esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.1 \"Coastguard\" Video Sequence
PDIVCOM performs even better than DIVCOM and achieves a coding efficiency of up to 9 kbps and PSNR gain of up to 0.17 dB. The small improvement in coding efficiency at lower quantization points is due to fewer bands undergoing the decoding process. However, the improvement becomes significant at higher quantization points because more bands undergo the decoding process. Upon further evaluation of the frames for each quantization point, a higher coding rate is observed for a few of the frames in the test sequence. This is due to a miscalculation of the CNM parameter by the proposed model. Even the PSNR quality is also compromised for those frames. The performance comparison of DIVCOM with Intra H.264 shows that DIVCOM achieves better coding efficiency for all quantization points with a bit rate saving of up to 41.92 kbps. For low to intermediate quantization points, DIVCOM achieves a PSNR gain of up to 0.69 dB. However, at the highest quantization point, the PSNR degrades by up to 0.28 dB. PDIVCOM shows better coding efficiency for all quantization points compared to Intra H.264 with bit rate savings ranging from 30.23 kbps to 42.83 kbps. At the same time, PDIVCOM achieves PSNR gains up to 0.71 dB for all quantization points. However, similar to DIVCOM its PSNR performance degrades at the highest quantization point by 0.29 dB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Video** \\ **Sequence** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Qm** \\ **DIS** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average Channel Decoding Rate per WZF (bits)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average PSNR Gain per WZF Compared to DIS (dB)** \\ \end{tabular} } \\ \cline{3-3} \cline{5-6}  & & & & **DIS** \\ \hline \multirow{6}{*}{Coastguard} & 1 & 4545 & 4444 & 4326 & 0.0521 & 0.0751 \\ \cline{2-6}  & 2 & 5404 & 5199 & 5176 & 0.0731 & 0.0449 \\ \cline{2-6}  & 3 & 7722 & 7486 & 7523 & \(-\)0.006 & 0.0396 \\ \cline{2-6}  & 4 & 18,070 & 17,425 & 17,384 & 0.1957 & 0.1231 \\ \cline{2-6}  & 5 & 22,979 & 22,028 & 21,891 & 0.2417 & 0.2507 \\ \cline{2-6}  & 6 & 33,856 & 32,678 & 32,546 & 0.3549 & 0.3323 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coding efficiency evaluation of proposed novel online CNM for “Coastguard” video sequence. Figure 2: Rate-distortion (RD) performance graphical evaluation of “Coastguard” video sequence with frame rate of 15 Hz and GOP 2.
# Volatile Composition of Smoked and Non-Smoked Iranian Rice
## Results
### Principal Component Analysis (PCA)
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**ANOVA \({}^{\ddagger}\)**} & **Non-Smoked Rice** & **Smoked Rice** \\ \cline{2-4}  & & & **Relative Abundance (\%)** \\ \hline Benzofuran & \({}^{\star}\) & 0.06 \(\pm\) 0.01 b & 0.66 \(\pm\) 0.15 a \\
2-Propionylfuran & NS & 0.06 \(\pm\) 0.01 & 0.17 \(\pm\) 0.09 \\ \(p\)-Methylanisole & \({}^{\star}\) & 0.12 \(\pm\) 0.04 b & 0.61 \(\pm\) 0.16 a \\ Limonene & \({}^{\star}\) & 0.60 \(\pm\) 0.15 a & 0.34 \(\pm\) 0.06 b \\
3-Octen-2-one & NS & 0.18 \(\pm\) 0.05 & 0.23 \(\pm\) 0.07 \\
2-Methyl-phenol & *** & 0.11 \(\pm\) 0.04 b & 2.76 \(\pm\) 0.09 a \\
