming potential (GWP) of R170 is less than that of R41, and R170 belongs to the hydrocarbon category, the use of the R170–R404A combination in a cascade refrigeration system can be recommended as an alternative to R41–R404A.
Pilot Study on the Deep Treatment of Sulfuric-Acid–Titanium-Dioxide Wastewater Using an Ultrafiltration/Reverse Osmosis Process
Authors: Li Liangfu , Li Sirui , Dong Yiming , Yang Ping , Wang Runkai
Published: May 2023

The production of titanium dioxide via the sulfuric acid process generates large amounts of acidic wastewater. Investigating the possibility of reusing this wastewater after deep treatment can reduce pollutant discharge and conserve water resources. In a pilot study, a dual-membrane method of ultrafiltration (UF) and reverse osmosis (RO) was employed to perform deep treatments of sulfuric-acid–titanium-dioxide wastewater. The findings showed that the multimedia and precision filters reduced the turbidity of water from an external drainage to as low as 0.18 NTU, with a turbidity removal rate of approximately 50%, reaching a maximum of 68%. When the UF effluent had a membrane flux of 70–100 L/m2 h and a water production rate of 85–90%, the SDI15 was <5.0 and the turbidity was <1.0, meeting RO water supply requirements. Additionally, RO achieved a TDS removal rate of >95%, a CODCr removal rate of 85%, and a desalination rate of >98.5%. At a smooth operation system water recovery rate of 50%, the highest system recovery rate obtained was 64%. The water produced via RO adhered to reuse water standards. UF/RO deep treatment of sulfuric-acid–titanium-dioxide production wastewater and its reuse can realize comprehensive wastewater use and conserve water resources.
Research on Properties of Ash and Slag Composite Cementitious Materials for Biomass Power Plants
Authors: Zhang Yanru , Zhao Baofeng , Zhu Jianjun , Wang Zhenjiang , Ren Changzai , Xie Hongzhang , Guan Haibin , Zhu Di
Published: May 2023

The effects of ash and slag from a biomass power plant on the compressive strength, setting time and fluidity of the pastes of Portland cement (P.O) and sulfoaluminate cement (SAC) were studied, and the hydration products and microstructure at the age of 7 days were analyzed via XRD, SEM and other test methods. The results show that the compressive strength of the composite cementitious material decreases, the setting time prolongs and the fluidity increases with the increase in the ash and slag content in the power plant. The microscopic analysis shows that the ash and slag of the biomass power plant can promote the hydration of Portland cement and sulfoaluminate cement paste, and increase the generation of hydration products. The results showed that replacing SAC clinker with 20–30% biomass power plant ash (BPPA) decreased the cement strength, and that an appropriate amount of BPPA (10–15%) could significantly improve the mechanical strength of SAC blended cement. The compressive strength of blended BPPA composite cementitious material in 28 days could reach 60 MPa. This study provided solutions to utilizing the BPPA as a building material admixture to minimize the consumption of energy-intensive cement and to meet the growing needs of the construction industry.
Modification and Validation of a Dynamic Thermal Resistance Model for Wet-State Fabrics
Authors: Wu Zijiang , Shi Yunlong , Yang Ruiliang , Qian Xiaoming , Fang Shuting
Published: May 2023

