bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Short Carbon Fiber Reinforcement on Mechanical Properties of 3D-Printed Acrylonitrile Butadiene Styrene
Authors: Lobov Evgeniy , Dobrydneva Anastasia , Vindokurov Ilia , Tashkinov Mikhail
Published: April 2023

The effect of short carbon fiber (SCF) filler on the mechanical properties of 3D-printed acrylonitrile butadiene styrene (ABS) was investigated. The fused filament fabrication (FFF) method was used for the manufacturing of samples. Elastic properties and strength characteristics of samples made of conventional ABS and SCF-reinforced ABS were compared in tensile and bending tests. Fracture toughness and critical strain energy release rate were also determined. In addition, 3D-printed monofilament SCF-reinforced samples were fabricated, the internal structure of which was analyzed using microcomputed tomography (micro-CT). Based on the tomography data, finite-element (FE) models of representative volume elements (RVEs) of the reinforced material were created and used for the numerical calculation of effective characteristics. Numerical and experimental results for the effective elastic properties were compared with the Mori-Tanaka homogenization technique. The ABS samples filled with SCF showed considerably higher mechanical characteristics than those of the conventional ABS. Finally, the dependence between the strength characteristics and elastic properties of the samples on the diameter of the nozzle used for 3D printing was established. 3D-printed ABS reinforced with SCF demonstrated a gain in tensile strength and fracture toughness by 30% and 20%, respectively. Interlayer adhesion strength in flexure tests showed an increase of 28% compared to pure ABS samples.
Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm
Authors: Ouyang Mingsan , Wang Yipeng , Wu Fan , Lin Yi
Published: April 2023

To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility.
The Upper Triassic Braided River Thin-Bedded Tight Sandstone in the Yanchang Formation, Ordos Basin: Sedimentary Characteristics, Seismic Forecasting Method, and Implication
Authors: Lou Tongyang , Feng Congjun , Sun Mengsi , Chen Zhiqiang
Published: April 2023

In the Ordos Basin, Chang 81, a Member of the Yanchang Formation, features the development of braided river thin-bedded tight sandstones. These sandstones constitute one of the main production layers of tight oil and gas in the Yanchang Formation within the basin. This study integrates data from core samples, drilling, and seismic information to identify braided river thin-bedded sandstones in the Chang 81 Member at Daijiaping, Ordos Basin, using a method of constrained correlation between seismic waveform and seismic facies. This approach aids in determining the sedimentary microfacies types and reservoir characteristics of thin-bedded tight sandstones. We establish a quantitative fitting formula for the width-to-thickness ratio of braided channel sand bodies to finely characterize sand body stacking patterns and spatial distribution of thin-bedded tight sandstones in braided channels. Braided delta plain deposits in the Chang 81 Member at Daijiaping mainly comprise four types of sedimentary microfacies: braided channels, crevasse channels, floodplains, and swamps. The thickness of the reservoir sand body of Chang 81 member is mainly concentrated between 5–25 m, with low porosity and permeability, making it a typical thin-bedded tight sandstone reservoir. A method of constrained correlation between seismic waveforms and seismic facies was employed to identify sand bodies of braided river thin-bedded sandstones in the Chang 81 Member, summarizing four sand body stacking patterns: longitudinal incision type, longitudinal separation type, lateral shifting type, and single channel type. Furthermore, a quantitative forecasting formula of width-to-thickness ratio was established for the river channel scale, providing accurate guidance for well deployment. Horizontal wells deployed from the sand body’s side towards its center in a river channel yield a production 1.8 times higher than that of horizontal wells deployed in the opposite direction. Thin-bedded tight sandstones in braided channels, characterized by flat-top and convex-bottom lenticular seismic facies, hold practical significance in guiding the deployment of horizontal well patterns for tight oil and enhancing oil and gas recovery.
Risk Assessment Model of Chemical Process Based on Interval Type-2 Fuzzy Petri Nets
Authors: Kan Zhe , Liang Yaxuan , Zhao Taoyan , Wang Xiaolei
Published: April 2023

