esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Analysis of Cinnamon Volatiles by SPME/GC-MS
#### 2.1.6 Miscellaneous  
Apart from the previously discussed volatile classes, other classes were identified, including phenol, ketone, pyran/furan, and oxide, though at much lower levels, Table S1. In authenticated drugs, phenols accounted for 0.12-4.2%, with the highest level detected in CVM attributed to carvacrol (P29) at 3.9%. Carvacrol was reported as a major volatile compound participating in the characteristic pungent and warm odor of cinnamon essential oil [28]. In contrast, oxide, ketone, and pyran/furan were minor classes in authenticated cinnamon, amounting for 0.08-0.8%, 0.2-0.5%, and 0.1-0.6%, respectively.  
It is noteworthy to report that phenols were detected in commercial products at 0.4-3.9%, with likewise carvacrol as a major component, especially in CP-4 at 3.8%. The other classes were also identified at trace levels. Nevertheless, furfural (P5) and dihydro-3-methylene-5-methyl-2-furanone (P13) were found to be relatively abundant in CP-3 (4.7%) and CP-2 (2.9%), respectively. These products are well known to be associated with the roasting process [23; 40], and whether thermal treatment was employed in the preparationof commercial products can be assessed by monitoring other markers, i.e., melanoidins, to be more conclusive.  
Other (_E_)-cinnamaldehyde-related metabolites were identified in various phytochemical-ical classes exemplified by hydrocinnamic acid (P34) and cinnamic acid (P54), detected at 0.01-1.1% and 0.02-0.3% in authenticated samples, respectively. However, they were observed at higher levels in commercial products. For instance, cinnamic acid levels reached 2.2% in CP-4, Table S1. Such an increase might confirm the hypothesis of thermal degradation revealed by the presence of furans.
_applied sciences_  
_Systematic Review_  
Effects of Various Foot Wedges on High Muscle Activity  
during Squatting in Healthy Adults: A Systematic Review  
and Meta-Analysis  
[PERSON] 1\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\) and [PERSON] 1,3,*\({}^{\copyright}\)  
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positions is the use of foot wedges [5]. This strategy has been used for changing lower limb biomechanics [12; 27; 28], muscle activity [5; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], and stability [31] during squatting. Based on the ankle joint position, foot wedges can be classified as anterior (dorsiflexion), posterior (plantarflexion), medial (inversion), and lateral (eversion).  
The results of previous studies on the effect of foot wedges on thigh muscle activity are inconsistent [5; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], which makes clinical decision-making problematic. While some studies showed decreased [5; 12; 15] or increased [11; 17; 19; 32] thigh muscle activity with foot wedges, others have reported no significant difference [16; 18; 33]. With regard to the effects of wedges on thigh muscle activation levels, previous studies examined the rectus femoris [10; 15; 18; 19; 27; 32], vastus lateralis [5; 12; 14; 16; 17; 18; 19; 20; 32], vastus medialis [5; 11; 12; 13; 14; 17; 18; 19; 20; 32], biceps femoris [10; 11; 13; 14; 18; 19; 27; 32], and semitendinosus [19]. However, the types of wedges used varied between studies, which may be the source of conflicting findings. For example, the effect of anterior wedge on thigh muscle activity was studied in five studies [5; 12; 13; 14; 18], but this wedge was different from other wedges applied in other studies [5; 10; 11; 14; 15; 16; 17; 18; 19; 20; 27; 32]. In order to clarify the effects and potential use of foot wedges, the aim of this study was to perform a systematic review and meta-analysis to identify the effects of four types of foot wedges on thigh muscle activation during squatting. We hypothesized that different types of foot wedges would selectively influence the activation levels of thigh muscles during squatting in healthy adults. The results can help specialists to achieve the desired effects in the areas of rehabilitation, injury prevention, physical preparation, and occupational optimization.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 1 Introduction
In this work, we address one of the major issues in such a setup, i.e., the tradeoff between throughput and quality of service (QoS) by means of a cognitive and dynamic approach for frequency allocation. This approach, based on re-enforcement learning, is named RL-CAA (for Reinforcement Learning--Channel Assignment Algorithm). The specific contributions presented in this paper are:  
* A context aware channel allocation RL-CAA approach in which the radio environment is first sensed, and based on the feedback received in the form of signal to noise ratio (SNR), the approach decides the fate of the channel. If the channel has a better SNR than other available channels, and if there is no co-channel interference, the channel is assigned to that particular gateway. Based on the throughput requirement, one or more channels can be assigned. The algorithm is robust to traffic changes; QoS satisfaction is its main objective. * The IEEE 802.15.6 standard has not formulated an off-body mathematical channel model. However, the IEEE standard document provides experimented values that can be used as the basis for such a model. Based on those experimental results, we have formulated a mathematical model for the off-body Channel Model 4 (CM4) using MATLAB that takes on-body posture and shadowing effects into account. * We propose a novel power allocation algorithm that aims at achieving the desired interference level at the boundary of the gateway's coverage area. Its primary convergence criterion is interference minimization towards other gateways. The rest of this paper is organized as follows: the current state of the art related to WBANs is described in Section 2. In Section 3, the detailed system model is presented and our simulation scenario is described. The simulation results are presented and discussed in Section 4. Finally, the last section concludes the paper.
