esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Scenario 1 versus Scenario 2
#### 3.2.3 Particle Matter (PM\({}_{2.5}\))
During COVID-19 lockdown, NOx emissions were in general lower in many cities due to the absence of road transport, but a significant reduction in PM\({}_{2.5}\) concentrations in many European cities was surprisingly not found [44; 45]. The reason for that absence of the reduction or even a small increment in PM\({}_{2.5}\) concentrations is that over urbanised areas, lower NOx\({}_{2}\) concentrations at constant or similar NMVOC concentrations lead to an increase of O\({}_{3}\) values, which is a reactive oxidant. Therefore, increasing levels of O\({}_{3}\) concentrations lead to an enhancement of the atmospheric oxidising capacity, which might lead to an increase in the Secondary Organic Aerosol (SOA) formation [46; 47; 48; 49; 50]. For example, SOA is formed through a series of chemical reactions of gaseous precursors, mainly volatile, intermediate-volatile, or semi-volatile organic compounds (IVOCs) with O\({}_{3}\), OH, and nitrate radical oxidants (NO\({}_{3}\)) [50]. IVOCs are mainly emitted by the road transport sector. Scenario 1 versus Scenario 2 Figure 7: PM\({}_{2.5}\) concentrations (\(\mu\)g/m\({}^{3}\)) at city level for the Reference Case, and the two counterfactual emission reduction scenarios. Evidently, also the EU PM\({}_{10}\) limit value of 50 \(\upmu\)g/m\({}^{3}\) is exceeded by the Reference Case. Under current EU air quality legislation, this daily limit value may not be exceeded more than 35 times per year. We can see that for Warsaw and Milan, the PM\({}_{10}\) limit values are exceeded 37 and 51 times per year respectively, see Figure 8c,d. In SC1 and SC2, the number of exceedance days for Warsaw reduced to 32 days for both scenarios. While for Milan, the number of exceedance days dropped to 47 and 45 days for the two scenarios, respectively. This indicates that applying the counterfactual scenarios reduces, to some extent, the number of days for which the daily limit values of PM\({}_{10}\) are exceeded, which is important for decision-makers.
## 5 Results and Discussion
### Data Reconciliation Without Gross Errors
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Design** & \multicolumn{3}{c}{**Measured**} & \multicolumn{3}{c}{**Reconciliated**} & \multirow{2}{*}{**Un.**} \\  & **Val.** & **Val.** & **De.** & **Un.** & **Val.** & **De.** & **Un.** & \\ \hline Mass flow of gas & 92.900 & 94.240 & 1.44\% & 5.00\% & 92.910 & 0.01\% & 2.54\% & kg/s \\ H hot side output temperature & 72.690 & 73.379 & 0.95\% & 0.58\% & 72.821 & 0.18\% & 0.46\% & \({}^{\circ}\)C \\ H cold side & 517.600 & 516.579 & \(-\)0.20\% & 0.25\% & 517.768 & 0.03\% & 0.12\% & \({}^{\circ}\)C \\ output temperature & 517.600 & 516.579 & \(-\)0.20\% & 0.25\% & 517.768 & 0.03\% & 0.12\% & \({}^{\circ}\)C \\ Mass flow of CO\({}_{2}\) in ST1 & 69.890 & 67.033 & \(-\)4.09\% & 10.00\% & 70.358 & 0.67\% & 2.56\% & kg/s \\ SC inlet pressure & 8.000 & 8.041 & 0.51\% & 2.00\% & 8.009 & 0.12\% & 1.36\% & MPa \\ SC output pressure & 24.000 & 24.154 & 0.64\% & 2.00\% & 24.081 & 0.34\% & 1.39\% & MPa \\ SC inlet temperature & 32.000 & 31.799 & \(-\)0.63\% & 0.66\% & 31.973 & \(-\)0.08\% & 0.19\% & \({}^{\circ}\)C \\ HTR cold side & 380.330 & 380.323 & \(-\)0.002\% & 0.31\% & 379.920 & \(-\)0.11\% & 0.13\% & \({}^{\circ}\)C \\ output temperature & 380.330 & 136.822 & 3.24\% & 10.00\% & 133.456 & 0.70\% & 2.60\% & kg/s \\ Mass flow of CO\({}_{2}\) in ST2 & 62.640 & 63.773 & 1.81\% & 10.00\% & 63.08 & 0.73\% & 2.75\% & kg/s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reconciliation