esa-sceva/satcom-chunk-collection huggingface dataset:
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 1 Introduction
### Literature Review
#### 1.1.1 Location Covering Problems  
Covering problems have been discussed for more than 50 years finding a wide area of applications [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] (Based on the Scopus database there were 4634 papers published on Set Covering Problem in the area of computer science or mathematics (5 November 2020). Since Scopus does not support multiple-word queries, such as set covering problem, the results may contain false positive matches). It has a decisive role in the success of supply chains with applications including locating gas stations, schools, plants, landfills, police stations, design of sensor networks, etc. Currently, there exists a plethora of models that fall into the category of covering problems such as edge covering [24], vertex covering [25], capacitated vertex covering [26] and others. In this paper, the focus is especially given to the covering problems that are targeting the optimization of the network facilities locations in the selected areas. For the goals stated in this paper, in regards to the optimization of static network infrastructure, the base covering model is LSCP together with its extensions. These were developed from the SCP model and, thus, share the same aspects such as its minimal computational complexity. The three extensions of capacity-based LSCP can be considered: (i) CLSCP-CA; (ii) CLSCP-EA; (iii) Capacitated Location Set Covering Problem-System Optimal (CLSCP-SO). The first one concerns the use-case when the closest service centre is used for handling the requirements within the customer locations. The second one is concerned with the equal assignment of capacities over the accessible
## 2 Fundamental Theory
### A Generalized Fractional-Order Model for Viscoelastic ABH Composite Beams
Therefore, the distance \(D\) is as follows:  
\[D=\frac{\left(E_{1}\frac{\partial^{2}w}{\partial x^{2}}+\mathbf{\eta}_{1}\frac{ \partial^{4}w}{\partial t^{2}}\frac{\partial^{2}w}{\partial x^{2}}\right)\left( h_{1}h_{2}+h_{1}^{2}\right)}{2\left[E_{2}\frac{\partial^{2}w}{\partial x^{2}}h_{2}+ \left(E_{1}\frac{\partial^{2}w}{\partial x^{2}}+\mathbf{\eta}_{1}\frac{\partial^{ 4}w}{\partial x^{4}}\frac{\partial^{2}w}{\partial x^{2}}\right)h_{1}\right]}, \tag{7}\]  
The bending moment \(M\) on the membrane-covered beam section is the sum of the bending moments of each layer:  
\[M(x,t)=-\int_{-h_{2}-D}^{h_{2}-D}z\mathbf{\sigma}_{2}(x,z,t)dA-\int_{h_{2}-D}^{h_{ 2}-D+h_{1}}z\mathbf{\sigma}_{1}(x,z,t)dA, \tag{8}\]  
Due to the force and bending moment balance on the micro-fracture, the vibration dynamic equation of the beam can be derived:  
\[\frac{\partial^{2}M(x,\ t)}{\partial x^{2}}+\mathbf{\rho}A(x)\frac{\partial^{2}w} {\partial t^{2}}=q(x,\ t), \tag{9}\]  
where \(q(x,\ t)\) is external excitation; \(\rho A(x)=\rho_{1}A_{1}(x)+\rho_{2}A_{2}(x)\); \(\rho_{1}\) and \(\rho_{2}\) are the density of the film coverage and base material; \(A_{1}(x)\) and \(A_{2}(x)\) are the cross-sectional areas of the film coating and the base. Substituting Equations (5) and (8) into (9), the fractional dynamic equation of the composite beam structure is obtained as follows:  
\[E_{2}bH_{hh}(x)\frac{\partial^{4}w}{\partial x^{4}}+E_{1}bH_{h}(x)\frac{ \partial^{4}w}{\partial x^{4}}+\mathbf{\eta}_{1}bH_{h}(x)\frac{\partial^{4}w}{ \partial t^{4}}\frac{\partial^{4}w}{\partial x^{4}}+\rho A(x)\frac{\partial^{ 2}w}{\partial t^{2}}=q(x,\ t), \tag{10}\]  
where \(H_{hh}\) and \(H_{h}\) are  
\[H_{hh}(x)=\frac{h_{2}^{3}(x)}{12}+h_{2}(x)D^{2}\] \[H_{h}(x)=\left[\frac{h_{2}(x)}{2}-D\right]^{2}\!h_{1}(x)+\left[ \frac{h_{2}(x)}{2}-D\right]\!h_{1}^{2}(x)+\frac{h_{1}^{3}(x)}{3}\,\]  
