bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Root Morphological Traits Using 2D-Imaging among Diverse Soybeans (Glycine max L.)
Authors: Tripathi Pooja , Abdullah Jamila S. , Kim Jaeyoung , Chung Yong-Suk , Kim Seong-Hoon , Hamayun Muhammad , Kim Yoonha
Published: November 2021

Roots are the most important plant organ for absorbing essential elements, such as water and nutrients for living. To develop new climate-resilient soybean cultivars, it is essential to know the variation in root morphological traits (RMT) among diverse soybean for selecting superior root attribute genotypes. However, information on root morphological characteristics is poorly understood due to difficulty in root data collection and visualization. Thus, to overcome this problem in root research, we used a 2-dimensional (2D) root image in identifying RMT among diverse soybeans in this research. We assessed RMT in the vegetative growth stage (V2) of 372 soybean cultivars propagated in polyvinyl chloride pipes. The phenotypic investigation revealed significant variability among the 372 soybean cultivars for RMT. In particular, RMT such as the average diameter (AD), surface area (SA), link average length (LAL), and link average diameter (LAD) showed significant variability. On the contrary RMT, as with total length (TL) and link average branching angle (LABA), did not show differences. Furthermore, in the distribution analysis, normal distribution was observed for all RMT; at the same time, difference was observed in the distribution curve depending on individual RMT. Thus, based on overall RMT analysis values, the top 5% and bottom 5% ranked genotypes were selected. Furthermore, genotypes that showed most consistent for overall RMT have ranked accordingly. This ultimately helps to identify four genotypes (IT 16538, IT 199127, IT 165432, IT 165282) ranked in the highest 5%, whereas nine genotypes (IT 23305, IT 208266, IT 165208, IT 156289, IT 165405, IT 165019, IT 165839, IT 203565, IT 181034) ranked in the lowest 5% for RMT. Moreover, principal component analysis clustered cultivar 2, cultivar 160, and cultivar 274 into one group with high RMT values, and cultivar 335, cultivar 40, and cultivar 249 with low RMT values. The RMT correlation results revealed significantly positive TL and AD correlations with SA (r = 0.96) and LAD (r = 0.85), respectively. However, negative correlations (r = −0.43) were observed between TL and AD. Similarly, AD showed a negative correlation (r = −0.22) with SA. Thus, this result suggests that TL is a more vital factor than AD for determining SA compositions.
Monoterpene Enrichments Have Positive Impacts on Soil Bacterial Communities and the Potential of Application in Bioremediation
Authors: Chalkos Dimitris , Karamanoli Katerina , Vokou Despoina
Published: November 2021

We study here how soil bacterial communities of different ecosystems respond to disturbances caused by enrichments with monoterpenes that are common essential oil constituents. We used fenchone, 1,8-cineol and α-pinene, and soils from phrygana, a typical Mediterranean-type ecosystem where aromatic plants abound, and from another five ecosystem types, focusing on culturable bacteria. Patterns of response were common to all ecosystems, but responses themselves were not always as pronounced in phrygana as in the other ecosystems, suggesting that these enrichments are less of a disturbance there. More specifically, soil respiration and abundance of the bacterial communities increased, becoming from below two up to 16 times as high as in control soils (for both attributes) and remained at high levels as long as these compounds were present. Bacteria that can utilize these three compounds as substrates of growth became dominant members of the bacterial communities in the enriched soils. All changes were readily reversible once monoterpene addition stopped. Bacteria with the ability to utilize these monoterpenes as carbon sources were found in soils from all ecosystems, 15 strains in total, suggesting a rather universal presence; of these, six could also utilize the organic pollutants toluene or p-xylene. These results suggest also potential novel applications of monoterpenes in combating soil pollution.
Calcification in Three Common Calcified Algae from Phuket, Thailand: Potential Relevance on Seawater Carbonate Chemistry and Link to Photosynthetic Process
Authors: Buapet Pimchanok , Sinutok Sutinee
Published: November 2021

