esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 4 Model Construction
### SHAP-Based Feature Interpretation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multirow{2}{*}{**Train Set**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**Test Set**} & \multirow{2}{*}{**Mean**} \\  & & 1 & & 1 & **SHAP Value** \\ \hline
1 & AGE & 0.574 & AGE & 0.558 \\
2 & LTE & 0.353 & DWL & 0.364 \\
3 & DWL & 0.346 & EM & 0.353 \\
4 & EM & 0.332 & LTE & 0.320 \\
5 & TS & 0.248 & TS & 0.286 \\
6 & BTH & 0.216 & BTH & 0.188 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sorting feature importance based on SHAP. Figure 8: Scatter plot of SHAP values. By utilizing the SHAP interpretation method, operators can gain insights into the extent of influence different indicators have on model predictions or outcomes. This can assist operators in understanding how various factors impact the final results and making informed decisions based on this understanding, ultimately enhancing the model's interpretability and reliability in practical business domains. In addition to providing global explanations for the model, SHAP provides local explanations for each individual sample. Figure 9 shows the SHAP interpretation of the prediction result for a sample with a predicted value of 0.514 mm and a true value of 0.5 mm. The figure illustrates each feature's contribution and its value in pushing the model's output from the baseline value, E[f(X)] (the average prediction of the CatBoost model on the entire dataset), towards the model's output. A horizontal bar with an arrow pointing to the right represents a feature that increases the faulting value from the baseline, while an arrow pointing to the left represents a feature that decreases the faulting value. The sum of the values on the horizontal bars plus the baseline value of the sample gives the model's output, which is the predicted faulting value for that sample. For example, in this particular sample, the DWL is smaller than the overall average DWL of the database. Since DWL has a negative correlation with the faulting value prediction, a larger DWL has a negative effect on the prediction. The same analysis applies to other variables. It is important to note that since this figure shows the prediction result of an individual sample, the feature importance order may not be the same for each sample, and it may also differ from the overall feature ranking of the dataset.
# A Ka-Band Circular Polarized Waveguide Slot Antenna with a Cross Iris
## 3 Antenna Analysis
The long side of (\(L_{1}\)) of the cross iris has a more dominant effect on the amplitude of \(E_{2}\) mode than on \(E_{1}\) mode. The \(E_{1}\) mode changes more with the variation in the length of short side (\(L_{2}\)). Therefore, we can conclude that the amplitude of \(E_{2}\) mode increases as \(L_{1}\) increases and that the amplitude of \(E_{1}\) mode increases as \(L_{2}\) increases. When \(L_{1}\) = 6 mm and \(L_{2}\) = 5 mm, a good magnitude match occurs between the two modes with a wide 90\({}^{\circ}\) phase difference range, leading to the wide axial ratio bandwidth. Additionally, because the cross iris has a height of 1.2 mm, it acts as a step between the rectangular waveguide and the radiation slot resulting in gradual change in the impedance so that the matching condition is improved [27]. Figure 11 shows the simulated reflection coefficients when the height of the cross iris (\(H_{4}\)) varies from 0.8 mm to 1.2 mm. When \(H_{4}\) equals 1.2 mm, an enhanced impedance bandwidth is obtained. As a result, as shown in Figure 7, the proposed antenna achieves the simulated 3 dB axial ratio bandwidth of 6479 MHz (31.97 GHz to 38.45 GHz) and a -10 dB reflection coefficient bandwidth of 6840 MHz (31.74 GHz to 38.58 GHz). Figure 9: Simulated amplitudes of \(E_{1}\) and \(E_{2}\) modes of (**a**) the second reference antenna and (**b**) the proposed antenna. Figure 3:0 Simulated amplitudes of \(E_{1}\) and \(E_{2}\) modes for various values of **(a)**\(L_{1}\) (\(L_{2}\) is fixed at 5 mm) and (b) \(L_{2}\) (\(L_{1}\) is fixed at 6 mm).
