esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
All the implants and abutments under analysis have been verified, and the results showed that, even with very high loads acting on the implants, the maximum stress, evaluated with the [PERSON] criterion, was always lower than the yield strength of the implant and the abutment, respectively, 483 MPa and 860 MPa.  
In conclusion, the peri-crestal study of the first and third systems (Cono-in 3.4 and Cono-in 5.2) confirmed the following:  
* The 3.4 mm Cono-in system was more stressed by large occlusal forces in the peri-crestal area with a much lower stress distribution;
* The 5.2 mm implant resisted higher occlusal loads than the first one thanks to its more consistent geometry; but, in the peri-crestal area, it had values and distributions of the most important stresses due to the inclination of the abutment, which needs more attention;
* An inclined load leads to an increase in stress in the abutment-implant connection area.  
Such a study can, therefore, help the clinician to accurately assess the size of the implant and take into account any parafunctional loads such as bruxism, avoiding failures that would lead to increased costs on the part of the patient and the clinician. The next investigation should attempt to correlate the results with clinical results. Currently, there is still no correspondence between FEA studies and in vivo studies. In doing so, it improves the validity of the models. In addition, it is necessary to simulate the consequences of saliva, infection, and fatigue failure under repetitive, realistic, and cyclical load conditions.  
Conceptualization, B.T.; Methodology, C.C., M.C. and A.S.; Formal analysis, A.S.; Investigation, C.C.; Resources, B.T.; Data curation, M.C.; Writing - original draft, C.C., M.C., A.S. and B.T.; Writing - review & editing, A.P.; Visualization, A.P.; Supervision, B.T.; Project administration, B.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank Company 3P ImplaFavourite for its valuable participation in the modeling process and technical consult.  
The authors declare no conflict of interest.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach
### State Representations of Virtual Linkage and Virtual Link  
In mechanical engineering, the state of a rigid body can be represented as a vector \((r,q)\), where \(r\) and \(q\) are the position and attitude of the rigid body. In order to describe the state of each link, a frame attached to each link is defined. That is, \(F^{\mathit{v}j}\) is attached rigidly to link \(j\). Following the Denavit-Hartenberg convention [30], which is used for selecting the reference frame and analyzing the state of a linkage, the reference frames of links are laid out as follows:  
1. The \(z\)-axis lies in the direction of the joint axis.
2. The \(x\)-axis is parallel to common normal: \(x_{i}=z_{i}\times z_{i-1}\) points from joint \(i-1\) to \(i\).
3. The \(y\)-axis is formed by the right-hand rule to complete the \(j\)th frame. Meanwhile, the virtual joint angle \(\theta_{i}\) is defined as the angle from axis \(x_{i-1}\) to \(x_{i}\) measured about \(z_{i-1}\)  
Figure 7: Centralized architecture based on the virtual structure approach.  
The coordinate frame \(F^{\
u j}\) corresponding to the \(j\)th virtual link is shown in Figure 8. The state of the \(j\)th virtual link is represented as a vector \(\left(r_{\
u j},q_{\
u j}\right)\), where \(r_{\
u j}\) and \(q_{\
u j}\) are the real position and attitude of the \(j\)th virtual link. In reality, only a parameter \(\theta_{ld}\) besides the length of the two links is required to describe the shape of the virtual linkage. Thus, the \(F^{R}\) is chosen to be the same with \(F^{\
u 1}\) and the state of the virtual linkage can also be denoted as \(\left(r_{R},q_{R}\right)\), where \(r_{R}\) and \(q_{R}\) are the position and orientation of the reference coordinate frame \(F^{R}\) (See Figure 8).
## 6 Optimization Benefit Analysis  
### Efficiency and Economic Benefits Analysis  
The construction efficiency is primarily affected by the ratio of drifts. A smaller ratio of left and right drifts results in a smaller excavation section, making mechanical excavation and slagging more challenging. Manual excavation and cleanup of soil and rock left at the upper bench platform are necessary in this scenario. For instance, with a ratio of 0.2, the area of both sides of the drifts is only 28 m\({}^{2}\). Five people are needed for each cleanup, taking 3 h per cycle. With a section length of 70 m using the double-side drift method, the total time required is 280 h. Conversely, with a ratio of 0.3, the area of the guide holes increases to 48 m\({}^{2}\), allowing for mechanical excavation and slagging on the benches, thus saving 280 h of manpower.  
