bluuebunny/crossref_metadata_2025 huggingface dataset:
Determinants for the Development of the Activity of the Catholic Church in Poland in the Field of Social Communication
Authors: Gawroński Sławomir , Tworzydło Dariusz , Bajorek Kinga
Published: October 2021

In recent years, the Catholic Church has been forced to change its attitude towards social communication and mass media. It has had to face not only religious but image challenges. Worldview matters, contemporary problems regarding the institution of marriage or pedophilia in the Church are constantly being debated in mass media, thus creating dangers in terms of critical public reception. This situation has also been occurring in Poland in recent years. The observed progress regarding opening the Church to media relations and the use of rich instruments of social communication have their reasons rooted not only in the history of the social and political events of the past several decades but also in the changes in the sphere of mass media and social communication. This article is an attempt to generate a peculiar typology of determinants regarding the development of social communication within the institutional church in Poland. Within our framework of methodological conditions, a literature query with available statistical analyses and observations of current events was applied. Our conclusions show the current attitude of the Catholic Church in Poland regarding issues related to the marketization of faith and the medialization of religion.
This Strange Creature: Plato and Conversion Experiences
Authors: Cimakasky Joe , Romano Joseph J. , Sheeley Kristian
Published: October 2021

In Plato’s corpus, the Greek word ἐξαίφνης appears precisely thirty-six times. Translated generally as “all of a sudden” or “the instant” in his Parmenides, ἐξαίφνης emerges in some of the most significant passages of Plato’s dialogues. Put simply, ἐξαίφνης connotes illumination of the highest realities and philosophical conversion experience. In addition to providing a review of Plato’s conception and use of ἐξαίφνης in Parmenides, Republic, Symposium, and the Seventh Letter, our paper brings an ancillary link to light. Namely, the appearance of ἐξαίφνης as a mark for conversion experiences in the New Testament’s Acts of the Apostles and Plotinus’s Enneads. We reveal how the same pattern and employment of ἐξαίφνης established by Plato emerge in both Acts and the Enneads. This pattern suggests a prolonged period of thinking and training, followed by a flash of understanding. Thus ἐξαίφνης, as evidenced by our survey of its strange instantiation in Plato’s dialogues and then subsequently in Acts and the Enneads, becomes a sign for enlightenment, assimilation with the divine, and conversion experience.
Diversity and Interpretation. Toward a Pluralist Realist Description of Religious Experience
Authors: Gómez Rincón Carlos Miguel
Published: October 2021

This paper attempts to offer a pluralist realist account of the diversity of religious experience. In the first part, I show that an influential trend in contemporary philosophy of religious experience and religious pluralism is based on the mediational image of knowledge and a problematic notion of interpretation, which generates irresoluble problems. I then attempt a redescription based on an extension of Heidegger’s theory of understanding as pre-theorical engagement with the world, which allows for the conciliation of the diversity of religious experience with its claimed epistemic force. To develop this argument, finally, I present the experience of diversity proper of the contemporary world as a type of spiritual experience in which the traits of a pre-theoretical religious understanding can be found. As a result, the paper suggests a move from epistemology to spirituality for a better understanding of religious experience.
Direct Aerial Visual Geolocalization Using Deep Neural Networks
Authors: Harvey Winthrop , Rainwater Chase , Cothren Jackson
Published: October 2021