To investigate the dynamic thermal resistance of woven fabrics in different wetting states, ten commonly used clothing fabrics were selected and tested for fabric thermal resistance under different levels of water saturation in accordance with Chinese national standards. Based on Mangat’s eight thermal resistance prediction models, the study improved the models by replacing the original moisture content with water content saturation. The suitability of the eight models in predicting the thermal resistance of woven fabrics in wet states was compared using the sum of squared deviations (SSD), sum of absolute deviations (SAD), and correlation coefficient (R2). The results showed that during the process from initial wetting to complete immersion, the measured thermal resistance values of the ten fabric samples were consistent with the predicted values from Model 5 in the theoretical model of thermal resistance (R2 > 0.955). The characteristic of Model 5 is that the air thermal resistance and water thermal resistance are first connected in parallel and then connected in series with the fiber thermal resistance. The corrected predicted values from Model 5 were highly consistent with the experimental measurement values and can be used to approximate the thermal resistance of woven fabrics in wet states.
Techno-Economic Assessment of PEM Electrolysis for O2 Supply in Activated Sludge Systems—A Simulation Study Based on the BSM2 Wastewater Treatment Plant
Authors: Parra Ramirez Mario Alejandro , Fogel Stefan , Reinecke Sebastian Felix , Hampel Uwe
Published: May 2023

The conversion of renewable energy into hydrogen (H2) by power-to-gas technologies involving electrolysis is seen today as a key element in the transition to a sustainable energy sector. Wastewater treatment plants (WWTP) could be integrated into future green H2 networks as users of oxygen (O2) produced alongside H2 in water electrolysis. In WWTPs, O2 is required for biological treatment steps, e.g., in activated sludge (AS) systems. However, the production costs of electrolysis O2 should be competitive with those of conventional O2 production processes. In this study, mathematical models of a polymer electrolyte membrane electrolyser (PEME) plant and the WWTP of the Benchmark Simulation Model No. 2 (BSM2) were used to simulate electrolysis O2 supply to an AS system and estimate net costs of production (NCP) for produced O2 via a techno-economic assessment (TEA). Assuming that produced H2 is sold to a nearby industry, NCPs for O2 were calculated for two different PEME plant dimensions, four alternatives regarding electricity supply and costs, and three sets of assumptions regarding system performance and market conditions. The analyses were performed for 2020 as a reference year and 2030 based on forecasts of relevant data. Results of the dimensioning of the PEME show the O2 demand of a municipal WWTP with an installed capacity of 80,000 population equivalents (PE), such as the one of the BSM2, can be covered for more than 99% of the simulated period by either a 6.4 MW PEME operated for 4073 full load hours or a 4.8 MW PEME operated for 6259 full load hours. Investment costs for the PEME stacks and the operational costs for electricity make up most of the NCP of electrolysis O2. The projected decrease in PEME stack costs and renewable energy prices in favourable market conditions can result in a competitive NCP for electrolysis O2 in 2030. The approach described in this study can be applied to analyse O2 supply to biological wastewater treatment in WWTPs with different characteristics, in processes different from AS, and under different assumptions regarding economic conditions.
Improvements in the Modeling and Kinetics Processes of the Enzymatic Synthesis of Pentyl Acetate
Authors: Lorenzo Beatriz , Fernández Luis , Ortega Juan , Domínguez Leandro
Published: May 2023

In this work, the enzymatic synthesis of pentyl acetate obtained from acetic acid and pentan-1-ol using the commercial immobilized lipase Lipozyme®435 was studied. Specifically, the effects of several variables of the process on the kinetics were shown, such as the initial concentration of the acetic acid, the alcohol/acid molar ratio, and the possible reuse of the enzyme, while other variables, such as temperature, agitation, and the enzyme/acid ratio were held constant. The kinetics were determined by assessing the acetic acid concentration throughout the reactive process. Experimental data were correlated with the rate equation consisting of a modified version of the Bi–Bi Ping-Pong mechanism. The results showed that when no hydrophobic solvents were used with the reagents in stoichiometric proportion, a high molar fraction of acetic acid (x0,acid ≈ 0.50) caused the loss of enzymatic activity, achieving a conversion of only 5%. However, when there was an excess of pentan-1-ol, the reaction occurred successfully. Under optimal conditions (solvent-free conditions, x0,alcohol/x0,acid = 2, and x0,acid = 0.33), it was found that the enzyme could be reused up to 10 times without a loss of activity, reaching conversions higher than 80% after 8 h. Therefore, those conditions are advantageous in terms of productivity.
They Are the Treasure of the Commonwealth: Franciscan Charisma and Merchant Culture in Medieval Barcelona
Authors: Aurell Jaume
Published: May 2023

