bluuebunny/crossref_metadata_2025 huggingface dataset:
Olive Mill by-Products Thermochemical Conversion via Hydrothermal Carbonization and Slow Pyrolysis: Detailed Comparison between the Generated Hydrochars and Biochars Characteristics
Authors: Azzaz Ahmed Amine , Matei Ghimbeu Camélia , Jellai Salah , El-Bassi Leila , Jeguirim Mejdi
Published: January 2022

In this research work, an ecofriendly approach was adopted for the treatment of two abundant liquid and solid agricultural wastes generated by olive mill industry: olive mill wastewater (OMWW) and raw olive pomace (ROP). It consists, firstly of the impregnation of ROP by OMWW and the conversion of the resulting impregnated sample (IROP) into hydrochars and biochars through hydrothermal carbonization (HTC) and slow pyrolysis methods, respectively. The impact of the treatment temperature on the physico-chemical properties of the derived chars was assessed by various analytical techniques. The hydrochars production yields were found to be higher than the biochars ones and associated to the lower temperature used. However, the generated biochars possess higher carbon percentage and lower volatile matter contents. In addition, the increase of the hydrothermal and pyrolysis carbonization temperatures shows an increase of macro-elements contents and a significant decrease of the O/C ratios which led to an enhancement of the high heating value for both hydrochars and biochars. The FTIR and Boehm titration analysis highlighted a significant alteration of the biochars surface chemistry compared to hydrochars evidenced by the lower amount of functional groups. The collected data emphasize on the efficient potential application of hydrochars and biochars for agricultural and environmental applications, respectively. Furthermore, it was noted that both chars have high energetic potentials and could be used for co-firing with coal in industrial boilers.
Acknowledgment to Reviewers of Proteomes in 2021
Authors:   Proteomes Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
The Future of Islamism through the Lens of the Past
Authors: Lynch Marc
Published: January 2022

Islamist movements today face perhaps their most difficult conditions in decades. After seizing political openings after the Arab uprisings, the Muslim Brotherhood and other Sunni political Islamist organizations have suffered from military coups, electoral defeats, social and political polarization, and extreme repression. This is not the first time they have faced such catastrophic conditions, however, and historically Islamist organizations have proven to be resilient and able to return to public life. This article examines the history of Islamist movements in the Middle East recovering from extreme setbacks in order to identify nine key mechanisms that facilitated those rebounds and then considers which of those factors might be operative today. It concludes that many of those factors are less available than in the past, but that the global turn toward populism, the persistent governance failures of Arab states, and the adaptability of Islamists create greater opportunities for recovery than might initially appear plausible.
Public Relations in the Perspective of the Catholic Church in Poland
Authors: Sulkowski Lukasz , Ignatowski Grzegorz , Seliga Robert
Published: January 2022

The issue of the use of marketing tools by religious organisations is a research problem because for moral reasons, churches declare that they do not use marketing communication explicitly. In religious circles, marketing tends to be associated with unethical practices, especially public relations, which in practice can be associated with propaganda. A careful analysis of the activities carried out by churches shows that many marketing communication methods and tools are used by religious organisations. To be successful, companies must identify the basic elements determining customer satisfaction and meet them more effectively than their competitors. At the same time, it is not about one-off transactions, but about building long-term relationships. This model is also slowly finding acceptance in religious circles, despite arguments that satisfying individual needs will be at the expense of church doctrine or will result in long-standing church traditions being abandoned and replaced by pop-cultural attitudes. The article discusses the specificity of building the brand image of the Catholic Church in Poland and the use of modern marketing tools in this process. It also presents the results of the authors’ research, which leads to the final conclusions verifying the research hypotheses set out in the research methodology. The article aims to initiate a wider discussion on the controversial topic of implementing commercial marketing tools into the image management processes of the Catholic Church. The conducted research results indicate the need for a change in the perception of the Catholic Church in Poland of the communication processes leading to the building and strengthening of its image. A major challenge for the Catholic Church in Poland seems to be changing the attitudes of non-believers towards the Catholic Church.
The Unspoken and The Forbidden in André Scrima’s Thought as a Way to Encounter the Other
Authors: Danca Mihai-Iulian
Published: January 2022