Unmanned aerial vehicles (UAVs) must keep track of their location in order to maintain flight plans. Currently, this task is almost entirely performed by a combination of Inertial Measurement Units (IMUs) and reference to GNSS (Global Navigation Satellite System). Navigation by GNSS, however, is not always reliable, due to various causes both natural (reflection and blockage from objects, technical fault, inclement weather) and artificial (GPS spoofing and denial). In such GPS-denied situations, it is desirable to have additional methods for aerial geolocalization. One such method is visual geolocalization, where aircraft use their ground facing cameras to localize and navigate. The state of the art in many ground-level image processing tasks involve the use of Convolutional Neural Networks (CNNs). We present here a study of how effectively a modern CNN designed for visual classification can be applied to the problem of Absolute Visual Geolocalization (AVL, localization without a prior location estimate). An Xception based architecture is trained from scratch over a >1000 km2 section of Washington County, Arkansas to directly regress latitude and longitude from images from different orthorectified high-altitude survey flights. It achieves average localization accuracy on unseen image sets over the same region from different years and seasons with as low as 115 m average error, which localizes to 0.004% of the training area, or about 8% of the width of the 1.5 × 1.5 km input image. This demonstrates that CNNs are expressive enough to encode robust landscape information for geolocalization over large geographic areas. Furthermore, discussed are methods of providing uncertainty for CNN regression outputs, and future areas of potential improvement for use of deep neural networks in visual geolocalization.
Societal Implications of Forest and Water Body Area Evolution in Czechia and Selected Regions
Authors: Huertas Bernal Diana Carolina , Purwestri Ratna Chrismiari , Perdana Mayang Christy , Hájek Miroslav , Tahri Meryem , Palátová Petra , Hochmalová Miroslava
Published: October 2021

Land cover evolution is an environmental factor that can be used to characterize forest ecosystem services (FES). This study aims to analyze the change in forest cover and water bodies between 1990 and 2018 in the whole Czech Republic, and in the Central Bohemian and South Moravian regions, and its effects on freshwater provision. Additionally, we attempt to understand the societal implications of water quality, public perception, and environmental investment on natural ecosystems. Forest cover and water body data were obtained from the Corine land cover database, while water quality and investment were compiled from the Czech Statistical Office. Public perceptions on the Czech FES were collected from a national survey. Between 1990 and 2018, forest cover has increased by 3.94% and water bodies by 7.65%; however, from 2014 to 2018, severe droughts were reported that compromised the availability of surface water, presumably on artificial structures, causing an increase in the occupied area. Regarding public perception, respondents with less education, and the older population, obtained an assessment of the low performance of the FES, while the water quality and investment indicate that environmental funding has contributed to improving the quality of outflow water from the wastewater treatment plants, fulfilling all the allowed limits of the urban wastewater treatment directive. Hence, a multidisciplinary approach can help decision makers promote policies that integrate environmental management measures, investment protection, and contribute to sustainable development.
Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition
Authors: Du Lan , Li Lu , Guo Yuchen , Wang Yan , Ren Ke , Chen Jian
Published: October 2021

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.
Remote Sensing of Urban Poverty and Gentrification
Authors: Lin Li , Di Liping , Zhang Chen , Guo Liying , Di Yahui
Published: October 2021

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.
Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees
Authors: Shashikant Veena , Mohamed Shariff Abdul Rashid , Wayayok Aimrun , Kamal Md Rowshon , Lee Yang Ping , Takeuchi Wataru
Published: October 2021

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.
Comparison of Hydrological Patterns between Glacier-Fed and Non-Glacier-Fed Lakes on the Southeastern Tibetan Plateau
Authors: Sun Fangdi , He Bin , Liu Caixia , Zeng Yuchao
Published: October 2021