The vibration equation for the uncoated length range (\(0\leq x<x_{0}\)) on the ABH is given by Equation (2), while the vibration equation for the coated length range (\(x_{0}\leq x\leq L\)) is described by Equation (10). In conclusion, the vibration equation of the whole structure is  
\[E(x)\frac{\partial^{4}w}{\partial x^{4}}+\mathbf{\rho}A(x)\frac{ \partial^{2}w}{\partial t^{2}}=q(x,\ t),(0\leq x<x_{0}) \tag{11}\] \[[E_{2}bH_{hh}(x)+E_{1}bH_{h}(x)]\frac{\partial^{4}w}{\partial x^ {4}}+\mathbf{\eta}_{1}bH_{h}(x)\frac{\partial^{4}w}{\partial t^{2}}\frac{\partial ^{4}w}{\partial x^{4}}+\mathbf{\rho}A(x)\frac{\partial^{2}w}{\partial t^{2}}=q(x, \ t),(x_{0}\leq x\leq L)\,\]  
Equation (11) can degenerate into an integer order model when \(\alpha\) is zero, while when \(\alpha\) is 1, Equation (11) degenerates to an integer-order viscoelastic model, which implies that the fractional-order dynamic model for composite beams is a more generalized model. In subsequent analysis, the adopted vibration analysis method must address the continuity issue between these two vibration equations.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation
### Target Response Estimation Performance  
Figure 5a displays the MSE achieved by the proposed scheme with regard to the estimation of target response. This plot demonstrates an improved MSE performance for the two-step optimization as compared with the conventional MAP approach, the Kalman filtering method and the MI optimization (step 2) modules, particularly for the first few iterations.  
It is evident from Figure 5 that the MSE performance offered by the proposed scheme is superior to the conventional MAP approach and the Kalman filtering method. The MSE performance offered by the proposed scheme is superior to MI optimization (step 2) criterion. Similarly, Figure 5b shows the MSE performance offered by the proposed scheme with respect to the estimate of target response. The MSE of target response estimation provided by the two-step scheme and other several approaches are compared to reveal the benefit of the proposed scheme.
## 3 Results and Discussion
### Physical and Chemical Properties
The binding energy of Ti-O-Si is 531.6 eV because the O1s binding energy of Si-O bond was weakened by the formation of Ti-O-Si and the electron density of Si is higher than Ti [28]. The formation of Ti-O-Si bond confirmed that TiO\({}_{2}\) was loaded on the surface of CS by chemical bond, which is tighter than physical connection and could improve the catalysis durability of PCM effectively [29]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Total Porosity (\%)** & **Pore Volume (mL/g)** \\ \hline
1 PCS-0.1–0.6 & 61.19 & 0.8438 \\
1 PCS-0.6–1.25 & 43.00 & 0.4053 \\
1 PCS-1.25–235 & 43.38 & 0.3973 \\
2 PCS-1.25–2.35 & 41.83 & 0.3655 \\
3 PCS-1.25–2.35 & 42.72 & 0.4070 \\
4 PCS-1.25–2.35 & 41.97 & 0.3706 \\
5 PCS-1.25–2.35 & 42.84 & 0.3806 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The total porosity and pore volume of all PCS samples. Figure 6: XPS photographs of PCS (4 PCS-1.25–2.35); **(a)** survey pattern; **(b)** O1s XPS spectra; XPS: X-ray photoelectron spectroscopy.
## 8 Final Conclusions  
Pulsed centrifugal propulsion is based on well-established laws of physics, i.e., the energy theorem, the principle of linear and rotational momentum, and [PERSON]'s axioms. We provide traceable evidence that the propulsion of small vehicles (UAV) can be generated by the conversion of the rotational energy of airborne rotors. By precise setting/resetting unbalances during each rotor rotation, short centrifugal force pulses can be induced and exactly directed. Thus, by controlled unbalance propulsion (CUP), the rotational energy of a rotor can be transformed into translational energy of the rotor-carrying vehicle.  