2-Octenal & * & 0.29 \(\pm\) 0.11 b & 0.50 \(\pm\) 0.08 a \\ \(p\)-Cresol & *** & 0.07 \(\pm\) 0.02 b & 5.44 \(\pm\) 0.83 a \\ Guaiocal & *** & 0.35 \(\pm\) 0.17 b & 11.3 \(\pm\) 1.1 a \\ Methylbenzoate & * & 0.12 \(\pm\) 0.06 b & 0.41 \(\pm\) 0.16 a \\ Nonanal & *** & 9.89 \(\pm\) 1.30 a & 3.33 \(\pm\) 0.45 b \\
2-Ethylphenol & *** & 0.06 \(\pm\) 0.02 b & 1.44 \(\pm\) 0.34 a \\
2-Nonanal & * & 0.15 \(\pm\) 0.06 b & 0.43 \(\pm\) 0.12 a \\
4-Ethylphenol & *** & 0.05 \(\pm\) 0.01 b & 3.48 \(\pm\) 0.84 a \\
3-Ethylphenol & ** & 0.08 \(\pm\) 0.02 b & 0.84 \(\pm\) 0.39 a \\
2-Methoxy-4-methylphenol & *** & 2.06 \(\pm\) 0.48 b & 6.99 \(\pm\) 0.06 a \\ Decanal & *** & 2.33 \(\pm\) 0.44 a & 0.45 \(\pm\) 0.16 b \\ Cinnamaldehyde & * & 0.13 \(\pm\) 0.03 b & 0.51 \(\pm\) 0.06 a \\
2-Decenal & NS & 0.84 \(\pm\) 0.40 & 0.82 \(\pm\) 0.36 \\ \hline Tridecane & *** & 0.35 \(\pm\) 0.09 b & 1.74 \(\pm\) 0.47 a \\ \(p\)-Ethylguaiocal & *** & 13.7 \(\pm\) 4.0 a & 3.74 \(\pm\) 1.20 b \\ Tetradecane & *** & 32.9 \(\pm\) 4.9 a & 4.12 \(\pm\) 1.35 b \\ \hline \hline \end{tabular} \({}^{\dagger}\) NS = not significant F ratio (\(p>0.05\)), * *** significant at \(p<0.05\), 0.01, and 0.001, respectively. \({}^{\ddagger}\) Treatment means of the ANOVA test (values are the mean value of three replications). \({}^{\text{V}}\) Values followed by the same letter, within the same row, were not significantly different (\(p<0.05\)), [PERSON]’s multiple-range test.  
\end{table} 
Table 3: _Cont._playing a differential role. The volatiles V6 (heptanal), V11 (benzaldehyde), V19 (limonene), V34 (2-decenal), and V36 (p-ethylguaiacol) were those common to all non-smoked samples.
# Finite Difference/Collocation Method for a Generalized Time-Fractional KdV Equation
## 2 Preliminaries of Fractional Calculus and Jacobi Polynomials
### Jacobi Polynomials  
In recent years, more concentration has been focused on Jacobi polynomials \(\int_{n}^{\beta,\gamma}\) for solving differential equations because the Jacobi parameters \(\beta\) and \(\gamma\) make such polynomials easy to implement. The well-known Jacobi polynomials \(\int_{n}^{\beta,\gamma}\) with indices \(\beta,\gamma>-1\) of degree \(n\) are defined on interval \([-1,1]\) and can be expressed as  
\[\int_{n}^{\beta,\gamma}(x)=\frac{\left(-1\right)^{n}}{2^{n}n!}\left(1-x\right) ^{-\beta}\left(1+x\right)^{-\gamma}\frac{d^{n}}{dx^{n}}\left(\left(1-x\right) ^{\beta+n}\left(1+x\right)^{\gamma+n}\right), \tag{6}\]  
which are solutions of a class of Sturm-Liouville problems [30].  
The integer derivative of \(\int_{n}^{\beta,\gamma}\) on the interval \([-1,1]\) can be represented in the form  
\[\frac{d^{m}}{dt^{m}}\int_{n}^{\beta,\gamma}(x)=\frac{\Gamma\left(n+m+\beta+ \gamma+1\right)}{\Gamma\left(n+\beta+\gamma+1\right)2^{m}}\int_{n-m}^{\beta+m,\gamma+m}(x). \tag{7}\]  
Let \(\left\{x_{j},\tilde{w}_{j}\right\}_{j=0}^{N}\) be a set of Jacobi-Gauss-Lobatto nodes and weights. The corresponding discrete inner product, denoted by \(\left\langle\cdot,\cdot\right\rangle_{N,\tilde{w}}\), can be defined as  
\[\left\langle u,v\right\rangle_{N,\tilde{w}}=\sum_{j=0}^{N}u\left(x_{j}\right) v\left(x_{j}\right)\tilde{w}_{j}.\]  
The exactness of the quadratures implies  
\[\left\langle u,v\right\rangle_{N,\tilde{w}}=\int_{-1}^{1}uv\tilde{w}^{\tilde{ w},\tilde{\beta}}dx=\left(u,v\right)_{\tilde{w}^{\tilde{w},\tilde{\beta}}},\quad \forall u\cdot v\in P_{2N-1}, \tag{8}\]  
where \(P_{2N-1}\) is denoted by the space of polynomials whose degree does not exceed \(2N-1\).  