Lakes on the Tibetan Plateau have experienced variations over the last several decades, and the delineation of lake dynamics is favorable for the regional water cycle and can serve as important information for plateau environmental research. This study focused on 57 lakes near the Tanggula Mountains on the southeastern Tibetan Plateau. Yearly inundations of the lakes in 1989–2019 and altimeter data available for 2003–2020 were integrated to illustrate the changing patterns of glacier-fed and non-glacier-fed lakes. These two groups of lakes presented very similar evolution stages. They both increased in 1989–1992, decreased in 1992–1996, increased rapidly in 1998–2005, and had batch-wise fluctuations since 2005, with respective areas of around 5305.28 and 1636.79 km2 in the last decade. The non-glacier-fed lakes were more sensitive to precipitation variation, and glacier-fed lakes were more sensitive to temperature changes. Based on lakes with obvious changes in water level, the whole water storage variations of the studied lakes were 1.90 Gt/y in 2003–2009, including 1.80 Gt/y for glacier-fed lakes and 0.10 Gt/y for non-glacier-fed lakes. The contribution from glacier melting in 2003–2009 amounted to 16.11% of the whole lake volume increase. In 2010–2020, water mass changes were 0.42 Gt/y for glacier-fed lakes and −0.14 Gt/y for non-glacier-fed lakes, respectively. The volume increase of glacier-fed lakes in 2010–2020 was mainly due to the expansion of Selin Co. Selin Co experienced a water increase of about 0.46 Gt/y, and the other glacier-fed lakes experienced a decreasing volume of −0.04 Gt/y. In 2010–2020, 99.43% of the glacier contribution supplied Selin Co.
Proof of Concept Study of an Electrochemical Sensor for Inland Water Monitoring with a Network Approach
Authors: Sabatini Anna , Zompanti Alessandro , Grasso Simone , Vollero Luca , Pennazza Giorgio , Santonico Marco
Published: October 2021

The technologies most suitable for monitoring the ecosystem of inland waters are image spectrometry and electrochemical sensors. The reason is that these instruments are able to ensure accuracy in the surveillance of very large areas through reliable and frequent measurements performed remotely. Electrochemical systems provide low-cost, miniaturized, reliable sensors that can be organized, when equipped with commercial on the shelf (COTS) low-power radio components implementing LoRaWAN, Sigfox or NB-IoT communications, in a dense network of sensors achieving the aforementioned requirements. In this work, a low-cost, low-size and low-noise electrochemical sensor endowed with protocols for network configuration, management and monitoring is presented. The electronic interface of the sensor allows high reproducible responses. As proof of concept for its utilization in inland water monitoring, the device has been tested for water composition analysis, bacteria identification and frequent pollutant detection: atrazine, dichloromethane and tetrachloroethene. The results are promising, and future investigations will be oriented to unlock the true potential of a general-purpose approach exploiting the continuous fusion of distributed data in each of the three considered application scenarios. A new device, with reduced power consumption and size, has been also developed and tested; this new device should be a node of a large network for inland water monitoring.
Road Traffic Monitoring from UAV Images Using Deep Learning Networks
Authors: Byun Sungwoo , Shin In-Kyoung , Moon Jucheol , Kang Jiyoung , Choi Sang-Il
Published: October 2021

In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV). The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating the speeds of vehicles on the roads. Our method can automatically measure the speed of the vehicles from the only videos recorded using UAV without additional information in both directions on the roads simultaneously. In our experiments, we evaluate the performance of the proposed method with the visual data at four different locations. The proposed method shows 97.6% recall rate and 94.7% precision rate in detecting vehicles, and it shows error (root mean squared error) of 5.27 km/h in estimating the speeds of vehicles.
The Fusion Strategy of 2D and 3D Information Based on Deep Learning: A Review
Authors: Zhao Jianghong , Wang Yinrui , Cao Yuee , Guo Ming , Huang Xianfeng , Zhang Ruiju , Dou Xintong , Niu Xinyu , Cui Yuanyuan , Wang Jun
Published: October 2021

Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.
Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States
Authors: Sun Luyao , Chen Haonan , Li Zhe , Han Lei
Published: October 2021

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.
Use of Oblique RGB Imagery and Apparent Surface Area of Plants for Early Estimation of Above-Ground Corn Biomass
Authors: Khun Kosal , Tremblay Nicolas , Panneton Bernard , Vigneault Philippe , Lord Etienne , Cavayas François , Codjia Claude
Published: October 2021

Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.
Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification
Authors: Lin Feng-Cheng , Chuang Yung-Chung
Published: October 2021

