esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Machine Learning Algorithms
### Support Vector Machine (SVM)  
SVM is an efficient learning algorithm being used in classification problems. SVM mapped nonlinear data into a high-dimensional feature space and separated the data by maximizing the hyperplane boundaries. SVM maximizes the gap between data points with hyperplane boundaries, which is the support vectors. SVM originally solved binary classification (classify two classes labeled \(+1\) and \(-1\)) and could be extended to multi-classes classification by combining multiple binary classifiers. Thus, this section presents key points of the SVM method for binary classification.  
Giving a training dataset \(D=\{(x_{i},y_{i})\}\) with \(i=1,2,3,\ldots,m,x_{i}\in R^{n}\) and \(y_{i}=\pm 1\), our goal is to find a hyperplane parameterized by \(\omega\in R^{h}\) and \(b\in R\) such that it correctly separates most of the samples. The hyperplane is formulated by Equation (11), where \(g(.)\) is a nonlinear equation mapping the input into a high dimensional space. SVM minimizes the loss function \(L\) to maximize the margin between the hyperplanes and data points, as described by Equation (11).  
\[h(x)=\omega^{T}\cdot g(x)+b=0 \tag{11}\]  
\[min\Big{\{}L=\frac{1}{2}\omega^{T}\omega+C\sum_{i=1}^{n}\theta_{i}\Big{\}}\] (12) subject to : \(y_{i}\big{(}\omega^{T}\cdot g(x)+b\big{)}\geq 1-\theta_{i},\) with \(\theta_{i}\geq 0,\ i=1,2,\ldots,m\)  
where \(C\) is the regulation factor and \(\theta_{i}\) is imperfection allowed error from the data point \(i\) to the hyperplane. To solve the optimization problem, Lagrange multiplier method could be used, which is described by Equation (13); where \(\alpha\) and \(\beta\) are the Lagrange multiplier. The decision function is then could be calculated by Equation (13); where \(K(x,x_{i})\) is the kernel function. Some common kernel functions are linear, polynomial, radial basic function, and multilayer perceptron [19]. To find hyperparameters of \(C\) and \(K\), we used the grid search method.  
\[\Gamma=L-\sum_{i=1}^{m}\alpha_{i}(y_{i}[\omega^{T}\cdot g(x)+b]-1+\theta_{i})- \sum_{i=1}^{m}\beta_{i}\theta_{i} \tag{13}\]  
\[h(x)=\sum_{i=1}^{m}\alpha_{i}K(x,x_{i})+b \tag{14}\]  
Figure 9 shows the validation accuracy of \"mixing\" datasets using the SVM with the different number of principal components (PCs). The optimum kernel is a linear \(K(x,x_{i})=x^{T}x\). As the number of PCs increases from 1 to 45, the classification accuracy quickly increases. The accuracy reaches its maximum at about 97%, with the number of PCs over 45. This is reasonable because the cumulative explained variance \(E\) increases as  
Figure 8: Classification accuracy of SVM model and cumulative explained variance with the number of principal components.  
the number of PCs increases. Thus, we could use a few features (99% of E is used in the later results) to train the machine-learning models.
## Results and Discussion
### Diversity of volatiles isolated by headspace solid-phase microextraction (HS-SPME)
#### Diversity of volatiles isolated by ultrasonic solvent extraction (USE)  
A total of 57 volatile and semi-volatile compounds were identified in _A. fruticosa_ honey isolated by USE followed by GC and GC-MS. A representative chromatogram is presented in Figure 2.  
2-Phenylethanol was the major compound of USE extracts from all _A. fruticosa_ honey samples, with percentages ranging from 12.1 to 16.8%, followed by minor percentages of other benzene derivatives such as benzyl alcohol (0.5-1.3%) and phenylacetaldehyde (0.0-0.1%). Another abundant compound was 2-phenylacetamide (3.2-7.4%), that is described as one of the major odor compounds of honey, irrespective of its floral origin [24]. More important compounds (although not so abundant) with respect to the honey botanical origin were shikimate pathway derivatives such as methyl syringae (5.8-8.2%), benzoic acid (0.5-1.1%), phenylacetic acid (0.8-2.1%), 4-hydroxybenzoic acid (0.9-2.3%), 4-hydroxyphenylacetic acid (0.2-0.7%), ferulic acid (0.6-1.2%) and others (Table 2). Methyl syringae was detected as the second most abundant compound of _A. fruticosa_ honey volatiles and was present in the honeys obtained from plants of different botanical families [25], but only in asphodel honey did it reach the highest level [26]. However, in comparison with other unifloral honeys neither of the previous compounds can be emphasized as specific marker of _A. fruticosa_ honey. Figure 2: Representative TIC chromatogram of _A. fruticosa_ honey VOCs isolated by USE. Numbers refer to the compounds listed in Table 2.