Since the beginning of the twentieth century, it has been a truism that the emergence of Protestantism was the main cause of the development of capitalism. However, a careful analysis of primary sources, especially in Latin countries, shows a quite different reality. Three centuries before the emergence of Protestantism, the Franciscans generated a discourse that made it possible to begin to legitimize the commercial practices that would later enable the emergence of capitalism. Based on these premises, this article aims to explore the discursive and juridical primary sources of medieval Barcelona—especially the testimonies of the Franciscan intellectual Francesc Eiximenis and merchant wills—to provide relevant new data and interpretations of how Franciscan charisma brought about a better understanding and legitimization of mercantile work. I intend to use the concept of charisma to better understand the great paradox of how those who aspired to a life of extreme poverty—the Franciscans—succeeded in legitimizing the work of those who aspired to a comfortable life, namely, the emerging merchant group. The merchants provided the Franciscans with the material capital necessary for their establishment in the city, while the Franciscans granted the merchants symbolic capital that was indispensable for the development of their mercantile work, social recognition, and religious legitimacy.
Identifying Optimal Cell Size for Geodiversity Quantitative Assessment with Richness, Diversity and Evenness Indices
Authors: Lopes Catarina , Teixeira Zara , Pereira Diamantino I. , Pereira Paulo
Published: May 2023

The importance of quantitatively assessing the spatial patterns of geodiversity, and their intrinsic relationship with biodiversity and the ecosystem services provided to society, has been signalized by several authors, due to the relevance of this information in territorial management, the planning of environmental and conservation strategies. Within geodiversity method assessments, the grid system is the most widely used GIS spatial approach to calculate a geodiversity index. Preferred for its simplicity, it implies the fundamental decision of choosing the scale of the analysis, defined by the selection of cell size, determinant for the accuracy and correctness of the final maps. Although this topic has been occasionally approached by some authors within geodiversity assessments, there is no formal procedure for cell size selection. This is a key issue, and, in the scope of the present work, an empirical procedure to select optimal cell size(s) was tested on the national scale in Portugal, in lithology and geomorphology datasets. The quantitative method based on geodiversity indices was applied, using richness, diversity and evenness indices, in a hexagonal analytical grid, through eight cell dimensions. Several descriptive statistical parameters were analyzed, with particular emphasis on dispersion statistical measures. Optimal cell size corresponded to the minimum cell size, once dispersion values were significatively reduced or stabilized, and distributions from evenness and diversity indices were closer to symmetry, which provided more accurate results and higher spatial differentiation, although the final decision should always consider the main purposes of the analysis.
Carbon-Energy Impact Analysis of Heavy Residue Gasification Plant Integration into Oil Refinery
Authors: Podolský Slavomír , Variny Miroslav , Kurák Tomáš
Published: May 2023

A gasification plant may partially replace an industrial thermal plant and hydrogen production plant by polygenerating valuable products (hydrogen, power, steam) from low-value materials. Carbon energy analysis is one way of conceptually evaluating such processes. In this paper, the integration of a heavy residue (HR) gasification plant into a mid-size oil refinery (5 million t per year crude processing rate) is conceptually assessed via the comparison of electricity, natural gas and heavy residue consumption, and CO2 emissions. The main purpose of the integration is to reduce the consumption of natural gas currently used for hydrogen production at the expense of increased HR consumption and to achieve a reduction in CO2 emissions. Two case studies with different modes of operation were compared to base case showing that annual reduction of 2280 GWh in natural gas consumption with constant heat and hydrogen production is possible, accompanied with a slight increase in electricity purchase by 28 GWh per year. HR processing in the refinery increases by over 2800 GWh per year. The refinery’s CO2 emissions increase by more than 20% (up to 350 kt per year) as a result, while, after incorporating external emissions into the balance, a decrease of more than 460 kt CO2 per year can be achieved. This confirms that the integration of gasification plants within industrial enterprises and clusters has a positive environmental and energy impact and supports the idea of converting low-value material to more valuable products in polygeneration plants. The economics of HR gasifier integration in varying operations under real refinery conditions remain to be explored.
Current Status and Challenges of BDS Satellite Precise Orbit Products: From a View of Independent SLR Validation
Authors: Li Xingxing , Liu Chengbo , Yuan Yongqiang , Zhang Keke
Published: May 2023