When original aerial photographs are combined with deep learning to classify forest vegetation cover, these photographs are often hindered by the interlaced composition of complex backgrounds and vegetation types as well as the influence of different deep learning calculation processes, resulting in unpredictable training and test results. The purpose of this research is to evaluate (1) data preprocessing, (2) the number of classification targets, and (3) convolutional neural network (CNN) approaches combined with deep learning’s effects on high-resolution aerial photographs to identify forest and vegetation types. Data preprocessing is mainly composed of principal component analysis and content simplification (noise elimination). The number of classification targets is divided into 14 types of forest vegetation that are more complex and difficult to distinguish and seven types of forest vegetation that are simpler. We used CNN approaches to compare three CNN architectures: VGG19, ResNet50, and SegNet. This study found that the models had the best execution efficiency and classification accuracy after data preprocessing using principal component analysis. However, an increase in the number of classification targets significantly reduced the classification accuracy. The algorithm analysis showed that VGG19 achieved the best classification accuracy, but SegNet achieved the best performance and overall stability of relative convergence. This proves that data preprocessing helps identify forest and plant categories in aerial photographs with complex backgrounds. If combined with the appropriate CNN algorithm, these architectures will have great potential to replace high-cost on-site forestland surveys. At the end of this study, a user-friendly classification system for practical application is proposed, and its testing showed good results.
Effects of Variable Eruption Source Parameters on Volcanic Plume Transport: Example of the 23 November 2013 Paroxysm of Etna
Authors: Rizza Umberto , Donnadieu Franck , Magazu Salvatore , Passerini Giorgio , Castorina Giuseppe , Semprebello Agostino , Morichetti Mauro , Virgili Simone , Mancinelli Enrico
Published: October 2021

The purpose of the present paper is to investigate the effects of variable eruption source parameters on volcanic plume transport in the Mediterranean basin after the paroxysm of Mount Etna on 23 November 2013. This paroxysm was characterized by a north-east transport of ash and gas, caused by a low-pressure system in northern Italy. It is evaluated here in a joint approach considering the WRF-Chem model configured with eruption source parameters (ESPs) obtained elaborating the raw data from the VOLDORAD-2B (V2B) Doppler radar system. This allows the inclusion of the transient and fluctuating nature of the volcanic emissions to accurately model the atmospheric dispersion of ash and gas. Two model configurations were considered: the first with the climax values for the ESP and the second with the time-varying ESP according to the time profiles of the mass eruption rate recorded by the V2B radar. It is demonstrated that the second configuration produces a considerably better comparison with satellite retrievals from different sensors platforms (Ozone Mapping and Profiler Suite, Meteosat Second-Generation Spinning Enhanced Visible and Infrared Imager, and Visible Infrared Imaging Radiometer Suite). In the context of volcanic ash transport dispersion modeling, our results indicate the need for (i) the use of time-varying ESP, and (ii) a joint approach between an online coupled chemical transport model like WRF-Chem and direct near-source measurements, such as those carried out by the V2B Doppler radar system.
Feasibility Study on Estimation of Sea Ice Drift from KOMPSAT-5 and COSMO-SkyMed SAR Images
Authors: Park Jeong-Won , Kim Hyun-Cheol , Korosov Anton , Demchev Denis , Zecchetto Stefano , Kim Seung Hee , Kwon Young-Joo , Han Hyangsun , Hyun Chang-Uk
Published: October 2021