## Materials and Methods
### Equivalent Radial Stiffness Calculation  
To provide an overview of the tube, the surrounding rock classification is introduced here. On the basis of integrality, the uniaxial compressive strength (UCS) of the saturated rock sample (\(R_{b}\)) and the self-stability time of the surrounding rock, the surrounding rock was classified into 5 categories in the underground mining industry in China, namely, (I) stable strata, (II) fair stable strata, (III) medium-stable strata, (IV) poor stable strata, and (V) unstable strata [37]. According to the magnitude of \(R_{b}\), it can be roughly simplified into 3 categories: for \(R_{b}>60\) MPa, the surrounding rock is strong; for \(30<R_{b}<60\) MPa, the surrounding rock is medium-strong; and for \(R_{b}<30\) MPa, the surrounding rock is weak [38]. According to the statistical relationship between UCS and the elastic properties of the rock, the strong, medium-strong, and weak surrounding rocks correlate to \(E_{r}>50\) GPa and \(v_{r}=0.15-0.20\), \(E_{r}=15-50\) GPa and \(v_{r}=0.20-0.25\), and \(E_{r}<15\) GPa and \(v_{r}=0.25-0.30\), respectively.  
In China, borehole diameter was usually about 30 mm in the bolting support of a coal mine, i.e., a = 30 mm. Accordingly, by substituting the above parameters into Equation (3), the radial stiffness of the PVC (\(E\) = 4.0 GPa and \(v\) = 0.35) tube corresponds to the weak surrounding rock (\(E_{r}\) = 1.0-3.5 GPa, \(v_{r}\) = 0.25-0.30). The radial stiffness of the \(\#6061\) Al (\(E\) = 68.9 GPa and \(v\) = 0.33) tube corresponds to the medium-strong surrounding rock (\(E_{r}\) = 15-50 GPa, \(v_{r}\) = 0.20-0.25). The radial stiffness of the \(\#20\) steel (\(E\) = 206 GPa and \(v\) = 0.3) tube corresponds to the strong surrounding rock (\(E_{r}\) = 50-140 GPa, \(v_{r}\) = 0.15-0.20). Figure 2 illustrates the calculation results.  
Figure 2: Relationship between tube wall thickness and the elastic modulus of the surrounding rock (I–V corresponding to surrounding rock classification).  
Figure 2 shows that, on the basis of the equivalent radial stiffness principle, tubes composed of PVC, #6061 Al, and #20 steel with proper wall thickness nearly cover the full range of the surrounding rock's strength. Figure 2 does not give the corresponding tube materials and wall thickness of the surrounding rock with the elastic modulus of 3.5 to 15 GPa, and materials such as C20 to C40 concrete can be used to mimic the surrounding rock or used material with an elastic modulus of roughly 20 to 30 GPa. Currently, the material with an elastic modulus of roughly 20 to 30 GPa has not been found to be available, and using C20 to C40 concrete is the universal method used by many researchers. Consequently, a series of laboratory SEPT were conducted to investigate the anchorage performance of dextral bolts under different confining materials.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 2 Methods
### Fabrication  
To prove that the P-WIM is feasible of working properly under realistic conditions, performing the on-site evaluation with prototype P-WIMs is necessary. To achieve the objectives, four prototype P-WIMs are fabricated in the laboratory. For each P-WIM, nine piezoelectric disks (Disk-shaped material is used to eliminate the stress concentration when force is applied) are sealed in a protective package made from engineering plastic, which is insulating and able to resist the external impact from vehicles with a long lifespan. Another function of this package is keeping fluid or other chemical  
Figure 1: The illustration of 33- (**a**) and 31- (**b**) coupling mode of piezoelectric energy harvesting.  
and contaminants out from the piezoelectric materials, which prevents any form of the short circuit of the electric circuit caused by fluid. Figure 2a shows a conceptual sketch of the prototype P-WIM. The cover part of the protective package is transmitting the load applied by vehicles uniformly onto the piezoelectric material. Ununiformed stress distribution on the piezoelectric material will significantly reduce their service life since they are naturally brittle (Some of the prototype P-WIMs failed quickly in lab tests because the force was not uniformly distributed onto the piezoelectric materials). Figure 2b shows a prototype P-WIM without a cover. All the positive and negative poles of the piezoelectric material are connected by several bar-shaped copper electrodes integrated into the protective package. The 850 series PZT piezoceramic disks are selected based on the criterions mentioned previously and in the material selection section. The piezoelectric charge constant, d\({}_{33}\), of the 850 series piezoceramic is \(400\times 10^{-12}\) C/N, the piezoelectric voltage constant; g\({}_{33}\), is \(24.8\times 10^{-3}\) Vm/N, and Young's modulus along 33 directions is \(5.4\times 10^{10}\) N/m\({}^{2}\).
