esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Empirical Regression Models for GPP Estimation  
For the eight stations, regression models were used to estimate daily GPP (g C m\({}^{-2}\) day\({}^{-1}\)) following previously tested model formulations for our study region by [PERSON] et al. (2017), one model for each ICOS site:  
\[GPP=a\times EVI2\times E+b, \tag{3}\]  
where the variable \(EVI2\) is daily averaged EVI2 reconstructed by the box constrained method from Sentinel-2 MSI data and MODIS data. \(E\) is the EC tower measured daily sum of PPFD (umol m\({}^{-2}\) day\({}^{-1}\)), the daily average air temperature T\({}_{\text{a}}\) (\({}^{\circ}\)C), the daily average VPD (hPa) or the product of them. Available light and temperatures above the freezing point are requirements for photosynthesis. Therefore, only dates with both a daily sum PPFD larger than 0 umol m\({}^{-2}\) day\({}^{-1}\) and a daily average T\({}_{\text{a}}\) above 0 \({}^{\circ}\)C were used in the further model calculation. \(E\) in the model was smoothed by a moving average with a 7-day moving window for reducing the noise of the data. Although there would be six possible model formulas based on the combinations between satellite-derived EVI2 and the different environmental variables at each site, a single model formula was required to enable a direct comparison between Sentinel-2 MSI EVI2 and MODIS EVI2 at each site. The selection of this single model formula was achieved by assessing the coefficient of determination values (R\({}^{2}\)) and root mean square error (RMSE) between the EC GPP and model estimated GPP.  
To evaluate the EVI2-driven GPP models, we computed the root mean square error (RMSE), mean absolute error (MAE) and R\({}^{2}\) between EVI2-driven daily GPP (\(GPP_{EVI2}\)) and EC-driven daily GPP (\(GPP_{EC}\)) for each GPP site:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(GPP_{EVI2}-GPP_{EC}\right)^{2}}{n}}, \tag{4}\]  
Figure 4: An example of the corresponding Sentinel-2 and MODIS pixels and the flux footprint site SE-Lnn. Sentinel-2 10 m resolution pixels (red) cover the region that contributed 80% of the total flux in 2017. The flux footprint region is covered by two MODIS 250m pixels (hatched) and one MODIS 500m pixels (black). The background image (green) shows Sentinel-2 EVI2 at the flux tower site SE-Lnn on day of year 182 in 2017.  
\[MAE=\frac{\sum_{i=1}^{n}|GPP_{EVI2}-GPP_{EC}|}{n}, \tag{5}\]  
where \(n\) is the number of valid days for modelling GPP at the site.  
In order to further clarify whether the RMSE, MAE, and R\({}^{2}\) at different spatial resolutions (10 m, 250 m and 500 m) are significantly different, we used the Wilcoxon signed-rank test to perform pairwise tests with a null hypothesis of zero medians for the difference of RMSE/MAE/R\({}^{2}\) between paired sample sites.
## 4 The Performance Analysis
### The Performance Analysis of the ODLM  
To analyze the ODLM performance, the ODLM is compared with the existing Long Short Term Memory (LSTM) and Deep Belief Networks (DBN) in the computation speed and reliability of a Neural Network Model. The Training Data set and Test data are used to analyze the ODLM performance. The ODLM, the LSTM, and the DBN decides the computation speed and reliability with the computation time and error rate occurring in the computation of Test data.  
Table 3 shows a control variable value for simulation. The simulation is done three times on the basis of the control variable and compares a Training Dataset learning speed, the response speed of Test Data analysis, and an error rate.  
The 1 st experiment compares the average response time by classifying Test data according to the number of sensors and the result is shown in Figure 13.  
According to Figure 13, the average response time of the ODLM is 14.28 ms in case of 20 sensors, 15.61 ms in case of 30 sensors, and 26.4 ms in case of 50 sensors. The average response time of the DBN is 13.81 ms in case of 20 sensors, 14.69 ms in case of 30 sensors, and 31.51 ms in case of 50 sensors. The average response time of the LSTM is 20.34 ms in case of 20 sensors, 23.05 ms in case of 30 sensors, and 37.44 ms in case of 50 sensors.  
Therefore, the average response time of the LSTM is the longest and the more the data, the shorter the average response time of the ODLM.  
The 2 nd experiment compares the error rate of diagnosis according to the number of Test data sets and the result is shown in Figure 14. In the experiment, the Test data for measurement were randomly selected.  
