esa-sceva/satcom-chunk-collection huggingface dataset:
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Comparison of the Results Obtained by MLP, Two Other ML Models, and the Weather Service
#### 4.6.1 Maximum Temperature Prediction
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**SVR**} & **Service** \\ \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**Train**} & **Test** & \\ \hline  & \multicolumn{2}{c}{**Mean**} & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & & \\ \hline MAE & 0.9097 & 0.0181 & 0.8795 & 0.0119 & 0.9354 & 0.0048 & 0.8551 & 0.0035 & 0.8042 & 0.8765 & 1.2526 \\ MSE & 1.3807 & 0.0559 & 1.3453 & 0.0276 & 1.4480 & 0.0135 & 1.2764 & 0.0041 & 1 for the weather service by 2.673, which shows the higher accuracy of the MLP over the weather service in terms of the MSE values. The value of the R\({}^{2}\) score for MLP was higher by 0.0175 compared with for the weather service; therefore, in terms of the R\({}^{2}\) score, the accuracy of MLP in forecasting the maximum temperature was higher. The accuracy of the MLP model for the maximum temperature forecasting was higher when compared with the weather service. Comparison with the LSTM and SVR model based on the accuracy metrics used showed a slightly higher accuracy of the MLP model over the compared models. All of the models tested were able to achieve greater accuracy in predicting the maximum temperature compared with the weather service. A slightly higher effectiveness of the MLP model compared with for the weather service is noticeable on the graph. The differences between the measured and predicted values in the graph are small, both for the MLP model and for the weather service. This shows the high accuracy of the MLP model for the maximum temperature forecasting for the next day.
# Optimal Cluster Expansion-Based Intrusion Tolerant System to Prevent Denial of Service Attacks
## 4 Performance Analysis
To more efficiently analyze resource waste, we define the VM processing rate parameter as the number of actual VMs that are used to process packets divided by the total number of VMs at a given time. Figure 6 shows the VM processing rate of each scheme under three different scenarios. When there is no DoS attack, the initial number of VMs is maintained and is fully used to process incoming packets for the duration of the simulation time, as shown in Figure 6a. In contrast, when there is a continuous or one-pulse DoS attack, the number of VMs increases in every system to process incoming DoS packets. The VM processing rates for the SCIT, ACT, and our proposed scheme preserve 100% without wasting resources, because all the additional VMs are fully used to process packets. In contrast, the VM processing rate of the exponential-ACT scheme decreases significantly because of the redundant VMs that are created by the expansion process but are not used for processing packets, as shown in Figure 6b,c. Figure 3: Scenario 1: normal packets. ACT: adaptive cluster transformation. (**a**) packets (count); (**b**) response time; (**c**) number of VMs. Figure 4: Scenario 2: continuous denial of service (DoS) packets. (**a**) packets (count); (**b**) response time; (**c**) number of VMs. Figure 5: _Cont._  
Figure 5: Scenario 3: one-pulse DoS packets. (**a**) packets (count); (**b**) response time; (**c**) number of VMs. Figure 6: VM processing rate in three scenarios. (**a**) normal packets; (**b**) continuous DoS packets; (**c**) one-pulse packets. Table 2 lists average response time, average VM processing rate, and total VM running time under three different scenarios. When there is no DoS attack, the average response times for four different systems are the same. However, under a DoS attack, the average response times for the SCIT and ACT schemes increase significantly, while the average response times for the exponential-ACT and our proposed scheme is reasonably small compared to the response times for the SCIT and ACT schemes. Although the response time performance of the exponential-ACT is similar to that of our scheme, the average VM processing rate of the exponential-ACT is much smaller than it is in our proposed scheme, because of the waste of resources. Therefore, we show that our proposed scheme can provide good QoS under a massive DoS attack without wasting resources by properly expanding cluster size.
