bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of Mucilage from Opuntia ficus-indica in the Manufacture of Probiotic Cream Cheese
Authors: Rodrigues Pamela Dutra , Fernandes Isabela de Andrade Arruda , de Marins Annecler Rech , Feihrmann Andresa Carla , Gomes Raquel Guttierres
Published: October 2024

Cream cheese is a type of fresh cheese with a thin consistency with great potential for adding probiotics. However, artificial thickeners have been used in its production, decreasing consumer satisfaction. This study suggests natural mucilage, specifically from the Cactaceae Opuntia ficus-indica, as a replacement for artificial thickeners due to its thick gelatinous properties. This study evaluated different cream cheese formulations by adding varying concentrations of Opuntia ficus-indica mucilage and the probiotic Lactobacillus acidophilus (L. acidophilus). Four formulations were created: formulation C (control, without mucilage), F1 (containing 1 mL/kg mucilage), F2 (2 mL/kg), and F3 (3 mL/kg mucilage). The physicochemical characteristics (pH, 4.90–5.57; 0.15–0.20% acidity; 1.78–2.42% protein; 29.98–30.88% fat; 38.27–41.63% moisture; and 1.25–1.63% ash) and microbiological analysis met the quality standards required by Brazilian legislation, and the cream cheese showed probiotic potential, with L. acidophilus counts above 108 CFU/mL within four weeks of storage. Regarding sensory evaluation, the texture received one of the highest scores (7.89), followed by aroma (7.11). Therefore, the Cactaceae mucilage has proven to be a viable alternative to replace artificial thickeners in cream cheese, making it an excellent option for probiotic supplementation.
Maleic Anhydride-Modified Water Hyacinth for Adsorption of Methylene Blue and Methyl Violet
Authors: Shen Liya , Xu Jing , Wang Xinru , Liu Yuanli
Published: October 2024

Removal of toxic pollutants is of the greatest concerns facing wastewater treatment. In this study, a chemical modification method was used to prepare the maleic anhydride-modified water hyacinth (MA-EC) for the removal of methylene blue (MB) and methyl violet (MV) from water. The maleic anhydride-modified water hyacinth biosorbent was characterized and adsorption experiments were conducted. The prepared MA-EC demonstrated considerable adsorptive efficiency toward MV and MB. It was confirmed that the maximum adsorptive capacities were 1373.58 and 434.70 mg/g for MV and MB, respectively. The adsorptive data were also fitted using Langmuir and Freundlich isotherms, and the results showed that the Langmuir isotherm adsorption model could better describe the adsorptive process. Adsorption–desorption cycling experiments demonstrated that the MA-EC adsorbent had good reusability, with adsorptive capacities of 538.88 mg/g for MV and 215.56 mg/g for MB after four cycles of desorption–adsorption.
Numerical Study of the Combustion Process in the Vertical Heating Flue of Air Staging Coke Oven
Authors: Hu Xiaolei , Zhang Jiale , Yu Zihan , Liu Zhenzhen , Guo Jiayi , Xu Changhua
Published: October 2024

To investigate the combustion process and reduce Nitric Oxide (NO) emissions in the vertical heating flue of air-staged coke ovens, a three-dimensional computational fluid dynamics method was applied to simulate the combustion process. The model integrates the k-ε turbulence model with a multi-component transport combustion model. The impact of air staging on the flow field and NO emissions in the vertical fire chamber was assessed through comparative validation with experimental data. The impact of air staging on the flow field and NO emissions in the vertical fire chamber was assessed through comparative validation with experimental data. Based on this research, the effects of the excess air coefficient and air inlet distribution ratio on NO emission levels at the flue gas outlet were further investigated. Analysis of the flow field structure, temperature at the center cross-section, component concentration, and NO emission levels indicates that as the excess air coefficient increases, the NO emission levels at the flue gas outlet initially decrease and then increase, accompanied by corresponding changes in outlet temperature. At an air excess factor of 1.3 and an air inlet distribution ratio of 7:3, NO emission levels are at their lowest—53% lower than those in a conventional coke oven—and the temperature distribution in the riser channel is more uniform. These results provide a theoretical foundation for designing the air-staged coke oven standing fire channel structure.
Ultrasonic-Assisted Extraction and Antioxidant Evaluation of Resveratrol from Peanut Sprouts
Authors: Xu Xianmeng , Zhang Dandan , Liu Xiaohua , Zheng Rong , Jiang Tingqi
Published: October 2024