As an essential infrastructure that provides positioning, navigation, and timing services, China constructed the BeiDou Navigation Satellite System (BDS). The last BDS satellite was launched in June 2020, which represents the completion of BDS. BDS’s constellation consists of Medium Earth Orbit (MEO), Inclined Geosynchronous Orbit (IGSO), and Geostationary Orbit satellites. The precise modeling of non-conservative forces for BDS satellites is a challenging task. As an independent observation, Satellite Laser Ranging (SLR) is an important validation method of GNSS orbit modeling. In this paper, we validated the precise orbit products of different Analysis Centers (ACs) by using SLR observations, focusing on the BDS orbit modeling. By comparing BDS precise orbit products generated by four ACs with respect to SLR observations for the period of February 2017 to March 2021, we proved that an obvious satellite signature effect exists in the SLR residuals of BDS observed by multi-photon stations. The result indicates that multi-photon stations have a root mean square (RMS) of SLR residuals about 5 mm lower than that of single-photon detectors. The slope of SLR residuals with regard to nadir angle of IGSO satellites for single-photon and multi-photon stations is −2.0 and −2.5 mm/deg, respectively, while the slope of MEO satellites for these stations is about −0.6 to −0.3 and −1.0 to −0.4 mm/deg, respectively. To assess the effect of non-conservative force modeling, we selected seven high-performing stations, including five single-photon and two multi-photon stations. By comparing the SLR residuals of four ACs’ orbits, we analyzed the effect of the solutions of orbit processing, especially solar radiation pressure (SRP) models. We found that some centers may have modeling defects, including BDS-3 orbits of the Deutsches GeoForschungsZentrum and BDS-2 orbits of the European Space Agency, inferred from the large RMS of SLR residuals. Modeling the SRP of BDS satellites is challenging, while an appropriate prior box-wing model can improve the accuracy of SRP modeling and provide a more stable performance.
Multimodal Wildland Fire Smoke Detection
Authors: Bhamra Jaspreet Kaur , Anantha Ramaprasad Shreyas , Baldota Siddhant , Luna Shane , Zen Eugene , Ramachandra Ravi , Kim Harrison , Schmidt Chris , Arends Chris , Block Jessica , Perez Ismael , Crawl Daniel , Altintas Ilkay , Cottrell Garrison W. , Nguyen Mai H.
Published: May 2023

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires.
Sequential Generation of Multi-GNSS Multi-Frequency PPP-RTK Products and Their Performance Using the EUREF Permanent GNSS Network
Authors: Platz Hans Daniel
Published: May 2023

