esa-sceva/satcom-chunk-collection huggingface dataset:
# Weight Assignment Method and Application of Key Parameters in Shale Gas Resource Evaluation
## 2 Resource Evaluation Methods and Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primary Influencing Factors** & **Secondary Influencing Factors** & **Third-Level Influencing Factors** \\ \hline \multirow{8}{*}{Tectonic action} & \multirow{2}{*}{Basin formation} & Basin types \\  & & Basin area \\  & & Basin evolution \\ \cline{2-3}  & \multirow{4}{*}{Thermal history} & Terrestrial heat flow \\  & & Thermal maturity \\  & & Hydrocarbon formation \\ \cline{2-3}  & \multirow{2}{*}{Tectonic deformation} & Fracturing \\  & & History of buried depth \\ \hline \multirow{8}{*}{Sedimentation} & \multirow{2}{*}{Shale distribution} & Shale thickness \\  & & Shale area \\ \cline{2-3}  & \multirow{2}{*}{Organic geochemistry} & Organic matter type \\  & & Organic matter abundance \\ \cline{2-3}  & \multirow{2}{*}{Rock minerals} & Mineral composition \\  & & Clay minerals \\  & & Brittle mineral \\ \hline \multirow{8}{*}{Diagenesis} & \multirow{4}{*}{Storage physical properties} & Porosity \\  & & Pore structure \\ \cline{1-1}  & & Permeability \\ \cline{1-1}  & & Density \\ \hline \multirow{4}{*}{Comprehensive geological processes} & \multirow{2}{*}{Gas content} & Gas content \\ \cline{1-1}  & & Gas-bearing structure \\ \cline{1-1}  & & Reserve recoverability \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of Factors Influencing Resource Evaluation. be introduced into the calculation of resource evaluation, thereby improving the accuracy of resource estimation.
## 2 System Model
The DRL algorithms also do not consider the network as a whole, so they cannot utilize the global graph of the network state to dynamically adapt to changes in the network. Therefore, in order to better optimize routing, it is necessary to consider network topological information and node state information comprehensively and analyze the network as a whole to dynamically adapt to changes in the network. Therefore, this paper proposes the GNN-DRL algorithm, which is a network routing optimization architecture based on graph neural networks and deep reinforcement learning. First of all, the graph neural networks are used to learn the relationships between graph elements in the network topology as well as the node composition rules. Then deep reinforcement learning algorithms are used to make routing decisions. The input state of DRL agent is expected to include not only the current node state and link state, but also the overall network state of the node according to the dynamic characteristics of LEO satellite network. Then we need to aggregate network information through an aggregation algorithm. However, due to the large number of nodes in LEO satellite network, if the common GCN is used, it will need a large number of calculations and cannot complete inductive representation learning. So, this paper uses GraphSAGE to aggregate the attributes of the node itself and its limited number of neighbors. The algorithm model diagram is shown in Figure 1, and the algorithm has following processes:Step1: For the current topology from \(t_{n}\) to \(t_{n}+t_{0}\), a graph neural network is utilized to learn the network topological information and perform information extraction, through which the network state is represented. Step2: The network state outputted by the graph neural network combined with the user state is input into the DRL to make routing decisions and update the network state. And the best decision-making action is selected based on the node user state as well as the network state to maximize the discounted cumulative rewards. And the policy parameters \(\theta\) are updated every \(\gamma\) (\(t_{0}=n\gamma\)).
## 4 Experiments and Discussion
### Ablation Study  
The ablation experiment is to verify the effectiveness of each innovation point. The idea of ablation experiment is to control variables and experiment on innovation points one by one.  
Two-stage augmentation. In order to show the effectiveness of our two-stage augmentation, we performed the following ablation study. In this ablation study, we train the MobileNet-v2 without data augmentation, with dynamic augmentation, with static augmentation, with one-stage augmentation. Results are shown in Table 6. Our two-stage augmentation method has the highest TPR/TNR, which means that our augmentation method is the best and can effectively improve the performance of defect detection.  
Multi-scale cascade. In order to verify the performance improvement of multi-scale cascade, we conducted a performance comparison experiment between our multi-scale cascade network MobileNet-v2-dense and MobileNet-v2. It should be noted that the accuracy of network changes is basically unchanged if no data augmentation is used. Therefore, we train the network based on the two-stage augmentation, and the loss function is the CE loss. The experimental results are shown in Table 7. Compared with MobileNet-v2, the TPR/TNR of our MobileNet-v2-dense is improved.  