## 2 Background  
### Reflection and Transmission Coefficients  
The four-hydrophone measurement method is frequently used to measure reflection and transmission coefficients [39]. The measuring impedance tube model is shown in Figure 1. The impedance tube is designed to obtain the acoustic information of the upstream and downstream sections in this tube by using four microphones. Such information can be used to calculate the reflection and transmission coefficients in accordance with corresponding formulas.  
A primary sound source in the upstream section excites incidence waves, transmits them to the downstream section after passing the test sample, and meets the boundary condition in the form of acoustic load. Incidence and reflected waves are formed on the left (\(A\) and \(B\)) and right (\(C\) and \(D\)) hands of the test sample, respectively. The sound pressure of both sides of the sample can be expressed as:  
\[p(x<0)=Ae^{-jkx}+Be^{+jkx}, \tag{1}\]  
\[p(x>d)=Ce^{-jkx}+De^{+jkx}, \tag{2}\]  
where \(d\) is the thickness of the test sample; \(j\) is the imaginary unit; and represents the wave-number, which can be obtained using the formula \(k=w/c\), where is the angular frequency and is the sound velocity. The amplitudes of the hydrophones at four positions (\(p_{1},p_{2},p_{3}\), and\(p_{4}\)) are known, and the incidence wave (\(A\)), reflection wave (\(B\)), transmission-incidence wave (\(C\)), and transmission-reflection wave (\(D\)) can be shown as:  
\[A=j\frac{p_{1}e^{+jkx_{2}}-p_{2}e^{+jkx_{1}}}{2\sin[k(x_{1}-x_{2})]}, \tag{3}\]  
\[B=j\frac{p_{2}e^{-jkx_{1}}-p_{1}e^{-jkx_{2}}}{2\sin[k(x_{1}-x_{2})]}, \tag{4}\]  
\[C=j\frac{p_{3}e^{+jkx_{4}}-p_{4}e^{+jkx_{3}}}{2\sin[k(x_{3}-x_{4})]}, \tag{5}\]  
\[D=j\frac{p_{4}e^{-jkx_{3}}-p_{3}e^{-jkx_{4}}}{2\sin[k(x_{3}-x_{4})]}. \tag{6}\]  
The transmission-reflection wave (\(D\)) will be removed if the end of the impedance tube is an anechoic termination. In that condition, the complex reflection coefficient (\(R\)) and transmission coefficient (\(T\)) of the test sample can be obtained as follows:  
\[R=\frac{B}{A}=\frac{p_{2}e^{-jkx_{1}}-p_{1}e^{-jkx_{2}}}{p_{1}e^{+jkx_{2}}-p_{ 2}e^{+jkx_{1}}}, \tag{7}\]  
\[T=\frac{C}{A}=\frac{p_{3}e^{+jkx_{4}}-p_{4}e^{+jkx_{3}}}{p_{1}e^{+jkx_{2}}-p_{ 2}e^{+jkx_{1}}}\cdot\frac{\sin[k(x_{1}-x_{2})]}{\sin[k(x_{3}-x_{4})]}. \tag{8}\]
## 5 Conclusions
The PCB and PAH contents obtained using the integrated determination methods were significantly different from those obtained using the specific methods for each of the groups of compounds. The integrated methods did not provide adequate extract cleanup. The extract fractionation step on alumina columns in method 3 [32] and alumina and silica gel columns in method 4 [33] were insufficient for removing compounds that interfered with the final detections. As with PCBs, the PAH contents obtained in the material tested differed depending on the methods applied. Based on the literature, it is apparent that integrated methods for determinations of PCBs and PAHs from one solvent extract (methods 3 and 4) are less labor intensive and less time consuming, and they utilize smaller quantities of reagents. However, because the differences in individual PCB congener recoveries using methods 3 and 4 were too high compared to those in method 1, neither of these methods was accurate in testing fruit matrices, and thus, they are unsuitable for quantitative determinations of PCBs in food. The determinations of individual PAHs in methods 3 and 4 and the specific analytical method for PAHs (method 2) were consistent for light PAHs, but determinations of heavy PAHs with a higher molecular weight were overestimated in methods 3 and 4. In reference to the results of the study, sample cleanup with integrated methods 3 and 4 was satisfactory only when standard solutions were used in the model experiment. The reasons for these differences should be sought in the insufficient removal of interfering substances, mainly lipids, that were also extracted. Therefore, it is necessary to further optimize these methods or to use other sorbents. The advantage of these methods is the possibility of performing simultaneous determinations of PCBs and PAHs, which are less labor intensive, save time, and utilize fewer reagents. However, because of inconsistent results, it is necessary to further optimize the extraction and purification stages of these methods, especially when they are applied to materials with higher fat contents. Conceptualization, A.C. and A.W. ; methodology, A.C. and A.W. ; validation, A.C., A.W. and K.P.-N.; investigations, A.C. and A.W. ; original draft and manuscript preparation, A.C., A.W. and K.P.-N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflict of interest.
