esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Theoretical Basis  
In numerical computations for models, the damping form typically adopts linear Rayleigh damping. In this case, the damping matrix \([\mathbf{C}]\) is represented as a linear combination of the mass matrix \([\mathbf{M}]\) and the stiffness matrix \([\mathbf{K}]\), as depicted in Equation (1) [23].  
\[[\mathbf{C}]=\alpha[\mathbf{M}]+\beta[\mathbf{K}], \tag{1}\]  
where \(\alpha\) and \(\beta\) can be determined by assuming that the damping ratio for a specific second-order frequency is known.  
The damping constant can be expressed using two frequencies of different magnitudes as follows:  
\[\alpha=\frac{2\left(\xi_{i}\omega_{i}-\xi_{i}\omega_{j}\right)}{\left(\omega_{ i}+\omega_{j}\right)\left(\omega_{i}-\omega_{j}\right)}\omega_{i}\omega_{j} \right\}, \tag{2}\]  
where \(\xi_{i}\) and \(\xi_{j}\) are the damping ratios of the \(i\)-th and \(j\)-th order circular frequencies (\(\omega_{i}\) and \(\omega_{j}\)), respectively.  
In general, engineering structures are usually assumed to have a damping ratio that remains relatively constant over a wide frequency range, with a typical value of 0.05 often used. The physical and mechanical parameters of the boundary elements in practical applications are determined by the materials of the adjacent surrounding rock medium, and specific calculation formulas can be found in the reference [24].  
The input method for seismic waves acting on the computational model adopts the form of inputting stress waves at the viscoelastic artificial boundary, which is known as the equivalent boundary force method [25]. This method requires that the equivalent load applied to the artificial boundary makes the stress and displacement on the boundary identical to the original wave field. At this juncture, the equivalent boundary stress \(F_{B}(t)\) can be expressed as follows:  
\[F_{B}(t)=\sigma_{B}(t)+C\dot{u}_{B}(t)\ +Ku_{B}(t), \tag{3}\]  
where \(\sigma_{B}(t)\) represents the stress at the artificial boundary of the infinite domain model; \(\dot{u}(t)\) and \(u(t)\) represent the vibration velocity and displacement at the artificial boundary. \(C\) and \(K\), respectively, denote the damping coefficient and spring elasticity coefficient set at the artificial boundary.  
When accounting for the fluctuation effects of the normal and tangential waves at the artificial boundary, researchers have analyzed the incident wave field of seismic waves in spherical coordinates and derived expressions for the vertical incident P-wave and S-wave at the artificial boundary, respectively [26].  
Considering the incident non-attenuated S-wave in the far field, the corresponding expression is:  
\[F_{BT}(t)=2\rho C_{S}\dot{u}_{B}(t)\ +\frac{2G}{R}u_{B}(t), \tag{4}\]  
where \(R\) represents the distance from the artificial boundary to the ground or structure; \(G\) represents the shear modulus of the adjacent medium in the artificial boundary region; \(\lambda\) and \(\rho\) represent the Lame constants and density of the corresponding medium.
## 2 Materials and Methods
### Data Processing and Analysis
The following set of relationships allows the quantification of discharge coefficient, density and velocity, through the jet temperature:  
\[\gamma=e^{k1}\cdotp_{0}^{k2}\cdotp_{m}^{k3}, \tag{3}\]  
\[\rho_{m,CR}=\rho_{0}\cdot\left(\frac{2}{k+1}\right)^{\frac{k}{k-1}}, \tag{4}\]  
\[w_{m,CR}=\sqrt{k\cdotR_{gas}\cdot T_{m,CR}}, \tag{5}\]  
\[T_{m,CR}=T_{0}\frac{2}{k+1}, \tag{6}\]  
where \(e\) is the Euler's number, \(p\) is gas pressure, \(D_{m}\) is the orifice outlet diameter, \(0\) refers to the conditions upstream of the nozzle, \(k\) is defined as the ratio between specific heat capacity at constant pressure and specific heat capacity at constant volume of the involved gas, \(R_{gas}\) is the specific gas constant, \(T_{m}\) is jet temperature, \(T\) is temperature and \(k1\), \(k2\) and \(k3\) are coefficients specific to the nozzle. Density values were taken from the _CoolProp_ free library [25] by selecting that of nitrogen corresponding to temperature and pressure measured upstream of the nozzle (Section 2.1, Figure 1a). The \(k1\), \(k2\) and \(k3\) coefficients were experimentally quantified by VdS Schadenverhutung GmbH for the selected nozzles, in order to also evaluate their outflow area. The evaluation of mass flow rate in the case of the FK-5-1-12 discharge was performed by processing the readings of the employed scale (Section 2.1, Figure 1b). As previously mentioned, the derivative of mass with respect to time was calculated by the approximation of the differential to finite difference:  
\[\dot{Q}_{m}=\frac{dm}{dt}\approx\frac{\Delta m}{\Delta t}=\frac{m_{i}-m_{i+1} }{\frac{1}{f}}, \tag{7}\]  
where \(m\) is mass, \(t\) is time, \(f\) is acquisition frequency and \(i\) is the index referring to the generic \(i\)th acquisition (\(i=0,\dots\ n\), where \(n\) is the last but one acquisition). This approach is relatively common to extrapolate mass flow rate from mass readings [26; 27; 28]; no moving average was applied in this calculation, since the acquired mass trend proved relatively bias-free in terms of statistical noise.
# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 3 Results
### Performance of the Algorithm  
The performance of the best model can be seen in Table 3. The table shows that only using the LA views was enough to achieve comparable results to humans by considering the standard deviations as well (3-4%). This is important, because the contrast agent can be injected after the long-axis measurements (if the algorithm indicates it and the experts accept it), then the short-axis cine images can be acquired, since the late enhancement images could be acquired at least 10 min after contrast material administration. This approach can save significant amounts of time and can also warn the on-site medical staff that the MRI protocol should be changed in order to avoid further, unnecessary examinations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scores** & **R1** & **R2** & **R3** & **R4** & **R5** & **R6** & **Mean** \\ \hline Overall & 89.7 & 91.5 & 91.5 & 88.0 & 89.7 & 90.6 & 90.0 \\ Hyp-Norm & 92.8 & 95.7 & 91.3 & 94.2 & 89.9 & 94.2 & 93.0 \\ HCM & 85.4 & 89.6 & 91.7 & 85.4 & 93.8 & 85.4 & 88.6 \\ Precision & 97.8 & 97.9 & 88.9 & 92.3 & 87.3 & 95.8 & 93.3 \\ Recall & 93.6 & 95.8  
The box plots in Figures 5 and 6 were calculated by repeating the test evaluation on 20 randomly sampled subsets of the test data, and in each sample, we used 70% of the test data. This method is similar to bootstrapping. Both images show the same relative performance. The algorithm using only the LA views had lower performance, but when short-axis and long-axis views were combined, the human level and the algorithm scores became close to each other, especially in the case of the recall. The results showed lower F1 and recall for the only short-axis case (see Table 3), which can be a result of the higher complexity of the data. More samples for the SA case could scale up the performance. Similarly, the algorithm (SA+LA) had lower performance than the experts, but we claim that a larger dataset would reduce the gap.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **F1** & **Precision** & **Recall** \\ \hline only LA2 & 90 & 86 & 92 \\ only LA4 & 86 & 81 & 90 \\ only LA3 & 91 & 86 & 92 \\ only SA & 86 & 90 & 83 \\ all LAs & 89 & 84 & 90 \\ LA+SA & 91 & 88 & 96 \\ \hline v. LA+SA & 93 & 91 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of the best model on the test sets. The last row shows the result on the validation set when the long-axis views were combined with the short-axis views.  
Figure 5: Comparison of the human (expert) and algorithm (auto) performances. The \(p\)-value between auto (LA) and auto (SA+LA) is lower than 0.001, which means using the short-axis images contributed to a significantly better performance. Between the auto (SA+LA) and expert group, the \(p\)-value was less than 0.001. For calculating the \(p\)-values, we used two-sample \(t\)-tests.
## 4 Results and Analysis
### Raman Denoising Experimental Results  
To thoroughly validate the denoising performance of the VMD-EWT-EBiLSTM network architecture, comprehensive performance analysis is carried out on the test set under 20 dB and 30 dB random noise conditions, by examining the denoising outcomes from each algorithm. The MSE and SNR objective metrics are utilized to quantify the results, which are presented in Figures 11 and 12.  
Figure 10: Comparative study on different denoising algorithms for 30 dB noisy signals.  