results of some parameters in Condition 2. The distribution of standard deviation in Conditions 1 and 2 are shown in Figures 4 and 5. The data reconciliation has a good effect on the deviation aggregation of parameters of such a thermal system, and the overall distribution curve of the deviation in Conditions 1 and 2 after reconciliation has an obvious effect of concentration towards the origin than that before reconciliation. The distribution of uncertainty in Conditions 1 and 2 are shown in Figures 6 and 7. The uncertainty distributions of the two are basically the same, which indicates that the data reconciliation has a good correction for random errors and always converges on the data results with higher precision. At the same time, similar to the deviation distribution, the distribution curve of the uncertainty after reconciliation also has an obvious effect on the origin concentration before reconciliation, which also explains the improvement of the overall data accuracy. Figure 4: Distribution of standard deviation in Condition 1. Figure 5: Distribution of standard deviation in Condition 2. Figure 6: Distribution of uncertainty in Condition 1.
# On the Melting Thresholds of Semiconductors under Nanosecond Pulse Laser Irradiation
## 3 Results and Discussion
### Cadmium Telluride  
We have applied our model to CdTe irradiated by a KrF excimer laser (248 nm) for the conditions of [PERSON] et al. [37], where TRR measurements and numerical simulations of the pulsed laser heating of CdTe were performed. For this material, reliable physical and optical properties are extensively reported in the literature. In our simulations, the value of thermal conductivity was taken from Refs. [57; 58] for solid and liquid state, respectively. The specific heat for both solid and liquid state was taken from Ref. [59]. The same thermophysical properties were also used in simulations [31; 37]. Measurements of the optical properties of CdTe using spectroscopic ellipsometry and modeling were performed in [60] for a wide range of wavelengths. Reflectivity and absorption are the same for solid and liquid state and independent of temperature.  
The authors [37] identified a laser fluence of 50 mJ/cm\({}^{2}\) as the melting threshold. In their simulations, this value corresponds to the molten layer with a thickness of the laser absorption depth. Their TRR measurements detected an abrupt although small rise of the reflectivity at a laser fluence of 48-50 mJ/cm\({}^{2}\). These results are in excellent agreement  
Figure 3: (**a**) The number of Ga and As atoms emitted from the GaAs surface irradiated by 694-nm, 20-ns laser pulses as a function of laser fluence as derived from mass spectrometric measurements (adapted with permission from Ref. [13]). Transient uneven and developed manifestations of increased reflectivity indicating the appearance of the liquid phase are marked by the shaded region and the solid line, respectively. Our simulated melting threshold is shown by a vertical dashed line. (**b**) The simulated dynamics of the surface temperature with the identified melting threshold of 282 mJ/cm\({}^{2}\). The laser pulse profile is shown by the dashed line.  
with our simulations (Table 1). Indeed, for \(F=50\) mJ/cm\({}^{2}\), our model gives the depth of the molten layer of 7 nm, very close to the absorption depth of CdTe at 248 nm (\(\sim\)9 nm). According to our definition of the melting threshold, achieving the melting temperature at the very surface of the irradiated sample, the calculated threshold is slightly lower, 46 mJ/cm\({}^{2}\) (Table 1).  
