esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This provides further evidence that the SITE model is capable of simulating the partitioning of available energy into sensible and latent heat fluxes. For example, [PERSON] [24] assessed the seasonal and annual behavior of energy partitioning and the energy balance closure in Caatinga. The authors identified a remarkable seasonal variability in the partitioning. Furthermore, they observed that during the dry period, more than half of the net radiation was converted into H, while in the wet season, this proportion reduced to less than 50%. In the study carried out by [PERSON] et al. [52], the dry and wet seasons in the Caatinga region were analyzed. The authors observed that the conversion of energy into H is more expressive during the dry season. Mean LE values, on the other hand, reached approximately 200 W m\({}^{-2}\) during the wet season, while in the dry season, it was nearly zero. This behavior was also explained by the closure of stomates and leaf senescence in most Caatinga plants as an adaptation to drier conditions, reducing water loss through transpiration. Furthermore, the authors observed that H, instead of LE, is the main driver of energy balance in the biome during drought years, mostly due to deficits in soil water availability, which lead to low photosynthetic activity. Other studies compared densely vegetated Caatinga sites with other sites with more sparsely and heterogeneous vegetation cover [53]. Results of these studies show that most parts of the net radiation were converted into H, as expected in semiarid environments. In these regions, LE surpassed H during the wet season, while the inverse occurred in the dry season. Furthermore, the difference in H and LE values during the dry season was more noticeable among studied sites. Additionally, LE was higher and more intense in the densely vegetated Caatinga region. Low LE values in the Caatinga region may also represent a defense mechanism by plants, associated with leaf loss, which can be indirectly measured by remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI). Deforestation affects the relationship between rainfall and NDVI [54], with degraded lands presenting lower productivity even in high water availability conditions. In preserved areas, however, NDVI responds more smoothly to rainfall variability, remaining relatively high even in drier conditions, compared with degraded lands.
## 1 Introduction
### Functionality of Race Simulations  
In [2], we presented a RS that simulates a race taking into account the most important effects on lap times: tire degradation, burned fuel mass, and interactions between the drivers, i.e., overtaking maneuvers. It simulates the race lap-wise, i.e., lap for lap. In each lap \(l\), it calculates the lap time \(t_{\text{lap}}\) for each driver. This is done by adding several time components, see Equation (1) [2]. The base lap time \(t_{\text{base}}\) represents the track characteristics. This can be regarded as the minimum lap time that can be achieved by the fastest driver-car combination under optimum conditions. It is increased by \(t_{\text{fire}}\) to include the effect of tire degradation (depending on the tire age \(a\) and compound \(c\)), \(t_{\text{fuel}}\) for the effect of fuel mass aboard the car, and \(t_{\text{car}}\) and \(t_{\text{driver}}\) that represent car and driver abilities. At the start of the race, \(t_{\text{grid}}\) adds a time loss that varies in relation to the grid position \(p_{\text{g}}\). Pit stops also increase the lap time. This is incorporated by \(t_{\text{pit,in-lap/out-lap}}\). The definition of the pit stops, i.e., in-laps, tire compound choices, and the amount of fuel to be added, currently has to be prescribed by the user as an input to the simulation.  
\[t_{\text{lap}}(l)=t_{\text{base}}+t_{\text{fire}}(a,c)+t_{\text{fuel}}(l)+t_{ \text{car}}+t_{\text{driver}}+t_{\text{grid}}(l,p_{\text{g}})+t_{\text{pit,in-lap/ out-lap}}(l) \tag{1}\]  
Consecutive lap times can be summed up to obtain the race time \(t_{\text{race}}(l)\) at the end of a lap as given by  
\[t_{\text{race}}(l)=\sum_{i=1}^{l}t_{\text{lap}}(i). \tag{2}\]  
A comparison of different drivers' race times at the end of a lap allows the algorithm to check whether overtaking maneuvers have occurred, which would result in a modification of the lap times of the drivers concerned. When a race is fully simulated, the RS returns each driver's final race durations as the main output. These inherently include the final positions. In [3], the RS was significantly extended by probabilistic effects, such as lap time variation, variation in pit stop duration, and FCY phases. They are evaluated by performing thousands of simulation runs according to the Monte Carlo principle, resulting in a distribution of the result positions.  
There are two applications in which the RS can be used. Before the race, the user is interested in determining a fast basic strategy to start with and assessing several what-if scenarios to prepare him for the race. During the race, the RS can be used to continually re-evaluate the strategy by predicting the race outcomes of different strategic options, starting from the current state.