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Quality Assessment of Spatiotemporal Blended Images  
Validation of the generated image fusion products is performed either visually, i.e., using a qualitative assessment [106] or by employing a quantitative metric [1; 107]. Among these metrics we can refer to spectral angle mapper [108], peak signal-to-noise ratio [109], structural similarity index used to assess the spatial distortion of the fused image [12], image quality index proposed by [110] and its vector extension [111], absolute difference [60; 112], root mean squared error (RMSE) [112], cross-correlation [112] and Erreur Relative Globale Adimensionnelle de Synthese [113]. The performance of the developed data fusion methods is evaluated by either using real data or synthetic data [32; 51; 60]. There are studies which evaluated the quality of the fused images by using evaluation metrics which do not require reference data [114; 115]. The most used quantitative evaluation metrics available in the literature are presented in Table 2.  
Currently, there is no agreement on which spatiotemporal image fusion method performs best for blending fine spatial resolution images with high temporal coverage images [42]. [PERSON] et al. [64] compared the methods developed by [32; 44; 50] for generating high temporal and spatial resolution LST product from ASTER and MODIS LST data in different landscape areas and concluded that all methods perform satisfactorily especially in desert areas. Furthermore, the spatiotemporal data method developed by [PERSON] et al. [50] is capable to deal with noises in the data much better than the other two evaluated methods. [PERSON] et al. [42] compared several image fusion methods and concluded that those relying on reconstruction-based concepts and theories are more stable than the learning-based methods.  
[PERSON] et al. [60] argued that none of the available image fusion methods perform well under all conditions of remote sensing applications. To test this hypothesis, we need a ready to use and open access library of the available image fusion methods [70] that would allow us to compare them across different landscapes [42] and agroecological regions, taking into account their sensitivity to noises in the data or to spatial and temporal variances [58]. Additionally, a hybrid image fusion framework can serve as a viable solution to combine methods that work well for heterogeneous landscape with those which perform well under homogeneous landscape conditions.  
Uncertainty analysis of the predicted/fused spatiotemporal images has been neglected by many fusion methods presented in this paper. Recently, [PERSON] and [PERSON] [119] proposed a spatiotemporal fusion method based on the geostatistical ordinary kriging method that allows the estimation of the prediction uncertainty. The method proposed by [PERSON] and [PERSON] [120] enables not only the estimation of the uncertainty of the predicted image, but it accounts also for the uncertainties of the input images used for the fusion purpose.
# Seismic Assessment of Historical Masonry Buildings at Different Scale Levels: A Review
## 2 Large-Scale Approaches
### Empirical Methods  
The empirical (i.e., macroseismic) methods began to spread around the 70s based on the post-earthquake damage survey and on the qualitative evaluation and statistical elaboration of a rather reduced parameter list. Their empirical nature represents, meanwhile, their strength and their weakness: it allows the analysis of a large number of buildings at a territorial or even national scale, but, on the other hand, rather rough results are achieved since affected by (only) qualitative information and by the need of determining approximately homogeneous classes where buildings are finally located. The roughness of the result is even because the post-earthquake damage survey is often incomplete, be ing focused on the most damaged areas near the earthquake's epicentre and therefore underestimating the real number of undamaged buildings [11].  
Empirical methods are particularly useful when attempting to define the damage scenario at a large-scale level; the numerous studies performed in the field led to the outline of three different approaches: (i) damage probability matrix (DPM) method, (ii) fragility curves method, and (iii) vulnerability index method (VIM); see Table 1.
## 2 Experiment and Characterization  
### Polishing Test  
The test sample was the n-type of a 6H-SiC single crystal substrate (Tianke Heda, Beijing, China) with a thickness of 0.4 mm, a diameter of 50.8 mm, and an initial surface roughness of 6-7 nm. The SiC was pasted to the center of the carrier table with paraffin wax before the test, and was dry polished on the C side (0001) at room temperature using the ZYP230 rotary oscillating gravity lapping and polishing machine (Kemai, Shenyang, China). The processing principle is shown in Figure 1, and the polishing process parameters are shown in Table 1. The polishing pad used for the test was a polyurethane polishing pad,and the polishing medium was five typesof solid-phase oxidant. The specific compositions are shown in Table 2.  