According to Figure 14, the ODLM shows an error rate 0% in case of 20 Test data sets, 0% in case of 30 Test data sets, 0% in case of 50 Test data sets and 1.5% in case of 400 Test data sets. The DBN shows an error rate 10% in case of 20 Test data sets, 6.3% in case of 30 Test data sets, 8% in case of 50 Test data sets and 9% in case of 400 Test data sets. The LSTM shows an error rate 5% in case of 20  
\begin{table}
\begin{tabular}{c c} \hline
**Control Variables** & **Values** \\ \hline the number of Hidden Layers & 2 \\ the number of each Hidden Layer Nodes & 20 \\ a Learning Rate & 0.1 \\ weight (all) & 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: A Control Variable Value for Simulation.  
Figure 12: The comparison of the existing in-vehicle protocols with the In-VGM in message transformation time.  
Test data sets, 2% in case of 30 Test data sets, 2% in case of 50 Test data sets and 3% in case of 400 Test data sets.  
Therefore, in the 1 st experiment the ODLM improved the analysis speed higher than the DBN by 0.05 s and the LSTM by 0.1 s. In the 2 nd experiment, the ODLM showed the lower error rate than the LSTM by 10.5% and the DBN by 1.5%.
# Behavior of Airport Concrete Pavement Slabs
###### Abstract  
The behavior of pavement slabs was measured over a two-year period from the placement of concrete at a construction site in Incheon International Airport using temperature sensors and strain gauges. The influence of various environmental factors on the slab behavior was investigated by collecting weather data. Laboratory tests were conducted to obtain the material properties of the concrete slab, such as the elastic modulus, Poisson's ratio, and modulus of rupture. The time of final setting of the concrete slab at the zero point of the strain gauges was determined according to the position and depth of the slab using the maturity of concrete obtained by a mortar penetration test. The real and shrinkage strains were calculated according to the position and depth of the slab using the coefficient of thermal expansion of the concrete, strain gauges, and correction factors of the strain gauges. The effects of environmental factors, such as temperature and moisture variations, were analyzed with respect to the strain in the concrete slab for the first seven days and two years after the placement of the concrete slab. The results obtained by the study shall be used to quantify the environmental effects on slabs for developing a method of designing airport concrete pavements.  
Keywords:airports; pavement design; weather; concrete; measurement +
Footnote †: journal: Applied Sciences
## 3 Experimental Results and Discussion  
### Tensile Experimental Results  
In this section, the possible influence of the orientation of the transversal connections on the tensile mechanical response of the composite is discussed. Both the knitted 3D and woven 3D TRC configurations were compared for loading along their respective x- and y-direction. The orientations are schematically shown in Figure 8a,b for the knitted 3D layup and in Figure 9a,b for the woven 3D layup. Additionally, a comparison was performed with an equivalent 2D TRC layup (Figure 1c). The equivalent 2D TRC had the same geometry and in-plane volume fraction as the 3D alternatives, with omission of the transversal connections.  
Figure 8: Schematic representation of loading in (**a**) x-direction, (**b**) y-direction for the knitted 3D TRC system.  
Figure 6: Tensile test setup  
Figure 7: Four point bending test setup.  
The macroscopic tensile behavior of a TRC composite shows three stages: a first stage without macro cracks, a second stage during which multiple cracking develops, and a third stage corresponding to crack opening. It can be assumed that this behavior follows a tri-linear pattern, corresponding to these three stages, with respective stiffnesses E\({}_{1}\), E\({}_{2}\) and E\({}_{3}\). In the following sections, the curves linearly connect the following anchor points: (i) point of zero loading (0,0), (ii) stress and strain at first significant load drop due to crack formation (\(\sigma_{\rm c}\),\(\epsilon_{\rm c}\)), (iii) stress and strain at the end of the multiple cracking (last significant load drop) (\(\sigma_{\rm mc}\),\(\epsilon_{\rm mc}\)) and (iv) failure stress and strain (\(\sigma_{\rm f}\),\(\epsilon_{\rm f}\)).
