bluuebunny/crossref_metadata_2025 huggingface dataset:
Composite Fibers Based on Polycaprolactone and Calcium Magnesium Silicate Powders for Tissue Engineering Applications
Authors: Busuioc Cristina , Alecu Andrada-Elena , Costea Claudiu-Constantin , Beregoi Mihaela , Bacalum Mihaela , Raileanu Mina , Jinga Sorin-Ion , Deleanu Iuliana-Mihaela
Published: October 2022

The present work reports the synthesis and characterization of polycaprolactone fibers loaded with particulate calcium magnesium silicates, to form composite materials with bioresorbable and bioactive properties. The inorganic powders were achieved through a sol–gel method, starting from the compositions of diopside, akermanite, and merwinite, three mineral phases with suitable features for the field of hard tissue engineering. The fibrous composites were fabricated by electrospinning polymeric solutions with a content of 16% polycaprolactone and 5 or 10% inorganic powder. The physico-chemical evaluation from compositional and morphological points of view was followed by the biological assessment of powder bioactivity and scaffold biocompatibility. SEM investigation highlighted a significant reduction in fiber diameter, from around 3 μm to less than 100 nm after the loading stage, while EDX and FTIR spectra confirmed the existence of embedded mineral entities. The silicate phases were found be highly bioactive after 4 weeks of immersion in SBF, enriching the potential of the polymeric host that provides only biocompatibility and bioresorbability. Moreover, the cellular tests indicated a slight decrease in cell viability over the short-term, a compromise that can be accepted if the overall benefits of such multifunctional composites are considered.
Force-Extension Curve of a Polymer Chain Entangled with a Static Ring-Shaped Obstacle
Authors: Zhang Qihao , Li Jianfeng
Published: October 2022

The way to theoretically approach dynamic and static topological constraints of polymer entanglements still presents a great challenge in polymer physics. So far, only the problem of static entanglement with multiple simple objects has been solved in theory by a superspace approach in our previous work. This work is devoted to extending the superspace approach to study a polymer chain entangled with a relatively complicated object—a ring-shaped object with genus one. Taking advantage of the axial symmetry of the model setup, the 3D diffusion equations in the superspace can be numerically solved within the 2D coordinates using a specially designed alternating-direction implicit (ADI) scheme. A series of numerical calculations reveal that the topological entanglement effect of the ring will exert a topological entropy attractive force on the linear chain, which can be used to explain the viscosity-increase phenomenon observed in recent simulations and experiments. Furthermore, the influences of the ring size and the entangling modes on the topological entropy force are also investigated by examining the corresponding force-extension curves. This work, together with our previous work, might pave the path toward the complete formulation of static topological constraints.
Supercritical Carbon Dioxide Extraction Process for Hibiscus taiwanensis S. Y. Hu and Tyrosinase Inhibitory Activity of Its Extract
Authors: Yang Jingguo , Pan Fawu , Du Liyong
Published: October 2022

In this study, the bioactive components in the stem of Hibiscus taiwanensis S. Y. Hu were extracted by supercritical carbon dioxide (SC-CO2), and the inhibition activity of the extract tyrosinase was analyzed. When the particle size of the powder was controlled to be 50 mesh, the effects of temperatures (40, 45, 50, 55, and 60 °C), pressures (15, 20, 25, 30, and 35 MPa), concentrations of entrainment agent (ethanol) (60, 80, and 95%) and CO2 flow rates (80, 100, 120, and 140 L/min) on the extraction rate, respectively, were studied with the single-variable method. The optimal parameters included an extraction temperature of 55 °C, an extraction pressure of 30 MPa, 80% ethanol as the entrainment agent, and the CO2 flow rate of 100 L/min. Under the optimal conditions, the extraction rate of Hibiscus taiwanensis S. Y. Hu reached more than 0.5% within 2 h. At the same time, the chemical compositions of the extract were investigated by using high-resolution liquid chromatography-mass spectrometry (LC-MS) equipped with a linear ion trap-electrostatic field orbital trap. In addition, the related activity of extract in the stem Hibiscus taiwanensis S. Y. Hu was evaluated by tyrosinase inhibition experiments. The results showed that the inhibition rate of the extract with a concentration of 0.25 mg/mL to tyrosinase reached 35.60%, indicating that the extract by SC-CO2 had the potential to be used for whitening active raw materials.
Quantitative vs. Qualitative Assessment of the Effectiveness of the Removal of Vascular Lesions Using the IPL Method—Preliminary Observations
Authors: Lipka-Trawińska Aleksandra , Wilczyński Sławomir , Deda Anna , Koprowski Robert , Lebiedowska Agata , Wcisło-Dziadecka Dominika
Published: October 2022

