esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficacy of Pilates in Functional Body Composition: A Systematic Review
## 4 Discussion
In a cross-sectional study, with 75 elderly women, distributed among practitioners of Pilates, water aerobics, dance, and control groups, the highest values of WHR, diastolic blood pressure, and resting heart rate [10] were attributed to the control group. In a study of males, the resting heart rate and blood pressure improved as a function of Pilates [51]. Returning to the molecular topic, serum levels of Decorin, Follistatin, and Myostatin [44] were also investigated. In this case, the practice of Pilates did not promote significant changes (\(p>0.05\)) in these proteins and, consequently, in FBC. The authorsindicated that a greater intensity or longer duration of intervention was required to bring about the levels of protection projected in this study. There were gains in muscle strength and endurance compared to the control group. In the same sense/set of results, a study was developed with women with breast cancer, which also did not find differences between the Pilates practitioner group and the control group in the variables of BW, BMI, and WHR [45]. The findings of another study comparing the practice of low-intensity, high-repetition exercises versus the practice of Pilates indicated an improvement in some aspects of the physical fitness of low-intensity, high-repetition exercise practitioners [46]. As far as Pilates is concerned, its practice was associated with increased flexibility. In these studies, the issue of intensity stood out, which should be further studied to understand its influence on the FBC. To wrap up this molecular topic, a combined procedure testing FBC and blood tests on 28 overweight women who performed mat Pilates for 12 weeks revealed significant in BMI, cholesterol, and triglycerides [47]. Additionally, 25-hydroxy vitamin D levels, as well as HDL levels, showed positive improvements associated with this practice. In a final study, no significant differences were registered between two groups of Pilates practice over a period longer than 3 months. However, higher levels of body fat were associated with the group that had the least amount of practice time [48]. Overall, in the present review, a wide range of benefits and the influence of the Pilates method were collected with respect to FBC. This can be better seen in Table 2, with the majority of studies pointing to a positive trend between Pilates and FBC. On a final note, we consider limitations of this review in the period defined (5 years) and in the number of databases searched. Both factors may have decreased the number of eligible studies on this topic, which should be considered in a future review.
## 2 WDM Photodetector Array for Performance Monitoring
The inset grating teeth in Figure 5 show the rough top surface of the side wall, which demonstrated the internal responsivity of a single 1,532-nm wavelength channel as only \(\sim\)0.025 A/W (photocurrent divided by the waveguide power) on the photocurrent output rather than the optical power from the reflectively coupled WDM PDA. Because the coefficient between responsivity and quantum efficiency of the InGaAs photodetector can be derived as 1.233 A/W (= 1.6E-19 \(\times\) 1532/6.626E-34/3E17) at a wavelength of 1,532 nm, the internal quantum efficiency for WDM PDA was 0.0338 (= 0.025/1.233/0.6) after considering the quantum efficiency 0.6 from the 1.4-\(\upmu\)m thick i-region of the InGaAs photodiode. Therefore the echelle grating insertion loss was as high as \(\sim\)15 dB, which can be further improved by the grating tooth surface roughness quality besides the sharpness and verticality maintenance. The internal responsivity of 0.54 for WDM PDA can be expected after the echelle grating might be appropriately processed to achieve 3-dB loss [18] and 0.87 quantum efficiency from the 3-\(\upmu\)m thick i-region of the InGaAs photodiode. The bandwidth allocations and management in transparent dense WDM optical networks, which are essential to lightwave communications, offer new high-bandwidth services while saving operational costs [16]. A WDM photodetector array precisely connected to a waveguide tap port [15] can take a small portion of light signal from the main transmission channel and provide real-time optical channel performance monitoring information on the system. For the case here, our receiver module detector array was allocated onto a Rowland focus circle with PDL free TIR mirror reflection to increase the detector resolution suitable for very dense WDM applications.