The orthogonal array design method was used to optimize ultrasonic-assisted extraction of resveratrol from peanut sprouts. The results showed that the highest extraction yield of resveratrol using ultrasonic-assisted extraction could be up to 1.1%. The optimal extraction conditions were liquid to solid ratio of 30:1 (mL/g) and ethanol concentration of 80% (v/v) as solvent for 40 min at the temperature of 70 °C. AB-8 macroporous adsorption resin was used to purify the crude extract and the resveratrol content increased to 47.5% after one treatment run. The optimal adsorption parameters were initial concentrations in the sample solution of 2 mg/mL, a pH of 5.0, a flow rate of 2 mL/min, and a temperature of 25 °C. The optimal desorption parameters were 60% ethanol and a flow rate of 1 mL/min. The chemical composition of the peanut sprout’s resveratrol sample was investigated via HPLC, and the predominant constituents were found to be protocatechuic acid, catechins, caffeic acid, epicatechuic acid, resveratrol, and rutin. The antioxidant activities of the resveratrol were measured via the following different analytical methods: reducing power, 2,2-diphenyl-1-picrylhdrazyl (DPPH), hydroxyl radical-scavenger activity, superoxide radical-scavenger activity, the β-carotene bleaching test, and the scavenging nitrite test. The results indicated that the resveratrol in peanut sprouts have significant antioxidant activities and can be used as a source of potential antioxidant. And peanut sprout’s resveratrol has the potential and valuable application to be used as a new type of resveratrol resource. The finding of this study can provide some theoretical reference for the comprehensive utilization of peanut resources in the development of antioxidant health foods.
Prediction of Heat Transfer in a Hybrid Solar–Thermal–Photovoltaic Heat Exchanger Using Computational Fluid Dynamics
Authors: Pérez Grajales Sandro Guadalupe , Hernández Ortíz Teresa , Martinez-Oropeza Rogelio , Torres Tabai , Adrián López-Pérez Luis , Delgado-Gonzaga Javier , Huicochea Armando , Juárez-Romero David
Published: October 2024

Solar energy is one of the main renewable energy resources due to its abundance. It can be used for two purposes, thermal or photovoltaic applications. However, when the resource obtained is mixed, it is called photovoltaic thermal hybrid, where the solar panels generate electricity and are provided with a heat exchanger to absorb energy through a water flow. This is one of the techniques used by the scientific community to reduce the excess temperature generated by solar radiation in the cells, improving the electrical efficiency of photovoltaic systems and obtaining fluid with higher temperature. In this work, the thermal behavior of a heat exchanger equipped with fins in its interior to increase the thermal efficiency of the system was analyzed using CFD (Computational Fluid Dynamics). The results showed that the average fluid outlet temperature was 75.31 °C, considering an incident irradiance of 1067 W/m2 and a fluid inlet temperature of 27 °C. The operating conditions were obtained from published experimental studies, achieving 97.7% similarity between the two. This was due to the boundary conditions of the heat flux (1067 W/m2) impinging directly on the coupled cells and the heat exchanger in a working area of 0.22 m2.
Phenolic Class Analysis in Honey: Comparison of Classical and Single UV Spectrum Methodologies
Authors: Paula Vanessa B. , Sousa-Dias Miguel L. , Seixas Natália L. , Combarros-Fuertes Patricia , Estevinho Letícia M. , Dias Luís G.
Published: October 2024

