esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## Appendix A Pre-Test and Post-Test Questions
### Numerical Approximation of Gradients Post-Test Questions  
1. What is the main purpose of gradient numerical approximation? (a) Directly calculate the derivative of a function at a certain point (b) Test the accuracy of the derivative by approximation method (c) Taylor series expansion of the solution function (d) Computational efficiency of optimization function
* What kind of function is the gradient numerical approximation method mainly suitable for? (a) Differentiable continuous functions (b) Functions that are not differentiable (c) Discrete function (d) Any function
* Which of the following formulas is the standard formula for the two-sided difference method? (a) \((f(x+h)-f(x))/h\) (b) \((f(x)-f(x-h))/h\) (c) \((f(x+h)-f(x-h))/2h\) (d) \((f(x+h)+f(x-h))/2h\)
* What is the main difference between unilateral difference and bilateral difference in gradient numerical approximation? (a) Computational complexity
* The magnitude of the approximation error
* Type of function used
* Whether a derivation formula is needed
* In the gradient test, which one is more effective?
* Unilateral difference
* Bilateral difference
* In gradient test, the main calculation of unilateral difference is
* Gradient caused by a large change in a function at a certain point in a certain direction
* An approximation of the gradient caused by a small change in the function at a point in a certain direction
* The average value of the gradient caused by the change of the function in all directions at one point
* The gradient difference caused by a change in the opposite direction of a function at a certain point
* The main difference between two-sided difference and one-sided difference in calculating the gradient approximation is
* Bilateral difference only takes into account changes in one direction
* Bilateral differences consider changes in two opposite directions
* Bilateral difference does not consider the change of the function, only the gradient
* There is no difference in calculation between bilateral difference and unilateral difference
* In practical applications, what factors may affect the choice of unilateral difference or bilateral difference for gradient testing?
* Derivability of the function
* Limitations on computing resources
* Requirements for accuracy
* All of the above factors
* In practical applications, which strategies might be adopted to balance computational efficiency and accuracy of gradient tests?
* Select the difference method according to the function characteristics
* Always use bilateral differences
* Always use unilateral difference
* No gradient test
* Please briefly describe basic principles and differences of unilateral difference and bilateral difference.
## 3 Results
### Impact Resistance  
To ascertain the impact resistance of concrete, the weight-dropping method, a simple and widely preferred technique, was employed. A load of 5.6 kg was released from a height of 180 mm onto a beam specimen measuring 80 \(\times\) 100 \(\times\) 400 mm (Figure 12). The impact energy applied to the specimens was calculated according to the formulas provided below [80; 81].  
The weight employed in the impact experiment was dropped from a height of 180 mm onto beam elements with dimensions of 80 \(\times\) 100 \(\times\) 400 mm, after a curing period of 28 days. The experimental outcomes are presented in Table 3.  
\[\text{Impact resistance}=\text{n}\times\text{U} \tag{1}\]  
\[\text{H}=\text{gt}^{2}/2 \tag{2}\]  
\[\text{U}=\text{m}\,\text{V}^{2}/2 \tag{3}\]  
Figure 11: The relationship between the mixing ratio of the material used and the flexural-strength values.  
Figure 10: The flexural-strength values of the geopolymer mortar.  
\[\text{V}=\text{g}\,\text{t} \tag{4}\]  
\[\text{M}=\text{W}/\text{g} \tag{5}\]  
Let H stand for the height of the fall (mm), V for the hammer's impact velocity (mm/s), W for the hammer's weight (N), m for the hammer's mass (kg), g for the acceleration caused by gravity (mm/s\({}^{2}\)), t for the hammer's fall time from a height of 180 mm, and n for the number of blows.  
Impact experiments were conducted on specimens obtained from the prepared mixtures under identical conditions (hammer mass, weight, and height). The specimens were subjected to an impact energy of 10.02 kN/mm, calculated according to the aforementioned formulas.  