When the ratio of left and right drifts is small, mechanical excavation and slag removal from the upper platform becomes challenging. Manual supplementary excavation is necessary after the main mechanical excavation, followed by clearing soil and rock to the lower platform for loading and transportation out of the tunnel. In Section 6.1, it is shown that with a drift ratio of 0.2, the area of the drift is only 28 m\({}^{2}\) and the cyclic footage is 1.5 m. According to calculations, the upper bench of both side drifts requires manual labor to clear 21 m\({}^{3}\) of soil and rock. With a tunnel segment length of 70 m using the double-side drift method, the total soil and rock to be cleared manually is estimated at 2614 m\({}^{3}\), resulting in a manual cleaning cost of CNY 52,280. Optimizing the construction drift ratio can help reduce the labor costs associated with this process.
## 2 System and Channel Model
### Causal Factors in RL Structure
#### 2.4.2 Structural Causal Model  
The wireless environment exhibits ubiquitous causality, leading to causal changes in the wireless channel over time. Obtaining the causality of the time-varying wireless channel enables efficient modeling even with limited channel measurements. The key to representing wireless channel causality lies in developing suitable structural causal models (SCMs) [33]. In this paper, we denote the SCM as \(M\), which is a tuple \(<\mathbf{U},\mathbf{V},\mathcal{F},P(u)>\). \(\mathbf{V}=[V_{1},\ldots V_{n}]\) represents a set of endogenous variables, i.e., variables influenced by other variables in the study. \(\mathbf{U}=[U_{1},\ldots,U_{m}]\) represents a set of exogenous variables, i.e., variables in the study not influenced by other variables. A set of structural functions determining \(V\) is defined as \(\mathcal{F}=[f_{1},\ldots,f_{n}]\). \(P(u)\) represents the distribution over \(\mathbf{U}\).  
Next, we formalize the multi-RIS assisted wireless system in the causal domain as Figure 2 shows.  
Figure 2: The causal graphs for both reward (**left**) and state (**right**) depict the situation of the state \(s_{\text{t}}\) in the RIS-assisted wireless electric vehicle charging network. In this graphical representation, all elements are classified into three tiers: the top layer corresponds to the outcome variables, represented by the green text box; the bottom layer corresponds to exogenous variables \(\mathbf{U}\), consisting of manipulable variables, depicted in blue text boxes (\(X\)); and the variables that have a direct impact on the outcome variables (\(Z^{R}\) and \(S^{R}\)) are marked in orange, positioned between the green and blue layers. These variables are related to the internal factors denoted by \(\mathbf{V}\). The interactions between these tiers are depicted by gray arrows, illustrating the intricate causal relationships facilitated by the structural functions \(\mathcal{F}\). At each time step \(t\), controllers adjust the blue nodes, influencing subsequent observations of values in the orange and green nodes.
## 2 Materials and Methods  
YOLO-v3 is a one-stage network based on the method of regression to extract the features and directly predict and classify the input images; it not need to generate a large number of candidate windows compared with two-stage networks, and has excellent recognition speed and detection accuracy. Specifically, YOLO-v3 first resizes the input image to the 416 \(\times\) 416 pixels fed into deep neural networks for training, and then divides the image into S \(\times\) S grids, with each grid being responsible for predicting the object within it. The feature extraction network of YOLO-v3 is composed of Darknet-53 [39] and multi-scale feature fusion (26 \(\times\) 26 feature maps are fused with 52 \(\times\) 52 feature maps via up-sampling, similarly, the 13 \(\times\) 13 feature maps are fused with the 26 \(\times\) 26 ones via up-sampling). The detection accuracy can be greatly improved by the combination of the deep feature and the shallow feature.  
