esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The contributions of this work are summarized as follows:  
* A generalized linguistic rule-based methodology (LR-TED) that is scalable to other domains to automatically extract eyewitness reports without relying on domain experts or platform-specific metadata, such as Twitter. * Crafting and evaluating grammar rules to extract linguistic features from the textual content of a message and using them for annotating eyewitness messages. * Using disaster-related linguistic features to train and evaluate several machine learning and deep learning models to classify the real-world Twitter dataset into \"Eyewitness\", \"Non-Eyewitness\", and 'Unknown\" classes. * Comparative analysis of the proposed methodology with a baseline manually crafted dictionary-based approach using several evaluation metrics such as precision, recall, and F-score. The rest of the paper is structured as follows--Section 2 reviews related work to summarize previous research on disaster event classification. Section 3 describes the methodology adopted for fine-tuning grammar rules to extract eyewitness features automatically. Evaluation and comparison results are discussed in Section 4. The concluding discussion and future work are presented in Section 5.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.6 SportE2  
Similarly, energy management consumption in buildings for sport facilities through ICT solutions has been examined in the \"SportE2\" project. This project has been run by participants in four countries. Initially, the project aimed at studying how energy is used in sport facilities through smart metering and then to provide with the necessary interfaces that would enable control of heating, cooling and lighting. For this purpose, a sensor network was deployed along with various communication solutions. The smart meter communication technologies varied among the different sites and included PLC and wireless smart metering solutions, like ZigBee and GSM. For the realisation of the sensor network, standard communication protocols have been used, including Ethernet and ZigBee [86].
###### Abstract  
A simple and accurate evaluation method of broken rock zone thickness (\(BRZT\)), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (\(BRZT\)), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (\(GP\)) and particle swarm optimization algorithm (PSO)-\(GP\) method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (\(ED\)), drift span (\(DS\)), surrounding rock mass strength (RMS) and joint index (\(JI\)) were selected to train the intelligence model, while broken rock zone thickness (\(BRZT\)) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (\(VAF\)), determination coefficient (\(R^{2}\)) and root mean squared error (\(RMSE\)) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (\(BRZT\)). In addition, the sensitive effect of collected variables on broken rock zone thickness (\(BRZT\)) can be listed as \(JI\), \(ED\), \(DS\) and RMS, and \(JI\) was found to be the most sensitive factor.  
broken rock zone (BRZ); k-fold cross-validation; Gaussian process (\(GP\)); particle swarm optimization (PSO); sensitivity analysis +
Footnote †: journal: Applied Sciences
## 5 Natural Gas Exploration Potential  
### Hydrocarbon Generation  
The Permian Fengcheng Formation source rocks in the Junggar Basin have a high abundance of organic matter, suitable organic matter types, and moderate organic matter maturity evolution. Based on the study of geochemical characteristics, distribution, and gas generation potential, it could be concluded that the Fengcheng source rock can generate medium to large-scale natural gas reservoirs in the depths of more than 6500 m.  
The burial depth of the Fengcheng source rocks in the Mahu Sag is generally less than 6500 m, and several decades' hydrocarbon explorations show that the resources are mainly oil and less natural gas. Despite the number of exploration wells drilled in the sag for shale oil exploration, no large-scale natural gas reservoirs have been found.  
In the Penyijingxi and Shawan Sags, the burial depth of the Fengcheng Formation source rocks is at depth between 8500 m and 10,000 m, respectively. As the burial depth is far greater than 6500 m, it could be concluded that the Fengcheng source rocks in the Penyijingxi and Shawan Sags have great potential of natural gas generation. Recently, natural gas reservoirs have been found around the Penyijingxi and Shawan Sags, and several exploration wells have achieved the industrial gas flow standards in their production. In summary, the oil and natural gas exploration status is consistent with the research on the natural gas generation potential of Fengcheng Formation source rocks.  
Furthermore, in addition to the Fengcheng Formation source rocks, the burial depth of Jurassic source rocks in the southern margin of the Junggar Basin is over 10,000 m. And the Jurassic coal-bearing source rocks are widely distributed, with high organic matter abundance, mainly Type-II and Type-III organic matters, and high natural gas generation potential [28; 29].  
In summary, both the Permian source rocks in the Penyijingxi and Shawan Sags and the Jurassic source rocks in the southern margin of the Junggar Basin have a relatively higher potential of natural gas generation.
# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
###### Abstract  
Pathologies in concrete structures, such as cracks, splintering, efflorescence, corrosion spots, and exposed steel bars, can be visually evidenced on the concrete surface. This paper proposes a method for automatically detecting these pathologies from images of the concrete structure. The proposed method uses deep neural networks to detect pathologies in these images. This method results in time savings and error reduction. The paper presents results in detecting the pathologies from wide-angle images containing the overall structure and also for the specific pathology identification task for cropped images of the region of the pathology. Identifying pathologies in cropped images, the classification task could be performed with 99.4% accuracy using cross-validation and classifying cracks. Wide images containing no, one, or several pathologies in the same image, the case of pathology detection, could be analyzed with the YOLO network to identify five pathology classes. The results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion. Pathology identification in concrete photos can be optimized using deep learning.  
concrete pathologies; deep learning; classification; detection Article
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 6 Discussion and Limitations
When a user selects a recommended node displayed in the system as the hit, the system automatically constructs the next process node based on the user's selection, eliminating the need for manualconstruction. This demonstrates the effectiveness of the proposed method in supporting practical application scenarios. Our conclusion is that in the new era of digitization and informatization, efficient and accurate process modeling technology will become the core value of BPM applications. By extracting the edge-disjoint paths of the process through the EPE and further mining the behavioral knowledge of the process using CPSPan, recommending process nodes based on their process-matching, confidence, lift, and support degrees can effectively promote efficient and accurate modeling. However, despite our efforts, our technology still has certain limitations:  
1. Our approach recommends the next best node, which provides the greatest flexibility for the modeler to choose the next process activity. However, recommending process segments or complete process models can in some way facilitate the modeler to model more easily and quickly. 2. Our method is based on the complex structures of process library models for process recommendation. Although event logs do not contain information such as complex structures, event logs contain a large amount of abstract information such as the frequency of process branch directions, which can also serve as a powerful basis for process recommendation. 3. From a business process optimization perspective, our approach meets the time requirements and provides highly accurate recommended results. However, it only focuses on the next best node in the business process modeling design, taking into account both the process model and the business requirements, without considering the reliability of the services/components involved in each process node during the modeling process. For instance, we did not consider factors such as process execution time, task execution time, the number of exception events, timeouts, failures, and retries associated with each service/component. However, as this study aimed to address the problem of accuracy in short-process models, there are few business process datasets that contain both short-process models and reliability metrics in the current business process domain.
## 1 Introduction
Ground-penetrating radar (GPR) is another nondestructive testing method used to complement deflectometric testing by measuring the thickness of pavement layers and assessing the condition of the subgrade, e.g., voids, defects, settlement, and moisture ingress into pavement layers [18; 23; 24]. Advanced methods for the nondestructive detection of the depth of origin of cracks using GPR have also been proposed in the literature [6; 25]. To a lesser extent, the use of other nondestructive testing methods, such as ultrasonic testing and seismic survey methods, has also been reported [1]. Despite the challenges mentioned above, NDT methods for assessing the condition of concrete pavements are not standardized for three main reasons. The first reason is that the main focus of road authorities is on asphalt pavements, which make up the majority of the road network, while concrete pavements may account for only a small percentage in different countries. Secondly, the large number of different NDT systems makes it difficult to standardize the test methods used in practice. Thirdly, the evaluation of concrete pavements is often neglected in pavement management due to their high material strength, which means that these pavements may not be structurally monitored or maintained on a regular basis [26]. This, in turn, can lead to a lack of performance data over the life of the pavement, which hinders the development of robust management strategies. Therefore, many researchers involved in field pavement evaluation are forced to resort to case-based methods when inspecting concrete pavements, although there is a consensus that the FWD is the most commonly used method. This is also confirmed by many reports from the USA where, unlike other countries, the proportion of concrete pavements is significant. Therefore, the use of the FWD is recommended because of its versatility in assessing deflection changes over the length of the investigated areas; thus, many authorities worldwide are familiar with this system, and the data collected are accurate overall [4,5]. With this background, the authors present a methodological framework developed ad hoc for the condition assessment of a considerable number of toll stations on a highly trafficked urban highway with a JPCP section. A trial campaign was organized that included a detailed visual inspection of the study area, deflectometric testing with an FWD, and core drilling to assess the thickness of the concrete slab and support the interpretation of the FWD data. The aim of this campaign was to assess the condition of individual pavement slabs or lanes and to assist relevant decision-makers in the condition mapping and strategic management of these pavements. The main issues in this particular case of the study were the age of the pavements at the time of testing, which was more than fifteen years, and, most importantly, the lack of previous monitoring data that could be used as a reference for assessing the evolution of pavement performance over time.