An interval type-2 fuzzy set and fuzzy Petri net combined risk assessment model for chemical production was proposed to solve the problems of disorganized hierarchy and poorly targeted measures, as well as the requirement for complex equipment associated with chemical production risk assessment. First, four different types of risk databases were established according to the production process of cyclohexane. Considering the intrinsic relationship between the risk factors in the fault database, the interval type-2 fuzzy set was used to improve the semantic transformation accuracy and calculate the confidence in the risk factors. The fuzzy Petri net model was used to simulate the dynamic development of accidents, and the parallel relationship between risk factors was intuitively described. Thereafter, the external relationship between risk factors was analyzed, and the net structure of each layer was divided to build a multilevel model. Finally, the catalyst activation process during cyclohexane production was taken as an example for risk assessment calculation, and the accident risk probability was calculated by multilevel fuzzy reasoning. The results demonstrate that the model is an improvement over traditional methods and can be used for precise prevention and control. Moreover, it can accurately analyze risk probability during chemical production, determine the risk associated with the reaction process, effectively prevent accidents, and provide a reference for risk evaluation and risk classification.
Ability of Deep Eutectic Solvent Modified Oat Straw for Cu(II), Zn(II), and Se(IV) Ions Removal
Authors: Dimitrijević Jelena , Jevtić Sanja , Marinković Aleksandar , Simić Marija , Koprivica Marija , Petrović Jelena
Published: April 2023

In the proposed study, agro-waste biomass oat straw (OS) was considered a potential adsorbent for Cu(II), Zn(II), and Se(IV) removal from aqueous solutions. In order to obtain material with better adsorption abilities, the OS was modified by a deep eutectic solvent (DES). Structural changes caused by the applied modification route were considered by pHpzc, SEM, FTIR, and DSC/TG analysis. These methods discovered that lignocellulosic biomass degradation and material functionalization were achieved by DES treatment. Preliminary adsorption tests showed an over fourfold increase in capacity upon modification. The kinetic parameters implied that adsorption on modified material followed the pseudo-second-order kinetic model. Different isotherm models were applied to experimental data, while the Sips isotherm model best describes the equilibrium of the adsorption process on the tested modified material. According to this isotherm model, the maximum achieved adsorption capacities of Cu(II), Zn(II), and Se(IV) were 48.21, 55.06, and 87.85 mg/g, respectively. The summarized experimental results revealed that the adsorption process of selected cations on modified OS was predominantly caused by chemisorption, while, in addition to chemisorption, electrostatic forces were also responsible for Se(IV) removal. Desorption test showed that the prepared material could be reused for at least 3 cycles, with minimal efficiency loss. Briefly, this study reinforces that DES-modified agro-waste biomass could be used as a promising adsorbent for cations and oxyanions from wastewater.
Leggett–Garg-like Inequalities from a Correlation Matrix Construction
Authors: Ben Porath Dana , Cohen Eliahu
Published: April 2023

The Leggett–Garg Inequality (LGI) constrains, under certain fundamental assumptions, the correlations between measurements of a quantity Q at different times. Here, we analyze the LGI and propose similar but somewhat more elaborate inequalities, employing a technique that utilizes the mathematical properties of correlation matrices, which was recently proposed in the context of nonlocal correlations. We also find that this technique can be applied to inequalities that combine correlations between different times (as in LGI) and correlations between different locations (as in Bell inequalities). All the proposed bounds include additional correlations compared to the original ones and also lead to a particular form of complementarity. A possible experimental realization and some applications are briefly discussed.
An Exploration of Marian Spiritual Practices: Toward a Daily Transcendent Spiritual Life with Mother Mary
Authors: Lee Yong-Gil
Published: April 2023