Compared with most other object detection models, YOLO-v3 has the advantages of faster detection speed and higher detection accuracy, but it still has several challenges when directly applied for insulator detection in aerial images from transmission lines (e.g., missed detection in small object detection, insufficient extraction of low resolution feature, and Recall of the model is low, etc.). In order to solve the above issues, on the basis of YOLO-v3 and DenseNet, a Cross Stage Partial Dense YOLOv3 (CSPD-YOLO) model is proposed in this paper. The entire structure of the CSPD-YOLO model is shown in Figure 2, which is composed of a feature extraction network, feature pyramid network (FPN), and detection network. To promote feature reuse and propagation in the low-resolution feature layers, Cross Stage Partial Dense (CSPD) blocks were adopted to the feature extraction network of the CSPD-YOLO model. To obtain multi-scale features of insulator faults, a structure of feature fusion was adopted to the CSPD-YOLO model. To avoid gradient vanishing, three residual units were adopted in front of YOLO headers. To improve the detection accuracy of insulator faults, an improved loss function was adopted to the CSPD-YOLO model.
## 2 Related Work
### Clustering
#### 2.2.1 K-Means  
The K-means clustering algorithm is a representative method of partitional clustering, and it divides data into K preset clusters. Figure 1 compares the situations before and after clustering. The red clusters represent data around the first center point, the orange clusters represent data around the second center point, and the green clusters represent data around the third center point. These visualizations give you an intuitive sense of the distribution and centroids of each cluster. The basic process of the algorithm starts by the random selection of K cluster centroids, and each data point is assigned to the closest centroid. The nearest centroid is calculated based on Euclidean distance. Then, a new center point for each cluster is determined by averaging the data points within that cluster. After repeated processes of reassigning each data point to the closest cluster based on the newly calculated centroid, the algorithm converges when the centroid or assignment no longer changes [23].  
The K-means algorithm has the advantages of simplicity and computational efficiency. It is widely used in many fields because it can be applied quickly even on large datasets [24]. However, it requires specifying the number of K clusters in advance, causing difficulties in determining the optimal number of clusters. Further, since K-means assumes spherical clusters, it may not be suitable for data with nonspherical structures, and the results may vary depending on the initial centroid settings [18].
# Dynamic SDN Controller Load Balancing
## 6 Simulations
Figure 6: Difference bound and the final difference results. Figure 7: Distance between the difference bound and actual difference. As noted, the initialization of step 1 in the DCC algorithm reduces the number of replacements required in step 2. Figure 10 depicts the number of replacements required, with and without initialization of step 1. The results are for 75 controllers and 15 clusters over 50 different network scenarios. As mentioned previously in Section 5.1.1, during the initialization we can also consider the constraint on the distance (although it is not mandatory and in Section 5.1.2 we presented an initialization based on load only). Thus, if a controller-to-controller maximal distance constraint is important, we have to compute the lower bound on the maximal distance. By adding this lower bound to the offset defined by the user, an upper bound called \"\(Cnt\)\" is calculated (Equation (8)). Figure 11 shows the final maximal distance that remains within the upper and lower bounds. The results are for 30 controllers and 10 clusters with offset 20 over 30 network scenarios. Figure 10: Number of replacements with and without initialization. Figure 9: Increase in the number of replacements with an increase in the number of clusters. Finally, we compare our method of dynamic clusters with another method of fixed clusters. As a starting point, the controllers are divided into clusters according to the distances between them (Heuristic 1). In each time cycle, the clusters are rearranged according to the controllers' loads of the previous time cycle. The change in the load status from cycle to cycle is defined by the following transition function:  
\[f(n)=\left\{ \begin{array}{cc}max(l(t)_{i}+random(range),P)&random(0,1)=1\\ max(l(t)_{i}+random(range),0)&else\end{array} \right\}\]  
where \(P\) is the number of requests per second a controller can handle. The load in each controller increases or decreases randomly. We set the range at 20, and \(P\) at 1000. Figure 12 depicts the results with 50 controllers partitioned into 10 clusters. The results show that the differences between the clusters' loads are lower when the clusters are dynamic. Following Figure 12, we ran simulation (see Table 4) for different configuration than the one in Figure 12, where 50 controllers were considered. Here, we simulated the comparison on different random topologies, with a different number of controllers and clusters in each topology. The number of cycles we run is also randomly chosen. The simulation results indicate that the difference is improved fivefold by the dynamic clusters in comparison to the fixed clusters. Figure 11: Maximal distance between lower and upper bounds. Figure 12: Comparison results of dynamic clusters vs. fixed clusters.