The aim of the study was to develop a methodology for the acquisition of skin images in visible light in a repeatable manner, enabling an objective assessment and comparison of the skin condition before and after a series of IPL treatments. Thirteen patients with erythematous lesions, vascular skin and/or rosacea were examined. Treatments aimed at reducing the erythema were carried out using the Lumecca™ (InMode MD Ltd., Yokneam, Israel) The research used the FOTOMEDICUS image acquisition system (Elfo, Łódź, Poland). The RGB images were recorded and decomposed to individual channels: red, green and blue. Then, the output image (RGB) and its individual channels were transformed into images in shades of gray. The GLCM and QTDECOMP algorithms were used for the quantitative analysis of vascular lesions. Image recording in cross-polarized light enables effective visualization of vascular lesions of the facial skin. A series of three treatments using the IPL light source seems to be sufficient to reduce vascular lesions in the face. GLCM contrast and homogeneity analysis can be an effective method of identifying skin vascular lesions. Quadtree decomposition allows for the quantitative identification of skin vascular lesions to a limited extent. The brightness analysis of the images does not allow quantification of the vascular features of the skin. Mexametric measurements do not allow for a quantitative assessment of the skin’s blood vessel response to high-energy light.
Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0
Authors: Alarcon Claudio , Shene Carolina
Published: October 2022

Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
Fe–Mn Oxide Composite Activated Peroxydisulfate Processes for Degradation of p-Chloroaniline: The Effectiveness and the Mechanism
Authors: Shi Yu , Ma Panfeng , Qiao Lin , Liu Bingtao
Published: October 2022

The chemical co-precipitation method was used to prepare magnetically separable Fe–Mn oxide composites, and the degradation of p-chloroaniline (PCA) using MnFe2O4 activated peroxydisulfate (PDS). The MnFe2O4 catalyst exhibited highly catalytic activity in the experiments. XRD, FTIR, SEM and TEM were used to characterize the catalytic materials. MnFe2O4 calcined at 500 °C was more suitable as a catalytic material for PCA degradation. The elevated reaction temperature was beneficial to the degradation of PCA in neutral pH solution. The reaction mechanism of the MnFe2O4 catalyzed oxidative degradation of PCA by PDS was investigated by free radical quenching experiments and XPS analysis. The results showed that sulfate radicals (SO4•−), hydroxyl radicals (•OH) and singlet oxygen (1O2) may all be participated in the degradation of PCA. XPS spectra showed that the electron gain and loss of Mn2+ and Fe3+ was the main cause of free radical generation. The possible intermediates in the degradation of PCA were determined by HPLC-MS, and possible degradation pathways for the degradation of PCA by the MnFe2O4/PDS system were proposed.
Mass Spectrometry-Based Proteomics of Human Milk to Identify Differentially Expressed Proteins in Women with Breast Cancer versus Controls
Authors: Aslebagh Roshanak , Whitham Danielle , Channaveerappa Devika , Mutsengi Panashe , Pentecost Brian T. , Arcaro Kathleen F. , Darie Costel C.
Published: October 2022