## 5 Formulation of the LMI Stability Criterion
### Design of the LMI Condition
\right]<0 \tag{76}\]  
It is important to emphasize that, based on the matrix inequality (76), we can derive four different LMI requirements for the gain \(\mathcal{K}\) of the proposed control law (25). The first method assumes that both \(\epsilon\) and \(\bar{\delta}\) are known parameters that are predefined before solving the LMI. In the second method, \(\bar{\delta}\) is fixed as a known parameter, while \(\epsilon\) is treated as an unknown variable to be minimized. Conversely, the third method considers \(\epsilon\) as a fixed known parameter, while \(\bar{\delta}\) is treated as an unknown variable to be maximized. Finally, the fourth method addresses the scenario where both \(\epsilon\) and \(\bar{\delta}\) are unknown, with the objective being to maximize \(\bar{\delta}\) while minimizing \(\epsilon\).
# Fast 3D Content Update for Wide-Angle Holographic Near-Eye Display
## 5 Numerical Investigation of Hologram Update Algorithm
To evaluate the image quality, every tenth frame was numerically reconstructed. In addition, for every tenth frame, the full CGH with A-PAS algorithm was generated and reconstructed as a reference. The test was evaluated for overlap factors \(F\) = 0.5 and 0.75. The amplitude ratios for on-axis point P0, midpoint P2, and off-axis point P4 comparing update holograms with the reference holograms are shown in Figure 6.  
The update algorithm remains stable after 1000 cycles. This shows that the proposed method preserves the high accuracy of the encoded object information. The amplitude ratio for midpoints is close to 1 during the entire test. For the off-axis point, the amplitude ratio constantly grows during the stability test. In the investigated geometry, the viewpoint rotates around the object as shown in Figure 4c. Thus, the off-axis point with each frame gets closer to the center area, as marked in 'View B' in Figure 4c, where a smaller approximation error is introduced. Figure 5: (**a**) Accuracy test of points distributed along the diagonal of image space for different overlap sizes after a single hologram update cycle; (**b**) reconstructed point \(P_{4}\) from the starting hologram, first and thousandth hologram obtained with proposed update method. Figure 6: Amplitude ratios of reconstructed on-axis point \(P_{0}\), midpoint \(P_{2}\), and off-axis point \(P_{4}\) from holograms made with update algorithm and A-PAS in 1000 cycle test for (**a**) \(F\) = 0.75, and (**b**) \(F\) = 0.5. The last numerical test compared the calculation time of the holographic frame with the proposed update algorithm and the full calculation of new holographic frames. The generated CGHs had the same parameters as in the accuracy test. As a test object, a large point cloud made of one million randomly distributed points was used. In the speed test, 100 holographic frames were generated with the update method for three different values of overlap factor \(F\): 0.5, 0.75, and 0.875 and with the PAS method. For each hologram, calculation time was measured and the average time computed. For the proposed update algorithm, time measurements for reuse of the overlap region and filling of the blank region were made separately. The results are presented in Figure 7.  
The process of the update algorithm changes linearly with the size of the blank region. A-PAS generating hologram data for this area is a major component of calculation time. The time needed to update the overlap region is a small part of total generation time and is proportional to the size of the overlap region.
## 2 Materials and Methods
### Estimation of Equivalent R-C Network Model  
Once the state-space representation has been obtained, it is possible to estimate resistive-capacitive network arrays (Figure 5) or ladder networks. For instance, [PERSON] et al. used a similar approach to obtain lung RC networks from state-space representation of lung models [28]. To illustrate the process, consider the model of the electrochemical cell constructed using a set of Kelvin-Voigt elements [23; 24].  
Figure 4: Modified Kelvin–Voigt model.  
Assuming that the system's poles are different from the system's zeros and that \(G\)(s):  
\[G(s)=\frac{b_{ms}^{m}+b_{m-1}s^{m-1}+\cdots+b_{1}s+b_{0}}{s^{n}+a_{n-1}s^{n-1}+ \cdots+a_{1}s+a_{0}} \tag{7}\]  
which represents the model, is a proper rational function (m \(\leq\) n) corresponding to a linear, time-invariant, underdamped, stable system, the R-C network parameters can be deduced from the partial fraction expansion of \(G\)(s).  
