esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 6 Feature Importance of Each Drilling Parameter  
Although tunnel geology conditions were predicted well by the neural network model, we do not know which drilling parameters played relatively important roles. Many drilling parameters can be acquired by a drilling rig, and it can be difficult to find a quantitative model to effectively characterize rock properties when too many drilling parameters are used. If we can determine which drilling parameters play relatively important roles, then geology predictions can be achieved by a more concise and more easily applicable neural network model. To determine the most important drilling parameters for geological prediction, a permutation feature importance analysis was performed. Permutation feature importance is defined as the decrease in a model's score when a single feature value is randomly shuffled [30]. The greater decrease in model accuracy, the more importance is attributed to the parameter.  
Table 3 lists the feature importance of each drilling parameter. The water supply rate showed the greatest impacts on predictive accuracy, while the water supply pressure showed the third greatest impact. These results can be explained by the higher water flow required by the drilling rig when discontinuities or soft rock are drilled, due to the quantities of drill cuttings and/or water leakage into the discontinuities [11]. EV energy was the second most important factor impacting predictive accuracy, thus affirming the usefulness of drilling energy analysis methods for geology prediction [3; 4; 5; 6]. Tables 4 and 5 list the top ten features and their importance to permutate two-parameter and three-parameter groupings. These lists also indicate that water supply rate, EV energy, and water supply pressure substantially influenced predictive accuracy. Other drilling parameters, when combined with EV energy, water supply pressure, and water supply rate, also significantly influenced predictive accuracy.
## 5 Experimental Results and Analysis
### Experimental Results and Discussion on Algorithms Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(1.04\times 10^{11}\) & \(4.49\times 10^{10}\) & \(6.91\times 10^{10}\) & \(7.09\times 10^{10}\) & \(1.62\times 10^{10}\) \\ MOPSO & \(2.17\times 10^{10}\) & \(9.26\times 10^{9}\) & \(1.44\times 10^{10}\) & \(1.45\times 10^{10}\) & \(3.58\times 10^{9}\) \\ MOAO & \(2.43\times 10^{10}\) & \(1.06\times 10^{10}\) & \(1.74\times 10^{10}\) & \(1.74\times 10^{10}\) & \(4.30\times 10^{9}\) \\ MOHBA & \(2.03\times 10^{10}\) & \(1.07\times 10^{10}\) & \(1.63\times 10^{10}\) & \(1.59\times 10^{10}\) & \(3.07\times 10^{9}\) \\ MOWOA & \(1.90\times 10^{10}\) & \(9.07\times 10^{9}\) & \(1.27\times 10^{10}\) & \(1.32\times 10^{10}\) & \(2.64\times 10^{9}\) \\ MOGWO & \(1.68\times 10^{10}\) & \(7.24\times 10^{9}\) & \(1.24\times 10^{10}\) & \(1.27\times 10^{10}\) & \(2.76\times 10^{9}\) \\ MOFECO & \(8.29\times 10^{10}\) & \(2.44\times 10^{10}\) & \(3.86\times 10^{10}\) & \(4.27\times 10^{10}\) & \(1.57\times 10^{10}\) \\ LSMOFECO & \(1.80\times 10^{11}\) & \(4.62\times 10^{10}\) & \(1.00\times 10^{11}\) & \(1.09\times 10^{11}\) & \(3.39\times 10^{10}\) \\ MOFECO-MS & \(1.87\times 10^{11}\) & \(7.27\times 10^{10}\) & \(1.37\times 10^{11}\) & \(1.34\times 10^{11}\) & \(3.35\times 10^{10}\) \\ \hline \end{tabular}
\end{table} 
Table 23: Statistical results of HV indicator of the eight comparison algorithms for \(ch150\_n149\). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(1.04\times 10^{11}\) & \(4.49\times 10^{10}\) & \(6.91\times 10^{10}\) & \(7.09\times 10^{10}\) & \(1.62\times 10^{10}\) \\ MOPSO & \(2.17\times 10^{10}\) & \(9.26\times 10^{9}\) & \(1.44\times 10^{10}\) & \(1.45\times 10^{10}\) & \(3.58\times 10^{9}\) \\ MOAO & \(2.43\times 10^{10}\) & \(1.06\times 10^{10}\) & \(1.74\times 10^{10}\) & \(1.74\times 10^{10}\) & \(4.30\times 10^{9}\) \\ MOHBA & \(2.03\times 10^{10}\) & \(1.07\times 10^{10}\) & \(1.63\times 10^{10}\) & \(1.59\times 10^{10}\) & \(3.07\times 10^{9}\) \\ MOWOA & \(1.90\times 10^{10}\) & \(9.07\times 10^{9}\) & \(1.27\times 10^{10}\) & \(1.32\times 10^{10}\) & \(2.64\times 10^{9}\) \\ MOGWO & \(1.68\times 10^{10}\) & \(7.24\times 10^{9}\) & \(1.24\times 10^{10}\) & \(1.27\times 10^{10}\) & \(2.76\times 10^{9}\) \\ MOFECO & \(8.29\times 10^{10}\) & \(2.44\times 10^{10}\) & \(3.86\times 10^{10}\) & \(4.27\times 10^{10}\) & \(1.57\times 10^{10}\) \\ LSMOFECO & \(1.80\times 10^{11}\) & \(4.62\times 10^{10}\) & \(1.00\times 10^{11}\) & \(1.09\times 10^{11}\) & \(3.39\times 10^{10}\) \\ MOFECO-MS & \(1.87\times 10^{11}\) & \(7.27\times 10^{10}\) & \(1.37\times 10^{11}\) & \(1.34\times 10^{11}\) & \(3.35\times 10^{10}\) \\ \hline \end{tabular}
\end{table} 
Table 24: Statistical results of SP indicator of the eight comparison algorithms for \(ch150\_n149\).
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Mobility Based Protocols  
Mobile Delay-Tolerant Data Dolphin (DDD) [46] uses mobile nodes (Dolphins) to gather data from the stationary nodes. Mobicast Routing Protocol for Underwater Sensor Networks [47] is a 3D routing protocol based on mobile sinks, i.e., AUVs. Nodes close to an AUV form a 3D zone and the AUVs collect their data. For nodes to be ready, a sleeping node in the next zone is woken up in advance by the AUV.
## 3 Methodology  
To conduct this comprehensive survey on trust evaluation techniques for 6G networks with a focus on fuzzy algorithm approaches, a systematic methodology was employed to ensure thoroughness and rigor. The methodology comprises several key phases: literature review, categorization, analysis, and synthesis. Each phase was meticulously designed to build upon the previous one, resulting in a cohesive and comprehensive understanding of the current state and future direction of trust evaluation in 6G networks. The literature review phase involved defining the scope of the survey and establishing selection criteria for the literature, using keywords such as \"6G networks\", \"trust evaluation\", \"fuzzy logic\", \"security\", \"privacy\", and \"trust\" to guide the search across major academic databases, including IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Only peer-reviewed articles, conference papers, and high-impact journal articles from the last decade were included to ensure relevance and quality. Articles not directly related to trust evaluation in 6G networks, or those not involving fuzzy logic, were excluded, resulting in a curated list of significant publications.  
In the classification stage, the selected articles were thematically grouped into the following primary categories: trust evaluation models, trust evaluation methods using fuzzy algorithms, categorization based on different types of networks (considering the unique criteria and challenges faced in each), and critical metrics. A comparative framework was established to systematically compare and contrast different techniques based on parameters such as scalability, privacy, dynamicity, integrity, availability, and context awareness. The analysis phase involved a critical evaluation of each article to understand the strengths, weaknesses, and unique contributions of the proposed techniques. Key methodologies, results, and conclusions were extracted, and different techniques were compared with established parameters. Through this analysis, common trends and emerging patterns in trust evaluation techniques were identified, along with gaps in current research that require further investigation. The final synthesis phase involved integrating the findings into a coherent narrative; summarizing key insights; highlighting innovative approaches, particularly those involving fuzzy logic; and proposing potential areas for future research based on the identified gaps and trends. The survey was structured to present the information logically and coherently, starting with an introduction to 6G networks and the importance of trust evaluation, followed by detailed sections on each identified category and concluding with a discussion on future research directions. Figure 2 provides a simplified overview of the study's structure.