Calcifying macroalgae contribute significantly to the structure and function of tropical marine ecosystems. Their calcification and photosynthetic processes are not well understood despite their critical role in marine carbon cycles and high vulnerability to environmental changes. This study aims to provide a better understanding of the macroalgal calcification process, focusing on its relevance concerning seawater carbonate chemistry and its relationship to photosynthesis in three dominant calcified macroalgae in Thailand, Padina boryana, Halimeda macroloba and Halimeda opuntia. Morphological and microstructural attributes of the three macroalgae were analyzed and subsequently linked to their calcification rates and responses to inhibition of photosynthesis. In the first experiment, seawater pH, total alkalinity and total dissolved inorganic carbon were measured after incubation of the macroalgae in the light and after equilibration of the seawater with air. Estimations of carbon uptake into photosynthesis and calcification and carbon release into air were obtained thereafter. Our results provide evidence that calcification of the three calcified macroalgae is a potential source of CO2, where calcification by H. opuntia and H. macroloba leads to a greater release of CO2 per biomass weight than P. boryana. Nevertheless, this capacity is expected to vary on a diurnal basis, as the second experiment indicates that calcification is highly coupled to photosynthetic activity. Lower pH as a result of inhibited photosynthesis under darkness imposes more negative effects on H. opuntia and H. macroloba than on P. boryana, implying that they are more sensitive to acidification. These effects were worsened when photosynthesis was inhibited by 3-(3,4-dichlorophenyl)-1,1-dimethylurea, highlighting the significance of photosynthetic electron transport-dependent processes. Our findings suggest that estimations of the amount of carbon stored in the vegetated marine ecosystems should account for macroalgal calcification as a potential carbon source while considering diurnal variations in photosynthesis and seawater pH in a natural setting.
Obtaining Salt Stress-Tolerant Eggplant Somaclonal Variants from In Vitro Selection
Authors: Hannachi Sami , Werbrouck Stefaan , Bahrini Insaf , Abdelgadir Abdelmuhsin , Siddiqui Hira Affan , Van Labeke Marie Christine
Published: November 2021

An efficient regeneration protocol was applied to regenerate shoots on salt stress-tolerant calli lines of aubergine (Solanum melongena). These NaCl-tolerant cell lines were obtained by two different methods. On the one hand, the developed callus tissue was transferred to a medium with a continuous salt content of 40, 80, 120, or 160 mM NaCl. On the other hand, the callus tissue was subjected to a stepwise increasing salinity to 160 mM NaCl every 30 days. With the second method, calli which could be selected were characterized by compact growth, a greenish color, and absence of necrotic zones. When grown on salt-free medium again, NaCl-tolerant calli showed a decline in relative growth rate and water content in comparison to the control line. This was more obvious in the 120 mM NaCl-tolerant callus. Lipid peroxidase activity increased in 40 and 80 mM NaCl-tolerant calli; yet did not increase further in 120 mM-tolerant callus. An increase in ascorbic acid content was observed in 80 and 120 mM NaCl-tolerant calli compared to the 40 mM NaCl-tolerant lines, in which ascorbic acid content was twice that of the control. All NaCl-tolerant lines showed significantly higher superoxide dismutase (SOD) (208–305–370 µmol min−1 mg−1 FW) and catalase (CAT) (136–211–238 µmol min−1 mg−1 FW) activities compared to control plants (231 and 126 µmol min−1 mg−1 FW). Plants were regenerated on the calli lines that could tolerate up to 120 mM NaCl. From the 32 plants tested in vitro, ten plants with a higher number of leaves and root length could be selected for further evaluation in the field. Their high salt tolerance was evident by their more elevated fresh and dry weight, their more increased relative water content, and a higher number and weight of fruits compared to the wild-type parental control. The presented work shows that somaclonal variation can be efficiently used to develop salt-tolerant mutants.
Core-Shell Sr2CeO4@SiO2 Filled COC-Based Composites with Low Dielectric Loss for High-Frequency Substrates
Authors: Wang Qinlong , Wang Hao , Zhang Caixia , Zhang Qilong , Yang Hui
Published: November 2021

