esa-sceva/satcom-chunk-collection huggingface dataset:
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 7 Results, Evaluation of the Solutions
### Materials and Methods  
For the analysis, random graphs based on the model of Albert-Barabasi [20] were generated. The algorithms are implemented in C++ using the Leda library [21]. The DCMSHs and DCMSTs were computed using the CPLEX Ilog server [22]. The heuristic and the computation of the MSTSL are in polynomial time, but the exact solutions need expensive computations. Fortunately, in the tested random graphs the computation of the exact solutions required from a few seconds to a few hours and was feasible.  
Parameter Settings  
In the experiment, some parameters were variable: the number of nodes in the random graphs, the degree bounds on the nodes, and the edge costs. Medium-size topologies were generated containing 80-170 nodes. Notice that the number of edges in the graphs was not deterministic, since the edges were generated randomly. In each experiment presented hereafter, we indicate the average number of edges. The degree bounds were set randomly from an interval \([D_{min},D_{max}]\) using a uniform) distribution. The edge costs were also uniformly generated from the interval \([1,C_{max}]\).
## 5 Results and Discussion
### Analysis of Feeding Displacement and Staking Load  
The feeding displacement and the staking load directly determines the deformation degree of the flanging lip, and determines the working performance of the bearing after the roller staking process. Therefore, reasonable control of the loading process of the feeding displacement and the staking load is very necessary to control the staking quality [23; 24]. The relationship curve between the feeding displacement and the staking load was obtained for the limit states of the \"State 5\" and \"State 3\" are shown in Figure 8, respectively.  
The black line in Figure 8 is the staking load curve obtained by finite element numerical simulation, and the red line is the smooth curve fitted to it. It can be seen from Figure 8 that in the initial stage of the roller staking process, the staking load increases linearly with the change of the feeding displacement. When the feeding displacement is about 0.70 mm, the corresponding deformation state is \"State 3\" (as shown in Figure 8b). After this state, the staking load increases sharply. For other types of bearings mainly used in aviation at this stage, the roller staking process was analyzed using the same simulation method, and the general rule is as follows: if the feeding displacement is approximately equal to about 0.5-0.6 times depth of the V groove (\(k\)) and keep the staking load at a certain level, a better staking quality can be obtained, and most of the bearings after staking meet the requirement of the practical application.  
Figure 8: Relationship curve between the feeding displacement and the staking load: (**a**) the whole process of downward to “State 5”; (**b**) the process of downward to “State 3”.
## 4 Results and Discussion
### Relationship between Aspect Ratio and Crack Density  
Individual equations for aspect ratio and crack density were combined in this work to form a single relationship between both parameters based on exponential function (Figure 13). The mathematical relationship between aspect ratio and crack density is shown in Equation (15).  
\[\text{Aspect ratio}=0.1625e^{(14.989\text{-}crack\text{ }density)},\ R^{2}=0.8837 \tag{15}\]  
The aspect ratio and crack density values, studied by [PERSON] and [PERSON] [39], [PERSON] et al. [13], and [PERSON] et al. [36], are also described in Figure 13 and they are mostly included in the trend  
Figure 12: GRG results among variables.  
Figure 13: Relationship between aspect ratio and crack density.  
line proposed in this study. However, Equation (15) has a limitation in explaining the deviation of aspect ratio in the range of 0.15-0.2 in crack density, and thus more data are necessary to understand the behavior.
applied sciences  
An Adaptive Classification Model for Predicting Epileptic Seizures Using Cloud Computing Service Architecture  
**[PERSON] 1,2** and [PERSON] 1,3,**  
Adaptive Machine Learning (ML) consists of a continuous training process for the classification model to respond to the change in data distribution. The delay in obtaining the actual values of the class label after the classification decision restricts the adaptation process. For this reason, the classification should have the ability to overcome this delay by considering them as missing values, for example, after waiting for a specified period of time. Imputation refers to predicting the most reasonable values instead of the missing values. The prediction is performed using the available information, which has known values [4]. The model-based imputation utilizes statistical methods for building the prediction model for the missing values based on the data distribution. The task of building an ensemble and adaptive classifier required centric and high computational resources to handle the ongoing patient data stream, and these resources can be provided by cloud computing services [5].  