## 2 Materials and Methods
### Machine Learning Algorithms  
Skin image classification is a very important aspect of skin image analysis. For the skin Contact Capacitive image classification, we used different Deep Learning Neural Networks, such as AlexNet [17], GoogLeNet [18], VGG [19], ResNet [20], Inception [21], MobileNet [22], DenseNet [23], SqueezeNet [24], InceptionResNet [25] and Xception [26]. They are pre-trained Convolutional Neural Networks (CNNs) that have been trained on over 1 million images from the ImageNet Dataset [27]. They can classify images into 1000 object categories (e.g., keyboard, mouse, coffee mug, pencil). Through TransferLearning [28], we can take the advantages of pre-trained models and re-train these models over the skin Contact Capacitive images, and achieve classification over different skin sites.  
For the skin High-Resolution Ultrasound Imaging classifications, we have developed two new feature-based classification methods. One is based on the luminosity values (0 to 255) from the red, green or blue channels of the images. In this approach, Logistic Regression, K-Nearest Neighbor (KNN), Neural Networks (NNs) and Random Forest were used as classifiers. Logistic Regression is a parametric model where KNN is a non-parametric model, and KNN is comparatively slower than Logistic Regression. KNN supports non-linear solutions where Logistic Regression supports only linear solutions. NNs need a large volume of training data compared to KNN to achieve sufficient accuracy. NNs need lot of hyperparameter tuning compared to KNN.  
Another new classification method is based on skin ultrasound image texture. In this approach, ten types of textural features were extracted from each skin ultrasound image. Of the ten textural features, five were traditional textural features and five were from the output of one intermediate layer of pre-trained convolutional networks, such as DenseNet, MobileNet, ResNet, VGG and Xception. We then applied principal component analysis (PCA) on each feature class and retained the first two principal components to generate scatter plots for exploratory data analysis.  
Although the classification algorithms developed in this study are mainly for differentiating skin sites, they can also be used for other classification tasks, such as differentiating dry skin from normal skin, damaged skin from intact skin, young skin from aged skin, healthy skin from diseased skin, and even different types of skin diseases.
# Deep Learning Cluster Structures for Management Decisions: The Digital CEO +
## 3 Deep Learning Cluster Structures for Management Decisions
### Deep Learning Cluster Structures
#### Deep Learning Management Cluster  
The DL management clusters take the overall routing management decision (Figure 7). The QoS and Cyber management clusters analyze the output from their associated QoS and Cyber DL clusters respectively. If the Cyber management cluster detects a failure in the cyber certificates; the CEO management cluster routes the network Cognitive Packets as safe mode using the QoS DL clusters, otherwise, if the Cyber certificates are valid the CEO management cluster chooses the route provided by the RNN-RL routing algorithm as normal mode.  
