esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Measurement**} & **Initial** & **Predicted** & **Achieved** & **Predicted and Achieved** & **Accuracy (\%)** & **Difference** \\ \cline{2-9}  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Correlation** & \(p\)**-Value** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **95\% CI** \\ \hline Overjet (mm) & 2.85 \(\pm\) 1.39 & 2.57 \(\pm\) 0.72 & 2.77 \(\pm\) 1.11 & 0.33 & \(<\)0.001 & 75.77 \(\pm\) 20.74 & 0.20 \(\pm\) 1.11 & (0.05, 0.35) \({}^{\star}\) \\ \hline Interincisal Angle (\({}^{\circ}\)) & 128.67 \(\pm\) 11.99 & 129.4 \(\pm\) 7.62 & 131.96 \(\pm\) 9.59 & 0.77 & \(<\)0.001 & 96.23 \(\pm\) 3.07 & 2.56 \(\pm\) 6.16 & (1.71, 34) \({}^{\star}\) \\ \hline Overbite (mm) & 2.60 \(\pm\) 1.60 & 1.66 \(\pm\) 0.71 & 2.44 \(\pm\) 1.46 & 0.48 & \(<\)0.001 & 60.04 \(\pm\) 23.99 & 0.77 \(\pm\) 1.29 & (0.59, 0.95) \({}^{\star}\) \\ \hline Upper Intercanine Width (mm) & 35.84 \(\pm\) 2.25 & 36.81 \(\pm\) 2.01 & 36.26 \(\pm\) 2.21 & 0.88 & \(<\)0.001 & 97.97 \(\pm\) 2.49 & 0.53 \(\pm\) 1.05 & (0.39, 0.67) \({}^{\star}\) \\ \hline Lower Intercanine Width (mm) & 27.79 \(\pm\) 1.71 & 28.21 \(\pm\) 1.58 & 27.99 \(\pm\) 1.64 & 0.82 & \(<\)0.001 & 97.67 \(\pm\) 2.65 & 0.53 \(\pm\) 1.05 & (0.10, 0.36) \({}^{\star}\) \\ \hline Upper Intercimal Width (mm) & 54.25 \(\pm\) 3.07 & 55.81 \(\pm\) 2.89 & 54.86 \(\pm\) 3.16 & 0.77 & \(<\)0.001 & 97.58 \(\pm\) 3.09 & 0.95 \(\pm\) 2.05 & (0.67, 1.23) \({}^{\star}\) \\ \hline Lower Intercimal Width (mm) & 50.63 \(\pm\) 3.09 & 51.59 \(\pm\) 2.92 & 51.10 \(\pm\) 3.19 & 0.77 & \(<\)0.001 & 97.72 \(\pm\) 3.21 & 0.49 \(\pm\) 2.08 & (0.20, 0.78) \({}^{\star}\) \\ \hline Upper Crowding (mm) & 1.88 \(\pm\) 1.56 & 0.76 \(\pm\) 1.05 & 1.77 \(\pm\) 1.32 & 0.16 & 0.03 & 38.79 \(\pm\) 26.42 & 1.01 \(\pm\) 1.55 & (0.80, 1.22) \({}^{\star}\) \\ \hline Lower Crowding (mm) & 2.45 \(\pm\) 2.08 & 0.55 \(\pm\) 0.91 & 2.21 \(\pm\) 2.63 & 0.30 & \(<\)0.001 & 30.02 \(\pm\) 25.55 & 1.66 \(\pm\) 2.51 & (1.32, 2.00) \({}^{\star}\) \\ \hline \end{tabular}  
* Statistically significant (confidence interval does not include 0). \({}^{\star}\) Clinically significant (confidence interval lower limit \(\geq\) 0.5 mm [or \(\geq\)2\({}^{\circ}\)] or upper limit \(\leq\) \(-\)0.5 mm [or \(\leq\)\(-\)2\({}^{\circ}\)]).  
\end{table} 
Table 1: This table is assessing the changes in the different dimensions of the patient’s current stage and the final proposed stage by ClinCheck®.