The cloud provides storage and computing capabilities that are highly expandable. Despite that, it is not practical to store and process all the data there, especially in applications that require a real-time or near-real-time response. Edge computing models have been developed to handle this type of application by adding a new layer of storage and computing resources that are closer to the source of data (end-user device) [6]. In addition to minimizing the computation latency, utilizing edge computing can provide more reliability against failures of the network, minimizing the network traffic and increasing the capability of the cloud by shifting the computing to the edge infrastructure. On the other hand, edge devices have a limitation to utilize the adaptive ensemble classifiers due to the limited resources.  
In this work, we focus on processing and analyzing Electroencephalography (EEG) data to classify the brain activity signals of epileptic patients in the cloud environment. This goal can be achieved by building an adaptive classification model that accurately detects an epileptic seizure, thereby warning the patient's relatives and the medical staff. It deals with EEG data in their original form as a stream (not a batch file) when generating it without the need to store and retrieve it.  
The proposed model is characterized by utilizing the adaptive mechanism of online bagging for handling the concept drift and the fast response of VFDT as a base learner in the ensemble model. It utilizes Edge computing by combining a light, fast, and local classifier that will be used only for the inference process with an adaptive and global classifier in the master cloud node that performs the training process and sends the latest version of the classifier to all edge nodes. The model reduces the effect of delayed labels by considering them as missing values, and the model-based imputation method is used to handle them in the global classifier.
# Non-Classic Atmospheric Optical Turbulence: Review
## 3 Theoretical Modeling of Optical Turbulence
### Obukhov-Corrsin Theory
Function \(g\) is a constant in the inertial-convective range function (hence the spectrum is of Kolmogorov \(-11/3\) type), and then it increases to a maximum in the viscous-convective regime and then decreases to zero in the viscous-diffusive regime. Function \(g\) depends on \(Pr\). In the inertial-convective regime, the 3D power spectrum is described by Kolmogorov's power law [51] as follows:  
\[\Phi_{T}(\kappa)=0.033C_{T}^{2}\kappa^{-11/3}, \tag{22}\]  
where \(C_{T}^{2}\) [m\({}^{-2/3}\)] is the temperature structure parameter as follows:  
\[C_{T}^{2}=0.9\Gamma(1/3)C_{0}\chi_{T}\varepsilon^{-1/3}, \tag{23}\]  
where \(\Gamma\) denotes the Gamma function. For higher spatial frequencies, the spectrum involves function \(g_{T}\):  
\[\Phi_{T}(\kappa)=0.033C_{T}^{2}\kappa^{-11/3}g_{T}(\kappa\eta_{K}). \tag{24}\]  
It is a convention in atmospheric propagation studies to use inner scale \(l_{0}\) instead of \(\eta_{K}\). The two quantities are proportional:  
\[Ma=\frac{u}{u_{s}}. \tag{16}\]0  
reducing to \(l_{0}=7.42\eta_{K}\) for atmospheric case. The widely used models for function \(g_{l}(\kappa l_{0})=g_{T}(\kappa\eta_{K})\) were suggested in [52] as follows:  
\[Ma=\frac{u}{u_{s}}. \tag{16}\]1  
and in [53]  
\[ \begin{split} g_{l}(\kappa l_{0})&=\exp(1.1090l_{0 }\kappa)\Bigl{[}1+0.70937l_{0}\kappa+2.8235(l_{0}\kappa)^{2}\\ &-0.28086(l_{0}\kappa)^{3}+0.08277(l_{0}\kappa)^{4}\Bigr{]}.\end{split}  \tag{27}\]  
While [PERSON]'s model gives a smooth decay, the Freilich model predicts the occurrence of a bump as spatial frequencies transition from inertial-convective to viscous-diffusing regime (see Figure 7). Other models (e.g., [54]) have been suggested but were later shown to somewhat undershoot/overshoot a constraint that must be imposed on the power spectrum to be consistent with the first principles of thermodynamics [55]. More generally, in the 1978 papers of [PERSON] [56; 57], several analytical and numerical models were suggested for more precise modeling of power spectra that can be directly related to the thermodynamic state of the air. For instance, in the maritime atmosphere, the power spectrum may have to be substantially modified at high spatial frequencies, as compared to the over-the-ground turbulence, as was revealed in Reference [58].
