bluuebunny/crossref_metadata_2025 huggingface dataset:
A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks
Authors: Ang Koon Meng , Chow Cher En , El-Kenawy El-Sayed M. , Abdelhamid Abdelaziz A. , Ibrahim Abdelhameed , Karim Faten Khalid , Khafaga Doaa Sami , Tiang Sew Sun , Lim Wei Hong
Published: December 2022

Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems.
“Verum Ipsum Factum”, Factum Ipsum Bellum: Death Drive or Driving towards Death?
Authors: Girardi Paolo , Modesti Martina Nicole , Del Casale Antonio
Published: December 2022

Is war evitable? Besides being a question to which humanity is struggling to answer, it is the title of the letter exchange between Freud and Einstein that inspired this editorial. The aim is to address this question and analyse whether there is a pattern behind some historical events and mass behaviour, starting with Freud and ending with the possibility of applying chaos theory. What has been hypothesized for centuries is that the masses behave in a manner that can overcome the individual’s interest but are subject to the same risks and patterns as the individual is, i.e., hypernarcissism that fuels the vicious circle of victims and executioners. Is there a reason why historical memory does not stop humankind from pursuing war? Is there an intrinsic death drive that fuels man against one another, or are there other, more complex reasons why war could be (or could not be) inevitable?
Dynamically Updated Alive Publication Date
Authors: Gorbunov-Posadov Mikhail
Published: December 2022

A scientific work posted on the internet, which its authors constantly keep up to date, is called an ‘alive’ publication. The genre of alive publishing has many attractive features. However, it requires a certain expansion of the composition of the meta-attributes of the publication: along with the traditional attributes, the date of the appearance of the new, fresh revision is brought to the fore here. Such a date is placed in a prominent place in the text of the publication. Along with this, it becomes highly desirable to include a dynamically (“on the fly”) generated date in a bibliographic reference to an alive publication. The currently used methods of dynamic extraction of this date are considered for a simple online publication, for a publication that has received a DOI through Crossref, and for a publication posted in arXiv. Thanks to adding this meta-attribute, references to alive publications will improve any bibliographic list.
Schrödinger–Newton Equation with Spontaneous Wave Function Collapse
Authors: Diósi Lajos
Published: December 2022

Based on the assumption that the standard Schrödinger equation becomes gravitationally modified for massive macroscopic objects, two independent proposals have survived from the 1980s. The Schrödinger–Newton equation (1984) provides well-localized solitons for free macro-objects but lacks the mechanism of how extended wave functions collapse on solitons. The gravity-related stochastic Schrödinger equation (1989) provides the spontaneous collapse, but the resulting solitons undergo a tiny diffusion, leading to an inconvenient steady increase in the kinetic energy. We propose the stochastic Schrödinger–Newton equation, which contains the above two gravity-related modifications together. Then, the wave functions of free macroscopic bodies will gradually and stochastically collapse to solitons, which perform inertial motion without momentum diffusion: conservation of momentum and energy is restored.
Palaeoenvironment, Settlement, and Land Use in the Late Neolithic—Bronze Age Site of Colombare di Negrar di Valpolicella (N Italy, On-Site)
Authors: Tecchiati Umberto , Salzani Paola , Gulino Fiorenza , Proserpio Barbara , Reggio Chiara , Putzolu Cristiano , Rattighieri Eleonora , Clò Eleonora , Mercuri Anna Maria , Florenzano Assunta
Published: December 2022