Furthermore, we conclude that controlled unbalance propulsion could provide excellent flight performance data for small UAVs. Our calculations indicate that high thrusts and vehicle velocities up to orbital levels could be achieved. Thus, the working range of high-performance CUP drones could be extended to near-Earth space and seems not limited to Earth's atmosphere. Additionally, exceptional maneuverability and levitation can be attained, making these UAVs superior to conventional drones. Targeted action in the design and performance dimensioning of centrifugal force drives is possible based on this information. This paper allows for specific actions to be taken when designing and determining the performance of centrifugal force drives. The practicability of inertial drives can be tested with state-of-the-art components.  
Last but not least, a second CUP generation applying combined inertial/electric propulsion of vehicles appears to be feasible. Here, the unbalance-controlled rotor is part of an electric motor drive/generator circuit. Initially, additional electrical power is stored in (low-weight) onboard batteries during the prestart phase. This process occurs in parallel with the electrical loading of the rotor to its maximum rotation frequency, \(\omega_{0}\). Later, during flight, the stored electrical energy can be used to compensate for the decrease in rotational energy during the vehicle's thrust phases. This allows the rotor to maintain a constant frequency, \(\omega_{0}\), during vehicle operation, which should simplify the guidance software and energy management. Furthermore, due to the increase in total onboard energy, the operational range of the vehicle should be expanded.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflicts of interest.
## 3 Application
### Failure Probability Analysis Using MCS
#### 3.2.4 MCS  
MCS was implemented by extracting randomly repeated variable values from a predefined probability distribution. The variables required for analyzing the structural failure of a diversion structure with a concrete cofferdam are loads and parameters related to overturning, sliding, and base pressure on the foundation. Specifically, the mass density of concrete, allowed support force of the dam foundations, friction angle, and adhesive force of the soil were selected as variables. The variables of the material properties of the deposit were height, mass density, and friction coefficient. The probability function, mean, and standard deviation of these variables were taken from previous studies [10; 11; 12] and presented in Table 3. Failure probabilities with a design flood frequency of 1-, 2-, 5-, and 10-year return periods were then calculated using MCS. The stability analysis for overturning, sliding, and base pressure on the foundations was performed 500,000 times after the sampling of random variables with predefined statistical characteristics. The calculation sheet for the stability analysis of the concrete cofferdam was built using Macro supported by Microsoft Excel. Additionally, data of the observed annual maximum discharge for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Condition** & **Deadload** & **Headwater** & **Tailwater** & **Uplift** & **Silt** & **Earthquake** \\ \hline Unusual loading condition (construction) & O & X & X & X & X & X \\ \hline Usual loading condition (normal operating) & O & Normal operating level & Minimum & O & O (I.F.) * & X \\ \hline Unusual loading condition (flooding discharge) & O & Standard project flood & O & O & O (I.F.) & X \\ \hline Extreme loading condition (construction with an OBE **) & O & X & X & X & X & OBE \\ \hline Unusual loading condition (normal operation with an OBE) & O & Normal operating level & Minimum & At pre-earthquake level & O (I.F.) & OBE \\ \hline \hline \end{tabular}  
* I.F.: If applicable. ** OBE: Operational Basis Earthquake, which has a 50% chance of being exceeded within 100 years (or a 144-year return period).  
\end{table}
Table 2: Loading conditions and combinations for the cofferdam.  
53 years were used to calculate the upstream water level in the flood routing analysis. The results of the probability of failure for overturning, sliding, base pressure, and concurrent occurrence are presented in Figure 6.