## 5 Experimental Details
### Comparative Experiment  
In this experiment, the results of the SA algorithm and WSO algorithm optimization models are not significantly different and smaller than the SSA-optimized decision tree model; on the contrary, the decision tree optimized with the SSA algorithm shows high prediction accuracy and is much better than the traditional decision tree model; its R\({}_{2}\) is significantly improved, and the prediction accuracy is improved by 9.236%; MAE is down 8.65 \(\times\) 10\({}^{-2}\), MAPE is down 3.21 \(\times\) 10\({}^{-3}\), MSE is down 1.04 \(\times\) 10\({}^{-1}\), and RMSE is down 1.0581 \(\times\) 10\({}^{-1}\). It can also be seen that the SSA-CART optimization model is more evident than the WSO-CART model and the SA-CART optimization model. Additionally, it is evident that the SA optimization model is better than the WSO optimization model, but less successful than the SSA (Sparrow Search Algorithm) optimization.  
The MSE of the SSA algorithm decreased by 3.58 \(\times\) 10\({}^{-2}\) compared to the WSO algorithm, and the MSE of the WSO algorithm decreased by 4.94 \(\times\) 10\({}^{-3}\) compared to the SA algorithm. The specific reduction indicators for the parameters are shown in Figure 5.  
Regarding the indicators displayed in Figure 5, the figure demonstrates that the SSA optimization model has an outstanding level of the fit and is accurate in predicting pork prices. Figure 6 depicts the decision tree coefficients of various models as well as the prediction time of each model, and it can be seen that after using different optimization algorithms, the prediction effect of the decision tree is well improved. Using optimization algorithms to optimize the decision tree sacrifices time but its accuracy has been improved,  
Figure 5: Evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Set** & **MAE** & **MAPE** & **MSE** & **RMSE** & **R\({}_{2}\)** \\ \hline CART & 0.47351 & 0.017259 & 0.29788 & 0.54579 & 0.73623 \\ SSA-CART & 0.38697 & 0.014049 & 0.19358 & 0.43998 & 0.82859 \\ SA-CART & 0.42771 & 0.015481 & 0.2399 & 0.48979 & 0.78758 \\ WSO-CART & 0.42277 & 0.0153 & 0.237 & 0.48682 & 0.79015 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of four models.  
In the combined models, the Sparrow Search Algorithm in this study performs the best of all models, achieving the closest approximation to the real values; the predictive power of the optimized decision tree outperforms that of individual decision trees.
## 3 Material and Methods
### ECG Signal Preprocessing
#### 3.3.3 High Pass Filter  
An ECG signal has a frequency range of approximately 0.5 Hz-85 Hz. Alternating current fields in the patient's cable caused distortion. Using a Butterworth technique A high-pass filter was applied to remove the noise with a frequency cutoff value of 1.5 Hz. The frequency response of this filter is shown below:  
\[H(f)=\frac{f}{\sqrt{(1+f^{2/2.5\times 10^{-4}})}} \tag{4}\]  
where \(f\) is the frequency in Hz. Note that the gain of the filter increases with increasing frequency, reaching a value of 0.707 (\(-\)3 dB) at the cut-off frequency of 0.5 Hz. This means that frequencies below 0.5 Hz are attenuated by the filter, while frequencies above 0.5 Hz are passed through. Using a high-pass filter with a cut-off frequency of 0.5 Hz, the low-frequency noise and baseline wander can be removed from the ECG signal. Using the Neurokit [48] tool, low-frequency noise is removed.
