esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Optimization Parameters and Objective Function
#### 2.4.2 Mesh Generator  
After the definition of the bowl geometry profile, the case should be constructed for the CFD calculations. Here, the next step is to generate a mesh capable of reproducing the piston geometry, which also changes for every case. A code in Python was developed to generate all the meshes automatically by using the technique of dynamic mesh layering developed by [PERSON] et al. in [26]. This technique divides the domain in different blocks defined by control points and also maintains as fixed the number of cells around the spray region. The control points can move depending on the piston bowl geometry and the spray angle of each case, but the number of cells inside each block is constant. Then, the total number of cells for all simulations is the same. Furthermore, the orientation of the cells near the nozzle varies as a function of the spray angle. Figure 5 represents the definition of the control points, the blocks used to group the cells, the spray angle and the orientation of the cells.  
\begin{table}
\begin{tabular}{l l} \hline Condition & \(f_{1}(\text{soot})\)....... (7) \\ \hline if \(f_{\text{CFD}}<f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}\) \\ if \(f_{\text{CFD}}\geq f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}+1000\cdot(log(f_{\text{CFD}})-log(f_{lim}))^{2}\) \\ \hline Condition & \(f_{2}(\text{eff})\)....... (8) \\ \hline if \(f_{\text{CFD}}>f_{lim}\) & \(\frac{f_{lim}}{f_{\text{CFD}}}\) \\ if \(f_{\text{CFD}}\leq f_{lim}\) & \(\frac{f_{lim}}{f_{\text{CFD}}}+100\cdot(log(f_{\text{CFD}})-log(f_{lim}))^{2}\) \\ \hline Condition & \(f_{3}(p_{max})\)..... (9) \\ \hline if \(f_{\text{CFD}}<f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}\) \\ if \(f_{\text{CFD}}\geq f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}+50\cdot(f_{\text{CFD}}-f_{lim})^{2}\) \\ \hline Condition & \(f_{4}(\text{CO})\)...... (10) \\ \hline if \(f_{\text{CFD}}<f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}\) \\ if \(f_{\text{CFD}}\geq f_{lim}\) & \(\frac{f_{\text{CFD}}}{f_{lim}}+100\cdot(f_{\text{CFD}}-f_{lim})^{2}\) \\ \hline \end{tabular} where the _CFD_ index represents the value obtained from the CFD simulation and the _lim_ index represents the baseline level of each component. The objective function is important in the optimization process because it is the one that feeds the NS algorithm with the evaluation of the inputs and their effect on the efficiency and emission reduction, leading to the convergence of the solution.  
\end{table}
Table 7: Component functions of the parameters to optimize and the constraints.  
Figure 4: Example of bowls obtained for the combustion chamber geometry.  
of the cells. Moreover, each mesh is constructed as a sector based on the injector holes number, which is another input for the optimization. The simulation is performed for the combustion chamber with only one spray based on the axy-symmetric assumption to save computational time, because a large number of cases are required for the convergence of the optimization process.
## 2 Literature Review
The most widely used encoding methods are label-based encoding [38; 44; 45; 46; 47; 48; 49] and locus-based encoding [39; 50; 51; 52; 53; 54]. The bulk of the implementations of genetic algorithms make use of modularity as the fitness function [38; 44; 45; 48; 49; 52; 54]. Additionally, fitness functions such as the community score [40; 51] and modularity density [46; 47] are utilised. The initial population determination is conducted randomly in most of the existing methods. Also, the network's centrality is not considered while evaluating its community structure. Many methods have been used in literature to detect the most influential nodes in the network. The existing methods can be classified into topology-based methods and diffusion-based methods. Degree-based methods [55], centrality-based methods [5; 6; 7], and K-Shell [56] decomposition methods are topology-based. PageRank [8], LeaderRank [2; 9], and HITS Score [57] methods are diffusion-based. The topology-based methods take into consideration the attributes of the nodes in the network, like degree, centrality, etc., whereas the diffusion-based methods take into consideration the nodes visited by the diffusion process. Based on the findings of the preceding investigation, we determined that the existing techniques take longer to converge as the size of the network increases in scale. The existing algorithms are incapable of dealing with the massive amounts of data generated across the world.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 1 Introduction
WFPV, although it can achieve high accuracy on public datasets, is not effective in other non-periodic motion states. Although the FSM-based method improves accuracy, the method is not applicable to real-time monitoring devices. Based on this, a new FSM-based heart rate estimation algorithm is proposed in this paper. Based on the acceleration signals, RLS filtering is applied to the PPG signal to attenuate the MA and combined with EWT for further denoising to obtain the spectral information of the intervals in which the true heart rate is likely to occur so as to reduce the error caused by the inaccuracy of the reference signal. According to the rules of FSM, the current state is judged by the signal of the previous state as well as the current window. The spectral peak tracking is performed adaptively according to the state, which can reduce the interference generated by the close distance between the peak corresponding to the motion artifacts and the true heart rate to a certain extent. Our algorithm combines RLS filtering and EWT methods, effectively addressing the limitations of single denoising techniques. Additionally, it employs an adaptive spectral peak tracking strategy, maintaining high accuracy and stability even under significant motion artifacts. Compared to the traditional algorithms, this method significantly reduces computational complexity while ensuring the accuracy of heart rate estimation. This design greatly improves computational efficiency and enhances the practical applicability of the algorithm.
