esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Criteria  
In the present research, the following performance metrics (Equations (7)-(10)) were applied: correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), ratio of RMSE to standard deviation (RSD) [62; 63; 64]:  
\[R=\frac{\sum_{i=1}^{N}\bigl{(}t_{exp}-\overline{t_{exp}}\bigr{)}\bigl{(}t_{pre }-\overline{t_{pre}}\bigr{)}}{\sqrt{\sum_{i=1}^{N}\left(t_{exp}-\overline{t_{ exp}}\right)^{2}}\sum_{i=1}^{N}\bigl{(}t_{pre}-\overline{t_{pre}}\bigr{)}^{2}} \tag{7}\]  
\[NSE=1-\frac{\sum_{i=1}^{N}\bigl{(}t_{pre}-t_{exp}\bigr{)}^{2}}{\sum_{i=1}^{N} \bigl{(}t_{exp}-\overline{t_{exp}}\bigr{)}^{2}} \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}t_{pre}-t_{exp}\bigr{)}^{2}} \tag{9}\]  
\[RSD=\frac{RSME}{\sum_{i=1}^{N}\bigl{(}t_{exp}-\overline{t_{exp}}\bigr{)}} \tag{10}\]  
where \(t_{exp}\) and \(t_{pre}\) denote the experimental and predicted target variable values, respectively. \(\overline{t_{exp}}\) and \(\overline{t_{pre}}\) are the mean of experimental and predicted target variable values, respectively. \(N\) denotes the total number of data. The \(R\) index, which is in the range of (0,1) (with \(R=1\) as the ideal value), shows the selected predictors suitability in predicting the target variable. NSE, with the range of (\(-\infty\), 1) and ideal value equal to unity, is used for assessing the capability of the proposed methods. Therefore, a value equal to unity shows perfect fitting between the actual and measured target values, and a negative value means bad performance of the model with respect to the arithmetic mean of the used models. RMSE and RSD with the range of (0, +\(\infty\)) and ideal value of zero are used to assess the accuracy.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 5 Numerical Examples
### The 3D Poisson Problem  
The 3D Poisson problem considered in this example is defined as follows:  
\[Lu\equiv-\
abla^{2}u=f\quad\text{in}\ \Omega \tag{23}\]  
where the domain \(\Omega\) is an irregular hexahedron as shown in Figure 9 with a homogeneous Dirichlet boundary condition on all boundaries. The function \(f\) is chosen so that the exact solution is of the form  
\[\left\{\begin{array}{ll}u=\cos(\pi a/2)\cos(\pi b/2)\cos(\pi b/2)\quad a=3x/(9+ y-z)\\ b=(y+2z-30+\sqrt{y^{2}+4 yz+4z^{2}+48y-120z+576}/6\\ c=(y+2z+15-\sqrt{y^{2}+4 yz+4z^{2}+48y-120z+576}/9\end{array}\right. \tag{24}\]  
The errors of the nodal displacements, \(e_{i,\text{max}}^{h}\), \(\tilde{e}_{i,\text{max}}^{h}\), and the errors of interior displacements, \(e_{\text{max}}^{h}\), \(e_{\text{max}}^{*}\), \(\tilde{e}_{\text{max}}^{*}\), are presented in Table 4, from which similar conclusions to those in Section 5.1 can be obtained. For comparison purposes, the quadratic element solutions using the same FE mesh are also shown in Figure 10, and it is noted that the improved EEP for linear elements achieves the same convergence order as that of quadratic elements.  