# Effect of Pore Size Distribution and Amination on Adsorption Capacities of Polymeric Adsorbents
## 3 Results and Discussion
### Adsorption Kinetics  
The adsorption kinetic data were analyzed using pseudo-first-order and pseudo-second-order rate equations via nonlinear fitting [21; 22]; Table 3 lists the corresponding parameters. The correlation coefficient (R\({}^{2}\)) suggested that the pseudo-second-order rate equation was a better fitting model.  
Figure 5 shows the adsorption kinetic curves of the three adsorbates on NDA-2 and NDA-3. Phenol rapidly reached equilibrium in approximately 0.5 h, ABS acid reached  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Specific Surface**} & \multicolumn{2}{c}{**Pore Volume**} & \multirow{2}{*}{**Average Pore**} & \multirow{2}{*}{**Modification**} & \multirow{2}{*}{**Polarity**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Residual**} & \multirow{2}{*}{**Chlorine**} & \multirow{2}{*}{**Capacity**} \\  & & & & & & & & & & & **Chlorine** (\%) \\ \hline NDA-1800 & 852.3 & 93.3 & 1.470 & 0.022 & 7.47 & / & nonpolar & 0.4–0.6 & / & / \\ NDA-1801 & 630.6 & 84.1 & 0.944 & 0.021 & 5.99 & & \begin{tabular}{c} Primary \\ amine \\ \end{tabular} & polar & 0.4–0.6 & 6.8 & 1.13 \\ NDA-1802 & 628.4 & 54.7 & 1.230 & 0.009 & 7.86 & \
\begin{tabular}{c} Secondary \\ amine \\ \end{tabular} & polar & 0.4–0.6 & 7.1 & 1.49 \\ NDA-1803 & 602.6 & 59.0 & 0.961 & 0.010 & 6.38 &
\begin{tabular}{c} Tertiary \\ amine \\ \end{tabular} & polar & 0.4–0.6 & 5.4 & 5.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of the modified resins.  
Figure 4: Mesopore diameter distribution of NDA-1800, NDA-1801, NDA-1802, and NDA-1803.  
equilibrium in approximately 8 h, and RBB4 reached equilibrium in approximately 20 h. The kinetics of the different adsorbates varied because the adsorption process on the inner surface of the resin is often very fast, and is mainly controlled by internal diffusion into the resin pores [23]. Owing to the relative size of the resin pores and the molecular size of phenol, the diffusion of phenol at the resin pore interface is similar to molecular diffusion in which there is free diffusion and a relatively high diffusion rate. However, RBB4 has a larger molecular volume, which leads to more frequent collisions with the pore wall, greater diffusion resistance, and a slower diffusion rate.  
The rate constant, k\({}_{2}\), of phenol adsorption was in the following order: NDA-2 > NDA-3, while this was the opposite for RBB4. The equilibrium adsorption capacities of NDA-2 for phenol and ABS acid were greater than those of NDA-3, while this was the opposite for RBB4. The pore diameter of NDA-2 was smaller than that of NDA-3, leading to a higher equilibrium adsorption capacity for micromolecules such as phenol and ABS acid. In contrast, NDA-3 had higher equilibrium adsorption capacity for the macromolecule RBB4. Therefore, the pore diameter distribution of the resin not only affected the adsorption rate, but also influenced the adsorption capacity of the resin for an adsorbate.
# Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle
## 5 Bifurcation Flight Dynamic Analysis of a Micro Aerial Vehicle
It can be seen that there is also a slight area of stable spins near the elevator deflection angle of \(\delta_{e}\approx 12^{\circ}\). The waveform of flight parameters indicates that attractors are present within branch E, hence, this equilibrium branch does not play a significant role in the dynamics of disturbed spins [26; 27]. For an angle of attack range of \(65^{\circ}<\alpha<85^{\circ}\), most equilibrium positions are unstable. There can be no steady spins on unstable branches, while one can expect solutions of deterministic chaos nature on these branches [13]. The spin dynamics is significantly impacted by two Hopf bifurcations present at \(\delta_{e}=23.4^{\circ}\) and \(\delta_{e}=-13.2^{\circ}\) and a period-doubling bifurcation present at \(\delta_{e}=12.5^{\circ}\). All three bifurcations appear for angles of attack of \(\alpha>65^{\circ}\). In the case of an elevator deflection angle of \(\delta_{e}=-13.2^{\circ}\), the periodic orbits created through the Hopf bifurcation are unstable throughout the entire range of bifurcation parameters. The period-doubling bifurcation appearing at \(\delta_{e}=12.5^{\circ}\) can lead to a spin of chaotic nature [13]. The periodic orbit created through Hopf bifurcation at \(\delta_{e}=23.4^{\circ}\) is more significant. It is initially unstable and then branches into a stable periodic orbit through the torus bifurcation at \(\delta_{e}=-15.9^{\circ}\). The stable periodic orbit of branch F (Figure 5) is significant, since in this case, the average angle of attack is \(\alpha\approx 70^{\circ}\), with high values of deflection angular velocities. Therefore, it can be concluded that branch F corresponds to steep oscillation spin states. It can also be inferred that MAV oscillation spins occur at angles of attack of approximately \(70^{\circ}\). Also, a second stable periodic orbit can be identified on branch G (Figure 6) within the range of high angles of attack. This solution is true for a closed curve that is not connected with other curves. It is the outcome  
Figure 8: Bifurcation diagram. Quasi-steady states for various elevator deflection angles; V(\(\delta_{e}\)). of torus bifurcation present on branch F (for \(\delta_{\epsilon}=-15.9^{\circ}\)). Due to torus bifurcations, the periodic orbit becomes unstable, and MAV flight dynamics equation solutions are attracted by branch G periodic orbit. Solutions to micro aerial vehicle motion equations over branch G represent more rapid, oscillation spins, relative to branch F solutions [26, 27].
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 8 Conclusions  
In this paper, we propose a genetic algorithm (GA)-based Ad Hoc On-Demand Multipath Distance Vector routing protocol (GA-AOMDV). The fitness function is based on the energy consumption of nodes and therefore the AOMDV routes are optimized. As well-known, AOMDV selects the routes based on the minimum number of hops; therefore, GA-AOMDV applies two metrics to select the routes which are the least number of nodes in each route in addition to the minimum energy used. Before applying the GA, routes with low fitness scores are dropped and this accordingly reduces the processing time and speeds up the solution convergence. This is crucial in various applications bounded by time such as in FANETs and VANETs, particularly for alert messages communications. GA-AOMDV outperforms other protocols based on the QoS parameters. Particularly, our protocol reduces the energy consumption during data packets communications through mobile nodes. Therefore, the lifetime of the network is extended. Our protocol achieves an average gain of 7 to 22% over other protocols. As a result, it is obvious that the GA-based protocol enhances the performance of mobile networks. Moreover, GA-AOMDV does not require large processing resources as other protocols need.  
Conceptualization, J.P. and H.E.-O.; methodology, J.P. and H.E.-O.; software, J.P. validation, J.P. and H.E.-O.; formal analysis, J.P. and H.E.-O.; investigation, J.P. and H.E.-O.; resources, J.P.; data curation, J.P. and H.E.-O.; writing--original draft preparation, J.P.; writing--review and editing, H.E.-O.; visualization, J.P. and H.E.-O.; supervision, H.E.-O.; project administration, H.E.-O.; funding acquisition, No funding. [PERSON] agreed on the first draft and any changes made by H.E.-O. of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Authors would thank [PERSON] for his great support for this article.  
The authors declare no conflict of interest.