Mother Mary has been and can be a spiritual role model for Korean Catholics to live out their faith, a faith that is based on a dynamic understanding of a spiritual model of Mary, that is, spiritual growth coming from Eastern Mariology, particularly Mariology based on Gregory Palamas. For this dynamic understanding of Marian spirituality, I review the Scriptures on Mary, including Luke, and Palamas’ reflection on Mary’s spiritual life. This dynamic understanding of Mary and Her spiritual life never contradicts the static approach to Mariology, the Immaculate Conception, through which Jesus, who is God, was born. I believe Mother Mary in Heaven still loves Her Son and God, and Her love is becoming deeper and deeper in Heaven, which means that some virtues, such as wisdom, knowledge, prudence, vigilance, and endurance, could be interim virtues “until heaven and earth pass away” (Mt 5:19), for love and intimacy with God are permanent in Heaven. This is the belief that we can grow our love continuously with Mary forever.
Sacralizing a Battlefield: The Religious Heritage of the Battle of Warsaw 1920
Authors: Baraniecka-Olszewska Kamila
Published: April 2023

This article presents a process of creating religious heritage that goes beyond the “sacralization of heritage” or the “heritagization of religion”. It is grounded in the assumption that religious heritage may be an element of lived religion, while the strategies of managing the past (and of preserving and transmitting values) observable within Roman Catholicism all fit within the discourse of heritage. This text focuses on the heritage of the Battle of Warsaw developed in Roman Catholic religious practice. The battles fought in 1920 are also referred to as the “Miracle on the Vistula”, since the Polish victory in that confrontation is seen as resulting from a divine intervention. Such an image of the battle permeates its heritage, allowing the Roman Catholic Church to shape it in accordance with religious teachings. This article is based on fieldwork on the anniversary events of the Battle of Warsaw. It aims to illustrate how the battlefield may become sacralized, blurring the lines between religious practices and heritage. The haptically experienced materiality of the battlefield makes it possible to localize heritage perceived not only as the site of bloody conflict, but also as the scene of the Virgin Mary’s intercession and divine intervention.
The Virtue of Religio in Thomas Aquinas: Between Justice and Love
Authors: Nemec Rastislav , Blaščíková Andrea
Published: April 2023

The aim of this article is to interpret the virtue of religio in the thinking of Thomas Aquinas against the background of his Summa Theologiae. In Summa Theologiae, the issue is placed in the context of justice and injustice; thus, this article seeks to show the deeper reason as well as the possible connections between religio and iustitia. Justice, according to Thomas, is preferably realised where there are differences among people and some debt (debitum) occurs that needs to be settled. The inclusion of the virtue of religio under justice is justified by the existence of a relationship in which a debitum also arises, but settlement is impossible. Aristotle, who inspired Thomas, claims that true friendship rests on similarity and equality. This article wants to present how this opinion of Aristotle could be manifested in Thomas’s description of the virtue of religio. The article posits the question of whether the gift of friendship with God does not permit us to give a new interpretation of the virtue of religio. The authors conclude that, together with love, the virtue of religio is infused into man, thanks to which he can perform deeds commensurate with the goal of ultimate beatitude. For a person who loves God, the act of religio is not only a means of satisfying the demand of justice towards the Creator of all things but also a grace in which he returns his love to God.
CSP2Turtle: Verified Turtle Robot Plans
Authors: MacConville Dara , Farrell Marie , Luckcuck Matt , Monahan Rosemary
Published: April 2023

Software verification is an important approach to establishing the reliability of critical systems. One important area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains. Our particular interest is in checking the plans that robots are expected to follow to detect errors that would lead to unreliable behaviour. Python is a popular programming language in the robotics domain through the use of the Robot Operating System (ROS) and various other libraries. Python’s Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP). Our interactive toolchain CSP2Turtle with CSP models and Python components enables plans for the turtle agent to be verified using the FDR model-checker before being executed in Python. This means that certain classes of errors can be avoided, providing a starting point for more detailed verification of Turtle programs and more complex robotic systems. We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world. We evaluate our approach and discuss future work, including how our approach could be scaled to larger systems.
Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method
Authors: Mayoral Baños José Carlos , From Pål Johan , Cielniak Grzegorz
Published: April 2023