Figure 9: Comparative study on different denoising algorithms for 20 dB noisy signals.
# Thermal Comfort and Sustainability in University Classrooms: A Study in Mediterranean Climate Zones
## 3 Results
### A Case Study on the Spatial Distribution of Students and Associated Factors
On the other hand, students in positions further away from the openings, especially in the centre and at the back of the classrooms, reported predominantly \"Warm\" or even \"Hot\" sensations. In these areas, cross-ventilation is insufficient to maintain a constant airflow, which leads to heat build-up due to a lack of effective circulation. In ESS03, this inequality is particularly evident, as the range of TSV varied from \"Neutral\" in the vicinity of the open windows and doors to \"Hot\" in the central and rear areas. This may be because this classroom had fewer openings available, and the existing ones are poorly distributed, limiting the ability to evenly ventilate the entire space. In contrast, ESS04 showed a more balanced thermal perception due to the higher number of open windows and doors, which increased the cross-ventilation effectiveness. In this case, areas close to the openings maintained \"Slightly warm\" or \"Neutral\" sensations, while in the centre and back of the classroom, \"Warm\" sensations were less frequent compared to ESS03. This highlights the importance of the number and location of openings to improve airflow distribution in naturally ventilated classrooms [93]. In addition, the variability in thermal perception in these classrooms reflects not only the structural limitations of natural ventilation but also the impact of other factors, such as classroom occupancy and heat generation by students, which may contribute to greater heat build-up in less ventilated areas [66]. Also, the orientation of windows and doors concerning the prevailing air may have played a key role, as poor alignment with external air currents could reduce the effectiveness of cross-ventilation, especially in ESS03. Finally, despite the differences in environmental conditions created by the modes of operation, clothing insulation values show a similar trend in both cases, ranging from 0.11 to 0.40 clo. However, there are subtle but relevant differences between men and women that impact their thermal perception. In general, men tend to wear slightly more insulating clothing than women, with average clo values close to 0.35 versus 0.30 for women. This pattern is related to cultural norms in Portugal, where male students tend to dress more formally in educational settings [22]. This higher level of dress could affect their perception of thermal comfort, especially in warmer environments, such as areas away from windows in NV or areas less influenced by air vents in AC.
# Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology
## 3 Results
### HEV Simulation Results
#### 3.1.1 Validation of the Vehicle Model  
The sole purpose of validation was to observe whether the control system was capable of and feasible for integation with any given data and specifications. This is a way of checking whether the control system was reliable before beginning the actual simulations, so that if there were any need to perform modifications, these could be done at the earlier stages, rectifying the issue, instead of proceeding with a faulty control system. For this stage, the user is able to validate the model in comparison to their own experimental test results and, for example, it can be used for the development of simulations that can be used for making engineering predictions on the basis of quantified confidence. This specification can be used in the simulation model to further observe results such as the SOC of the battery and fuel consumption. Figure 7 shows an example of the validation of torque demands, instantaneous CO2, and accumulated CO2 on cold WLTC by means of the simulation compared to chassis dynamometer test data in the case of a vehicle test without hybridization. The ICE is 1.6 L modern compression ignition (CI) engine with a variable-geometry turbocharger (VGT) system. According to the comparison, the trends of simulated engine torque and CO2 emissions are in good agreement with those of the experimental results. The role of a vehicle's transmission model is to adapt the traction performance of the ICE to the road load and acceleration demand of the driver. The gear position was estimated using driver pedal acceleration and transmission/vehicle speed. The gear selection by the model simulation was in excellent agreement with the experimental results. From the figure, it can be observed that the energy management and the control system work well, as the simulation results show a similar trend for both the torque demand and CO2, proving that it is reliable and can be adapted to any data, providing significant results.  
Figure 7: Simulation validation for torque and CO2 on a C-segment vehicle test without hybridization (cold WLTC).
## 3 Results and Discussion
### Control System  
According to this study, the platform had to synchronize with VR images in real-time. Therefore, the control system needed to build on a high degree of synchronization and high transmission speed. In this study, the high transmission speed serial EtherCAT was selected to control the platform. EtherCAT was used to control 3 sets of 4.5 kW and 1 set of 750 W servo drive equipment in real-time, and collocation with planetary gear reducer with a reduction ratio of 1:50. The system was set up in the Visual Studio C# development environment, and the position control commands were compiled on the upper-axis control card. The compiled instructions were transmitted to the servo drive, and the drive sent the position control to the motors through the received instruction. Then, the motors would make the platform motions, and the multi-axis control was performed between the drives by using the master station and multiple sets of slave stations in series. The control architecture was a three-loop closed-loop system, and the position loop was the main control loop. The communication method is shown in Figure 29.  