Considering that the transfer function, \(G\)(s), is the result of a combination of different first-order elements (i.e., Kelvin-Voigt-type element models as shown in Figure 4) and there are no repeated poles, \(G\)(s), can be simplified to:  
\[G(s)=\sum_{\Delta=0}^{m-n}d_{\Delta}s^{\Delta}+\sum_{\alpha=1}^{n}\frac{k_{ \alpha}}{(s-P_{\alpha})} \tag{8}\]  
where the coefficients \(k_{\alpha}\) are calculated using the Heaviside cover-up method, as:  
\[k_{\alpha}=\lim_{s\to P_{\alpha}}\left[(s-P_{\alpha})\ G(s)\right]\qquad for \quad\alpha=1,\ 2,\ldots,n \tag{9}\]  
The partial fraction expansion representation results in a summation of first-order functions, which for a proper function can be rewritten as:  
\[G(s)=(d_{0})+\frac{\left(\frac{k_{1}}{-P_{1}}\right)}{\left(\frac{s}{-P_{1}} +1\right)}+\frac{\left(\frac{k_{2}}{-P_{2}}\right)}{\left(\frac{s}{-P_{2}}+1 \right)}+\frac{\left(\frac{k_{3}}{-P_{3}}\right)}{\left(\frac{s}{-P_{3}}+1 \right)}+\cdots \tag{10}\]  
Recalling Equation (4), the individual RC network parameters can be calculated as  
\[C_{\alpha}=\left[\left(\frac{1}{k_{\alpha}}\right)\right];\ R_{\alpha}=\left[ \left(\frac{k_{\alpha}}{-P_{\alpha}}\right)\right];\ R_{0}=[d_{0}]for\ \alpha=1..n \tag{11}\]  
Thus, the linear approximation of the electrochemical cell [20] can be represented as the network model as shown in Figure 5, using a combination of passive components.
## 2 Materials and Methods
### The Proposed Deraining Network
#### 2.2.3 Improvement of Loss Function  
The MSE loss function is the most commonly used loss function in image processing networks, which overcomes the problems of low stability and unstable output of the L1 loss function. The MSE loss function is defined as:  
\[L_{MSE}=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}\Big{(}Y_{true}-Y_{pred} \Big{)}^{2}, \tag{2}\]  
where \(Y_{true}\) and \(Y_{pred}\) represent the real value and predicted value respectively, and \(N\) is the number of numerical values. However, the incorrect predictions will be produced by MSE loss function when outliers are present. Therefore, a perceived loss function is incorporated to improve this situation. The perceived loss function can be used to quantify and estimate the visual differences between images through the pre-trained VGG16 network. Thus, the possibility of abnormal difference in the original MSE loss function is reduced. The calculation formula of perceived loss function is defined as follows:  
\[L_{A}=\frac{1}{C\times W\times H}\underset{C=1}{\overset{C}{\sum}}\sum_{W=1} ^{W}\underset{H=1}{\overset{H}{\sum}}\underset{2}{\overset{2}{\sum}}, \tag{3}\]  
where \(C\), \(W\), and \(H\) are the number, width, and height of output channels respectively, \(V\) represents the pre-training model VGG16 used, and \(V()\) is the outputs of the VGG16 model.  
In addition, slight noise on the image may have a great impact on the restoration result for image deraining. Some regularization terms need to be added to the model of the optimization problem to maintain the image smoothness. Therefore, on the basis of improving MSE loss function, the TV (total variation) loss function is also introduced in this paper. The TV loss function is a commonly used regular term, which is used to reduce the difference between adjacent pixel values by using constrained noise in coordination with other loss functions. The expression of the TV loss function is as follows:  
\[L_{TV}=\underset{i,j}{\sum}\Big{(}\big{(}x_{i,j-1}-x_{i,j}\big{)}^{2}+\big{(} x_{i+1,j}-x_{i,j}\big{)}^{2}\Big{)}^{\frac{\delta}{2}}, \tag{4}\]  
where \(x\) is the pixel points of the image. The default value for \(\beta\) is fixed to 2. Finally, the total loss function of the deraining network designed in this paper is defined as:  
\[L_{OUR}=L_{MSE}+\alpha L_{A}+\delta L_{TV}, \tag{5}\]  
where \(\alpha\) and \(\delta\) are the weight values of the perceived loss function and the TV loss function, respectively, which are 0.04 and 0.02 after a series of training adjustments.  
