esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Cluster Based Protocols  
The Distributed Minimum-Cost Clustering Protocol (MCCP) [32] forms clusters based on the total energy required to send data to the cluster head, the residual energy of the nodes of a cluster,and the cluster heads' relative distance to the sink. Each cluster head candidate constructs its set of neighbors. The costs of the clusters are exchanged to minimize the overall cost. MCCP uses a centralized approach, designating more cluster heads around the sink node to balance the traffic load and the energy consumption by the cluster heads. In the Distributed Underwater Clustering Scheme (DUCS) [14] protocol, the nodes keep rotating the role of cluster head among themselves to conserve energy. In the Temporary Cluster Based Routing (TCBR) [33], multiple sinks are used to increase the delivery ratio. Special nodes, called couriers, collect data and transmit it to one of the sink nodes. TCBR is not suitable for time critical applications.  
Greedy Geographic Forwarding based on Geospatial Division (GGFGD) and Geographic Forwarding based on Geospatial Division (GFGD) [34] are 3D routing protocols. In GGFGD, first the target cluster is selected. The distance of the target cluster from the sink must be shorter than the distance from the current cluster. This may make the total path longer. Then, the next hop is selected within the target cluster. GFGD imposes the constraint that the target cluster must be surface-adjacent. GGFGD always has a longer path than GFGD, but, compared to Multipath Power-Control Transmission (MPT) (described in Section 2.5), both GGFGD and GFGD have longer paths because they take path loss, transmission delay, and residual energy level as part of the selection criteria as well, and, thus have higher average delay.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 3 Results and Discussion
### Antioxidant Activity Analysis
#### 3.4.2 ABTS Radical Scavenging Activity  
Hydrogen atoms or electrons can be accepted via the reaction between ABTS radicals and antioxidants. It is a commonly used method to detect antioxidant capacity in vitro [35]. As shown in Figure 3B, it is clear that all samples exhibited ABTS radical scavenging in a significant concentration-dependent manner. At the maximum dose of 10 mg/mL, the values of the ABTS\({}^{+}\) scavenging activity of GPPs and VC were 52.01% and 96.51%, respectively. Therefore, it could of great significance and value to extract GPPs and apply them as natural antioxidants in the food industry.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**Percentage of Each Monosaccharide Group (\%)**} \\ \cline{2-7}  & **Ara** & **Rha** & **Gal** & **Glc** & **Man** & **Glc-UA** \\ \hline GPPs & 20.79 & 9.63 & 18.67 & 18.76 & 16.09 & 7.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Monosaccharide composition of grape pomace polysaccharides.  
Figure 3: Antioxidant activity of grape pomace polysaccharides. **(A)** DPPH radical scavenging capacity, **(B)** ABTS radical scavenging capacity and **(C)** OH\({}^{-}\) scavenging capacity. All treatments were performed in triplicate, and data are shown as means \(\pm\) standard deviations. Ascorbic acid was the positive control.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 7 Simulation Results
### Sensing Performance Simulation  
For radar simulation, we added Doppler to a strong reflection and used a CSI-based approach only, as discussed in [5]. The results obtained are presented in Figure 17. As the data transmission does not influence the radar subsystem and in the CSI-based approach only pilots are used for target detection, we define input SNR as the ratio of the average power of active pilot carriers to noise on the input of the receiver. The output SNR is the average power of the detected peak compared with the average power in the DD grid cells without any targets. As expected, the ZP-OTFS has no processing gain, and the processing gain of the RP-OTFS depends on the number of active pilot carriers.  
Figure 16: BER for channels with 10 reflections and strong reflection on zero Doppler with enabled and disabled the proposed pilot-to-data interference compensation technique.  
Figure 17: \(SNR_{out}\) as a function of \(SNR_{in}\) for the facial channel and different modulation techniques.  
Figure 18 presents the simulated probability of the right detection depending on the SNR, for a constant false alarm ration \(10^{-4}\) and for different pilot lengths \(L\) and different velocities of the target. In our simulation, we took into account the interferences between carriers in the OFDM-based pilot. The ZP-OTFS was used as the baseline for our simulations. The performance in the case of ZP-OTFS does not depend on the velocity of the target (until it remains in one resolution cell during the whole OTFS frame). In this simulation, we do not take into account the cell migration of the target.