Palynological and archaeobotanical analyses have been carried out as part of the interdisciplinary project of Colombare di Negrar, a prehistoric site in the Lessini Mountains (northern Italy). The palaeoenvironmental and economic reconstruction from the Late Neolithic to the beginning of the Early Bronze Age was based on 16 pollen samples and three samples of macroremains taken from two contiguous trenches. The landscape reconstruction shows the presence of natural clearings in the wood. Forest cover was characterised by oak wood, with Ulmus and Tilia. The intermediate morphology of size and exine of Tilia cordata/platyphyllos pollen may be regarded as the first palynological evidence of lime hybrids in palaeorecords. Hygrophilous trees and Vitis vinifera testify to the presence of riparian forests and moist soils. Among trees supplying fruits, in addition to the grapevine, hazelnut (Corylus avellana) and walnut (Juglans regia) were present. A mixed economy based on animal breeding and cultivation of cereals (Hordeum vulgare, Triticum monococcum, T. dicoccum, T. timopheevii) emerged from the data. The combined analysis of pollen and plant macroremains suggests that different activities were carried out simultaneously in Colombare and a relationship between natural resources and the socio-economic and cultural evolution of the territory.
Seeing as Worldmaking: Ten Views of a Lingbi Rock and Yogācārin Epistemology in Late Ming China
Authors: Oh Seung Hee
Published: December 2022

This paper examines Wu Bin’s (c. 1543–c. 1626) Ten Views of a Lingbi Rock (1610) from the perspective of Buddhist epistemological notions in seventeenth-century China. In studying a series of gazes focusing on a single object—a stone with a very complex surface—my discussion posits an act of excessive seeing as a process of making worlds. I take a theoretical cue from contemporaneous intellectual discourses, especially those that flourished with the revival of Yogācāra Buddhism in late Ming China. This paper will show how an art object comes into being in perceivable worlds interconnected by the individual’s sensory experiences. My study aims to inquire into the role of illusion as sensory experiences, phenomenological processes, and even notions of soteriological efficacy beyond formal artistic devices. To that end, this paper is the first attempt to situate Ten Views of a Lingbi Rock alongside Buddhist thoughts and artmaking.
Dent Disease Type 1: Still an Under-Recognized Renal Proximal Tubulopathy: A Case Report
Authors: Vitkauskaitė Monika , Čerkauskaitė Agnė , Miglinas Marius
Published: December 2022

Dent disease is a rare renal tubular disorder that appears almost exclusively in males. The diagnosis is still challenging, and therefore Dent disease is occasionally misdiagnosed. We report a case of a 45-year-old man with Dent disease who developed renal failure. Since the age of 7 months, he persistently exhibited proteinuria. At the age of 24 years, he underwent kidney biopsy, which revealed focal segmental glomerulosclerosis. The patient’s brother was found to have proteinuria since he was 2 years old. At the age of 45 years, the patient was transferred to a tertiary care nephrologist, and Dent disease was suspected. Genetic testing revealed a CLCN5 mutation. We highlight the broad spectrum of clinical manifestations in Dent disease and the importance of having a high clinical suspicion to attain a definitive diagnosis. Furthermore, future research regarding the clinical course of the disease, prognosis, and effective treatment options is needed.
Heavy Metal Accumulation in Sediments of Small Retention Reservoirs—Ecological Risk and the Impact of Humic Substances Distribution
Authors: Bartoszek Lilianna , Gruca-Rokosz Renata , Pękala Agnieszka , Czarnota Joanna
Published: December 2022

Anthropogenic pollutants that accumulate in bottom sediments may pose a serious threat to the aquatic environment and humans. The aim of the study was to determine the ecological risk related to the contamination of sediment with heavy metals and the relationship between the accumulation of heavy metals and various granulometric fractions and humic substances in the bottom sediments of small retention reservoirs located in catchments of varying anthropopressure. The research objects were five small dam reservoirs located in south-eastern Poland. The sediments of the reservoir exposed to the greatest anthropopressure from the catchment area posed a serious threat to aquatic organisms feeding at the bottom. The bottom sediments of the remaining reservoirs showed a low level of potential toxicity (or non-toxicity). The observed relationship between the enrichment of sediments with organic matter (OM) and the increased risk of their ecotoxic impact on aquatic organisms was determined by excessive exposure to heavy metal contamination. The sand content did not appear to have a clear effect on the metal accumulation, although it was associated with enrichment in OM. Due to diverse environmental conditions, it was not possible to unequivocally confirm that the accumulation of heavy metals in the sediments of small retention reservoirs directly depends on the content of organic matter and humic substances, but such relationships were observed in most of the objects.
Important Preliminary Insights for Designing Successful Communication between a Robotic Learning Assistant and Children with Autism Spectrum Disorder in Germany
Authors: Horstmann Aike C. , Mühl Lisa , Köppen Louisa , Lindhaus Maike , Storch Dunja , Bühren Monika , Röttgers Hanns Rüdiger , Krajewski Jarek
Published: December 2022