Figure 4: Structure of pyramid multi-scale pooling layer [31].
# Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
## 3 Overall Design of Tooth Rail Wind Shaft Inspection Robot
### Design of Detection Module  
The detection module is an important guarantee for the stable acquisition of shaft wall state information, and it carries sensors such as visual sensors, temperature and humidity meters and necessary equipment such as batteries. At the same time, the top of the detection shell is equipped with a lifting wire rope wedge sleeve and a wire rope connecting device, which can ensure the stable connection between the lifting wire rope and the detection shell. The structure of the detection module is shown in Figure 5, which is mainly composed of detection shell, intrinsically safe camera, anti-shake pan/tilt, miner's lamp, camera battery, miner's lamp battery, switch and armored optical fiber. The detection shell is divided into two parts: the upper part is hexagonal, which can maximize the installation space, and the lower part is symmetrical, which can greatly reduce the wind resistance. The two sides of the detection shell are designed with guide devices of guide ropes, which can reduce the left-right swing of the detection shell under the action of guide ropes. The intrinsically safe camera is installed on the side of the detection shell, which can ensure the maximum shooting angle, and can also reduce the influence of particles such as dust in the wind well on the camera to a certain extent. Moreover, the anti-shake tripod head is installed at the bottom of the camera, which can further reduce the influence of mechanical vibration and wind disturbance on the shooting effect of the camera. In addition, because the detection module is located in the shaft of the wind well, it is difficult to supply power by wire, so the battery is installed in the detection shell by means of battery power supply. In order to improve its endurance, multiple groups of battery modules can be arranged inside the shell.  
Figure 4: Overall structure of inspection system.
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## 4 Results
### CFD Cases
Therefore, as stated before, cases 2 and 3, which are very similar from an aerodynamic point of view, offer, in general, a better aerodynamic performance than the one from case 1. The mass flow rate throughout the scramjet in terms of the angle of attack of the three CFD cases is presented in Figure 17 where, for the three cases, increases with it. In addition to that, all cases have approximately the same mass flow rate for \(AoA=[-4,\ -2,\ 0]^{\circ}\); at higher angle of attack, the mass flow rate of case 1 is the highest one, followed by the one from case 2 and finally case 3. For \(AoA=4^{\circ}\), the mass flow rate of cases 1 and 3 are higher than that of case 2 as the aircraft flies with the nose optimised configuration. All of the presented parameters of the three CFD cases are shown in Tables 7 and 8 for the zero pitching moment and Vertical Force Balance conditions, respectively. For the first condition, all cases have a negative lift coefficient and aerodynamic efficiency since the angle of attack is negative; case 2 presents a lift coefficient slightly least negative than the rest, while case 3 has the lowest drag coefficient. This leads case 2 to reach the least negative aerodynamic efficiency and case 3 to have the lowest \(AoA_{(C_{M_{CG}}=0)}\). It is important to highlight that the negative lift coefficient obtained in the present study is caused by the non consideration of the stabilizers. These elements would be able to compensate this coefficient allowing the aircraft to fly. For the second condition, case 2 also gets the highest aerodynamic efficiency and the lowest drag coefficient. However, case 3 achieves this condition with a lower \(AoA_{(VFB)}\), which translates into a better aerodynamic performance from a \"lift point of view\". In addition to that, when the Vertical Force Balance condition is achieved, all cases present their corresponding maximum aerodynamic efficiency. Despite case 1 being the worst case in all aerodynamic aspects, it has the highest mass flow rate for both conditions. Figure 7:1 Pressure field of case 2 (**a**) and case 3 (**b**) for \(AoA=0^{\circ}\). Figure 7:2 Aerodynamic parameters in terms of the angle of attack of the three CFD cases, (**a**) \(C_{L}\) vs. \(AoA\), (**b**) \(C_{D}\) vs. \(AoA\), (**c**) \(E\) vs. \(AoA\), (**d**) \(C_{M^{c}c}\) vs. \(AoA\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & \(AoA\)(\({}_{C_{M_{CG}}}\)= 0) (\({}^{\circ}\)) & \(C_{L}\) & \(C_{D}\) & \(E\) & \(C_{M_{CG}}\) & \(\textit{in}_{S}\) (kg/s) \\ \hline
1 & \(-1.45\) & \(-0.0299\) & \(0.0122\) & \(-2.45\) & 0 & \(10.55\) \\
2 & \(-1.53\) & \(-0.0139\) & \(0.0094\) & \(-1.48\) & 0 & \(10.49\) \\
3 & \(-2.15\) & \(-0.0149\) & \(0.0093\) & \(-1.6\) & 0 & \(9.87\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Aerodynamic parameters and mass flow rate for the zero pitching moment condition (\(C_{M_{CG}}=0\)) of the three CFD cases. Figure 17: Mass flow rate throughout the scramjet in terms of the angle of attack of the three CFD cases.