## 3 Methodologies
### Support Vector Machine  
SVM (Support Vector Machine) algorithms have been widely applied in binary classification tasks [64]. Initially developed for linear classification, they were later extended to non-linear classifiers and eventually adapted to regression tasks. The core concept of SVM is to construct an optimal margin classifier, where the complexity is a function of the number of support vectors, which means that only a small subset of the data that is crucial for constructing the classifier is required. Consequently, the SVM algorithm is particularly well suited for scenarios involving sparse samples and large-scale high-dimensional datasets [65].  
For the classification of complex non-linear data, the use of a kernel function has been proposed to project the dataset into a higher-dimensional feature space, allowing for linear separation within this newly transformed space. Common kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid kernels. By applying these kernel functions, SVM is able to transform non-linear problems in the original feature space into linearly separable problems in a higher-dimensional space, effectively increasing its flexibility and ability to handle a wide variety of complex relationships within data.
## 3 Empirical Results
### Exploratory Data Analysis  
A summary of the datasets of the two radiometric stations was done based on their main characteristics through observing descriptive statistics. The summary statistics in Table 3 show that the maximum GHI for the VEN data is 1179.16 W/m\({}^{2}\). The coefficient of skewness is 1.31 indicating the distribution is right-skewed. The coefficient of kurtosis is greater than zero meaning that the distribution has light tails. As for the SUN data, the maximum GHI is 1106.53 W/m\({}^{2}\) and the mean is 220.58. The coefficient of skewness is 1.29, the value is positive, indicating that the data are skewed to the right. The coefficient of kurtosis is 0.398 which means the distribution appears moderately distributed.  
Table 4 shows the correlations between GHI of each of the radiometric stations with the important weather variables which are used as covariates in this study. There is a fairly strong positive correlation between GHI and temperature. Similarly, there is strong negative correlation between GHI and relative humidity, while the relationship between GHI and barometric pressure suggests a weak negative correlation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Temp** & **BP** & **RH** & **WD** & **WD\_SD** & **WS** \\ \hline GHI(VEN) & 0.589 & \(-\)0.100 & \(-\)0.528 & \(-\)0.221 & 0.492 & 0.168 \\ GHI(SUN) & 0.626 & \(-\)0.104 & \(-\)0.640 & 0.251 & 0.189 & 0.242 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlations between GHI and weather variables.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Min** & **Q1** & **Median** & **Mean** & **Q3** & **Max** & **Skew** & **Kurt** \\ \hline GHI(VEN) & 0.0005 & 238.99 & 368.39 & 368.39 & 378.31 & 1179.16 & 1.31 & 0.84 \\ GHI(SUN) & 0.0002 & 15.73 & 28.19 & 220.58 & 387.88 & 1106.53 & 1.29 & 0.398 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics for VEN and SUN data.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.5 Multimodal Fusion Strategies  
Multimodal fusion strategies are widely employed to integrate diverse data sources for enhanced modelling. Studies have adopted early, late, and hybrid fusion techniques to combine modalities like EEG, facial expressions, gaze, and body posture [51; 96; 98]. Decision- and feature-level fusion methods have also been applied to improve recognition of engagement and mental states [35; 46; 49; 99].  