It is thought that accurate risk assessment and early diagnosis of breast cancer (BC) can help reduce cancer-related mortality. Proteomics analysis of breast milk may provide biomarkers of risk and occult disease. Our group works on the analysis of human milk samples from women with BC and controls to investigate alterations in protein patterns of milk that could be related to BC. In the current study, we used mass spectrometry (MS)-based proteomics analysis of 12 milk samples from donors with BC and matched controls. Specifically, we used one-dimensional (1D)-polyacrylamide gel electrophoresis (PAGE) coupled with nanoliquid chromatography tandem MS (nanoLC-MS/MS), followed by bioinformatics analysis. We confirmed the dysregulation of several proteins identified previously in a different set of milk samples. We also identified additional dysregulations in milk proteins shown to play a role in cancer development, such as Lactadherin isoform A, O-linked N-acetylglucosamine (GlcNAc) transferase, galactosyltransferase, recoverin, perilipin-3 isoform 1, histone-lysine methyltransferase, or clathrin heavy chain. Our results expand our current understanding of using milk as a biological fluid for identification of BC-related dysregulated proteins. Overall, our results also indicate that milk has the potential to be used for BC biomarker discovery, early detection and risk assessment in young, reproductively active women.
Learning Mixed Strategies in Quantum Games with Imperfect Information
Authors: Silva Agustin , Zabaleta Omar Gustavo , Arizmendi Constancio Miguel
Published: October 2022

The quantization of games expand the players strategy space, allowing the emergence of more equilibriums. However, finding these equilibriums is difficult, especially if players are allowed to use mixed strategies. The size of the exploration space expands so much for quantum games that makes far harder to find the player’s best strategy. In this work, we propose a method to learn and visualize mixed quantum strategies and compare them with their classical counterpart. In our model, players do not know in advance which game they are playing (pay-off matrix) neither the action selected nor the reward obtained by their competitors at each step, they only learn from an individual feedback reward signal. In addition, we study both the influence of entanglement and noise on the performance of various quantum games.
Deviation and Fantasy: On the Question of the Status of the Bible as Fantasy
Authors: Dagan Hagai
Published: October 2022

The paper explores the possibility of classifying the Bible as a work of fantasy, or as a work including fantastic elements. It looks into the unique status of Biblical fantasy and the challenges to such a classification, including its literary features and presumption of ontological realism. The paper defines fantasy as a literary work whose content is characterized by surfeit or deviation to which no extra-textual ontological pretension is attributed. However, the argument goes beyond the ontological theme to stress the significance of the transcendent in both religious literature and fantasy. The Bible is viewed as fantasy in this sense and in some additional senses already within some of its own parts (such as Lamentation and Job). The paper studies these texts in this context and in relation to the theory of fantasy.
Judaism, Experience, and the Secularizing of Life: Revisiting Walter Benjamin’s Montage of Quotation
Authors: Sax Benjamin E.
Published: October 2022

Most scholarship on the life and thought of Walter Benjamin does not seriously engage the phenomenon of religion or the philosophy of religion in his thought. While some scholarship considers Benjamin a German-Jewish thinker, placed in the company of luminaries such as Martin Buber, Franz Rosenzweig, and Gershom Scholem, most readers assume that Benjamin’s secular identity motivated most of his inquiries and critical thinking. However, focusing on a secular sensibility obscures important elements of religious traditions in Benjamin’s writings. In fact, Benjamin suggested that widely contemporary institutions like capitalism, art, and even at times science contained poignant traces of religion and religious thought. In this article, I examine these traces by revisiting his montage of quotation, which, I argue, is where we see the most salient aspects of the use of Judaism in Benjamin’s thought. His desire to secularize life was inexorably related to his interpretations of experience and of Judaism. I will argue that not only did Benjamin, in fact, use Jewish theological language and imagery through his montage of quotation, but also, he used this method to secularize contemporary theological-political-aesthetic paradigms. I will also argue that this method—primarily understood through his idiosyncratic use of Jewish imagery—is critical to the writing of history.
Smartphone Religious Networking: Negotiating Contested Identities within a Pious Community over WhatsApp
Authors: Mishol-Shauli Nakhi , Golan Oren
Published: October 2022

Online social networks can be considered harbingers of modernity and are claimed to encourage individualization in religious practices. Nevertheless, religious minority groups, including reclusive communities, legitimize their use for religious and communal purposes. Accordingly, social networks are emerging as dynamic third spaces of identity reflections on key issues of lived religion. This study examined how members of a religious group negotiate their identity over online social networks. Accordingly, we conducted a content analysis of 70 ultra-Orthodox Jewish public (Haredi) WhatsApp groups and 40 semi-structured interviews with participants. Findings revealed three primary facets of identity performance: communal affinity; proclaimed conformism and practiced agency; and contesting dogmatism and pragmatism. Through these facets, a new social identity is crystallized within the Haredi sector in Israel. Thus, the secluded spaces of WhatsApp groups enable a marginalized grassroots religious public to promote incremental social change without shattering communal boundaries.
From Abstract Form to Concrete Materialization: An Analysis of Mazu’s Image in Statues and Images
Authors: Zhang Beibei , Shu Xiaping
Published: October 2022