# Analysis of AMI Communication Methods in Various Field Environments
## 5 Experimental Results
### Measurement Results of PLC and Regression Analyses
The model with these coefficients is shown as a black dotted line in Figure 22a. The noise power ranged from \(-47.3\) dBm to \(-34.0\) dBm. The difference between the maximum and minimum was about 13.3 dBm, which is also relatively small. The maximum value is indicated by a red dotted line and the average value (\(-40.0\) dBm) by the blue dotted line in Figure 22b. The average noise power at the Overhead Rural Area was rather large because of the solar-power panels at the Overhead Rural Area. Note that the solar-power panel system usually generates a lot of background noise. The Overhead Factory Area was characterized by small numbers of branches and short path length. Figure 23a shows the received powers with respect to the distance. The measurements were made at 5 MPs. There were two MPs at Site 4-3 and they had equal path lengths at 10 m. An MP had about \(-24.6\) dBm for the received power, while the other MP had about \(-37.3\) dBm. In spite of the same path length, the difference of the received signal powers was about 12.7 dBm, which is a rather large value. The results at Site 4-1 show that the first MP at 10 m suffers more attenuation than the case of the second MP at 22 m. These results show somewhat different trends than those obtained in other areas. These trends will be discussed in Section 6 in more detail. With the 5 MP results, a linear regression analysis was conducted to model the received power with respect to the distance for the Overhead Factory Area. The coefficients for the model were found as \(\alpha=-0.145\) and \(\beta=-28.3\). The model with these values is shown as a black dotted line in Figure 23a. The noise power ranged from \(-49.0\) dBm to \(-35.1\) dBm. The difference between the maximum and minimum  
Figure 20: Overhead Residential Area. (**a**) Received power. (**b**) Noise power. was about 13.9 dB, which is relatively small. The maximum value is indicated by a red dotted line and the average value by a blue dotted line in Figure 23b. The average noise power was \(-43.4\) dBm, which is relatively large compared to those of other areas. The measurements at Underground Areas were taken at 7 MPs. Figure 24a shows the received powers with respect to the distance. With the 7 MP results, a linear regression analysis was conducted and the coefficients for the model were found as \(\alpha=-0.127\) and \(\beta=-38.1\). The model with these values is shown as a black dotted line in Figure 24a. The noise power ranges from \(-56.0\) dBm to \(-38.6\) dBm. The difference between the maximum and minimum was about 17.4 dB. The maximum value is indicated by a red dotted line and the average value (\(-49.6\) dBm) is indicated by a blue dotted line in Figure 24b. Figure 21: Overhead Commercial Area. (**a**) Received power. (**b**) Noise power. Figure 22: Overhead Rural Area. (**a**) Received power. (**b**) Noise power.