Early therapeutic intervention programs help children diagnosed with Autism Spectrum Disorder (ASD) to improve their socio-emotional and functional skills. To relieve the children’s caregivers while ensuring that the children are adequately supported in their training exercises, new technologies may offer suitable solutions. This study investigates the potential of a robotic learning assistant which is planned to monitor the children’s state of engagement and to intervene with appropriate motivational nudges when necessary. To analyze stakeholder requirements, interviews with parents as well as therapists of children with ASD were conducted. Besides a general positive attitude towards the usage of new technologies, we received some important insights for the design of the robot and its interaction with the children. One strongly accentuated aspect was the robot’s adequate and context-specific communication behavior, which we plan to address via an AI-based engagement detection system. Further aspects comprise for instance customizability, adaptability, and variability of the robot’s behavior, which should further be not too distracting while still being highly predictable.
A Morphing-Based Method for Paleotopographic Reconstruction of the Transverse Canyon
Authors: Shen Yangen , Li Anbo , Xu Shiyu , Xie Xianli
Published: December 2022

The transverse canyon is a V-shaped, fluvial-genetic canyon, a secondary valley formed by transverse drainage crossing a tectonically uplifted mountain. Paleotopography of the transverse canyon is vital to drainage connection and river capture, offering insight into the processes that link large-scale river systems, analyzing paleodrainage patterns, and recreating headward erosion. Notably, modern paleotopographic reconstruction methods are usually limited to reconstructions of paleotopography in vast sedimentary basins and denuded hills in orogenic belts. When applied to transverse canyons, a specific secondary valley found in tiny locations, these techniques are difficult, expensive, and ineffective. This paper proposes an automated method for reconstructing the paleotopography of the transverse canyon using the digital elevation model (DEM) and river. (1) Restore the ridgeline above the transverse canyon based on the ridgelines of the mountains on both sides; (2) create a buffer zone based on the river centerline with unequal buffer distances on each side; (3) construct a mesh surface by interpolating transition curves from the morphing method, using the three-edge type; (4) apply a spatial interpolation method to the elevation points on the mesh surface to construct the DEM above the transverse canyon and stitch it to the input DEM to obtain the paleotopographic DEM; (5) calculate the spatial attributes. The objective of this study is to reconstruct the paleotopography of eight typical transverse canyons in the comb-like fold belt of northern Chongqing. As part of the paleotopographic reconstruction of the transverse canyon, we address the effects of dislocated mountains, erosion gullies, and different morphing techniques, as well as the applicability of the proposed method to reconstructing other secondary valleys. In conclusion, we reconstruct paleotopographic DEMs of transverse canyons to replicate headward erosion processes, assess paleodrainage patterns, and build three-dimensional solid models.
Tropical Cyclone Planetary Boundary Layer Heights Derived from GPS Radio Occultation over the Western Pacific Ocean
Authors: Wang Li , Yang Shengpeng , Lin Lin
Published: December 2022