Estimating the sea ice drift field is of importance in both scientific study and activities in the polar ocean. Ice motion is being tracked at large scale (10 km and larger) on a daily basis; however, a higher resolution product is desirable for more reliable monitoring of rapid changes in sea ice. The use of wide-swath SAR has been extensively studied; yet, recent high-resolution X-band SAR sensors have not been tested enough. We examine the feasibility of KOMPSAT-5 and COSMO-SkyMed for retrieving sea ice motion by using the dataset of the MOSAiC expedition. The ice drift match-ups extracted from consecutive SAR image pairs and buoys for more than seven months in the central Arctic were used for a performance evaluation and validation. In addition to individual tests for KOMPSAT-5 and COSMO-SkyMed, a cross-sensor combination of two sensors was tested to overcome the drawback, a relatively long revisit time of high-resolution SAR. The experimental results show that higher accuracies are achievable from both single- and cross-sensor configurations of high-resolution X-band SARs compared to wide-swath C-band SARs, and that sub-daily monitoring is feasible from the cross-sensor approach.
Coastal Retreat Due to Thermodenudation on the Yugorsky Peninsula, Russia during the Last Decade, Update since 2001–2010
Authors: Leibman Marina , Kizyakov Alexander , Zhdanova Yekaterina , Sonyushkin Anton , Zimin Mikhail
Published: October 2021

Thermodenudation on the Kara seacoast, the Yugorsky Peninsula, Russia, is studied by analyzing remote-sensing data. Landforms resulting from the thaw of tabular ground ice, referred to as thermocirques, are formed due to polycyclic retrogressive thaw slumps, during the last decade 2010–2020. We calculate the retreat rate of the thermocirque edge using various statistical approaches. We compared thermocirque outlines by the end of each time interval defined by the dates of available very-high-resolution imagery. Six thermocirques within two key sites on the Yugorsky peninsula are monitored. We correlate each of the thermocirque edge’s retreat rates to various climatic parameters obtained at the Amderma weather station to understand the interrelation patterns better. As a result, we find a very low correlation between the retreat rate of each thermocirque and summer warmth, rainfall, and wave action. In general, the activity of thermodenudation decreases in time from the previous decade (2001–2010) to 2010–2020, and from 2010 towards 2020, although the summer warmth trend increases dramatically. A single thermocirque or series of thermocirques expand in response to environmental and geological factors in coastal retreat caused by thermodenudation.
Expansion and Evolution of a Typical Resource-Based Mining City in Transition Using the Google Earth Engine: A Case Study of Datong, China
Authors: Xue Minghui , Zhang Xiaoxiang , Sun Xuan , Sun Tao , Yang Yanfei
Published: October 2021

China’s resource-based cities have made tremendous contributions to national and local economic growth and urban development over the last seven decades. Recently, such cities have been in transition from resource-centered development towards human-oriented urbanization to meet the requirements of long-term sustainability for the natural environment and human society. A good understanding of urban expansion and evolution as a consequence of urbanization has important implications for future urban and regional planning. Using a series of remote sensing (RS) images and geographical information system (GIS)-based spatial analyses, this research explores how a typical resource-based mining city, Datong, has expanded and evolved over the last two decades (2000–2018), with a reflection on the role of urban planning and development policies in driving the spatial transformation of Datong. The RS images were provided and processed by the Google Earth Engine (GEE) platform. Spatial cluster analysis approaches were employed to examine the spatial patterns of urban expansion. The results indicate that the area of urban construction land increased by 132.6% during the study period, mainly along with the Chengqu District, the Mining Area, and in the southeast of the Nanjiao District, where most new towns are located. Reflection on the factors that influence urban expansion shows that terrain, urban planning policies, and social economy are driving Datong’s urban development.
Combining Tracking and Remote Sensing to Identify Critical Year-Round Site, Habitat Use and Migratory Connectivity of a Threatened Waterbird Species
Authors: Batbayar Nyambayar , Yi Kunpeng , Zhang Junjian , Natsagdorj Tseveenmyadag , Damba Iderbat , Cao Lei , Fox Anthony David
Published: October 2021