## 5 Discussion
### Practical Implications  
The practical implication of the research encompasses a wide range of applications, ranging from autonomous vehicles, healthcare, agriculture, security, and surveillance [59]. All these applications usually include visual extremes, the proposed luminance-based algorithm is probable to add in digitization and automation of these applications. The revolutionization may arise with digitized healthcare, SMART manufacturing and agriculture, architectural sustainability, and so on. All these automated applications can incorporate this algorithm as a mini framework for the elimination of visual extremes.  
In addition to that, the novel luminance-based algorithm can aid to classification of existing images in dark, semi-dark and bright images, thereby reducing the costs of creating sheer number of annotations. Using the proposed algorithm, we also introduced well-known datasets, i.e., MSCOCO [18], Berkeley [52] and Stanford background [53] dataset, with enhanced features by including the classification labels for Dark, Semi-dark and Bright images.
## 4 Localization Algorithm
### Validation Gate  
It is difficult to determine if all the position correction results of ICP maps are highly reliable. The measurement of the vehicle position computed by ICP is used as an bservation inside a Kalman filter framework. A validation gate was set up in the localization filter in order to figure out the issue of false matching by estimating the covariance of the ICP algorithm. Only the measurements of the covariance value of the ICP algorithm that properly pass the validation gate are used to update the estimator. The equations of the proposed validation gate can be obtained as follows:  
\[\begin{array}{l}e^{2}=\left(Z_{k}-H\overline{X}_{k|k-1}\right)^{T}\!\!S_{k} {}^{-1}\!\!(Z_{k}-H\overline{X}_{k|k-1})\\ e_{longi}=\left|x_{localiz}-x_{path,map}\right|,\;e_{tati}=\left|y_{localiz}-y_ {path,map}\right|\\ e_{yava}=\left|\psi_{localiz}-\psi_{path,map}\right|\\ V_{k}=\left\{\begin{array}{l}Z_{k}:(e^{2}<g^{2})\delta\!\epsilon(e_{longi}< \gamma_{longi})\dots\\ \delta\!\epsilon(e_{tati}<\gamma_{tati})\delta\!\epsilon(e_{yava}<\gamma_{yav}) \end{array}\right.\end{array}\right\}\end{array} \tag{10}\]  
where \(g^{2}\) is selected as the confidence level. The normalized error, \(e^{2}\), differs as a chi-squared distribution with the number of measurement degrees of freedom. \(e_{longi}\), \(e_{tati}\), \(e_{yav}\) are the differences between the localization results and the references. \(\gamma_{longi}\), \(\gamma_{tati}\), \(\gamma_{yav}\) are the error thresholds of each state, which were determined as 10.0, 3.0, and 45 degrees in this research.
## 3 Results and Discussion
### Effects of Treated Straw
#### 3.5.3 Compression Strength  
Figure 16b shows the influence of pre-treatment on the compression strength of rice straw particleboards. Comparing the untreated rice straw particleboard, a significant improvement was observed thanks to the treatment with hot water: The compression strength increased from 1.7 to 3.4 MPa. For rice straw particleboards that have been treated with NaOH, the compressive strength increased to 1.8 MPa. The higher compressive strength can mainly be attributed to the extraction of hemicellulose, wax, and other non-polar extractives from the rice straw through the pre-treatment. This procedure improves the compatibility between rice straw and the UF resin. These results clearly indicate that the pre-treatment with NaOH in comparison to the treatment with hot water has a negative effect on the compression strength of the boards. The results proved that the decrease of the compressive strength of the particleboards treated with NaOH may be due to the lower pH-value of particles and residual of NaOH on the surface and in the interior of the rice straw particles. This can cause a pre-curing of the UF resin in NaOH treated particleboards. This phenomenon of hardening therefore also partly explains the reduction of compression strength in treatment with NaOH compared to treatment with hot water.
# RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization
## 4 Outage Probability Analysis of RIS-AP-FNOMA  
Most traditional studies on passive RIS presume its deployment to be far from the BS and UE. As a result, the benefits claimed by the RIS may not be realized since RIS receives weaker signals. This makes RIS-SR-FNOMA unrealizable in practical environments. In practice, RIS is effective when it is placed near the transmitter or receiver [42]. In this system, RIS with \(N\) elements is installed nearest to BS/AP. Due to this, the effect of fading between the AP and RIS is negligible. As a result, RIS-AP and users communicate in a single hop. The conceptual diagram of RIS-AP-FNOMA is illustrated in Figure 2.  