Figure 8: Distributions of effective index: (**a**) component in the \(x\) direction; (**b**) component in the \(y\) direction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(N_{e}\)} & \multicolumn{2}{c}{**FEM**} & \multicolumn{2}{c}{**Improved FEM**} & \multicolumn{2}{c}{**FEM**} & \multicolumn{2}{c}{**EEP**} & \multicolumn{2}{c}{**Improved EEP**} \\ \cline{2-10}  & & \(e_{i,\text{max}}^{h}\) & \(\rho\) & \(\tilde{e}_{i,\text{max}}^{h}\) & \(\rho\) & \(e_{\text{max}}^{h}\) & \(\rho\) & \(\tilde{e}_{\text{max}}^{*}\) & \(\rho\) & \(\tilde{e}_{\text{max}}^{*}\) & \(\rho\) \\ \hline \multirow{4}{*}{1} & \(8\times 8\) & \(8.68\times 10^{-2}\) & - & \(9.72\times 10^{-3}\) & - & \(8.68\times 10^{-2}\) & - & \(8.78\times 10^{-2}\) & - & \(3.06\times 10^{-2}\) & - \\  & \(16\times 16\) & \(2.26\times 10^{-2}\) & 1.94 & \(6.56\times 10^{-4}\) & 3.89 & \(2.39\times 10^{-2}\) & 1.86 & \(2.26\times 10^{-2}\) & 1.96 & \(3.05\times 10^{-3}\) & 3.33 \\  & \(32\times 32\) & \(5.58\times 10^{-3}\) & 2.01 & \(4.84\times 10^{-5}\) & 3.76 & \(6.17\times 10^{-3}\) & 1.95 & \(5.58\times 10^{-3}\) & 2.01 & \(3.70\times 10^{-4}\) & 3.04 \\  & \(64\times 64\) & \(1.40\times 10^{-3}\) & 2.00 & \(3.32\times 10^{-6}\) & 3.87 & \(1.55\times 10^{-3}\) & 1.99 & \(1.40\times 10^{-3}\) & 2.00 & \(4.59\times 10^{-5}\) & 3.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Convergence orders of displacement component in \(y\) direction.  
Figure 9: Irregular domain.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub
### Interaction between Physical CPS Resources  
Let us take an example of the CPS hub deployment case showing interaction between physical CPS resources in the traffic accident case. The prerequisite steps of the case, attachments of related physical CPS resources to the CPS hub are described in Section 3.4 and Figure 11. In the example interaction, Figure 18, a smart watch of Polar, is interacting via the CPS hub with a remotely located air quality sensor of IMEC.  
The solution applies bluetooth low energy (BLE) technology for interaction locally with the devices and gateways/infrastructure. The Polar smart watch is maintaining a BLE network for sensors in the BLE host role, and simultaneously acts as BLE client to the smart phone controlled BLE network. The smart phone operates as a gateway to the CPS hub. Depending on the smart watch capabilities, a gateway can act as a protocol translator, allowing the passing of messages directly to the Internet, like in this case. The gateway allows simpler and more power-efficient smart watches by reusing the existing mobile Internet connections. In this way, there is no need for additional ISP licenses/security devices/fees/maintaining.  
The IMEC air quality sensor applies bluetooth LE and a gateway for communication with the related Amazon cloud-based services. The CPS hub connector is applied in the gateway for registration and publishing CO\({}_{2}\) measurements to the CPS hub. The CPS hub acts as the communication intermediary realizing the communication spaces for the owners, Polar and IMEC in this example case; Figure 11.  
The interaction process of the smart watch via the CPS hub with remotely located air quality sensor is shown in the Figure 18. First, registration of the presences of the devices to the CPS hub is required. The smart watch registers its presence to the Polar communication space (polar@m2 mgrids.erve.vtt.fi/Polar.M600.1) and CO\({}_{2}\) (imea@m2 mgrids.erve.vtt.fi/cp2-sensor1), respectively. Then, the trust creation process between the referred owners/resources needs to be done.  
Figure 18: Interaction process of smart watch via the CPS hub with remotely located air quality sensor.  
After this, the IMEC air quality sensor can start publishing the measured CO\({}_{2}\) level and location of the sensor. The Polar smart watch subscribes to the CO\({}_{2}\) level and location information of the sensor. When the information is available and the trust relationship between Polar and IMEC is present, the delivery of the CO\({}_{2}\) level and location information of the sensor can happen. After the information delivery, the CO\({}_{2}\) level can be shown on the screen of the smart watch.
## 2 Edge Enabled Network Slicing  
In the current context, where there is a wide variety of services and devices that wireless networks have to deal with, both network slicing and edge computing techniques help us to satisfy QoS and QoE requirements for heterogeneous use cases. However, to achieve a complete cross-layer solution and to manage, program, and slice a heterogeneous wireless network, both paradigms seem to be necessary [22]. This is because neither network slicing nor edge computing is able to satisfy the strict timing and performance requirements of the 5G services alone [23]. Indeed, as shown in Figure 1, MEC plays different roles for different network slices [24; 25]. For ultra-reliable, low-latency communication (URLLC) services, both storage and computational resources to undertake the function of data processing, analysis, and storage can be provided to ensure low latency and high reliability. In case of enhanced Mobile Broadband (eMBB) services, as high bandwidth is crucial, caching and traffic offloading at the edge can help to increase overall capacity of the mobile core. Similarly, for massive machine type communication (mMTC) applications, to provide high performance and scalability, computational and storage resources can be provided by the edge to deal with huge amounts data generated by IoT devices.  