## 4 Application and Analysis of FBG Strain Detection Piles in Slope Deformation Monitoring
According to the deformation data of IN 1\(\sim\)IN 5, it was found that the strain variation in the shallow surface on the north side of the slope is large, especially at a depth of 6 m of IN 1; the maximum strain value obtained by the FBG sensors is 426 \(\upmu\)e, and the maximum strain change value obtained at 4 m is nearly 200 \(\upmu\)e, which is much larger than the other positions, so it can be assumed that shear deformation occurs at a depth of 6 m on the north side of the slope. Corresponding to the strongly weathered fractured structural surface between the residual slope deposit of clay and the underlying mudstone, the strain value at a depth of 8 m\(\sim\)22 m is very small, so it is assumed that the positions in this depth range are located in a weakly weathered and stable zone of bedrock. The strain value increases at a depth of 22 m to 28 m, with the maximum value reaching 149 \(\upmu\)e, which suggests that there may be fissure water inside the soil body at this place. When rainfall occurs, the infiltration of rainfall will cause changes in the water content of the slope, resulting in the corresponding adjustment of the stress field and displacement field in the soil. During the period from 2 April to 5 April, continuous rainfall occurred in the monitoring area. Infiltration of rainwater into the ground increases the soil saturation, which is inimical to slope stability. Because the overlying slope residue on the north side of the slope is thicker, ground movement due to significant rainfall infiltration also leads to obvious lateral strain change of IN1 in the shallow areas. The average strain values at IN 2\(\sim\)IN 4 were relatively small in changes, and the structure is comparatively stable. The strain variation in the  
Figure 12: Distribution of FBG sensors in a slope borehole. The average strain values at IN 2\(\sim\)IN 4 were relatively small in changes, and the structure is comparatively stable. The strain variation in the shallow surface on the south side of the slope is large, especially at a depth of 6 m of IN 5, corresponding to the depth of the rock boundary location. In addition, the longitudinal strain distribution characteristics indicated that the cumulative deformation of the shallow soil on the northernmost and southernmost sides of the slope is significantly larger than that of the deep soil. Moreover, there is a zone of stability with a small variation in the strain at the bottom of boreholes IN 1, IN 2, and IN 4. The presence of these stable zones suggests that the bottom of the borehole  
Figure 13: Longitudinal distribution of strain changes in 30 m monitoring detection pile IN 1\(\sim\)IN 5 in the slope. is positioned in a zone of stability within the bedrock. In summary, the utilization of FBG strain sensors to grasp the stress-strain law of the slope provides an effective monitoring method for slope stability evaluation.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
## 1 Introduction
However, research concerning multi-functional dynamic changes to land use focus mainly on macro (national) and middle (provincial) scales, with insufficient research carried out on a micro (county) scale, and there are few discussions regarding the ethnic regions at the county-scale. Most research studies are taken from the perspective of the dimensions of time or space. Therefore, research using a combination of space and time with regard to land use is required. In addition, land use change has also brought a series of adverse effects [25; 26], which restricts the sound development of the land systems. For measuring the function of land, it is also necessary to clarify factors that create obstacles for the versatile use of land, but few studies have conducted such analyses. Xiangxi is typically representative of less developed areas in China, and it is also a minority-concentrated area. Based on the land-use history of this area, we arrive at three questions. 1. What changes have taken place with the land-use function of this region? 2. What are the main factors restricting the land function there? 3. What measures should be taken to mitigate the adverse effects? To answer these questions, bases on the logic of \"analysis of functions-diagnosis of barrier factors-policy recommendations,\" this study is conducted from the following aspects. First, we classified the land function types and constructed an evaluation index system. Second, we evaluated the temporal and spatial dynamics of land multifunction in the last five years. Third, we explored the obstacles in depth. Lastly, through comparative analyses between Xiangxi and other regions, we put forward the corresponding policy recommendations. The aims of this study are: (1) to realize efficient positioning, quantitative expression [27], (2) identify obstacles to constrain land use efficiency in multifunctional land use, and (3) provide policy suggestions for the rational land use in minority areas.
###### Abstract
Over the years, several studies have investigated the levels of PAHs in seafood caught in the Adriatic [12, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35], Ionian [9, 20, 34, 36, 37, 38, 39], and Tyrrhenian Seas [7, 14, 16, 17, 19, 30, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], with the aims of monitoring the environmental status of particularly impacted areas [39, 48], improving the knowledge of factors influencing PAHs levels in marine species [12, 35], and, ultimately, informing decisionmakers when it comes to fixing the limits to PAH levels in fishery products [9]. The aim of this work was to collect and harmonize the results of those studies, in order to obtain a database of georeferenced observations on PAHs in seafood caught in the Western and Central Mediterranean Sea. For each observation, information on the taxonomy and the ecology of the investigated species are reported, as well as any details on the hydrocarbon, temporal and spatial information on sampling. Additionally, two health risk indexes were calculated for each record and included in the database. Finally, we conducted a meta-analysis of the data on Mediterranean mussel, Manila clam, red mullet \begin{table}
\begin{tabular}{c c c} \hline \hline
**Foodstuffs** & **Maximum Levels for BaP** & **Maximum Levels for PAH4 1**  \\  & **(mg/kg)** & **(mg/kg)** \\ \hline Muscle meat of smoked fish & & \\ and smoked fishery products & 0.002 & 0.012 \\ Bivalve molluss (fresh, & \\ chilled or frozen) & 0.005 & 0.030 \\ Bivalve molluss (smoked) & 0.006 & 0.035 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum levels for PAHs in seafood, set by EU in 2011 [11]. Concentrations are expressed in mg/kg. and common sole, identifying trends that could be related to the biology of the investigated organisms, as well as to the physico-chemical properties of each hydrocarbon and to the water circulation in this part of the Mediterranean.