Upon examining Figure 13, it is observed that the impact-resistance values of the mixtures ranged between 10 and 33.3 kNmm. Similar to compressive and flexural strength, a decrease in impact resistance was noted with the increase in the ratios of FA and LP. Simultaneously, an increase in the BFS ratio resulted in an augmentation of impact resistance. The maximum impact resistance, 33.3 kNmm, was attained in mix No. 8 and mix No. 9. The mixture yielding the lowest impact resistance was mix No. 1 with a value of 10 kNmm, as depicted in Figure 14, which illustrates the effects of the constituent materials on impact resistance.  
Figure 12: Impact test setup.  
Figure 13: The impact-resistance values of the geopolymer mortar.  
The impact resistance of the prepared geopolymer mortar aligned consistently with the factors influencing flexural and compressive strength.
## 3 Hardware Implementation
### ECG R-Peak Detection Engine  
Figure 7 shows the detailed architecture of the R-peak detection engine, which comprises five components: the R-peak detection state machine, process element (PE), data buffer, parameter storage, and positive zero-crossing (PZC) detection unit. We utilize a 1-KB SRAM for the data buffer, which stores both raw ECG and intermediate data. The filter coefficients are stored in the parameter SRAM. The computational flow is controlled by the R-peak detection state machine. To enhance resource utilization, each computational stage shares the same PE.  
Figure 6: Overall schematic of the arrhythmia detection processor.  
Figure 7: The detailed architecture of the ECG R-peak detection engine.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection
### Feature Extraction  
Feature extraction plays a crucial role in building EEG-based BCI applications. Thus, we extracted the EEG frequency bands by using a power spectral density (PSD) method called the Welch method. Subsequently, we used the resulting frequency bands to calculate the valence as a preference indicator. Figure 4 presents the block diagram of feature extraction.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix B Proof of Theorem 2
### Coefficient's Derivation and Error Probability Analysis
\begin{split} E\Big{[}\varepsilon_{1}\gamma_{2}^{R}\Big{]}& =\|\mathbf{h}_{\text{sd}}\|\sqrt{\frac{P}{\Phi}}\sqrt{\alpha_{1}},\\ E\Big{[}\gamma_{2}\gamma_{2}^{R}\Big{]}&=\|\mathbf{h }_{\text{sd}}\|^{2}\frac{P}{\Phi}+\|\mathbf{h}_{\text{sd}}\|^{2}P\bigg{(}1- \frac{1}{\Phi}\bigg{)}+\sigma^{2},\end{split}  \tag{104}\]  
and  
\[\alpha_{2}=\alpha_{1}\Bigg{(}1+\frac{\|\mathbf{h}_{\text{sd}}\|^{2}\frac{p}{ \Phi}}{\|\mathbf{h}_{\text{sd}}\|^{2}P\Big{(}1-\frac{1}{\Phi}\Big{)}+\sigma^{2 }}\Bigg{)}^{-1}. \tag{105}\]At time instant \(n(3\leq n\leq N)\), \(\epsilon_{n}=\epsilon_{n-1}-\frac{E\left[\left\{\epsilon_{n-1}I_{n}^{\mathcal{H}} \right\}\right]}{E\left[I_{n}I_{n}^{\mathcal{H}}\right]}\mathbf{I}_{n}\), where  
\[E\Big{[}\epsilon_{n-1}\mathbf{I}_{n}^{\mathcal{H}}\Big{]}=\sqrt{ \alpha_{n-1}}\Bigg{(}\|\mathbf{h}_{\text{sd}}\|\sqrt{\frac{P}{\Phi}}+\| \mathbf{h}_{\text{sr}}\|\cdot|h_{rd}|\beta\sqrt{P}\rho_{n-1}\Bigg{)}, \tag{108}\] \[E\Big{[}\mathbf{I}_{n}\mathbf{I}_{n}^{\mathcal{H}}\Big{]}=\Bigg{(}\| \mathbf{h}_{\text{sd}}\|\sqrt{\frac{P}{\Phi}}+\|\mathbf{h}_{\text{sr}}\|\cdot |h_{rd}|\beta\sqrt{P}\rho_{n-1}\Bigg{)}^{2}\] \[+\|\mathbf{h}_{\text{sr}}\|^{2}|h_{rd}|^{2}\beta^{2}P\Bigg{(} \bigg{(}1-\frac{1}{\Phi}\bigg{)}-\bigg{(}1-\frac{1}{A_{n-1}}\bigg{)}^{2}\rho_{ n-1}^{2}\Bigg{)}+F,\]  