High-frequency communication equipment urgently needs substrate materials with lower dielectric loss, better heat dissipation, and higher stability, to ensure real-time low-loss and high-speed signal transmission. The core-shell structure of Sr2CeO4@SiO2 was prepared by the sol-gel method, and the modified powders with different volume contents were introduced into the cyclic olefin copolymer (COC) to prepare hydrocarbon resin-based composites. Due to the protective effect of the SiO2 shell, the stability of the powders is significantly improved, and the moisture barrier and corrosion resistance of the composites are enhanced, which is conducive to the normal operation of electronic equipment in harsh and complex environments. When the filler content is 20 vol%, the composite has a dielectric loss of 0.0023 at 10 GHz, a dielectric constant of 3.5, a thermal conductivity of 0.9 W·m−1·K−1, a water absorption of 0.32% and a coefficient of thermal expansion of 37.7 ppm/°C. The COC/Sr2CeO4@SiO2 composites exhibit excellent dielectric properties and thermal conductivity, while maintaining good moisture resistance and dimensional stability, which shows potential application prospects in the field of high-frequency substrates.
Surface Modification of Metallic Inserts for Enhancing Adhesion at the Metal–Polymer Interface
Authors: Novák Libor , Fojtl Ladislav , Kadlečková Markéta , Maňas Lukáš , Smolková Ilona , Musilová Lenka , Minařík Antonín , Mráček Aleš , Sedláček Tomáš , Smolka Petr
Published: November 2021

A combination of mechanical and chemical treatments was utilized to modify the surface textures of copper and duralumin inserts in order to enhance the adhesion at the metal–polymer interface and provide an adhesive joint with a high loadbearing capacity. Pretreatment of the surfaces with sandblasting was followed by etching with various chemical mixtures. The resulting surface textures were evaluated with a scanning electron microscope (SEM) and an optical confocal microscope. Surface geometry parameters (Sa, Sz, and Sdr) were measured and their relationships to the adhesion joint strength were studied. It was found that the virgin and purely mechanically treated inserts resulted in joints with poor loadbearing capacity, while a hundredfold (duralumin) and ninetyfold (copper) increase in the force to break was observed for some combinations of mechanical and chemical treatments. It was determined that the critical factor is overcoming a certain surface roughness threshold with the mechanical pretreatment to maximize the potential of the mechanical/chemical approach for the particular combination of material and etchant.
Investigation on the Microscopic/Macroscopic Mechanical Properties of a Thermally Annealed Nafion® Membrane
Authors: Pham Tuyet Anh , Koo Seunghoe , Park Hyunseok , Luong Quang Thien , Kwon Oh Joong , Jang Segeun , Kim Sang Moon , Kim Kyeongtae
Published: November 2021

The Nafion® electrolyte membrane, which provides a proton pathway, is an essential element in fuel cell systems. Thermal treatment without additional additives is widely used to modify the mechanical properties of the membrane, to construct reliable and durable electrolyte membranes in the fuel cell. We measured the microscopic mechanical properties of thermally annealed membranes using atomic force microscopy with the two-point method. Furthermore, the macroscopic property was investigated through tensile tests. The microscopic modulus exceeded the macroscopic modulus over all annealing temperature ranges. Additionally, the measured microscopic modulus increased rapidly near 150 °C and was saturated over that temperature, whereas the macroscopic modulus continuously increased until 250 °C. This mismatched micro/macroscopic reinforcement trend indicates that the internal reinforcement of the clusters is induced first until 150 °C. In contrast, the reinforcement among the clusters, which requires more thermal energy, probably progresses even at a temperature of 250 °C. The results showed that the annealing process is effective for the surface smoothing and leveling of the Nafion® membrane until 200 °C.
The Effect of Agarose on 3D Bioprinting
Authors: Gong Chi , Kong Zhiyuan , Wang Xiaohong
Published: November 2021

In three-dimensional (3D) bioprinting, the accuracy, stability, and mechanical properties of the formed structure are very important to the overall composition and internal structure of the complex organ. In traditional 3D bioprinting, low-temperature gelatinization of gelatin is often used to construct complex tissues and organs. However, the hydrosol relies too much on the concentration of gelatin and has limited formation accuracy and stability. In this study, we take advantage of the physical crosslinking of agarose at 35–40 °C to replace the single pregelatinization effect of gelatin in 3D bioprinting, and printing composite gelatin/alginate/agarose hydrogels at two temperatures, i.e., 10 °C and 24 °C, respectively. After in-depth research, we find that the structures manufactured by the pregelatinization method of agarose are significantly more accurate, more stable, and harder than those pregelatined by gelatin. We believe that this research holds the potential to be widely used in the future organ manufacturing fields with high structural accuracy and stability.
Central Composite Design, Kinetic Model, Thermodynamics, and Chemical Composition of Pomelo (Citrus Maxima (Burm.) Merr.) Essential Oil Extraction by Steam Distillation
Authors: Dao Tan Phat , Nguyen Thanh Viet , Tran Thi Yen Nhi , Le Xuan Tien , An Ton Nu Thuy , Anh Nguyen Huu Thuan , Bach Long Giang
Published: November 2021