We tracked 39 western flyway white-naped cranes (Antigone vipio) throughout multiple annual cycles from June 2017 to July 2020, using GSM-GPS loggers providing positions every 10-min to describe migration routes and key staging areas used between their Mongolian breeding and wintering areas in China’s Yangtze River Basin. The results demonstrated that white-naped cranes migrated an average of 2556 km (±187.9 SD) in autumn and 2673 km (±342.3) in spring. We identified 86 critical stopover sites that supported individuals for more than 14 days, within a 100–800 km wide migratory corridor. This study also confirmed that Luan River catchment is the most important staging region, where white-naped cranes spent 18% of the annual cycle (in both spring and autumn) each year. Throughout the annual cycle, 69% of the tracking locations were from outside of the currently protected areas, while none of the critical staging areas enjoyed any form of site protection. We see further future potential to combine avian tracking data and remote-sensing information throughout the annual range of the white-naped crane to restore it and other such species to a more favourable conservation status.
Dynamic Service Function Chaining Orchestration in a Multi-Domain: A Heuristic Approach Based on SRv6
Authors: Wu Yutong , Zhou Jinhe
Published: October 2021

With the emergence of virtualization technology, Network Function Virtualization (NFV) and Software Defined Networking (SDN) make the network function abstract from the hardware and allow it to be run on virtual machines. These technologies can help to provide more efficient services to users by Service Function Chaining (SFC). The sequence of multiple VNFs required by network operators to perform traffic steering is called SFC. Mapping and deploying SFC on the physical network can enable users to obtain customized services in time. At present, a key problem in deploying SFC is how to reduce network resource consumption and load pressure while ensuring the corresponding services for users. In this paper, we first introduce an NFV architecture for SFC deployment, and illustrate the SFC orchestration process which is based on SRv6 in multi-domain scenario. Then, we propose an effective SFC dynamic orchestration algorithm. First, we use Breadth-First Search algorithm to traverse network and find the shortest path for deploying VNFs. Next, we use the improved Ant Colony Optimization algorithm to generate the optimal deployment scheme. Finally, we conduct a series of experiments to verify the performance of our algorithm. Compared with other deployment algorithms, the results show that our solution effectively optimizes end-to-end delay, bandwidth resource consumption and load balancing.
Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks
Authors: Nisar Kashif , Sabir Zulqurnain , Zahoor Raja Muhammad Asif , Ibrahim Ag Asri Ag , Mahmoud Samy Refahy , Balubaid Mohammed , Rawat Danda B. , Rodrigues Joel J. P. C.
Published: October 2021

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.
Green Smart Campus Monitoring and Detection Using LoRa
Authors: Tseng Kuo-Hsiung , Chung Meng-Yun , Chen Li-Hsien , Chang Pei-Yao
Published: October 2021

Along with the rapid development of sensing systems and wireless transmission technology, the scope of application of the IoT has substantially increased, and research and innovation that integrate artificial intelligence. This study integrated civil engineering and electrical engineering to establish a universal and modularized long-term sensing system. Aiming at positive construction in civil engineering, the campus of National Taipei University of Technology was used as the experimental site as a green campus. This paper focused on the cooling effect of the green roof and the temperature difference of the solar panel to effectively isolate the direct sunlight on the roof of the building. To achieve long-term monitoring, energy consumption must be minimized. Considering that the distance between sensor nodes in the experimental site was over dozens of feet, LoRa transmission technology was selected for data transmission. LoRa only consumes a small amount of energy during data transmission, and it can freely switch between work modes, achieving optimal power utilization efficiency. The greening-related research results indicated that the shade from solar panels on the rooftop could effectively reduce the temperature increase caused by direct sunlight on concrete surfaces. The temperature reduction effect was positively correlated with whether the solar panels provided shade. After 1 week of monitoring, we observed that having plants on the rooftop for greening negatively correlated with temperature reduction efficiency. Permeable pavement on the ground was positively correlated with temperature reduction efficiency. However, its temperature reduction efficiency was inferior to that of solar panel shading. The temperature difference between high-rise buildings and the ground was approximately 1–2 °C. At the same elevation, the temperature difference between buildings with and without greening was approximately 0.8 °C. Regarding the sensing system designed for this site, both hardware and software could be flexibly set according to the research purposes, precision requirements of the sites, and the measurement scope, thereby enabling their application in more fields.
Software-Defined Networking Solutions, Architecture and Controllers for the Industrial Internet of Things: A Review
Authors: Urrea Claudio , Benítez David
Published: October 2021