where \(F=\|\mathbf{h}_{\text{sd}}\|^{2}P\Big{(}1-\frac{1}{\Phi}\Big{)}+|h_{rd}|^{2} \beta^{2}\sigma_{r}^{2}+\sigma^{2}\). So, at time instant \(N\), the variance of the final estimation error \(\epsilon_{N}\) is as follows  
\[\alpha_{N}=\alpha_{2}\Bigg{(}1+\frac{\bigg{(}\|\mathbf{h}_{\text{sd}}\|\sqrt{ \frac{P}{\Phi}}+\|\mathbf{h}_{\text{sr}}\|\cdot|h_{rd}|\beta\sqrt{P}\rho^{*} \bigg{)}^{2}}{\Gamma}\Bigg{)}^{2-N}, \tag{109}\]  
where  
\[\Gamma=\|\mathbf{h}_{\text{sr}}\|^{2}|h_{rd}|^{2}\beta^{2}P\Bigg{(}\bigg{(}1- \frac{1}{\Phi}\bigg{)}-\bigg{(}1-\frac{1}{A(\rho^{*})}\bigg{)}^{2}(\rho^{*})^{ 2}\Bigg{)}+F, \tag{110}\]  
\(\alpha_{2}\) is given in (107).
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## 4 Results
### An Intuitive Visual Comparison to Assess the Performances at a Glance  
The fluctuations with time of the slowly varying component of the X-band solar spectral irradiance values listed in Table 11 are shown in Figure 2: the 1 st column values are on the abscissa, while the other five columns are on the ordinate. The DRAO reference is in red and the radar-derived horizontal (vertical) values are in blue (green). It is clear that all the retrieved values (both from the original data and after noise subtraction) are able to capture and describe the (\(\sim\)27 day) periodic evolution of the microwave signal emitted by the sun, despite a relatively poor S/N ratio and the fact that the amplitude of the slowly varying component at 3.2 cm is only \(\sim\)69% (see Equation (2)) of the maximum oscillation, which is observed at the S-band. To the best of our knowledge, this is the first time that the solar slowly varying component has been monitored at the X-band by dual-pol receivers. As it will be seen in Section 5, the retrieved values at 3.2 cm are far from the accurate monitoring of solar activity performed by DRAO: the explained variance between 57 data pairs is of the order of 55% (41%) for the vertical (horizontal) polarization (performances after having subtracted the estimated noise). The situation is obviously better at longer wavelengths: for instance, the explained variance by the C-band MeteoSwiss Albis radar during 220 days in 2014 was 83% (see Table 2 in [13]).  
The DRAO reference and the radar retrieved curves both show four pronounced relative maxima in February, April, May and July. There is also agreement for the lowest values of the relative minima. As expected, given the relatively poor sun-to-noise ratio, the \"raw\" Sun + Noise values (upper thin blue and green curves) are not very close to the DRAO reference: on average, +1.38 (+1.32) dB can be observed for the horizontal (vertical) channel. After the noise subtraction, the average residual bias is reduced significantly: on average, \(-0.38\) (\(-0.46\)) dB can be observed for the horizontal (vertical) channel, as seen in the 2 nd line in Table 2.
# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
## 2 Related Work
Other researchers proposed hybrid optimization strategies that combine two or more heuristic optimization techniques [33; 34], which could integrate the merits of different algorithms or achieve a near optimal solution quickly. Considering that DS will fall into local optimum when optimizing the SSTP model, we propose LSPSO (Particle Swarm Optimization algorithm combined with Local Search strategy), which uses the Pareto dominance relationship to measure the advantages and disadvantages of the solution, and uses local search for non-dominated solution sets to enhance the local search ability. LSPSO has a strong ability to explore and can approach the true Pareto frontier.