# Experimental Study of the Mechanical Properties of a Flexible Grid Filling Body
## 4 Analysis of the Uniaxial Compressive Strength Characteristics of the Flexible Grid Filling Body
### Analysis of the Compressive Strength of the Flexible Grid Filling with Different Spacings  
According to the design scheme in Table 3, the uniaxial compression test was carried out, and the stress-strain curves of the flexible grid filling bodies with different spacings with different ash-sand ratios (1:4 and 1:8) were plotted, according to the test results, as shown in Figures 8 and 9.  
Figures 8 and 9 show that the stress-strain curve in the uniaxial compressive test of the reinforced filling body and the unreinforced filling body was consistent, and they all went through the compaction stage, elastic stage, fracture development stage, and failure stage in turn; however, compared with the unreinforced filling body, the uniaxial compressive strength of the reinforced filling body increased, the stress peak point shifted back, the ductility increased, and the britt  
Figure 8: Uniaxial compression test results of the different flexible grid backfill spacings with a cement-to-sand ratio of 1:4. (**a**) One-dimensional grid, (**b**) two-dimensional grid, and (**c**) three-dimensional grid.  
Figure 7: YNS600 electrohydraulic servo universal testing machine uniaxial compression test layout. (**a**) Testing machine. (**b**) Specim on the machine.  
Figure 9: Uniaxial compression test results of the different flexible grid backfill spacings with a cement-to-sand ratio of 1:8. (**a**) One-dimensional grid, (**b**) two-dimensional grid, and (**c**) three-dimensional grid.  
In order to further analyze the influence of the different pitch flexible grids on the compressive strength of the filling body, the uniaxial compressive strength of the filling body with different flexible grid spacings was obtained, according to the test results of Figures 7 and 8, as shown in Table 4 and Figure 10.  
It can be seen from Table 4 and Figure 9 that the uniaxial compressive strength of the flexible grid filling body gradually increased with the decrease in the grid spacing (increase of grid density), but the increase was not large; that is, for every 10 mm decrease, the uniaxial compressive strength only increased by about 5%. This shows that reducing the spacing of the flexible grid improved the uniaxial compressive strength of the filling body, but the effect was not obvious.
## 1 Introduction
### Structure of the Article  
The remainder of this article is structured as follows. First, Section 2 overviews related surveys and research articles that provide an overview of CO frameworks. Then, Section 3 presents our research method to perform the systematic assessment. Thereafter, Sections 4 and 5 present the qualitative assessment of the genericity of the CO frameworks: Section 4 presents the assessment of the common features and functional (sub)-aspects, i.e., research questions RQ1-RQ2, and Section 5 presents the assessment of unique features, i.e., research question RQ3. Subsequently, Section 6 presents the quantitative analysis with respect to the genericity property, i.e., research questions RQ4-RQ6. Thereafter, Section 7 summarizes the results of assessing the maturity and pioneering nature of the CO frameworks, i.e., research questions RQ7-RQ8, and the assessment of the stability of the CO frameworks, i.e., research questions RQ9-RQ10. Then, Section 8 presents a look-ahead into the future of CO frameworks and outlines important missing aspects and open research questions. Thereafter, Section 9 discusses the threats to validity and the limitations of the study. Subsequently, Section 10 presents a synthesis with respect to the findings for genericity, maturity and stability and summarizes the main distinguishing characteristics of the CO frameworks. Finally, Section 11 concludes.  
Note, all the collected data including hyperlinks to relevant documentation pages of CO frameworks at GitHub is available at Zenodo [30]. Moreover, an extensive technical report with detailed comparisons between the CO frameworks and detailed assessments of all the data is also available [31].