The analytical results from a study of 16 honey samples (extra white to dark honey color range) of phenolic compounds obtained using the single UV spectrum methodology and classical spectrophotometric methods (Folin–Ciocalteu and AlCl3 methods) are presented. The first method quantified all classes of phenolic compounds in honey’s SPE-C18 extract: the total hydroxybenzoic acid content (concentrations between 0.37 ± 0.05 and 4.46 ± 0.37 mg of gallic acid/g of honey), total hydroxycinnamic acid content (0.13 ± 0.03 and 2.76 ± 0.13 mg of ferulic acid/g of honey), and total flavonoid content (0.15 ± 0.03 and 1.63 ± 0.17 mg of quercetin/g of honey). The total phenolic contents were, on average, 1.86 ± 0.72 and 1.78 ± 0.79 times higher than the results obtained for raw honey and the SPE-C18 extract, respectively, using the classical Folin–Ciocalteu method. The total flavonoid contents, on average, were 6.02 ± 3.14 times larger and 0.66 ± 0.33 times smaller than the results obtained using the classical AlCl3 method for raw honey and SPE-C18 extract, respectively.
Conceptual Design and Economic Optimization of Different Valorization Routes for Orange Peel Waste: The Application of the Biorefinery Concept for an Integral Use of Raw Material
Authors: Arango-Manrique Sergio , Agudelo Patiño Tatiana , Matallana Pérez Luis Gerónimo , Ortiz-Sanchez Mariana , Cardona Alzate Carlos Ariel
Published: October 2024

Biorefineries are novel biotechnological routes designed to generate sustainable processes from renewable raw materials. The valorization of orange peel waste (OPW) provides high-value products based on their composition. The economic optimization of biorefineries through conceptual design and generation of superstructures based on the analysis of processing units is a topic of great interest. This work aimed to obtain the most profitable biorefinery through economic optimization strategies based on high-value-added products from OPW. Two stages were considered: The first stage consisted of the conceptual design of multiple OPW processing units (production of essential oil, mucic acid, phenolic compounds, biogas, among others). An OPW flow rate of 140 kg/h was selected as the base case. From the stand-alone units, a biorefinery superstructure (second stage) was designed. Finally, the units with the best mass and energy results were selected in order to maximize the net present value (NPV) and obtain an optimal biorefinery configuration. The results evidenced that the production of essential oil and biogas presented the best yields (2.61 mL and 0.028 m3 per kg OPW, respectively). This biorefinery configuration obtained an NPV of −7.7 mUSD from the base case. Through the evaluation of the different superstructure configurations, the combined production of essential oil, biogas, and mucic acid and a scale-up of over 22 times the base case generated the minimum processing scale. Under a Colombian context, the implementation of the biorefineries analyzed are promising since the minimum processing scale contemplated only 8.8% of the OPW production. Efforts to increase yields and decrease capital and operating expenses while keeping environmental impacts low should be pursued.
Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes
Authors: Roblek Matjaž , Kern Tomaž , Andrašec Eva Krhač , Brezavšček Alenka
Published: October 2024

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.
Development of Fruit-Based Carbohydrate Gel for Endurance Athletes
Authors: Assis Renata , Valentim Ashley , Barbosa Isabele , Silva Julyana , Aquino Andrea , Viana José , Rabelo Claisa , Sousa Paulo , Maia Carla , Fernandes Victor , Vieira Ícaro , Alves Carlucio
Published: October 2024

The aim of this study was to produce a carbohydrate gel based on genipap and banana and analyze its physico-chemical, rheological, and sensory quality, as well as its proximate composition and antioxidant activity. Three gel samples were formulated containing different concentrations of genipap and clarified banana juice. The formulated samples followed the minimum parameters required and were subjected to analyses of their pH, soluble solids, titratable acidity, moisture, ash, lipids, proteins, glucose, fructose, sucrose, polyphenols, antioxidant activity, and rheology. Commercial carbohydrate gel was used as a control sample. It can be concluded that the gel formulations were formulated following the minimum parameters required, with a moderate sensory acceptance. The physico-chemical parameters and proximate composition the developed gels were similar to the commercial gel, while their glucose, sucrose, fructose, polyphenol, and antioxidant activity contents were higher and their rheological properties were within the expected range for this category of commonly marketed products. In the two blocks of analysis mentioned above, data variability was mostly explained by PC1–PC3 at almost 100%. Rheologically, the commercial gel is considered to be a Newtonian fluid, and the developed formulations can be considered as pseudoplastic fluids due to the insoluble solids still present.
Continuous Biological Ex Situ Methanation of CO2 and H2 in a Novel Inverse Membrane Reactor (IMR)
Authors: Haitz Fabian , Jochum Oliver , Lasota Agnieszka , Friedrich André , Bieri Markus , Stalder Marc , Schaub Martin , Hochberg Ulrich , Zell Christiane
Published: October 2024