# Factorial Design Analysis for Localization Algorithms
## 2 Related Work
### Evaluation Scenarios Reported  
These localization techniques have been evaluated in different real-world scenarios [23; 24; 25] and simulations [19; 22]. In [24] the authors describe a real-world outdoor scenario with a sensing area measuring 10 m \(\times\) 10 m and four RNs on the corners of the sensing area, where range-free localization techniques are evaluated [19; 22] for their simple implementation in hardware. In this scenario, the log-normal shadowing propagation model is used to calculate the RSSI between the NOI and the four RNs. Since this scenario is represented in a small area, the MSE is evaluated as a function of the noise \(\sigma_{dB}\in\{0.5,1,2,\ldots,10\}\), because the number of RNs is always constant and node density is assumed to be unitary, i.e., one NOI on the 10 m \(\times\) 10 m grid. CDF represents the localization error distribution, when two localization techniques are compared, that technique with a localization error distribution concentrated on the small values is the better [19], since this implies that the technique has a high probability of having small localization errors. Up to now no large sensing area scenario has
## 5 Conclusions  
In this study, the PFA model was developed using a crack band model. Using the Hashin criteria, failure initiation was detected at various failure modes. The damage evolution behavior was calculated based on the crack-band-model-implemented damage variables. To verify the effectiveness of the suggested PFA model, the test results of the open-hole composite laminate were compared to the analysis results. The conclusions drawn from this study are as follows.  
1. The developed PFA model demonstrated results that were less-mesh-dependent in comparison to the MLT-PFA model that has been used in various studies. The lower mesh dependency occurred  
Figure 19: Load-displacement curve for two-open-hole compressive analysis result with each damage points and the corresponding calculated damage contour when the (**a**) shear failure and (**b**) matrix tension failure damage variable become \(1\) or greater and (**c**) final fracture occurs.  
Figure 20: Fractured test specimen for (**a**) one-open-hole tensile test and (**b**) two-open-hole compressive test.  
because each element failed when considering the constant fracture energy regardless of the element size when using the damage variable in the crack-band-model.
2. The analysis results were in good agreement with the experimental ones regardless of the stacking sequences, the number of notches, and the loading direction. This conclusion was determined by examining the load-displacement behavior and the strain distribution of the PFA results while doing a comparison to the experimental results.
3. Using the developed PFA model, the failure behavior of the composite laminate containing open holes was studied. Tensile failure behavior shows a final load-drop induced from the fiber failure damage while the compressive failure behavior shows that the final load-drop occurred by shear and matrix failure damage. The different fracture mechanism according to the loading direction was confirmed by comparing the fractured specimens.  
As a result, it was verified that the developed PFA model can accurately predict the failure behavior of the composite laminate with less mesh dependency. The suggested model, which allows for a good representation of behavior of the notched geometry and less mesh dependency, is expected to benefit the design process of complex composite structures. In addition, it is considered that such advantages may be useful in predicting the structural health in the field of sound absorption that uses the geometrical discontinuities as a design factor (e.g., rigid inclusions and cavities in a host material [1; 36; 37; 38]).  