Pomelo peel-derived essential oils have been gaining popularity due to greater demand for stress relief therapy or hair care therapy. In this study, we first performed optimization of parameters in the pomelo essential oil extraction process on a pilot scale to gain better insights for application in larger scale production. Then extraction kinetics, activation energy, thermodynamics, and essential oil quality during the extraction process were investigated during the steam distillation process. Three experimental conditions including material mass, steam flow rate, and extraction time were taken into consideration in response surface methodology (RSM) optimization. The optimal conditions were found as follows: sample weight of 422 g for one distillation batch, steam flow rate of 2.16 mL/min and extraction time of 106 min with the coefficient of determination R2 of 0.9812. The nonlinear kinetics demonstrated the compatibility of the kinetic model with simultaneous washing and unhindered diffusion with a washing rate constant of 0.1515 min−1 and a diffusion rate constant of 0.0236 min−1. The activation energy of the washing and diffusion process was 167.43 kJ.mol−1 and 96.25 kJ.mol−1, respectively. The thermodynamic value obtained at the ΔG° value was −35.02 kJ.mol−1. The quality of pomelo peel essential oil obtained by steam distillation was characterized by its high limonene content (96.996%), determined by GC-MS.
Automatic Tolerance Analysis of Permanent Magnet Machines with Encapsuled FEM Models Using Digital-Twin-Distiller
Authors: Orosz Tamás , Gadó Krisztián , Katona Mihály , Rassõlkin Anton
Published: November 2021

Tolerance analysis is crucial in every manufacturing process, such as electrical machine design, because tight tolerances lead to high manufacturing costs. A FEM-based solution of the tolerance analysis of an electrical machine can easily lead to a computationally expensive problem. Many papers have proposed the design of experiments, surrogate-model-based methodologies, to reduce the computational demand of this problem. However, these papers did not focus on the information loss and the limitations of the applied methodologies. Regardless, the absolute value of the calculated tolerance and the numerical error of the applied numerical methods can be in the same order of magnitude. In this paper, the tolerance and the sensitivity of BLDC machines’ cogging torque are analysed using different methodologies. The results show that the manufacturing tolerances can have a significant effect on the calculated parameters, and that the mean value of the calculated cogging torque increases. The design of the experiment-based methodologies significantly reduced the calculation time, and shows that the encapsulated FEM model can be invoked from an external system-level optimization to examine the design from different aspects.
Photocatalytic Processes for Environmental Applications
Authors: Monfort Olivier , Wu Yanlin
Published: November 2021

Photocatalysis, especially heterogeneous photocatalysis, is one of the most investigated processes for environmental remediation [...]
Modelling Sessile Droplet Profile Using Asymmetrical Ellipses
Authors: Tran Du Tuan , Nguyen Nhat-Khuong , Singha Pradip , Nguyen Nam-Trung , Ooi Chin Hong
Published: November 2021

Modelling the profile of a liquid droplet has been a mainstream technique for researchers to study the physical properties of a liquid. This study proposes a facile modelling approach using an elliptic model to generate the profile of sessile droplets, with MATLAB as the simulation environment. The concept of the elliptic method is simple and easy to use. Only three specific points on the droplet are needed to generate the complete theoretical droplet profile along with its critical parameters such as volume, surface area, height, and contact radius. In addition, we introduced fitting coefficients to accurately determine the contact angle and surface tension of a droplet. Droplet volumes ranging from 1 to 300 µL were chosen for this investigation, with contact angles ranging from 90° to 180°. Our proposed method was also applied to images of actual water droplets with good results. This study demonstrates that the elliptic method is in excellent agreement with the Young–Laplace equation and can be used for rapid and accurate approximation of liquid droplet profiles to determine the surface tension and contact angle.
Study of the Transcription Effects of Pressing Dies with Ultrasonic Polishing on Glass Molding
Authors: Cheng Ken-Chuan , Huang Chien-Yao , Hung Jung-Chou , Wang A-Cheng , Lin Yan-Cherng
Published: November 2021

