bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Glove with Self-Compensation Mechanism Based on High-Sensitive Elastic Fiber-Optic Sensor
Authors: Yu Hui , Zheng Daifu , Liu Yun , Chen Shimeng , Wang Xiaona , Peng Wei
Published: December 2022

With the development of virtual reality (VR) interaction technology, data glove has become one of the most popular devices for human-computer interaction. It’s valuable to design high-sensitive and flexible sensor for data glove. Therefore, a low-cost data glove based on self-compensating elastic optical fiber sensor with self-calibration function is proposed. The tunable and stretchable elastic fiber was fabricated by a simple, economical and controllable method. The fiber has good flexibility and high stability under stretching, bending and indentation deformation. The optical fibers are installed in the sensor in a U shape with a bending radius of 5 mm. Compared with the straight fiber, the response sensitivity of the U-shaped fiber to deformation is increased by about 7 times at most. The reference optical fiber is connected to the sensor, which effectively improves the stability and accuracy of the sensor system. In addition, the sensors are easy to install so that the data gloves can be customized for different hand shapes. In the gesture capture test, it can respond quickly and guide the manipulator to track the gesture. This responsive and stable data glove has broad development potential in motion monitoring, telemedicine and human-computer interaction.
A Study on High and Low Temperature Rheological Properties and Oil Corrosion Resistance of Epoxy Resin/SBS Composite Modified Bitumen
Authors: Xue Zhuo , Xu Wenyuan
Published: December 2022

In order to investigate the high and low temperature rheological properties and fuel corrosion resistance of epoxy resin on SBS modified asphalt, epoxy resin/SBS composite modified asphalt (ER/SBS) was prepared by high-speed shear. Moreover, composite modified bitumen with different proportions were designed based on the uniform design method and the basic performance index test was performed. The optimal composite mixing ratio of the ER and SBS modifier in composite modified asphalt (2.3% and 3.8%, respectively) was determined. Temperature scanning and a multiple stress creep test (MSCR) on ER/SBS composite modified asphalt with different ER content before and after oil corrosion was carried out using a dynamic shear rheometer (DSR). In addition, the high temperature rheological properties of different ER contents and composite modified asphalt after oil corrosion were evaluated by combing DSR measurements with the test data. The creep stiffness (S) and creep rate (m) indexes were obtained by a bending rheometer (BBR), and the effect of ER on the low-temperature rheological properties of SBS modified bitumen was investigated. The influence of the modifier incorporation on the micromorphology of asphalt and the change of micromorphology of asphalt after oil corrosion were analyzed by fluorescence microscopy. The test results show that the incorporation of 2.3% ER and 3.8% SBS can effectively improve the high temperature performance of SBS modified asphalt under the premise of cost saving. Moreover, the composite modified asphalt doped with ER can effectively improve the resistance of SBS modified asphalt to fuel corrosion at high temperatures, and the greatest improvement in the oil corrosion resistance of composite modified asphalt is observed at the ER content of 2.3%.
Microbial Natural Products with Wound-Healing Properties
Authors: Hillman Prima F. , Lee Chaeyoung , Nam Sang-Jip
Published: December 2022

Wound healing continues to pose a challenge in clinical settings. Moreover, wound management must be performed properly and efficiently. Acute wound healing involves multiple cell divisions, a new extracellular matrix, and the process of formation, such as growth factors and cytokines, which are released at the site of the wound to regulate the process. Any changes that disrupt the healing process could cause tissue damage and prolong the healing process. Various factors, such as microbial infection, oxidation, and inflammation, can delay wound healing. In order to counter these problems, utilizing natural products with wound-healing effects has been reported to promote this process. Several natural products have been associated with wound healing, most of which are from medicinal plants. However, secondary microbial metabolites have not been extensively studied for their wound-healing properties. Further, investigations on the wound-healing control of natural microbial products are required due to a lack of studies. This review discussed the in vivo and in vitro research on the wound healing activities of natural microbial products, which may assist in the development of better wound treatments in the future.
Aqueous Two-Phase Systems Based on Ionic Liquids and Deep Eutectic Solvents as a Tool for the Recovery of Non-Protein Bioactive Compounds—A Review
Authors: Buarque Filipe , Gautério Gabrielle , Coelho Maria , Lemes Ailton , Ribeiro Bernardo
Published: December 2022