## 2 Design of the State Vector
### Simplification of the State Vector  
To simplify further discussion, the subscripts of the variables described in the world coordinate system are omitted; thus, the ICEKF state vector in Equation (1) is rewritten as follows:  
\[\mathbf{X}=\left\{\mathbf{p}_{ic}{}^{T}\mathbf{v}_{c}{}^{T}\mathbf{v}_{i}{}^{T}\overline{\mathbf{ q}}_{ic}{}^{T}\overline{\mathbf{q}}_{i}{}^{T}\omega_{c}{}^{T}\mathbf{\omega}_{i}{}^{T} \mathbf{b}_{a}{}^{T}\mathbf{b}_{a}{}^{T}\lambda\right\}^{T}, \tag{6}\]  
The derivatives of the variables in the vector are as follows:  
\[\dot{\mathbf{p}}_{ic}=\mathbf{v}_{ic}=\mu_{v}\mathbf{v}_{i}+\lambda(1-\mu_{v})\mathbf{R}_{c}^{ i}\mathbf{v}_{c},\ \dot{\mathbf{v}}_{c}=\mathbf{n}_{v},\ \dot{\mathbf{v}}_{i}=\mathbf{R}_{i}(\mathbf{a}_{mi}-\mathbf{b}_{ai}-\mathbf{n}_{ \mathbf{a}_{mi}})-\mathbf{g},\]  
\[\dot{\overline{\mathbf{q}}}_{ic}=\frac{\mu_{v}}{2}\overline{\mathbf{q}}_{i}{}^{\mu_{ \omega}}\otimes\overline{\mathbf{u}}_{i}\otimes\overline{\mathbf{q}}_{c}{}^{1-\mu_{ \omega}}+\frac{1-\mu_{w}}{2}\overline{\mathbf{z}}_{ic}\otimes\overline{\mathbf{w}}_{c}, \ \dot{\overline{q}}_{i}=\frac{1}{2}\overline{\mathbf{q}}_{i}\otimes\overline{\mathbf{w}}_{ i},\ \dot{\mathbf{\omega}}_{c}=\mathbf{n}_{\mathbf{\omega}_{c}},\ \dot{\mathbf{\omega}}_{i}=\frac{d}{dt}(\mathbf{\omega}_{mi}-\mathbf{b}_{\mathbf{\omega}_{i}}- \mathbf{n}_{\mathbf{\omega}_{mi}}), \tag{7}\]  
\[\dot{\mathbf{b}}_{ai}=\mathbf{n}_{\mathbf{b}_{ai}},\ \dot{\mathbf{b}}_{ai}=\mathbf{n}_{\mathbf{b}_{ai}}, \dot{\lambda}=\mathbf{n}_{\lambda},\]  
where \(\mathbf{v}_{c}\), \(\mathbf{\omega}_{c}\), and \(\lambda\) are modeled as random walk, and notably \(\overline{\mathbf{q}}_{c}=\overline{\mathbf{q}}_{c}^{i}\otimes\overline{\mathbf{q}}_{w}^{c}= \overline{\mathbf{q}}_{w}^{\omega^{\prime}}\).
## 4 Results and Discussion
### Total System Uncertainty
According to these results, the probabilities of collapse for the archetypes--except for the 30\({}^{\circ}\) archetype in the x-direction--were very low. It is also worth noting that, with the exception of the 0\({}^{\circ}\) archetype, the collapse probabilities in the y-direction were consistently lower than those in the x-direction. This highlights that the structural stiffening effect induced by the slope reduced the lateral displacements and, consequently, the extent of damage experienced by the structures. For the 30\({}^{\circ}\) archetype, the collapse probability remained below 10% in both principal directions; however, a noticeably higher value was observed in the x-direction. This outcome was attributed to the combination of two factors: the lowest collapse capacity (SCT) and the highest seismic demand (SMT) among all archetypes in that direction. This condition was further explained by the fact that the fundamental period of this archetype aligned with the peak of the MCE response spectrum, as illustrated in Figure 5 (the elastic and inelastic spectra for the 0\({}^{\circ}\) building archetype). This finding suggests that a structural redesign may be necessary to reduce the vulnerability of the 30\({}^{\circ}\) archetype in the x-direction. A comparative analysis of the collapse probabilities revealed consistently lower values in the y-direction across all archetypes. This outcome was directly associated with higher SCT values in the y-direction, which represented a greater structural capacity to resist collapse. This increased capacity was attributed to the greater stiffness of the buildings in this direction, requiring higher seismic acceleration levels to reach the median collapse threshold (SMT). The percentage differences in the SCT between the y- and x-directions were calculated as 12%, 22%, 27%, and 39% for the archetypes with slope inclinations of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\), respectively. These findings suggest a clear trend: as the slope became steeper, the geometric adaptation of the structure led to increased rigidity in the y-direction, thereby enhancing its ability to withstand collapse.
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 2 Effect of Horizontal Two-Layer Soil Characteristics on Grounding System Performances
### Number of Meshes  
The number of meshes in a grounding grid can be increased to protect the effectiveness of a grounding system, according to a common understanding of the grounding system. The greater the number of meshes in a grounding grid, the more fault current flows through it and into the soil, decreasing potential gradients above it [39; 51].  