## 4 Conclusions  
It is possible to measure different monoterpene enantiomers in white wine using an HS-SPME-MDGC-MS method. This method was able to quantify concentrations of the different enantiomers down to 1 ng/L. It is not possible to use a single calibration curve for all wines as a significant matrix effect, related to sugar content, was noted. Additionally, the fiber age also impacted the measurement of some of the chiral terpenes. In particular the rose oxide isomers, and limonene isomers were most impacted by fiber life and the matrix. However this method resulted in good separation and sensitive, accurate and reproducible measurement of chiral monoterpenes when different wine matrixes were used. A difference in chiral monoterpene content was noted between Pinot Gris and Riesling wines, with Riesling wines containing a larger number of the chiral terpenes than Pinot Gris wines. Terpenes are known to impact the floral and citrus aromatics of wine, but the identification and quantitation of the individual isomers may explain differences in these aromatics between wines. Further investigations will determine if the different terpene isomers impact wine aroma.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 6 Numerical Results
### Topological Metric  
We start with the topological metric in Figure 4 illustrating the topology similarity index as a function of the successive algorithm time steps. It can be observed that the GAR algorithm provides a closer overall topology resemblance to the original physical graph, while both GAR and the proposed UVCS approaches improve the virtual topology in time. We specifically emphasize the ability of the proposed UVCS algorithm to converge much faster--in less than 100 steps. The Geo routing algorithm based on physical coordinates is not included in that figure, as it relies on the real nodes coordinates, resulting in the one-to-one similarity.
# Subjective Distance Estimates and Sense of Agency in Robotic Wheelchair Control
## 1 Introduction
### Intentional Binding  
One of the most discussed implicit correlates of the sense of agency is intentional binding (IB), originally reported by [PERSON] et al. [16]. IB is the phenomenon of subjective temporal convergence of a voluntary action and its sensory effect. In the experiment by [PERSON] et al., participants heard a tone 250 ms after pressing a key. It was found that they perceived the action as happening later than it actually occurred, and the tone as sounding earlier than it actually did. This phenomenon was not observed if they pressed the key not voluntarily but due to transcranial magnetic stimulation. The authors suggested that IB correlates with SoA. In this original study, participants watched [PERSON]'s clock [17] to determine the moment of a particular event (either action or tone). This method requires careful observation of the clock during the activity, and also does not allow the subjective timing of both events to be recorded within a single trial. [PERSON] et al. [18] found that IB was reproducible when participants gave direct estimates of time intervals between actions and their effects. The intervals were estimated as shorter for voluntary actions.  
IB has been repeatedly applied to determine the convenience of interfaces in human-machine interaction. For example, it has been used to evaluate various input methods [19,20] or to evaluate how the degree of automation affects the attribution of responsibility during complex tasks [21]. As an indicator, IB has the advantage of not requiring expensive equipment and being easy to implement in experiments.  
However, currently there is a debate in the literature as to whether IB is a specific indicator of SoA or whether it arises from the predictability of events. The separation of the factors of agency and predictability is difficult, and it is a major limitation of many SoA studies, although attempts were made to solve this problem [22]. The predictability of sensory consequences of an action is one of the mechanisms that low-level SoA relies on [23]. Nevertheless, as agents, we do not consider all predictable events to be the results of our actions. When we act, we usually know when exactly the action and its consequences take place. It is widely accepted that as the predictability of the type and timing of feedback decreases, IB declines as well [24]. Yet, it is still unclear whether this happens because SoA is highly dependent on predictability or because IB reflects the predictability of the event for the agent rather than the perceived degree of their involvement in the action. In light of this, there is a need for studies where the predictability of the consequences of voluntary and involuntary actions is equated as much as possible. In our study, we attempted to do this, specifically in the context of spatial binding.
## 2 Materials and Methods
### Statistics and Analysis Methods  
This study conducted experiments with students with autism (6 individuals) and typical students with social difficulties (5 individuals), collecting a total sample size of 11. A quasi-experimental mixed design was used, employing a two-factor mixed design: social curriculum as the within-subject factor with pre-test and post-test levels, and student characteristics as the between-subject factor, divided into two levels: the autism group and the group of students with social difficulties. The study utilized five methods for statistical analysis, including the following:  
* Distribution of the Data and Test for Normality This study utilizes the Normal Q-Q Plot, the Shapiro-Wilk test (which is suitable for small samples), and the widely used Kolmogorov-Smirnov test to ensure that the sampled participants' test scores conform to a normal distribution.
* Descriptive Statistics This is primarily used to analyze preliminary data from the \"Social Skills Effectiveness Opinion Survey\", \"Social Skills Performance Rating Scale\", and the \"Social Skills Behavior Checklist for Elementary and Middle School Students (Teacher Version)\".