Aqueous two-phase systems (ATPS) based on ionic liquids (IL) and deep eutectic solvents (DES) are ecofriendly choices and can be used to selectively separate compounds of interest, such as bioactive compounds. Bioactive compounds are nutrients and nonnutrients of animal, plant, and microbial origin that benefit the human body in addition to their classic nutritional properties. They can also be used for technical purposes in food and as active components in the chemical and pharmaceutical industries. Because they are usually present in complex matrices and low concentrations, it is necessary to separate them in order to increase their availability and stability, and ATPS is a highlighted technique for this purpose. This review demonstrates the application of ATPS based on IL and DES as a tool for recovering nonprotein bioactive compounds, considering critical factors, results and the most recent advances in this field. In addition, the review emphasizes the perspectives for expanding the use of nonconventional ATPS in purification systems, which consider the use of molecular modelling to predict experimental conditions, the investigation of diverse compounds in phase-forming systems, the establishment of optimal operational parameters, and the verification of bioactivities after the purification process.
Developing Trusted IoT Healthcare Information-Based AI and Blockchain
Authors: AlGhamdi Rayed , Alassafi Madini O. , Alshdadi Abdulrahman A. , Dessouky Mohamed M. , Ramdan Rabie A. , Aboshosha Bassam W.
Published: December 2022

The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry’s efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment.
Effect of Pouch Size on Sterilization of Ready-to-Eat (RTE) Bracken Ferns: Numerical Simulation and Texture Evaluation
Authors: Jung Hwabin , Lee Yun Ju , Yoon Won Byong
Published: December 2022

Bracken fern (Pteridium aquilium, BF) is a widely consumed vegetable. It has the potential to be manufactured as a ready-to-eat (RTE) product as a cooking ingredient and a side dish. The aim of the current study was to develop sterilized BF RTE products and to investigate textural qualities depending on the size of the pouches. The F0-value at the cold point according to pouch size (100, 150, and 200 g) targeted at 15 min was determined through heat transfer simulation using the calibrated heat transfer coefficient. The location of the cold points in the stand-up pouches was moved upward from the bottom of the pouch by increasing the pouch size. The sterilization time for 100, 150, and 200 g was evaluated as 35.0, 41.5, and 47.5 min, respectively. The textural properties measured using the cutting test showed significant differences according to the location in the pouch. The textural degradation of BF in the top part of the pouch was more extensive than that at the bottom due to the smaller dimensions. In addition, the percentage of textural degradation in the top part increased with increasing pouch sizes. The methods introduced in this study can be applied to validate the degree of sterilization and the texture of various stalk vegetables used for ready-to-eat products packed in stand-up pouches.
The Spatial Effect and Threshold Characteristics of Green Technological Innovation on the Environmental Pollution of Thermal Power, etc., Air Pollution-Intensive Industrial Agglomeration in China
Authors: Zhou Jingkun , Li Yating , Tian Juan , Ma Zhifei
Published: December 2022

Serious air pollution has occurred in China since 2012. With the increasing investment in technological innovation in China, the role of green technological innovation in reducing air pollution has attracted more and more attention. By constructing the spatial Durbin model and threshold regression model and using the statistical data of China’s provinces, this study explores the spatial effects and threshold characteristics of China’s green technology innovation on the environmental pollution of China’s air pollution-intensive industrial agglomeration. The research objective is to find out the spatial effects and threshold characteristics of green technology innovation on the environmental pollution of China’s air pollution-intensive industrial agglomeration. The results show that thermal power, etc., air pollution-intensive industrial are important sources of sulfur dioxide emissions; however, their degree of concentration is gradually increasing, resulting in rising sulfur dioxide emissions in these areas. When the level of green technological innovation is greater than 8.0523, its inhibition effect on sulfur dioxide emissions in these industries is significantly increased. Improving green technology innovation ability in thermal power, etc., air pollution-intensive industrial agglomeration areas can effectively reduce pollution in the atmosphere. The level of green technology innovation in key zones must be increased to adjust the concentration of pollution-intensive industries, improve China’s industrial structure, and reduce atmospheric environment pollution.
Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine
Authors: Liu Yinlong , Li Jinze
Published: December 2022