Note that the analytical theory predictions [28] of the melting thresholds of CdTe at a laser wavelength of 694 nm are in reasonable agreement with our simulations over the studied range of pulse durations, although the theoretical values are systematically higher than ours (Table 1). We should also note that, in Refs. [32; 56], an effect of the enhanced evaporation of Cd atoms enriching the surface by tellurium upon laser heating was studied. It was shown that this effect can have an impact on the melting and ablation processes. This effect was not taken into account in this work, nor in [28; 37].
## 3 Methods
### Spatial Auto-Correlation  
The Global Moran's index is used to analyze the global spatial correlation of EE, while the local Moran's index is employed to analyze the local spatial aggregation characteristic. These indices can be calculated using the following equations [46; 47; 48]:  
\[\begin{split}\text{Global Moran's Index}=\frac{\sum\limits_{i} \sum\limits_{j=1}^{n}\sum\limits_{j=1}^{m}w_{ij}z_{i}z_{j}}{\sum\limits_{i=1}^ {n}z_{i}^{2}}\\ \text{Local Moran's Index}=\frac{nz_{i}\sum\limits_{j=1}^{m}w_{ ij}z_{j}}{\sum\limits_{i=1}^{n}z_{i}^{2}}\end{split} \tag{15}\]  
The deviation of the RSEI value at spatial cell \(i\) from its mean value, denoted as \(z_{i}=\left(x_{i}-\bar{X}\right)\), is used in the formula, where \(\bar{X}\) is the mean value of the RSEI. The spatial weight matrix is represented by \(w_{ij}\), \(n\) is the total number of elements, and \(S_{0}\) represents the aggregation of all spatial weights.  
\[S_{0}=\sum\limits_{i=0}^{n}\sum\limits_{j=0}^{m}w_{ij} \tag{16}\]  
The Moran index \(I\) ranges from \(-1\) to \(1\), and a higher absolute value indicates a stronger spatial autocorrelation. A positive \(I\) value implies a positive correlation, while a negative value implies a negative correlation. An \(I\) value of \(0\) indicates no spatial correlation. The local Moran index, calculated using the Local Indicators of Spatial Association (LISA), is used to assess the degree of local spatial correlation. LISA plots are categorized into five groups: Insignificant, high-high (HH), low-low (LL), low-high (LH), and high-low (HL).
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
## 5 Discussion  
In this study, we analyzed the mechanism and performance of Pd-deposited MoS\({}_{2}\)-based hydrogen sensors. The foundational role of MoS\({}_{2}\) in adsorbing oxygen in the absence of H\({}_{2}\) and the action of adsorbing hydrogen in the presence of H\({}_{2}\) are combined to reveal the characteristics of sensing H\({}_{2}\). This \"spillover\" effect, characterized by H\({}_{2}\) dissociation in Pd and the subsequent diffusion to MoS\({}_{2}\), highlights the importance of Pd in sensing mechanisms, especially in influencing the Schottky barrier at the metal-semiconductor junction. As shown in Table 1, faster responses at room temperature are prominent compared to previous studies. The fast response time shown by the Pd-deposited MoS\({}_{2}\)-based hydrogen sensor was caused by various characteristics. The first one contributed to the fast response time by enabling fast current change due to the high electron mobility of MoS\({}_{2}\). Both Raman spectroscopy and PL spectroscopy provided a perspective on the structural dynamics of MoS\({}_{2}\) for optimization through thickness adjustment. Second, the property of Pd, which quickly adsorbs and dissociates H\({}_{2}\) molecules, contributes to a faster reaction time, and Pd thickness optimization ensures the nanoparticle structure of Pd and the wide surface area for efficient H\({}_{2}\) adsorption and reaction. In addition, the change in the Schottky barrier due to the interaction of Pd and MoS\({}_{2}\) accelerates the electron flow, thereby facilitating the reaction. The optimized thickness of the MoS\({}_{2}\) layer can speed up  
Figure 5: The sensitivity of the Pd-deposited MoS\({}_{2}\)-based hydrogen sensor using H\({}_{2}\) concentration at room temperature: (**a**) H\({}_{2}\) concentration is 1%; (**b**) H\({}_{2}\) concentration is 3%; (**c**) H\({}_{2}\) concentration is 4%.  