A promising approach for carbon dioxide (CO2) valorization and storing excess electricity is the biological methanation of hydrogen and carbon dioxide to methane. The primary challenge here is to supply sufficient quantities of dissolved hydrogen. The newly developed Inverse Membrane Reactor (IMR) allows for the spatial separation of the required reactant gases, hydrogen (H2) and carbon dioxide (CO2), and the degassing area for methane (CH4) output through commercially available ultrafiltration membranes, enabling a reactor design as a closed circuit for continuous methane production. In addition, the Inverse Membrane Reactor (IMR) facilitates the utilization of hydraulic pressure to enhance hydrogen (H2) input. One of the process’s advantages is the potential to utilize both carbon dioxide (CO2) from conventional biogas and CO2-rich industrial waste gas streams. An outstanding result from investigating the IMR revealed that, employing the membrane gassing concept, methane concentrations of over 90 vol.% could be consistently achieved through flexible gas input over a one-year test series. Following startup, only three supplemental nutrient additions were required in addition to hydrogen (H2) and carbon dioxide (CO2), which served as energy and carbon sources, respectively. The maximum achieved methane formation rate specific to membrane area was 87.7 LN of methane per m2 of membrane area per day at a product gas composition of 94 vol.% methane, 2 vol.% H2, and 4 vol.% CO2.
Prediction of Capillary Pressure Curves Based on Particle Size Using Machine Learning
Authors: Qi Xinghua , Wei Yuxuan , Wang Shimao , Wang Zhuwen , Zhou Mingyu
Published: October 2024

Capillary pressure curves are usually obtained through mercury injection experiments, which are mainly used to characterize pore structures. However, mercury injection experiments have many limitations, such as operation danger, a long experiment period, and great damage to the sample. Therefore, researchers have tried to predict capillary pressure data based on NMR data, but NMR data are expensive and unstable to obtain. This study aims to accurately predict capillary pressure curves. Based on rock particle size data, various machine learning methods, such as traditional machine learning and artificial neural networks, are used to build prediction models and predict different types of capillary pressure curves, aiming at studying the best prediction algorithm. In addition, through adjusting the amount of particle size characteristic data, the best amount of particle size characteristic data is explored. The results show that three correlation coefficients of the four optimal algorithms can reach more than 0.92, and the best performance is obtained using the Levenberg–Marquardt method. The prediction performance of this algorithm is excellent, with the three correlation coefficients being all higher than 0.96 and the root mean square error being only 5.866. When partial particle size characteristics are selected, the training performance is gradually improved with an increase in the amount of feature data, but it is far less than the performance of using all the features. When the interpolation increases the particle size characteristics, the best performance is achieved when the feature data volume is 50 groups and the root mean square error is the smallest, but the Kendall correlation coefficient decreases. This study provides a new way to obtain capillary pressure data accurately.
Is Citation Count a Legitimate Indicator of Scientific Impact? A Case Study of Upper (1974) “The Unsuccessful Self-Treatment of a Case of Writer’s Block” and Its Derivatives
Authors: Yeung Andy Wai Kan
Published: October 2024

The work by Upper (1974) was a blank paper. Multiple replication studies were published. This work examined the number of citations received by these papers, and manually checked the citing papers to determine why they made the citations. The Dimensions literature database was queried with the search string: (unsuccessful treatment writer’s block). The search yielded 14 articles, two of which were irrelevant and excluded. The 12 papers remained after screening included the original study by Upper (1974), nine replication studies, one review, and one meta-analysis. The original work received 43 citations, but related works had fewer than 10 citations each. One fourth of citations of Upper (1974) were being satiric on “nothing” or “precise” from papers dealing with unrelated concepts, and five citations were deemed erroneous/digressed. One citation was made to acknowledge the reviewer’s comments to Upper (1974), which did not involve Upper’s own ideas. This work exposed a scenario where there were limitations of using citation count as the only metric to gauge scientific impact of journal articles.
Temporal Evolution of Bradford Curves in Academic Library Contexts
Authors: Xue Haobai
Published: October 2024