The solid-phase oxidant was spread evenly on the polishing pad, as shown in Figure 2a, and the dry polishing process had a dosing rate of 20g/h. Figure 2b,c show the beginning of the dry polishing process, during the dry polishing process, and after the completion of dry polishing, respectively. The single-factor method was used for the experiments and analysis to explore the oxygen production mechanisms of different solid-phase oxidant polishing.  
The mass of each sample was measured using a precision electronic balance.Before and after its processing, the difference was calculated, and the material removal rate (MRR, nm/min) for polishing was calculated using Equation (1). The surface roughness and 3D morphology of the sample before and after polishing were measured on a ContourGTk-1 3D profile inspection system (Bruker, Billerica, MA, USA).  
\[MRR=\frac{\Delta m}{\rho r^{2}\tau t}\times 10^{7} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & **Speed of Polishing** & **Speed of Polishing** & **Polishing Pressure P (psi)** & **Time t (h)** \\ \hline Parameters & 60 & 45 & 2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Polishing process parameters.  
Figure 1: The schematic of tribochemica Imechanical polishing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** \\ \hline
6H-SiC & Na\({}_{2}\)CO\({}_{3}\)—1.5 H\({}_{2}\)O\({}_{2}\) & NaOH & KIO\({}_{3}\) & KClO\({}_{3}\) & KMnO\({}_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Solid-phase oxidant and its composition used in the test.  
Figure 2: Test process. (**a**) The oxidant is evenly distributed on the polishing pad. (**b**) Start polishing. (**c**) Finish polishing.  
where, \(\Delta m\) is the mass difference before and after polishing, g, \(t\) is the processing time, min, \(\rho\) is the density of SiC, g/cm\({}^{3}\), which is taken as 3.2 g/cm\({}^{3}\), \(r\) is the radius of the test sample, mm.
# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
## 2 Theory
### Massively Parallel Room Acoustics Solver
#### 2.1.2 DDM-Based Parallel Computation  
The DDM-based parallel computing strategy implemented into the explicit TD-FEM is described here. Actually, DDM-based parallel computing techniques of two kinds can use overlapping or non-overlapping subdomains. The present explicit TD-FEM uses non-overlapping subdomains. The DDM-based parallel computation performs each subdomain calculation simultaneously by distributing subdomains into different computational nodes or processors while communicating with other computation processes. The message passing interface (MPI) is used for communication.  
In our implementation, the whole FE volume mesh is first partitioned into the non-overlapping subdomain FEs as a pre-process of room-acoustics simulations. The Metis Library [28] was used for partitioning. Then, in the room-acoustics simulation phase, each computational process reads the assigned subdomain FE volume mesh file. It enables us to compute the matrices \(\mathbf{M}\), \(\mathbf{K}\), and \(\mathbf{D}\) rapidly. All processes read the same global surface mesh file for constructing boundary condition matrix \(\mathbf{C}\) for ease of implementation. This boundary condition management increases the computational time of matrix construction in massively parallel computation cases because it induces competition in the file reading among the computational processes. Nevertheless, its effect on the total computational time is quite small because the matrix composition wastes much less time than the time-marching computation time, as explained in Section 3. After finishing matrix constructions, each process conducts time-marching calculations in parallel. Details of the procedure were presented in our earlier work [9].
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 4 Numerical Results
### BER Performance  
Simulation results of BER are shown in Figures 6 and 7 when the parameter \(N\) in (8) and (12) is equal to 4 and 16 QAM is adopted. The proposed methods of different coefficient values have similar BER performances and negligible decreases compared with the CAP method. The decrease in BER performance compared with the CAP method mainly results from power multiplexing and the successive interference cancellation algorithm we adopted to recover the sacrificed data symbols for intrinsic interference cancellation. As shown in Figure 8, it is understandable that the MSE curves of the CAP and proposed methods are overlapped because their BER performance gap is very small, whereas we add Figure 8 for its wide use in similar studies.  