Safe navigation is a key objective for autonomous applications, particularly those involving mobile tasks, to avoid dangerous situations and prevent harm to humans. However, the integration of a risk management process is not yet mandatory in robotics development. Ensuring safety using mobile robots is critical for many real-world applications, especially those in which contact with the robot could result in fatal consequences, such as agricultural environments where a mobile device with an industrial cutter is used for grass-mowing. In this paper, we propose an explicit integration of a risk management process into the design of the software for an autonomous grass mower, with the aim of enhancing safety. Our approach is tested and validated in simulated scenarios that assess the effectiveness of different custom safety functionalities in terms of collision prevention, execution time, and the number of required human interventions.
Regional Algorithm for Estimating High Coccolithophore Concentration in the Northeastern Part of the Black Sea
Authors: Vazyulya Svetlana , Deryagin Dmitriy , Glukhovets Dmitry , Silkin Vladimir , Pautova Larisa
Published: April 2023

A modified regional algorithm to quantify the coccolithophore concentration in the northeastern part of the Black Sea under conditions of intense bloom is presented. To modify the algorithm, the data of in situ measurements of coccolithophore Emiliania huxleyi abundance performed in June 2017 and 2022 (when the maximum values were 9 × 106 and 13 × 106 Cells L−1, respectively), as well as the data from hydro-optical and satellite measurements, were used. In addition, the ratio between the number of detached coccoliths and coccolithophore cells was taken into account. Based on the expanded array of in situ data, the optimal values of the regional algorithm parameters were obtained. The modified algorithm makes it possible to obtain more accurate results in areas of high coccolithophore concentrations and takes into account the contribution of coccoliths. To test the sensitivity of the algorithm to variations in bio-optical characteristics, model calculations were performed using Hydrolight software. The updated algorithm is significantly less sensitive to variations in chlorophyll concentration and CDOM absorption coefficient than its previous version.
An Effective Deep Learning Model for Monitoring Mangroves: A Case Study of the Indus Delta
Authors: Xu Chen , Wang Juanle , Sang Yu , Li Kai , Liu Jingxuan , Yang Gang
Published: April 2023

Rapid and accurate identification of mangroves using remote sensing images is of great significance for assisting ecological conservation efforts in coastal zones. With the rapid development of artificial intelligence, deep learning methods have been successfully applied to a variety of fields. However, few studies have applied deep learning methods to the automatic detection of mangroves and few scholars have used medium-resolution Landsat images for large-scale mangrove identification. In this study, cloud-free Landsat 8 OLI imagery of the Indus Delta was acquired using the GEE platform, and NDVI and land use data were used to produce integrated labels to reduce the complexity and subjectivity of manually labeled samples. We proposed the use of MSNet, a semantic segmentation model fusing multiple-scale features, for mangrove extraction in the Indus Delta, and compared the performance of the MSNet model with three other semantic segmentation models, FCN-8s, SegNet, and U-Net. The overall performance ranking of the deep learning methods was MSNet > U-Net > SegNet > FCN-8s. The parallel-structured MSNet model was easy to train, had the fewest parameters and the highest validation accuracy, and provided the best results for the extraction of mangrove pixels with weak features. The MSNet model not only maintains the high-resolution features of the image and fully learns the pixels with weak features during the training process but also fuses the multiple-scale underlying features at different scales to enhance the semantic information and improve the accuracy of feature recognition and segmentation localization. Finally, the areas covered by mangroves in the Indus Delta in 2014 and 2022 were extracted using the best-performing MSNet. The statistics show an increase in mangrove-covered areas in the Indus Delta between 2014 and 2022, with a reduction of 44.37 km2, an increase of 170.48 km2, and a net increase of 126.11 km2.
Landsat-8 Observations of Foam Coverage under Fetch-Limited Wave Development
Authors: Dulov Vladimir A. , Skiba Ekaterina V. , Kubryakov Arseny A.
Published: April 2023