# Industry 4.0 Roadmap: Implementation for Small and Medium-sized Enterprises
## 3 Results
### Mixed Reality for Manufacturing and Shipping Processes  
HoloLens coupled with the software solution Microsoft 365 Guides is a hands-free computer for the operator that achieves the following:  
* It displays the manufacturing instructions as interactive Standard Operating Procedures (SOPs) and renders high-definition holograms that respond in a similar manner to physical objects. It goes where you go, sees what you see and does what you say;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Month/2020** & **Savings to JOP** & **Number of Non-Conformances** \\ \hline February & - & 145 \\ March & 270 h & 118 \\ April & 650 h & 80 \\ May & 720 h & 73 \\ June & 850 h & 60 \\ July & 920 h & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Andon system—quantitative results. JOP: jumping-off point.  
Figure 7: Implementation of the Andon system.  
* It displays digital pick lists. Order details are automatically assigned and sent to each picker, where they can follow instructions on their HoloLens, resulting in a more efficient process. A digital picking list can more easily include additional information such as product images, serving as an additional quality control check and as a validation that the picker is grabbing the correct item.  
Mixed reality uses one of the technologies selected in Section 2.1.2: VR. The implementation following the roadmap is described in Table 5.  
The total investment for the mixed reality system is $3520 [50]:  
* HoloLens 2 ($3455);
* Microsoft 365 Guides ($65 per user/month).  
Quantitative Results  
The results of mixed reality implementation are not available at this point in the research. The initial qualitative results gained from the operators in the Munich manufacturing site are presented in Section 4 below.
## 3 Results
### NF-kB, Nrf2, and HO-1 Expression in PBMC Based on CIMT  
A significantly increased expression of the three studied genes in the age < 55 MetS subgroup was detected (Figure 2). The increase in mRNA levels was as follows: 83% for NF-kb (\(p\) < 0.05), 251% for Nrf (\(p\) < 0.05), and 337% for HO-1 (\(p\) < 0.05). It is obvious that the expression level of these genes in PBMC is elevated in patients under 55 years of age, and HO-1 appears to be the most affected among the studied genes. In contrast, in the MetS subgroup with individuals over 55 years of age, the genes were under-regulated, achieving the levels of the control group, resulting in a statistically significant difference between the age < 55 and age \(\geq\) 55 MetS subgroups (Figure 1).  
### NF-kB, Nrf2, and HO-1 Expression in PBMC Based on CIMT  
The NF-kB, NRF2, and HO-1 expression, depending on CIMT, followed the same pattern as the dependence on age. The MetS group was subdivided according to the values of their CIMT: MetS with CIMT < 0.90 mm and MetS with CIMT \(\geq\) 0.90 mm. A CIMT of 0.90 mm was considered a cut-off value.  
Figure 1: Average CIMT according to the age of the MetS patient. Data are presented as mean \(\pm\) SEM. Control group: CIMT < 0.9 mm, age < 55. The MetS group was divided into two subgroups: MetS with age < 55 and MetS with age \(\geq\) 55. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001.  
Figure 2: Relative expression levels of NF-kB, NRF2, and HO-1 in PBMC, depending on age in MetS. Control group: age < 55. The MetS group was divided into two subgroups: MetS with age < 55 and MetS with age \(\geq\) 55. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001.  
A significantly increased expression of the three studied genes in the MetS group with CIMT < 0.90 mm was detected as follows: 80% for NF-kB (\(p\) < 0.01); 260% for Nrf2 (\(p\) < 0.05), and 303% for HO-1 (\(p\) < 0.05). Again, a trend was observed with NF-kB, NRf2, and HO-1 expression levels in PBMC changing with age and HO-1 being the most affected among the three studied genes. Similar to the results about the dependence of NF-kB, NRf2, and HO-1 expression on age, here, data show that in the MetS subgroup with individuals with CIMT \(\geq\) 0.90 mm, these genes were under-regulated, achieving the levels of the control group or even lower, with a statistically significant difference between the MetS subgroups with different CIMT being established (Figure 3).
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## 3 5G Field Trials
### 5G Network  
#### 3.2.1 Network Architecture  
The 5G network architecture deployed for the test-bed is based on a network slicing approach.  