Asymmetric loss. In experiment 3, we train with CE loss, focal loss, and asymmetric loss in the same MobileNet-v2-dense and two-stage data augmentation. The results are listed in Table 8. It can be seen that the asymmetric loss function can improve the TNR to 99.93% for the DAGM data set, with an increase of 2.28% compared to Focal loss.
### Experimental Tests and Data  
This study uses a medium-carbon steel, type P20, with the chemical composition presented in Table 1.  
The experiments used in this study mirror those previously conducted by [PERSON] et al. [14]. These tests involve hot compression on a Gleeble-3800 of P20 steel cylinders with initial dimensions of \(\phi_{0}=10\) mm and \(h_{0}=15\) mm. Only the most relevant information about those experiments is reported hereafter. In order to have a more complete knowledge about the compression tests conducted during this study, we refer to [PERSON] et al. [14]. Hot compression tests were performed for five temperatures, \([1050,1100,1150,1200,1250]\)\({}^{\circ}\)C and six strain rates \([0.001,0.01,0.1,1,2,5]\) s\({}^{-1}\). Figure 1 reports a plot of all 30 strain/stress curves extracted from the experiments.  
The experimental database is composed of all strain/stress data for all 30 couples of strain rate and temperature. Each strain/stress curve contains 701 equidistant strains \(\varepsilon=[0.0,0.7]\) in \(\Delta\varepsilon=0.001\) increments. The complete database contains 21,030 quadruplets (\(\varepsilon\), \(\dot{\varepsilon}\), \(T\) and \(\sigma\)). This dataset will be used here after to identify the ANN flow law parameters depending on the selected hyperparameters of the ANNs.  
Section 2 is dedicated to the presentation of the ANN based flow law. The first part is dedicated to a reminder of the basic notions on ANNs, with a section on the choice of activation functions to be used in the formulation. The architecture chosen for the formulation of the flow laws based on a two-hidden-layer network will then be presented, together with the formulation of the derivatives of the output with regard to the input variables. The learning methodology and the results in terms of network performance as a function of the activation functions selected will then be presented. Section 3 is dedicated to the FE simulation of a compression test using the explicit version of Abaqus, integrating the ANN implemented as a user routine in Fortran. The quality of the numerical solution obtained and its performance in terms of computational cost will be analyzed as a function of the network structure. Finally, the last section concerns conclusions and recommendations.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Element** & **C** & **Mn** & **Mo** & **Si** & **Ni** & **Cr** & **Cu** \\ \hline Wt \% & 0.30 & 0.89 & 0.52 & 0.34 & 0.68 & 1.86 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of medium-carbon steel. Fe = balance.
###### Abstract  
In deep, geologically complex environments characterized by high in situ stress and elevated formation temperatures, the mechanical behavior of rocks often transitions from brittle to ductile, differing significantly from that of shallow formations. Traditional rock failure criteria frequently fail to accurately assess the strength of rocks under such deep conditions. To address this, a novel failure criterion suitable for high-temperature and high-pressure conditions has been developed by modifying the Mohr-Coulomb criterion. This criterion incorporates a quadratic function of confining pressure to account for the attenuation rate of strength increase under high confining pressure and a linear function of temperature to reflect the linear degradation of strength at elevated temperatures. This criterion has been used to predict the strength of granite, shale, and carbonate rocks, yielding results that align well with the experimental data. The average coefficient of determination (\(R^{2}\)) reached 97.1%, and the mean relative error (\(MRE\)) was 5.25%. Compared with the Hoek-Brown and [PERSON] criteria, the criterion proposed in this study more accurately captures the strength characteristics of rocks under high-temperature and high-pressure conditions, with a prediction accuracy improvement of 1.70-4.09%, showing the best performance in the case of carbonate rock. A sensitivity analysis of the criterion parameters \(n\) and \(B\) revealed notable differences in how various rock types respond to these parameters. Among the three rock types studied, granite exhibited the lowest sensitivity to both parameters, indicating the highest stability in the prediction results. Additionally, the predictive outcomes were generally more sensitive to changes in parameter \(B\) than in \(n\). These findings contribute to a deeper understanding of rock mechanical behavior under extreme conditions and offer valuable theoretical support for drilling, completion, and stimulation operations in deep hydrocarbon reservoirs.  