## 2 Materials and Methods
### Biome-BGC Model  
The Biome-BGC model is a process-based ecosystem model that operates using a daily time step that simulates the dynamics of carbon, water, and nitrogen in a specific terrestrial ecosystem [24]. The carbon dynamics simulated by Biome-BGC includes net primary productivity (NPP), gross primary production (GPP), net ecosystem productivity (NEP), and net ecosystem exchange (NEE). Farquhar biochemical model [25] was adopted by Biome-BGC model to estimate GPP. NPP is the residue of GPP from plant growth and maintenance respiration as shown in Equation (1) [26]. NEP equals to NPP minus soil heterotrophic respiration [26], according to Equation (2).  
\[\mathrm{NPP}=\mathrm{GPP}-\mathrm{gr}-\mathrm{mr} \tag{1}\]  
where NPP is the net primary productivity, GPP is the gross primary production, gr is the plant growth, and mr is maintenance respiration.  
\[\mathrm{NEP}=\mathrm{NPP}-\mathrm{hr} \tag{2}\]  
where NEP indicates net ecosystem productivity and hr indicates heterotrophic respiration.  
The model has been successfully applied and validated over a number of ecosystems, spatial-temporal scales, and climate regimes, including various forest ecosystems from different continents [27; 28; 29; 21]. The model has been evaluated via sensitivity analyses as well as validations that compared with field measured evapotranspiration (ET), primary productivities, and carbon  
Figure 1: The study area and the distribution of major land cover types in Central Asia.  
dynamics at several sites. The model, its physiological and ecological parameters, and the validation results have been described extensively in previous studies [14; 21; 22]. The sand dunes in the desert and built-up areas were excluded from the simulation in this study because of their extremely low biological activities.
## 3 Results and Discussion
### Optimization of Experimental Conditions and Validation of the Models  
The optimization of the deshelled _Carica papaya_ seeds dosage, pH and initial turbidity for turbidity removal and coagulation activity was performed using the desirability function. The best combination found was found using deshelled _Carica papaya_ seeds dosage of 196 mg/L at pH 4 with initial turbidity of synthetic kaolin water of 500 NTU. Under this condition, the turbidity removal and coagulation activity predicted by the adjusted regression models were 93.0% and 85.0%, respectively, with a desirability of 0.9596. The results from the model were compared to those obtained through experimentation, and the relative error was determined. A result of 88.0% turbidity removal and 83.0% coagulation activity were recorded against the predicted optimum responses. It can be evidenced that the mathematical models are able to satisfactorily predict the turbidity removal, and coagulation activity with the relative error between the predicted values and the values obtained in the experiment was \(\pm\)5%. This observation validates and confirms the robustness of the models that have been generated.  
In comparison to the tann extract bio-coagulant, the required optimum dosage was low; 50 mg/L for the diethanolamine (DEA) modification and 25 mg/L for the ethanolamine (ETH) modification, resulted to the \(>\)90% turbidity removal [12]. Higher dosage required by _Carica papaya_ seed is probably due to the less extracted active coagulant agent. As mentioned earlier, this bio-coagulant was extracted chemical-free, only using the distilled water. Ability of the distilled water to extract active coagulant agent from deshelled _Carica papaya_ seeds may not be as good as other solvents or chemical modifications but sufficient in resulting in high turbidity removal and coagulation activity exceeded 90%. In general, this study contributes significantly on the encouragement of the use of natural resources for the removal of polluted waters [45; 46; 47; 48; 49; 50; 51; 52].
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
###### Abstract  
The world's high-resolution images are supplied by a radar system named Synthetic Aperture Radar (SAR). Semantic SAR image segmentation proposes a computer-based solution to make segmentation tasks easier. When conducting scientific research, accessing freely available datasets and images with low noise levels is rare. However, SAR images can be accessed for free. We propose a novel process for labeling Sentinel-1 SAR radar images, which the European Space Agency (ESA) provides free of charge. This process involves denoising the images and using an automatically created dataset with pioneering deep neural networks to augment the results of the semantic segmentation task. In order to exhibit the power of our denoising process, we match the results of our newly created dataset with speckled noise and noise-free versions. Thus, we attained a mean intersection over union (mIoU) of 70.60% and overall pixel accuracy (PA) of 92.23 with the HRNet model. These deep learning segmentation methods were also assessed with the McNemar test. Our experiments on the newly created Sentinel-1 dataset establish that combining our pipeline with deep neural networks results in recognizable improvements in challenging semantic segmentation accuracy and mIoU values.  