In this paper, we aimed to clarify the problem of foam coverage dependence on wave fetch, which is of interest in satellite microwave radiometry, but for which controversial results were reported previously. The classical approach to investigating developing waves was applied. That is, the waves are considered as coming from the coast under approximately constant wind velocity. The study includes two scenes of intensive katabatic winds in the Gulf of Lion and the Gulf of Tehuantepec. We used two Bands of Landsat OLI images to extract the wave spectral peak frequency and the sea fraction covered by foam simultaneously along the wave fetch. The distributions of the spectral peak frequency along the fetch obeying the classical wave growth law clearly showed that we observed the developing waves. Along the fetch, the sea surface covered with foam grows about three times with the power law. This development of foam coverage occurred at the range of dimensionless fetches from 50 up to 7000 if the fetch is scaled using wind velocity and gravity acceleration. A simple model of the foam coverage growth with wave fetch is suggested. We modeled wave energy dissipation rate using the JONSWAP wave spectrum for developing seas. The model explains the observations at the quantitative level. Reported results can be applied to investigations of tropical cyclones using satellite microwave radiometry.
Different-Classification-Scheme-Based Machine Learning Model of Building Seismic Resilience Assessment in a Mountainous Region
Authors: Wen Haijia , Zhou Xinzhi , Zhang Chi , Liao Mingyong , Xiao Jiafeng
Published: April 2023

This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas.
UAV Network Path Planning and Optimization Using a Vehicle Routing Model
Authors: Chen Xiaotong , Li Qin , Li Ronghao , Cai Xiangyuan , Wei Jiangnan , Zhao Hongying
Published: April 2023

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications
Statistical Study of the Ionospheric Slab Thickness at Beijing Midlatitude Station
Authors: Zhang Yuqiang , Zhou Yong , Zhang Fubin , Feng Jian , Xu Tong , Deng Zhongxin , Zhu Jiawei , Liu Yi , Wang Xiang , Zhao Zhengyu , Zhou Chen
Published: April 2023

The ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2) is known as the ionospheric equivalent slab thickness (EST, also known as τ), and it is a crucial indicator of the ionosphere. Using TEC and NmF2 data from the years 2010 to 2017, this work conducts a comprehensive statistical analysis of the ionospheric slab thickness in Beijing, which is in the midlatitude of East Asia. The outcomes show that the τ have different diurnal variations at different seasons for high/low-solar-activity years. On the whole, daytime τ significantly greater than nighttime τ in summer, and it is the opposite for the τ in winter regardless of the solar cycle, whereas the τ during equinox shows different morphology for high/low-solar-activity years. Specifically, daytime τ is larger than nighttime τ during equinox in years of high-solar activity, while the opposite situation applies for the τ during equinox in years of low-solar activity. Moreover, the pre-sunrise and post-sunset peaks are most pronounced during winter for low-solar-activity years. In summer, there is a great increase in τ during the morning hours when compared with other seasons. Furthermore, the τ decreases with the solar activity during nighttime, whereas it seems there is no correlation between daytime τ and solar activity. This paper explained the primary diurnal variations in τ across different seasons during high-/low-solar-activity years by analyzing relative fluctuations of TEC and NmF2 throughout the corresponding period. In addition, based on the disturbance index (DI), which is calculated by instantaneous τ and its corresponding median, this paper found that the storm-time τ might increase when compared with its median value during the daytime, while it may both increase and decrease during the nighttime, especially around dawn and dusk hours. To further analyze the physical mechanism, an example on 2 October 2013 is also presented. The results indicate that the positive disturbance of τ during the main phase of a geomagnetic storm might be caused by the prompt penetration electric field and neutral wind during the storm, and the τ increases during the early recovery phase might be due to the disturbance dynamo electric field as well as the neutral wind during the storm. Moreover, there is a negative disturbance of τ in the recovery phase during the most disturbed sunrise hours, and it might be due to the electric field reversal, neutral wind or other factors during this period. This paper notes the differences of τ in midlatitude between different longitudinal sectors from the related climatology and storm-time behavior, as it would be helpful to improve the current understanding of τ at midlatitudes in East Asia.
Early Freeze-Up over the Bering Sea Controlled by the Aleutian Low
Authors: Wang Weibo , Jing Chunsheng , Guo Xiaogang
Published: April 2023