André Scrima’s interest in Christian–Muslim dialogue goes beyond a historical or comparative approach. In this sense, knowledge from within the two monotheisms is achieved through an apophatic theology. Through it, the spiritual and mystical language that is characteristic of any authentic religious experience offers the possibility of a renewed approach to the differences between Islam and Christianity, and at the same time, clears a path for a truly authentic encounter. Fresh insight into the faith of the other does not put into parentheses the specific identity of the two monotheisms, but deepens it from the perspective of its apophatic content, where the possibility of experiencing the transcendent in a much deeper way is rooted, not by making new affirmations, but by adopting an attitude of stillness and silence.
Acknowledgment to Reviewers of Resources in 2021
Authors:   Resources Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
The Interaction of the EEU Member States and Risks of Their Mutual Trade during the COVID-19 Pandemic: Implications for the Management of Corporate Social Responsibility
Authors: Yelikbayev Kuanysh , Andronova Inna
Published: January 2022

The research problem is that the COVID-19 pandemic has become a threat to the sustainable development of the EEU and caused uncertainty in terms of the management of corporate social responsibility. This paper is aimed at identifying the impact of the COVID-19 pandemic on sustainable development of the EEU from the perspective of the interaction of the member states of the integration association and their mutual trade risks through the prism of the management of corporate social responsibility. The methodological foundation of the research is composed of the provisions of a comprehensive approach that has been used as a basis for determining the cause-and-effect relationship between the member states of the integration association and their mutual trade risks in the age of the COVID-19 pandemic in 2020. The analysis of statistical data is based on the methodology of econometric theory; in particular, the methods of horizontal and trend analysis. This paper analyzes the measures that were taken by the EEU member states to fight the pandemic of a new coronavirus infection both at the national level and at the level of the EEU institutions. The authors showed the asynchronous nature of measures introduced at the national level, while in certain circumstances the economic inefficiency of the introduction of measures, taken at the supranational level, and the impact of imposed restrictions on the current situation with mutual trade in goods and services, and free movement of workers. It has been substantiated that the examination of the economic interaction of the EEU member states in the period of restrictions dictated by a new coronavirus infection has revealed several endemic problems and had a major impact on the achievement of the main objectives of the integration association, transforming the terms for the management of corporate social responsibility. The originality of the paper is that the unique experience of the integration association of the EEU is for the first time studied from the perspective of the impact of the risks of mutual trade during a pandemic on it through the prism of the management of corporate social responsibility.
Financial Innovation of Mass Destruction—The Story of a Countrywide FX Options Debacle
Authors: Sławik Anna , Bohatkiewicz-Czaicka Joanna
Published: January 2022

Astonishingly little attention has been paid in academic literature to the 2008–2009 foreign exchange (FX) options debacle in Poland, the scale of which was unheard of. It affected not only an individual organization but a significant part of economy, being an example of a situation in which operational risk at the company level could have impacted systemic risk. The research provides evidence of the dark side of financial innovations through an analysis of a countrywide case on an emerging market, utilizing a primary qualitative content analysis (QCA) of over 750 documents (including press releases, public authorities’ accounts, and corporate statements). It documents that the FX options debacle was caused by financial institutions which shrouded some aspects of innovative securities or took advantage of information asymmetry to exploit uninformed clients. The study concludes that both adequate legal regulations and proper operational risk management are crucial to avoid similar corporate failures.
Three-Dimensional Magnetotelluric Characterization of the Travale Geothermal Field (Italy)
Authors: Pace Francesca , Martí Anna , Queralt Pilar , Santilano Alessandro , Manzella Adele , Ledo Juanjo , Godio Alberto
Published: January 2022