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 4 Aircraft Wake Identification Results and Discussion
### Visualisation of Identification Results  
The improved ParNet classifies and visualises some images of the test set, as shown in Figure 13. The class is zero when the wake is identified, and the closer to zero the score is, the better the recognition performance is. The class is one when the wake is not identified, and the closer to one the score is, the better the recognition performance is. We purposely selected the representative samples with different time periods, different flights, and different background winds.  
Figure 12: Verification accuracy of attention module comparison experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Attention Module** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Value (\%)** & **MFLOPs** \\ \hline SSE & 96.88 & 92.64 & 96.92 & 94.74 & 0.84 \\ ECA & 98.28 & 99.04 & 97.50 & 98.26 & 0.73 \\ SE & 95.62 & 98.02 & 93.12 & 95.51 & 0.74 \\ CA & 97.19 & 98.07 & 95.63 & 96.84 & 0.75 \\ CBAM & 98.91 & 99.06 & 98.90 & 98.90 & 0.75 \\ \hline \end{tabular}
\end{table}
Table 3: Recognition results of attention comparison experiment.  
It is seen in Figure 13 that although both the ParNet and improved ParNet output correct the recognition results, the scores of the improved ParNet approximate the intended values more, which shows that the improved ParNet can effectively identify aircraft wake. It is also noted that when the left and right vortices separated and dissipated along with the evolution of the atmospheric environment, the model had better generalisation capability because the attention module was able to learn the characteristics of the wake at different stages.  
The confusion matrices of the wake recognition using the ordinary ParNet and improved ParNet are demonstrated in Figure 14. It is clearly seen that the improved ParNet improves the recognition accuracy for the wake and non-wake, respectively. Concurrently, the improved ParNet considerably decreases the probabilities of false alarms and miss detection. The improved ParNet appears to have good performance in recognizing the aircraft wake.  
Figure 14: Confusion matrix (**a**) ParNet; (**b**) improved ParNet.  
Figure 13: Visualization of wake recognition. (**a,b,e,f,i,j**) with wake; (**c,d,g,h,k,l**) without wake; (**a,c,e,g,i,k**) by ParNet; (**b,d,f,h,j,l**) by improved ParNet.
## 3 Case Study and Results
### Process of Establishing the IPSO-LSSVM Model
#### 3.3.2 Comparison of the Accuracy of the Models  
The coefficient of determination (\(R^{2}\)), the mean absolute error (\(MAE\)), and the root-mean-square error (\(RMSE\)) were adopted as the evaluation indexes of model accuracy. The accuracy of the IPSO-LSSVM models of \(K_{\rm c}\), \(P_{\rm min}\), and \(P_{\rm max}\) were compared with that of RSM and PSO-LSSVM models. The comparison results are shown in Table 5. The comparison between the predicted value and true value of \(K_{\rm c}\), \(P_{\rm min}\), and \(P_{\rm max}\) is shown in Figures 8-10.  
It can be seen from Table 5 that the accuracy of the RSM models is the lowest, indicating a highly nonlinear relationship between the influencing factors and the safety indexes of the concrete gravity dam. The accuracy of the remaining two models is higher, among which the IPSO-LSSVM models have the highest accuracy. The accuracy of the IPSO-LSSVM models is significantly improved compared with that of the PSO-LSSVM models, indicating that the IPSO algorithm is better at tuning the hyper-parameters of the LSSVM model than the PSO algorithm. Therefore, the IPSO-LSSVM model has significant advantages for dealing with highly nonlinear problems, and the IPSO algorithm is outstanding for the optimal solution of complex problems.  
Figures 8-10 show that the linear relationship between the predicted and true value is \(y=x\), indicating that the IPSO-LSSVM models is reliable, which can be used for the reliability calculation of concrete gravity dams.