This model defines the QoS management cluster as:  
* \(\mathbf{I_{qmc}}\) = (i\({}^{\text{qmc}}\)1, i\({}^{\text{qmc}}\)2,... i\({}^{\text{qmc}}\)c), a \(C\)-dimensional vector \(\mathbf{I_{qmc}}\in[0,1]^{C}\) with the values of the QoS Metrics for each QoS cluster;
* \(\mathbf{w_{qmc}}^{-}\)(c) is the \(C\)-dimensional vector of weights that represents the Goal = (\(\alpha_{\text{Delay}}\), \(\beta_{\text{Loss}}\), \(Y_{\text{Bandwidth}}\));
* \(Y_{\text{qmc}}\), a scalar Y\({}_{\text{qmc}}\in[0,1]\) that represents the best QoS metric routing decision to be taken. Cyber management cluster as:
* \(\mathbf{I_{cmc}}\) = (i\({}^{\text{cmc}}\)1, i\({}^{\text{cmc}}\)2,... i\({}^{\text{cmc}}\)c), a \(C\)-dimensional vector \(\mathbf{I_{cmc}}\in[0,1]^{C}\) with the values of the key errors for each Cyber cluster (User, Packet, Node);
* \(\mathbf{w_{cmc}}^{-}\)(c) is the \(C\)-dimensional vector of weights that represents the relevance of each Cyber Cluster;
* \(Y_{\text{cmc}}\), a scalar Y\({}_{\text{cmc}}\in[0,1]\) that represents if the packet has passed the Cyber network security. CEO management cluster as:
* \(\mathbf{I_{CEOmc}}\), a scalar I\({}_{\text{CEOmc}}\in[0,1]\) with the values of the QoS management cluster;
* \(\mathbf{w_{CEOmc}}^{-}\) a scalar w\({}_{\text{CEOmc}}^{-}\in[0,1]\) that represents the error of the Cyber management cluster;
* \(Y_{\text{CEOmc}}\), a scalar Y\({}_{\text{CEOmc}}\in[0,1]\) that represents the final routing decision.
## 3 Cable Models and Methodology
### Current Distribution and Frequencies  
Analyses are performed at four frequencies: \(0,50,150,250\) Hz. A total of 10 A is applied to the coupled nodes of each cable cross-section area (2D) or the coupled nodes of one end of the extruded structure (3D). In multi-segment cables, this current is shared equally among segments (e.g., 3.33 A per segment for three segments) by coupling the voltage degree of freedome of each segment's nodes individually and applying the corresponding current portion to a node of each coupled set. By using an identical current input across all models, the comparisons of \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\) and AC loss factors remain consistent.
## 4 SVM Fuel Consumption Model and IPSO Optimization
### IPSO Optimization Process  
To obtain the optimal shoveling trajectory with different engine speeds. IPSO is used to optimize the SVM fuel consumption model. The indicators of optimization are the input features of the SVM model, which are the initial velocity, the displacement of the wheel loader in the insertion phase, the shoveling weight, the operation time, and the displacement of the wheel loader in the scooping phase. Among them, the initial velocity exists in different intervals depending on the engine speed. The displacement of the wheel loader in the scooping phase is not involved in the optimization search due to the limitation of trajectory planning, and the results are obtained by the calculation of wheel loader displacement in the insertion phase. As for the operation time and shoveling weight, which is a posteriori data, as long as they correspond to the upper and lower limits of the sample data, the optimization intervals of each parameter are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of File** & **ARE (\%)** & **RMSE** & **R\({}^{2}\)** \\ \hline Gravel & 4.91 & 5.91 & 0.9764 \\ Sand & 3.49 & 3.08 & 0.9494 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fuel consumption model evaluation indicators.  
Figure 9: Prediction performance of fuel consumption model. (**a**) Gravel fuel consumption model; (**b**) Sand fuel consumption model.  
In the IPSO algorithm, \(c_{1}\) and \(c_{2}\) are set to 1.5. The number of iterations is set to 200. The population size is set to 30. The optimal trajectories of the gravel model and the sand model are optimized for three different engine speeds, respectively. The optimization process is shown in Figure 11, and the optimization results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Interval**} & \multicolumn{4}{c}{**Gravel Model**} & \multicolumn{4}{c}{**Sand Model**} \\ \cline{3-8}  & & **900 RPM** & **1200 RPM** & **1500 RPM** & **900 RPM** & **1200 RPM** & **1500 RPM** \\ \hline \multirow{2}{*}{Initial velocity (m\(\cdot\)s\({}^{-1}\))} & Upper limit & 0.6 & 0.95 & 1.3 & 0.6 & 0.95 & 1.3 \\ \cline{2-8}  & Lower limit & 0.3 & 0.65 & 1.05 & 0.3 & 0.65 & 1.05 \\ \hline \multirow{2}{*}{Displacement of wheel loader in insertion phase (mm)} & Upper limit & & & & 1000 & & \\ \cline{2-8}  & Lower limit & & & & 600 & & \\ \hline \multirow{2}{*}{Shoveling weight (kg)} & Upper limit & 5526 & 5545 & 5558 & 7294 & 7441 & 7520 \\ \cline{2-8}  & Lower limit & 4459 & 4609 & 3913 & 5400 & 5773 & 5519 \\ \hline \multirow{2}{*}{Operation time (s)} & Upper limit & 25 & 23 & 21 & 25 & 26 & 23 \\ \cline{2-8}  & Lower limit & 12 & 12 & 10 & 11 & 12 & 10 \\ \hline \multirow{2}{*}{Displacement of wheel loader in scooping phase (mm)} & \multicolumn{4}{c}{Calculated according to Equation (2)} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimization intervals for each parameter.  