Figure 28: VR image of aircraft.  
Figure 29: The communication method of EtherCAT.
## 3 System Model
### Proposed Polymorphic Heterogeneous Security Architecture
In device-to-edge-to-cloud scenarios, the edge node equipped with our architecture receives data streams from various terminal devices such as programmable logic controllers (PLCs), remote terminal units (RTUs), and distributed control systems (DCS). These inputs are simultaneously processed by multiple heterogeneous executors, each implementing different algorithmic approaches while maintaining functional equivalence. The arbitration and scheduling module conducts privacy-preserving comparison operations on these outputs without exposing the actual content, thereby protecting sensitive industrial process data. After validation, the aggregated results are transmitted to the cloud platform for further analytics, long-term storage, or enterprise-level decision support. Similarly, in cloud-to-edge-to-device scenarios, control commands or configuration updates originating from cloud applications undergo the same heterogeneous processing and validation at the edge node before being distributed to terminal devices.
## 4 Experiment and Result
### Additional Assessment of Learning Outcomes via a Multi-Goal Strategy
Furthermore, when MAC was combined with PHER to obtain the results, it demonstrated faster convergence compared to existing methods and achieved a success rate of approximately 75.96%, reflecting a performance improvement of around 2%. Figure 10: Simulation environments for multi-goal tasks of (**a**) FetchReach-v2, (**b**) FetchPush-v2, (**c**) FetchPickAndPlace-v2, and (**d**) FetchSlide-v2. The FetchPush-v2 environment, used for accurate performance evaluation, is designed to push objects from a random location to a target location. As shown in Figure 13, PHER showed more consistent performance than HER in the push environment. However, after 100 iterations, the performance difference between HER (75.60%) and PHER (76.10%) was minimal. Otherwise, when MAC and PHER were combined, the performance increased to 79.59%. Overall, we observed faster convergence and an approximate 3% improvement in performance. The final environment we experimented with FetchSlide-v2, presents more challenging learning conditions compared to the other environments. This is because the purpose of the slide environment is to push and pull randomly generated objects toward the target. As shown in Figure 14, we can observe that PHER's performance is significantly better than HER in the slide environment. At convergence, HER achieved a success rate of 16.42%, while PHER reached 45.44%, indicating a performance improvement of around 29%. Furthermore, the combination of MAC and PHER converged to a success rate of 67.27%, representing a performance improvement of approximately 22%. Figure 11: Experimental results in FetchReach-v2 for average success rate. Figure 12: Experimental results in FetchPickAndPlace-v2 for average success rate.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 5 Results and Discussion  
### Local Scour and Bed Topographic Change  
#### 5.1.1 Qualitative Analysis  
The bed configurations at the final stage are shown in Figure 5. The numerical model was able to predict most of the topographic features observed in the experiments, such as the mainstream erosion zone, the local scour hole, and the corresponding wake deposition in the proximity of the grouped spur dikes. When the inlet condition was determined, the bed configuration was related to the spacing of the grouped spur dikes. With the small spacing, the first spur dike's shielding effect on the rear spur dikes was strong, but as the spacing grew larger, the shield effect weakened. As can be seen from Figure 5, as the spacing between spur dikes changed, the bed surface morphology changed accordingly, and the shielding effect of spur dike I also gradually changed. With respect to spur dike spacing, with a spacing of 2\(l\), an integral scour zone was formed in the mainstream area, with the simulation results matching the experimental results. With a spacing of 3\(l\), in the experimental results the scouring range of spur dike II was largely covered by the scour range of the spur dike I, as part of the spur dike I downstream scouring. The local scour holes of spur dike III and IV remained basically intact. The simulation results showed two scour pits. With a spacing of 4\(l\), the experimental results showed that the shielding effect of spur dike I only partially affected a section of spur dike II. It can be clearly seen that the local scour morphology of each spur dike remained relatively complete, forming four largely independent scouring holes. In contrast to the experimental results, the simulation results show two separate scour pits, because the shielding effect of spur dike I only affects spur dike II, and the shielding effect of spur dike III only affects spur dike IV. Both the experimental result and the simulation result indicated that the shielding effect of the first spur dike was weakening. On the whole, the numerical simulations reproduce quite well the experimental outcomes under a qualitative point of view.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Grid Number**} & \multirow{2}{*}{
\begin{tabular}{c} **Grid Size** \\ **(cm)** \\ \end{tabular} } & \multicolumn{5}{c}{\(F_{8}\)=1.25} \\ \cline{3-6}  & & \(r\)=\(\frac{d_{k}}{d_{k+1}}\) & \(p\) & \(h\) **(cm)** & \(\frac{\text{Relative Error}}{\varepsilon}\) & \(GCI\) (\%) \\ \hline
520,800 & 1 & — & 1 & 10.75 & — & — \\ \hline
1,223,768 & 0.75 & 1.33 & 1 & 10.45 & 0.028708 & 10.77 \\
1,978,886 & 0.5 & 1.5 & 1 & 10.48 & 0.002863 & 0.72 \\
2,864,400 & 0.4 & 1.25 & 1 & 10.5 & 0.001905 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid convergence index (GCI) Calculation.  