The geoelectrical features of the Travale geothermal field (Italy), one of the most productive geothermal fields in the world, have been investigated by means of three-dimensional (3D) magnetotelluric (MT) data inversion. This study presents the first resistivity model of the Travale geothermal field derived from derivative-based 3D MT inversion. We analyzed MT data that have been acquired in Travale over the past decades in order to determine its geoelectrical dimensionality, directionality, and phase tensor properties. We selected data from 51 MT sites for 3D inversion. We carried out a number of 3D MT inversion tests by changing the type of data to be inverted, the inclusion of static-shift correction at some sites where new time-domain electromagnetic soundings (TDEM) were acquired, the grid rotation, as well as the starting model in order to assess the connection between the inversion model and the geology. The final 3D model herein presents deep elongated resistive bodies between the depths of 1.5 and 8 km. They are transverse to the Apennine structures and suggest a correlation with the strike-slip tectonics. Comparison with a seismic velocity model and well log data suggests a highly-fractured volume of rocks with vapor-dominated circulation. The outcome of this study provides new insights into the complex geothermal system of Travale.
The Prototype of a Fast Vertical Ionosonde Based on Modern Software-Defined Radio Devices
Authors: Shindin Alexei V. , Moiseev Sergey P. , Vybornov Fedor I. , Grechneva Kseniya K. , Pavlova Viktoriya A. , Khashev Vladimir R.
Published: January 2022

The description and test results of the prototype of a fast ionosonde for the vertical sounding of the ionosphere, which makes it possible to record ionograms once a second, are presented. Such a high rate of registration of ionograms is required to study the fast processes of redistribution of electron concentration during heating experiments, for registration of fast quasiperiodic and moving ionospheric disturbances in the F, E, and Es layers. The key feature of the presented development is the usage of publicly available radio-electronic components. This provided a significant reduction in the cost of creating the prototype. In the current version, the prototype is based on the software-defined radio (SDR) devices Red Pitaya SDRlab 122-16 and LimeSDR. The test results showed that the quality of the ionograms recorded using the prototype is not worse than the quality of ionograms recorded using the professional CADI ionosonde. The low cost of the components allows providing multi-position registration of ionograms for determination the dynamics of natural and artificial ionospheric disturbances in 3D region of space at a lower expenses rate, as well as to create a network of ionospheric observation points with an increased number of ionosondes.
Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies
Authors: Guan Zhen , Abd-Elrahman Amr , Whitaker Vance , Agehara Shinsuke , Wilkinson Benjamin , Gastellu-Etchegorry Jean-Philippe , Dewitt Bon
Published: January 2022

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an Adj R2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving Adj R2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning.
Predicting Dynamics of the Potential Breeding Habitat of Larus saundersi by MaxEnt Model under Changing Land-Use Conditions in Wetland Nature Reserve of Liaohe Estuary, China
Authors: Chang Yu , Chang Chang , Li Yuxiang , Liu Miao , Lv Jiujun , Hu Yuanman
Published: January 2022

Identifying waterfowl habitat suitability under changing environments, especially land-use change, is crucial to make waterfowl habitat conservation planning. We took Wetland Nature Reserve of Liaohe Estuary, the largest breeding area of Saunders’s Gulls (Larus saundersi) in the world, as our study area, generated land-use-type maps through interpretation of satellite images from four different years (1988, 2000, 2009, 2017), and predicted the potential breeding habitat of Saunders’s Gulls by MaxEnt model based on the land-use map, along with other environmental variables (NDVI, distance to roads and artificial facilities, distance to rivers and water bodies, DEM and distance to shoreline) for the four years, respectively. The models were evaluated using the area under the curve (AUC). We analyzed the changes of the breeding habitat from 1988 to 2017 and utilized RDA to explore the relationships among the changes of suitable habitat of Larus saundersi and the dynamics of land uses. Our results showed that the most suitable habitat decreased by 1286.46 ha during 1988-2009 and increased by 363.51 ha from 2009 to 2017. The suitable habitat decreased by 582.48 ha from 1988 to 2009 and then increased to 1848.96 ha in 2017, while the unsuitable habitat increased by 2793.87 ha during 1988–2009 and then decreased by 178.83 ha from 2009 to 2017. We also found that land use, distance to the coastline, distance to artificial facilities, distance to rivers, distance to roads, and NDVI had certain degrees of impact on the Larus saundersi distribution. The contribution of land use ranged from 16.4% to 40.3%, distance to coastline from 34.7% to 48.0%, distance to artificial facilities from 5.9% to 11.1%, distance to rivers from 5.5% to 11.0%, distance to roads from 3.9% to 12.5%, and NDVI from 0.3% to 6.3%. The change in suitable habitat of Larus saundersi has a positive relationship with the change of seepweed marsh. Human-induced changes in seepweed marsh and coastline position are the main factors influencing the potential breeding habitat of Saunders’s Gulls. We suggest strict conservation of seepweed marsh and implementation of habitat management practices to better protect Saunders’ Gull’s breeding habitat.
A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
Authors: Abdelhady Hazem Usama , Troy Cary David , Habib Ayman , Manish Raja
Published: January 2022