the movement of electrons and improve the response time. Adjusting the Schottky barrier may affect the sensitivity of the sensor.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Error Analysis
#### 3.3.2 Geometric Condition Check  
In the kinematic scenario, the antenna is fixed on the vehicle roof so it can be considered as a rigid body. Therefore, the angle between the two baselines in the local frame should meet the condition.  
\[\overset{-n}{b}_{1}\overset{-n}{b}_{2}-\left\|b_{1}^{b}\right\|\left\|b_{2}^{b }\right\|\cos\alpha<\tau_{geo} \tag{31}\]  
where \(\tau_{geo}\) is the empirical threshold, set as 0.2 in this paper.
## 4 Result and Discussion
### Comparison of Forward Prediction Results  
Using the production parameters as input data and the quality indicators of the finished board as output data, each prediction model was run 20 times, and the average value was taken. The evaluation indicators for the forward prediction of each model are shown in Table 3 Taylor diagrams are commonly used to compare the predictive performance between different predictive models. The comparison of the forward prediction performance of each model is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Predictive Indicators** & \(\mathbf{R^{2}}\) & \(\mathbf{R^{2}}\)**on Average** & **MAE** & **MSE** & **t (s)** \\ \hline \multirow{4}{*}{cHGWOSCA-SVR} & TSR & 0.9482 & & 1.1249 & 1.5176 & \multirow{4}{*}{85.929} \\  & WSR & 0.9499 & & 0.3438 & 0.1458 & \\  & MOR & 0.9899 & 0.9591 & 5.7306 & 50.1214 & \\ \cline{1-1}  & HSS & 0.9662 & & 0.8592 & 0.9565 & \\ \cline{1-1}  & MOE & 0.9456 & & 342.7807 & 136,144.1207 & \\ \hline \multirow{4}{*}{GWO-SVR} & TSR & 0.9125 & & 1.5249 & 2.5987 & \multirow{4}{*}{93.342} \\  & WSR & 0.8378 & & 0.6997 & 0.5622 & \\ \cline{1-1}  & MOR & 0.9660 & 0.91698 & 10.0157 & 138.6215 & \\ \cline{1-1}  & HSS & 0.9410 & & 1.1925 & 1.6383 & \\ \cline{1-1}  & MOE & 0.9276 & & 411.1318 & 186,459.0483 & \\ \hline \multirow{4}{*}{PSO-SVR} & TSR & 0.8248 & & 2.2711 & 5.6653 & \multirow{4}{*}{83.88} \\  & WSR & 0.7110 & & 1.0997 & 1.3279 & \\ \cline{1-1}  & MOR & 0.9318 & 0.8359 & 14.6236 & 275.8683 & \\ \cline{1-1}  & HSS & 0.8067 & & 2.3635 & 6.0800 & \\ \cline{1-1}  & MOE & 0.9052 & & 481.7232 & 249,018.1210 & \\ \hline \multirow{4}{*}{SVR} & TSR & 0.4973 & & 2.5656 & 11.3046 & \multirow{4}{*}{1.7771} \\  & WSR & 0.3717 & & 0.8815 & 1.2895 & \\ \cline{1-1}  & MOR & 0.6577 & 0.5495 & 25.3245 & 1180.2426 & \\ \cline{1-1}  & HSS & 0.5908 & & 2.2869 & 9.3753 & \\ \cline{1-1}  & MOE & 0.6298 & & 641.8169 & 754,863.8304 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of forward prediction results among algorithms.  
Figure 5: Performance comparison of forward prediction algorithms; (**a**) thickness swelling rate upon water absorption, (**b**) width swelling rate upon water absorption, (**c**) modulus of rupture, (**d**) horizontal shear strength, and (**e**) modulus of elasticity.  