Bradford’s law of bibliographic scattering is a fundamental principle in bibliometrics, offering valuable guidance for academic libraries in literature search and procurement. However, Bradford curves can exhibit various shapes over time, and predicting these shapes remains a challenge due to a lack of causal explanation. This paper attributes the deviations from the theoretical J-shape to integer constraints on the number of journals and articles, extending Leimkuhler’s function to encompass highly productive core journals, where the theoretical journal number falls below one. Using the Simon–Yule model, key parameters of the extended formulas are identified and analyzed. The paper explains the reasons for the Groos droop and examines the critical points for shape changes. The proposed formulas are validated with empirical data from the literature, demonstrating that this method can effectively predict the evolution of Bradford curves, providing academic libraries with a valuable tool for evaluating journal coverage, optimizing resource allocation, and refining Collection Development Policies (CDP).
Christianity and Boxing: A Review Essay and Position Statement
Authors: Weir J. Stuart
Published: October 2024

The sport of boxing has existed since ancient times, with fights sometimes ending in death. In the modern world, with our greater medical knowledge, the risks of brain and other life-changing injuries have called into question whether boxing has a place in contemporary society. In fact, professional boxing has been banned in some European countries. There are also questions about whether it is appropriate to encourage spectators to watch two individuals attempting to punch each other into oblivion and about the effect this can have on those watching. The fact that, in recent years, we have seen the development of women’s boxing both in the Olympics and at the professional level raises further questions. The particular emphasis of this paper is to note the number of committed Christians involved in boxing from grassroots to the elite level and to consider whether involvement in such a violent sport can be reconciled with the teaching of Jesus Christ. The main source of data is published biographies and autobiographies of Christian boxers and some interviews. There is very little published material at an academic level on the topic that we are considering. The conclusion is that despite the sincerity of the Christian boxers involved in the sport, it is difficult to justify boxing as a sport in relation to Christian teaching.
Equivalence—A Useful Yet Complex Concept in Natural Resource Science
Authors: Ridoutt Bradley
Published: October 2024

The concept of equivalence is widely employed to aggregate different types of resource depletion or emissions. The practice offers convenience for reporting and can assist policymaking. However, equivalence is typically established using a model based only on selected criteria. If alternative criteria are used, the relative importance of different types of resource depletion or emissions can change. This communication addresses the subject of greenhouse gas (GHG) emissions and carbon dioxide equivalence (CO2e). Using the Australian beef cattle and sheep meat sectors as a case study, radiative forcing (RF) footprints were quantified, and a method is presented to express these as CO2e emissions rather than the units W/m2. RF footprints are an alternative way of aggregating GHG emissions. They incorporate RF from current-year emissions and RF from historical emissions that remain in the atmosphere, avoiding the need to choose an arbitrary time horizon. There is simplicity and familiarity associated with the CO2e unit. However, it is concluded that whenever GHG emissions are reported as CO2e emissions, there is a need for transparency about the basis for equivalence, justification of the approach, as well interpretation of the results, and potential implications of selecting other bases of equivalency.
Role of Lake Morphometric and Environmental Drivers of Ice Cover Formation and Occurrence on Temperate Lakes: A Case Study from the Eastern Baltic Lakeland, Poland
Authors: Ptak Mariusz , Amnuaylojaroen Teerachai , Huang Wenfeng , Wang Li , Sojka Mariusz
Published: October 2024