The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three machine learning algorithms of BP-NN, MLR, and LS-SVM were chosen for training forecasting models, with the data on population, GDP, mean temperature, sunshine, rainfall, and frost days in 1993–2019 serving as the input variables. The total data were divided by 70% into the training set (1993–2011) and 30% into the test set (2012–2019), in chronological order. RMSE, MAPE, and MaxError were adopted as the performance criteria. The statistical results show that the gross population of the UK increases year by year from 1993 to 2020. The GDP generally increases before 2007 but has a decline, and then varies with a large amplitude afterward. The electricity and energy consumption of the UK generally increase from 1993 and reach a peak around 2005. Afterward, a decline occurs basically year by year until 2019. The simulation results reveal that all three models predict well on the training set but have some overestimation on the test set. The LS-SVM model has the best forecasting performance among the three models on the training set. The results show that it is feasible to use machine learning algorithms to predict the future electricity and energy consumption of a country based on past economic and livelihood data. In this way, economic decision-makers can rely on the predicted values to make a well-founded layout for future energy construction and investment to avoid waste or a shortage of resources.
Numerical Study of the Purge Flow’s Effect on the Loss Mechanism of the Blocking and Shear Effects
Authors: Bai Tao , Yang Qingzhen , Liu Jian
Published: December 2022

The loss mechanism of shear and upstream blockage caused by the interaction of the purged flow and ingested gas needs to be systematically studied to optimize the flow near the rim. In order to study the causes and influence factors of blocking and shearing effects and quantify their losses reasonably, the three-dimensional unsteady numerical method validated by the experiment data was adopted to study the turbine with three kinds of sealing structures. The block and shear loss are quantified by integrating the dissipation coefficient in the volume where the specific aerodynamic loss occurs. The result indicated that there was a larger radial velocity and smaller tangential velocity of the purged flow relative to the main flow caused the blocking effect. Therefore, its loss is affected by the seal flow and seal structure. The shear effect is mainly affected by the tangential velocity gradient and the axial velocity gradient near the cavity exit. The contribution of the tangential velocity gradient to shear loss is increased with the enlarged sealing efficiency. Through research, it is clear that increasing the purge flow’s tangential velocity is beneficial to reducing the shear loss and has a positive significance for weakening the blocking effect in the main flow channel. Furthermore, the influence of sealing structure on blocking and shear effect must be particularly considered since both are related to sealing efficiency.
Evaluation of the Performance of a Heat Pipe for Pre-Frozen Soil around a Solar Support by a Numerical Method
Authors: Li Dong , Yang Xinpeng , Zhao Xuefeng , Yang Ruitong , Meng Lan , Fu Shaojie
Published: December 2022

The base of solar collector systems is usually installed in soil that contains moisture. In cold regions, due to the low ambient temperature, the moisture in the soil freezes, creating a risk of frost heave. This study analyzed the frost heave mechanism of power transmission and transformation foundation, clarified the factors affecting soil frost heave and the way to solve soil layer frost heave, and proposed the use of heat transfer elements to pre-frozen soil layers to prevent the foundation of the solar collector system from freezing. A numerical model of the ground heat exchange pipes in soil was established. The effects of different soil types, soil moisture content, and the effective radius and operating time on the heat transfer performance of the system were investigated by the verified numerical model. The results show that the heat pipe pre-freezing technology can reduce the drop in soil temperature, thereby increasing the temperature difference between the ground heat exchange pipe and the far-field soil. In terms of the ability to delay the decline in soil temperature, reducing the water content and selecting certain clays can increase the degree and speed of the drop in soil temperature.
Cellulose Acetate Film Containing Bonechar for Removal of Metribuzin from Contaminated Drinking Water
Authors: Mielke Kamila , Castro Gustavo , Mendes Kassio
Published: December 2022