## 4 Implementation of the Risk Index
### Vulnerability (V)  
Assessing the vulnerability of an asset (in this case, a classroom) is crucial for informing decisions and shaping policies for future adjustments, as it helps set priorities that influence the classroom's architecture and design. In this regard, the vulnerability value aims to measure the capacity of the classroom, understood as an architectural space with its design and installation limitations, to maintain good indoor air quality. Thus, the size of the classroom along with the effectiveness of the ventilation system are the main factors here considered to qualitatively assess the classroom vulnerability. To assess such effectiveness, the concept of air changes per hour (ACH) will be here used. ACH refers to the number of times per hour that the total air volume within a specific space is replaced; in this case, a certain classroom is replaced with supply and/or recirculated air (using natural,  
Figure 2: Values of the H for **(a)** fixed ac of 1.6 met and **(b)** fixed y of 12 years.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Location and Proximity to Traffic** & **Category** & **H\({}_{\text{co2}}^{2}\)** \\ \hline Urban location in immediate proximity to high traffic street & L5 & 1.10 \\ Urban background & L4 & 1.07 \\ Residential location with a high street less than 400 m away & L3 & 1.05 \\ Residential location with a high street more than 400 m away & L2 & 1.02 \\ Suburban or rural & L1 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of the variable [H\({}_{\text{co2}}^{2}\)] depending on the location and proximity to traffic of the classroom being assessed.  
mechanical or hybrid means). Consequently, factors such as the occupancy, volume and airflow ventilation ratio are here considered.  
In this regard, the EN 16798-3 standard [38] for ventilation in non-residential buildings identifies four air quality categories (1: optimum, 2: good, 3: medium and 4: low) with outdoor air flow rates [in dm\({}^{3}\)/s per occupant] of 20.0, 12.5, 8.0 and 5.0, respectively [38]. For spaces dedicated to teaching and learning, the Spanish Regulation on Building Heating Installations (RITE) requires a minimum Category 2 [39]. In Portugal, national regulations [40] establish a minimum outdoor air flow rate of 24 m\({}^{3}\)/(h-occupant) for teaching/learning spaces, falling between Category 3 and Category 4 of [38].  
Based on such values, Equation (3) was designed in order to assign a vulnerability factor according to classroom ventilation capacity defined as ACH values. This relationship, shown in Figure 3, assigns a maximum score of 10 points when ACH is zero and decreases linearly to 0 when ACH reaches 10. Likewise, higher ACH values are also associated with the absence of vulnerability.  
\[V=\left\{\begin{aligned} & 10-ACH&\quad for\ 0\leq\ ACH\leq 10\\ & 0&\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad
## 7 Conclusions  
In this paper, an HGESO-ASMPFC scheme was proposed to design a path following controller for an underactuated vessel sailing in restricted waters with varying water depths. First, a sideslip observer-based guidance law is designed based on a high-gain extended state observer. Theoretical analysis was carried out for the kinematic system based on the cascade system theory; the results showed that the proposed guidance system could guarantee ISS, and effectively compensate the drift force due to the vessel-bank interaction in varying water depths. Then, an adaptive PI SMC was designed based on the Nomoto equations considering model uncertainties, unknown parameters, and external disturbances. Analysis based on Lyapunov theory has proven that the yaw tracking control system is GUAS, and the stabilization objective \(\underset{i\rightarrow\infty}{\overset{\leftarrow}{\underset{i\rightarrow \infty}{\inf}}}=0\) can be achieved.  
Finally, the full kinematic-dynamic closed-loop system was analyzed, and the result showed that the closed-loop system was GUAS, which indicated that the entire HGESO-ASMPFC scheme could accurately follow the desired straight-line path in varying water depths. The analysis results were validated via simulation experiments. In particular, comparisons with ILOS, ALOS, and ELOS demonstrated the superiority of the proposed guidance law, while the lateral position in the proposed guidance law could converge to the different desired straight-line paths with the smallest overshoot and the fastest convergence speed in the varying water depths.  
[PERSON] put forward the original concept, proposed the control strategy and wrote the article. [PERSON] and [PERSON] gave their valuable suggestions on the research design. Further, [PERSON], [PERSON] and [PERSON] analyzed and discussed the experimental results.  
**Funding:** This work is supported by the National Natural Science Foundation of China (Grant No. 51479156).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**0.1L\(\rightarrow\)0.05L**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Overshoot**} & \multicolumn{2}{c}{**Settling Time (s)**} \\ \hline ILOS & 5.333 m & 78.02\% & 4446+ \\ ALOS & 6.077 m & 88.91\% & 3959 \\ ELOS & 6.205 m & 90.78\% & 1880 \\ HGESO & 5.009 m & 73.28\% & 1346 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of overshoot and settling time at varying desired paths.  