Figure 10: Dimension of fuel consumption influence factors and model accuracy.
# An Approach to Dynamic Sensing Data Fusion
###### Abstract  
For the research and development of sensor systems, the collection and fusion of sensing data is the core. In order to make sensor data acquisition change with the change in environment, a dynamic data acquisition and fusion method based on feedback control is proposed in this paper. According to the sensing data acquisition and fusion model, the optimal acquisition of sensor data is achieved through real-time dynamic judgment of the collected data, decision-making of the next acquisition time interval, and adjustment. This model enables the sensor system to adapt to different environments. An experimental study of the proposed model was carried out on an experimental platform, and the results show that the proposed model can not only reflect the change in sensing data but also improve the transmission efficiency.  
dynamic acquisition; feedback control; sensor device; sensing data +
Footnote †: journal: Sensors
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 6 Thermal Numerical Analyses of the Hybrid CLT-glass Component Level
### Expected Thermal Performance Indicators
To avoid condensation, the minimum surface temperature (\(T_{min}\)) on the internal face of glass (i.e., inside the cavity volume) needs to be higher than the dew point temperature (\(T_{dp}\)) corresponding to the air conditioning inside the panel:  
\[T_{min}\geq T_{dp}=f(RH,T). \tag{6}\]  
The minimum measured surface temperature for the FE configuration was calculated to be a minimum of 9.15 \({}^{\circ}\)C (corner edge in the frame area, see Table 8). According to the reference European standard, the \(T_{\rm dp}\) value should be checked based on mock-up experiments including the continuous monitoring of temperature and humidity values inside the cavity (see, for example, [56]). A similar approach was proposed in [26] for a novel curtain wall system with a metal frame, but this approach is a key design step for glass envelopes in general. Conservatively, the calculation step was first carried out in this paper for the worst condition in the cavity volume, corresponding to an air temperature of 9 \({}^{\circ}\)C and an RH = 80%. This assumption results in \(T_{dp}\) = 5.7 \({}^{\circ}\)C and consequently suggests that the examined CLT-glass facade element has appropriate thermal performance (Equation (6)). Its overall  
Figure 15: Numerical thermal performance assessment of a full-size CLT-glass facade element. Temperature distribution: (**a**) in the frame cross-section (legend values in \({}^{\circ}\)C) and (**b**) in glass thickness (with CP_n_ = 1, 2, and 3 diagonal control points at a distance of 50 mm, 90 mm, and 150 mm from the corner; ABAQUS). thermal behavior, however, will be further explored in the next stages of the research project with the support of extended mock-up registrations. Another relevant parameter is then represented by the minimum surface temperature on the indoor face of the glass (\(T_{si}\), with \(T_{si}\) = 11.6 \({}^{\circ}\)C at the corner edge and \(T_{si}\) = 14 \({}^{\circ}\)C at the exposed corner of the glass, see Figure 15b). Here, for indoor conditions of 20 \({}^{\circ}\)C (air temperature) and RH = 50%, the recommended reference value is \(T_{dp}\) = 6 \({}^{\circ}\)C, thus suggesting again optimal satisfaction of the limit conditions for the CLT-glass solution.