# Streaming-Based Anomaly Detection in ITS Messages
## 2 Materials and Methods
### Data Generation, Pre-Processing and Transformation  
A real-world dataset of CAMs that was collected from 80 cars in France between September 2018 and August 2019 as part of a C-ITS project was used as seed data to generate simulated data for CAMs. Simulation of vehicular ad hoc network (VANET) applications requires simulating both vehicle-to-vehicle wireless data transmission and vehicle mobility. Simulations were carried out using the objective modular network testbed (OMNET++) network simulator [35], and simulation of urban mobility (SUMO) road traffic simulator [36]. The artery vehicle-to-everything (V2X) simulation framework [37] was used to combine the network simulator with the road traffic simulator, allowing for efficient communication. This connection is especially crucial in C-ITS, which provides applications for traffic safety and efficiency.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization  
The physical and chemical properties of polluted sites are adjusted and changed through stabilization materials of chromium and arsenic, and the chemical form of chromium or arsenic elements is changed through adsorption, oxidation-reduction, chemical precipitation, and ion exchange, thereby reducing the mobility and bioavailability of chromium and arsenic to achieve the goal of stabilization. For example, studies have shown that iron oxide, due to its rich surface functional groups and high specific surface area, can effectively stabilize chromium and arsenic in soil through adsorption [66; 67]. Sulfides (such as Na\({}_{2}\)S) can reduce hexavalent chromium (Cr(VI)) to trivalent chromium (Cr(III)) via redox reactions, while fixing arsenic in the form of insoluble sulfides [68; 69]. Phosphates can form chromium phosphate precipitates with chromium and arsenate precipitates with arsenic, thereby reducing their solubility and mobility in water [70]. Zeolite can effectively remove chromium and arsenic from water through the ion exchange mechanism [71; 72]. Figure 9 shows the mechanism types of the stabilization process, which is complex and involves multiple mechanisms working together [73; 74; 75; 76]. These studies indicate that inorganic stabilization materials hold significant potential for application in the treatment of chromium and arsenic pollution, thereby providing a scientific basis for the remediation of contaminated sites.  
Figure 9: The mechanism of the stabilization process of chromium and arsenic.
###### Abstract  
Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.  
heating load; cooling load; energy efficient residential buildings; artificial intelligence Article
# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 3 Methodology
### Estimate the CQW of the Alternative Based on Its Capacity  
Estimating the CQWs of different criteria is crucial when evaluating the value of alternative options. The determination of these weights can be either objective or subjective. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **RII** & **S** & **K** & **q** & **W** \\ \hline
**C6** & 0.857143 & & 1.000 & 1.000 & 0.171 \\ \hline
**C4** & 0.825397 & 0.038 & 1.038 & 0.963 & 0.165 \\ \hline
**C8** & 0.793651 & 0.040 & 1.040 & 0.926 & 0.159 \\ \hline
**C7** & 0.777778 & 0.020 & 1.020 & 0.907 & 0.156 \\ \hline
**C9** & 0.619048 & 0.256 & 1.256 & 0.722 & 0.124 \\ \hline
**C2** & 0.380952 & 0.625 & 1.625 & 0.444 & 0.076 \\ \hline
**C5** & 0.301587 & 0.263 & 1.263 & 0.352 & 0.060 \\ \hline
**C1** & 0.253968 & 0.188 & 1.188 & 0.296 & 0.051 \\ \hline
**C3** & 0.190476 & 0.333 & 1.333 & 0.222 & 0.038 \\ \hline \end{tabular}
\end{table} 
Table 5: Weightage calculations through SWARA. It is essential to note that in many cases, evaluating alternatives requires a subjective approach, necessitating the expertise and opinions of knowledgeable individuals. This process demands significant effort and time to execute, irrespective of the subsequent preparations and operations that follow the evaluations. To address this challenge, a method has been devised to determine the CQW value of alternatives based on their capacity. This method is founded on the premise that quality depends on the resource capacity and the nine characteristics above. Therefore, the PCA was employed to determine the coefficients of the nine criteria related to capacity. Determining the CQW by PCA  
To empirically establish the link between a supplier's production capacity and its performance across the nine significant quality criteria (C1-C9), PCA was employed. The objective of PCA in this context was to reduce the multi-dimensional expert opinions into a single, representative factor that captures the 'general quality impact of capacity'. The input for the analysis was a 20 \(\times\) 9 data matrix, where the rows represented the 20 expert respondents and the columns contained their ratings (from \(-\)5 to \(+\)5) on how production capacity influences each of the nine criteria (as detailed in Table 6). Before proceeding, the suitability of the data for factor analysis was confirmed. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.708 (well above the 0.5 threshold), and [PERSON]'s test of sphericity was significant (\(p<0.001\)), as shown in Table 7. These results indicate that the correlations between the criteria were strong enough for PCA to be a valid technique.