In the classic Precise Point Positioning (PPP) approach, observations of Global Navigation Satellite Systems (GNSS) are processed at the network level to generate satellite clocks and positions. This information can be used to enable accurate point positioning for a single GNSS receiver. In the PPP Real-Time Kinematic (PPP-RTK) approach, satellite phase biases are considered as well, enabling ambiguity resolution at the network and user levels. In this research, 30 s multi-frequency raw GPS, Galileo, and BDS-2/3 observations are processed at the network and user levels in a sequential Kalman filter. PPP-RTK enabling products are generated for up to five frequencies, and ambiguity resolution is performed at the network and user levels using a flexible ambiguity reparameterization approach, comparable to wide- and narrow-laning, which has shown to yield a significantly improved single epoch coordinate solution when multi-frequency observations are available. Different assumptions regarding the time stability of receiver and satellite phase biases have been made and compared. The availability of a precise user coordinate solution when multi-frequency and dual-frequency observations are processed is assessed and compared. A precise ambiguity-fixed solution is available in three epochs or fewer in 77% of all cases with an average of 24 visible satellites for static and kinematic receivers when multi-frequency observations are processed. When only dual-frequency observations are considered, a fixed solution is available in seven epochs or fewer in 71% of all cases. The fastest fixed solution was found in two epochs with multi-frequency observations and in six epochs with dual-frequency observations. Estimating a reference phase clock did not lead to an improvement in the coordinate solution. The findings indicate that a fixed solution can potentially be found faster than often suggested, with potential for further improvement when more satellites or regional atmospheric corrections are considered.
In Situ Calibration and Trajectory Enhancement of UAV and Backpack LiDAR Systems for Fine-Resolution Forest Inventory
Authors: Zhou Tian , Ravi Radhika , Lin Yi-Chun , Manish Raja , Fei Songlin , Habib Ayman
Published: May 2023

Forest inventory has been relying on labor-intensive manual measurements. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMSs) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high-quality point clouds for accurate, fine-resolution forest inventory. To improve the quality of the acquired point clouds, a system-driven strategy for mounting parameters estimation and trajectory enhancement using terrain patches and tree trunks is proposed. By minimizing observed discrepancies among conjugate features captured at different timestamps from multiple tracks by single/multiple systems, while considering the absolute and relative positional/rotational information provided by the GNSS/INS trajectory, system calibration parameters and trajectory information can be refined. Furthermore, some forest inventory metrics, such as tree trunk radius and orientation, are derived in the process. To evaluate the performance of the proposed strategy, three UAV and two Backpack datasets covering young and mature plantations were used in this study. Through sequential system calibration and trajectory enhancement, the spatial accuracy of the UAV point clouds improved from 20 cm to 5 cm. For the Backpack datasets, when the initial trajectory was of reasonable quality, conducting trajectory enhancement significantly improved the relative alignment of the point cloud from 30 cm to 3 cm, and an absolute accuracy at the 10 cm level can be achieved. For a lower-quality trajectory, the initial 1 m misalignment of the Backpack point cloud was reduced to 6 cm through trajectory enhancement. However, to derive products with accurate absolute accuracy, UAV point cloud is required as a reference in the trajectory enhancement process of the Backpack dataset.
Unsupervised Classification of Riverbed Types for Bathymetry Mapping in Shallow Rivers Using UAV-Based Hyperspectral Imagery
Authors: Kwon Siyoon , Gwon Yeonghwa , Kim Dongsu , Seo Il Won , You Hojun
Published: May 2023

Passive remote sensing is a practical and widely used method for bathymetry mapping in shallow rivers. However, the accuracy of this approach is limited because of different riverbed types; therefore, it is important to classify the riverbed types for improving bathymetry mapping accuracy and providing useful information for fluvial systems. In this study, we proposed a Gaussian mixture model (GMM)-based clustering method that utilizes hyperspectral imagery to classify riverbed types without sampling the bed material. We evaluated the proposed method in two shallow streams with different bed mixture conditions: (i) sand and vegetation and (ii) sand and moss-covered sand. The results showed that the GMM method accurately identified the spectral variability caused by diverse riverbed materials, enabling the precise classification of riverbed types. Moreover, by combining the GMM method with optimal band ratio analysis, we observed a reduction in error for the bathymetry mapping results by approximately 0.05 to 0.07 m. While our proposed method exhibits potential applications in various river environments, further research is needed to validate its effectiveness in classifying more complex riverbed types and conditions. Overall, our study findings suggest that the GMM-based clustering method using hyperspectral imagery is a promising tool for improving bathymetry mapping accuracy and classifying riverbed types in shallow rivers.
A GRACE/GFO Empirical Low-Pass Filter to Extract the Mass Changes in Nicaragua
Authors: Jian Guangyu , Wang Nan , Xu Chuang , Lin Jiayi , Li Meng
Published: May 2023