**3. Numerical Scheme and Stability Analysis**  
The integer-order KdV equation,  
\[\frac{\partial u(x,t)}{\partial t}+\zeta u(x,t)\frac{\partial u(x,t)}{\partial x }+\delta\frac{\partial^{3}u(x,t)}{\partial x^{3}}=f\left(x,t\right),\quad(x,t )\in(a,b)\times(0,T], \tag{9}\]is firstly derived to describe one-dimensional shallow-water waves with small but finite amplitudes. Generally, \(\xi\) is a positive parameter and \(\delta>0\) indicates the wave number. Replacing the time derivative by the Caputo-type generalized fractional derivative (see (5)), we get  
\[\left({{}^{*}D_{a+}^{\alpha}u}\right)\left({x,t}\right)+\xi u(x,t)\frac{\partial u (x,t)}{\partial x}+\delta\frac{\partial^{3}u(x,t)}{\partial x^{3}}=f\left({x,t }\right),\quad\left({x,t}\right)\in\left({a,b}\right)\times\left({0,T}\right], \tag{10}\]  
which is called a generalized fractional KdV equation.  
Note that the second term of (10) makes it a nonlinear equation. In order to solve this equation, we impose the following initial and boundary conditions  
\[\left\{{\begin{array}{*{20}c}{u(x,0)=\phi(x),\;\;x\in[a,b],}\\ {u(a,t)=u(b,t)=0,}\\ {\frac{{\partial u(b,t)}}{{\partial x}}=0,\;\;t\in[0,T].}\end{array}}\right. \tag{11}\]  
In the following parts, as a premise, we make an assumption that the problem (10) and (11) is well-posed.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 6 Performance Evaluation  
This section includes two contributions: first a profiling of SimuLTE execution time, varying the number of UEs and the packet rates, also assessing the impact of D2D communications; and, second, an analysis of D2D-enabled scenarios, whose purpose is to show the impact of some of the factors that we described in Section 5 (namely, the interference radius, the mode-switching policy and the transmission horizon) on the observable performance.
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 2 2D-BIBD Code for SAC-OCDMA Systems
### Construction of BIBD Code Families  
The 2D-BIBD code construction is based on a 1D-BIBD code. A 1D-BIBD has a simple code structure, which is illustrated in the following steps.
## 4 Discussion
### Limitations  
This study has several limitations. First, the study is based on secondary data. Cross-country comparison of country-specific aggregated data does not allow conclusions about causality. Second, data have different levels of accuracy. Incidence and prevalence data are obtained from survey-based data and cannot be considered as accurate as mandatory collected mortality data drawn from vital statistics. Usually, they are more vulnerable to bias because only a sample is taken instead of a collection of mortality data across the whole population. Third, although they are less inconsistent, retrospective data collection and death certification are marked with a certain robustness, which should be considered during the interpretation of data. Also, methodologies for deciding on causes of death might differ among countries regardless of the existence of the WHO ICD coding system and efforts to standardize coding. The problem of adequate reporting and filling in the reports on death certificates can never be excluded. This could lead to over or under-reporting of death since in the elderly population, mostly affected with IHD, there may be more than a few possible causes of death. Fourth, the comparisons between countries during certain periods are complex because there are differences in definitions of metrics used, quality of data, and methods of acquisition. Still, changes within a country should be more consistent as each country will likely preserve a generally consistent methodology over time. Fifth, the period before the COVID-19 pandemic was purposefully chosen because the pandemic affected health systems and medical procedures and their outcomes in different ways across countries.  
Despite the abovementioned limitations and potential methodological difficulties, this study provides new insights into cardiovascular epidemiology among Mediterranean countries. Standardized rates allow comparisons both over time and between countries. Although aggregated data are challenging to interpret regarding causality, their quantification provides indicative leads for future country-specific research with more complex study designs.