## 3 Results
### Physical Fitness Outcomes
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Outcome** & **Group** & **Week 0** & **Week 10** & \
\begin{tabular}{c} **Estimated Mean** \\ **Difference [95\% CI]** \\ \end{tabular} & \(p\) **(Time)** &
\begin{tabular}{c} \(p\) **(Group \(\times\)** \\ **Time)** \\ \end{tabular} \\ \hline Total & & & & & \\ cholesterol & All & \(222.88\pm 25.95\) & \(218.88\pm 31.10\) & \(-4.00\) [\(-\)14.04, 6.04] & 0.422 & 0.920 \\ (mg/dL) & EX + WB-EMS & \(219.19\pm 29.90\) & \(215.69\pm 37.15\) & \(-3.50\) [\(-\)23.14, 16.14] & & \\  & EX & \(226.56\pm 21.65\) & \(222.06\pm 24.43\) & \(-4.50\) [\(-\)24.14, 15.14] & & \\ HDL (mmol/L) & All & \(64.78\pm 12.88\) & \(65.88\pm 14.08\) & \(1.09\) [\(-\)2.16, 4.34] & 0.497 & 0.547 \\  & EX + WB-EMS & \(66.19\pm 15.38\) & \(68.25\pm 15.64\) & \(2.06\) [\(-\)4.29, 8.42] & & \\  & EX & \(63.38\pm 10.10\) & \(63.50\pm 12.37\) & \(0.13\) [\(-\)6.23, 6.48] & & \\ LDL (mmol/L) & All & \(142.45\pm 32.64\) & \(135.67\pm 25.70\) & \(-6.78\) [\(-\)18.05, 4.49] & 0.229 & 0.624 \\  & EX + WB-EMS & \(134.20\pm 22.76\) & \(130.15\pm 28.67\) & \(-4.05\) [\(-\)26.10, 18.00] & & \\  & EX & \(150.70\pm 39.23\) & \(141.19\pm 21.88\) & \(-9.51\) [\(-\)31.56, 12.54] & & \\ Triglycerids & All & \(93.84\pm 35.21\) & \(86.97\pm 23.22\) & \(-6.88\) [\(18.28,4.53\)] & 0.228 & 0.903 \\ (mmol/L) & EX + WB-EMS & \(94.00\pm 41.79\) & \(86.44\pm 22.22\) & \(-7.56\) [\(-\)29.88, 14.75] & & \\  & EX & \(93.69\pm 28.56\) & \(87.50\pm 24.91\) & \(-6.19\) [\(-\)28.50, 16.13] & & \\ Glucose & All & \(90.78\pm 10.52\) & \(94.97\pm 10.16\) & **4.19 [0.88, 7.50]** & **0.015** & 0.848 \\ (mmol/L) & EX + WB-EMS & \(91.38\pm 11.88\) & \(95.88\pm 8.39\) & \(4.50\) [\(-\)1.97, 10.97] & & \\  & EX & \(90.19\pm 9.30\) & \(94.06\pm 11.89\) & \(3.88\) [\(-\)2.59, 10.34] & & \\ Cardiometabolic risk score & All & \(-0.26\pm 0.88\) & \(-0.07\pm 0.52\) & \(0.19\) [\(-\)0.13, 0.51] & 0.235 & 0.574 \\  & EX + WB-EMS & \(-0.42\pm 1.09\) & \(-0.14\pm 0.44\) & \(0.28\) [\(-\)0.35, 0.91] & & \\  & EX & \(-0.10\pm 0.62\) & \(0.00\pm 0.59\) & \(0.10\) [\(-\)0.53, 0.73] & & \\ \hline \hline \end{tabular} Data are presented as mean \(\pm\) SD. WB-EMS: Whole body electromyostimulation. EX + WB-EMS: Voluntary exercise with WB-EMS; EX: Voluntary exercise group. Significant mean differences and \(p\)-values (\(p\) \(\leq\) 0.05) are shown in bold. Abbreviations: HDL-high-density lipoprotein cholesterol; LDL—low-density lipoprotein, EX: exercise only group; MD: mean difference; CI: confidence interval; \(p\): \(p\) Value.  
\end{table} 
Table 4: Summary of between-group changes on hematological measures. mean differences between pre- and post-tests of EX+WB-EMS group (155.54 m 95% CI [122.99, 188.08], \(p_{Bonferroni}<0.001\)), but not in the EX group (25.66 m 95% CI [ \(-6.88\), 58.21], emphp\({}_{Bonferroni}=0.201\)). Statistically significant between-group differences were shown in post-test (\(-117.63\) m 95% CI [ \(-174.54\), 60.71], \(p_{Bonferroni}<0.001\)), but not in pre-test (12.25 m 95% CI [\(-44.66\), 69.16], \(p_{Bonferroni}=1.0000\)).