# Optimization of Buoy Shape for Wave Energy Converter Based on Particle Swarm Algorithm
## 2 Theory and Methodology  
### Numerical Model  
Successful international wind and wave co-generation devices, such as the semi-submersible triangular wind and wave energy hybrid system W2 Power developed by Wave energy in Norway and the Wave Star basic array WEC developed in Denmark, are all WEC built on ocean platforms. The efficiency of the WECs varies depending on the coupling to the ocean platform. In this paper, a three-dimensional diagram of a WEC based on an offshore fixed wind turbine is shown in Figure 1.  
The above 3D model is simplified to obtain a schematic diagram of the operation of a single buoy coupled to a wind turbine. As shown in Figure 2, for this multi-degree of freedom WEC, there are two rotational joints, located at the beginning and end of the pendulum arm, with one end connected to the buoy and the other to the turbine base. \(H\) is the distance from the buoy restraint point to the PTO articulation point, \(h_{1}\) is the distance from the buoy center to the restraint point, \(h_{2}\) is the distance from the PTO articulation point to the upper surface of the buoy, and \(D\) is the tower diameter. In the time domain analysis, two rotational joints were established. The rotational joint connected to the tower incorporated the entire pendulum system to establish a rotating PTO system, with a damping coefficient \(C_{PTO}\) to generate electrical energy output. A rotational constraint was set at the rotational joint connecting the buoy and the swing arm, with a damping coefficient \(C\).
### Calculation Method of Transmitted Impact Energy \(E_{t}\)  
The energy \(E_{\mathrm{t}}\) transmitted to the RC beam during a weight collision was calculated under the assumption of a perfect plastic collision where the weight and the RC beam moved together without repulsion after the collision. The formula for calculating \(E_{\mathrm{t}}\) was derived, as follows.  
First, the momentum conservation laws, immediately before and after the weight collides with the beam, are expressed as follows:  
\[M_{\mathrm{w}}V=(M_{\mathrm{be}}+M_{\mathrm{w}})\;V_{\mathrm{a}} \tag{2}\]  
\[V_{\mathrm{a}}=(M_{\mathrm{w}}/(M_{\mathrm{be}}+M_{\mathrm{w}}))\;V \tag{3}\]  
where \(M_{\mathrm{be}}\) is the equivalent mass of the beam obtained by assuming that the vibration mode of the beam is equivalent to the first-order bending mode and multiplying the mass of the beam within its clear span \(M_{\rm b}\) by 17/35. Furthermore, \(M_{\rm w}\) is the mass of the impact weight, \(V\) is the velocity of the weight immediately before collision, and \(V_{a}\) is the velocity of the mass point, including the falling weight and the beam, immediately after the collision. Subsequently, we considered the kinetic energy before collision \(E_{\rm k}\) can be estimated using Equation (4), and the energy after collision \(E_{\rm ka}\) can be derived using Equations (5)-(7) as follows:  
\[E_{\rm k}=1/2~{}M_{\rm w}~{}V^{2}~{}(\text{before collision}) \tag{4}\]  
\[E_{\rm ka}=1/2~{}(M_{\rm be}+M_{\rm w})~{}{V_{\rm a}}^{2}~{}(\text{after collision}) \tag{5}\]  
\[={M_{\rm w}}^{2}~{}/~{}(2~{}(M_{\rm be}+M_{\rm w}))~{}V^{2} \tag{6}\]  
\[=(M_{\rm w}/(M_{\rm be}+M_{\rm w}))~{}E_{\rm k} \tag{7}\]  
where \(E_{\rm ka}\) is the kinetic energy of the combined weight and beam immediately after the collision and corresponds to the energy transmitted to the beam \(E_{\rm t}\). The energy \(E_{\rm t}\) can be determined using Equation (8).  
\[E_{\rm t}=(M_{\rm w}/(M_{\rm be}+M_{\rm w}))~{}E_{\rm k} \tag{8}\]  
\[M_{\rm be}=(17/35)~{}\rho\rm{AL} \tag{9}\]  
where \(\rho\) is the unit mass of the RC beam (=2.5), \(A\) is the sectional area of the beam, and \(L\) is the clear span of the beam.