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 2 Data and Methods
### Trends in Regional XCO\({}_{2}\) Summer Drawdown  
#### 2.2.1 Satellite-Observed XCO\({}_{2}\)  
Multiple satellite-observed XCO\({}_{2}\) products derived from SCIAMACHY, GOSAT, and OCO-2 platforms were integrated to obtain the longest and most complete record possible [58]. SCIAMACHY on-board the European ENVISAT was launched in 2002, with a temporal resolution of 36 days and spatial resolution of 60 km, available from 2003 to 2012 [59]. GOSAT was launched in 2009 by Japan, with a temporal resolution of 3 days and spatial resolution of 10 km, available from 2009 to the present [60]. NASA's OCO-2 was launched in 2014, with a temporal resolution of 16 days and spatial resolution of \(1.29\times 2.25\) km at nadir, available from 2014 to present [61]. SCIAMACHY XCO\({}_{2}\) was retrieved by the full-physics based Bremen Optimal Estimation-DOAS (BESD) algorithm (v02.01.01), referred to as BESD-XCO\({}_{2}\)[43,44] and was downloaded from the European Space Agency (ESA, [[http://www.esa-ghg-cci.org/](http://www.esa-ghg-cci.org/)]([http://www.esa-ghg-cci.org/](http://www.esa-ghg-cci.org/))). GOSAT was retrieved by the Atmospheric CO\({}_{2}\) Observations from Space (ACOS) project team (v7.3), referred to as ACOS-XCO\({}_{2}\)[45]. OCO-2 was retrieved by the OCO-2 team (v7r), referred as OCO-XCO\({}_{2}\)[46]. ACOS-XCO\({}_{2}\) and OCO-XCO\({}_{2}\) were downloaded from NASA ([[https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/)]([https://disc.gsfc.nasa.gov/](https://disc.gsfc.nasa.gov/))). XCO\({}_{2}\) from these different observations during 2003 to 2016 was integrated and mapped, described in Section 2.2.2, into global maps of XCO\({}_{2}\) (referred as GM-XCO\({}_{2}\)) with temporal and spatial resolution of 8 days and \(1\times 1\) degree.  
Figure 2: Example of how the growing period is identified from aggregated enhanced vegetation index (EVI) values from 2003 to 2016 data for a single grid cell (Lat = 33.5\({}^{\circ}\)N, Lon = 104.5\({}^{\circ}\)E). The symbol legends explain the original observations and the mean values. The original EVI values (gray points) for all years, mean seasonal EVI (red points), and EVI fitting results (black for spring increase and blue for fall decrease) are shown in the top panel. Curvature and derivative of the curvature of EVI fitting (black for increase and blue for decrease) are shown in bottom panel. The period between the two red dash lines was identified as the growing period for this grid cell.
## Case Study
### Strategic Verification
#### 5.2.1 Introduction of the Simulation Platform  
The structure of the hardware-in-loop co-simulation platform is shown in Figure 10. RT-Lab was used to simulate the physical power system, while OPNET was used to simulate the communication system. Furthermore, the smart terminals and HIG were incorporated as actual physical equipment. In a loop of simulation, the RT-Lab sends electrical quantities to the smart terminals through the power amplifier unit (PAU). The smart terminals then upload the measured information to the HIG, where the information is processed and forwarded to the LA through the router. Next, the UP and DRAS receive the information from the LA via the virtual network in OPNET.  
By adding smart terminals and the HIG, the platform can reflect the operation of the system on the customer side more precisely. OPNET and RT-Lab are able to undertake real-time simulations of wide-area internet networks and power systems, respectively. Therefore, the platform can accomplish more accurate simulations of the influence of the proposed system on the power system.  
Figure 11 shows the workflow of the active load shedding (ALS) method based on the \"local detection and action\" strategy in the proposed DR system to assist primary frequency control. Firstly, the UP regularly summarizes the information stored in the DRAS to evaluate the operating status of the power system. Then, the load shedding quantities and thresholds are determined and distributed to corresponding LAs, where more accurate decisions are made to determine which loads are in turn to act as the reserve. Finally, the thresholds are distributed to corresponding smart terminals. When a power system fault occurs, the smart terminal can act automatically based on the threshold and frequency collected.  
Figure 10: Hardware-in-loop co-simulation platform.
# Investigating the Learning Process of Folk Dances Using Mobile Augmented Reality
## 5 Results
### Motion Capture Data Comparison  
Filtered data were analyzed as shown in Section 5.1. Our hypothesis is that mobile AR application is better for learning how to dance compared to a large projector screen. Mean values and standard deviation (SD) regarding used device for individual best scores (best performance AR and best performance projection screen (PS)) and average of three performances (average AR and average PS) obtained from the filtered data are shown in Table 2. Lower score means better result. Statistical tests were performed using IBM SPSS. A Shapiro-Wilk test was performed on best performance and Average scores regarding used type of visualization and data have normal distribution except for best performance PS scores (\(p\) = 0.027), presented in Figure 7. An independent samples T-test was used to examine significant differences between average scores and best performance scores regarded used device (AR application or 3D animation on projection screen) and \(p\)-values was read from the outcome table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scores** & **Mean** & **SD** \\ \hline Best performance AR & 1954.8196 & 335.93332 \\ Best performance PS & 1982.7728 & 540.72122 \\ Average AR & 2087.3117 & 355.38190 \\ Average PS & 2170.4143 & 522.37585 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between AR and PS.  
Table 2 represents results for filtered data and mean value and SD of scores regarding used device are shown. Mean values for AR application are lower but without significant difference (\(p=0.682\) for average scores and \(p=0.891\) for best performances).  