Figure 5: Spectral efficiency when the 16 QAM and frame structures in Figure 4 are adopted.  
Figure 6: BER performances when the LS channel estimation method is adopted (broken line) and when channel state information is known perfectly (solid line). Note that \(c=0.15\) is adopted in simulations.  
The variation in \(c\) has an opposite influence on \(\hat{\mathbf{a}}_{2,K}\) and \(\hat{a}_{1}\) about SNR, whereas they both influence the overall BER performance. Similarly, the increase in \(K\) benefits the SNR of \(\hat{a}_{1}\) but results in more coded symbols in (9), which interfered due to \(a_{1}\). The values of \(c\) and \(K\) contributing to the optimal overall BER performance are difficult to be determined in theory, but fortunately, suboptimal values also work well. As shown in Figure 9, we selected a few values of \(c\) and \(K\) to simulate BER performance at \(E_{b}/N_{0}\ =30\) dB, and it is easy to find that the optimal value of \(c\) is around 0.15 and that different values of \(K\) have similar BER performances when \(c\approx 0.15\). Note that the other \(E_{b}/N_{0}\) has a similar selection process of suboptimal \(c\), and Figure 10 illustrates the suboptimal \(c\) corresponding to different values of \(E_{b}/N_{0}\). These suboptimal values are easy to find after a few attempts, and they are close to the optimal one, which means that there is no need for complex calculations for the optimal value.  
Figure 8: MSE performances when the LS channel estimation method is adopted.  
Figure 7: Zoomed-in version of a portion of Figure 6.
## 1 Introduction
Since the user can feel even small difference in heat loss as less as 1 W [30], it is paramount that the manikin used in evaluation of helmet performance represents the actual human head-shape as close as possible such that a realistic performance validation can be performed. Hence, a need for an anthropometrically developed biofidelic thermal manikin was identified. The developed biofidelic thermal manikin should provide more segmentation and high spatial resolution [31] such that the zonal heat-transfer characteristics can be studied in detail, which could be more useful in understanding the heat-transfer mechanisms specific to local zones. Hence, as suggested by this study [31], the manikin used in this study was modelled with nine measurement zones representing nine zones of human head. Thermal manikins have been consistently used to analyze different heat-transfer modes. The convective characteristics of helmets have been studied in detail using manikins: To assess the influence of comfort-angle on helmet ventilation [30], to evaluate the global and local characteristics of bicycle helmets [31], to evaluate helmet-design parameters [32], to understand rowing head-gear characteristics [33], for local ventilation-efficiency quantification [34], to quantify variation among helmets and helmet-ventilation efficiency [35], convective characteristics of cricket helmets [36], and ventilation changes in full-face motorcycle helmets [37] and industrial helmets [23; 27]. Thermal manikins were also used in evaluating the radiant heat-transfer characteristics of rowing head gear [33] and bicycle helmets [38] in combination with forced convection. Evaporative heat-transfer helmet characteristics were also studied using thermal manikins in pure evaporation studies for cricket helmets [36], and in combination with convection for industrial helmets [23; 27]. However, combined convective and evaporative heat-transfer studies using a biofidelic thermal manikin have not been performed. Hence, the focus of this study was to perform both a convective, and a combined convective and evaporative heat-transfer study to evaluate the influence of evaporation on helmet thermal performance. Most of the studies mentioned in this section dealt with evaluating different kinds of headgear, such as rowing headgear [33], motorcycle helmets [37], industrial helmets [23; 27], and the convective characteristics of bicycle helmets [31; 32; 33; 34; 35; 38]. In this study, the focus is on bicycle helmets, since helmet thermal performance plays a vital role in aiding users' decision-making process on whether to wear a helmet or not. Hence, in this research, we aimed to evaluate the combined convective and evaporative performance of bicycle helmets using an anthropometrically developed nine-zone biofidelic thermal manikin depicting an average European head shape. Results from this study can be used to establish the heat- and mass-transfer properties of helmet ensembles that could result in improved bicycle helmets for thermal comfort.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Parallel Power Stage Topologies  
The alternative way to increase the rated power of the drive is by operating at higher currents. This is a fairly common solution in traction, where sufficient cooling is provided by a forced flow of liquid through the heatsink of the power stage and, not so rarely, also through the stator winding. The literature review on parallel topologies in electric drives focuses on multiplication of three-phase power stages while maintaining a three-phase electric machine [76].  