Figure 12: The desired heading angle vs. the true heading angle in the proposed guidance law at varying desired paths.
## 2 Materials and Methods
### Specific Set Up for the Compressor and Nozzle  
This case has two additional degrees of freedom: the internal mass flow \(m_{c}\), directly imposed on the compressor and nozzle boundary conditions; and the specific power transmitted to the flow, imposed on the nozzle through the total temperature \(T_{t,n}\). Both are varied in order to meet the following specifications:  
* Net thrust (\(N_{T}\)) equal to 0. This ensures that the case is comparable with the _base_ one. The net thrust is computed as follows: \[N_{T}=\dot{m}_{c}(V_{n}-V_{c})+p_{n}A_{n}-p_{c}A_{c}-D_{wall}.\] (5)
* Same isentropic efficiency \(\eta\) throughout all cases, equal to 0.07.  
The fact that the value for the isentropic efficiency is so low can be explained due to its definition. This parameter tries to collect in one number all the processes that occur inside the hyperloop capsule and, for simplicity, are not modeled in this work. These are the flow compression on the turbomachinery, internal ducting, and flow expansion on a turbine and a nozzle. All the inefficiencies of these three processes are included in \(\eta\).  
The \(T-s\) diagram shown in Figure 8 represents thermodynamically what is occurring to the non-modeled flow and helps to understand the definition of the efficiency. In this figure, the three evolutions shown are:  
* Intake and compressor (\(c\)): from 2 to 3.
* Internal ducting (\(f\)): from 3 to 4.
* Turbine and nozzle (\(t\)): from 4 to 5.  
With these values in mind, the isentropic efficiency is defined as follows:  
\[\eta=\frac{T_{25 ts}-T_{2t}}{T_{5t}-T_{2t}}=\frac{\pi\frac{T_{7}-1}{T_{7}}-1}{T _{2t}}-1=\frac{\pi\frac{T_{7}-1}{T_{7}}-1}{\frac{T_{7t}}{T_{7t}}-1}, \tag{6}\]  
Figure 8: \(T-s\) diagram showing the thermodynamic evolution of the internal flow of the capsule. In this diagram, the intake is the station 2 and the nozzle exhaust is the station 5.  
where \(\pi\) is the total pressure ratio on the compressor and the nozzle.  
It is not the purpose of the authors to further detail what occurs to the internal flow or to optimize this efficiency. The only purpose of this definition is to have a way to measure the non-modeled flow and ensure equality among the different cases.  
Having said that, the values obtained for the compressor and nozzle boundary conditions that match the prescribed net thrust and efficiency are collected in Table 2.
# Aroma Characteristics of Lavender Extract and Essential Oil from _Lavandula angustifolia_ Mill.
## 2 Results and Discussion
### Odor Property of Lavender Extracts
#### 2.1.5 Verification Experiments on the Preparation of LEs
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **RT** & **RI** & **Volatile Compounds** & **LEO (\%)** & **ID \({}^{*}\)** \\ \hline