Figure 4: The plan view of the computational grid.
## 2 Materials and Methods
### Ordinary Kriging  
Generally, two assumptions are made when using the kriging method. First, the data must have a normal distribution; if they do not, then a variable transformation is performed such that the predictor and dependent variables are linear. In this case, the value estimated by kriging can be easily inversely transformed; however, the variance estimate is not accurate [49]. The following assumption is made: the data should be continuous and stationary. Most spatial data exhibit a specific trend or have a problem in that the mean changes with the location. When kriging is applied to data with strong trends, misspecification of spatial dependence may occur, and the predicted values may be biased [50]. Therefore, the predicted value reflecting the spatial characteristics of the target data can be calculated only when kriging is applied by removing the tendency of the data.  
The unknown value \(Z(x_{0})\) is interpreted as a random variable located in \(x_{0}\), as well as the values of the neighboring samples \(Z(x_{i}),\ i=1,\ \ldots\,\)N. The estimator \(\hat{Z}(x_{0})\) is also interpreted as a random variable located in \(x_{0}\), which is a result of the linear combination of variables. To deduce the kriging system from the assumptions of the model, the following error committed while estimating \(Z(x)\) in \(x_{0}\) is declared: the two quality criteria previously referred to can now be expressed in terms of the mean and variance of the new random variable, \(\epsilon(x_{0})\).  
\[\epsilon(x_{0})=\hat{Z}(x_{0})-Z(x_{0})=\sum_{i=1}^{N}w_{i}(x_{0})\times Z(x_ {i})-Z(x_{0}). \tag{11}\]
# Active Tunable Elastic Metasurface for Abnormal Flexural Wave Transmission
## 1 Introduction
Currently, most metasurfaces are actively modulated by negative-capacitance circuits, but negative-capacitance circuits are difficult to achieve in real-word applications. In order to satisfy the requirement of modulating wave transmission in real time, the piezoelectric patches were attached at the upper and lower surfaces of the unit cells, with the upper surface's piezoelectric patch acting as a brake, the lower surface piezoelectric patch acting as a sensor, and the piezoelectric patch externally connected to a negative proportional feedback control strategy [40]. This strategy was employed to provide positive active control stiffness to the piezoelectric sensor/actuator patch in the present work. This method can be utilized to implement anomalous wave manipulations in an active manner [37; 41; 42] for the flexural wave. In such systems, the key is to exploit the relationship between flexural wave phase velocity and stiffness by controlling the equivalent stiffness to determine the phase velocity. We derived the relationship between the equivalent stiffness and the phase velocity of the flexural wave and analyzed the transmission coefficients and phase shifts of each cell using the transfer matrix method. Based on the GSL, the adaptive unit cells were cleverly arranged to construct appropriate phase gradients, thus enabling the abnormal refraction [43; 44], focusing [45; 46; 47; 48], and transforming the guided wave of the metasurface to be realized at different frequencies. The paper is organized as follows. Section 2 outlines the theoretical derivation of the adaptive unit cell and metasurface design. Section 3 demonstrates the abnormal modulation of flexural waves via metasurfaces through finite element simulations. Section 4 summarizes some conclusions.