The signal received at the FU is  
\[r_{FU}=\left(\sum_{i=\frac{N}{2}+1}^{N}g_{i}e^{i\phi_{i}}\right)x_{s}+n_{FUI}, \tag{30}\]  
where \(g_{i}\) is the channel condition between the \(i^{th}\) RIS element and UE. The passive RIS introduces phase shift \(\phi_{i}\) at each reflecting element, which is  
\[\phi_{i}=-\psi_{i}. \tag{31}\]  
Substituting \(g_{i}\) and (31) in (30), the received signal at the FU is  
\[r_{FU}=\left(\sum_{i=\frac{N}{2}+1}^{N}\beta_{i}\right)x_{s}+n_{FU}. \tag{32}\]  
Figure 2: The conceptual diagram of RIS-AP-FNOMA system.  
Let \(C=\sum_{i=\frac{N}{2}+1}^{N}\beta_{i}\) be the cumulative channel effect between RIS and FU. Using steps similar to (5) to (11) discussed in Section 3, the outage probability of FU is determined as  
\[P_{F\bar{U}}^{o,AP}=\frac{\sigma_{C}\Big{\{}erf\Big{(}\frac{v_{F\bar{U}}-m_{C}}{ \sqrt{2}\;\sigma_{C}}\Big{)}+erf\Big{(}\frac{m_{C}}{\sqrt{2}\;\sigma_{C}}\Big{)} \Big{\}}}{2\sigma_{C}}. \tag{33}\]  
where \(m_{C}=\frac{N_{F\bar{U}}\tau}{4}\) is the mean and \(\sigma_{C}^{2}=N_{F\bar{U}}\Big{[}1-\frac{\tau^{2}}{16}\Big{]}\) is the variance of \(C\).  
The signal received by the NU is  
\[r_{NU}=\left(\sum_{i=1}^{\frac{N}{2}}g_{i}e^{i\phi_{i}}\right)x_{s}+n_{N\bar{U }}. \tag{34}\]  
Substituting \(g_{i}\) and (31) in (34), the received signal at the NU is  
\[r_{N\bar{U}}=\left(\sum_{i=1}^{\frac{N}{2}}\beta_{i}\right)x_{s}+n_{N\bar{U}}. \tag{35}\]  
Let \(D=\sum_{i=1}^{\frac{N}{2}}\beta_{i}\) be the cumulative channel effect between RIS and NU. Using steps similar to (15) to (28) discussed in Section 3, the outage probability of NU is determined as  
\[P_{N\bar{U}}^{o,AP}=\frac{\sigma_{D}\Big{\{}erf\Big{(}\frac{v_{N\bar{U}}-m_{D} }{\sqrt{2}\;\sigma_{D}}\Big{)}+erf\Big{(}\frac{m_{D}}{\sqrt{2}\;\sigma_{D}} \Big{)}\Big{\}}}{2\sigma_{D}}. \tag{36}\]  
where \(m_{D}=\frac{N_{N\bar{U}}\tau}{4}\) is the mean, and \(\sigma_{D}^{2}=N_{N\bar{U}}\Big{[}1-\frac{\tau^{2}}{16}\Big{]}\) is the variance of \(D\).
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
## 4 Conclusions  
Software using the mass action law for obtaining the plasma composition was developed. Under the assumption of LTE, a variation of species densities with temperature can be obtained for atmospheric pressures of up to 300 bar. For such high pressures, the virial corrections that are associated with classical Debye-Huckel corrections are systematically used in ours developments. After a presentation of the theory for calculating the plasma composition, the results of the software were validated with compositions reported from the literature concerning argon and air plasmas at atmospheric pressure. Additional routines were developed using Python to automatically collect the required data from the NIST and JANAF Internet databases. To consider the high pressures, potential interactions that are formally used in virial correction were studied. We focused on the (12-6) Lennard-Jones potential, which is very commonly used in the literature. For this kind of potential, two parameters are necessary: \(\sigma\), which corresponds to the distance where the potential is zero, and energy \(\varepsilon\), corresponding to the depth of the potential. A different set of values can be found in the literature, or determined from measurements of gas mass density or viscosity. Some authors have also directly given values for the second and third virial coefficients. Nevertheless, a wide disparity appears in these two coefficients, leading to quite large differences in composition and hence mass density. Most of the time, the data given in the literature are only valid for a given pressure value, but they are not optimized for other values. We then developed a computing code based on a genetic algorithm to optimize these parameter values from the experimental data over a wide range of pressures. If no measurements are available, we suggest the use of the Stiel formulation, based on the critical values for temperature and pressure. Indeed, for temperatures and pressures that are not too close to the critical values, this formulation leads to a good compromise for values of \(\sigma\) and \(\varepsilon\). The software was applied to obtain results for SF\({}_{6}\) and CH\({}_{4}\) plasma compositions at a pressure P = 100 bar.  