1 & 2.56 & 748 & 3-Methyl-2-butenal & 0.01 \(\pm\) 0.00 & MS, RI, S \\
2 & 3.49 & 852 & 1-Hexanol & 0.05 \(\pm\) 0.00 & MS, RI, S \\
3 & 4.54 & 929 & \(\alpha\)-Thujene & 0.10 \(\pm\) 0.06 & MS, RI \\
4 & 4.65 & 931 & \(\alpha\)-Pinene & 0.28 \(\pm\) 0.04 & MS, RI, S \\
5 & 4.95 & 958 & Camphene & 0.33 \(\pm\) 0.09 & MS, RI, S \\
6 & 5.46 & 963 & 1-Octen-3-ol & 0.47 \(\pm\) 0.08 & MS, RI, S \\
7 & 5.65 & 989 & 3-Octanone & 0.28 \(\pm\) 0.18 & MS, RI \\
8 & 5.74 & 990 & 9-Pinene & 0.70 \(\pm\) 0.05 & MS, RI, S \\
9 & 6.23 & 1003 & 3-Carene & 0.78 \(\pm\) 0.06 & MS, RI \\
10 & 6.47 & 1011 & \(p\)-Cymene & 0.13 \(\pm\) 0.03 & MS, RI, S \\
11 & 6.54 & 1015 & \(o\)-Cymene & 0.35 \(\pm\) 0.01 & MS, RI, S \\
12 & 6.65 & 1018 & Limonene & 0.95 \(\pm\) 0.18 & MS, RI, S \\
13 & 6.73 & 1022 & 1,8-Cineole & 1.02 \(\pm\) 0.27 & MS, RI \\
14 & 6.85 & 1034 & (E)-\(\beta\)-Ocimene & 4.38 \(\pm\) 0.91 & MS, RI, S \\
15 & 7.13 & 1036 & (Z)-\(\beta\)-Ocimene & 1.71 \(\pm\) 0.37 & MS, RI, S \\
16 & 7.44 & 1047 & \(\gamma\)-Terpinene & 0.09 \(\pm\) 0.04 & MS, RI, S \\
17 & 8.21 & 1081 & Linalool oxide II ((E)-furanoid) & 0.22 \(\pm\) 0.07 & MS, RI, S \\
18 & 7.83 & 1098 & Linalool oxide I ((Z)-furanoid) & 0.28 \(\pm\) 0.03 & MS, RI, S \\
19 & 8.68 & 1103 & Linalool & 29.01 \(\pm\) 0.33 & MS, RI, S \\
20 & 8.79 & 1106 & 1-Octen-3-yl acetate & 1.25 \(\pm\) 0.18 & MS, RI \\
21 & 9.15 & 1118 & _allo_-Ocimene & 0.33 \(\pm\) 0.09 & MS, RI \\
22 & 9.53 & 1121 & Camphor & 0.38 \(\pm\) 0.03 & MS, RI \\
23 & 9.68 & 1137 & Nerol oxide & 0.02 \(\pm\) 0.01 & MS, RI \\
24 & 9.98 & 1161 & Camphol & 1.65 \(\pm\) 0.66 & MS, RI \\
25 & 10.00 & 1162 & Lavandulol & 1.72 \(\pm\) 0.71 & MS, RI, S \\
26 & 10.20 & 1167 & Terpinen-4-ol & 2.24 \(\pm\) 1.05 & MS, RI \\
27 & 10.39 & 1170 & Cryptone & 0.31 \(\pm\) 0.04 & MS, RI \\
28 & 10.54 & 1172 & \(\alpha\)-Terpineol & 2.03 \(\pm\) 0.22 & MS, RI, S \\
29 & 10.81 & 1196 & Euacarone & 0.08 \(\pm\) 0.05 & MS, RI \\
30 & 10.99 & 1226 & (Z)-Carveol & 0.03 \(\pm\) 0.00 & MS, RI \\
31 & 11.15 & 1237 & Nerol & 0.40 \(\pm\) 0.08 & MS, RI \\
32 & 11.37 & 1239 & \(\alpha\)-Methylbenzenepropanal & 0.23 \(\pm\) 0.03 & MS, RI \\
33 & 11.44 & 1240 & Carvone & 0.04 \(\pm\) 0.03 & MS, RI \\
34 & 11.68 & 1251 & Linaly acetate & 27.33 \(\pm\) 5.05 & MS, RI, S \\
35 & 12.02 & 1280 & Phellandral & 0.04 \(\pm\) 0.01 & MS, RI \\
36 & 12.25 & 1294 & Lavandulyl acetate & 7.00 \(\pm\) 3.86 & MS, RI \\
37 & 12.31 & 1295 & Cuminol & 0.04 \(\pm\) 0.01 & MS, RI \\
38 & 13.33 & 1367 & Neryl acetate & 0.60 \(\pm\) 0.06 & MS, RI \\
39 & 13.63 & 1383 & Geranyl acetate & 1.23 \(\pm\) 0.15 & MS, RI, S \\
40 & 14.21 & 1418 & \(\alpha\)-Santalene & 0.77 \(\pm\) 0.15 & MS, RI \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile compositions of lavender essential oils.