The micro lens array (MLA) has played an important role in optical systems for the past few years, and the precision of pressing dies has dominated the quality of MLAs in glass molding. Few studies have covered the transcription effects on surface roughness of pressing dies for this technology. Therefore, this research utilized pressing dies to produce a sine-wave lens array on glass molding, to transform the Gauss-distributed spotlight into a uniform straight one and then characterize the transcription effects of these lenses. Pressing dies with a sine-wave shape were firstly cut by wire electrical discharge machining (WEDM), and then ultrasonic polishing using diamond abrasives was applied to finish the sine-wave surface with an original roughness of 0.2 μm Ra. Next, the sine-wave lens arrays were pressed by glass molding at the appropriate pressure and temperature, before evaluating the transcription effects of transforming the Gauss-distributed spotlight into a uniform straight one. The result showed that the sine-wave lens array stuck easily to the pressing die and then ruptured during glass molding due to the poor surface roughness of pressing tool. However, the diamond abrasive with appropriate sizes could establish good surface roughness on pressing dies via ultrasonic polishing, and the pressing die with a low surface roughness of 0.08 μm Ra was able to successfully perform MLA in the glass molding. However, only pressing dies with a surface roughness smaller than 0.023 μm Ra could produce precision glass lenses to transform the Gauss-distributed spotlight into a uniform straight one.
An Effective Communication Prototype for Time-Critical IIoT Manufacturing Factories Using Zero-Loss Redundancy Protocols, Time-Sensitive Networking, and Edge-Computing in an Industry 4.0 Environment
Authors: Kiangala Kahiomba Sonia , Wang Zenghui
Published: November 2021

The Industrial Internet of things (IIoT), the implementation of IoT in the industrial sector, requires a deterministic, real-time, and low-latency communication response for its time-critical applications. A delayed response in such applications could be life-threatening or result in significant losses for manufacturing plants. Although several measures in the likes of predictive maintenance are being put in place to prevent errors and guarantee high network availability, unforeseen failures of physical components are almost inevitable. Our research contribution is to design an efficient communication prototype, entirely based on internet protocol (IP) that combines state-of-the-art communication computing technologies principles to deliver a more stable industrial communication network. We use time-sensitive networking (TSN) and edge computing to increase the determinism of IIoT networks, and we reduce latency with zero-loss redundancy protocols that ensure the sustainability of IIoT networks with smooth recovery in case of unplanned outages. Combining these technologies altogether brings more effectiveness to communication networks than implementing standalone systems. Our study results develop two experimental IP-based industrial network communication prototypes in an intra-domain transmission scenario: the first one is based on the parallel zero-loss redundancy protocol (PRP) and the second one using the high-availability seamless zero-loss redundancy protocol (HSR). We also highlight the benefits of utilizing our communication prototypes to build robust industrial IP communication networks with high network availability and low latency as opposed to conventional communication networks running on seldom redundancy protocols such as Media Redundancy Protocol (MRP) or Rapid Spanning Tree Protocol (RSTP) with single-point of failure and delayed recovery time. While our two network communication prototypes—HSR and PRP—offer zero-loss recovery time in case of a single network failure, our PRP communication prototype goes a step further by providing an effective redundancy scheme against multiple link failures.
OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots
Authors: Cornejo-Lupa Maria A. , Cardinale Yudith , Ticona-Herrera Regina , Barrios-Aranibar Dennis , Andrade Manoel , Diaz-Amado Jose
Published: November 2021

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.
Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data
Authors: Qin Yuqing , Su Jie , Wang Mingfeng
Published: November 2021

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.
Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds
Authors: Krisanski Sean , Taskhiri Mohammad Sadegh , Gonzalez Aracil Susana , Herries David , Muneri Allie , Gurung Mohan Babu , Montgomery James , Turner Paul
Published: November 2021

Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.
An Adaptive-Parameter Pixel Unmixing Method for Mapping Evergreen Forest Fractions Based on Time-Series NDVI: A Case Study of Southern China
Authors: Yang Yingying , Wu Taixia , Zeng Yuhui , Wang Shudong
Published: November 2021