According to GPS radio occultation data from previous studies, the height of the planetary boundary layer (PBLH) is defined as the altitude at which the vertical gradient of refractivity N is at its local minimum, called the gradient approach. As with its density, the atmosphere’s refractivity falls broadly exponentially with height. The spherically symmetric refractivity Nss(r) was established to account for the standard deviation of atmospheric refractivity with altitude. Ni is the residual from the fundamental vertical variations of refractivity, defined as Ni(r) = N(r) − Nss(r). In this study, the vertical gradient of N is replaced by the vertical gradient of Ni to optimize the gradient approach, called the local gradient approach. Using the US radiosonde and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultations (ROs) data from 2007–2011, these two PBLH-determining approaches are evaluated. The PBLHs estimated by the gradient approach and the local gradient approach have RMSE values of 0.73 km and 0.65 km, respectively. The PBLH obtained by the local gradient approach is closer to the radiosonde-derived value. In this paper, the COSMIC-2 ROs data and the western Pacific typhoon best track data are collocated in time and space during 2020–2021, and the axisymmetric composite structural characteristics of the tropical cyclone (TC) PBLs are analyzed. The lowest vertical gradients of N and Ni of TCs correspond closely with the average PBLHs. We find that the mean PBLHs of tropical depressions (TD), tropical storms (TS), and typhoons (TY) all have their local maxima at a radial distance of 125 km with heights of 1.03 km, 1.12 km, and 1.36 km, respectively. After 375 km, 575 km, and 935 km of TD, TS, and TY radial distances, the mean PBLHs become stable and cease to vary. The mean PBLH undulations increase significantly with the increase in tropical cyclone intensity. Niwet is the residual from the fundamental vertical variations of wet refractivity, defined as Niwet(r) = Nwet(r) − Nsswet(r). Local minima of Niwet and Ni vertical gradients of TD, TS, and TY have comparable distributions and are concentrated between 0.5 km and 1 km.
Giant Planet Observations in NASA’s Planetary Data System
Authors: Chanover Nancy J. , Bauer James M. , Blalock John J. , Gordon Mitchell K. , Huber Lyle F. , Mace Mia J. T. , Neakrase Lynn D. V. , Tiscareno Matthew S. , Walker Raymond J.
Published: December 2022

While there have been far fewer missions to the outer Solar System than to the inner Solar System, spacecraft destined for the giant planets have conducted a wide range of fundamental investigations, returning data that continues to reshape our understanding of these complex systems, sometimes decades after the data were acquired. These data are preserved and accessible from national and international planetary science archives. For all NASA planetary missions and instruments the data are available from the science discipline nodes of the NASA Planetary Data System (PDS). Looking ahead, the PDS will be the primary repository for giant planets data from several upcoming missions and derived datasets, as well as supporting research conducted to aid in the interpretation of the remotely sensed giant planets data already archived in the PDS.
Extreme Change Events of Stratospheric HCl and N2O in the Mid-Latitude Region of the Northern Hemisphere
Authors: Han Yuanyuan , Xie Fei , Cui Fei , Wang Feiyang , Li Xin , Feng Wuhu
Published: December 2022

Hydrogen chloride (HCl) is the main reservoir species of chlorine and chemical decomposition of nitrous oxide (N2O) is the primary source of NOx (=NO + NO2) in the stratosphere. Changes in stratospheric HCl and N2O play a critical role in modulating variations in stratospheric ozone. Thus, long-term trends in stratospheric HCl and N2O have been investigated in many studies, whereas short-term changes have not received enough attention. Here, using satellite observations and a chemical transport model, we found that two extreme change events for HCl and N2O in the Northern Hemisphere mid-latitude middle and lower stratosphere have occurred over past decades, which are characterized by a sharp increase in HCl and a decrease in N2O over several months; for example, HCl increased (and N2O decreased) by 0.135 ppbv (−33.352 ppbv) in 1987/1988 and by 0.196 ppbv (−28.553 ppbv) in 2010/2011. Further analysis shows that the extreme change events of stratospheric HCl and N2O in these two periods are closely related to anomalous residual circulation caused by the joint effects of the strong easterly phase of the semi-annual oscillation and the strong polar vortex.
3D Graph-Based Individual-Tree Isolation (Treeiso) from Terrestrial Laser Scanning Point Clouds
Authors: Xi Zhouxin , Hopkinson Chris
Published: December 2022

Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects aligned with forest inventory standards due to noise, redundancy, and geometric complexity. A practical model treeiso based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The treeiso followed the local-to-global segmentation scheme, which grouped points into small clusters, large segments, and final trees in a hierarchical manner. Seven tree attributes were investigated to understand the underlying determinants of isolation accuracy. Sensitivity analysis based on the PAWN index was performed using 10,000 parameter combinations to understand the treeiso’s parameter importance and model robustness. With sixteen reference TLS plot scans from various species, an average of 86% of all trees were detected. The mean intersection-over-union (mIoU) between isolated trees and reference trees was 0.82, which increased to 0.92 within the detected trees. Sensitivity analysis showed that only three parameters were needed for treeiso optimization, and it was robust against parameter variations. This new treeiso method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.
MPFINet: A Multilevel Parallel Feature Injection Network for Panchromatic and Multispectral Image Fusion
Authors: Feng Yuting , Jin Xin , Jiang Qian , Wang Quanli , Liu Lin , Yao Shaowen
Published: December 2022

The fusion of a high-spatial-resolution panchromatic (PAN) image and a corresponding low-resolution multispectral (MS) image can yield a high-resolution multispectral (HRMS) image, which is also known as pansharpening. Most previous methods based on convolutional neural networks (CNNs) have achieved remarkable results. However, information of different scales has not been fully mined and utilized, and still produces spectral and spatial distortion. In this work, we propose a multilevel parallel feature injection network that contains three scale levels and two parallel branches. In the feature extraction branch, a multi-scale perception dynamic convolution dense block is proposed to adaptively extract the spatial and spectral information. Then, the sufficient multilevel features are injected into the image reconstruction branch, and an attention fusion module based on the spectral dimension is designed in order to fuse shallow contextual features and deep semantic features. In the image reconstruction branch, cascaded transformer blocks are employed to capture the similarities among the spectral bands of the MS image. Extensive experiments are conducted on the QuickBird and WorldView-3 datasets to demonstrate that MPFINet achieves significant improvement over several state-of-the-art methods on both spatial and spectral quality assessments.
Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System
Authors: Ma Yuanxu , Sun Dongqi , Liu Weihua , You Yongfa , Wang Siyuan , Sun Zhongchang , Wang Shaohua
Published: December 2022

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m3. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m3 corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m3, which was higher than that in rivers with a range of 2–4 mg/m3. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems.
A Comparison of Information Content at Microwave to Millimeter Wave Bands for Atmospheric Sounding
Authors: Xiao Xianjun , Weng Fuzhong
Published: December 2022

The brightness temperatures and their Jacobians with respect to atmospheric temperature and humidity at the microwave to millimeter wave spectral bands from 23 GHz to 424 GHz are simulated with the ECMWF IFS-137 profiles as inputs to the Advanced Radiative transfer Modeling System (ARMS). The information content of temperature and humidity is then calculated individually through the Shannon entropy which is contributed by a-priori background information and observations. For a typical set of measurement uncertainties, a high information content for atmospheric temperature is mainly obtained from V band near 50–70 GHz, whereas that for water vapor comes from G band near 183 GHz and Y1 band near 380 GHz. The channels within the G band have a large temperature information content mainly for lower and middle layers of troposphere and the Y1 band has a relatively large humidity information content for the entire troposphere. A large measurement uncertainty can significantly reduce the information content of each band. Thus, to make a best use of the data from each band, it is important to reduce the instrument calibration noise and increase the accuracy in forward radiative transfer simulation.
Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds—A Case Study in Southern China
Authors: Gu Yu , Chen Yangbo , Sun Huaizhang , Liu Jun
Published: December 2022

Urbanization has significant impacts on watershed hydrology, but previous studies have been confirmatory and not comprehensive; in particular, few studies have addressed the impact of urbanization on flooding in highly urbanized watersheds. In this study, this effect is studied in Chebei Creek, a highly urbanized watershed in the Pearl River Delta, southern China. Landsat satellite images acquired in 2015 were used to estimate land use and cover changes using the Decision Tree (DT) C4.5 classification algorithm, while the Liuxihe model, a physically based distributed hydrological model (PBDHM), is employed to simulate watershed flooding and hydrological processes. For areas with high degrees of urbanization, the duration of the flood peak is only 1 h, and the flood water level shows steep rises and falls. These characteristics increase the difficulty of flood modeling and forecasting in urbanized areas. At present, hydrological research in urbanized watersheds generally focuses on the quantitative simulation of runoff from urban areas to the watershed, flood flows, peak flood flow, and runoff depth. Few studies have involved real-time flood forecasting in urbanized watersheds. To achieve real-time flood forecasting in urbanized watersheds, PBDHMs and refined underlying surface data based on remote sensing technology are necessary. The Liuxihe model is a PBDHM that can meet the accuracy requirements of inflow flood forecasting for reservoir flood control operations. The accuracies of the two flood forecasting methods used in this study were 83.95% and 97.06%, showing the excellent performance of the Liuxihe model for the real-time flood forecasting of urbanized rivers such as the Chebei Creek watershed.
Indirect Assessment of Watershed SDG7 Development Process Using Nighttime Light Data—An Example of the Aral Sea Watershed
Authors: Chen Chaoliang , Sun Jiayu , Qian Jing , Chen Xi , Hu Zengyun , Jia Gongxu , Xing Xiuwei , Wei Shujie
Published: December 2022

The accurate calculation of sustainable development indicators is essential for the accurate assessment of the Sustainable Development Goals. This study develops a methodology that combines nighttime light indices, population distribution data, and statistics in order to examine changes and key drivers of SDG7 in the Aral Sea Basin from 2000–2020. In this study, the best-performing combination of four light indices and five simulation methods (two linear regression methods and three machine learning methods) was selected to simulate the spatial distribution of GDP in the Aral Sea Basin. The results showed that: (1) The prediction using the XGBoost model with TNL had better performance than other models. (2) From 2000 to 2020, the GDP of the Aral Sea Basin shows an uneven development pattern while growing rapidly (+101.73 billion, +585.5%), with the GDP of the lower Aral Sea and the Amu Darya River gradually concentrating in the middle Aral Sea and Syr Darya River basins, respectively. At the same time, the GDP of the Aral Sea Basin shows a strong negative correlation with the area of water bodies. (3) Although there is a small increase in the score (+6.57) and ranking (+9) of SDG7 for the Aral Sea Basin from 2000 to 2020, it is difficult to achieve SDG7 in 2030. Deepening inter-basin energy cooperation, enhancing investment in renewable energy, and increasing energy intensity is key to achieving SDG7.
Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing
Authors: Liu Yanjie , Wang Chao , Wu Heng , Wei Yanlong , Ren Meixuan , Zhao Changsen
Published: December 2022

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm.
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
Authors: Amarasingam Narmilan , Gonzalez Felipe , Salgadoe Arachchige Surantha Ashan , Sandino Juan , Powell Kevin
Published: December 2022

White leaf disease (WLD) is an economically significant disease in the sugarcane industry. This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction. This study evaluated the performance of the existing DL models such as YOLOv5, YOLOR, DETR, and Faster R-CNN to recognize WLD in sugarcane crops. The experimental results indicate that the YOLOv5 network outperformed the other selected models, achieving a precision, recall, mean average precision@0.50 (mAP@0.50), and mean average precision@0.95 (mAP@0.95) metrics of 95%, 92%, 93%, and 79%, respectively. In contrast, DETR exhibited the weakest detection performance, achieving metrics values of 77%, 69%, 77%, and 41% for precision, recall, mAP@0.50, and mAP@0.95, respectively. YOLOv5 is selected as the recommended architecture to detect WLD using the UAV data not only because of its performance, but this was also determined because of its size (14 MB), which was the smallest one among the selected models. The proposed methodology provides technical guidelines to researchers and farmers for conduct the accurate detection and treatment of WLD in the sugarcane fields.
UBathy (v2.0): A Software to Obtain the Bathymetry from Video Imagery
Authors: Simarro Gonzalo , Calvete Daniel
Published: December 2022