Early freeze-up affects the local marine environment and ecosystem throughout the entire Bering Sea. However, the process governing early freeze-up, which is responsible for the most significant interannual variation in the December sea ice area (SIA), is not well understood. Here, we show that the SIA in December is modulated by the Aleutian low in November by altering poleward heat transport (PHT). The stronger the November PHT is, the lower the December SIA. The rise in heat transport across the Bering Strait in November is consistent with the decrease in SIA in December, with a correlation of −0.71, further validating the regulatory role of PHT. The Aleutian low anomaly controls the local wind field, further altering the sea surface temperature and PHT. The center of the anomalous low-pressure in the east (west) generates the northerly (southeasterly) anomaly over the northern Bering Sea, leading to acceleration (suppression) of seawater cooling and weakening (enhancement) of the PHT. It is also found that a strong northerly surface current has a greater influence on the later SIA than warm water temperature. Hence, atmospheric forcing causing changes in ocean forcing is imperative to understand early freeze-up.
MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure
Authors: Wang Dehao , Weng Liguo , Xia Min , Lin Haifeng
Published: April 2023

The change-detection task is essentially a binary semantic segmentation task of changing and invariant regions. However, this is much more difficult than simple binary tasks, as the changing areas typically include multiple terrains such as factories, farmland, roads, buildings, and mining areas. This requires the ability of the network to extract features. To this end, we propose a multi-branch collaborative change-detection network based on Siamese structure (MHCNet). In the model, three branches, the difference branch, global branch, and similar branch, are constructed to refine and extract semantic information from remote-sensing images. Four modules, a cross-scale feature-attention module (CSAM), global semantic filtering module (GSFM), double-branch information-fusion module (DBIFM), and similarity-enhancement module (SEM), are proposed to assist the three branches to extract semantic information better. The CSFM module is used to extract the semantic information related to the change in the remote-sensing image from the difference branch, the GSFM module is used to filter the rich semantic information in the remote-sensing image, and the DBIFM module is used to fuse the semantic information extracted from the difference branch and the global branch. Finally, the SEM module uses the similar information extracted with the similar branch to correct the details of the feature map in the feature-recovery stage.
Combining Drones and Deep Learning to Automate Coral Reef Assessment with RGB Imagery
Authors: Giles Anna Barbara , Ren Keven , Davies James Edward , Abrego David , Kelaher Brendan
Published: April 2023

Coral reefs and their associated marine communities are increasingly threatened by anthropogenic climate change. A key step in the management of climate threats is an efficient and accurate end-to-end system of coral monitoring that can be generally applied to shallow water reefs. Here, we used RGB drone-based imagery and a deep learning algorithm to develop a system of classifying bleached and unbleached corals. Imagery was collected five times across one year, between November 2018 and November 2019, to assess coral bleaching and potential recovery around Lord Howe Island, Australia, using object-based image analysis. This training mask was used to develop a large training dataset, and an mRES-uNet architecture was chosen for automated segmentation. Unbleached coral classifications achieved a precision of 0.96, a recall of 0.92, and a Jaccard index of 0.89, while bleached corals achieved 0.28 precision, 0.58 recall, and a 0.23 Jaccard index score. Subsequently, methods were further refined by creating bleached coral objects (>16 pixels total) using the neural network classifications of bleached coral pixels, to minimize pixel error and count bleached coral colonies. This method achieved a prediction precision of 0.76 in imagery regions with >2000 bleached corals present, and 0.58 when run on an entire orthomosaic image. Bleached corals accounted for the largest percentage of the study area in September 2019 (6.98%), and were also significantly present in March (2.21%). Unbleached corals were the least dominant in March (28.24%), but generally accounted for ~50% of imagery across other months. Overall, we demonstrate that drone-based RGB imagery, combined with artificial intelligence, is an effective method of coral reef monitoring, providing accurate and high-resolution information on shallow reef environments in a cost-effective manner.
Clear-Air Bragg Scattering Observed above the Convective Boundary Layer in the Morning
Authors: Teng Yupeng , Li Tianyan , Chen Hongbin , Ma Shuqing
Published: April 2023