To cope with the increased current, a simple parallel connection of semiconductor power switches or the simple parallelization of the power stages may be insufficient. Instead, the parallel connection of several transistor branches (transistor legs with additional coupled inductors) form the output (phase) terminal (Figure 7). The coupling inductors are indispensable as they ensure safe operation when the triggering signals of the transistors from different branches are not fully synchronized. This allows the use of switches whose rated current is lower than the total phase current, and, consequently, a better distribution of power losses across the converter, a possible minimization of the input capacitor voltage ripple, the possibility of interleaving carrier signals, etc. [77]. If the connecting inductors are strongly coupled, additional care must be taken concerning the current ripple of the branches, which might be significantly increased in some cases of interleaved operation [78].  
A significant advantage of parallel power topologies is their inherent fault tolerance [76] as with failure of one of the parallel stages, the rest of the parallel stages can continue to operate resulting only in reduced available power.
# Evolutionary Strategy for Practical Design of Passive Optical Networks
## 4 Proposed Algorithm for PON Optimization
### Genetic Algorithm  
The first research on GAs was developed and published by [PERSON] [30]. Since then, genetic algorithms have been successfully applied to a wide range of optimization and machine learning problems, including the optimization of PONs planning [21].  
Initially, when a GA is executed, \(P\) individuals are generated randomly and each individual represents a possible solution to the problem. Each individual is composed  
Figure 2: A simple, non-oriented graph, in which \(V\) represents vertices (or nodes) and \(E\) represents edges (or links).  
of chromosomes and these are represented by a set of genes. As a further process of the algorithm, this set of individuals (population) is evaluated by using a fitness function. The better-fitting individuals are selected from the current population, and unfitted individuals are discarded. From this point on, the selected individuals have their genes modified through genetic operators known as crossover and mutation. The new generation of possible solutions is then used in the next iteration of the algorithm. This cycle is repeated until an appropriate solution is found or the number of interactions (generations), previously defined, is reached. The main goal of this technique is to find the individual that most fit the environment during the evolution process [31].  
One of the greatest advantages of GAs is the simplicity in the optimization problem formulation [32]. Usually, fixed-length bit strings are used as input for the algorithm, which perfectly adapts to problems involving graphs [33; 34; 35]. Another advantage is the fast convergence time, in comparison to polynomial algorithms, for problems that involve a large number of variables [36]. Although GA is an algorithm that usually presents robust results, as it is a meta-heuristic method, the solution can converge to a local minimum, presenting a false optimal response. However, this premature convergence can be minimized with an appropriate adjustment in the number of generations, population size and genetic operators [37; 38].
## 3 Methodology
### Proposed ML Models
#### 3.7.5 K-Nearest Neighbors (KNN)  
K-nearest neighbors (KNN) is a non-parametric algorithm used for classification and regression tasks. It classifies a data point by identifying the majority class among its k-nearest neighbors in the feature space. For regression, the prediction is the average value of the neighbors. KNN is simple and intuitive because it requires no prior model training and assumes that similar data points are near each other. However, it can be computationally intensive with large datasets and is sensitive to the choice of k and feature scaling, affecting its performance and accuracy [35].
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Adjustments
Considering each device as an on/off traffic source and using a binomial probability formula, the spectral efficiency can be adjusted as: \[\eta^{col}_{w}=\left(1-\frac{Np(1-p)^{N-1}}{2}\right)\eta_{w}.\] (15) A flow diagram of the spectrum calculation methodology can be observed in Figure 1.