The religious color of folk beliefs and customs, a concrete and ideal artistic presentation of the Mazu (媽祖) image reflected in ancient books, folk customs and myths, endows Mazu with its unique image and humanistic connotation. The visual image, which implies the meaning of the thought, serves as important material for historical research. Therefore, this paper refers to and draws on three changing images of Mazu summarized from the perspectives of state and society, social function, and region of belief, respectively, by anthropological researcher Mr. Bozhong Li in his study of Mazu. Based on the analysis of Mazu image data, three significant images of Mazu are proposed, namely, the initial image, the transformation image and the typical image, along with a comprehensive analysis of its evolution and relationship. The image of Mazu in the statue is not only an artificially constructed social and cultural symbol but also a folk art symbol with religious and folk characteristics, and secular and emotional characteristics. It is also the visual form of Mazu’s intuitive concrete materialization, carrying a certain representative thought and concept, meaning and spirit, and the various pieces of information it contains is more intuitive than words, bringing the incomparable dissemination power of knowledge and information from the classics. Moreover, it can transcend language barriers and regional boundaries, stimulate personal inner original feelings, interact with Mazu’s beliefs and customs, and serve as an important medium for spreading Mazu’s beliefs and customs.
Are Climate-Dependent Impacts of Soil Constraints on Crop Growth Evident in Remote-Sensing Data?
Authors: Ulfa Fathiyya , Orton Thomas G. , Dang Yash P. , Menzies Neal W.
Published: October 2022

Soil constraints limit plant growth and grain yield in Australia’s grain-cropping regions, with the nature of the impact dependent on climate. In seasons with low in-crop (short for “during the crop growing season”) rainfall, soil constraints can reduce yield by limiting soil water infiltration, storage, and crop water uptake. Conversely, soil constraints can exacerbate waterlogging in seasons with high in-crop rainfall. When average in-crop rainfall is experienced, soil constraints may only have a limited impact on yields. To investigate the relationship between climate and the impact of soil constraints on crop growth, long-term time series yield information is crucial but often not available. Vegetation indices calculated from remote-sensing imagery provide a useful proxy for yield data and offer the advantages of consistent spatial coverage and long history, which are vital for assessing patterns of spatial variation that repeat over many years. This study aimed to use an index of crop growth based on the enhanced vegetation index (EVI) to assess whether and how the within-field spatial variation of crop growth differed between years with different climates (dry, moderate, and wet years, as classified based on in-crop rainfall). Five fields from the grain-growing region of eastern Australia were selected and used to assess the consistency of the spatial variation of the index for years in the same in-crop rainfall category. For four of the five fields, no evidence of patterns of climate-dependent spatial variation was found, while for the other field, there was marginal evidence of spatial variation attributable to wet years. The correlation between measured data on soil sodicity (a soil constraint that might be expected to impact crop growth most in wetter years) and average EVI was investigated for this field. The results showed a stronger negative correlation between average EVI and sodicity in wet years than in dry years, suggesting that sodicity—through its impacts on soil structure and water movement—might be a driver of the spatial variation of crop growth in wet years for this field. Our results suggest that although there may be cases when climate-dependent within-field spatial variation of crop growth is detectable through remote-sensing data (through the multi-year consistency of the within-field variation), we should not expect this to be evident for fields as a matter of course.
Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
Authors: Patra Raj Kumar , Patil Sujata N. , Falkowski-Gilski Przemysław , Łubniewski Zbigniew , Poongodan Rachana
Published: October 2022

In remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long Short-Term Memory (BiLSTM) method to reduce the overfitting problem and increase the performance of classification in change detection applications. Additionally, data usage and accuracy in remote sensing activities, particularly CD, can be significantly improved by a large number of training models based on BiLSTM. Normalization techniques are applied to input images in order to enhance the quality and reduce the difference in pixel value. The AlexNet and VGG16 models were used to extract useful features from the normalized images. The extracted features were then applied to the FWA-BiLSTM model, to give more weight to the unique features and increase the efficiency of classification. The attention layer selects the unique features that help to distinguish the changes in the remote sensing images. From the experimental results, it was clearly shown that the proposed FWA-BiLSTM model achieved better performance in terms of precision (93.43%), recall (93.16%), and overall accuracy (99.26%), when compared with the existing Difference-enhancement Dense-attention Convolutional Neural Network (DDCNN) model.
Building Change Detection in Remote Sensing Images Based on Dual Multi-Scale Attention
Authors: Zhang Jian , Pan Bin , Zhang Yu , Liu Zhangle , Zheng Xin
Published: October 2022

Accurate change detection continues to pose challenges due to the continuous renovation of old urban areas and the emergence of cloud cover in coastal areas. There have been numerous methods proposed to detect land-cover changes from optical images. However, there are still many flaws in many existing deep learning methods. In response to the problems of unpredictable change details and the lack of global semantic information in deep learning-based change detection models, a change detection model based on multi-scale and attention is proposed. Firstly, a multi-scale attention module is proposed to effectively obtain multi-scale semantic information to build an end-to-end dual multi-scale attention building change detection model. Secondly, an efficient double-threshold automatic data equalization rule is proposed to address the imbalance of data categories existing in the building change detection dataset, which effectively alleviates the severely skewed data distribution and facilitates the training and convergence of the model. The validation experiments are conducted on three open-source high-resolution building change detection datasets. The experimental results show that the proposed method in this paper can detect the location and area of the actual building changes more accurately and has better results in the detail detection part. This verifies the effectiveness and accuracy of the proposed method.
Increased Mass Loss of Glaciers in the Sawir Mountains of Central Asia between 1959 and 2021
Authors: Bai Changbin , Wang Feiteng , Bi Yanqun , Wang Lin , Xu Chunhai , Yue Xiaoying , Yang Shujing , Wang Puyu
Published: October 2022

Glacier mass balance can be regarded as a major direct index of climate variations. In this paper, a geodetic method was used to evaluate the mass balance of Sawir glaciers based on topographic map DEM (Digital Elevation Model), SRTM 30 m DEM, ASTER 30 m DEM, and Sentinel-1 Synthetic Aperture Radar 10 m DEM between 1959–2021, in order to explore the response to climatic alterations. In the case of Muz Taw glacier, the first comprehensive dataset concerning mass-balance readings for the 2014–2021 period was provided based on the eight-year consecutive field measurements. The glaciological average mass balance reached –883.4 ± 130 mm a–1 during this period. The geodetic mass balance for all glaciers of the Sawir Mountain range was −0.43 ± 0.12 m w. e. a−1 between 1959 and 2000, and accelerated to −0.56 ± 0.13 m w. e. a−1 between 2000 and 2021. A comparison of field measurements and remote-sensing approaches for determining the Muz Taw glacier’s mass balance between 2014–2021 proves the feasibility of the remote-sensing approach, which involves mass-balance monitoring based on DEMdata. In addition, our findings support the contention that air temperature is the dominant factor for accelerated glacier mass loss and surface elevation change.
Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin
Authors: Leal Helvecio B. , Calheiros Alan J. P. , Barbosa Henrique M. J. , Almeida Adriano P. , Sanchez Arturo , Vila Daniel A. , Garcia Sâmia R. , Macau Elbert E. N.
Published: October 2022

Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) that impact tracking of these variables. In order to represent the physical aspects of rain propagation over the Amazon region, several methods of correction and displacement detection were studied. Different parameters were used to validate the methods based on the extrapolated rain cell. A probability detection of 78.4% and 68.6% was achieved for 20 dBZ thresholds during the wet and dry season, respectively. However, the POD decreases for higher reflectivity thresholds. The results for corrections by Inner Nuclei showed that embedded convection can dictate the propagation of rain cells. Split and merge corrections performed well; however, they applied only to a few cases. Corrections performed better for precipitating systems with larger areas and longer duration. The correction methods showed similar skills for both seasons. Which shows that they are able to monitor rain cells throughout the year. The automated combination of different methods for the 20 dBZ threshold proved to be the best choice for tracking rainfall in the Amazon region.
Detection and Identification of Mesoscale Eddies in the South China Sea Based on an Artificial Neural Network Model—YOLOF and Remotely Sensed Data
Authors: Cao Lingjuan , Zhang Dianjun , Zhang Xuefeng , Guo Quan
Published: October 2022