Figure 8 shows device-based comparison of different body parts of participants, respectively. The greatest impact on total scores were from left and right arm and left and right leg for both, AR and back-projector screen and comparison will be based on these body parts. Participants that used AR performed better for right arm, while participants that used back-projector screen performed better for left arm. Again, this supports results that there is no significant difference between AR and back-projector screen.  
Figure 8: Device-based comparison of raw and filtered data for different body parts.  
Figure 7: Boxplots for best performance scores for filtered data.
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 2 Materials and Methods
### The Methodological Approach
The analysis reveals that a resolution of 14.5 s combined with an 80% overlap yields the most pronounced entropy changes that support the optimal detection of the P-wave onset for all available datasets. Seismic records were segmented into time windows of 240 s. During this period, 219 seismic events were recorded at the seven selected stations. The complete dataset consists of 1260 recorded event traces. Each trace includes 120 s of data before and 120 s after the arrival of the P-wave. We used a 38 s sliding window centered on the arrival time of the P-wave [15]. Figure 3: A heatmap of the spectral entropy gain \(\Delta H\) as a function of the _pspertrum_ parameters: the time resolution (window length) and overlap percentage. The analysis was performed using the MATLAB function _pentropy_, which internally relies on _pspertrum_ with a default Hamming window. The results indicate that the entropy growth is maximized in the region with a 12–15 s resolution and a 70–80% overlap. This parameter combination provides the best contrast between the pre- and post-P-wave entropy, enhancing the detection sensitivity. \[P(m)=\frac{S(m)}{\sum_{i}S(i)} \tag{1}\]0 The sensitivity of the method to the onset of the P-wave (orange) and the corresponding changes in the signal’s composition (blue). The value \(H_{1}\) represents the average spectral entropy computed over a fixed-length window preceding the P-wave onset, while \(H_{2}\) represents the average entropy in the window following the onset. Their difference, \(\Delta H=H_{2}-H_{1}\), captures the increase in the spectral complexity associated with the event. During seismic events, the spectral characteristics of the signal change, typically leading to an increase in spectral entropy—indicating a transition to a more complex and informative state. To evaluate the effect of the window duration on the entropy performance, we conducted a sensitivity analysis using varying symmetric time windows around the P-wave's arrival (ranging from 20 to 60 s). The results presented in Figure 4 indicate that the spectral entropy difference \(\Delta H\) is unstable for windows shorter than 20 s but becomes stable and consistent for durations greater than 40 s. The chosen window of 38 s provides a trade-off between temporal localization and entropy resolution, justifying its use throughout this study. Following the analysis of seismograms from the seven stations, a slow baseline drift was observed at specific sites, particularly BTLS and JNK. Unlike in our previous study, this drift was not filtered out and was retained in its original form for the entropy analysis.
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
###### Abstract  
Increased data traffic resulting from the increase in the deployment of connected vehicles has become relevant in vehicular social networks (VSNs). To provide efficient communication between connected vehicles, researchers have studied device-to-device (D2D) communication. D2D communication not only reduces the energy consumption and loads of the system but also increases the system capacity by reusing cellular resources. However, D2D communication is highly affected by interference and therefore requires interference-management techniques, such as mode selection and power control. To make an optimal mode selection and power control, it is necessary to apply reinforcement learning that considers a variety of factors. In this paper, we propose a reinforcement-learning technique for energy optimization with fifth-generation communication in VSNs. To achieve energy optimization, we use centralized Q-learning in the system and distributed Q-learning in the vehicles. The proposed algorithm learns to maximize the energy efficiency of the system by adjusting the minimum signal-to-interference plus noise ratio to guarantee the outage probability. Simulations were performed to compare the performance of the proposed algorithm with that of the existing mode-selection and power-control algorithms. The proposed algorithm performed the best in terms of system energy efficiency and achievable data rate.  
Keywords:5G; D2D communication; vehicle-to-vehicle communication; mode selection; power control; vehicular social network +
Footnote †: journal:
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.5 Handover  
In [116], the authors propose an offline RL algorithm to optimize Handover decisions. The model is able to decrease excess Handover up to 70% by studying the prolonged user's connectivity. This model can also achieve higher than conventional Handover reduction approaches. In [117], a DRL framework is proposed for handover optimizing and timing in mm-wave systems. The model uses camera images for predicting future data rate of mm-wave links and ensuring that proactive Handover is performed before the presence of obstacles leads to decreasing system's data rate. The proposed approach achieves better performance results than conventional model and is also able to predict the degradations of date rate 500 ms before the occur. In [118], a distributed RL model for Handover optimization in mm-wave systems is proposed, with results showing reduction in signal overhead.