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.
A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years
Authors: Thonfeld Frank , Gessner Ursula , Holzwarth Stefanie , Kriese Jennifer , da Ponte Emmanuel , Huth Juliane , Kuenzer Claudia
Published: January 2022

Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.
Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches
Authors: Bhatt Parth , Maclean Ann , Dickinson Yvette , Kumar Chandan
Published: January 2022

Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral enhancement techniques such as principal component analysis (PCA) and independent component analysis (ICA) were integrated. The study evaluated the importance of biogeophysical parameters such as topography, soil characteristics and gray level co-occurrence matrix (GLCM) textures, together with the normalized difference vegetation index (NDVI) and NAIP water index (WINAIP) spectral indices, using the joint mutual information maximization (JMIM) feature selection method and various machine learning algorithms (MLAs) to accurately map the natural habitat communities. Individual habitat community classification user’s accuracies (UA) ranged from 60 to 100%. An overall accuracy (OA) of 79.45% (kappa coefficient (k): 0.75) with random forest (RF) and an OA of 75.85% (k: 0.70) with support vector machine (SVM) were achieved. The analysis showed that the use of the biogeophysical ancillary data layers was critical to improve interclass separation and classification accuracy. Utilizing widely available free high-resolution NAIP imagery coupled with an integrated classification approach using MLAs, fine-scale natural habitat communities were successfully delineated in a spatially and spectrally complex Laurentian Mixed Forest environment.
Extracting Buildings from Remote Sensing Images Using a Multitask Encoder-Decoder Network with Boundary Refinement
Authors: Xu Hao , Zhu Panpan , Luo Xiaobo , Xie Tianshou , Zhang Liqiang
Published: January 2022

Extracting buildings from high-resolution remote sensing images is essential for many geospatial applications, such as building change detection, urban planning, and disaster emergency assessment. Due to the diversity of geometric shapes and the blurring of boundaries among buildings, it is still a challenging task to accurately generate building footprints from the complex scenes of remote sensing images. The rapid development of convolutional neural networks is presenting both new opportunities and challenges with respect to the extraction of buildings from high-resolution remote sensing images. To capture multilevel contextual information, most deep learning methods extract buildings by integrating multilevel features. However, the differential responses between such multilevel features are often ignored, leading to blurred contours in the extraction results. In this study, we propose an end-to-end multitask building extraction method to address these issues; this approach utilizes the rich contextual features of remote sensing images to assist with building segmentation while ensuring that the shape of the extraction results is preserved. By combining boundary classification and boundary distance regression, clear contour and distance transformation maps are generated to further improve the accuracy of building extraction. Subsequently, multiple refinement modules are used to refine each part of the network to minimize the loss of image feature information. Experimental comparisons conducted on the SpaceNet and Massachusetts building datasets show that the proposed method outperforms other deep learning methods in terms of building extraction results.
Spectral Retrieval of Eucalypt Leaf Biochemical Traits by Inversion of the Fluspect-Cx Model
Authors: Lamsal Krishna , Malenovský Zbyněk , Woodgate William , Waterman Melinda , Brodribb Timothy J. , Aryal Jagannath
Published: January 2022