Mesoscale eddies are typical mesoscale ocean phenomena that exist widely in all oceans and marginal seas around the world, playing important roles in ocean circulation and material transport. They also have important impacts on the safe navigation of ships and underwater acoustic communications. Traditional mesoscale eddy identification methods are subjective and usually depend on parameters that must be pre-defined or adjusted by experts, meaning that their accuracy cannot be guaranteed. With the rise of deep learning, the “you only look once” (YOLO) series target recognition models have been shown to present certain advantages in eddy detection and recognition. Based on sea level anomaly (SLA) data provided over the past 30 years by the Copernicus Marine Environment Monitoring Service (CMEMS), as well as deep transfer learning, we propose a method for oceanic mesoscale eddy detection and identification based on the “you only look once level feature” (YOLOF) model. Using the proposed model, the mesoscale eddies in the South China Sea from 1993 to 2021 were detected and identified. Compared with traditional recognition methods, the proposed model had a better recognition effect (with an accuracy of 91%) and avoided the bias associated with subjectively set thresholds; to a certain extent, the model also improved the detection of and the identification speed for mesoscale eddies. The method proposed in this paper not only promotes the development of deep learning in the field of oceanic mesoscale eddy detection and identification, but also provides an effective technical method for the study of mesoscale eddy detection using sea surface height data.
Mapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning
Authors: Negri Rogério G. , Luz Andréa E. O. , Frery Alejandro C. , Casaca Wallace
Published: October 2022

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product.
Landsat 9 Cross Calibration Under-Fly of Landsat 8: Planning, and Execution
Authors: Kaita Edward , Markham Brian , Haque Md Obaidul , Dichmann Donald , Gerace Aaron , Leigh Lawrence , Good Susan , Schmidt Michael , Crawford Christopher J.
Published: October 2022

During the early post-launch phase of the Landsat 9 mission, the Landsat 8 and 9 mission teams conducted a successful under-fly of Landsat 8 by Landsat 9, allowing for the near-simultaneous data collection of common Earth targets by the on-board sensors for cross-calibration. This effort, coordinated by the Landsat Calibration and Validation team, required contributions from various entities across National Aeronautics and Space Administration and U.S. Geological Survey such as Flight Dynamics, Systems, Mission Planning, and Flight Operations teams, beginning about 18 months prior to launch. Plans existed to allow this under-fly for any possible launch date of Landsat 9. This included 16 ascent plans and 16 data acquisition plans, one for every day of the Landsat orbital repeat period, with a minimum of 5 days of useful coverage overlap between the sensors. After the Landsat 9 launch, the plan executed, and led to the acquisition of over 2000 partial to full overlapping scene pairs. Although containing less than the expected number of scenes, this dataset was larger than previous Landsat mission under-fly efforts and more than sufficient for performing cross-calibration of the Landsat 8 and Landsat 9 sensors. The details of the planning process and execution of this under-fly are presented.
Pixel Representation Augmented through Cross-Attention for High-Resolution Remote Sensing Imagery Segmentation
Authors: Luo Yiyun , Wang Jinnian , Yang Xiankun , Yu Zhenyu , Tan Zixuan
Published: October 2022

