esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evolution of Side Temperatures  
In this paper, we only present the temperature evolution measured at each side of the regenerator. The effect of frequency on the temperature is plotted in Figure 7 for three types of regenerators, Str_30%, Str_35%, and Str_40%, at a length of \(L_{1}\) = 60 mm.  
At low frequency, the fluid temperature in the hot side increases in amplitude due to the heating effect as the piston motion pushes fluid from the hot side to the cold side of the regenerator. The fluid temperature in the cold side also increases due to the heating effect from the hot side. The temperature fluid drops when the piston motion pushes fluid from the cold side to the hot side. It should also be noted that fluid temperatures reach their maximum value earlier for Str_35% and Str_40% regenerators than for the 30% one at low frequencies. Moreover, maximum temperatures are reached before the mid-period.  
When increasing the rotation velocity of the motor, the maxima are reached for a rotation angle closest to 180\({}^{\circ}\). The fluid maximum temperature reaches 80 \({}^{\circ}\)C on the hot side and 35 \({}^{\circ}\)C on the cold side (Figure 7). These temperatures are 10 and 25 \({}^{\circ}\)C higher than those of the hot and cold exchangers, respectively. This is due to compressibility effects (pressure and, therefore, density) implied by alternations, as can be seen in Sections 3.3 and 3.4. The evolutions of the maximum temperatures of the hot side and cold side for the three types  
Figure 7: Evolution of temperatures at hot and cold inlet sides of regenerator ((**a**): Str_30%, (**b**): Str_35%, (**c**): Str_40%. \(L_{1}\) = 60 mm, \(C_{1}\) = 64.4 mm) versus phase for temperature gradient \(\Delta T_{1}\) = 70 \({}^{\circ}\)C/10 \({}^{\circ}\)C.  
of regenerator are out of phase by about 180\({}^{\circ}\). Neither geometry nor porosity affects this value. This was also observed by [PERSON] [34] and [PERSON] [35] in studies on heat engines operating on alternating flow.
## 4 Results and Discussions
### Distribution Laws of Possible Probability Distributions  
For random variables, each value corresponds to a probability of what happens in the trial. To show the distribution law of potential distribution intuitively, the possible value range of the median pore diameters is divided into eight intervals. Figure 10 shows the distribution laws of potential probability distributions and measured median pore size at four NS contents. In general, the probability value of the middle segment is greater than that of the boundary segment, and the probability values of the two boundary segments are equal approximately. As presented in Figure 10, normal and lognormal distributions generally maintain similar distribution laws with sample distribution, while Gamma and Weibull distributions differ greatly from sample distribution, especially Weibull distribution. However, the partial distribution law of the lognormal distribution at an NS content of 1% and 3% is the opposite of the sample distribution law. From the perspective of the median pore diameters, normal distribution is the best effect to describe the statistical characteristics of the median pore diameters of NS-modified silty clay.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**NS Content** & \multicolumn{4}{c}{**0**} & \multicolumn{4}{c}{**0.25\%**} \\ \hline \(\Delta\) (\%) & \(\Delta_{00}\) & \(\Delta_{01}\) & \(\Delta_{02}\) & \(\Delta_{03}\) & \(\Delta_{04}\) & \(\Delta_{00}\) & \(\Delta_{01}\) & \(\Delta_{02}\) & \(\Delta_{03}\) & \(\Delta_{04}\) \\ \hline MV & 0 & 0.53 & 0.61 & 0.65 & 2.07 & 0 & \(-\)0.01 & 0.17 & 0.04 & 1.74 \\ Var & 0 & 3.75 & 4.61 & 4.75 & 4.84 & 0 & 3.85 & 4.28 & 4.32 & 4.21 \\ SD & 0 & 19.36 & 21.48 & 21.79 & 22.00 & 0 & 19.63 & 20.70 & 20.79 & 20.52 \\ \hline