deep formations; high temperature and high pressure; Mohr-Coulomb criterion; nonlinear failure criterion 2025158048
## 3 Metaverse Classroom: A Layered Architectural View
### Collaboration and Social Layer  
#### 3.4.1 Avatar-Based Interaction  
Avatar-based interaction in the Metaverse classroom enhances student engagement by allowing them to represent themselves visually in a digital space [157]. Customizable avatars help students feel more connected to their virtual environment and peers, reducing the sense of distance often associated with online learning [158]. This visual representation makes learning more personal and interactive, fostering a stronger sense of community and collaboration [14]. For instance, seeing peers represented as avatars can reduce feelings of isolation and encourage students to participate actively in class activities. Avatars also enable nonverbal communication through gestures, facial expressions, and body language, essential for creating meaningful social interactions [159]. In traditional classrooms, non-verbal cues play a crucial role in communication, and avatars replicate this in the Metaverse [160]. For example, a student can not to show agreement, raise a hand to ask a question or express surprise through facial animations. These interactionsmake virtual exchanges feel more authentic and help teachers gauge student engagement and adjust their instruction accordingly [91]. Emotional expression through avatars builds empathy and stronger relationships among students [161]. Avatars that display emotions allow students to express feelings such as encouragement or frustration during group work, fostering a supportive learning environment [162]. For example, a student might smile or express enthusiasm through their avatar during a group project, boosting team morale and creating a positive dynamic [163]. Emotional expression humanizes the virtual learning experience, improving collaboration and class cohesion.  
Avatars also support cultural expression and identity, allowing students to showcase aspects of their background through their virtual appearance [158]. Students can customize avatars with culturally significant symbols, attire, or colors, promoting cultural inclusivity and awareness [164]. For instance, students may choose clothing or accessories that reflect their heritage, introducing opportunities for intercultural dialogue and helping classmates appreciate different perspectives [165]. This personalization encourages self-expression and fosters pride in one's identity, creating a more inclusive classroom environment [129]. Finally, avatars serve as the foundation for interactive learning experiences that go beyond traditional classroom activities [3]. Students can use avatars in virtual simulations or role-play scenarios to enhance their understanding of the topic [130]. For example, in a history lesson, students might take on avatars representing historical figures, adding depth to their exploration of the topic [129]. In language learning, avatars can role-play real-life conversations, helping students practice practical skills in an engaging way [166]. By participating actively through avatars, students immerse themselves more fully in the material, improving both comprehension and retention [167].
## 2 Literature Review
These models were tested against the results from 18 decomposition models that were used to predict DNI from GHI. The separation methods were found to be reasonable but not exceptionally accurate under clear conditions and significantly inaccurate under non-clear sky conditions. [PERSON] [4] investigated 18 models for estimating GHI based on atmospheric data. Data from Oklahoma, Illinois, Colorado, Hawaii, and Saudi Arabia were compared to four simple and eight complex clear sky models. The study revealed that various models were capable of accurately estimating GHI. The REST2 model performed best, followed by the Inichen model [69]. [PERSON] [67] used hourly GHI from four Indian regions to analyze three GHI clear sky models. The REST model was found to be more accurate in Indian areas than the other two models, as indicated in Table 1. Using monthly average GHI data spanning 14 years, [PERSON] et al. [72] investigated the comparative capabilities of four models for three locations in Israel's Negev region. To identify clear-sky days, [PERSON]'s [73] filter was used, which categorized days only based on their daily clearness index [72; 73]. The authors discovered that the Berger-Duffie model, a model presented by [PERSON] and [PERSON] [74], and a model proposed by [PERSON] [75] fared the best.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 5 Conclusions  
A CT-video matching method based on a depth map was proposed for the ureteroscopy scene. We applied the method to clinical data and compared it with the traditional 2D-2D registration method. The results show that our method outperforms the traditional method in terms of accuracy and time performance, with a 26% improvement in accuracy and a five times improvement in speed for the top 10. In addition, even though the time performance is improved by a factor of five, our time performance of 1.26 s per image is still far from meeting the clinical requirements. In fact, the depth map-based matching method in this paper is not limited to ureteroscopy application scenarios but can also be considered for other endoscopy scenarios. We believe that that with the continuous exploration of deep learning technology, future research work can optimize the matching method based on this paper to achieve better matching accuracy and speed trade-off.  