# Go Source Code Vulnerability Detection Method Based on Graph Neural Network
## 4 Methodology
### Graph-Level Feature Extraction
This approach enhances the understanding of local graph structure features, efficiently captures control and data dependencies, and provides a novel method for obtaining global graph features and contextual information, potentially improving classification performance. The overall framework of GraphSAGE is illustrated in Figure 4.  
At each iteration \(k\), we randomly sample a fixed-size neighborhood \(S_{v}^{k}\) for each node \(v\). Then, we apply a mean aggregation function to merge the feature vectors of the neighboring nodes and update the representation of node \(v\). The mean aggregation function is defined as follows:  
\[h_{N(v)}^{(k)}=\frac{1}{|S_{v}^{k}|}\sum_{u\in S_{v}^{k}}h_{u}^{(k-1)} \tag{1}\]  
where \(h_{N(v)}^{(k)}\) denotes the aggregated feature vector of node \(v^{\prime}s\) neighborhood at the \(k\)-th layer. Specifically, we average the feature vectors \(h_{u}^{(k-1)}\) of all sampled neighbors \(u\in S_{v}^{k}\) to obtain a representation of the local neighborhood of node \(v\) at the current iteration. Subsequently, node \(v\) updates its own representation by incorporating the aggregated neighborhood feature vector \(h_{N(v)}^{(k)}\), using the following update function:  
\[sh_{v}^{(k)}=\sigma(W^{(k)}\cdot CONCAT(h_{v}^{(k-1)},h_{N(v)}^{(k)})) \tag{2}\]  
where \(sh_{v}^{(k)}\) represents the weight matrix at the \(k\)-th layer, \(\sigma\) is the nonlinear activation function, and \(W^{(k)}\) is the learnable weight matrix. The operator \(\text{CONCAT}(\cdot)\) denotes concatenation, which combines the current features of node \(v\) with the aggregated features from its neighbors. To prevent excessive feature scale growth and maintain consistency, we normalize each node's feature vector after every update. Specifically, we apply L2 normalization as follows:  
\[h_{v}^{(k)}=\frac{h_{v}^{(k)}}{\left\|h_{v}^{(k)}\right\|_{2}} \tag{3}\]  
Figure 4: Sampling and aggregation process of graph random walk network. The final graph representation \(h_{G}\) integrates the entire graph's structural and feature information without relying on expensive matrix operations or requiring storage of the complete graph structure. Therefore, in practical applications, GraphSAGE efficiently and accurately learns to distinguish the graph patterns of vulnerable and benign Go code, enabling effective detection of potential security vulnerabilities in Go source code.
## 4 Results and Discussion
### Experimental Result from Schlieren Method
The pressure, Mach number, and velocity distribution curves obtained from the simulation results using the \(k\)-\(\omega\) turbulence model were compared with the Schlieren experiment result as shown in Figure 18.  
From the figure, it can be seen that in Section 1, the crests and troughs (peak and valley) in the velocity and Mach number distribution curve show some similarity with the curve drawn at the outline of gas flow distribution extracted from Schlieren results. However, in contrast to the velocity and Mach number distribution curve, the pressure distribution curve shows the opposite characteristics. Specifically, at positions indicated by lines 1, 3,  
Figure 11:0 Outline of the flow along the kerf. (**a**) Starting and end points of cut kerf, (**b**) result of image processing for edge detection, (**c**) distance between Schlieren edge and line Z2. Figure 11:1 Comparison between density gradient fluctuation predicted by numerical simulation with different turbulent models and experimental results. and 5, the velocity curve, Mach number curve, and the curve extracted from the outline of gas flow show the crest, whereas the pressure curve shows the trough. The crest in the velocity curve is due to the Mach shock disk as observed in the Mach number distribution curve. Whereas at the positions indicated by lines 2 and 4, the trough can be observed in velocity distribution, Mach number distribution, and gas flow outline curve and vice versa for the pressure curve. In conclusion, the similarity between the calculated velocity distribution from the numerical simulation and the results of the Schlieren experiment for the position of Mach disk distribution shows a high degree of similarities and can be applied to validate the proposed model.