Bonechar presents high sorption capacity for mobile herbicides retained in soil and water. However, its use in a granulated and/or powder form makes it difficult to remove water. The objective of this study was to produce a cellulose acetate film with bonechar as a viable alternative to remove metribuzin from water. The treatments were composed of 2 and 3 g of bonechar fixed on a cellulose acetate film, pure bonechar, and a control (no bonechar). The sorption and desorption study was carried out in the equilibrium batch mode with five concentrations of metribuzin (0.25, 0.33, 0.5, 1, and 2 mg L−1). The water used in the experiment was potable water. Herbicide analysis was performed by High-Performance Liquid Chromatography (HPLC). The addition of 2 and 3 g of the bonechar fixed on the acetate film sorbed 40% and 60%, respectively, of the metribuzin at the lowest concentrations (0.25, 0.33, and 0.5 mg L−1). For both additions, desorption was low, being 7% and 2.5% at 24 and 120 h, respectively. There are still no reports of the production of cellulose acetate film with bonechar for herbicide removal in water, considered an alternative of easy handling and indicated for water treatment plants.
Influence of Tip Clearance on Cavitation Characteristics of an Inducer of Turbopump: CFD Study
Authors: Han Huan , Xiang Le , Xu Kaifu , Geng Da , Ren Zibo , Wu Guohong , Liu Shuhong , Zuo Zhigang
Published: December 2022

The tip clearance, a compact gap between the inducer blade tip and casing wall, is critical to both the liquid leakage and cavitation-induced forces of a turbopump. In this study, we numerically investigate the effect of tip clearance on the cavitation characteristics of an inducer. Six different tip clearances, 0.1, 0.3, 0.5, 1.0, 1.5, and 2 mm, namely Models A–F, were designed to evaluate the cavitation performance, cavity structure, blade loading, radial force, etc. Model D (1.0 mm) had the relatively highest head coefficient and smallest cavity area on each blade as compared to all other models. The pressure coefficient distribution and blade loading further confirmed that Model D can maintain a higher pressure head and better suppress the cavitation onset than the other models. The radial force signals in the time and frequency domains show that Model D has an intermediate force magnitude with slightly higher noises at the rotating frequency and its harmonic frequencies. Model D also has a relatively smaller vortex region and smaller vortex strength (λ2 criterion). In short, all results show that Model D is the best alternative to balance the complex interactions of the bulk flow and tip leakage flow, compromising the hydraulic head and rotating cavitation.
“Religious-Zionism”: Signifier without Signified? Or—Is Religious-Zionism Still Alive?
Authors: Sagi Avi
Published: December 2022

In the public discourse and the research literature, the signifier “religious-Zionism” is usually viewed as denoting a specific group located midway between secular and ultra-Orthodox Jews. This location does not turn religious-Zionism into a residual category including whoever is not part of the two others. Quite the contrary. Religious-Zionism used to be a group with unique characteristics, including values and a normative fullness of its own. I argue in this article that, at present, the category “religious-Zionism” no longer signifies a specific group due to a series of centrifugal processes affecting it. Its ethos, myth, textual web, and authority principle have collapsed and the signifier reflects no more than a political and rabbinic discourse attempting to control the breakdown.
Hortus Conclusus—A Mariological Metaphor in Some Renaissance Paintings of the Annunciation in the Light of Medieval Liturgical Hymns
Authors: Salvador-González José María
Published: December 2022

This article seeks to shed light on the doctrinal meanings of the closed garden included in some Renaissance paintings of the Annunciation. To justify the iconographic interpretations that we will give of these paintings, we will base them on the analysis of many medieval liturgical hymns that poetically designate the Virgin Mary through the metaphorical expression hortus conclusus (closed garden) with which the Husband or Bridegroom requisites the Wife or Bride in the Song of Songs. We will divide our article into two parts as a strategy for analysis. First, we will analyze an extensive series of fragments of liturgical hymns that repeatedly praise Mary through this biblical metaphor. In the second part, we will examine some artistic representations of the Annunciation that, in the Italian Renaissance, depict a closed garden in the scene. From this double comparative analysis, textual and iconic, we will conclude that, in direct and essential correlation, those hymnic texts and those paintings clearly illustrate that the hortus conclusus is an eloquent symbol of the virginal divine motherhood of Mary and her perpetual virginity, as well as the excellence and fullness of her supernatural virtues and privileges.
‘Ka asi kasya asi, kalyāṇi?’ The Ambiguity of the yakṣas in the Araṇya Parva of the Mahābhārata
Authors: Sterken Arjan
Published: December 2022