**NS Content** & \multicolumn{4}{c}{**1\%**} & \multicolumn{4}{c}{**3\%**} \\ \hline \(\Delta\) (\%) & \(\Delta_{00}\) & \(\Delta_{01}\) & \(\Delta_{02}\) & \(\Delta_{03}\) & \(\Delta_{04}\) & \(\Delta_{00}\) & \(\Delta_{01}\) & \(\Delta_{02}\) & \(\Delta_{03}\) & \(\Delta_{04}\) \\ \hline MV & 0 & 0.67 & 1.37 & 1.48 & 2.52 & 0 & 0 In this part, the possible value range of the cumulative volume of pore size smaller than 0.1 um is divided into seven or eight intervals. The distribution laws of potential probability distributions and measured \(V_{D<0.1\ \upmu\text{m}}\) at four NS additions are shown in Figure 11. When the NS additions are 0, 0.25%, 1%, and 3%, the corresponding maximum probabilities of measured \(V_{D<0.1\ \upmu\text{m}}\) are in the intervals of 33-34%, 34-35%, 35-36%, and 51-52%, respectively. Additionally, the probabilities are 0.21, 0.17, 0.18, and 0.20, respectively. In general, for the \(V_{D<0.1\ \upmu\text{m}}\), the distribution law of normal distribution has a high similarity with the sample distribution. However, the lognormal, Gamma, and Weibull distributions are different from the sample distribution law, especially the Weibull distribution.  
Figure 10: The distribution laws of four distributions and measured median pore radius.
## 2 Materials and Methods
### Experimental Soil Collection, Analysis and Incubation  
Agricultural soil was collected, from 0 to 15 cm below the surface, from the Mont Brunt area located in the Abitibi-Temiscamingue region, Northern Quebec, Canada. The soil was air-dried and sieved to lower than a 2 mm fraction. Initial characterization of soil was performed in terms of pH as described in Section 2.1, conductivity, oxygen redox potential (ORP), and dissolved oxygen (DO) by electrochemistry using an HQ40d portable multi-parameter (HACH, Mississaug, ON, Canada), and organic matter was determined by Computrac (Max 5000 XL, Arizona Instrument, Chandle, AZ, USA). Metals including (Al, Cd, Ca, Cr, Co, Cu, Fe, Mg, Mn, Ni, Pb, K, Na, and Zn) were analyzed using inductively coupled plasma-mass spectrometer (ICP-MS 7800 instrument, Agilent Technologies, Toronto, ON, Canada). Agricultural soil was artificially contaminated with a synthetic solution of sodium selenite (Na\({}_{2}\)SeO\({}_{3}\)) and sodium selenate (Na\({}_{2}\)SeO\({}_{4}\)) provided by Sigma-Aldrich, Inc. (St. Louis, MO, USA) to reach final Se concentrations of 1, 2, and 3 mg/kg. After two weeks of incubation in dark, the biochar was added at 1% and the activated biochar was added at 1 and 5% and mixed up the three selenium doses. Each treatment was performed in triplicate. In total, two types of controls were considered: (i) agricultural soil without Se treatment nor biochar addition (ii) Se-treated agricultural soil without biochar addition. Both controls did not have biochar materials addition. Soil samples were monitored for pH, electrical conductivity, ORP, and DO every week.