## 5 Interpretation and Discussion
### Tectonic Setting  
Several researchers including [6; 9; 27; 28; 29] have envisaged that tectonic settings control or influence the chemical compositions of clastic sedimentary rocks, indicating that variety of tectonic settings have terrain-specific signatures. Different tectonic setting discrimination diagrams have been used to separate between different tectonic settings, and they all provide consistent results for siliciclastic rocks that have not been strongly affected by post-depositional weathering and metamorphism [6; 27; 28]. Similarly, the bivariate and ternary plots of major and trace element geochemistry have been widely used by several researchers to determine the tectonic setting of shales [14]. The most used discrimination diagrams are major element-based discrimination diagrams of [27; 28]. The geochemical data of the studied samples were plotted on binary plots of SiO\({}_{2}\) against Log (K\({}_{2}\)O/Na\({}_{2}\)O), TiO\({}_{2}\) versus (Fe\({}_{2}\)O\({}_{3}\) + MgO), and ternary plot of Th-Sc-Zr/10. The binary plots of SiO\({}_{2}\) against K\({}_{2}\)O/Na\({}_{2}\)O and TiO\({}_{2}\) versus (Fe\({}_{2}\)O\({}_{3}\) + MgO) show that the sedimentation of the studied samples were related to both active continental and passive continental settings (Figures 9 and 10). Likewise, the ternary plot of Th-Sc-Zr/10 revealed that the samples were mainly deposited in a passive continental setting, with little contribution from active continental margin setting (Figure 11).  
Figure 9: Bivariate plot of SiO\({}_{2}\) versus K\({}_{2}\)O/Na\({}_{2}\)O for the studied samples showing tectonic setting (Background field after [28]).  
Figure 11: Th-Sc-Zr/10 ternary plot for the studied samples showing tectonic setting (Background field after [6]). Note: PM, ACM, CIA and OIA represent Passive Margin, Active Continental Margin, Continental Island Arc, and Oceanic Island Arc, respectively.  
Figure 10: Bivariate plot of TiO\({}_{2}\) (wt%) versus (Fe\({}_{2}\)O\({}_{3}\) + MgO) (wt%) for the studied samples showing tectonic setting (Background field after [6]). Note: PM, ACM, CIA and OIA represent Passive Margin, Active Continental Margin, Continental Island Arc, and Oceanic Island Arc, respectively.
# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Number** & **Mutation Rate** & **Generation** & **Genome** & **Total Genome** & **Distance** & **Time** \\ \hline \multirow{8}{*}{3 inputs} & 0.025 & 3 & 0 & 120 & 263.11 & 41.60 \\ \cline{2-6}  & 0.055 & 5 & 25 & 225 & 262.46 & 53.25 \\ \cline{2-6}  & 0.105 & 6 & 0 & 240 & 259.23 & 54.12 \\ \cline{2-6}  & 0.155 & 5 & 2 & 202 & 259.90 & 82.82 \\ \cline{2-6}  & 0.205 & 5 & 7 & 207 & 268.57 & 33.03 \\ \cline{2-6}  & 0.255 & 11 & 2 & 442 & 263.36 & 59.85 \\ \cline{2-6}  & \multicolumn{4}{c}{Average} & & 239.33 & 262.77 & 54.11 \\ \hline \multirow{8}{*}{5 inputs} & 0.025 & 4 & 3 & 163 & 255.79 & 70.86 \\ \cline{2-6}  & 0.055 & 13 & 24 & 544 & 256.00 & 47.68 \\ \cline{2-6}  & 0.105 & 9 & 0 & 360 & 266.94 & 32.38 \\ \cline{2-6}  & 0.155 & 2 & 1 & 81 & 258.46 & 64.35 \\ \cline{2-6}  & 0.205 & 7 & 2 & 282 & 254.00 & 54.00 \\ \cline{2-6}  & 0.255 & 8 & 1 & 321 & 254.00 & 35.91 \\ \cline{2-6}  & \multicolumn{4}{c}{Average} & & 291.83 & 257.53 & 50.86 \\ \hline \multirow{8}{*}{7 inputs} & 0.025 & 3 & 1 & 121 & 262.41 & 70.95 \\ \cline{2-6}  & 0.055 & 5 & 27 & 227 & 262.43 & 71.06 \\ \cline{2-6}  & 0.105 & 1 & 0 & 40 & 269.13 & 75.92 \\ \cline{2-6}  & 0.155 & 5 & 19 & 219 & 271.37 & 49.21 \\ \cline{2-6}  & 0.205 & 3 & 32 & 152 & 268.11 & 70.25 \\ \cline{2-6}  & 0.255 & 9 & 6 & 366 & 267.00 & 78.23 \\ \cline{2-6}  & \multicolumn{4}{c}{Average} & & 187.50 & 266.74 & 69.