Leaf biochemical traits indicating early symptoms of plant stress can be assessed using imaging spectroscopy combined with radiative transfer modelling (RTM). In this study, we assessed the potential applicability of the leaf radiative transfer model Fluspect-Cx to simulate optical properties and estimate leaf biochemical traits through inversion of two native Australian eucalypt species: Eucalyptus dalrympleana and E. delegetensis. The comparison of measured and simulated optical properties revealed the necessity to recalibrate the refractive index and specific absorption coefficients of the eucalypt leaves’ biochemical constituents. Subsequent validation of the modified Fluspect-Cx showed a closer agreement with the spectral measurements. The average root mean square error (RMSE) of reflectance, transmittance and absorptance values within the wavelength interval of 450–1600 nm was smaller than 1%. We compared the performance of both the original and recalibrated Fluspect-Cx versions through inversions aiming to simultaneously retrieve all model inputs from leaf optical properties with and without prior information. The inversion of recalibrated Fluspect-Cx constrained by laboratory-based measurements produced a superior accuracy in estimations of leaf water content (RMSE = 0.0013 cm, NRMSE = 6.55%) and dry matter content (RMSE = 0.0036 g·cm−2, NRMSE = 21.28%). The estimation accuracies of chlorophyll content (RMSE = 8.46 µg·cm−2, NRMSE = 24.73%), carotenoid content (RMSE = 3.83 µg·cm−2, NRMSE = 30.82%) and anthocyanin content (RMSE = 1.69 µg·cm−2, NRMSE = 37.12%) were only marginally better than for the inversion without any constraints. Additionally, we investigated the possibility to substitute the prior information derived in the laboratory by non-destructive reflectance-based spectral indices sensitive to the retrieved biochemical traits, resulting in the most accurate estimation of carotenoid content (RMSE = 3.65 µg·cm−2, NRMSE = 29%). Future coupling of the recalibrated Fluspect with a forest canopy RTM is expected to facilitate retrieval of biophysical traits from spectral air/space-borne image data, allowing for assessing the actual physiological status and health of eucalypt forest canopies.
An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height
Authors: Zhao Junpeng , Zhao Lei , Chen Erxue , Li Zengyuan , Xu Kunpeng , Ding Xiangyuan
Published: January 2022

Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Considering the high cost of acquiring LiDAR data over large areas, we took a two-stage up-scaling approach in estimating forest canopy height and aimed to develop a method for quantifying the uncertainty of the estimation result. Based on the generalized hierarchical model-based (GHMB) estimation framework, a new estimation framework named RK-GHMB that makes use of a geostatistical method (regression kriging, RK) was developed. In this framework, the wall-to-wall forest canopy height and corresponding uncertainty in map unit scale are generated. This study was carried out by integrating plot data, sampled airborne LiDAR data, and wall-to-wall Ziyuan-3 satellite (ZY3) stereo images. The result shows that RK-GHMB can obtain a similar estimation accuracy (r = 0.92, MAE = 1.50 m) to GHMB (r = 0.92, MAE = 1.52 m) with plot-based reference data. For LiDAR-based reference data, the accuracy of RK-GHMB (r = 0.78, MAE = 1.75 m) is higher than that of GHMB (r = 0.75, MAE = 1.85 m). The uncertainties for all map units range from 1.54 to 3.60 m for the RK-GHMB results. The values change between 1.84 and 3.60 m for GHMB. This study demonstrates that this two-stage up-scaling approach can be used to monitor forest canopy height. The proposed RK-GHMB approach considers the spatial autocorrelation of neighboring data in the second modeling stage and can achieve a higher accuracy.
Monitoring Rock Desert Formation Caused by Ice–Snow Melting in the Qinghai-Tibet Plateau Using an Optimized Remote Sensing Technique: A Case Study of Yushu Prefecture
Authors: Jia Wei , Ma Weidong , Shi Peijun , Wang Jing’ai , Su Peng
Published: January 2022