The presence of ice cover on temperate lakes is a crucial factor in determining the functioning of these ecosystems. The isolation of water from atmospheric influences significantly alters physical, chemical, and biological processes, and the intensity of this impact depends on the duration of the ice cover. This study analyzed the basic parameters of ice cover on several dozen lakes in Northeastern Poland. The aim of this study is to investigate the influence of morphometric parameters, alongside environmental factors, on the variation of ice cover characteristics in lakes located within the Eastern Baltic Lakeland. Characterization of ice conditions in the analyzed lakes was based on basic statistics such as minimum and maximum values, mean, standard deviation, coefficients of variation, skewness, and kurtosis. Given that the dataset contains variables describing ice phenomena in the studied lakes and data describing location, morphometric parameters, and land cover directly adjacent to the lake (treated as independent variables), a method of Spearman’s rank correlations and constrained ordination method were decided upon. Despite the relatively small study area, significant variability was observed, with average differences as follows: 26 days for the onset of ice cover, 17 days for the end date, 15 cm for ice thickness, and a 30-day difference in the average duration of ice cover. Key factors included parameters such as lake volume, average depth, and land use (urbanized and agricultural areas). Understanding parameters such as the onset and end of ice cover is essential for lake ecosystems, both from an ecological and economic perspective. This knowledge is crucial for interpreting the behavior of living organisms, water quality, and economic considerations.
Managing Financial Risks of Global Companies Through Corporate Social Responsibility: The Specifics of Sustainable Employment in Developed and Developing Countries
Authors: Tursunov Bobir O. , Kulueva Chinara R. , Abdurakhmanov Olim K. , Shabaltina Larisa V. , Bezdenezhnykh Tatyana I.
Published: October 2024

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune “Global 500” rankings for 2021–2023. As a result, the consequences of global companies’ CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries.
Cloud Detection Using a UNet3+ Model with a Hybrid Swin Transformer and EfficientNet (UNet3+STE) for Very-High-Resolution Satellite Imagery
Authors: Choi Jaewan , Seo Doochun , Jung Jinha , Han Youkyung , Oh Jaehong , Lee Changno
Published: October 2024

It is necessary to extract and recognize the cloud regions presented in imagery to generate satellite imagery as analysis-ready data (ARD). In this manuscript, we proposed a new deep learning model to detect cloud areas in very-high-resolution (VHR) satellite imagery by fusing two deep learning architectures. The proposed UNet3+ model with a hybrid Swin Transformer and EfficientNet (UNet3+STE) was based on the structure of UNet3+, with the encoder sequentially combining EfficientNet based on mobile inverted bottleneck convolution (MBConv) and the Swin Transformer. By sequentially utilizing convolutional neural networks (CNNs) and transformer layers, the proposed algorithm aimed to extract the local and global information of cloud regions effectively. In addition, the decoder used MBConv to restore the spatial information of the feature map extracted by the encoder and adopted the deep supervision strategy of UNet3+ to enhance the model’s performance. The proposed model was trained using the open dataset derived from KOMPSAT-3 and 3A satellite imagery and conducted a comparative evaluation with the state-of-the-art (SOTA) methods on fourteen test datasets at the product level. The experimental results confirmed that the proposed UNet3+STE model outperformed the SOTA methods and demonstrated the most stable precision, recall, and F1 score values with fewer parameters and lower complexity.
Stability Assessment of the Maltravieso Cave (Caceres, Spain) Through Engineering Rock Mass Classification, Empirical, Numerical and Remote Techniques
Authors: Benrabah Abdelmadjid , Senent Domínguez Salvador , Collado Giraldo Hipolito , Chaves Rodríguez Celia , Jorda Bordehore Luis
Published: October 2024