Network slicing is considered one of the pillars of 5G systems. Specific functions can be designed to create and manage dedicated end-to-end logical networks, without losing the economies of scale of a common physical infrastructure. Each logical network is tailored to provide a specific service and/or provide a particular tenant with a given level of guaranteed network resources [15]. Consequently, 5G systems can support a wide variety of vertical markets that originate a wide range of services.  
The 5G network architecture considered in the field-test has a logical organization on three-layers (Figure 2). These are used for diversification of the service layer from the network functions and the physical infrastructure. Physical infrastructure is the first layer, whose main objective is the managing of the physical resources. Network functions is the second layer, whose scope is function customization. Service is the third layer and it maps the service level agreements (SLAs), quality of service (QoS), and required functionality into the slice's configurations. An orchestrator manages the three layers by mapping the resources available at different layers to the slices.
## 2 Methodology
### Plane-Map Generation  
Typically, CCTV installed at a construction site is affected by the perspective view, in which the location and dimension cannot be measured directly. A bird's eye view transformation of the perspective view to the top view, that is, a plane map, is necessary. Homography is an image processing method in which an image captured from a virtual angle is obtained using the corresponding point transformation relationship between two images, and a perspective view image can be transformed into a bird's eye view image, as shown in Figure 4a. The transformed bird's eye view is a virtual image that has not been photographed, but because it contains the same information as the plane map, normalized distance information can be obtained at all locations in the virtual image. An ROI consisting of at least four points is required to apply homography. The pixel coordinates (\(u\), \(v\)) inside the ROI are converted into plane coordinates (\(x\), \(y\)), as shown in Figure 4b.  
The plane map is mainly composed of entities of trucks and workers in this study. When tracking is completed in the case of a truck, a tracking path of the truck, a concatenated line of the center point (\(u_{truck},v_{truck}\)) of the bounding boxes in every frame, is determined. In the plane map, location (\(x_{truck},y_{truck}\)) and moving direction, termed as the orientation of the truck in this study, are identified. Given the dimension of the truck detected, it is mapped into the plane coordinates subjected to the orientation (Figure 5a).  
In the case of a worker determined to be in the \"upright posture\" state by the pose determination algorithm, the orientation of the worker is acquired in the perpendicular direction of the shoulder line, as the shoulder points acquired by pose estimation are converted to a bird's eye view through homography. A safety ellipse is generated in the blind area direction based on the shoulder line at the plane map (\(x_{worker},y_{worker}\)) point, where center point (\(u_{worker},v_{worker}\)) of the bounding box is converted (Figure 5b). A proximity  
Figure 4: Schematic of bird’s eye view transformation: (**a**) principal of its transformation; (**b**) coordinates of each image.  
Figure 3: Vision-dependent safety ellipse: (**a**) human vision and blind area; (**b**) safety ellipse, example 1; (**c**) safety ellipse, example 2.  
visualization of the degree of safety based on the distance between workers and trucks is developed by overlapping the plane map with the space occupied by the truck and the safety ellipse of the worker.
## 3 Results and Discussion
### Textural Evaluation
The first component (PC1) separates the M1 and M2 margarine group samples from M3 margarine samples based on monounsaturated fatty acids (MN), 10 mm spherical penetrometer hardness, cone plasticity, and the long-chain saturated fatty acid (LCS) content. The second component (PC2) separated the M2 group from the other samples based on the cone and 10 mm spherical fracturability values, with a smaller contribution having the 5 mm hardness and plasticity. From the loadings plot, it can be observed that the projection of M3 margarine samples was significantly influenced by the long-chain saturated fatty acids (LCS) and by the cone hardness values and polyunsaturated fatty acids (PN), which have a smaller influence. Of the outside and inside measured color parameters, only the b* color parameter indicates an influence in the M3 sample distribution; its values are also positively correlated with polyunsaturated fatty acids (PN). Regarding the projection of the M1 samples, it can be observed that moisture content has a strong influence on their positioning in the PCA diagram. The high value achieved in the dispersal of data variation highlights the utility of PCA in margarine classification using chemical composition, color parameters, and textural properties. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Probe** & \multicolumn{2}{c}{**PS\({}_{\textbf{5}}\)**} & \multicolumn{2}{c}{**PS\({}_{\textbf{10}}\)**} & \multicolumn{2}{c}{**PC**} \\ \hline
**Sample** & **H (N)** & **P (mJ)** & **F (N)** & **H (N)** & **P (mJ)** & **F (N)** & **H (N)** & **P (mJ)** & **F (N)** \\ \hline M1 & 4.76 (0.40) & 6.12 (0.62) & 0.68 (0.02) & 29.18 (1.85) & 63.48 (2.25) & 4.25 (0.92) & 19.02 (2.78) & 38.93 (3.54) & 1.36 (0.28) \\ \hline M2 & 7.26 (0.25) & 7.76 (0.32) & 0.35 (0.10) & 34.66 (1.22) & 73.37 (1.87) & 8.23 (1.20) & 30.06 (2.42) & 53.55 (5.10) & 2.66 (0.54) \\ \hline M3 & 8 (0.30) & 7.79 (0.43) & 0.74 (0.12) & 41.56 (1.89) & 82.84 (2.02) & 5.39 (0.33) & 52.28 (3.25) & 70.76 (4.83) & 0.42 (0.08) \\ \hline \end{tabular} SD—standard deviation, PS\({}_{\textbf{5}}\)—5 mm diameter spherical penetrometer, PS\({}_{\textbf{10}}\)—10 mm diameter spherical penetrometer, PC—120” conical penetrometer, H—hardness, P—plasticity, F—fracturability, SD—standard deviation.  