Reflected waves have been frequently observed via weather radar, even during sunny, cloudless days. Additionally, it is recognized that turbulence and biological scatterers can also dominate the scattering process. In previous studies, echo returns of turbulence from clear-air Bragg scattering (CABS) have been used to detect the height of the convective boundary layer (CBL). However, in a dual-frequency antenna multiplexing radar system, through which the dual-wavelength ratio (DWR) identifies the CABS clearly, CABS can be unexpectedly observed not only at the edge of the CBL but also above the CBL, highlighting the need for an expanded set of causes of clear-air echoes. It was further identified that the negative second derivative of horizontal wind speed, which is measured by a coherent Doppler Lidar, is consistent with the variation of the CABS layers’ height above the CBL. These results emphasize the presence of physical processes leading to turbulence in the troposphere, with implications for Bragg scattering studies and the theory of turbulence in general. This study will help the ecology of research better understand the laws of biological activity.
UWB Circular Fractal Antenna with High Gain for Telecommunication Applications
Authors: Nejdi Ibrahime Hassan , Bri Seddik , Marzouk Mohamed , Ahmad Sarosh , Rhazi Youssef , Ait Lafkih Mustapha , Sheikh Yawar Ali , Ghaffar Adnan , Hussein Mousa
Published: April 2023

The present study proposes a new, highly efficient fractal antenna with ultra-wideband (UWB) characteristics. The proposed patch offers a wide simulated operating band that reaches 8.3 GHz, a simulated gain that varies between 2.47 and 7.73 dB throughout the operating range, and a high simulated efficiency that comes to 98% due to the modifications made to the antenna geometry. The modifications carried out on the antenna are composed of several stages, a circular ring extracted from a circular antenna in which four rings are integrated and, in each ring, four other rings are integrated with a reduction factor of 3/8. To further improve the adaptation of the antenna, a modification of the shape of the ground plane is carried out. In order to test the simulation results, the prototype of the suggested patch was built and tested. The measurement results validate the suggested dual ultra-wideband antenna design approach, which demonstrates good compliance with the simulation. From the measured results, the suggested antenna with a compact volume of 40 × 24.5 × 1.6 mm3 asserts ultra-wideband operation with a measured impedance bandwidth of 7.33 GHz. A high measured efficiency of 92% and a measured gain of 6.52 dB is also achieved. The suggested UWB can effectively cover several wireless applications such as WLAN, WiMAX, and C and X bands.
Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions
Authors: Gu Zhiru , Liu Zhongwei , Wang Qi , Mao Qiyun , Shuai Zhikang , Ma Ziji
Published: April 2023

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon’s environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.
The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations
Authors: Prey Lukas , Ramgraber Ludwig , Seidl-Schulz Johannes , Hanemann Anja , Noack Patrick Ole
Published: April 2023

Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R2 = 0.27–0.81), but R2-values for the best across-trials models were lower only by 0.03–0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models.
A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges
Authors: An Qi , Rahman Saifur , Zhou Jingwen , Kang James Jin
Published: April 2023

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
A Hybrid Indoor Altimetry Based on Barometer and UWB
Authors: Si Minghao , Wang Yunjia , Zhou Ning , Seow Cheekiat , Siljak Harun
Published: April 2023