## 3 Results
### In-Hospital Death and MPV Relationship  
In contrast to the statistical results obtained for HF patients who died at 1 year based on the MPV value, regarding the hypothesis that HF patients who died in hospital would have a higher MPV value, the statistical results are not promising.  
By applying the logistic regression model, the estimated coefficients for which are shown in the table below (Table 8), the results did not show that there was a link between the value of MPV and in-hospital death in patients with HF, since the \(p\)-value associated with the coefficient for MPV (0.4739) is 0.344 and, therefore, statistically insignificant.  
The above-mentioned results are illustrated in all three graphs represented in the figure below (Figure 16), which imply the same conclusion, according to which there is no clear relationship between HF patients who died in hospital and MPV value. In the first graph of the figure, we observe that there was no significant difference in the MPV value between HF patients who died in the hospital compared to those who did not die in the hospital. The same information can be deduced from the second and third graphs.  
Although the above results do not indicate that there was a very clear relationship between MPV and in-hospital death in HF patients, it can be seen in Figure 17 that, as the MPV value increases, there is a minimal increase in the probability of the HF patients dying  
Figure 16: MPV value in HF patients who died in hospital (1) compared to the MPV value in HF patients who did not die in hospital (0).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dep.** & **In-Hospital** & \multicolumn{2}{c}{**No.**} & \multicolumn{2}{c}{**260**} \\
**Variable:** & **Death** & **Observations:** & & & \\ \hline Model: & Logit & Df Residuals: & 258 & & & \\ Method: & MLE & Df Model: & 1 & & & \\  & & Pseudo R-squ.: & 0.01374 & & & \\ Converged: & TRUE & LL-Null: & \(-\)32.208 & & & \\ \hline  & **coef** & std err & z & \(p>|z|\) & [0.025 & 0.975] \\ \hline Intercept & \(-\)7.8824 & 4.615 & \(-\)1.708 & 0.088 & \(-\)16.928 & 1.163 \\ MPV & **0.4739** & 0.5 & 0.947 & **0.344** & \(-\)0.507 & 1.455 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The relationship between MPV and the probability of in-hospital death in HF patients, explained by the logistic regression model.  
in hospital (10% probability for patients with an MPV value of 7 fl versus 41% probability for patients with an MPV value of 10 fl).
## Materials and Methods
### RNA Isolation and Quantitative Polymerase Chain Reaction (qPCR) Analysis of Apoptotic Marker Genes  
For qPCR analysis, RNA was extracted from HepG2 cells, both control and treated, with GNPs at a concentration of 25 \(\upmu\)g/mL for 24 h. The RNA was extracted with an RNeasy mini Kit (Qiagen) according to manufacturer's protocol. The purity of the RNA concentration was determined by a Nanodrop 8000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The cDNA was synthesized from treated and untreated cells, taking 1 \(\upmu\)g of RNA by a Reverse Transcriptase kit using MLV reverse transcriptase (GE Health Care, UK), as per the manufacturer's protocol. Gene expression was performed with a LightCycler\({}^{\text{\textregistered}}\)480 SYBR Green I Master on a real-time PCR (LightCycler\({}^{\text{\textregistered}}\)480, Roche Diagnostics, Rotkreuz, Switzerland). The final 20-\(\upmu\)L PCR reaction mixture contained 1x qPCR Green Master, 100 ng of the cDNA, and both forward-reverse primers. The primers used for the amplification were GAPDH F-5\({}^{\prime}\)CCACTCCTCCACCTTTGAC3\({}^{\prime}\), R-5\({}^{\prime}\)ACCCTGTT GCTG AGCCA3\({}^{\prime}\), Caspa se-3 F-5\({}^{\prime}\)ACATGGCGTGCTATAAAATACC3\({}^{\prime}\) R-5\({}^{\prime}\)CACAA AGCGA CTGGATGAAC3\({}^{\prime}\), p53 F-5\({}^{\prime}\)CCCCAGCCAAGAAGAAACCA3\({}^{\prime}\), R-5\({}^{\prime}\)TTC CAA GG CCTCATCAGCT3\({}^{\prime}\), Bax F-5\({}^{\prime}\)TGCTTCAGGGTTTCCATCAG3\({}^{\prime}\), and R-5\({}^{\prime}\) GGC GGCAATCATCTCG3\({}^{\prime}\). The GAPDH gene used as an internal house-keeping control for the expression levels of p53, caspase 3, and Bax was normalized. The cycling conditions were used for amplification: heat-denaturing at 95 \({}^{\circ}\)C for 10 min for the first step, then 45 cycles each at 95 \({}^{\circ}\)C for 20 s, annealing at 58 \({}^{\circ}\)C for 20 s, and an elongation period at 72 \({}^{\circ}\)C for 20 s. qPCR was done in triplicate. All data are expressed as the mean of three independent experiments, calculated by the 2-\(\Delta\Delta\)CT method.