The rapid and effective identification of the spatial distribution of rock deserts in ice–snow melting areas can provide useful information for the prevention of natural disasters. In this study, Landsat TM/OLI were used to track the evolution of snow lines in the snow melting areas of Yushu Tibetan Autonomous Prefecture of the Qinghai-Tibet Plateau (hereinafter referred to as Yushu Prefecture) during the last 30 years. A total of seven extraction schemes were used to classify the rock desert in the ice–snow melting area by remote sensing. Our results show that: (1) The accuracy of the multi-index factor compound analysis and object-oriented classification compound method was the highest. This method can provide a rapid and efficient reference scheme for rock desert extraction in the plateau ice–snow melting area. (2) The combinations of two single methods with higher accuracy can further improve the total accuracy. If a single method with lower accuracy is involved in the multi-method fusion, the accuracy of the method with lower accuracy can be improved. (3) In the past 30 years, there has been a large amount of ice–snow melting in Yushu Prefecture. The ice–snow melting area accounts for 53.78% (1451.04 km2) of the ice–snow area, and the bare rock and bare stone in the rock desert account for 63.77% of the total area of the ice–snow melting area. Bare sand and bare soil area account for 30.27% of the total area of ice and snow melting area.
Marine Online Platforms of Services to Public End-Users—The Innovation of the ODYSSEA Project
Authors: Majidi Nezhad Meysam , Neshat Mehdi , Piras Giuseppe , Astiaso Garcia Davide , Sylaios Georgios
Published: January 2022

Recently, various Earth Observation Networks (EONs) have been designed, developed and launched by in-situ, on-site and off-site collected data from fixed and moving marine sensors and remote sensing (RS) satellite data. This information can significantly help a wide range of public and private end-users better understand the medium- and high-resolution numerical models for regional, national and global coverage. In this context, such EON core services’ operational numerical data can be seen of the growing demand result for marine sustainability development of developing countries and the European Union (EU). In this case, marine platforms can offer a wide range of benefits to users of human communities in the same environment using meticulous analyses. Furthermore, marine platforms can contribute to a deeper discourse on the ocean, given the required regulations, technical and legal considerations and users to a common typology using clear scientific terminology. In this regard, firstly, the following six steps have been used to develop a better understanding of the essential data structure that is commensurate with the efficiency of the marine end-user’s service: (1) steps and challenges of collecting data, (2) stakeholder engagement to identify, detect and assess the specific needs of end-users, (3) design, develop and launch the products offered to meet the specific needs of users, (4) achieve sustainable development in the continuous provision of these products to end-users, (5) identify future needs and challenges, and (6) online platform architecture style related to providing these products to end-users. Secondly, the innovation of the ODYSSEA (Operating a Network of Integrated Observatory Systems in the Mediterranean Sea) platform project has been evaluated and reviewed as a successful project on marine online platforms to better understand how marine online platforms are being used, designed, developed and launched. The ODYSSEA platform provides a system that bridges the gap between operational oceanographic capabilities and the need for information on marine conditions, including for the end-user community. The project aims to develop a fully integrated and cost-effective cross-platform, multi-platform network of observation and forecasting systems across the Mediterranean Sea.
Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint
Authors: Zhang Peijing , Luo Xiaoyan , Ma Yan , Wang Chengyi , Wang Wei , Qian Xu
Published: January 2022

For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and terrain fluctuation distortion in the overlapping areas can represent different image characteristics. These time-varying properties cause traditional registration methods with known reference information to fault. Therefore, in this paper we propose a comprehensive coarse-to-fine registration (CCFR) algorithm. First, we design a low-rank constraint-based batch reference extraction (LRC-BRE) method. Under the low-rank constraint, the stable features with highly spatial co-occurrence can be reconstructed via matrix decomposition, and are set as reference images to batch registration. Second, we improve the general feature registration with block feature matching and local linear transformation (BFM-LLT) operators including match outlier filtering (MOF) on regional mutual information and dual-weighted block fitting (DWBF). Finally, based on combining LRC-BRE and BFM-LLT, CCFR is integrated. Experimental results show that the proposed method has a good batch alignment effect, especially in the registration of large difference image pairs. The proposed CCFR achieves a significant performance improvement over many state-of-the-art registration algorithms.
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
Authors: Adugna Tesfaye , Xu Wenbo , Fan Jinlong
Published: January 2022

The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation either from high or medium resolution images using various software, but not Python. In this paper, we compared the performance of these two algorithms for large area LC mapping of parts of Africa using coarse resolution imageries in the Python platform by the employing Scikit-Learn (sklearn) library. We employed a big dataset, 297 metrics, comprised of systematically selected 9-month composite FegnYun-3C (FY-3C) satellite images with 1 km resolution. Several experiments were performed using a range of values to determine the best values for the two most important parameters of each classifier, the number of trees and the number of variables, for RF, and penalty value and gamma for SVM, and to obtain the best model of each algorithm. Our results showed that RF outperformed SVM yielding 0.86 (OA) and 0.83 (k), which are 1–2% and 3% higher than the best SVM model, respectively. In addition, RF performed better in mixed class classification; however, it performed almost the same when classifying relatively pure classes with distinct spectral variation, i.e., consisting of less mixed pixels. Furthermore, RF is more efficient in handling large input datasets where the SVM fails. Hence, RF is a more robust ML algorithm especially for heterogeneous large area mapping using coarse resolution images. Finally, default parameter values in the sklearn library work well for satellite image classification with minor/or no adjustment for these algorithms.
Combining Deep Learning with Single-Spectrum UV Imaging for Rapid Detection of HNSs Spills
Authors: Mehdi Syed Raza , Raza Kazim , Huang Hui , Naqvi Rizwan Ali , Ali Amjad , Song Hong
Published: January 2022

Vital transportation of hazardous and noxious substances (HNSs) by sea occasionally suffers spill incidents causing perilous mutilations to off-shore and on-shore ecology. Consequently, it is essential to monitor the spilled HNSs rapidly and mitigate the damages in time. Focusing on on-site and early processing, this paper explores the potential of deep learning and single-spectrum ultraviolet imaging (UV) for detecting HNSs spills. Images of three floating HNSs, including benzene, xylene, and palm oil, captured in different natural and artificial aquatic sites were collected. The image dataset involved UV (at 365 nm) and RGB images for training and comparative analysis of the detection system. The You Only Look Once (YOLOv3) deep learning model is modified to balance the higher accuracy and swift detection. With the MobileNetv2 backbone architecture and generalized intersection over union (GIoU) loss function, the model achieved mean IoU values of 86.57% for UV and 82.43% for RGB images. The model yielded a mean average precision (mAP) of 86.89% and 72.40% for UV and RGB images, respectively. The average speed of 57 frames per second (fps) and average detection time of 0.0119 s per image validated the swift performance of the proposed model. The modified deep learning model combined with UV imaging is considered computationally cost-effective resulting in precise detection accuracy and significantly faster detection speed.
A Comparison of Sporadic-E Occurrence Rates Using GPS Radio Occultation and Ionosonde Measurements
Authors: Carmona Rodney A. , Nava Omar A. , Dao Eugene V. , Emmons Daniel J.
Published: January 2022

Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site and then combined to determine the most accurate GPS-RO technique for two thresholds on sporadic-E intensity: no lower limit and fbEs ≥3 MHz. Overall, the YuS4 method shows the closest agreement with ionosonde measurements for total Es occurrence rates without a lower limit on intensity, while the phase-based Chu technique shows the closest agreement for fbEs ≥3 MHz. This analysis demonstrates that the variation in GPS-RO derived sporadic-E occurrence rates is due to varying thresholds on the sporadic-E intensities in terms of fbEs.
Modified U-NET Architecture for Segmentation of Skin Lesion
Authors: Anand Vatsala , Gupta Sheifali , Koundal Deepika , Nayak Soumya Ranjan , Barsocchi Paolo , Bhoi Akash Kumar
Published: January 2022