## 4 Results and Discussion  
### Fabrication Using 3 DCP-SIT  
The fabrication processes of all the specimens are shown in Figure 9. The IR- series samples were prepared as shown in Figure 9a-e, with the rectangular nozzle tip, from extruding cement in (a) and (b), switching the nozzle tip to the SIA in (c), starting staple insertion in (d), and finally to finishing the insertion of one layer in (e). The IL- samples were prepared with the SIA and round nozzle tip in turn, as shown in (f) and (g), respectively. The typical samples are shown in (h), (i), and (j), with successfully inserted staples being 98.4% and 98.1% of the staples and two failed staples circled in (h) and (i), respectively. This proved the staple inserting mechanism can be fully automated in bulk manufacturing. The inserting operation extended the fabricating time. Further studies can be conducted on high-speed inserting: staple jetting, which is possible on large-scale printing activities, or developing an inserting mechanism along the material extrusion, as discussed in Section 4.7.  
Figure 8: Loading directions. (**a**) Positions of the flexural testing samples: (**b**) type-A loaded along the \(Z\)-axis, (**c**) type-B loaded along the \(Z\)-axis, (**d**) type-C loaded along the \(X\)-axis, (**e**) type-D along the \(X\)-axis, and (**f**) type-E along the \(Z\)-axis.  
Figure 9: Fabricating processes with the 3 DCP-SIA and typical samples. (**a**) the mortar extruder was ready to print, (**b**) mortar printing in process, (**c**) SIA repositioning in process, (**d**) staple inserting in process, (**e**) staple insertion was about to finish, (**f**) staple inserting to a IL sample, (**g**) mortar printing to cover the inserted staples, (**h**) a stapled IR sample with a failed staple in red circle, (**i**) a IR sample with three rows of staples, a failed staple in red circle, and (**j**) a printed and stapled IL sample.
# Voltage Distribution on Transformer Windings Subjected to Lightning Strike Using State-Space Method
## 2 Network Elements  
### Lightning Surge Model  
The Heidler lightning surge model used in this study features the direct lightning impulse model, which is one of its main categories. The Heidler model is among the most frequently used models for accurately analyzing transient overvoltages caused by lightning, owing to its realistic current profile, its endorsement as a reference model by standards organizations such as the International Electrotechnical Commission (IEC 62305-1), and its wide range of application areas. While the calculations involved in this model are more complex than those in other models and require more computations, this complexity can be viewed as a disadvantage. However, it ultimately yields more realistic results compared to simpler models. The function of the Heidler model is provided in (1) [14; 15].  
\[I(t)=\frac{I_{max}}{\eta}x(t)y(t)=\frac{I_{max}}{\eta}\frac{\left(\frac{t}{ \tau_{1}}\right)^{n}}{1+\left(\frac{t}{\tau_{1}}\right)^{n}e^{-\frac{t}{\tau_{ 2}}}} \tag{1}\]  
where \(I_{max}\) is the peak value of the lightning current, \(\eta\) is the correction factor of the peak value, \(t\) is the time, \(\tau_{1}\) and \(\tau_{2}\) are time constants depending on current rise (front) time and fall (decay) time, and \(n\) is the current steepness factor (constant of current gradient), respectively. The \(n\) value is a parameter that affects how the current rises. A higher \(n\) value provides a faster rise. The expression \(x(t)\) controls the phase of the current rise. With this expression, the current can reach its peak value quickly. The expression \(y(t)\) controls the phase of the current fall. This expression determines how the current decreases after it reaches its peak value. The mathematical expression of the correction factor in (2) is obtained by taking the derivative of (1), equating it to zero, and performing various calculations [16].  
\[\eta=e^{-\frac{\tau_{1}}{\tau_{2}}\ n+\sqrt{\frac{n\cdot\tau_{2}}{\tau_{1}}}} \tag{2}\]  
When modeling the Heidler lightning surge, a current source is connected in parallel with a resistance known as the lightning path impedance, as shown in Figure 1. The value of the lightning path impedance can vary based on factors such as the length of the lightning channel, atmospheric conditions, and the physical properties of lightning. In this study, the lightning path impedance is taken to be 400 \(\Omega\)[17].
## 5 Diagnostic Work Up  
The diagnosis of CR is assessed with the clinical history and evaluation using anterior rhinoscopy and nasal endoscopy, which should always be performed to exclude nasal infection, crust formation, and/or anatomic deformities. Notwithstanding, the SPT and the dosage of serum-allergen-specific IgE are determinants for the diagnosis of AR [36; 37].  
For instance, the position paper of the European Academy of Allergy and Clinical Immunology (EAACI) does not recommend allergen provocation testing, microbiological analysis, nasal cytology or biopsies, or the measurement of the total or allergen-specific IgE as diagnostic tests for NAR. However, NAC and the measurement of IgE in nasal secretions are of the utmost importance to distinguishing between LAR and NAR, whereas the nasal cytology or biopsies may help distinguish between an inflammatory and a neurogenic etiology [37]. Eosinophilic inflammation in the absence of systemic allergy may be attributed to LAR, nonallergic rhinitis with eosinophil (NARES), or to intolerance to drugs [37].  