[PERSON] and [PERSON] [39] conducted a study on the influence of the number of meshes in a grounding system on the ground resistance, Rg, in a two-layer soil structure. A 20 m \(\times\) 20 m grounding grid with 1, 4, 16, and 64 meshes are simulated. A 1000 A current is discharged into the grids and the depth of grid burial is kept at 0.5 m. Case 1 is referring to the top soil layer with lower resistivity than the bottom layer (K > 0), while Case 2 is referring to the top soil layer with higher resistivity than the bottom layer (K < 0).  
Figure 3 below indicates the ground resistance plotted against the number of meshes. The findings show that if the number of meshes for both cases is increased, the ground resistance decreases. However, as the number of meshes increased, the percentage of reduction in grid resistance for the high resistive top layer (case 2) is evident, which is about 41.67% relative compared to the low resistive top layer (case 1), which is just 10.72% (almost constant) as can be seen in Figure 3. This might be owing to the uniform current density dispersion across the grounding conductors in the high resistive top layer and concentrated current density at the grounding perimeter in the low resistive top layer.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Soil Resistivity of the Top Layer** & **Increasing Top Soil Layer Height** \\ \hline K \({}^{1}\) < 0 & Significant reduction in GPR value & Increases grid resistance \\ K \({}^{1}\) > 0 & A slight reduction in GPR value & Reduction in the grid resistance increases \\ Ref. & [40] & [19; 50] \\ \hline \end{tabular} \({}^{\dagger}\) K is the reflection factor between the first and second soil layers.  
\end{table}
Table 2: The effect of soil resistivity and depth of top layer in a two-layer soil structure.  
Figure 2: Grid resistance of four mesh grounding grids for different top-layer of soil resistivity [50].
## 6 Regression-Analysis-Based Hybrid Approach
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Transmission**} & & & \\
**Line Length** & **Frequency** & & & \\ \hline \multicolumn{4}{c}{**Existing Correlation**} & & \\ \hline
50 km & DC-1 MHz & 7.40 GB & **7.9 MB** \\  & DC-5 MHz & 35.25 GB & **38.9 MB** \\
100 km & DC-1 MHz & 14.9 GB & **15.9 MB** \\  & DC-5 MHz & 74.5 GB & **79.7 MB** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Memory space requirement: comparison between the existing correlation estimator and the hybrid approach. Figure 19: Comparison of the GPR models in the prediction of fault locations. f a few selected transfer faults, and the CCv of \(H_{STF}\) @ \(Z_{f}\) = 10 \(\Omega\) was higher. Therefore, for fault prediction, this scenario was selected to give 10.05 km. Similarly, for \(H_{fos}\) due to \(x_{f}\) = 35 km with \(Z_{f}\) = 500 \(\Omega\), as the CCv of \(H_{STF}\) @ \(Z_{f}\) = 1000 \(\Omega\) was higher, so it would be utilised for fault prediction, which was 35.13 km. The pseudo approach described in Section 4 will also require additional memory, because it takes GBs to store just one copy of \(H_{fes}\); as we mentioned in the case of a single-conductor transmission line, so the more memory space will be needed to store transfer function copies for each fault type. The memory burden once again was reduced by employing the hybrid approach, as argued before. The results provided in Table 3 are repeated in Table 8 using the hybrid method (requiring only a few selected transfer functions with a regression model). Even with a few selected transfer functions, the similarity of the results in Tables 3 and 8 is evident. It is important to note that, although a new database must be generated when the system topology is altered, this will not influence the method's efficiency because the data are prepared prior to fault occurrences, and this is not repeated. \begin{table}
\begin{tabular}{c c c c c} \hline
**Fault Type** & **Estimation (\(x_{g}\))** & \multicolumn{3}{c}{**Fault Estimation Stage Storage Data**} \\ \hline  & & **Phase a** & **Phases ab** & **Phases abc** \\ \hline Phase a & a & **44.99** & 40.73 & 36.2 \\  & b & 45 & 29.09 & 32.4 \\  & c & **44.99** & 37.2 & 28.5 \\ \hline Phases ab & a & 39.8 & **45.05** & 46.9 \\  & b & 49.5 & **45** & 43.8 \\  & c & 43.5 & **44.99** & 29.4 \\ \hline Phases abc & a & 38.35 & 46.63 & **45** \\  & b & 38.8 & 35.71 & **44.9** \\  & c & 30.66 & 27.25 & **45** \\ \hline \end{tabular}
\end{table} 
Table 8: Test fault location of 45 km having different scenarios of fault types and its estimation by the pseudo and hybrid approaches.