The use of Software-Defined Networking (SDN) in the communications of the Industrial Internet of Things (IIoT) demands more comprehensive solutions than those developed to date. The lack of an SDN solution applicable in diverse IIoT scenarios is the problem addressed in this article. The main cause of this problem is the lack of integration of a set of aspects that should be considered in a comprehensive SDN solution. To contribute to the solution of this problem, a review of the literature is conducted in this article, identifying the main requirements for industrial networks nowadays as well as their solutions through SDN. This review indicates that aspects such as security, independence of the network technology used, and network centralized management can be tackled using SDN. All the advantages of this technology can be obtained through the implementation of the same solution, considering a set of aspects proposed by the authors for the implementation of SDNs in IIoT networks. Additionally, after analyzing the main features and advantages of several architectures proposed in the literature, an architecture with distributed network control is proposed for all SDN network scenarios in IIoT. This architecture can be adapted through the inclusion of other necessary elements in specific scenarios. The distributed network control feature is relevant here, as it prevents a single fault-point for an entire industrial network, in exchange for adding some complexity to the network. Finally, the first ideas for the selection of an SDN controller suitable for IIoT scenarios are included, as this is the core element in the proposed architecture. The initial proposal includes the identification of six controllers, which correspond to different types of control planes, and ten characteristics are defined for selecting the most suitable controller through the Analytic Hierarchy Process (AHP) method. The analysis and proposal of different fundamental aspects for the implementation of SDNs in IIoT in this article contribute to the development of a comprehensive solution that is not focused on the characteristics of a specific scenario and would, therefore, be applicable in limited situations.
Design of a Low-Cost Small-Size Fluxgate Sensor
Authors: Shen Xiaoyu , Teng Yuntian , Hu Xingxing
Published: October 2021

Traditional fluxgate sensors used in geomagnetic field observations are large, costly, power-consuming and often limited in their use. Although the size of the micro-fluxgate sensors has been significantly reduced, their performance, including indicators such as accuracy and signal-to-noise, does not meet observational requirements. To address these problems, a new race-track type probe is designed based on a magnetic core made of a Co-based amorphous ribbon. The size of this single-component probe is only Φ10 mm × 30 mm. The signal processing circuit is also optimized. The whole size of the sensor integrated with probes and data acquisition module is Φ70 mm × 100 mm. Compared with traditional fluxgate and micro-fluxgate sensors, the designed sensor is compact and provides excellent performance equal to traditional fluxgate sensors with good linearity and RMS noise of less than 0.1 nT. From operational tests, the results are in good agreement with those from a standard fluxgate magnetometer. Being more suitable for modern dense deployment of geomagnetic observations, this small-size fluxgate sensor offers promising research applications at lower costs.
Filter Pruning via Measuring Feature Map Information
Authors: Shao Linsong , Zuo Haorui , Zhang Jianlin , Xu Zhiyong , Yao Jinzhen , Wang Zhixing , Li Hong
Published: October 2021