Some approaches integrate deep learning and attention mechanisms to align multimodal inputs, as seen in Transformer-based models and dual-stream frameworks for behaviour recognition [15; 42; 100]. Techniques such as PCA-based, entropy-optimised, and ensemble fusion further enhance model robustness and accuracy [59; 71; 101]. Software tools are used for feature extraction in hybrid models combining audio and visual cues [8; 102].  
Several studies have proposed innovative methods for integrating multimodal data in educational settings. For instance, the use of fast-slow neural networks has been explored for detecting student engagement, effectively combining asynchronous data streams like EEG and pose information for accurate on- and off-task state prediction [98].  
Hierarchical fusion approaches, such as hierarchical random forest for attention estimation and CRF for affective learning, have also been utilised to model student interest, achieving notable accuracy improvements [87].  
Additionally, complex network models incorporating attention mechanisms have been employed to capture teaching dynamics and student interactions, demonstrating superior predictive accuracy in classroom settings [69].  
For the analysis of group interactions, some approaches rely on GRUs to integrate written reflections and game trace logs, implementing both early and late fusion strategies for enhanced student assessment [96].  
Hybrid approaches that combine physical and digital modalities, including facial expressions, head posture, eye gaze, and EEG signals, have also been explored to create comprehensive engagement models for educational applications [51].  
Moreover, deep learning models like the state-aware deep item response theory integrate spatial features extracted from facial videos to assess student cognitive states, effectively bridging the gap between traditional assessment and real-time multimodal analytics [101].  
Other studies have implemented ensemble models that combine facial and body tracking for engagement detection, integrating sentiment analysis from audio signals for a more comprehensive understanding of student interactions [76].  
Finally, the use of novel algorithms like adaptive entropy minimisation and hybrid deep restricted Boltzmann machines has been proposed to optimise feature extraction and fusion, enhancing the generalisation ability of multimodal learning models [71].
# Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on \(L_{p}\) Decomposition
## 3 Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on \(L_{p}\) Decomposition
### Environmental Change Detection Operator  
Many researchers have studied environmental change detection operators. However, the current detection operators either ignore the detection of the constraint functions or mistake the small noise generated by the objectives or constraint functions in the evaluation process for environmental changes [23]. A new and improved environment change detection operator [30] is introduced and adopted in our proposed dMOEA/D-\(L_{p}\). It can detect the changes of objective functions and constraint functions at the same time, and set a threshold to eliminate the negative effects of noise as much as possible. The newly introduced operator is shown in Equation (7):  
Figure 1: Contour lines of the \(g^{L_{p}}\) function.  
\[\delta(t)=\frac{\sum_{i}^{K}\left(\frac{\left\|F(x^{i},t)-F(x^{i},t-1)\right\|}{ \left\|F(x^{i},t-1)\right\|+\varepsilon}+\frac{\left\|g(x^{i},t)-g(x^{i},t-1) \right\|}{\left\|g(x^{i},t-1)\right\|+\varepsilon}+\frac{\left\|h(x^{i},t)-h(x ^{i},t-1)\right\|}{\left\|h(x^{i},t-1)\right\|+\varepsilon}\right)}{K}>\delta, \tag{7}\]  
where \(F(x^{i},t)\) is an evaluation function, \(g(x^{i},t)\) is an inequality constraint function, and \(h(x^{i},t)\) is an equality constraint function. Since they all may change with time \(t\), we use the averaging method to detect three functions at the same time. \(\varepsilon\) is an arbitrary small positive number, \(\left\|.\right\|\) represents Euclidean distance, \(K\) represents the number of individuals randomly selected from the population (generally available as \(1\leq K\leq 5\)). According to [31], the Euclidean distance of the intergenerational target generally does not exceed \(10^{-2}\) in the case of no change for environment. Therefore, we set a pre-threshold \(\tilde{\delta}\left(10^{-3}\leq\tilde{\delta}\leq 10^{-2}\right)\). When \(\delta(t)>\tilde{\delta}\), it means the environment has changed.
## 2 Material and Methods
### The Physicochemical Parameters (WC-Water Content, pH, and Water Activity)  
The water content (WC) was determined according to PN ISO 1442:2000 [31]. A digital pH meter CPC-501 (Elmetron, Zabrze, Poland) equipped with a temperature sensor and pH electrode (ERH-111, Hydromet, Gliwice, Poland) was used to measure pH of the samples. The water activity (a\({}_{\text{w}}\)) was measured using a water activity analyzer (Novasina AG, Lachen, Switzerland), which gives temperature-controlled measurements. Measurements were made at a temperature of 20 \({}^{\circ}\)C. Analyzer had been calibrated with Novasina SAL-T humidity standards (33%, 75%, 84%, and 90% relative humidity).