However, deploying and managing a sliced wireless network at the edge to achieve an effective slicing mechanism is not an easy task. This is because both computation and storage resources are very limited at the edge of networks, which makes them hard to efficiently manage. Each task requested by applications need to be satisfied and to maximize the benefit of edge slicing, one should accurately compute and analyse the requirements of slices and prioritize if necessary. Another critical challenge aroused from the fact that the wireless channel and backhaul conditions are time-varying and cannot be  
Figure 1: The role of edge computing and slicing for different type of services.  
discovered in advance. Therefore, before deciding how to slice an edge resource, which is a long-term decision, several things need to be considered simultaneously to guarantee the QoS of multiple differentiated services.  
There are also set of challenges that sets MEC slicing apart from traditional resource allocation problems. Here, the key issue is that MEC resources are usually coupled, meaning that slicing one resource usually leads to a performance degradation in another type of resource [23]. Although algorithms that do not take isolation of slices into account can have lower time complexities compared to isolated ones, isolation among the partitions have better guarantees for QoS as well as Service Level Agreements (SLAs). Based on this observation, we classified all the studies we want to present into two categories as papers with non-isolated slices and with isolated slices.
## 4 Numerical Method of Support Design
### Evaluation of the Primary Support Performance  
Yielding steel arches, used as the primary support for the hoisting machine chamber and the technological shaft inset, were modeled as an arrangement of elastic beam elements with geometric parameters corresponding to the dimensions of the V29 and V36 profiles. To accurately represent the behavior of sliding joints, which enable the yielding steel arches to close in the circumferential direction, the axial stiffness of the beam elements was reduced to zero using the \"sliding gap\" option provided by the software manufacturer.  
It is important to note that the flexural behavior of the beam elements is not directly affected by the \"sliding gap\" option. The liners incorporated into the numerical models provide resistance to bending moments regardless of the status of the sliding gap [49]. Therefore, the assessment of the performance of yielding steel arches was carried out based on the bending moment diagrams obtained from numerical calculations, as well as the value of the strain ratio \(k\), calculated using the following equation:  
\[k=\frac{M_{max}\cdot m_{1}\cdot e}{f_{yd}\cdot(m+n_{1})\cdot W_{x}}. \tag{29}\]  
An exemplary bending moment diagram, obtained for the hoisting machine chamber, is depicted in Figure 6.  
The calculation results of the strain ratio of the yielding steel sets used as a primary support of the hoisting machine chamber (excavation A) and the technological shaft inset (excavation B), based on Equation (29), are summarized in Table 9.  
As indicated in Table 9, the values of the steel sets strain ratio \(k\) for both the hoisting machine chamber and the technological shaft inset are less than unity. Therefore, based on the numerical calculation results, it can be concluded that the primary support system, consisting of steel sets spaced at 0.6 and 0.5 m intervals--initially determined using the analytical design method (presented in Section 3 of this article)--should ensure initial excavation stability while maintaining the required safety factors.  
Figure 5: Numerical model for the technological shaft inset.
## 3 Results
### Evaluation on Forest Cover Dataset with Class 2 and 6  
This is a publicly available dataset to predict forest cover type.  
For the forest cover dataset with class 2 and 6, it was observed from Table 2 that, while precision was consistent for both of the sampling techniques, using SMOTE-ENC increased recall of the minority class observations, which resulted in marginal improvement of the F1-score for SMOTE-ENC at threshold 0.5.  
The precision-recall curve (Figure 6) showed that SMOTE-ENC was marginally better than SMOTE-NC for this dataset. However, the area under ROC and PR curve being high implied that the classes were easily separable, which makes the importance of sampling method less prominent. For this reason, we one-hot encoded the categorical features and applied SMOTE to resample the data. In this dataset, we had two categorical features, soil type and wilderness_area. While the former contained 40 labels, the latter had four labels.  
From Figure 7, it was observed that one-hot encoding of multi-label features decreased the area under ROC and PR curve. Thus, the importance of interpreting nominal features in a different way than interpreting continuous features in over-sampling method can be ascertained. We further showed that the importance of categorical features (soil type and wilderness_area) was picked up by SMOTE-ENC and SMOTE-NC but not by SMOTE (Figure 8).  