## 4 Experimental Setup and HPT Test Plan
According to Table 4, the HPT test program envisions the following representative operating parameters:  
* RF power level: Adjustable within 250 to 600 W to optimize plasma ignition and stability. Under this power envelope, a H-W mode transition is expected. * Propellant flow rates: Initially modulated within the range of 6-20 sccm, it was determined that 12 sccm is the optimal value for PWM operation. * Pulse timing: Includes the duration of RF pulses, gas flow timing, the delay between the initiation of gas flow and RF power, and the interval between successive pulses, all of which are critical for achieving the desired plasma characteristics. * Reflected Power: Maintained at minimal levels, typically between 30 and 50 W, to ensure efficient energy transfers and minimal losses. * Modulation period: Variable to control the reactor's operational mode, facilitating transitions between inductive (H) and wave (W) modes. * Post-pulse recovery time: The PWM testing sequences ensure a fast post-pulse recovery time. * Pressure conditions: During operation, a pressure in the range of \(3.5\cdot 10^{-3}\) mbar was maintained. The spectral intensity of the plasma in the downstream region of the magnetic nozzle was measured using a basic optical emission spectroscopy setup. The collected plasma light was transmitted through an optical fiber (ThorLabs--Liner-to-Linear Bundle model), 2 m long and with a 200 \(\upmu\)m core diameter, connected to a spectrometer (AvaSpec--UL54096 CL--EVO, Avantes BV, Apeldoorn, The Netherlands) with a wavelength range of 200 to 1100 nm. OES was involved in capturing the time-resolved optical emission intensities of both argon atomic (Ar I) and argon ion (Ar II) emissions. According to the principles of OES, the Ar I lines are generated through electron collisions with ground-state argon atoms while the Ar II lines are produced by the excitation of ionized argon \((Ar^{+})\) in its ground state. To validate the spectral data, the measured wavelengths and excitation energies of the emission lines were systematically cross-referenced with the NIST Atomic Spectra Database lines [72]. This verification ensured that the experimental data met international standards, enhancing the reliability of the results. Emission lines observed in the wavelength range of 700 to 950 nm were identified as low-energy electron-excited neutral argon lines (Ar I), and, conversely, emission lines in the 350 to 500 nm range were attributed to high-energy electron-excited ionic argon lines (Ar II) [73]. The spectral intensity measurements were conducted by applying pulsed RF power, beginning at 200 W and incrementally increasing up to 500 W.
## 2 Materials and Methods
### Variable Selection
#### 2.3.2 Variable Selection Algorithms
As is well known, when the number of variables is small, each variable holds significant importance. However, when the number of variables is large, the importance of any single variable diminishes. Therefore, this selection range is reasonable and allows for a comprehensive evaluation of the performance of variable selection algorithms across different numbers of selected variables. It should be noted that although filter methods often select variables based on a defined threshold, variable selection algorithms used in this study select spectral variables based solely on the number of variables, without applying additional thresholds or criteria. The parameters for the variable selection algorithms were set based on experience and references, and these parameters remained constant throughout the study. For MWPLS, the window width was set to 31. For MC-UVE, the number of Monte-Carlo simulations was set to 50, with the ratio of calibration samples to total samples at 0.6. For GA, the number of evaluations per run was 50. For RF, 500 simulations were run, and the initial number of variables was 500.
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
## 5 Simulation Results
With an increase in the number of nodes at intermediate levels, energy consumption increases for the flooding approach while it decreases in the proposed approach. The number of transmissions for the flooding approach increases with an increase in the number of nodes at the intermediate level. Contrastingly, with an increase in the number of nodes at the intermediate level, the number of transmissions for the proposed approach decreases. This is because a larger pool of candidates is available to be selected as a relay node. Consequently, a relay node that covers a large number of data receiving nodes in the next level has been selected for transmission. This results in a reduced number of transmissions and therefore reduces energy consumption. Figure 6: Average number of used channels versus the number of destination nodes.