## Materials and Methods
### Measuring the Cut Width  
To determine the width of the cut, a section profile of the image was built perpendicular to the cut. The section profile is the dependence of the image brightness on the spatial coordinate. An example of laser cuts and a cut cross section is shown in Figure 2A,B. A cut in a photo may have a white border, primarily on one side. We suppose that this is caused by a combination of optical effects, including refraction and total internal reflection. When the cut is illuminated by a microscope lamp, the rays of light travel within the bulk of the ZP and between the walls of the cut. The white border appears when the ray of light being reflected from a wall of the cut is directed right to the microscope camera. The opposite wall stays in shadow and the dark area indicates the opposite boundary of the cut. For thin cuts, the amount of light scattered by the wall decreases, and the white border may disappear. A mean value of \(D_{2}\) and \(D_{1}\) shown in Figure 2B was taken as a cut width. The difference between \(D_{2}\) and \(D_{1}\) determined the inaccuracy in width estimation. Averaging was carried out over 50 sections. The measurements were carried out three times for different embryos. The average value of the cut width \(D_{\mathrm{av}}\) was finally obtained, as well as \(\Delta D\), characterizing the minimum-maximum cut width spread in the series. It should be noted that the term \"cut\" here stands for laser-affected area of the ZP, including complete protein dissociation or density changes in the ZP causing alternations in its index of refraction.
## 4 Analysis of the Experimental Results
### Straight-Line Running Positioning Experiments
This is related to the drift characteristics of the inertial meter over time. To meet the long-term positioning requirements, a more accurate instrument can be used. In the case of straight line running, the discrete amount of the isolated points in the Y direction can be corrected by map matching. By projecting the isolated points on a straight line map, it can be ensured that the shearer is positioned on the scraper conveyor and reducing positioning errors. The positioning results after the map matching is shown in Figure 8;
5. The density of the coordinate points reflects the operating speed of the shearer. In the experiment, the cycle of positioning calculation is controlled by the system time. At the same sampling frequency, the slower the running speed, the more detailed the sampling data is. Therefore, the running speed of the coal shearer is related to the density of the coordinate points. The slower the shearer runs, the denser the coordinate points. Therefore, the positioning system can not only reflect the position information of the shearer in real time, but also display the speed in each operating section.
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 3 Module Implementation
### Self-Learning Processing Module Implementation  
Self-learning processing module implementation is mainly to extend OpenFlow actions, which can perform some services in the data plane, such as the L2-switch. All the action definitions are included in include/openflow directory. For example, in the openflow/openflow-1.3.h file, the structure named ofproto_action_type defines all types of actions. There are some action structure definitions. To support a new action, the new action type is to be added into enum ofp13_action_type like OFPAT13_SELF_LEARNING. Then an appropriate structure definition is to be defined like struct ofp13_action_self_learning in the same file (Algorithm 3):  
```
struct ofp13_Action_self_learning { ovs_be16 type; /*OFPAT11_SELF_LEARNING.*/ ovs_be16 len; /* Length is 8.*/ uint8_t pad [4]; };
```  
**Algorithm 3**  
All the actions are supported by OpenFlow users pace and some actions extended by OVS are defined in the file ofp-actions.h. All the necessary structures used by these actions are also defined in the same file. The macro which defines actions is as follows (Algorithm 4):  
```
#define OFPACTS DEFINE_OFPACT(OUTPUT, ofpact_output, ofpact) DEFINE_OFPACT(GROUP,ofpact_group,ofpact) DEFINE_OFPACT(CONTROLLER,ofpact_controller,ofpact) DEFINE_OFPACT(ENQUEUE,ofpact_enqueue,ofpact)
```  
**Algorithm 4**  
For self-learning action, we need to add the new action to the macro like DEFINE_OFPACT (SELF_LEARNING, ofpact_self_learning, ofpact).  
In addition to the above work, the flow chart (see in Figure 5) shows the remaining work for the process of Self_Learning module implementation. When a packet matches a rule containing the self-learning action, the corresponding action will be executed (see step 1 and step 2 in Figure 5). Therefore, a necessary function named xlate_self_learning_action should be defined in ofproto/ofproto-dpif-xlate.c. This function is to parse the packet and generate a new rule (see step 3 in Figure 5). Then it checks whether there is a rule as same as the new rule in the flow tables. If not, the new rule is to be inserted into the appropriate flow table. Otherwise, the old rule will be updated according to this new rule (see step 4 in Figure 5). Besides, we make some appropriate changes to some related functions in lib/ofp-actions.c file.  
If a controller sends a Flow-Mod message containing a new match field and a new action to OVS. OVS can't parse the new match field and the new action, so necessary modifications have to be made in some files to make them compatible with the new actions and match fields in the Flow_Mod message. These main files are lib/ofp-parse.c, lib/ofp-util.def, lib/match.c, meta-flow.c (struct mf_field), and nx_match.c.