SAR; SAR segmentation; deep learning; semantic segmentation; Sentinel-1
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 2 Materials and Methods
### Overall Architecture  
Figure 1 illustrates the overall two-stage architecture of the proposed origin traceability system for Chinese mitten crabs. The system consists of two main modules. (1) Object Detection Stage: The input image is first passed into the object detection module, MC-RTLNet, which is built upon an improved YOLOv10n architecture. It integrates ODConv, SlimNeck, and LSCD Head. This module is designed to accurately detect the presence and locations of Chinese mitten crabs in complex aquaculture environments. The output of this stage is a set of cropped image regions, each containing a single crab. (2) Classification Stage: The cropped crab images are then fed into the classification module. This module employs a customized IGINC classifier, which is based on the Inception architecture of GoogleNet and further incorporates AC.B and SE modules. These enhancements improve the model's ability to extract discriminative features relevant to geographic origin identification.  
The two modules are trained independently, offering two major advantages: first, each stage can utilize task-specific datasets tailored to either detection or classification; second, the decoupled structure allows for targeted optimization based on the distinct complexity and feature characteristics of the two tasks. This modular framework effectively combines the robustness of object detection with the fine-grained capability of origin classification, while also providing greater flexibility and adaptability for real-world deployment.
## 3 Test Results and Discussion  
### Failure Patterns of the Composite Columns under Compression Loading  
The axial compression failure patterns of the concrete-encased CFST composite columns subjected to high temperature were similar to those at ambient temperature, but the influence of the heating temperature, steel tube diameter, and concrete cover thickness was obvious, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Steel Type** & _d_/mm & **20 °C** & **200 °C** & **300 °C** & **400 °C** & **500 °C** & **600 °C** & **700 °C** & **800 °C** \\ \hline \multirow{2}{*}{Rebar} & 6 & 473.26 & 487.02 & 486.5 & 468.51 & 475.82 & 467.19 & 368.63 & 375.63 \\  & 12 & 432.04 & 431.38 & 431.18 & 433.96 & 435.46 & 416.87 & 367.66 & 380.05 \\  & 89 & 412.83 & 426.22 & / & 433.60 & / & 396.32 & / & 342.65 \\ \multirow{2}{*}{Steel tube} & 114 & 409.55 & 426.22 & 434.25 & 444.6 & 417.1 & 401.36 & 369.51 & 327.64 \\  & 138 & 361.27 & 380.01 & / & 382.95 & / & 343.21 & / & 296.24 \\ \hline \end{tabular}
\end{table}
Table 4: Residual yield strength of steel.  
Figure 4: Test instruments.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**T/\({}^{\circ}\)C** & **20 °C** & **200 °C** & **300 °C** & **400 °C** & **500 °C** & **600 °C** & **700 °C** & **800 °C** \\ \hline \(f_{\text{c}}^{{}^{\prime}}\) (T) /MPa & 54.56 & 43.11 & 32.74 & 26.68 & 19.9 & 15.76 & 10.84 & 5.89 \\ \hline \end{tabular}
\end{table}
Table 3: Residual compressive strength of concrete cylinder blocks.  
When the heating temperature \(T<500\)\({}^{\circ}\)C, the concrete cover did not completely peel off until failure, the vertical and oblique cracks staggered to form the main cracks, and the edge concrete collapsed and failed without longitudinal reinforcement bulking or stirrup deformation. When \(T\geq 500\)\({}^{\circ}\)C, the concrete cover was badly damaged and could not contribute to the ultimate bearing capacity of the specimen. At the same time, the damage to the outer concrete was serious, and the loading was mostly borne by the CFST with excellent deformation capacity, resulting in a large ultimate displacement until failure. The outer concrete was seriously peeled off, some stirrup bars were exposed and deformed, and the longitudinal reinforcements buckled.  
At the same heating temperature, the larger the diameter of the steel tube, the more pronounced the peeling of the concrete cover, and the more serious the apparent damage to the specimen. The damage degree of the outer concrete increased as the thickness of the concrete cover increased.