Among the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on temporal gravity products, the north–south stripe noise in the spherical harmonic coefficient (SHC) products contaminates the inversion of the Earth’s mass field. In this study, GRACE SHC products are adopted to estimate the mass changes in Nicaragua. To improve this estimation, we propose an empirical low-pass filter to suppress stripe noise. After only using our filter, the Nicaragua regional uncertainty diminishes from 123.26 mm to 69.11 mm, and the mean signal-to-noise ratio of all available months (2002–2021) improves from 1.67 to 1.8. Subsequently, our filter is employed to estimate the basin terrestrial water storage (TWS) change in Nicaragua. In the end, TWS change estimations are compared with various observations such as mascon products, hydrological models, and in situ groundwater observation. The main conclusions are as follows: (1) After using the wavelet coherent analysis, there is a negative resonance between TWS and the climate factor (El Nino–Southern Oscillation) with a period of 2~4 years; (2) The significant ~3.8-year periodic signal in groundwater storage change estimation is contributed by GRACE aliasing error. Our work can provide new knowledge and references for mass change in small areas.
Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development, Evaluation and Demonstration
Authors: Cao Xincheng , Chen Haochong , Gelbal Sukru Yaren , Aksun-Guvenc Bilin , Guvenc Levent
Published: May 2023

The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these connected and autonomous driving functions in this approach. This is an unsafe, costly and inefficient method. Motivated by these shortcomings, this paper introduces the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method is compared to the existing state-of-the-art. Its basic implementation for a path-following task is used to explain the method where the actual autonomous vehicle operates in a large empty area with its sensor feeds being replaced by realistic sensor feeds corresponding to its location and pose in the virtual environment. It is possible to easily change the development virtual environment and inject rare and difficult events which can be tested very safely. Vehicle-to-Pedestrian (V2P) communication-based pedestrian safety is chosen as the application use case for the VVE in this paper, and corresponding experimental results are presented and discussed. A no-line-of-sight pedestrian and vehicle moving towards each other on intersecting paths with different speeds are used in the experiments. Their time-to-collision risk zone values are compared for determining severity levels. The severity levels are used to slow down or brake the vehicle. The results show that V2P communication of pedestrian location and heading can be used successfully to avoid possible collisions. It is noted that actual pedestrians and other vulnerable road users can be used very safely in this approach.
Review of Atom Chips for Absolute Gravity Sensors
Authors: Li Dezhao , He Wenfeng , Shi Shengnan , Wu Bin , Xiao Yuhua , Lin Qiang , Li Long
Published: May 2023

As a powerful tool in scientific research and industrial technologies, the cold atom absolute gravity sensor (CAGS) based on cold atom interferometry has been proven to be the most promising new generation high-precision absolute gravity sensor. However, large size, heavy weight, and high–power consumption are still the main restriction factors of CAGS being applied for practical applications on mobile platforms. Combined with cold atom chips, it is possible to drastically reduce the complexity, weight, and size of CAGS. In this review, we started from the basic theory of atom chips to chart a clear development path to related technologies. Several related technologies including micro-magnetic traps, micro magneto–optical traps, material selection, fabrication, and packaging methods have been discussed. This review gives an overview of the current developments in a variety of cold atom chips, and some actual CAGS systems based on atom chips are also discussed. We summarize by listing some of the challenges and possible directions for further development in this area.
Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic Sensors
Authors: Zhang Xulong , Cheng Zihao , Du Li , Du Yuan
Published: May 2023

The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results.
MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array
Authors: Zhang Xiong , Wu Wenbo , Li Jialu , Dong Fan , Wan Shuting
Published: May 2023

Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.
A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning
Authors: Lei Tingjun , Chintam Pradeep , Luo Chaomin , Liu Lantao , Jan Gene Eu
Published: May 2023

In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.
The Influence of Anthropometric Variables and Filtering Frequency on Center of Pressure Data
Authors: Koltermann Jan Jens , Floessel Philipp , Hammerschmidt Franziska , Disch Alexander Carl
Published: May 2023

Good postural control is considered to be a key component of an active lifestyle, and numerous studies have investigated the Center of Pressure (CoP) as a way of identifying motor deficits. However, the optimal frequency range for assessing CoP variables and the effect of filtering on the relationships between anthropometric variables and CoP are unclear. The aim of this work is to show the relationship between anthropometric variables and different ways of filtering the CoP data. CoP was measured in 221 healthy volunteers using a KISTLER force plate in four different test conditions, both mono and bipedal. The results show no significant changes in the existing correlations of the anthropometric variable values over different filter frequencies between 10 Hz and 13 Hz. Therefore, the findings with regard to anthropometric influences on CoP, with a reasonable but less than ideal filtering of the data, can be applied to other study settings.
Rolling Bearing Composite Fault Diagnosis Method Based on Enhanced Harmonic Vector Analysis
Authors: Lu Jiantao , Yin Qitao , Li Shunming
Published: May 2023

Composite fault diagnosis of rolling bearings is very challenging work, especially when the characteristic frequency ranges of different fault types overlap. To solve this problem, an enhanced harmonic vector analysis (EHVA) method was proposed. Firstly, the wavelet threshold (WT) denoising method is used to denoise the collected vibration signals to reduce the influence of noise. Next, harmonic vector analysis (HVA) is used to remove the convolution effect of the signal transmission path, and blind separation of fault signals is carried out. The cepstrum threshold is used in HVA to enhance the harmonic structure of the signal, and a Wiener-like mask will be constructed to make the separated signals more independent in each iteration. Then, the backward projection technique is used to align the frequency scale of the separated signals, and each fault signal can be obtained from composite fault diagnosis signals. Finally, to make the fault characteristics more prominent, a kurtogram was used to find the resonant frequency band of the separated signals by calculating its spectral kurtosis. Semi-physical simulation experiments are conducted using the rolling bearing fault experiment data to verify the effectiveness of the proposed method. The results show that the proposed method, EHVA, can effectively extract the composite faults of rolling bearings. Compared to fast independent component analysis (FICA) and traditional HVA, EHVA improves separation accuracy, enhances fault characteristics, and has higher accuracy and efficiency compared to fast multichannel blind deconvolution (FMBD).
Real-Time Kinematically Synchronous Planning for Cooperative Manipulation of Multi-Arms Robot Using the Self-Organizing Competitive Neural Network
Authors: Zhang Hui , Jin Hongzhe , Ge Mingda , Zhao Jie
Published: May 2023

This paper presents a real-time kinematically synchronous planning method for the collaborative manipulation of a multi-arms robot with physical coupling based on the self-organizing competitive neural network. This method defines the sub-bases for the configuration of multi-arms to obtain the Jacobian matrix of common degrees of freedom so that the sub-base motion converges along the direction for the total pose error of the end-effectors (EEs). Such a consideration ensures the uniformity of the EE motion before the error converges completely and contributes to the collaborative manipulation of multi-arms. An unsupervised competitive neural network model is raised to adaptively increase the convergence ratio of multi-arms via the online learning of the rules of the inner star. Then, combining with the defined sub-bases, the synchronous planning method is established to achieve the synchronous movement of multi-arms robot rapidly for collaborative manipulation. Theory analysis proves the stability of the multi-arms system via the Lyapunov theory. Various simulations and experiments demonstrate that the proposed kinematically synchronous planning method is feasible and applicable to different symmetric and asymmetric cooperative manipulation tasks for a multi-arms system.