Supernatural entities are often described as ambiguous, but ambiguity is underdetermined and undefined. This article has a twofold goal: first of all, it constructs an ideal-type model for identifying and specifying ambiguity in supernatural beings; secondly, it examines the ambiguity of yakṣas in the Araṇya Parva of the Mahābhārata. This model for determining supernatural ambiguity utilizes five markers, which appear in either a positive or negative aspect: fulfilling or denying needs and desires; protecting or attacking humans; belonging to the same order as humansor rejecting this order; beautiful or hideous appearance; and living close by or far away from human communities. Four narratives are examined: the story of Nala and Damayantī, the First and Second War of the Yakṣas, and the story of the Drillling Woods. In all stories, each of the five markers are utilized to describe the yakṣas’ ambiguity. However, one should distinguish between ambiguity proper (when conflicting markers are present at the same time) verus ambiguity caused by the shifting of markers during a narrative.
‘A Policy of Sacrifice’: G.B.A. Gerdener’s Missionally Founded Racial Theory and the Religionization of Apartheid
Authors: Pienaar Jacques
Published: December 2022

In 1935 the Dutch Reformed Church (DRC) accepted its federal mission policy which had racial segregation enshrined in it as a core and divinely sanctioned principle. As the foremost missiologist within South African church circles during the middle half of the 20th Century, Gustav Bernhard Augustus Gerdener was the chief formulator and disseminator of this policy. Convinced that the future fate of South Africa’s multi-racial society rested squarely on evangelisation, white guardianship, and mission work, Gerdener lobbied for secular racial theory to be based on the formula of the DRC mission church. By 1946 this racial theory espoused by Gerdener, as well as the majority DRC, was internationally questioned by the post-World War Two onset of general human rights and rapid decolonialisation spearheaded by the newly constituted United Nations Organisation. This paper sets out to track the influence Gerdener had on the formulation of the DRC mission policy. It will make the case that as advocate of this policy and through his position as chairman of the DRC Federal Mission Council Gerdener played a critical role during the incubation years of the apartheid ideology leading up to the nationalist’s political victory in 1948. Finally, it will aim to elucidate the justification for apartheid which Gerdener’s racial theory afforded to a religious nation. A justification which formed the moral bedrock of South Africa’s opposition to the broader international context of decolonialisation and advocation of a domestic social system guised as one geared toward equal, albeit separate, development but which ultimately proved to be a new strain of colonialism.
Materiality, Experience and the Body: The Catholic Pilgrimage of Sheshan in Shanghai, China
Authors: Huang Jianbo , Zheng Xuwen , Lee Christine
Published: December 2022

This paper foregrounds the Turnerian experiences of pilgrims themselves, for whom pilgrimage is perhaps first and foremost the process of experiencing faith with their whole body and mind. At the Chinese Catholic pilgrimage site of Sheshan, located in western Shanghai, multiple meanings and possibilities are written onto the body of the pilgrim as it interacts with sacred materialities. In the process, the pilgrim materially orients themselves towards the transcendent other and to people and events throughout time. The boundary between subject and object is increasingly blurred in the pilgrim’s imagination, and pilgrimage becomes a ‘porous’ mind-body experience for them. In the process, as pilgrims repeatedly physically enact doctrine and doctrinal texts in the course of pilgrimage—while simultaneously rooting them in their own personal lives—Sheshan is, through the concrete actions of worshippers layered up over time, continually being re-made as sacred.
Islamic Religious Education Textbooks in a Pluralist Nigeria
Authors: Idoko Patricia Enedudu
Published: December 2022

Pluralism and religious diversity offer opportunities for deep learning across cultures and religions. However, in some cases, religious diversity also results in tensions and conflicts in communities because religious differences polarize people of different faith traditions. This article analyzes how religious instruction textbooks used in public junior high schools in Nigeria promote integration and inclusion. In particular, the paper will examine the approved texts used in teaching Islamic Religious Studies (IRS). In analyzing the IRS textbooks, the article focuses on two major points: how the texts present Islam to the students, and how students are equipped to deal with the question of contrasting truth claims within and outside their faith traditions. This article argues that the IRS textbooks used in high school in Nigeria should expand their discussion on Islam to include open interaction about the religious differences within and across religious traditions. I argue that IRS textbooks need to prepare students to deal with different theological truth claims in a way that is both self- and other-respecting. To compose a text that engages religious pluralism, I propose that the approach of teaching religious education in junior secondary schools in Ghana should be used as a model, as it could offer Nigeria some insights on how to teach religious knowledge from a pluralistic approach.
Deep Generators on Commodity Markets Application to Deep Hedging
Authors: Boursin Nicolas , Remlinger Carl , Mikael Joseph
Published: December 2022

Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case of commodities, it turns out that these generators can also be used to refine the price models by tackling the high-dimensional challenges. In this work, the synthetic time series of commodity prices produced by such generators are studied and then used to train deep hedgers on various options. A fully data-driven approach to commodity risk management is thus proposed, from synthetic price generation to learning risk hedging policies.
Opportunities for the Application of a Model of Cost Management and Reduction of Risks in Financial and Economic Activity Based on the OLAP Technology: The Case of the Agro-Industrial Sector of Russia
Authors: Khoruzhy Liudmila I. , Katkov Yuriy N. , Katkova Ekaterina A. , Khoruzhy Valeriy I. , Dzhikiya Meri K.
Published: December 2022

The development of cloud technologies enables companies to actively implement technologies for cost management and risk reduction in their financial and economic activities. The use of cloud-based models of risk management in the financial and economic activities of the enterprise will help small and medium-sized companies in the agro-industrial sector in Russia to make structural and strategic changes, as well as discover new opportunities for business expansion. The purpose of the study is to develop models for cost management and reduction of risks in the financial and economic activities of companies based on the OLAP technology for application in Russian agro-industrial enterprises. The study employs a qualitative approach based on the case study methodology. The paper discloses and substantiates the authors’ conceptual model of a cost management system that allows executives to make decisions proceeding from four types of cost prices. The distinguishing feature of the management system is the use of a digital twin, which makes it possible to manage risks at the early stages of decision-making. The application of OLAP systems improves the quality of analysis and visualization methods as part of the cost management system. In addition, the study provides practical insight into how the applied model will help small and medium-sized agro-industrial enterprises to develop different business vision strategies based on cost reduction, manage the level of risk at the early stages of decision-making, and analyze information from a geographically dispersed logistics chain of divisions (production facilities, warehouses, stores).
Measuring Performance: Metrics for Manipulator Design, Control, and Optimization
Authors: Russo Matteo
Published: December 2022

How good is a robot? Three challenges arise from this question: first, defining performance from the robot’s observable behavior; second, quantifying performance with an index that is obtainable through direct measurement or computation, and representative of the measured quantity; third, ensuring that this procedure is repeatable and general, to enable performance comparison, benchmarking, and an increase of safety and efficiency standards. However, the landscape of performance metrics for industrial manipulators is fragmented, and limited effort is being made toward a unified framework. This survey aimed at collecting, classifying, and analyzing the key works on the topic, with a focus on mechanical performance metrics for industrial robots. Two diverging trends are outlined, with commercial standards adopting a limited set of metrics and academic research encouraging the development of new performance indices. The shortcomings of both approaches are highlighted, providing a perspective on how future research could proceed.
An Improved Forest Height Model Using L-Band Single-Baseline Polarimetric InSAR Data for Various Forest Densities
Authors: Sui Ao , Michel Opelele Omeno , Mao Yu , Fan Wenyi
Published: December 2022

Forest density affects the inversion of forest height by influencing the penetration and attenuation of synthetic aperture radar (SAR) signals. Traditional forest height inversion methods often fail in low-density forest areas. Based on L-band single-baseline polarimetric SAR interferometry (PolInSAR) simulation data and the BioSAR 2008 data, we proposed a forest height optimization model at the stand scale suitable for various forest densities. This optimization model took into account shortcomings of the three-stage inversion method by employing height errors to represent the mean penetration depth and SINC inversion method. The relationships between forest density and extinction coefficient, penetration depth, phase, and magnitude were also discussed. In the simulated data, the inversion height established by the optimization method was 17.35 m, while the RMSE value was 3.01 m when the forest density was 100 stems/ha. This addressed the drawbacks of the conventional techniques including failing at low forest density. In the real data, the maximum RMSE of the optimization method was 2.17 m as the stand density increased from 628.66 stems/ha to 1330.54 stems/ha, showing the effectiveness and robustness of the optimization model in overcoming the influence of stand density on the inversion process in realistic scenarios. This study overcame the stand density restriction on L-band single baseline PolInSAR data for forest height estimation and offered a reference for algorithm selection and optimization. The technique is expected to be extended from the stand scale to a larger area for forest ecosystem monitoring and management.
3PCD-TP: A 3D Point Cloud Descriptor for Loop Closure Detection with Twice Projection
Authors: Wang Gang , Jiang Xudong , Zhou Wei , Chen Yu , Zhang Hao
Published: December 2022

Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scenes, driverless ground vehicles or robots usually revisit the same place from a different position, meaning that the descriptor cannot give a uniform description of similar scenes, failing LCD. Against this problem, this paper proposes a 3D point cloud descriptor with Twice Projection (3PCD-TP) for the calculation of the similarities between scenes. First, we redefined the origin and primary direction of point clouds according to their distribution and unified their coordinate system, thereby reducing the interference in position recognition due to the rotation and translation of sensors. Next, using the semantic and altitudinal information of point clouds, we generated the 3D descriptor 3PCD-TP with multidimensional features to enhance its ability to describe similar scenes. Following this, we designed a weighting similarity calculation method to reduce the false detection rate of LCD by taking advantage of the property that 3PCD-TP can be projected from multiple angles. Finally, we validated our method using KITTI and the Jilin University (JLU) campus dataset. The experimental results show that our method demonstrated a high level of precision and recall and exhibited greater performance in the face of scenes with reverse loop closure, such as opposite lanes.
Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination
Authors: Almeida Sílvia , Radeta Marko , Kataoka Tomoya , Canning-Clode João , Pessanha Pais Miguel , Freitas Rúben , Monteiro João Gama
Published: December 2022

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.
STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data
Authors: Wang Shuyu , Li Wengen , Hou Siyun , Guan Jihong , Yao Jiamin
Published: December 2022

Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of satellite data is reduced greatly, which hinders the applications of satellite data in practice. Therefore, a variety of methods have been proposed to conduct missing data imputation for satellite data to improve its quality. However, these methods cannot well learn the short-term temporal dependence and dynamic spatial dependence in satellite data, resulting in bad imputation performance when the data missing rate is large. To address this issue, we propose the Spatio-Temporal Attention Generative Adversarial Network (STA-GAN) for missing value imputation in satellite data. First, we develop the Spatio-Temporal Attention (STA) mechanism based on Graph Attention Network (GAT) to learn features for capturing both short-term temporal dependence and dynamic spatial dependence in satellite data. Then, the learned features from STA are fused to enrich the spatio-temporal information for training the generator and discriminator of STA-GAN. Finally, we use the generated imputation data by the trained generator of STA-GAN to fill the missing values in satellite data. Experimental results on real datasets show that STA-GAN largely outperforms the baseline data imputation methods, especially for filling satellite data with large missing rates.
An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking
Authors: Chai Xuchao , Chen Jianyu , Mao Zhihua , Zhu Qiankun
Published: December 2022

For the mosaicking of multiple remote sensing images, obtaining the optimal stitching line in the overlapping region is a key step in creating a seamless mosaic image. However, for very large remote sensing images, the computation of finding seamlines involves a huge amount of image pixels. To handle this issue, we propose a stepwise strategy to obtain pixel-level optimal stitching lines for large remote sensing images via an upscaling–downscaling image sampling procedure. First, the resolution of the image is reduced and the graph cut algorithm is applied to find an energy-optimal seamline in the reduced image. Then, a stripe along the preliminary seamline is identified from the overlap area to remove the other inefficient nodes. Finally, the graph cut algorithm is applied nested within the identified stripe to seek the pixel-level optimal seamline of the original image. Compared to the existing algorithms, the proposed method produces fewer spectral differences between stitching lines and less-crossed features in the experiments. For a wide range of remote sensing images involving large data, the new method uses less than 10 percent of the time needed by the SLIC+ graph cut method.
AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery
Authors: Gudzius Povilas , Kurasova Olga , Darulis Vytenis , Filatovas Ernestas
Published: December 2022