\end{table} 
Table 4: The textural parameters of margarine samples.
## 4 Solution Approach
```
1:\(usedOpenDepots\leftarrow\emptyset\)
2:\(\langle nbDepots_{0},maxNbDepots\rangle\gets computeDepotsBound(depots)\)
3:for\(nbDepots\gets nbDepots_{0}\) to \(maxNbDepots\)do
4:for\(iter\gets 1\) to \(iter_{max}\)do
5:\(openDepots\gets depotsToOpen(nbDepots)\)
6:if\(openDepots\
otin usedOpenDepots\)then
7:if\(capacity(openDepots)\geq demand(cust)\)then
8:\(usedOpenDepots\gets add(usedOpenDepots,openDepots)\)
9:\(map\leftarrow allocateCustomers(openDepots,cust)\)
10:\(lrpSol\gets CWS(map,\beta,vchCap)\)
11:\(poolSol\gets add(poolSol,lrpSol)\)
12:endif
13:endif
14:endfor
15:endfor
16:return\(sortingByCost(poolSol)\)
```  
**Algorithm 1** Constructive heuristic (\(cust\), \(depots\), \(vehCap\), \(\beta\), \(iter_{max}\))  
Algorithm 2 outlines Stages 2 and 3. During the second stage, each \"promising\" map generated by the constructive heuristic is processed by the simulation and the fuzzy components to estimate its safety stock (line 4). This procedure is carried out by performing a low number of runs, where a new value is assigned to each random or fuzzy element based on its probability distribution or fuzzy function, respectively. We use Monte Carlo simulation in order to estimate the stochastic variables, whilst a fuzzy inference system is used to estimate the fuzzy variables. Then, the objective function and the constraints are evaluated under the random/fuzzy generated values to compute the expected cost of each promising map. Next, the ILS metaheuristic tries to improve the set of \"promising\" maps by iteratively exploring the search space and conducting a second process of fuzzy/simulation runs. We start the process by perturbing the current base solution \(baseSol\) (line 8). In this phase we use two different strategies. In the first one, the algorithm randomly selects a set of customers and tries to reassign them in a random way to another facility without violating its capacity. Regarding the second strategy, the algorithm randomly exchanges the allocation of a percentage of customers among facilities. This process is dependent on the value of \(k\), which represents the degree of exchange to be applied. This value is updated in each iteration between \(K_{min}\) and \(K_{max}\), i.e., it is reset to \(K_{min}\) whenever a new solution _newSol_ outperforms the _baseSol_, and it is increased whenever the algorithm fails to improve the current solution until a maximum value \(K_{max}\). The strategy to be used in each iteration of the algorithm is randomly selected.
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.6. Results for XGBoost with Classical Feature Selection (XGB-RFECV)  
This time the average accuracy rate achieved was 0.7085, reaching a maximum of 0.7424, as shown in Table 2. A total of 20 variables were used, including sex, age, education, metabolic syndrome, apnea, IPAQ, and different variables associated with alcoholic beverages, among others. Figure 3 shows the evolution of the accuracy rate with the number of variables chosen. In this case, the maximum value is obtained with 20 variables.