Caves have long fascinated humanity, serving as shelters, canvases for artistic expression and now significant attractions in the realm of tourism. Among these remarkable geological formations, the Maltravieso cave in Extremadura, Spain, stands out for its rich archaeological and paleontological heritage, particularly its collection of Paleolithic rock art. Despite its cultural significance, there is a notable dearth of studies addressing the stability of the cave from an engineering perspective. This article presents a pioneering study aimed at assessing the stability of the Maltravieso cave through a multidisciplinary approach: using empirical geomechanical classifications such as the Q Index, Rock Mass Rating (RMR) and the recently formulated Cave Geomechanical Index (CGI), alongside other techniques like Structure from Motion (SfM), 2D numerical modeling and 3D wedge analysis. This research aims to fill the gap in our opinion of cave stability assessment. By combining field data collection with sophisticated analysis methods, this study seeks to provide valuable insights into the geomechanical properties of the Maltravieso cave and validate a simple yet effective methodology for evaluating the stability of natural caves. This work not only contributes to the body of knowledge regarding cave geomechanics but also underscores the importance of preserving these invaluable cultural and geological treasures for future generations.
Spectral Characteristics and Identification of Degraded Alpine Meadow in Qinghai–Tibetan Plateau Based on Hyperspectral Data
Authors: Qian Dawen , Li Qian , Fan Bo , Zhou Huakun , Du Yangong , Guo Xiaowei
Published: October 2024

Grassland degradation poses a significant challenge to achieving the Sustainable Development Goals (SDGs) on the Qinghai–Tibetan Plateau (QTP). Effective monitoring of grassland degradation is essential for ecological restoration. Hyperspectral technology offers efficient and accurate identification of degradation. However, the influence of observation time, data analysis methods and classification techniques on the accuracy of identifying alpine grasslands remains unclear. In this study, the spectral reflectance of degraded alpine meadow, alpine meadow, alpine shrub and Tibetan barley was measured from May to September 2023 using a ground spectrometer in the northeastern QTP. First-order derivatives (FDR) and continuum removal were applied to the spectra, and characteristic parameters and vegetation indices were calculated. Support vector machine (SVM), random forest (RF), artificial neural network (ANN) and decision tree (DT) were then used to compare the classification accuracy between different months, transformation methods and characteristic parameters. The results showed that the spectral reflectance peaked in July, with significant differences in the near infrared (NIR) bands between alpine meadow and degraded alpine meadow. Alpine shrub and Tibetan barley showed greater differences in reflectance compared to other vegetation types, especially in the NIR bands. Data transformations improved reflectance and absorption characteristics in the NIR and visible bands. Indices such as DVI, RVI and NDGI effectively differentiated vegetation types. Optimal accuracy for the identification of degraded alpine meadow in July was achieved using FDR transformations and ANN or SVM for classification. This study provides methodological insights for monitoring grassland degradation on the QTP.
The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision
Authors: Huang Qiuying , Wang Xiaoming , Li Haobo , Zhang Jinglei , Han Zhaowei , Liu Dingyi , Li Yaping , Zhang Hongxin
Published: October 2024

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction.
Three-Dimensional Geometric-Physical Modeling of an Environment with an In-House-Developed Multi-Sensor Robotic System
Authors: Zhang Su , Yu Minglang , Chen Haoyu , Zhang Minchao , Tan Kai , Chen Xufeng , Wang Haipeng , Xu Feng
Published: October 2024

Environment 3D modeling is critical for the development of future intelligent unmanned systems. This paper proposes a multi-sensor robotic system for environmental geometric-physical modeling and the corresponding data processing methods. The system is primarily equipped with a millimeter-wave cascaded radar and a multispectral camera to acquire the electromagnetic characteristics and material categories of the target environment and simultaneously employs light detection and ranging (LiDAR) and an optical camera to achieve a three-dimensional spatial reconstruction of the environment. Specifically, the millimeter-wave radar sensor adopts a multiple input multiple output (MIMO) array and obtains 3D synthetic aperture radar images through 1D mechanical scanning perpendicular to the array, thereby capturing the electromagnetic properties of the environment. The multispectral camera, equipped with nine channels, provides rich spectral information for material identification and clustering. Additionally, LiDAR is used to obtain a 3D point cloud, combined with the RGB images captured by the optical camera, enabling the construction of a three-dimensional geometric model. By fusing the data from four sensors, a comprehensive geometric-physical model of the environment can be constructed. Experiments conducted in indoor environments demonstrated excellent spatial-geometric-physical reconstruction results. This system can play an important role in various applications, such as environment modeling and planning.
Modeling of Solar Radiation Pressure for BDS-3 MEO Satellites with Inter-Satellite Link Measurements
Authors: Lv Yifei , Liu Zihao , Jiang Rui , Xie Xin
Published: October 2024