UBathy is an open source software developed for bathymetry estimation from video images. The proposed scheme is based on extracting the wave modes from videos of the nearshore surface wave propagation. These videos can be formed either from raw camera images, which must have been previously calibrated, or from georeferenced planviews. For each wave mode extracted from the videos, the wave frequency and the spatially dependent wavenumbers are obtained. The frequencies and wavenumbers from different videos are used to estimate the bathymetry by adjusting the dispersion relationship for linear surface water waves. The bathymetry at different times can further be weighted and aggregated using the Kalman filter. The new software is suitable for Argus-type video monitoring stations and for moving cameras mounted on drones or satellites, and it is meant for users familiar with coastal image processing and suitable for non-experienced users. The software and an application example are available on the GitHub platform.
Remote Radio-Physical Harbingers of Drought in Steppes of the South of Western Siberia
Authors: Romanov Andrey , Ryabinin Ivan , Khvostov Ilya , Troshkin Dmitry , Romanov Dmitry
Published: December 2022

Methods for remote sensing of the underlying surface in the microwave range based on moisture dependence of soil emissivity were successfully used in monitoring droughts and assessing water availability of the studied territories. Soil moisture influence on soil cover emissivity calibrated in units of the radio brightness temperature (TB) was studied. We used values of TB derived from SMOS satellite data. This paper presents the results of a comparative analysis of soil, meteorological conditions and physical characteristics of soils in the test territories of the Kulunda Plain. The experimental data were applied in computing trends of TB and physical temperature (T) described by linear dependencies. Volume fractions of water (W) in soil were calculated based on the satellite sensing data, the results of field studies and laboratory measurements of dielectric characteristics of soils. A map scheme of spatial distribution of W was constructed and the influences of snow cover, precipitation and surface wind velocity on drought were analyzed. The comprehensive analysis of remote, field and laboratory data suggest that the rate of change in the brightness temperature (dTBH/dD—up to 17 K per day), which characterizes the rate of fall in volume humidity of soil (ΔW—up to 0.009 cm3/cm3 per day), can be used as a short-term radio-physical harbinger of drought. An experimental dependence of the rate of change in radio brightness temperature on the rate of change in soil moisture was established.
Correcting Underestimation and Overestimation in PolInSAR Forest Canopy Height Estimation Using Microwave Penetration Depth
Authors: Luo Hongbin , Yue Cairong , Wang Ning , Luo Guangfei , Chen Si
Published: December 2022

PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R2 increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R2 value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R2 increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors.
Rényi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
Authors: Maksymiuk Radosław , Abratkiewicz Karol , Samczyński Piotr , Płotka Marek
Published: December 2022

This paper presents the first successful drone detection results using a 5G network as a source of illumination in a passive radar system. Furthermore, a novel adaptive strategy for signal integration is shown. The proposed approach is based on the Rényi entropy. It allows one to select time frames with a densely allocated downlink channel both in the time and frequency domains. The resource allocation is strongly related to a network load and has a crucial influence on 5G-based passive radar range resolution and detection capabilities. The proposed technique was validated using simulated and real-life signals, confirming the possibility of detecting unmanned aerial vehicles (UAVs) in 5G-network-based passive radars. Moreover, the proposed methodology can be directly used in passive radar systems where the illuminating signal duration and bandwidth are content-dependent, and the radar resolution may vary significantly.
Urban Flood Detection Using TerraSAR-X and SAR Simulated Reflectivity Maps
Authors: Baghermanesh Shadi Sadat , Jabari Shabnam , McGrath Heather
Published: December 2022

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.
Multi-Scale Feature Aggregation Network for Semantic Segmentation of Land Cover
Authors: Shen Xu , Weng Liguo , Xia Min , Lin Haifeng
Published: December 2022

Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentation in recent years, but there are few semantic segmentation algorithms for land cover. When dealing with land cover segmentation tasks, traditional semantic segmentation networks often have disadvantages such as low segmentation precision and weak generalization due to the loss of image detail information and the limitation of weight distribution. In order to achieve high-precision land cover segmentation, this article develops a multi-scale feature aggregation network. Traditional convolutional neural network downsampling procedure has problems of detail information loss and resolution degradation; to fix these problems, a multi-scale feature extraction spatial pyramid module is made to assemble regional context data from different areas. In order to address the issue of incomplete information of traditional convolutional neural networks at multiple sizes, a multi-scale feature fusion module is developed to fuse attributes from various layers and several sizes to boost segmentation accuracy. Finally, a multi-scale convolutional attention module is presented to enhance the segmentation’s attention to the target in order to address the issue that the classic convolutional neural network has low attention capacity to the building waters in land cover segmentation. Through the contrast experiment and generalization experiment, it can be clearly demonstrated that the segmentation algorithm proposed in this paper realizes the high precision segmentation of land cover.
Multi-Sensor Fusion of SDGSAT-1 Thermal Infrared and Multispectral Images
Authors: Qi Lintong , Hu Zhuoyue , Zhou Xiaoxuan , Ni Xinyue , Chen Fansheng
Published: December 2022

Thermal infrared imagery plays an important role in a variety of fields, such as surface temperature inversion and urban heat island effect analysis, but the spatial resolution has severely restricted the potential for further applications. Data fusion is defined as data combination using multiple sensors, and fused information often has better results than when the sensors are used alone. Since multi-resolution analysis is considered an effective method of image fusion, we propose an MTF-GLP-TAM model to combine thermal infrared (30 m) and multispectral (10 m) information of SDGSAT-1. Firstly, the most relevant multispectral bands to the thermal infrared bands are found. Secondly, to obtain better performance, the high-resolution multispectral bands are histogram-matched with each thermal infrared band. Finally, the spatial details of the multispectral bands are injected into the thermal infrared bands with an MTF Gaussian filter and an additive injection model. Despite the lack of spectral overlap between thermal infrared and multispectral bands, the fused image improves the spatial resolution while maintaining the thermal infrared spectral properties as shown by subjective and objective experimental analyses.
Exploring Endothelial Expansion on a Chip
Authors: Konopka Joanna , Kołodziejek Dominik , Flont Magdalena , Żuchowska Agnieszka , Jastrzębska Elżbieta , Brzózka Zbigniew
Published: December 2022

Angiogenesis is the development of new blood vessels from the existing vasculature. Its malfunction leads to the development of cancers and cardiovascular diseases qualified by the WHO as a leading cause of death worldwide. A better understanding of mechanisms regulating physiological and pathological angiogenesis will potentially contribute to developing more effective treatments for those urgent issues. Therefore, the main goal of the following study was to design and manufacture an angiogenesis-on-a-chip microplatform, including cylindrical microvessels created by Viscous Finger Patterning (VFP) technique and seeded with HUVECs. While optimizing the VFP procedure, we have observed that lumen’s diameter decreases with a diminution of the droplet’s volume. The influence of Vascular Endothelial Growth Factor (VEGF) with a concentration of 5, 25, 50, and 100 ng/mL on the migration of HUVECs was assessed. VEGF’s solution with concentrations varying from 5 to 50 ng/mL reveals high angiogenic potential. The spatial arrangement of cells and their morphology were visualized by fluorescence and confocal microscopy. Migration of HUVECs toward loaded angiogenic stimuli has been initiated after overnight incubation. This research is the basis for developing more complex vascularized multi-organ-on-a-chip microsystems that could potentially be used for drug screening.
Botnet Defense System: Observability, Controllability, and Basic Command and Control Strategy
Authors: Yamaguchi Shingo
Published: December 2022

This paper deals with the observability, controllability, and command and control strategy in the Botnet Defense System (BDS) that disinfects malicious botnets with white-hat botnets. The BDS defends an IoT system built over the Internet. The Internet is characterized by openness, but not all nodes are observable and controllable. We incorporated the concept of observability and controllability into the BDS design and theoretically clarified that the BDS can enhance its observability and controllability by utilizing its white-hat botnets. In addition, we proposed a Withdrawal strategy as a basic strategy to command and control white-hat botnets. Then, we modeled the BDS, adopted the Withdrawal strategy with agent-oriented Petri net PN2 and confirmed the effect through the simulation of the model. The result shows that even if considering observability and controllability, the BDS wiped out the malicious bots and reduced the white-hat bots to less than 1% as long as the white-hat worms were sufficiently infectious.