Dermoscopy images can be classified more accurately if skin lesions or nodules are segmented. Because of their fuzzy borders, irregular boundaries, inter- and intra-class variances, and so on, nodule segmentation is a difficult task. For the segmentation of skin lesions from dermoscopic pictures, several algorithms have been developed. However, their accuracy lags well behind the industry standard. In this paper, a modified U-Net architecture is proposed by modifying the feature map’s dimension for an accurate and automatic segmentation of dermoscopic images. Apart from this, more kernels to the feature map allowed for a more precise extraction of the nodule. We evaluated the effectiveness of the proposed model by considering several hyper parameters such as epochs, batch size, and the types of optimizers, testing it with augmentation techniques implemented to enhance the amount of photos available in the PH2 dataset. The best performance achieved by the proposed model is with an Adam optimizer using a batch size of 8 and 75 epochs.
An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources
Authors: Qafzezi Ermioni , Bylykbashi Kevin , Ampririt Phudit , Ikeda Makoto , Matsuo Keita , Barolli Leonard
Published: January 2022

The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources.
Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network
Authors: Saiz Fátima A. , Barandiaran Iñigo , Arbelaiz Ander , Graña Manuel
Published: January 2022

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network.
CNN-Based Smoker Classification and Detection in Smart City Application
Authors: Khan Ali , Khan Somaiya , Hassan Bilal , Zheng Zhonglong
Published: January 2022

To better regulate smoking in no-smoking areas, we present a novel AI-based surveillance system for smart cities. In this paper, we intend to solve the issue of no-smoking area surveillance by introducing a framework for an AI-based smoker detection system for no-smoking areas in a smart city. Moreover, this research will provide a dataset for smoker detection problems in indoor and outdoor environments to help future research on this AI-based smoker detection system. The newly curated smoker detection image dataset consists of two classes, Smoking and NotSmoking. Further, to classify the Smoking and NotSmoking images, we have proposed a transfer learning-based solution using the pre-trained InceptionResNetV2 model. The performance of the proposed approach for predicting smokers and not-smokers was evaluated and compared with other CNN methods on different performance metrics. The proposed approach achieved an accuracy of 96.87% with 97.32% precision and 96.46% recall in predicting the Smoking and NotSmoking images on a challenging and diverse newly-created dataset. Although, we trained the proposed method on the image dataset, we believe the performance of the system will not be affected in real-time.
Barrier Function Adaptive Nonsingular Terminal Sliding Mode Control Approach for Quad-Rotor Unmanned Aerial Vehicles
Authors: Alattas Khalid A. , Mofid Omid , Alanazi Abdullah K. , Abo-Dief Hala M. , Bartoszewicz Andrzej , Bakouri Mohsen , Mobayen Saleh
Published: January 2022

This paper proposes a barrier function adaptive non-singular terminal sliding mode controller for a six-degrees-of-freedom (6DoF) quad-rotor in the existence of matched disturbances. For this reason, a linear sliding surface according to the tracking error dynamics is proposed for the convergence of tracking errors to origin. Afterward, a novel non-singular terminal sliding surface is suggested to guarantee the finite-time reachability of the linear sliding surface to origin. Moreover, for the rejection of the matched disturbances that enter into the quad-rotor system, an adaptive control law based on barrier function is recommended to approximate the matched disturbances at any moment. The barrier function-based control technique has two valuable properties. First, this function forces the error dynamics to converge on a region near the origin in a finite time. Secondly, it can remove the increase in the adaptive gain because of the matched disturbances. Lastly, simulation results are given to demonstrate the validation of this technique.
Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools
Authors: Katona Jozsef
Published: January 2022

Writing a computer program is a complex cognitive task, especially for a new person in the field. In this research an eye-tracking system was developed and applied, which allows the observation of eye movement parameters during programming as a complex, cognitive process, and the conclusions can be drawn from the results. The aim of the paper is to examine whether the flowchart or Nassi–Shneiderman diagram is a more efficient algorithm descripting tool for describing cognitive load by recording and evaluating eye movement parameters. The results show that the case of the interpreting flowchart has significantly longer fixation duration, more number of fixations, and larger pupil diameter than the case of the Nassi–Shneiderman diagram interpreting. Based on the results of the study, it is clear how important it is to choose the right programming tools for efficient and lower cost application development.