Spectral unmixing remains the most popular method for estimating the composition of mixed pixels. However, the spectral-based unmixing method cannot easily distinguish vegetation with similar spectral characteristics (e.g., different forest tree species). Furthermore, in large areas with significant heterogeneity, extracting a large number of pure endmember samples is challenging. Here, we implement a fractional evergreen forest cover-self-adaptive parameter (FEVC-SAP) approach to measure FEVC at the regional scale from continuous intra-year time-series normalized difference vegetation index (NDVI) values derived from moderate resolution imaging spectroradiometer (MODIS) imagery acquired over southern China, an area with a complex mixture of temperate, subtropical, and tropical climates containing evergreen and deciduous forests. Considering the cover of evergreen forest as a fraction of total forest (evergreen forest plus non-evergreen forest), the dimidiate pixel model combined with an index of evergreen forest phenological characteristics (NDVIann-min: intra-annual minimum NDVI value) was used to distinguish between evergreen and non-evergreen forests within a pixel. Due to spatial heterogeneity, the optimal model parameters differ among regions. By dividing the study area into grids, our method converts image spectral information into gray level information and uses the Otsu threshold segmentation method to simulate the appropriate parameters for each grid for adaptive acquisition of FEVC parameters. Mapping accuracy was assessed at the pixel and sub-pixel scales. At the pixel scale, a confusion matrix was constructed with higher overall accuracy (87.5%) of evergreen forest classification than existing land cover products, including GLC 30 and MOD12. At the sub-pixel scale, a strong linear correlation was found between the cover fraction predicted by our method and the reference cover fraction obtained from GF-1 images (R2 = 0.86). Compared to other methods, the FEVC-SAP had a lower estimation deviation (root mean square error = 8.6%). Moreover, the proposed method had greater estimation accuracy in densely than sparsely forested areas. Our results highlight the utility of the adaptive-parameter linear unmixing model for quantitative evaluation of the coverage of evergreen forest and other vegetation types at large scales.
A Hybrid ECOM Model for Solar Radiation Pressure Effect on GPS Reference Orbit Derived by Orbit Fitting Technique
Authors: Tseng Tzu-Pang
Published: November 2021

A hybrid ECOM (Empirical CODE Orbit Model) solar radiation pressure (SRP) model, which is termed ECOMC in this work, is proposed for global navigation satellite system (GNSS) orbit modeling. The ECOMC is mainly parameterized by both ECOM1 and ECOM2 models. The GNSS orbit mainly serves as a reference datum not only for its ranging measurement but also for the so-called precise point positioning (PPP) technique. Compared to a complex procedure of orbit determination with real tracking data, the so-called orbit fitting technique simply uses satellite positions from GNSS ephemeris as pseudo-observations to estimate the initial state vector and SRP parameters. The accuracy of the reference orbit is mainly dominated by the SRP, which is usually handled by either ECOM1 or ECOM2. However, the reference orbit derived by ECOM1 produces periodic variations on orbit differences with respect to International GNSS Service (IGS) final orbit for GPS IIR satellites. Such periodic variations are removed from a reference orbit formed using the ECOM2 model, which, however, yields large cross-track orbit errors for the IIR and IIF satellites. Such large errors are attributed to the fact that the ECOM2 intrinsically lacks 1 cycle per revolution (CPR) terms, which stabilize the estimations of the even-order CPR terms in the satellite-Sun direction when the orbit fitting is used. In comparison, a reference orbit constructed with the ECOMC model is free of both the periodic variations from the ECOM1 and the large cross-track orbit errors from the ECOM2. The above improvements from the ECOMC are associated with (1) the even CPR terms removing the periodic variations and (2) the 1 CPR terms compensating for the force mismodeling at ∆u = 90° and 270°, where the ∆u is the argument of the latitude of the satellite with respect to the Sun. The parameter correlation analysis also presents that the direct SRP estimation is sensitive to the 1 and 2 CPR terms in the ECOMC case. In addition, the root-mean-square (RMS) of orbit difference with respect to IGS orbit is improved by ~40%, ~10%, and ~50% in the radial, along-track, and cross-track directions, respectively, when the SRP model is changed from the ECOM2 to the ECOMC. The orbit accuracy is assessed through orbit overlaps at day boundaries. The accuracy improvements of the ECOMC-derived orbit over the ECOM2-derived orbit in the radial, along-track, and cross-track directions are 13.2%, 14.8%, and 42.6% for the IIF satellites and 7.4%, 7.7%, and 35.0% for the IIR satellites. The impact of the reference orbit using the three models on the PPP is assessed. The positioning accuracy derived from the ECOMC is better than that derived from the ECOM1 and ECOM2 by approximately 13% and 20%, respectively. This work may serve as a reference for forming the GNSS reference orbit using the orbit fitting technique with the ECOMC SRP model.
Raytracing Simulated GPS Radio Wave Propagation Paths Experiencing Large Disturbances When Going through the Top of the Sub-Cloud Layer
Authors: Yang Shengpeng , Zou Xiaolei , Anthes Richard
Published: November 2021