## 2 Materials and Methods
### Implementing Digital Signal Processing Techniques for CSI Data Analysis
#### 2.5.2 Gaussian Filtering  
Post-Hampel filtering, the CSI data exhibit improved clarity but may still lack smoothness. To further refine the data, Gaussian Filtering is applied, enhancing the smoothness of the CSI sequence. An example of this effect is shown in Figure 7, contrasting a rough input with its smoother, filtered counterpart. The key parameter for this process, \(\sigma\), is denoted as \(\gamma\) in our study.  
The 1-D Gaussian kernel is given by the equation:  
\[g(x)=\frac{1}{\sqrt{2\pi\gamma^{  
\[h(x)=(f*g)(x)=\sum_{t=-r}^{r}f(x-t)\phi(t) \tag{8}\]  
where \(r\) is the radius of the kernel, defined as \(r=\lfloor\text{truncate}\cdot\gamma+0.5\rfloor\) if not specified.  
For a multidimensional input \(f(x)\), the Gaussian filter is applied independently along each dimension. The Gaussian kernel in \(n\) dimensions is:  
\[g(x)=\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi\gamma_{i}^{2}}}\exp\left(-\frac{x_{i}^{ 2}}{2\gamma_{i}^{2}}\right) \tag{9}\]  
The filtered output is given by:  
\[h(x)=\int_{-\infty}^{\infty}\cdots\int_{-\infty}^{\infty}f(t)g(x-t)\,dt \tag{10}\]  
The Gaussian filter is adjusted using the following parameters in our study:  
1. order: the order of the filter is 0.
2. \(\gamma\): the standard deviation of the Gaussian kernel is adjustable to minimize the error.
3. truncate: the filter is truncated with the value 4 at many standard deviations.
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Accronyms** & **Description** & **Accronyms** & **Description** \\ \hline APP & A-Posterior Probability & CDMA & Code Division Multiple Access \\ C-CDMA & Coded-CDMA & BER & Bit Error Rate \\ SNR & Signal-to-noise Ratio & MUD & Multi User Detection \\ PIC & Parallel Interference Cancellation & LLR & Log-likelihood Ratio \\ MAI & Multiple Access Interference & SISO & Single Input Single Output \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of Acronyms. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(\Lambda\) & LLR & \(\rho_{2}\) & Correlation Factor \\ \(\mu\) & Mean & \(\sigma^{2}\) & Variance \\ \(W_{1},W_{2}\) & Normal RV & \(E[.]\) & Expected Value \\ \(\Gamma\) & Received Signal & \(X\) & Symbol \\ \(\delta\) & Scaled value of LLR & \(K/N\) & System load \\ \(N\) & Gain & \(K\) & No. of users \\ \(L\) & Data Sequence Length & \(x\) & BPSK sybmol \\ \(\bar{x}\) & Estimated of \(x\) & \(\Lambda_{1}\) & LLR at output of PIC \\ \(\Lambda_{2}\) & LLR from previous iteration & \(\Upsilon_{\Gamma}\) & SNR of \(\Gamma\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of Symbol. * The notion of a 'feedback residue' for systems with APP feedback is introduced, and it is empirically shown that this residue term is a key consideration when determining the PIC output statistics. * Using the'residual feedback' model, it is shown that when APP feedback is utilised, data from previous cycles is not simply \"a scaled, noisy version\" of the current data. For this reason, benefits may be realised by APP feedback use. * LLRs with Extrinsic Feedback are derived. * It is demonstrated that extrinsic data, in the case of iterative receivers with suboptimal components, may not always yield the best results. * It is analytically shown that when extrinsic feedback is used in a coded CDMA system, no benefit will be realised by weighted delay filtering, as soft outputs from previous cycles are a merely scaled, noisy version of the most recent data.