Methodology, Y.P. and T.Y.; software, H.L. and Y.P.; validation, Z.F., C.Z. and X.Z.; investigation, Y.P.; resources, P.W., J.L., X.Y. and H.D.; data curation, P.W.; writing--original draft preparation, H.L. and Y.P.; writing--review and editing, J.L., P.W. and T.Y.; visualization, H.L.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Example of the matching ambiguity. (**a**) shows ureteroscope image and (**b**) shows corresponding depth map. Two rows in (**a**) show different anatomical positions, but two rows in (**b**) show similar depth maps.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant/Award Numbers: 31771072, 81827804 and the National Key Research and Development Program of China, Grant/Award Numbers: 2017 YFB1302803.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the retrospective design of the study and the fact that all data used were from existing and anonymized clinical datasets.  
**Informed Consent Statement:** Patient consent was waived due to the retrospective nature of this study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank [PERSON], [PERSON], and [PERSON] for their help in preparing the data in this paper.  
**Conflicts of Interest:** No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.
## 3 General Findings  
### Studies Distribution in Chronological Order  
The research on the BIS adoption has attained great attention over the last two decades, but prominent increase has been observed from 2009 to 2019 [3]. From 2000 to 2009, researchers had mostly focused on components of the BIS such as online analytical processing (OLAP) and data warehouse (DW) or some other tools such as dashboards etc. The focus of researchers has shifted to the BIS as a whole entity during 2009 to 2019 [3]. Therefore, the selected period of SLR is from 2011 to 2020 in the perspective of BIS adoption and acceptance as a whole entity. Thus, a total of 84 studies are part of this review. It is revealed by the analysis that limited papers are published in the years of 2011 (3), 2012 (7) and 2013 (5). Whereas the publications are increased little bit in 2014 (9), 2015 (8) and 2016 (9) but a significant increase is observed in 2017 (12) and 2018 (18). While sudden decline is noticed in 2019 (10) and 2020 is going on with 5 studies. The distribution of all studies is done according to the significant dimensions and the year of publication in Figure 2 and Table 4. It is revealed by the analysis of results that most studies were conducted in an organizational dimension as compared to other three dimensions. Individual (users) and environmental dimensions attained less attention from researchers, but it showed by Table 4 that the technological dimension was neglected by researchers.
# Dynamic RACH Partition for Massive Access of Differentiated M2M Services
###### Abstract  
In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay.  
M2M communications; overload control; preamble partition; access success probability; average access delay +
Footnote †: journal: Sensors
# Wood Defect Detection Based on Depth Extreme Learning Machine
## 2 Materials and Methods
### Optimized Convolution Neural Network
#### 2.2.4 Feature Extraction  
The optimized CNN model proposed in this paper was designed for wood surface feature extraction. Knots were used as example wood defects (Figure 3a) to test feature extraction via the following operations. The input image Figure 3a was directly processed by CNN algorithm to obtain image Figure 3b, which presented local irregularity and nonsmooth edges in the contour after enlargement [20]. The SLIC algorithm was used to process the input image (Figure 3a) followed by longitudinal convolution (Figure 3d). The image shown in Figure 3h was obtained after edge removal and fusion processing. The defect contour features of Figure 3h are substantially clearer compared to Figure 3b because the segmentation of wood images using CNN, which is expressed in pixels as a matrix without considering the spatial organization relationship between pixels, affects the end image segmentation results. The SLIC algorithm instead extracts the wood defect boundary and contour information from the original image and feeds back the information to the initial segmentation results of the CNN model.  
The above process reduces the redundancy of local image information in addition to the complexity and computation of the image processing. The pixel-level CNN model method does not accurately reveal the boundary of the defective region of wood image, but instead indicates only its general position. SLIC can extract a relatively accurate contour to supplement it and optimize the initial CNN model. To this effect, the proposed SLIC algorithm-based method improves the defect feature extraction of wood images over CNN alone.  
The input image (Figure 3a) was processed by the NSST algorithm to obtain the image shown in Figure 3e, then Figure 3f was obtained using the SLIC algorithm. Vertical convolution was carried out to obtain the image shown in Figure 3g. Figure 3i was obtained after edge removal and fusion processing. Consider Figure 3i compared to Figure 3h: although the wood defect contour feature extraction effects are not obvious, using NSST to preprocess the image reduces environmental interference and training depth to markedly decrease the computation and complexity of the image processing.  
Based on the above analysis, this paper decided to use the wood image processing frame shown in Figure 4 to obtain the wood defect feature map.