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed results from static environment experiment using proposed method (modified, neuronal GA). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Number** & **Mutation Rate** & **Generation** & **Genome** & **Total Genome** & **Distance** & **Time** \\ \hline \multirow{6}{*}{3 inputs} & 0.025 & 11 & 29 & 469 & 268.65 & 34.80 \\ \cline{2-6}  & 0.055 & 4 & 1 & 161 & 260.77 & 41.11 \\ \cline{2-6}  & 0.105 & 2 & 16 & 96 & 262.57 & 35.16 \\ \cline{2-6}  & 0.155 & 4 & 3 & 163 & 254.76 & 40.28 \\ \cline{2-6}  & 0.205 & 17 & 32 & 712 & 273.19 & 87.53 \\ \cline{2-6}  & 0.255 & 13 & 31 & 551 & 269.12 & 46.58 \\ \cline{2-6}  & \multicolumn{3}{c}{Average} & \multicolumn{3}{c}{358.67} & 264.84 & 47.58 \\ \hline \multirow{6}{*}{5 inputs} & 0.025 & 4 & 16 & 176 & 263.50 & 56.00 \\ \cline{2-6}  & 0.055 & 15 & 2 & 602 & 261.65 & 36.50 \\ \cline{2-6}  & 0.105 & 2 & 18 & 98 & 257.56 & 63.49 \\ \cline{2-6}  & 0.155 & 26 & 2 & 1042 & 275.83 & 32.56 \\ \cline{2-6}  & 0.205 & 14 & 4 & 564 & 255.98 & 30.98 \\ \cline{2-6}  & 0.255 & 4 & 2 & 162 & 265.65 & 34.60 \\ \cline{2-6}  & \multicolumn{3}{c}{Average} & \multicolumn{3}{c}{440.67} & 263.36 & 42.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed results from dynamic environment experiment using proposed method (modified, neuronal GA).
## 3 Results and Analysis  
### Finite Element Simulation Results  
Figure 5 shows the finite element results of the stresses in the TC4-DT titanium alloy lug plate and compression bushing expansion. The results in Figure 5 show that the compression bushing mainly exists in the form of compressive stresses, while the lug plates mainly show tensile stresses, and the maximum compressive stress area is near the hole wall of the compression bushing along the width of the lug. This is mainly because, during the cold extrusion strengthening process, in addition to the extrusion process of the bushing and lugs with the conical section of the mandrel, there is also a slight pulling process due to the movement of the mandrel along the axial direction. As a result, the lug can show tensile stresses in the form of transition under the bushing. Along the path of the extruded surface, S11 is plotted as a function of the distance from the lug plate hole wall. Along the path of the extruded surface, S11 is plotted as a function of the distance from the lug plate hole wall. The circumferential stresses gradually increase with distance from the lug sheet aperture wall, forming tensile stresses. The residual stress at the lug sheet aperture wall is about zero, and the residual tensile stress at the edge is about 350 MPa. In contrast to the pattern presented by the annular stresses, the radial stress curves generally show a decreasing and then increasing trend. However, as the distance from the lug plate hole increases, the radial compressive stress values tend to increase and then decrease. The compressive stress reaches its maximum value of about 225 MPa at 1.5 mm from the wall of the lug plate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline Voltage & 40 kV \\ Current & 40 mA \\ Crystal structure & Hexagonal \\ Diffraction crystal plane & [213] \\ Diffraction angle & 140\({}^{\circ}\) \\ Oscillation setting & 2\({}^{\circ}\) \\ Exposure time & 100 s \\ Collimator diameter & 1 mm \\ Peaking method & Half height and width method \\ \hline \hline \end{tabular}
\end{table}
Table 1: Residual stress determination conditions.  
Figure 5: The finite element results of the extruded annular stress of C4-DT titanium alloy lug plate and compression bushing. (**a**) Stress map. (**b**) Stress curve.
## 5 Simulation Result Analysis
### No Disturbance
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Shoulder Joint**} \\ \hline  & \multicolumn{2}{c}{ITSE (Deg.)} & \multicolumn{2}{c}{ISE (Deg.)} & \multicolumn{2}{c}{ITAE (Deg.)} & \multicolumn{2}{c}{IAE (Deg.)} \\ \hline Control & \(-20\%\) & \(+20\%\) & \(-20\%\) & \(+20\%\) & \(-20\%\) & \(+20\%\) & \(-20\%\) & \(+20\%\) \\ \hline Proposed & 7.529 & 7.534 & 1.52 & 1.52 & 16.83 & 16.84 & 3.377 & 3.379 \\ \hline IADRC [52] & 13.61 & 13.61 & 2.73 & 2.73 & 23.48 & 23.49 & 4.703 & 4.703 \\ \hline NLADRC [51] & 15.73 & 15.74 & 3.154 & 3.154 & 25.21 & 25.21 & 5.047 & 5.048 \\ \hline ADRC [40] & 18 & 18 & 3.607 & 3.607 & 26.96 & 26.97 & 5.398 & 5.399 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance indices of the shoulder joint \(\pm 20\%\) parameter variation and constant disturbance. The ITSE, ISE, ITAE, and IAE of the proposed method have values 7.501, 13.59, 15.71, and 17.97 with \(-20\%\) parameter variation and 7.522, 13.60, 15.72, and 17.99 for the elbow joint with \(+20\%\) parameter variation, respectively, which are minimum as compared IADRC, NLADRC, and ADRC. Minimum values of ITSE, ISE, ITAE, and IAE indicate the proposed method performs better in terms of trajectory tracking for ULRRE and can be observed form the Table 6.  
It has been observed that all the techniques mentioned above can reject disturbances. The proposed method outperforms all others in terms of trajectory tracking with disturbance rejection, as evidenced by consistent performance indices under the introduction of parametric uncertainties and disturbances. In comparison to previous ESO-based approaches, the proposed controller shows promise.
## 5 Related Work  
Selfish and malicious misbehavior in wireless networks has been extensively studied under various scenarios in different communication layers. The majority of previous work has concentrated on detecting or punishing MAC-layer misbehavior [18; 23; 24; 25]. In [18], [PERSON] and [PERSON] studied the MAC-layer misbehavior of selecting always small backoff values rather than random selection, and showed that such selfish misbehavior can seriously degrade the network performance. [PERSON] et al. [23] investigated multiple misbehavior policies in 802.11 MAC protocol, including backoff manipulations, and presented a detection framework, called DOMINO, which considers all possible strategies jointly and improves the detection accuracy. Several statistic-based frameworks for detecting misbehavior also have been introduced [24; 25]. Their common detection approach is to measure the inter-arrival time of target stations in terms of the number of backoff slots and verify whether the backoff time of the stations follows a legitimate pattern or not. The authors in [26] proposed a non-parametric CUSUM test in order to detect real-time selfish misbehavior in  
Figure 12: Impact of the first alarm threshold \(\theta_{AS}\) on detection time for UDP and TCP protocols with \(B_{el}=20\) Mbps.  