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. Since the BDS-3’s inter-satellite link (ISL) can enhance the orbit estimation of BDS-3 satellites, the aim of this study is to establish an a priori SRP model for the satellite body using 281-day ISL observations to reduce the systematic errors in the final orbits. The adjustable box wind (ABW) model is employed to refine the optical parameters for the satellite buses. The self-shadow effect caused by the search and rescue (SAR) antenna is considered. Satellite laser ranging (SLR), day-boundary discontinuity (DBD), and overlapping Allan deviation (OADEV) are utilized as indicators to assess the performance of the a priori model. With the a priori model developed by both ISL and ground observation, the slopes of SLR residual for the China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM) satellites decrease from −0.097 cm/deg and 0.067 cm/deg to −0.004 cm/deg and −0.009 cm/deg, respectively. The standard deviation decreased by 21.8% and 26.6%, respectively. There are slight enhancements in the average values of DBD and OADEV, and a reduced β-dependent variation is observed in the OADEV of the corresponding clock offset. We also found that considering the SAR antenna only slightly improves the orbit accuracy. These results demonstrate that an a priori model established for the BDS-3 MEO satellite body can reduce the systematic errors in orbits, and the parameters estimated using both ISL and ground observation are superior to those estimated using only ground observation.
Estimation of Glacier Outline and Volume Changes in the Vilcanota Range Snow-Capped Mountains, Peru, Using Temporal Series of Landsat and a Combination of Satellite Radar and Aerial LIDAR Images
Authors: Montoya-Jara Nilton , Loayza Hildo , Gutiérrez-Rosales Raymundo Oscar , Bueno Marcelo , Quiroz Roberto
Published: October 2024

The Vilcanota is the second-largest snow-capped mountain range in Peru, featuring 380 individual glaciers, each with its own unique characteristics that must be studied independently. However, few studies have been conducted in the Vilcanota range to monitor and track the area and volume changes of the Suyuparina and Quisoquipina glaciers. Notably, there are only a few studies that have approached this issue using LIDAR technology. Our methodology is based on a combination of optical, radar and LIDAR data sources, which allowed for constructing coherent temporal series for the both the perimeter and volume changes of the Suyuparina and Quisoquipina glaciers while accounting for the uncertainty in the perimeter detection procedure. Our results indicated that, from 1990 to 2013, there was a reduction in snow cover of 12,694.35 m2 per year for Quisoquipina and 16,599.2 m2 per year for Suyuparina. This represents a loss of 12.18% for Quisoquipina and 22.45% for Suyuparina. From 2006 to 2013, the volume of the Quisoquipina glacier decreased from 11.73 km3 in 2006 to 11.04 km3 in 2010, while the Suyuparina glacier decreased from 6.26 km3 to 5.93 km3. Likewise, when analyzing the correlation between glacier area and precipitation, a moderate inverse correlation (R = −0.52, p < 0.05) was found for Quisoquipina. In contrast, the correlation for Suyuparina was low and nonsignificant, showing inconsistency in the effect of precipitation. Additionally, the correlation between the snow cover area and the annual mean air temperature (R = −0.34, p > 0.05) and annual minimum air temperature (R = −0.36, p > 0.05) was low, inverse, and not significant for Quisoquipina. Meanwhile, snow cover on Suyuparina had a low nonsignificant correlation (R = −0.31, p > 0.05) with the annual maximum air temperature, indicating a minimal influence of the measured climatic variables near this glacier on its retreat. In general, it was possible to establish a reduction in both the area and volume of the Suyuparina and Quisoquipina glaciers based on freely accessible remote sensing data.
From Data to Decision: Interpretable Machine Learning for Predicting Flood Susceptibility in Gdańsk, Poland
Authors: Gulshad Khansa , Yaseen Andaleeb , Szydłowski Michał
Published: October 2024

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models’ performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability.
Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques
Authors: Useni Sikuzani Yannick , Mpanda Mukenza Médard , Kikuni Tchowa John , Kabamb Kanyimb Delphin , Malaisse François , Bogaert Jan
Published: October 2024

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas.