esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Discussion
### Publications Trends and Country Performance
a rapid expansion, which is anticipated to result in a massive market deployment in the near future. Figure 4 depicts the analysis of one country's contribution to the highest citation count of YIG papers. The contribution of one country is based on the first author's affiliation. The United States of America (USA) published the most papers (33) and is the leading publishing country in the world, followed by Germany (23), the United Kingdom (UK) (10), Brazil (6), China (5), and Japan (3). Furthermore, two papers were contributed by the countries Russia, India, Malaysia, and Spain. The remaining nations, including the Netherlands, South Korea, Iran, Taiwan, Mexico, Poland, Finland, and Sweden, each contributed a single paper. From 2012 to 2020, the United States has displayed a continuous publishing of top papers. The first-ranked paper is [13] from the USA. Germany and the UK have also contributed to paper growth, but less than the US has. Even though Germany is ranked second for its expanding contribution of top papers, the country has produced two top-ranked citation papers placed second [36] and third [18] (refer to Table 1). These top three productive countries (the USA, Germany, and the UK) should collaborate with other countries to provide high-quality YIG research.
## 3 Numerical Examples
### Example 2: Comparison with the Measurement of the Track  
On-site measurement of track vibration in the subgrade was reported in Ref. [29], and the same track and vehicle parameters are adopted. Figure 4 compares the vertical and lateral displacements between the presented model and the field measurements. It can be seen that the simulation results are very close to the measured displacements. The maximum vertical and lateral displacements of the rail are 0.364 mm and 0.162 mm respectively in the calculation model, while they are 0.357 mm and 0.167 mm, respectively, in the test. The small deviations in the amplitude are all within the allowable error range. This is due to the effect of uncertain excitation of track irregularity on the dynamic response of the track. The results indicate that the linearized Hertz wheel-rail contact has little effect on the vertical dynamics of the 3-D vehicle-rail-bridge interaction system. However, nonlinearity has an effect on the lateral dynamics of the track.
## 3 Results and Discussion
### Quantitative Analysis  
Images obtained using each method were subjected to image thresholding to calculate the number of individual adulterant pixels at each concentration and thus develop a quantitative model and compare the performances of each technique. The preprocessed band images (for univariate analysis) and band multiplication images (for bivariate analysis) were thresholded by converting all pixels with intensities below the threshold value into the background, with those with intensities above the threshold value ascribed to adulterant particles. The optimum threshold values were determined separately for each adulterant by examining the Raman spectra of pixels classified as either adulterant or paprika powder. Binary images were generated from rule images (Figure 5) by applying a threshold value calculated as mentioned in the Materials and Methods section. The contents of individual adulterants were then determined by calculating the percentage of adulterant pixels in binary images.  
To visualize the spatial distribution of each adulterant in paprika powder, images obtained by univariate analysis were combined as shown in Figure 6, with a similar procedure performed for bivariate and multivariate analyses. The concentration of color-coded pixels was low in images of the 0.1 wt % samples, increasing with increasing adulterant content and thus confirming that the detected number of component pixels was linearly correlated to adulterant content (R > 0.95 for each adulterant detected using different analysis methods).
## 4 Results and Discussion
### Sensitivity Analysis of Rankings  
The sensitivity analysis (Figure 11) examined how different weighting scenarios affect the ranking of the solutions.  
Impact of Priorities  
The sensitivity analysis reveals significant shifts in solution rankings based on decision-maker priorities:  
* Balanced Approach (50% efficiency, 50% sustainability): Solution P9 ranks first, offering 92.3% efficiency with 1758.2 kg CO\({}_{2}\)/day emissions.
* Efficiency Focus (70% efficiency, 30% sustainability): Solution P10 becomes optimal, achieving 92.7% efficiency at the cost of slightly higher emissions (1743.4 kg CO\({}_{2}\)/day).
* Sustainability Focus (30% efficiency, 70% sustainability): Solution P7 ranks highest, prioritizing lower emissions (1796.5 kg CO\({}_{2}\)/day) while maintaining 91.3% efficiency.  
The analysis demonstrates that solutions P7-P10 consistently rank among the top choices in different weighting scenarios, suggesting their robustness as compromise so  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Solution** & **Efficiency (\%)** & **Emissions (kg CO\({}_{2}\)/Day)** & **S+** & **S -** & **C\({}_{i}\)** & **Rank** \\ \hline Baseline & 87.2 & 2007.5 & 0.682 & 0.052 & 0.071 & 18 \\ P1 & 87.8 & 1980.3 & 0.654 & 0.089 & 0.120 & 17 \\ P18 & 95.2 & 1734.5 & 0.327 & 0.551 & 0.627 & 14 \\ \hline \end{tabular}
\end{table}
Table 4: TOPSIS Results for Pareto Solutions in Tube-Making Process.  
Figure 11: Sensitivity analysis of TOPSIS rankings based on different weighting scenarios.  
lutions. Extreme solutions (P1-P3 and P16-P18) show the highest sensitivity to weight changes, making them less suitable for scenarios where priorities may change.
## 1 Introduction
The main contribution of this work can be summarized as follows:  
1. We propose a fuzzy logic-based [15] pricing strategy to quantify the willingness and the capability of SVs for undertaking the computation tasks unloaded from UVs. The proposed pricing strategy takes multiple parameters such as the financial situation  
Figure 1: VEC task offloading model. and the volume of rentable resources of an SV, as well as the distance, speed difference and link reliability between an SV and the UV, into consideration for determining the unit price of an SV for executing the computation task. Other things being equal, the proposed pricing strategy promotes vehicles that have a small volume of rentable resources and difficult financial situations to the final SVs, thus encouraging most vehicles to join in the task offloading process and improving the overall resource utilization ratio. 2. We propose a Max-Min fairness-oriented [16] approach to solve the problem of finding the most suitable offloading decision. Essential to the approach is guaranteeing that vehicles that have few rentable resources and poor financial situations are treated equally to vehicles that have large resources and good financial conditions, in the process of determining the final service vehicles. This in turn is beneficial for the requesting UV to minimize the total payment, as well as for the system to maximize the resource utilization ratio and minimize the overall delay. Moreover, we present two heuristic algorithms to find the optimal solution to the formulated problem. The proposed algorithms achieve satisfying performance with applicable complexity and suit the practical IoV environment. 3. Extensive simulation results prove that the proposed approach achieves remarkable performance improvements in terms of the task completion delay, service cost and the utilization ratios of SVs. The rest of this paper is organized as follows: Section 2 introduces the related work. Section 3 describes the preliminaries. The problem formulation is given in Section 4. Section 5 introduces the proposed approach in detail. Section 6 shows the simulation results and evaluates the performance. Finally, Section 7 concludes this paper.
## 1 Introduction
This resulted in an improved loss function on the PhysioNet Cardiology Challenge 2017 dataset. Other researchers proposed using CNNs and LSTM to improve model performance using ECG signals from the MIT-BIH and BIDMC databases [19]. Additionally, other research papers investigated the use of transform-based deep learning algorithms and showed that applying a transformation to an ECG can slightly improve the model's performance [20; 21]. Overall, these studies demonstrate the potential of automated arrhythmia analysis using machine learning and deep learning techniques to improve the accuracy and speed of arrhythmia detection and classification. However, the traditional time-domain analysis of ECG signals often falls short of capturing the dynamic nature of cardiac events. In contrast, spectrograms offer a superior representation by jointly providing information about both the time and frequency components. This enables a more comprehensive understanding of cardiac rhythms, including subtle variations and transient phenomena that may be obscured in time-domain analysis. By leveraging the spectrogram's ability to reveal the time-varying spectral content of ECG signals, we aim to improve the classification accuracy. This study proposes a deep learning-based model with feature fusion for ECG signal classification. We convert the ECG time series signal into a spectrogram image. This spectrogram is then used to extract additional features and form descriptor images using a histogram of oriented gradients (HOG) and local binary patterns (LBPs). A HOG and LBPs are often considered superior to other feature descriptors in specific tasks due to their ability to capture essential local patterns and edge information effectively. Both descriptors handle variations in scale and rotation better than many pixel-based methods or more generalized descriptors, making them useful in diverse real-world scenarios. Three separate convolutional neural networks (CNNs) analyze these three image representations in parallel. To improve performance, the extracted features are concatenated and fed into a gated recurrent unit (GRU) module. The GRU processes these combined features before feeding them to the final classification layer. The remainder of this paper is organized as follows: Section 2 discusses the preparation and preprocessing of the ECG datasets used in this study and lists the employed algorithms. Section 3 describes the applied methodology and presents the proposed approach. Discussions of the obtained results and comparisons are conducted in Section 4, while Section 5 concludes the paper with comments and future directions.
## 3 _Pbc_ Analysis Method and Result
### Results of PBC Effect Analysis in FCW Installation
#### 3.5.3 Number of Collisions  
Considering the cognitive reaction time and deceleration of the driving simulation scenario, when the distance to the front vehicle is analyzed from the start of the scenario with a timetable, as shown in Figure 7, the distance to the front unexpected vehicle continues to decrease up to the point of the perception of a dangerous situation (cognitive reaction time), and the vehicle brakes after the perception of the dangerous situation. In the case without FCW installation at nighttime, one event of collision occurred, but after FCW installation, no events of collision occurred. After FCW installation, the safety distance from the front of the vehicle increased during both daytime and nighttime, confirming highly effective accident prevention due to FCW installation.  
Figure 5: Cognitive reaction time values before and after the installation of the FCW system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Daytime**} & \multicolumn{2}{c}{**Nighttime**} \\ \cline{2-5}  & **Non-FCW** & **FCW** & **Non-FCW** & **FCW** \\ \hline Degree of freedom & 69 & 69 & 69 & 69 \\ Sample mean & 1.66 & 1.46 & 1.95 & 1.66 \\
95\% confidence & Lower limit & 1.58 & 1.39 & 1.87 & 1.60 \\ interval of mean & Upper limit & 1.75 & 1.52 & 2.03 & 1.72 \\ Median & 1.73 & 1.51 & 1.97 & 1.66 \\ Variance & 0.12 & 0.07 & 0.11 & 0.06 \\ Standard Deviation & 0.35 & 0.27 & 0.33 & 0.25 \\ Normality verification & Shapiro-Wilk \(p\)-value & 0.001 & 0.020 & 0.002 & 0.008 \\ Kolmogorov-Smirnov & 0.001 & 0.097 & 0.010 & 0.058 \\ \multicolumn{2}{c}{\(p\)-value} & \multicolumn{1}{c}{0.000} & \multicolumn{1}{c}{0.000} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of reaction time(s).  
Figure 6: Deceleration values (m/s\({}^{2}\)) in a dangerous situation before and after installation of the FCW system.  
Figure 7: Vehicle travel distance and collision status after the event.
## 2 System Model
### The Proposed Secure Beam Hybrid Precoding Algorithm: GSVD-Sparsity
Consequently, the selection of the strongest power paths is inherently location-dependent, and so is the resulting hybrid precoder, thereby enhancing the security of the proposed algorithm. Precoding, as a physical layer security technique, aims to provide strong signal reception for the legitimate user while suppressing the signal strength at unauthorized users as much as possible. The GSVD-based secure beam precoding enhances security by suppressing the signal received by the eavesdropper, though at the cost of spectral efficiency loss for the legitimate user. The proposed GSVD-Sparsity algorithm exploits the sparsity of the channel space and the strongest propagation paths to maximize energy efficiency while maintaining the required level of security.
## 2 Experiment Setup and Model Configuration
### Experimental Setup for PIV Measurements
In each case, the mean velocity, turbulent kinetic energy (\(TKE=0.5\times(\overline{u^{\prime 2}}+\overline{v^{\prime 2}})/U_{\infty}^{2}\)), swirling strength, separation point, and other flow field characteristics were then obtained from instantaneous PIV measurements. Three-hundred pairs of images were collected for each case during the experiments. The image had a physical area of 338 mm \(\times\) 254 mm. The interrogation window was 32 \(\times\) 32 pixels, corresponding to an area of 7 mm \(\times\) 7 mm. Since an effective overlap of 50% of the interrogation windows was adopted in PIV image processing, the resultant spatial resolution was 3.5 mm \(\times\) 3.5 mm for the PIV measurements. In this experiment, the uncertainty of instantaneous velocity measurements was about 5.43%.
# Safety Concept for Textile-Reinforced Concrete Structures with Bending Load
## 2 Boundary Conditions
### Static System and Geometric Dimensions  
In this study, a single-span beam with length \(l\) loaded with a dead load \(g\) and a live load \(q\) is examined (Figure 1). The cross-section of the beam is rectangular with dimensions \(b\times h\) (width \(\times\) height). Furthermore, the distance from the extreme compression fiber to the centroid of the longitudinal tensile reinforcement (effective depth) \(d\) and the reinforcement cross-sectional area \(A_{t}\) are specified. Table 1 lists the statistical parameters of the geometric dimensions and Table 2 lists the statistical parameters of the effects.  
Figure 1: Structural system and dimensions of rectangular cross-section.  
Figure 1 shows the parameters that have an influence on the bending load capacity, namely \(g\), \(q\), \(l\), \(h\), \(d\), \(b\) and \(A_{\rm t}\). Two different effective depths \(d\) are examined in the reliability calculations: \(d_{\rm I}=15\) mm and \(d_{\rm II}=60\) mm. Both values are commonly used in carbon concrete constructions. Examples for the variation of effective depths \(d\) can be found on curtain facades and pedestrian bridges realized with AR-glass or carbon reinforcement [11; 12; 13; 14; 15; 16; 17; 18]. The corresponding component heights \(h_{\rm I}\) and \(h_{\rm II}\) are regarded as deterministic due to their low impact on the probability of failure; therefore, they are not random variables (i.e., scattering values). The same applies to the width \(b\) and the span \(l\). In order to investigate textile failure and concrete failure, the reinforcement ratio \(\rho_{\rm I}\) varies within the reliability analysis.  
The statistical properties of live loads \(q\) were taken from the literature [19; 20]. In contrast to steel-reinforced concrete, textile-reinforced components can be slimmer due to the corrosion-resistant reinforcement so that the typical load ratio of dead and live loads \(\eta\) changes. The amount of \(q\) in the total load increases due to the lower dead load. An example is the first pedestrian bridge made of carbon concrete in Ebingen [17], where the dead load of the construction is approximately 140 kN and the traffic loads are about 240 kN. Since the traffic loads are roughly double than the value of the permanent load, \(\eta_{\rm I}\) is close to 0.5. For comparison, the typical load ratio for steel-reinforced concrete components has a \(\eta_{2}\) value approximately equal to 1.0. Both ratios were used in the calculations.
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 2 Materials and Methods  
### Study Participants  
This cross-sectional study utilized secondary data from the 2019 Korea National Health and Nutrition Examination Survey (KNHANES), conducted annually by the Korea Disease Control and Prevention Agency. The survey targets a representative sample of the non-institutionalized South Korean population. Initially, 8110 participants were surveyed; however, those with incomplete data on fasting blood glucose or HOMA-IR levels were excluded. This resulted in a final dataset of 6134 participants for analysis.  
The protocol for collecting the KNHANES data was reviewed and approved by the Institutional Review Board of the Korea Disease Control and Prevention Agency (approval code: 2018-01-03-C-A), ensuring compliance with ethical standards.  
We classified participants as diabetic if they self-reported a physician's diagnosis of Diabetes Mellitus in the KNHANES survey, while those who reported no history of Diabetes Mellitus were categorized as non-diabetic. Only individuals who had valid fasting insulin measurements, fasting glucose levels, and Diabetes Mellitus status data were included.  
To assess whether excluding participants with missing data introduced any selection bias, we compared the excluded (\(n=1976\)) and included (\(n=6134\)) groups on key demographic and clinical variables (e.g., sex, age, waist circumference). This participant selection process is illustrated in Figure 1. As shown in Table 1, there were no statistically significant differences in these characteristics (all \(p>0.05\)), suggesting that the final analytic sample remained representative despite the exclusions. This assessment confirms the integrity of the analyzed dataset and indicates that missing data did not introduce systematic bias.
###### Abstract  
The deployment of Internet of Things (IoT) applications in remote areas, such as environmental sensing areas, requires Disruption Tolerant Networking (DTN) support due to the lack of continuous network connectivity. IoT devices in DTN generate and store data until a network link is available for data transmission. Data mules or ferries are scheduled to travel among intermittent networks to collect data and disseminate configuration updates from control centers to all participating nodes in a reliable manner. The incorporation of efficient, reliable multicast algorithms into DTN helps to overcome the current limitations in updating large numbers of nodes in remote areas with identical configurations. In this paper, the current state of multicast routing protocols in DTN is outlined according to their design aspects. Open issues are also discussed to provide impetus for further research into the Reliable Multicast DTN (RMDTN) protocol.  
disruption tolerant networking; Internet of Things; multicast protocols; reliable multicast; routing protocols +
Footnote †: journal: Electronics
## 5 Brief Description of Reduction Method for HOCIP
### Procedure for Denominator  
The denominator polynomial \(\widetilde{F}\left(s\right)\) of ROCIP is obtained by using improved Routh table given in [56] which always ensures the stability of reduced model. The improved Routh table for denominator polynomial of HOCIP is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \(T_{1,1}=B_{n}\) & \(T_{1,2}=B_{n-2}\) & \(T_{1,3}=B_{n-4}\) & \(\ldots\) \\ \(T_{2,1}=B_{n-1}\) & \(T_{2,2}=B_{n-3}\) & \(\ldots\) & \\ \(T_{3,1}\) & \(T_{3,2}\) & & \\ \(\vdots\) & \(\vdots\) & \(\ddots\) & \\ \(T_{n,1}\) & \(T_{n,2}\) & & \\ \(T_{n+1,1}\) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Modified Routh array.  
The formula for calculating the interval elements of Table 1 is given as  
\[T_{i,j}=T_{i-2,j+1}-\frac{\overset{\cap}{T_{i-2,1}}}{\overset{\cap}{T_{i-1,1}}}T _{i-1,j+1} \tag{23}\]  
where \(i\geq 3\) and \(1\leq j\leq(n-i+3)/2\). The element \(\overset{\cap}{T_{i,j}}\) denotes the mid-point of the interval \([T_{i,j}^{-},T_{i,j}^{+}]\) and its value is calculated according to  
\[\overset{\cap}{T_{i,j}}=\frac{1}{2}\Big{(}T_{i,j}^{-}+T_{i,j}^{+}\Big{)} \tag{24}\]  
While obtaining the elements of the modified Routh table (Table 1), two conditions are established to ensure the consistency of all elements.  
**Condition 1**.: _To ensure the existence of interval \(T_{i,j}\), the element \(T_{i-1,j+1}\) is modified to_  
\[T_{i-1,j+1}=\left[\max\left(T_{i-1,j+1}^{-},\overset{\cap}{T_{i-1,j+1}}-\frac {UL_{i-2,j+1}}{2}\right),\right. \tag{25}\]  
_where \(T_{i-1,j+1}^{-}\) and \(T_{i-1,j+1}^{+}\) are the lower and upper bounds of the interval \(T_{i-1,j+1}\); \(\overset{\cap}{T_{i-1,j+1}}\) is the mid-point of interval \(T_{i-1,j+1}\); \(L_{i-2,j+1}\) is the range of element \(T_{i-2,j+1}\) which is given as_  
\[L_{i-2,j+1}=T_{i-1,j+1}^{+}-T_{i-1,j+1}^{-} \tag{26}\]  
_and_  
\[U=\left(1/d\right)\left|\begin{matrix}\overset{\cap}{T_{i-1,1}}\\ \overset{\cap}{T_{i-2,1}}\end{matrix}\right| \tag{27}\]  
_with \(d>1\). This value of \(d\) is obtained as_  
\[d=\frac{\left|\overset{\wedge}{T_{i-1,1}}\right|+\left|\overset{\cap}{T_{i-2,1}}\right|}{\left|\overset{\cap}{T_{i-2,1}}\right|} \tag{28}\]  
**Condition 2**.: _This condition sets essential features on the denominator polynomial obtained from the Table 1. The denominator polynomial, \(\widehat{F}\left(s\right)\), of ROCIP is calculated from the \((n+1-r)\)th and \((n+2-r)\)th rows of Table 1 as_  
\[\widehat{F}\left(s\right)= T_{n+1-r,1}s^{r}+T_{n+2-r,1}s^{r-1}+T_{n+1-r,2}s^{r-2}+\cdots \tag{29}\]  
_Usually, the value of \(\overset{\cap}{T_{n+1-r,1}}\) is taken as the mid value of the interval \(T_{n+1-r,1}\)._