From the evaluation indexes of the forward prediction model, it can be seen that the average \(R^{2}\) value of the cHGWOSCA-SVR forward prediction model is 0.9591 which is closer to 1, the MAE and MSE values are lower compared to other models and the average running time of the algorithm is 85.929 s. Compared with other prediction models, the cHGWOSCA-SVR forward prediction model has higher prediction accuracy and minimizes the error value of the algorithm.
## 3 Results and Discussion
### Association Analysis and Discussion
Based on the above analysis, it can be concluded that the aroma differences in oolong tea samples from cultivars of different suitability were jointly affected by eight terpene metabolites (\(\beta\)-Ocimene, (Z)-Furan linalool oxide, linalool, (3E)-4,8-Dimethyl-1,3,7-nonatriene, (E)-Pyranoid linalool oxide, \(\gamma\) -Elemene, Humulene, and (Z,E)-\(\alpha\)-Farnesene), three carotenoid metabolites (\(\beta\)-Ionone, (Z)-Geranylacetone and 6-methyl-5-Hepten-2-one), three lipid metabolites (cis -3-Hexenyl (Z)-3-hexenoate, Butanoic acid hexyl ester, and (Z)-Jasmone), four amino acid metabolites (Methyl salicate, Geranyl isovalerate, indole, Phenylethyl alcohol), and six thermal reaction products (2-Pentyfuran, Octanal, Decanal, (E, E)-2,4-Nonadienal, (Z)-2-Decenal, and (E)-2-Undecenal).
## 3 Proposed Methodology (SLF-RPL)
### Trust Model  
The study aims to minimize internal attacks using limited resources by minimizing trust-related data congestion and conserving energy consumption. To do this, a trust model is utilized, and the Subjective Logic Framework (SLF) is used for trust calculation. The trust model has a separate controller responsible for processing the parameters required for evaluation, which helps to save resources at the node level. The model gathers measurements of the required parameters from different child nodes in the RPL tree topology and transfers them to the controller for processing. The collected data is then used to calculate the trust for each node separately. The trust model helps nodes to identify malicious nodes with a wormhole attack. To manage the trust model, the SLF controller layer handles various tasks, as illustrated in Figure 5 below and further discussed in the following sections.
## 3 Numerical Computations with MATLAB BVP4C Code  
The set of nonlinear ordinary differential equations, Equations (10)-(13) with boundary conditions (14) are solved numerically by using BVP4C numerical method under MATLAB for various values of the flow controlling parameters. In order to apply the BVP4C routine, we must rewrite the coupled non-linear differential equations as systems of first-order ODEs. The function BVP4C is a finite difference code that implements the three stages of Lobatto IIIa formula. It is a collocation formula, and the collocation polynomial provide us with a C1-continuous solution, which is fourth-order accurate uniformly in the interval where the function is integrated. In this solving approach, we have chosen a finite value of \(\eta_{\mathrm{max}}=14\) and fixing the relative tolerance to \(10^{-5}\) produces every numerical solution in this problem. Mesh selection and error control are based on the residual of the continuous solution. The range of parameters are: \(\delta>0,\gamma<0\), \(0<h_{2},h_{4},h_{6},h_{8}\leq 1\), \(0\leq Nb,Nt,Ec,Me,M\leq 5\), and \(0<Pe,Sc,Sb\leq 10\). To validate the present solution, comparison has been made with previously published data from the literature for the skin friction in Tables 1 and 2, and favourable agreement is achieved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(\mathbf{M}\)} & **[PERSON] et al.**[53] & **[PERSON] and** & **Present Results** & **Present Results** \\  & **(Modified Adomian** & **Mastroberardino** & **(BVP4C)** & **(VIM)** \\  & **Decomposition)** & **[48] (RKF45)** & **(BVP4C)** & **(VIM)** \\ \hline
0 & 1.00000 & 1.000008 & 1.0013962 & 1.0000002 \\