Hence, NAC is a helpful diagnostic tool; it is the _gold standard_ in assessing clinical reactivity to an allergen and accurately identifying LAR, as well as assessing accurate therapeutic management and choosing patients for clinical trials and translational and clinical research studies [37].  
Although NAC is a well-established technique included in the standard diagnostic workp for rhinitis, being useful in discriminating between the different rhinitis endotypes (AR, LAR, NAR, occupational rhinitis, etc.) and designing the composition of allergen immunotherapy (AIT), it remains a time- and resource-consuming technique, whose safety and reproducibility are still limited, especially in children [38]. In addition, NAC is not everywhere available, and it is difficult to apply. However, recently, [PERSON] et al. demonstrated that NAC is safe and reproducible and ready to be used in clinical practice in both children and adults with or without asthma [38].  
In the absence of systemic atopy, the presence of nasal sIgE and a positive NAC response confirm the diagnosis. According to [PERSON], the measurement of IgE in nasal lavage fluid may be considered an easy and rapid method to diagnose LAR in children [39]. IgE is generally determined in nasal lavage fluid during natural exposure or after NAC [40; 41]. Single aeroallergen NAC is an important diagnostic tool, and its sensitivity seems to be higher than nasal IgE, trytase, or ECP level determination [36; 40].  
The basophil activation test (BAT) is another diagnostic test for LAR, with a sensitivity of 50-66% for HDM and pollens, and with a specificity of >90%. Unlike nasal IgE, the BAT does not require a previous NAC to increase the sensitivity [3; 42; 43; 44; 45].  
Although recent studies have emphasized the use of next-generation sequencing (NGS) and quantitative real-time PCR (qRT-PCR) in advanced respiratory disease diagnostics, their role in LAR remains elusive. Furthermore, the current biomarker for the clinical use of LAR remains the level of the local IgE [46; 47].
# Theoretical and Numerical Simulation Research on Fire of Large-Span Spatial Structures
## 4 Modified Two-Zone Temperature Field Model
### Correction of the Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & \multicolumn{3}{c}{**CASE1**} & \multicolumn{3}{c}{**CASE2**} & \multicolumn{3}{c}{**CASE3**} & \multicolumn{3}{c}{**CASE4**} \\ \cline{2-13}  & **c** (°C)** & **s** (°C)** & **e** & **c** (°C)** & **s** (°C)** & **e** & **c** (°C)** & **s** (°C)** & **e** & **c** (°C)** & **s** (°C)** & **e** \\ \hline
14.4 & 103.2 & 102.2 & 0.01 & 98.3 & 89.7 & 0.10 & 84.7 & 81.7 & 0.04 & 78.6 & 75.0 & 0.05 \\ \hline
14.1 & 100.1 & 92.2 & 0.09 & 94.9 & 79.8 & 0.19 & 80.7 & 70.2 & 0.15 & 74.3 & 63.4 & 0.17 \\ \hline
13.8 & 97.0 & 96.3 & 0.01 & 91.5 & 83.9 & 0.09 & 76.6 & 74.9 & 0.02 & 70.0 & 68.5 & 0.02 \\ \hline
13.3 & 91.9 & 90.9 & 0.01 & 85.8 & 77.7 & 0.10 & 69.9 & 66.1 & 0.06 & 62.8 & 57.2 & 0.10 \\ \hline
12.8 & 86.8 & 88.7 & 0.02 & 80.1 & 76.1 & 0.05 & 63.1 & 65.8 & 0.04 & 55.7 & 57.9 & 0.04 \\ \hline
12.3 & 81.7 & 84.1 & 0.03 & 74.4 & 71.3 & 0.04 & 56.4 & 58.7 & 0.04 & 48.5 & 49.9 & 0.03 \\ \hline
11.8 & 76.6 & 81.5 & 0.06 & 68.8 & 68.4 & 0.01 & 49.6 & 54.0 & 0.08 & 41.3 & 44.2 & 0.07 \\ \hline
11.3 & 71.5 & 76.3 & 0.06 & 63.1 & 62.8 & 0.00 & 42.8 & 46.5 & 0.08 & 34.2 & 37.5 & 0.09 \\ \hline
10.8 & 66.4 & 73.1 & 0.09 & 57.4 & 60.1 & 0.04 & 36.1 & 44.6 & 0.19 & 27.0 & 36.1 & 0.25 \\ \hline
10.0 & 58.3 & 65.4 & 0.11 & 48.3 & 52.6 & 0.08 & 25.3 & 37.2 & 0.32 & 20.0 & 30.5 & 0.34 \\ \hline
9.2 & 50.1 & 57.7 & 0.13 & 39.2 & 44.4 & 0.12 & 20.0 & 31.0 & 0.35 & 20.0 & 26.8 & 0.25 \\ \hline \hline \end{tabular} Note: c represents the calculated temperature value; s represents the simulated temperature value; e is the error between the two.  