Figure 6: (a) Comparison between SMOTE-ENC and SMOTE-NC on forest cover dataset with class 2 and 6 by area under ROC curve. (b) Comparison between SMOTE-ENC and SMOTE-NC on forest cover dataset with class 2 and 6 by area under PR curve.  
Figure 7: (a) ROC curve for forest cover dataset with class 2 and 6 using SMOTE. (b) PR curve for forest cover dataset with class 2 and 6 using SMOTE.
# Underwater Wireless Communications for Cooperative Robotics with UWSim-NET
## 3 Results
### MAC Layer
The resulting data rates for each experiment are shown in Table 1. To illustrate the relevance of the MAC layer, the same experiment was done without considering any MAC protocol. The data flows were configured as with the CS-ALOHA. The performance achieved with each MAC protocol was compared in terms of throughput, efficiency, end-to-end delay and jitter. As expected, the throughput measured after the experiment (see Figure 7a), coincided with the values indicated in Table 1. However, when no MAC protocol was considered, only the data flows transmitted by the support, buoy and E2 succeeded in sending packets. The efficiency is the ratio of bytes successfully received to the total number of transmitted bytes. It is related with the power consumption of the communications system. It is appreciated in Figure 7b that the S-FAMA had a higher efficiency than CS-ALOHA in the acoustic channel, but the opposite occurred with RF channel between the I-AUVs and the support. This behavior might be understood considering the two different sizes of the packets used in each flux: data packets transmitted by leader, follower, support and buoy were 20 bytes long, but the image packets transmitted by the explorers were 400 bytes long. The less overhead of the CS-ALOHA, the smaller number of nodes in the RF  
Figure 6: Trajectories followed by the ROVs during the experiment. The trajectories of the explorers are represented in white lines. The lines in yellow and red correspond to the trajectories of the two I-AUVs, and the line in magenta corresponds to the support ROV. Each image corresponds to a different stage of the experiment: (**a**) approaching; (**b**) cooperative grasping; and (**c**) transportation. channel and the small likelihood of small packets to collide favored the better results observed with the CS-ALOHA. On the contrary, the larger packet sizes and the higher number of nodes favored the better performance of the S-FAMA in the acoustic channel. When no MAC protocol was considered, no positive results were obtained because very few packets were successfully transmitted and the efficiency was near zero. The end-to-end delay (Figure 6(c)), and jitter (Figure 6(d)) are related with the time lapse required to send the information between the ROVs. In general, S-FAMA achieved a lower end-to-end delay than CS-ALOHA (see Figure 6(c)). The low end-to-end delay achieved when no MAC protocol was used was due to the very low number of successfully transmitted packets. If the transmission succeeded, the packet was delivered immediately, and it had little contribution to the end-to-end delay. Concerning the jitter, comparable results were obtained with both protocols.
## 4 Results and Discussion
### Discussion
#### 4.2.2 Influence of Gasket Surface Friction Coefficient  
The O-ring is in contact with the gasket only in the deflation state, so the change of the surface friction coefficient of the gasket only affects the performance of the O-ring in the deflation state. Figure 16a shows the maximum von Mises stress curves of the O-ring under various gasket surface friction coefficients. As the surface friction coefficient of the gasket increases from 0.01 to 0.1 at 15 \({}^{\circ}\)C and 70 MPa, the maximum von Mises stress in the deflation state consistently larger than that in the inflation state. The results show a higher probability of O-ring damage in the deflated state. Additionally, the increase in von Mises stress in the deflation state is almost negligible, indicating that the influence of the friction coefficient on the von Mises stress in the deflation state can be disregarded.  
Figure 16b shows the maximum contact pressure curves of the O-ring under various gasket surface friction coefficients. The maximum contact pressures of both contact surfaces I and II are greater than 70 MPa, indicating that the O-ring effectively achieves its sealing function across a wide surface friction coefficient range. As the friction coefficient of the gasket surface increases from 0.01 to 0.1 at 15 \({}^{\circ}\)C and 70 MPa, the maximum contact pressure in the deflation state of contact surface I consistently remains lower than the other three situations (inflation contact surface I, inflation contact surface II, and deflation contact surface II), indicating relatively poorer sealing performance in the deflation state of contact surface I. Furthermore, the maximum contact pressure of contact surface II in the deflation state remains unchanged, while the maximum contact pressure of contact surface II in the deflation state fluctuates by approximately 3%. The results show that the influence of the surface friction coefficient on the sealing performance in the deflation state can be ignored. In summary, the influence of the surface friction coefficient on the performance of the combined sealing structure is found to be negligible.  