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m.
Backward Compatible Identity-Based Encryption
Authors: Kim Jongkil
Published: April 2023

In this paper, we present a new identity-based encryption (IBE) system that is named Backward Compatible Identity-based Encryption (BC-IBE). Our BC-IBE is proposed to solve the problem caused by the out-of-synchronization between users’ private keys and ciphertexts. Encryption systems such as revocable IBE or revocable Attribute-based Encryption (ABE) often require updating private keys to revoke users after a certain time period. However, in those schemes, an updated key can be used to decrypt the ciphertexts created only during the current time period. Once the key is updated and the previous keys are removed, the user, the owner of the updated key, will lose access to the past ciphertexts. In our paper, we propose BC-IBE that supports backward compatibility, to solve this problem. In our proposed system, user’s private keys and ciphertexts can be updated periodically with time tags, and these processes can be used to revoke users who do not receive an updated key as the other revocable encryption does. However, in our proposed system, a private key newly issued to a user is backward compatible. This means that it decrypts not only the ciphertexts at the present time period but also all past ciphertexts. This implies that our proposed scheme guarantees the decryption of all encrypted data even if they are not synchronized. Compared to the existing revocable identity-based encryption system, our proposed BC-IBE has the advantage of simplifying key management and securely delegating ciphertext updates. Our proposed scheme only requires a single backward-compatible private key to decrypt all past ciphertexts created. Moreover, the ciphertext update process in our proposed scheme does not require any special privileges and does not require decryption. This means that this process can be securely delegated to a third-party server, such as a cloud server, and it prevents the potential leakage of secrets. For those reasons, BC-IBE is suitable for a system where users are more dynamic, such as the Internet-of-Things (IoT) network, or a system that regularly updates the data, like cloud data storage. In this paper, we provide the construction of BC-IBE and prove its formal security.
A Practice-Distributed Thunder-Localization System with Crowd-Sourced Smart IoT Devices
Authors: Lu Bingxian , Wang Ruochen , Qin Zhenquan , Wang Lei
Published: April 2023

Lightning localization is of great significance to weather forecasting, forest fire prevention, aviation, military, and other aspects. Traditional lightning localization requires the deployment of base stations and expensive measurement equipment. With the development of IoT technology and the continuous expansion of application scenarios, IoT devices can be interconnected through sensors and other technical means to ultimately achieve the goal of automatic intelligent computing. Therefore, this paper proposes a low-cost distributed thunder-localization system based on IoT smart devices, namely ThunderLoc. The main idea of ThunderLoc is to collect dual-microphone data from IoT smart devices, such as smartphones or smart speakers, through crowdsourcing, turning the localization problem into a search problem in Hamming space. We studied the dual microphones integrated with smartphones and used the sign of Time Difference Of Arrival (TDOA) as measurement information. Through a simple generalized cross-correlation method, the TDOA of thunderclaps on the same smartphone can be estimated. After quantifying the TDOA measurement from the smartphone node, thunder localization was performed by minimizing the Hamming distance between the binary sequence and the binary vector measured in a database. The ThunderLoc system was evaluated through extensive simulations and experiments (a testbed with 30 smartphone nodes). The extensive experimental results demonstrate that ThunderLoc outperforms the main existing schemes in terms of effectively locating position and good robustness.
Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
Authors: Wang Fan , Shang Tao , Hu Chenhan , Liu Qing
Published: April 2023

Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of parameters of neural networks make them difficult to deploy in scenarios and receiver devices with strict requirements for low latency and storage. Therefore, this paper proposes a lightweight neural network-based AMC framework. To improve classification performance, the framework combines complex convolution with residual networks. To achieve a lightweight design, depthwise separable convolution is used. To compensate for any performance loss resulting from a lightweight design, a hybrid data augmentation scheme is proposed. The simulation results demonstrate that the lightweight AMC framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77%, without a degradation in performance.