Global positioning satellite system (GPS) radio waves that reach the tropical lower troposphere are strongly affected by small-scale water vapor fluctuations. We examine along-the-ray simulations of the impact parameter at every ray integration step using the high-resolution European Centre for Medium-Range Weather Forecasts ERA5 reanalysis as the input model states. We find that disturbances to the impact parameter arise when ray paths go through the top of the sub-cloud layer, where there is a pronounced reduction with increasing height in the humidity, and wet refractivity has a strong local vertical gradient, creating multipath. Additionally, the horizontal gradients of refractivity cause the impact parameter to vary along the ray. The disturbances to the impact parameter are confined to an area about 250 km horizontally and 4 km vertically from the perigee point. Beyond 250 km from the perigee, the impact parameter remains constant. The vertical gradient of refractivity is largest at the top of the sub-cloud layer, usually between 1.5 and 3.0 km, and becomes negligibly small above 4 km.
Azimuth Multichannel Reconstruction Based on Advanced Hyperbolic Range Equation
Authors: Xu Wei , Li Ruibo , Fang Chonghua , Huang Pingping , Tan Weixian , Qi Yaolong
Published: November 2021

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach.
Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion
Authors: Zhang Minghua , Xu Shubo , Song Wei , He Qi , Wei Quanmiao
Published: November 2021

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.
Analysis of the Impacts of Environmental Factors on Rat Hole Density in the Northern Slope of the Tienshan Mountains with Satellite Remote Sensing Data
Authors: Shi Haiyang , Pan Qun , Luo Geping , Hellwich Olaf , Chen Chunbo , Voorde Tim Van de , Kurban Alishir , De Maeyer Philippe , Wu Shixin
Published: November 2021

Understanding the impacts of environmental factors on spatial–temporal and large-scale rodent distribution is important for rodent damage prevention. Investigating rat hole density (RHD) is one of the most effective methods to obtain the intensity of rodent damage. However, most of the previous field surveys or UAV-based remote sensing methods can only evaluate small-scale RHD and its influencing factors. However, these studies did not consider large-scale temporal and spatial heterogeneity. Therefore, we collected small-scale and in situ measurement records of RHD on the northern slope of the Tien Shan Mountains in Xinjiang (NTXJ), China, from 1982 to 2015, and then used correlation analysis and Bayesian network (BN) to analyze the environmental impacts on large-scale RHD with satellite remote sensing data such as the GIMMS NDVI product. The results show that the built BN can better quantify causality in the environmental mechanism modeling of RHD. The NDVI and LAI data from satellite remote sensing are important to the spatial–temporal RHD distribution and the mapping in the future. In regions with an elevation higher than 600 m (UPR) and lower than 600 m (LWR) of NTXJ, there are significant differences in the driving mechanism patterns of RHD, which are dependent on the elevation variation. In LWR, vegetation conditions have a weaker impact on RHD than UPR. It is possibly due to the Artemisia eaten by the dominant species Lagurus luteus (LL) in UPR being more sensitive to precipitation and temperature if compared with the Haloxylon ammodendron eaten by the Rhombomys opimus (RO) in LWR. In LWR, grazing intensity is more strongly and positively correlated to RHD than UPR, possibly due to both winter grazing and RO dependency on vegetation distribution; moreover, in UPR, sheep do not feed Artemisia as the main food, and the total vegetation is sufficient for sheep and LL to coexist. Under the different conditions of water availability of LWR and UPR, grazing may affect the ratio of aboveground and underground biomass by photosynthate allocation, thereby affecting the distribution of RHD. In extremely dry years, the RHD of LWR and UPR may have an indirect interactive relation due to changes in grazing systems.
Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System
Authors: Shah Sayed-Chhattan
Published: November 2021

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.
Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump
Authors: Cerfoglio Serena , Galli Manuela , Tarabini Marco , Bertozzi Filippo , Sforza Chiarella , Zago Matteo
Published: November 2021

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.
Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
Authors: Abdelkader Ghadeer , Elgazzar Khalid , Khamis Alaa
Published: November 2021

In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.