## 3 Control Architecture
### Description of Tasks within an Action
To aid the terminal docking maneuver, we define a pre-docking position near the required dock position, at a safe distance from the docking station. A pre-docking position is essentially a point in free space with an associated orientation (pre-docking orientation) that the vehicle needs to reach (in position and orientation) prior to the execution of the docking maneuver. This pre-docking position can be used to attain different dock positions as depicted in Figure 5. Once the vehicle matches its position and orientation to the pre-docking position and orientation with the help of _Field Velocity_ Task and _Velocity Alignment_ Task discussed previously, it gradually surges or translates to the final dock position on the docking station with the help of _Terminal Dock_ Task. To complement the terminal docking maneuver, we use the _Dock Attitude_ Task for maintaining the vehicle orientation to the required dock orientation. In terms of general mission tasks, we also consider the need for the vehicle to follow a certain path in 2D. For instance, a typical surveying mission might require covering an area by moving in lawn-moving paths between way-points. The _Follow 2D Path_ Task allows the vehicle to achieve this by implementing the Line of Sight (LOS) [53] path following technique, reducing the along-track and cross-track errors to zero. Such a 2D path following task can be combined with a _Vehicle Depth_ Task, if the survey needs to be conducted at a constant depth, or with a _Vehicle Altitude_ Task, if it needs to be done at constant altitude  
Figure 4: Velocity field-based approach in 2D planar scenario. from the seafloor. Unlike the _Minimum altitude_ task that was discussed before, the _Vehicle Depth_ and the _Vehicle Altitude_ tasks always keep the vehicle altitude or depth close to a reference value. Finally, once the AUV has reached the end of one leg of the survey, we consider a _Survey Leg Alignment_ Task to control the overall attitude of the vehicle, aligning to the next leg while keeping the current position using _Hold Position_ Task before proceeding further with the path following technique. Normally, as these are slow maneuverings, the drag effects remain at very reasonable levels.
## 1 Introduction
### Motivation for a Systematic Literature Review  
A review is needed for UWA communication because the current research on ML/DL-based UWA communication is scattered. Researchers have frequently applied ML/DL techniques for enhancing channel estimation, adaptive modulation, and modulation recognition. However, they often use different methods, datasets, and evaluation tools. Moreover, their results exhibit differences in training loss, modulation schemes, throughput gains, and BER (Bit Error Rate) improvements. This makes it hard to compare results or draw clear conclusions. A well-organized review can consolidate the findings from multiple studies, offering a comparative analysis of ML/DL-based approaches. Given the ongoing challenges of multipath interference, extreme Doppler shifts, limited training data, and resource constraints in UWA systems, a systematic literature review (SLR) becomes important in guiding researchers toward developing more robust, scalable, and efficient AI-driven solutions.
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 2 UPQC in Dual Topology
### Shunt Active Power Filter (Shunt APF)  
The shunt APF of the dual UPQC aims to provide the load with a sinusoidal voltage of low harmonic distortion and maintain the balance on the three-phase system. The APF consists of a bi-directional power electronics inverter to which a capacitor must be added in parallel to the output.  
The transfer function of the shunt APF relates the output voltage with the duty cycle (see Figure 10). Additionally to the parameters defined previously for Figures 1 and 4, the term \(V_{\alpha a}\), \(V_{ob}\), and \(V_{oc}\) are the voltage in the load of the power grid; \(i_{fsu}\), \(i_{fgb}\), and \(i_{fsc}\) are the filtered currents supplied to the load using the series APF; \(C_{pa}\), \(C_{pb}\), and \(C_{pc}\) are the capacitors of the shunt APF; \(Q_{1}\)-\(Q_{6}\) are the duty cycle for three-phase inverter; and \(v_{ref}\) is a voltage reference for the voltage controller.  
The shunt inverter can be modeled as a single-phase equivalent by the split capacitor characteristic on the DC bus. The circuit in Figure 11 illustrates the equivalent circuit for voltage plant analysis.  
From the circuit presented in Figure 11, an equivalent circuit can be obtained as shown in Figure 12, for which the transfer function is given by Equation (20).  
\[Gp(s)=\frac{V_{o}(s)}{D(s)}\ =\frac{V_{B}}{L_{p}C_{p}}\frac{1}{s^{2}+\left( \frac{1}{C_{p}R_{L}}\right)\!s+\frac{1}{L_{p}C_{p}}}. \tag{20}\]  
Figure 11: Single-phase circuit of the shunt APF.  
Figure 10: Shunt active power filter (shunt APF) for the UPQC in dual topology.