Advancements in optical satellite hardware and lowered costs for satellite launches raised the high demand for geospatial intelligence. The object recognition problem in multi-spectral satellite imagery carries dataset properties unique to this problem. Perspective distortion, resolution variability, data spectrality, and other features make it difficult for a specific human-invented neural network to perform well on a dispersed type of scenery, ranging data quality, and different objects. UNET, MACU, and other manually designed network architectures deliver high-performance results for accuracy and prediction speed in large objects. However, once trained on different datasets, the performance drops and requires manual recalibration or further configuration testing to adjust the neural network architecture. To solve these issues, AutoML-based techniques can be employed. In this paper, we focus on Neural Architecture Search that is capable of obtaining a well-performing network configuration without human manual intervention. Firstly, we conducted detailed testing on the top four performing neural networks for object recognition in satellite imagery to compare their performance: FastFCN, DeepLabv3, UNET, and MACU. Then we applied and further developed a Neural Architecture Search technique for the best-performing manually designed MACU by optimizing a search space at the artificial neuron cellular level of the network. Several NAS-MACU versions were explored and evaluated. Our developed AutoML process generated a NAS-MACU neural network that produced better performance compared with MACU, especially in a low-information intensity environment. The experimental investigation was performed on our annotated and updated publicly available satellite imagery dataset. We can state that the application of the Neural Architecture Search procedure has the capability to be applied across various datasets and object recognition problems within the remote sensing research field.
AFL-Net: Attentional Feature Learning Network for Building Extraction from Remote Sensing Images
Authors: Qiu Yue , Wu Fang , Qian Haizhong , Zhai Renjian , Gong Xianyong , Yin Jichong , Liu Chengyi , Wang Andong
Published: December 2022

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images.
Estimation of Ground Subsidence Deformation Induced by Underground Coal Mining with GNSS-IR
Authors: Bo Huaizhi , Li Yunwei , Tan Xianfeng , Dong Zhoubin , Zheng Guodong , Wang Qi , Yu Kegen
Published: December 2022

In this paper, GNSS interferometric reflectometry (GNSS-IR) is firstly proposed to estimate ground surface subsidence caused by underground coal mining. Ground subsidence on the main direction of a coal seam is described by using the probability integral model (PIM) with unknown parameters. Based on the laws of reflection in geometric optics, model of GNSS signal-to-noise (SNR) observation for the tilt surface, which results from differential subsidence of ground points, is derived. Semi-cycle SNR observations fitting method is used to determine the phase of the SNR series. Phase variation of the SNR series is used to calculate reflector height of ground specular reflection point. Based on the reflector height and ground tilt angle, an iterative algorithm is proposed to determine coefficients of PIM, and thus subsidence of the ground reflection point. By using the low-cost navigational GNSS receiver and antenna, an experimental campaign was conducted to validate the proposed method. The results show that, when the maximum subsidence is 3076 mm, the maximum relative error of the proposed method-based subsidence estimation is 5.5%. This study also suggests that, based on the proposed method, the navigational GNSS instrument can be treated as a new type of sensor for continuously measuring ground subsidence deformation in a cost-effective way.
Detecting Woody Plants in Southern Arizona Using Data from the National Ecological Observatory Network (NEON)
Authors: Hutsler Thomas , Pricope Narcisa G. , Gao Peng , Rother Monica T.
Published: December 2022

Land cover changes and conversions are occurring rapidly in response to human activities throughout the world. Woody plant encroachment (WPE) is a type of land cover conversion that involves the proliferation and/or densification of woody plants in an ecosystem. WPE is especially prevalent in drylands, where subtle changes in precipitation and disturbance regimes can have dramatic effects on vegetation structure and degrade ecosystem functions and services. Accurately determining the distribution of woody plants in drylands is critical for protecting human and natural resources through woody plant management strategies. Using an object-based approach, we have used novel open-source remote sensing and in situ data from Santa Rita Experimental Range (SRER), National Ecological Observatory Network (NEON), Arizona, USA with machine learning algorithms and tested each model’s efficacy for estimating fractional woody cover (FWC) to quantify woody plant extent. Model performance was compared using standard model assessment metrics such as accuracy, sensitivity, specificity, and runtime to assess model variables and hyperparameters. We found that decision tree-based models with a binary classification scheme performed best, with sequential models (Boosting) slightly outperforming independent models (Random Forest) for both object classification and FWC estimates. Mean canopy height and mean, median, and maximum statistics for all vegetation indices were found to have highest variable importance. Optimal model hyperparameters and potential limitations of the NEON dataset for classifying woody plants in dryland regions were also identified. Overall, this study lays the groundwork for developing machine learning models for dryland woody plant management using solely NEON data.