**Plasma Compositions:** Even if the software is currently automated, it does not have a user-friendly interface. In the meantime, the authors can be contacted at the given email to obtain the plasma compositions and properties.  
**Author Contributions:** [PERSON] and G.J.-J.: investigation, software, validation, and original draft preparation; [PERSON] and G.J.-J.: conceptualization, methodology, resources, supervision, and original draft preparation.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Theory and Design Strategy
Single-mode propagation can occur when using the circuit model of the CPPW structure given in Figure 2. Furthermore, the reduced form of Equation (4) can be written taking into account the equivalent transmission line model only for \(P=M=1\) and \(n=1\)[5]:  
\[X_{\pm}=2\,\text{Im}(S_{1,1}\pm S_{1,2})=0. \tag{5}\]  
All notations are listed in Appendix B. Equation (5) can only be used for the dominant mode, not including higher-order mode interaction. Despite the fact that some deviations from the design objectives can occur in periodic filter design [50], the circuit model can be utilized for rapidly computing the dimensions of unit cells. The designer can significantly reduce computational burden by solving Equation (5) using the root-finding routine, as opposed to using the fine frequency sweeps of Equation (2) to derive the dispersion diagram of glide-symmetric double CPPWs. This fast approach reduces the size of the design space and provides a simple and approximate design method. Unit cell analysis is important for analyzing the dispersion diagram of the periodic structure and establishing approximate solutions. In addition, the finite number of unit cells examined gives the designer an important insight into the compatibility of the passband/stopband regions of the periodic structure and its filtering behavior. Based on this information, the applicability of the AFGSM method for the unit cell analysis of corrugated PPW structures and the dimensional synthesis of such filters will be tested. Firstly, Equation (5) is applied to determine the passband/stopband regions of a single corrugated PPW, and then the dimensional synthesis of the same structure in accordance with the design requirements is performed. A similar process will be applied for the analysis and dimensional design of glide-symmetric CPPWs. In this context, the following design strategy has been established:  
* Step 1: Select single/glide-symmetric double CPPW model and start appropriate unit cell configuration. * Step 2: Constrain the design space so that the dimensional parameters of the unit cell based on the circuit model given in Figure 2 are in the appropriate range. * Step 3: Modify the dimensions in the limited design space obtained in Step 2, and determine the appropriate unit cell parameters satisfying the given design requirements employing Equation (5). * Step 4: Connect the designed unit cells back-to-back a finite number of times to meet the design requirements and obtain the filter responses with full-wave simulators. Figure 1: Unit cell geometries of 1D corrugated parallel-plate waveguides. (**a**) single CPPW ; (**b**) glide-symmetric DCPPW.
## 4 Results and Analysis
### Experimental Analysis Based on CEC2017
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Function** & **COSGO** & **GWO** & **SCSO** & **WOA** & **AGWO-CS** & **PSOGSA** \\ \hline F1 & Mean & 2840 & 35,700 & \(1.9\times 10^{9}\) & 37,800,000 & 14,600.00 & 108,000.00 \\ \hline  & Worst & 8420 & 54,800 & \(1.92\times 10^{9}\) & \(1.14\times 10^{9}\) & 210,000.00 & 240,000.00 \\ \hline  & Best & 126 & 17,300 & \(1.89\times 10^{9}\) & 134 & 16,600.00 & 19,500.00 \\ \hline  & STD & 2380 & 9430 & 9,280,000 & 207,000 & 38,500.00 & 45,000.00 \\ \hline F3 & Mean & 300 & 1410 & 2460 & 877 & 2520.00 & 877.00 \\ \hline  & Worst & 301 & 4300 & 27,500 & 3830 & 8500.00 & 3830.00 \\ \hline  & Best & 300 & 546 & 300 & 302 & 378.00 & 302.00 \\ \hline  & STD & 0.221 & 960 & 6000 & 937 & 2170.00 & 937.00 \\ \hline F4 & Mean & 424 & 418 & 421 & 402 & 424.00 & 409.00 \\ \hline  & Worst & 527 & 464 & 529 & 403 & 527.00 & 440.00 \\ \hline  & Best & 403 & 407 & 404 & 400 & 403.00 & 406.00 \\ \hline  & STD & 27.8 & 19.8 & 28.1 & 0.818 & 27.80 & 6.01 \\ \hline F5 & Mean & 521 & 520 & 535 & 515 & 521.00 & 516.00 \\ \hline  & Worst & 537 & 542 & 565 & 529 & 537.00 & 535.00 \\ \hline  & Best & 510 & 506 & 505 & 508 & 510.00 & 504.00 \\ \hline  & STD & 7.06 & 10.4 & 13.4 & 4.6 & 7.06 & 9.11 \\ \hline F6 & Mean & 600 & 601 & 613 & 613 & 604.00 & 601.00 \\ \hline  & Worst & 600 & 605 & 643 & 640 & 614.00 & 605.00 \\ \hline  & Best & 600 & 600 & 602 & 600 & 600.00 & 600.00 \\ \hline  & STD & 0.0234 & 1.11 & 11.4 & 11.6 & 3.13 & 1.08 \\ \hline F7 & Mean & 719 & 731 & 763 & 724 & 738.00 & 734.00 \\ \hline  & Worst & 729 & 746 & 803 & 749 & 765.00 & 756.00 \\ \hline  & Best & 713 & 717 & 730 & 712 & 715.00 & 716.00 \\ \hline  & STD & 3.52 & 10 & 20.5 & 9.88 & 12.00 & 11.10 \\ \hline F8 & Mean & 812 & 816 & 827 & 834 & 819.00 & 817.00 \\ \hline  & Worst & 824 & 837 & 838 & 857 & 829.00 & 835.00 \\ \hline  & Best & 805 & 805 & 814 & 818 & 811.00 & 805.00 \\ \hline  & STD & 5.41 & 8.11 & 7.33 & 8.04 & 4.94 & 7.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for F1-F30 algorithms (CEC17) D10.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.1 Latency Constraints  
Fog computing will allow real-time processing and data analytics at the edges of the network, which will enable the deployment of delay-sensitive applications and the control of time-sensitive network functionalities close to end devices. This will allow coping with the strict requirements of the future smart city applications. This way, fog computing will help to reduce the service provisioning delay for most applications.
## 4 Results and Discussion
### Effects of Soil Thermal Conductivity
As for the Monte-Carlo simulation groups, the formation times of the desired frozen wall are summarized in the frequency histograms and scatter plots in Figures 16 and 17. The mean formation time is calculated as 23.3 days, 32.9 days and 55.8 days respectively, marked by black dashed lines in Figure 16. Compared with the deterministic results represented by red dashed lines, the average time of forming the desired frozen wall in Monte-Carlo groups is obviously longer. With the increase in seepage velocity, the difference between them becomes greater. When the seepage velocity reaches 1 m/d, 7% of the simulations fail to achieve the desired frozen wall even after 80 days, so 1 m/d is roughly considered as a critical reference velocity by writers, and additional freezing time and anti-seepage measures are needed when applying AGF technique in seepage fields higher than 1 m/d. Figure 16: Histograms of the formation time of the desired frozen wall under different velocities of seepage: (a) 0 m/d; (b) 0.5 m/d; (c) 1 m/d. The red dashed lines represent deterministic results, and the black dashed lines are the mean values of Monte-Carlo simulations. Figure 17: Scatter plots of the formation time of desired frozen wall under different velocities of seepage. Black line are mean values of Monte-Carlo simulations. In addition, Figure 17 clearly demonstrates that when no seepage flow occurs, the formation time of the frozen wall is relatively stable around the mean value, which indicates that the spatial variation of soil thermal conductivity shows little effect. However, the increase in seepage velocity leads to obvious fluctuations of the formation time. It is reasonable to believe that the spatial variation of soil thermal conductivity has a remarkable influence on soil freezing when high-speed seepage exists. To ensure the formation of the desired frozen wall, the additional freezing time is required to be longer, with an increase in seepage velocity. Based on the data from simulation results, Figure 18 provides the additional freezing time above the deterministic value against the seepage velocity. The solid curve demonstrates the adverse effect caused purely by seepage flow, while the joint effect of seepage flow and spatially varied soil thermal conductivity is represented by the dashed curves. Figure 18 can be used as a rule of thumb for practitioners to estimate the additional freezing time above the deterministic value in order to account for the seepage flow and spatial variation in soil thermal conductivity. [PERSON] et al. (2019) [PERSON] et al. (2019) pointed out that the mean velocity of groundwater flow is about 0.345 m/d, which means around 32% additional freezing time is required under an average level, and 55% under the conservative level according to Figure 18.