Figure 13: Impact of the second alarm threshold \(\theta_{S}\) for the given \(\theta_{AS}=3\) on detection time with \(B_{el}=20\) Mbps.  
802.11 networks. The problem of selfish misbehavior has also been addressed in several different layers and perspectives, including the coexistence between LTE and Wi-Fi systems in unlicensed bands [27], the emerging machine-to-machine (M2M) communications [28], relay networks [29], routing layers [30; 31], multi-channel protocols [32], and game theory-based behaviors [28; 33]. The problem of misbehavior using the unfair coexistence between LTE-LAA and Wi-Fi system is relatively new and unexplored in the literature. In [27], the problem of detecting misbehaving of LTE/Wi-Fi has been addressed. The authors of [34] presented a new model for analyzing the throughput performance for Wi-Fi and LTE-LAA coexistence, which can act as a baseline model to be exploited to distinguish misbehaving coexistence from the legitimate behavior. In [35], the authors have identified the effect of an 802.11 node's selfish behavior by manipulating the CCA threshold. They have shown that the selfish behavior can achieve higher throughput than other well-behaving nodes that are based on a game-theoretical model. The authors in [36; 37] addressed the selfish carrier sense problem in 802.11 Wi-Fi networks. The authors performed experimentation on a real-world testbed and showed that such selfish behaviors can cause extremely unfair allocations of the wireless medium. In [37], the detection of misbehavior in dense Wi-Fi networks has been addressed and a detection method that is based on a statistics has been presented. However, none of these studies considered the problem of the selfish problem exploiting Wi-Fi tethering environments.
## 6 Conclusions  
The 5G communication is expected to hit the market by 2020. After that, the 6G communication is predicted to be launched in between 2027 and 2030. Achieving the goals of 5G/6G and IoT on the basis of tactile internet is challenging. The most important and most challenging issues are the provision of high capacity, massive connectivity, low latency, high security, low-energy consumption, high QoE, and highly reliable connectivity for 5 GB communication systems. Only RF-based systems are unable to meet the high demands of future 5G/6G and IoT networks. OWC technologies are the best complementary solution of RF networks. The coexistence of RF and optical wireless systems can achieve the goals of such networks. This study presented a detailed observation of how OWC technologies, such as VLC, LiFi, OCC, and FSO, will provide an effective solution for the successful deployment of future 5G/6G and IoT networks. To do that, we briefly explained herein the characteristics of 5G, 6G, and IoT systems and features of OWC technologies. Each 5G, 6G, and IoT specification is individually explained herein with regard how OWC systems offer such features. The present OWC-related studies on 5G and IoT were also summarized in this paper. Therefore, this paper is highly expected to help in understanding the research contributions in different optical wireless systems for the deployment of future networks.  
Conceptualization, M.Z.C. and Y.M.J.; methodology, M.Z.C., M.S., and M.K.H.; software, M.S. and M.K.H.; validation, M.Z.C. and M.K.H.; formal analysis, M.Z.C. and M.S.; investigation, M.Z.C. and Y.M.J; writing--original draft preparation, M.Z.C and M.S.; writing--review and editing, M.Z.C; visualization, M.Z.C and M.S.; supervision, Y.M.J.; project administration, Y.M.J.; funding acquisition, Y.M.J. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00824, Development of Intelligent and Hybrid OCC-LiFi Systems for Next Generation Optical Wireless Communications), the National Research Foundation of Korea(NRF) grant funded by the Korea government (Performance Enhancement and Resource Management of Visual MIMO-based Camera Communication Networks) (No. NRF-2016R1D1A1A09919091), and the Korea Research Fellowship Program (2016H1D3A1938180). The authors declare no conflict of interest.