## 5 Case Studies
### The Performance of the DSEC Method Under Different IRs  
In order to study the classification performance of the DSEC method under different IRs, the value range of IR is set to [2; 4,..., 10] and new training sets and testing sets for studying are constructed based on the value of IR. The GM performance of the DSEC method is evaluated under different IRs. In addition, as a traditional machine learning method, the AdaBoost classifier is utilized for comparing with the DSEC method using the same sample set. The results of these two methods are illustrated and compared in Figure 6.  
As the IR increases, the GM performance of the AdaBoost classifier decreases continuously and when IR = 10, the GM value decreases to about 87%. The performance of the DSEC method is almost unaffected with the change of IR and all the GM values are all higher than 93% under different IRs. The results further demonstrate the effectiveness of the DSEC method in dealing with the imbalanced data problem of TSSP.  
Under different IRs, the increment of GM (IGM) value of the DSEC method over the AdaBoost classifier is shown in Table 7.  
An approximate linear function between the IR and the IGM value is fitted as follows:  
\[IGM=0.64\times 10^{-2}\times IR-0.35\times 10^{-2}. \tag{19}\]  
The discrete data points and the fitted linear function are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IR** & **2** & **4** & **6** & **8** & **10** \\ \hline
**IGM (\%)** & 0.61 & 2.10 & 4.43 & 4.61 & 5.79 \\ \hline \hline \end{tabular}
\end{table}
Table 7: IGM values under different IRs.  
Figure 6: GM performance comparison under different IRs.  
As shown in Figure 7, the IGM value almost increases linearly with the increase of IR, which means that the more severe the imbalanced data problem of TSSP, the greater the improvement of the DSEC method over the AdaBoost classifier makes.
# Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area
## 3 Effects of Different Parameters on Sound Pressure Level  
As outlined previously, the primary objective of this study is to characterize and analyze the noise levels and frequency content generated by railway traffic on the Porto metropolitan surface line. Given the specific operating conditions of the metro network, which include diverse types of vehicles, distinct track solutions, and unique track geometries with particular characteristics, along with a range of speeds integrated into these operational conditions, it becomes feasible to conduct a comparative analysis based on these described aspects. Therefore, the analysis conducted takes advantage of the context of the metro network to determine noise levels based on these specificities. With the available data, it is possible to compare the following aspects:  
* Dependence of emitted noise on tram type;
* Influence of traffic speed on different types of tracks;
* Influence of track type on sound pressure levels;
* Influence of track geometry on sound pressure levels;
* Influence of vehicles in acceleration phase and with zero acceleration;
* Influence of bridge noise.  
The calculation of the indicator \(L_{Aeq,T}\) (equivalent short-term continuous sound level), which represents the energy of the acoustic signal at the various receivers during a period that corresponds to the passage of the vehicle, is very useful for understanding how the different traffic conditions influence the noise levels. As a unique value, this indicator gathers all the information present in the collected signal and facilitates analysis. For this reason, for each of the scenarios under analysis, the corresponding \(L_{Aeq,T}\) is presented for the receiver furthest from the road. \(L_{Aeq,T}\) is calculated as presented in Equation (1), where \(P(t)\) is the acoustic signal under analysis, \(P_{0}\) is the reference pressure and \(t_{1}\) and \(t_{2}\) correspond to the time defined by the 20 dB maximum level.  
\[L_{Aeq,T}=10\log\left[\frac{1}{t_{2}-t_{1}}\int_{t_{1}}^{t_{2}}\frac{p_{A}^{2 }(t)}{p_{0}^{2}}dt\right]\left[dBA\right] \tag{1}\]
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art  
The simulation results of proposed schemes; GDGOR-IA, GRMC-SM, and GDGOR-SM against existing methodologies GEDAR, AUV-CH, and EnOR are presented in this section. The discussion is divided into different subsections; fraction of void nodes, depth adjustment, PDR, energy consumption, and end-to-end delay.