Natural imagery segmentation has been transferred to land cover classification in remote sensing imagery with excellent performance. However, two key issues have been overlooked in the transfer process: (1) some objects were easily overwhelmed by the complex backgrounds; (2) interclass information for indistinguishable classes was not fully utilized. The attention mechanism in the transformer is capable of modeling long-range dependencies on each sample for per-pixel context extraction. Notably, per-pixel context from the attention mechanism can aggregate category information. Therefore, we proposed a semantic segmentation method based on pixel representation augmentation. In our method, a simplified feature pyramid was designed to decode the hierarchical pixel features from the backbone, and then decode the category representations into learnable category object embedding queries by cross-attention in the transformer decoder. Finally, pixel representation is augmented by an additional cross-attention in the transformer encoder under the supervision of auxiliary segmentation heads. The results of extensive experiments on the aerial image dataset Potsdam and satellite image dataset Gaofen Image Dataset with 15 categories (GID-15) demonstrate that the cross-attention is effective, and our method achieved the mean intersection over union (mIoU) of 86.2% and 62.5% on the Potsdam test set and GID-15 validation set, respectively. Additionally, we achieved an inference speed of 76 frames per second (FPS) on the Potsdam test dataset, higher than all the state-of-the-art models we tested on the same device.
Urban Traffic Imaging Using Millimeter-Wave Radar
Authors: Yang Bo , Zhang Hua , Chen Yurong , Zhou Yongjun , Peng Yu
Published: October 2022

Imaging technology enhances radar environment awareness. Imaging radar can provide richer target information for traffic management systems than conventional traffic detection radar. However, there is still a lack of research on millimeter-wave radar imaging technology for urban traffic surveillance. To solve the above problem, we propose an improved three-dimensional FFT imaging algorithm architecture for radar roadside imaging in urban traffic scenarios, enabling the concurrence of dynamic and static targets imaging. Firstly, by analyzing the target characteristics and background noise in urban traffic scenes, the Monte-Carlo-based constant false alarm detection algorithm (MC-CFAR) and the improved MC-CFAR algorithm are proposed, respectively, for moving vehicles and static environmental targets detection. Then, for the velocity ambiguity solution problem with multiple targets and large velocity ambiguity cycles, an improved Hypothetical Phase Compensation algorithm (HPC-SNR) is proposed and complimented. Further, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to remove outliers to obtain a clean radar point cloud image. Finally, traffic targets within the 50 m range are presented as two-dimensional (2D) point cloud imaging. In addition, we also try to estimate the vehicle type by target point cloud size, and its accuracy reaches more than 80% in the vehicle sparse condition. The proposed method is verified by actual traffic scenario data collected by a millimeter-wave radar system installed on the roadside. The work can support further intelligent transportation management and extend radar imaging applications.
A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM
Authors: Liu Yang , Wang Yexin , Di Kaichang , Peng Man , Wan Wenhui , Liu Zhaoqin
Published: October 2022

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications.
Estimation of Water Balance for Anticipated Land Use in the Potohar Plateau of the Indus Basin Using SWAT
Authors: Idrees Muhammad , Ahmad Shakil , Khan Muhammad Wasif , Dahri Zakir Hussain , Ahmad Khalil , Azmat Muhammad , Rana Irfan Ahmad
Published: October 2022

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably.
Variation Characteristics of Ecosystem Water Use Efficiency and Its Response to Human Activity and Climate Change in Inner Mongolia
Authors: Mei Li , Tong Siqin , Yin Shan , Bao Yuhai , Huang Xiaojun , Alateng Tuya
Published: October 2022

Water use efficiency (WUE) reflects the balance between carbon assimilation and water consumption in terrestrial ecosystems. Considering the fragile conditions of global water resources, the evaluation of regional WUE variation characteristics and response mechanisms is critical for promoting sustainable ecological development and water resource utilization. Based on gross primary productivity (GPP) and evapotranspiration (ET) datasets, combined with vegetation and meteorological data, this study examined the spatiotemporal variations, annual variation contribution rate, and driving mechanism of WUE in Inner Mongolia from 2001 to 2020. The main results are as follows: (1) The interannual and spatial variations of GPP, ET, and WUE all exhibited increasing trends, with WUE increasing in approximately 70% of the region and significantly increasing in 22.35% of pixels exhibiting a significant increase. Areas with the most significant increases were located in the Horqin sandy land and Mu Us sandy land. (2) The highest WUE values were observed in the summer season, followed by autumn and winter, and the lowest in spring. (3) Among all vegetation types, the typical steppe ecosystems contributed most to the interannual variability (IAV) of GPP, ET, and WUE, with values of 169.89%, 141.09%, and 193.42%, respectively. While the coniferous forest contributed least or negatively to GPP, ET, and WUE IAV, with values of −36.28%, 28.20%, and −32.86%, respectively. (4) The primary driver of WUE variation was found to be GPP, which contributed 59.36%, mainly in the central and western regions. The remaining 40.64% was attributable to ET, concentrated in the northeast region. (5) Human activities significantly affected WUE, with a contribution (about 53.52%) larger than that of climate change (nearly 46.48%). Increased precipitation improves vegetation WUE and is the most important climate factor influencing WUE variations. These findings will aid the formulation of vegetation protection and water resource management strategies in water-stressed areas.
Mass Balance Reconstruction for Laohugou Glacier No. 12 from 1980 to 2020, Western Qilian Mountains, China
Authors: Wu Jiake , Sun Weijun , Huai Baojuan , Ding Minghu , Wang Lei , Wang Yuzhe , Zhang Junlong , Du Wentao , Chen Jizu , Qin Xiang
Published: October 2022