Conceptualization, D.Y., J.K. and Y.D.; Methodology, D.Y. and Y.D.; Validation, D.Y. and S.K.; Formal analysis, S.K. and Y.D.; Investigation, D.Y. and S.K.; Resources, J.K. and Y.D.; Writing-original draft, D.Y.; Writing-review and editing, D.Y. and J.K.; Visualization, D.Y. and S.K.; Supervision, J.K. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# A 60-GHz Ultra-Thin and Flexible Metasurface for Frequency-Selective Wireless Applications
## 3 Results and Discussion
### Experimental Testing Setup of the 60-GHz Metasurface  
In order to perform the experimental testing, a sample (metasurface under test-MUT) is placed in the path of converged radiation beams of the two horn antennas. The free-space quasi-optic bench is deployed for the testing purposes, which is comprised of a pair of corrugated horns with a bandwidth of 50-75 GHz acting as transmitter and receiver, two pairs of reflecting mirrors one of which is designed to create a full reflection at an angle of 90\({}^{\circ}\), and a sample holder which allows for the transmission of radiation beams through the sample. The experimental setup is presented in Figure 9, where the sample placement is shown in Figure 9a and the complete quasi-optic bench is depicted in Figure 9b. The horn antennas are screw-fitted to their respective frequency multiplier modules, which are connected to the ports of the vector network analyzer (VNA). The highly directional beams from the corrugated horn antennas are focused toward the sample through a specified path directed by reflecting mirrors for the measurements of transmission characteristics. The inner-most pair of mirrors are designed to collimate the radiating beams with a high-quality focus over a broadband range. The sample is oriented perpendicular to the direction of antenna beams, and a VNA is used for the measurements of phase and magnitude of the reflection and transmission characteristics.
# Improvement of Recycled Aggregates Properties by Means of CO\({}_{2}\) [PERSON]
## 3 Materials and Methods
### Accelerated Carbonation Test on RCAs  
The water content is an essential parameter in the carbonation process. If the water content of the RCA is low, little CO\({}_{2}\) is dissolved in the water, while a high water content slows down CO\({}_{2}\) diffusion since the diffusion rate in water is slower than in air. To improve the accelerated carbonation test and increase the capacity of an RCA to store CO\({}_{2}\), it was necessary to obtain the optimum water content for each RCA.  
The accelerated carbonation test on the RCAs (Figure 1) was carried out after the imposition of a certain water content by immersing 100 g of a dry RCA in water for 5 min. The desired water content was then obtained by using a balance placed in a desiccator. The relative humidity in the desiccator was measured with a humidity sensor, but only the water content was imposed upon the RCA.  
A layer of this RCA was placed in contact with an atmosphere containing CO\({}_{2}\) at a concentration of 15% or 100% for 24 h (Figure 1). The concentration of 100% CO\({}_{2}\) was chosen to obtain the maximum CO\({}_{2}\) uptake during the accelerated carbonation test. The concentration of 15% is the same concentration as in cement kiln gases. The aim was to simulate the concentration of an industrial application.  
Figure 1: Experimental setup for the accelerated carbonation test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**RCA** & **SFSA (\%)** & **Portlandite Rate (\%)** & **CaCO\({}_{3}\) Rate (\%)** & **Porosity (\%)** & **WAC (\%)** \\ \hline NC–RB 1–4 & 21.3 \(\pm\) 8.4 & \(\approx\)0 & 40.8 \(\pm\) 0.2 & 11.5 \(\pm\) 0.5 & 4.6 \(\pm\) 0.5 \\ NC–RB 10–20 & 19.3 \(\pm\) 7.7 & 1.0 \(\pm\) 0.1 & 36.1 \(\pm\) 0.5 & 15.5 \(\pm\) 0.2 & 7.1 \(\pm\) 0.1 \\ NC–RB 1–4 C & 26.0 \(\pm\) 6 & 0.4 \(\pm\) 0.1 & 35.3 \(\pm\) 0.3 & 11.4 \(\pm\) 0.5 & 5.1 \(\pm\) 0.3 \\ NC–CB 1–4 & 34.0 \(\pm\) 1.4 & 3.4 \(\pm\) 0.3 & 46.3 \(\pm\) 0.5 & 13.8 \(\pm\) 1.1 & 6.3 \(\pm\) 0.6 \\ NC–OC 1–4 & 44.9 \(\pm\) 3.9 & 4.0 \(\pm\) 0.2 & 61.9 \(\pm\) 0.4 & 11.7 \(\pm\) 0.1 & 4.8 \(\pm\) 0.1 \\ NC–OC 12–20 & 32.1 \(\pm\) 5.4 & 2.3 \(\pm\) 0.2 & 66.8 \(\pm\) 2.1 & 9.20 \(\pm\) 0.2 & 4.0 \(\pm\) 0.2 \\ NC–HPC 1–4 & 34.8 \(\pm\) 5.6 & 2.3 \(\pm\) 0.2 & 62.0 \(\pm\) 0.3 & 10.4 \(\pm\) 0.1 & 4.0 \(\pm\) 0.4 \\ NC–HPC 12–20 & 33.9 \(\pm\) 1.2 & 2.1 \(\pm\) 0.1 & 62.1 \(\pm\) 3.6 & 8.6 \(\pm\) 0.2 & 3.6 \(\pm\) 0.3 \\ \hline \end{tabular}
\end{table}
Table 3: Properties of the non-carbonated RCAs.  