\end{table} 
Table 9: Calculated values compared with simulated values. Figure 31: Temperature curves with height: (**a**) CASE1; (**b**) CASE2; (**c**) CASE3; (**d**) CASE4. The calculation results are shown in Figure 33, and the calculation curve is in good agreement with the simulation curve. The comparison between the calculation results and the simulation results is shown in Table 10. The maximum difference between the calculation results and the simulation results is about 10 \({}^{\circ}\)C, and the error is between 0-14%. The average relative error between the predicted temperature and the simulation results is 6%, indicating high accuracy. The fire temperature field analysis of large space structure is summarized, and the analysis process is shown in Figure 34. The logarithmic curve model proposed in this paper
## 3 Results and Discussion
### Adsorption measurements
#### 3.2.2 Optimization for Maximal Dye Removal
Figure 5: Effects of the (**a**) MCC dosage, (**b**) initial dye concentration, and (**c**) pH of solution on the RR120 dye removal over the MCC. From these univariate analysis results above, the optimum condition for the complete dye removal was achieved when using at least 0.6 g of the MCC over a wide range of solution pH (3-10) and initial dye concentrations (10-100 mg L\({}^{-1}\)). It is of note that the MCC used in this work could adsorb the dye completely over acidic, neutral, and basic conditions, which is due to the synergistic contribution of the hydrophobic interaction and electrostatic attraction between the dye and the MCC. Table S1 shows the comparison with other relevant adsorbents.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Jitter (ms)  
Figure 17 compares our proposed protocol's jitter to existing protocols. The results show that the protocol achieves a low jitter value (0.3 ms) at 100 nodes compared to other protocols. The jitter value decreases as the number of nodes increases. As a result, the introduced protocol is more efficient than existing protocols for other power sources. The jitter values of the existing MT-MAC, DHCO, ALOC, and M-GWO protocols are 0.9 ms, 0.8 ms, 0.7 ms, and 0.4 ms, respectively.
## 2 Simulation Model of CMIs with the CC Method
### Modeling the Complex Permeance of the Core
The real and imaginary parts of complex permeability can be described by Equations (3) and (4), using the RC circuit's real and imaginary components of complex admittance \(Y_{\text{m}}\), as depicted in Figure 3.  
\[\mu_{r}^{\prime}(f)=\text{ Im}(Y_{\text{m}})\cdot\frac{l_{c}}{2\pi f\mu_{0}A_{ \text{c}}} \tag{3}\]  
\[\mu_{r}^{\prime\prime}(f)=\text{ Re}(Y_{\text{m}})\cdot\frac{l_{e}}{2\pi f\mu_{0}A_{ \text{c}}} \tag{4}\]  
Using fitting parameters \(a_{1}\), \(a_{2}\), \(a_{3}\), \(b_{0}\), \(b_{1}\), \(b_{2}\), and \(b_{3}\), the admittance \(Y_{\text{m}}\) can be represented as  
\[Y_{\text{m}}(s)=\frac{a_{3}s^{3}+a_{2}s^{2}+a_{1}s}{b_{3}s^{3}+b_{2}s^{2}+b_{1 }s+b_{0}} \tag{5}\]  
where \(s=j\omega\). The real and imaginary parts of the complex permeability can be calculated by dividing Equation (5) into real and imaginary parts and substituting them into Equations (3) and (4). To identify the fitting parameters, curve fitting of the calculated results to the measured results obtained from Equations (1) and (2) is performed based on the least-squares method. Moreover, the admittance of the three-stage RC circuit is also represented by the following Equation (6). \[Y_{\text{m}}(s)=\frac{1}{R_{1}+\frac{1}{sC_{1}+\frac{1}{R_{2}+\frac{1}{cC_{2}+ \frac{1}{R_{3}+\frac{1}{c_{3}}}}}}} \tag{6}\]  
By comparing Equations (5) and (6), the modeling components of the complex permeance \(R_{1}\), \(R_{2}\), \(R_{3}\), \(C_{1}\), \(C_{2}\), and \(C_{3}\) can be calculated based on the identified fitting parameters.