1 & \(-\)1.41421 & \(-\)1.4142135 & \(-\)1.4142375 & \(-\)1.41422211 \\
5 & \(-\)2.44948 & \(-\)2.4494897 & \(-\)2.4494897 & \(-\)2.4494901 \\
10 & \(-\)3.31662 & \(-\)3.3166247 & \(-\)3.3166248 & \(-\)3.3166229 \\
50 & \(-\)7.14142 & \(-\)7.1414284 & \(-\)7.1414284 & \(-\)7.1414279 \\
100 & \(-\)10.04987 & \(-\)10.049875 & \(-\)10.049876 & \(-\)10.049868 \\
500 & \(-\)22.38302 & \(-\)22.383029 & \(-\)22.383029 & \(-\)22.383031 \\
1000 & \(-\)31.63858 & \(-\)31.638584 & \(-\)31.638584 & \(-\)31.638578 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of skin friction \(f^{\prime\prime}(0)\) for different values of \(M\) when \(\Pr=1,Me=\delta=\gamma=Nb=Nt=Ec=Sc=Pe=Sb=h_{2}=h_{4}=h_{6}=h_{8}=0\).
## 3 Results
### Results of the Unsupervised Method
#### 3.3.1 Choosing a Suitable Number of Clusters  
In Section 2.3, the method of choosing a suitable number of clusters, \(k\), was explained. The Elbow method was applied by calculating \(k\)-means with the range of values for the \(k\) from two to nine. In Figure 11, the plotted line with distortion score for equipment electricity consumption over a different number of clusters, \(k\), can be seen. The dotted line is showing the \"elbow\" of the curve which represents the selected value for \(k\) using the Elbow method. In the case of equipment consumption, the optimal number of clusters was three. A similar analysis was done for lighting electricity consumption and water consumption, which can be seen in Figure 12, where the optimal number of clusters was chosen to be four and six, respectively.  
Figure 11: Distortion score with a different number of clusters (\(k\)) for equipment electricity consumption. Using the elbow method the optimal number of clusters is selected to be three (**dotted line**).  
Figure 10: Day energy consumption profiles of each studied variable of the unsupervised method, where each line represents the consumption for one day in the dataset. Equipment consumption (**left**), water consumption (**centre**) and lighting consumption (**right**).
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were carried out using the software RStudio (v.1.2.5033, RStudio Inc., Boston, MA, USA). All the registered complete steps were considered for analysis and were classified according to the initial foot strike pattern (Rearfoot (RF), MidFoot (MF), or Forefoot (FF)) [44]. For each participant, the predominant foot strike pattern was determined based on the one that occurred in more than half of the total recorded steps. The independent variables (factors) were: footwear type or condition (MS vs. CS), minute in which the step was registered, initial foot strike pattern (RF, MF and FF), and if the step was from the dominant leg (Yes vs. No). The dependent variables were: mean pressure, peak pressure, contact time, center of pressure (CoP) velocity, relative force, and contact area. Firstly, outliers from the database were identified by the intersection of different outlier detection methods defined by the package \"OutlierDetection\" (v.0.1.1) and removed from the database. Descriptive data were then provided using the mean, standard deviation and 95% confidence intervals (CI) of the mean. Multivariable linear regression mixed models were carried out for each factor using the package \"nlme\" (v. 3.1-142), being the participant a random factor fitted by the intercept, and analysing the main effect of each independent variable, as well as the interaction of these variables with the condition variable. The assumptions of the mixed models were evaluated by checking the standardised residuals in relation to the fitted values, observing a normal distribution and similar variance within and between groups. Standardised effect sizes (ES) of the model factors were obtained using the \"emmeans\" package (v.1.4.8), and were interpreted as \(\geq\)0.2, small; \(\geq\)0.5, moderate; \(\geq\)0.8, large. The level of significance was set at \(p<0.05\).