Figure 16: Influence of surface friction coefficient on O–ring performance: (**a**) max von Mises stress; (**b**) max contact pressure.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 2 Research Object  
The main research object in this study is an equal pitch inducer with helical static blades, as shown in Figure 1. The inducer is designed as follows: the flow rate \(Q=5\) kg/s, the head \(H=7.5\) m, and the rotating speed \(n=4000\) r/min, and the main structure parameters are shown in Table 1. The helical static blade has the same pitch and the opposite turning direction to the inducer. A characteristic parameter \(k\) is defined as \(h_{r}\)/\(D_{t}\), in which \(h_{r}\) is the radial height of helical static blade and \(D_{t}\) is the tip diameter of the inducer, so that \(h_{r}\) can be described more reasonably. Table 2 shows the corresponding relationship between \(k\) and \(h_{r}\) in different schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Number of Blades** & **Inlet Hub Diameter \(d_{hl}\)/mm** & **Outlet Hub Diameter \(d_{h2}\)/mm** & **Tip Diameter \(D_{t}\)/mm** \\ \hline Value & 3 & 24 & 40 & 78 \\ \hline \end{tabular}
\end{table}
Table 1: Main parameters of the inducer.  
Figure 1: Structure of inducer with helical static blades.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 4 Discussion of Wave-Induced Pile-Seabed Response
### Pile-Stiffness Effect  
Pile stiffness is another important factor in pile lateral-displacement response. Two materials, plexiglass and aluminum, were used to fabricate the model piles. The ratio of stiffness between these two piles reached 1:22 to facilitate the comparison. Regarding the permanent displacement shown in Figure 12a, the aluminum pile in T6(f-\(H8\)-\(D3\)a) in Sample 1 seabed presented the smallest responses, showing the noticeable effect of pile stiffness.  
Figure 15: Oscillating pore pressure around a finned pile in Sample 2 seabed.  
Results similar to those obtained for Sample 2 seabed can be seen for the aluminum pile in T12(s-_H8-D3_a) (Figure 13a), where a small increment of permanent displacement was observed in the experiments. This was due to finite deflection being transmitted to the deep seabed, leading to small soil plastic strain and avoiding soil-strength degradation.
# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
## 5 Discussion  
### Result Verification  
A visual comparison of the results of crack detection on a brick wall using YOLOv5s, YOLOv7-tiny, YOLOv8n, and the improved CL-YOLOv8 algorithm (from top to bottom) is presented in Figure 9. The CL-YOLOv8 algorithm showed a clear improvement in detection accuracy compared to the other models. This enhancement can be attributed to the integration of the ConvNeXt V2 model, which significantly improved feature extraction through deeper and more complex convolutional layers, and the LSKA mechanism, which enhanced the receptive field and feature correlations. These architectural improvements allowed CL-YOLOv8 to better capture subtle crack features, even in challenging lighting and environmental conditions, as demonstrated by the higher confidence scores and more precise crack localization.  
From a numerical standpoint, CL-YOLOv8 achieved an accuracy of 85.3%, a recall rate of 83.2%, and a mean average precision (mAP) of 83.7%. These improvements in performance metrics were consistent with the visual results, as higher accuracy and recall rates translated into more reliable crack detections. In contrast, YOLOv5s and YOLOv7-tiny, while able to detect cracks, exhibited lower confidence scores and a reduced precision. This suggests that these models are less capable of distinguishing cracks from the surrounding texture or noise, particularly under less favorable conditions.  
Figure 9: The detection effect of different models. YOLOv5s (**a**), YOLOv7-tiny (**b**), YOLOv8n (**c**), and CL-YOLOv8 (**d**).  
In comparison to existing methods, such as traditional image processing or non-AI-based techniques like ultrasonic or infrared thermography, the proposed algorithm demonstrated a superior adaptability to varying conditions. While these traditional methods are effective under controlled circumstances, their sensitivity to environmental changes is a limitation. By leveraging the strengths of advanced deep learning architectures, CL-YOLOv8 offers a more comprehensive solution for crack detection in diverse environments, which is crucial for maintaining the structural integrity of historical buildings. The test results for whether the fair-faced wall had cracks are shown in Figure 9.