Once the carbonation process was completed, the RCA was dried to a constant mass at 80 \({}^{\circ}\)C to preserve its microstructure, remove the free water and measure the mass gain due to CaCO\({}_{3}\) formation [45].
## 4 Discussion
Further studies are needed to determine the test-retest reliability of hop-RSI across multiple hops, and then to investigate if (and which) hop-RSI metrics best predict performance. As a secondary finding, we reported small-to-moderate correlations between hop-RSI and sprint-RSI, as well as between hop-RSI and sprint performance. The only previous study to assess the relationship between hop-RSI and sprint performance reported a small-to-moderate correlation with change in direction ability (assessed by a 5-0-5 change in direction test), but not with 10 m sprint times [14]. However, the former study was performed on a homogenous sample of volleyball players who rarely performed linear sprinting. A recent systematic review reported small-to-moderate associations between RSI from drop jumps and acceleration (pooled r = 0.42) and top speed (pooled r = 0.32) [4]; however, this finding is not consistent across all studies [7]. The study is the first to point out that horizontal hop-RSI could also be related to sprinting performance across a 0-100 m distance, which reflects both acceleration ability and the maintenance of high velocity. Previous reports showed high correlation (r = 0.84-0.89) between triple-hop distance and 10 m sprint [16], which suggests that the horizontal-RSI particularly relates to sprint acceleration performance. Looking at the correlation coefficients between RSI and underlying metrics across hopping and sprinting tasks (Section 3.3), it seems that these associations are primarily driven by associations in contact times. This is expected, as contact times reflect joint/leg stiffness [26], which has been shown to be important for sprint performance [27]. Perhaps even larger associations with sprint would be achieved if we used horizontal hopping tasks with alternating legs, which is a movement that resembles sprinting more closely than unilateral hopping on a single leg. We also checked if inter-limb asymmetries in hop-RSI are related to sprint performance. Although previous studies have indicated that inter-limb asymmetries can be detrimental to athletic performance [28], we found no statistically significant associations between inter-limb asymmetries and sprint performance. There are some limitations of the study that must be acknowledged. Reliability analysis could not be performed, thus, the reliability of the hop-RSI metrics beyond triple hop tasks remains unknown. It would be reasonable to investigate if a consistent performance (stable RSI metric) is obtained on the level of the individual with multiple repetitions. Furthermore,we recruited a convenient sample of male kinesiology students for this study. As previous studies found significant differences in RSI among sports and between sexes [7,11,12], our findings cannot be generalized to elite athletes and females. Further studies are needed to investigate the behavior of RSI across consecutive hops in female populations and in elite athletes in general.
# Remote Water Quality Monitoring System for Use in Fairway Applications
###### Abstract
The effectiveness and reliability of the GSM-based remote water quality monitoring system were demonstrated in providing real-time data for the early detection of contaminants and supporting sustainable management of inland waterways. This article is divided into four sections and concludes with findings. Section 2 consists of two parts: one describing the architecture of the remote water quality monitoring system and the other describing the main server and peripheral devices. The results of the remote water quality monitoring system and their general discussion are presented in Section 3. Finally, Section 4 contains the final discussion and conclusions.