A long-series mass balance (MB) of glaciers can be used to study glacier–climate relationships. Using a distributed simplified energy balance model (SEBM) and an enhanced temperature-index model (ETIM), the MB of Laohugou Glacier No. 12 (LHG12) was reconstructed from 1980 to 2020, driven by a calibrated ERA5 reanalysis dataset. The simulation of SEBM performs better than that of ETIM. The results showed that the annual MB of LGH12 is a fluctuating trend of declining from 1980 to 2020, with annual means of −0.39 ± 0.28 m w.e. a−1 and cumulative value of −16 ± 4 m w.e. During 1980–1990, the annual MB fluctuated in a small range, while after 1990, LHG12 accelerated melting owing to rising air temperature, with annual means of −0.48 m w.e. a−1, three times as large as that of 1980–1990. The largest mass loss occurred during 2001–2010 at an average rate of −0.57 m w.e. a−1. The average equilibrium line altitude (ELA) was 4976 m a.s.l., and since 1980, the ELA has been increasing at a rate of 37.5 m/10 a. LHG12 is most sensitive to air temperature, and the MB sensitivity reaches −0.51 m w.e. a−1 with air temperature increase of 1 °C. The sensitivity of MB to incoming shortwave radiation (+10%) simulated by SEBM is −0.30 m w.e. a−1, three times larger than that simulated by ETIM. This is mainly because the two models have different conditions for controlling melting. Melting is controlled only by air temperature for ETIM, while for SEBM, it is controlled by air temperature and incoming shortwave radiation.
Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis
Authors: Al-Kindi Khalifa M. , Janizadeh Saeid
Published: October 2022

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.
A Migratory Biomass Statistical Method Based on High-Resolution Fully Polarimetric Entomological Radar
Authors: Yu Teng , Li Muyang , Li Weidong , Zhang Tianran , Wang Rui , Hu Cheng
Published: October 2022

Entomological radar is a specially designed instrument that can measure the behavioral and biological characteristics of high-altitude migrating insects. Its application is of great significance for the monitoring, early warning, and control of agricultural pests. As an important component of the local migratory biomass, insects fly in the air during the day and night. The fully polarimetric entomological radar was carefully designed with all-day, all-weather, and multi-function measurement capabilities. The fully polarimetric entomological radar measures the mass of a single insect based on the radar cross-sectional (RCS) measurement and then calculates the biomass of migrating insects. Therefore, the measurement accuracy of the insect RCS is the key indicator affecting the accuracy of migratory biomass statistics. Due to the radar’s lack of in-beam angle measurement ability, the insect RCS is usually measured based on the assumption that the insect is on the beam center. Therefore, the measured RCS will be smaller than true value if the insect deviates from the beam center due to the gain curve of the antenna. This leads to measurement errors in regard to the insect mass and migratory biomass. In order to solve this problem, a biomass estimation method, reported in this paper, was designed under the assumption of a uniform distribution of migrating insects in the radar monitoring airspace. This method can estimate the individual RCS expectation of migrating insects through a statistical method without measuring the position of the insects in the beam and then obtain the migratory biomass. The effectiveness of the model and algorithm is verified by simulations and entomological radar field measurements.