* Two-Way ANOVA (Mixed Design) It's mainly used to clarify whether \"this curriculum is suitable for typical students with social difficulties and students with autism\"
* Paired Sample \(t\)-test This test is primarily used to analyze the \"Social Skills Behavior Checklist for Elementary and Middle School Students (Teacher Version)\" and \"Students' EEG data output comparison\".  
* Chi-Squared Test This test is mainly used to analyze changes in students' responses to unexpected situations in the \"Behavioral Experiment of Unexpected Situations\" between the pre-test and post-test.
* Wilcoxon Sign Rank This analysis is used for the EEG experiment results. The final data analysis in this study was conducted using the EEG Analysis module in Matlab 2021b.
# An Application of Manifold Learning in Global Shape Descriptors
## 3 Method
### Algorithm  
Our proposed descriptor consists of three major steps. For a given 3D polygonal model \(G=(V,E)\) with a set of vertices \(V=(x_{1},\ldots,x_{n})\in\mathbb{R}^{3\times n}\) and a set of neighbor connections \(E\), the LESI descriptor is a \(d\)-dimensional vector \(\mathbf{\zeta}=(\lambda_{1},\ldots,\lambda_{d})\) of real and positive values.  
In the first step, we compute the \(n\times n\) real, symmetric, and sparse weight matrix (\(\mathbf{W}\)) for a 1-ring neighbor of every point, as stated in Equation (6) using the inner scaling factor given in Equation (7). Next, we form the generalized eigenvalue problem in Equation (10) by constructing Laplacian and degree matrices (\(\mathbf{L}\) and \(\mathbf{D}\), respectively) without difficulty. The matrix \(\mathbf{L}\) is sparse, real, symmetric and semi-positive. Utilizing the Dulmage-Mendelsohn decomposition, we find the number of connected components of \(\mathbf{L}\). The objective of the second step is to find the spectrum of the LB operator. For that purpose, we solve the generalized eigenvalue problem using the Lanczos method. Then, we leave out as many smallest eigenvalues as the number of connected components. Since in most cases a single 3D model is made up of one connected component, we only need to leave out one eigenvalue. The last step of the algorithm deals with scale normalization and noise reduction, in the case it is required, by taking the logarithm of the spectrum and subtracting the first element from the rest of the vector. Detailed steps of the algorithm are summarized in Algorithm 1.  
```
Input: A 3D polygonal model \(G=(V,E)\) with n vertices \(x_{i}\) and edge list \(E\) Output: A \(d\)-dimensional vector \(\mathbf{\zeta}=(\lambda_{1},\ldots,\lambda_{d})\)
1 Compute edge weights using Equations (6) and (7);
2 Construct the sparse, real, and symmetric \(n\times n\) matrices \(\mathbf{W}\), \(\mathbf{D}\), and \(\mathbf{L}\);
3 Find number of connected components (nConComp) from \(\mathbf{L}\);
4 Solve Equation (10) for \(nConComp+d\) eigenvalues;
5 Sort them in increasing order and leave out \(nConComp\) smallest ones;
6 ifnormalization is requiredthen
7 Compute \(log(\lambda_{i})-log(\lambda_{1})\) where
8\(1\leq i\leq d\);
9
10 end if
```  
**Algorithm 1**Laplacian Eigenmap-based scale-invariant global shape descriptor.
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Preprocessing  
The 50 Hz of the EEG signals was filtered using a moving average of order 5. In addition, EEG signals were filtered using a Butterworth low pass IIR filter with \(f=80\) Hz and order \(N=6\).
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 3 Experiments
### Efficiency Analysis  
We compare the memory and run-time requirements of recent transformer architectures, which present SOTA results, with our suggested method with a varying number of mixer layers. We use the ETTm2 dataset in a univariate setting with a batch size set to 8. The memory and run-time data were collected during the training phase for all compared architectures. The results are shown in Figure 3.  
Figure 3a,b show the comparison of the memory requirements with varying values for horizon length and look-back window length. Similarly, Figure 3c,d show the comparison of run-time with varying values for horizon length and LWL, respectively. From Figure 3 we can conclude that the suggested architecture is much more efficient as compared to the recently famous transformer architecture. The usage of resource-intensive self-attention, prob-sparse self-attention, and autocorrelation results in the inefficiency of the transformer, Informer, and Autoformer architectures.