Neural network pruning, an important method to reduce the computational complexity of deep models, can be well applied to devices with limited resources. However, most current methods focus on some kind of information about the filter itself to prune the network, rarely exploring the relationship between the feature maps and the filters. In this paper, two novel pruning methods are proposed. First, a new pruning method is proposed, which reflects the importance of filters by exploring the information in the feature maps. Based on the premise that the more information there is, more important the feature map is, the information entropy of feature maps is used to measure information, which is used to evaluate the importance of each filter in the current layer. Further, normalization is used to realize cross layer comparison. As a result, based on the method mentioned above, the network structure is efficiently pruned while its performance is well reserved. Second, we proposed a parallel pruning method using the combination of our pruning method above and slimming pruning method which has better results in terms of computational cost. Our methods perform better in terms of accuracy, parameters, and FLOPs compared to most advanced methods. On ImageNet, it is achieved 72.02% top1 accuracy for ResNet50 with merely 11.41M parameters and 1.12B FLOPs.For DenseNet40, it is obtained 94.04% accuracy with only 0.38M parameters and 110.72M FLOPs on CIFAR10, and our parallel pruning method makes the parameters and FLOPs are just 0.37M and 100.12M, respectively, with little loss of accuracy.
Design and Performance Analysis of an Ultrasonic System for Health Monitoring of Concrete Structure
Authors: Zhao Jinhui , Hu Tianyu , Zheng Ruifang , Ba Penghui , Zhang Qichun
Published: October 2021

The development and research of an ultrasonic-based concrete structural health monitoring system encounters a variety of problems, such as demands of decreasing complexity, high accuracy, and extendable system output. Aiming at these requirements, a low-cost extendable system based on FPGA with adjustable system output has been designed, and the performance has been evaluated by different assessment parameters set in this paper. Besides the description of the designed system and the experiments in air medium, the residual similarity and Pearson correlation coefficients of experimental and theoretical data have been used to evaluate the submodules’ output. The output performance of the overall system is evaluated by the Pearson correlation coefficient, root-mean-square error (RMSE), and magnitude-squared coherence with 40 experimental data. The maximum, median, minimum, and mean values in three-parameter datasets are analyzed for discussing the working condition of the system. The experimental results show that the system works stably and reliably with tunable frequency and amplitude output.
A High-Speed Demodulation Technology of Fiber Optic Extrinsic Fabry-Perot Interferometric Sensor Based on Coarse Spectrum
Authors: Zhang Peng , Wang Ying , Chen Yuru , Lei Xiaohua , Qi Yi , Feng Jianghua , Liu Xianming
Published: October 2021

A fast real-time demodulation method based on the coarsely sampled spectrum is proposed for transient signals of fiber optic extrinsic Fabry-Perot interferometers (EFPI) sensors. The feasibility of phase demodulation using a coarse spectrum is theoretically analyzed. Based on the coarse spectrum, fast Fourier transform (FFT) algorithm is used to roughly estimate the cavity length. According to the rough estimation, the maximum likelihood estimation (MLE) algorithm is applied to calculate the cavity length accurately. The dense wavelength division multiplexer (DWDM) is used to split the broadband spectrum into the coarse spectrum, and the high-speed synchronous ADC collects the spectrum. The experimental results show that the system can achieve a real-time dynamic demodulation speed of 50 kHz, a static measurement root mean square error (RMSE) of 0.184 nm, and a maximum absolute and relative error distribution of 15 nm and 0.005% of the measurement cavity length compared with optical spectrum analyzers (OSA).
A New Payload Swing Angle Sensing Device and Its Accuracy
Authors: Grosinger Patrik , Rybář Jan , Dunaj Štefan , Ďuriš Stanislav , Hučko Branislav
Published: October 2021

Measuring the swing angle of a crane load is a relatively well-known but unsatisfactorily solved problem in technical practice. This measurement is necessary for the automatic stabilization of load swing without human intervention. This article describes a technically simple and new approach to solving this problem. The focus of this work is to determine the accuracy of the measuring device. The focus of this work remains on the design, the principle of operation of the equipment, and the determination of accuracy. The basic idea is to apply the strain gauge on an elastic, easily deformable component that is part of the device. One part of the elastic component is fixedly connected to the frame; the other part is connected to the crane rope by means of pulleys close to the rope. In this way, the bending of the elastic component in proportion to the swing angle of the payload is ensured.