## 3 Result and Discussion
### Electrical Characteristics of FEGLD  
The distinction in discharge characteristics can be observed at pulse voltage duration times of positive and negative. Hence, the waveforms of the discharge current and topical voltage in positive pulse voltage and negative pulse voltage are presented in Figure 4a,b, under the experimental condition of 12 mm discharge gap, 30 kV pulse peak voltage, and 3.5 L/min gas flow rate. Figure 4a shows that the discharges mainly occur at the rising edge of positive pulse voltage, which has the rising time of about 250 ns. When the gas is broken down, one discharge current peak with 1.5 us pulse width can be observed. In addition, three burr-like current peaks appear at the rising edge of the main discharge current peak. Each of those burr-like current peaks has the width of 50 ns. The characteristic of discharge current waveforms in negative pulse voltage is similar to that in positive pulse voltage, except their opposite polarity. In addition, there are a number of oscillations and a long plateau for voltage in each pulse. It is due to the power supply and capacitive loads in  
\begin{table}
\begin{tabular}{c c c c} \hline
**NO.** & **Reaction** & **Rate Constant** & **Ref.** \\ \hline
**1** & He\({}^{*}+\) N\({}_{2}\)\(\rightarrow\) N\({}_{2}^{+}+\)He + e & 7.0 \(\times\) 10\({}^{-11}\) cm\({}^{3}\) s\({}^{-1}\) & [35] \\
**2** & He\({}^{*}+\) O\({}_{2}\)\(\rightarrow\) O\({}_{2}^{+}+\)He + e & 2.6 \(\times\) 10\({}^{-16}\) cm\({}^{3}\) s\({}^{-1}\) & [36] \\
**3** & He\({}^{*}+\) H\({}_{2}\)O \(\rightarrow\) He + OH\({}^{+}\) + H\({}^{+}\)+e & 1.5 \(\times\) 10\({}^{-10}\) cm\({}^{3}\) s\({}^{-1}\) & [37] \\
**4** & e + N\({}_{2}\)(X) \(\rightarrow\) e + N\({}_{2}\)(A) & 1.1 \(\times\) 10\({}^{-10}\) cm\({}^{3}\) s\({}^{-1}\) & [33] \\
**5** & N\({}_{2}\)(A) + N\({}_{2}\)(A) \(\rightarrow\) N\({}_{2}\)(C) + N\({}_{2}\)(X) & 8.0 \(\times\) 10\({}^{-11}\) cm\({}^{3}\) s\({}^{-1}\) & [33] \\
**6** & e + N\({}_{2}\)(A) \(\rightarrow\) 2 e + N\({}_{2}^{+}\) & 2.4 \(\times\) 10\({}^{-12}\) cm\({}^{3}\) s\({}^{-1}\) & [34] \\
**7** & e + O\({}_{2}\)\(\rightarrow\) e + O + O & 3.2 \(\times\) 10\({}^{-11}\) cm\({}^{3}\) s\({}^{-1}\) & [34] \\
**8** & e + H\({}_{2}\)O \(\rightarrow\) e + H + OH & 2.6 \(\times\) 10\({}^{-12}\) cm\({}^{3}\) s\({}^{-1}\) & [34] \\
**9** & OH(A) + O\({}_{2}\)(X) \(\rightarrow\) HO\({}_{2}\) (X) + O(\({}^{3}\)P) & — & [38] \\ \hline \end{tabular}  
* Metastable state.  
\end{table}
Table 1: Related reactions for active species in FEGLD.  
Figure 3: Typical optical emission spectrum generated by FEGLD plasma in the wavelength range of (**a**) 300–450 nm and (**b**) 450–800 nm.
## 4 Methods
### VAE Dimensionality Reduction and Denoising
#### 4.3.3 Variational Lower Bound  
VAE is trained by maximizing the log-likelihood \(logp(x)\) of the data. However, directly computing \(p(x)\) is infeasible, Therefore, VAE uses variational inference by maximizing the variational lower bound (ELBO) to accurately reconstruct user behavior data and capture the interactions between users and locations, while ensuring that the latent space structure is reasonable, thereby learning the latent interests and behavior patterns of users.  
The ELBO is calculated as shown in Equation (11):  
\[\mathcal{L}(\theta,\phi;x)=\mathbb{E}_{q(z|x)}[\log p(x|z)]-D_{\mathrm{KL}}(q( z|x)||\mathrm{p}(z)) \tag{11}\]  
Figure 3: Basic framework of VAE, consisting of three parts: encoder, latent variables, and decoder. The encoder maps the input data to the latent space, the decoder generates reconstructed data based on the latent variables, and the entire process is achieved by optimizing the variational lower bound.  
where \(\theta\) and \(\phi\) represent the neural network parameters of the decoder and encoder networks, respectively.  
\(q(z|x)\) is the approximate posterior distribution of the latent variable zzz output by the encoder network, typically a Gaussian distribution;  
\(p(x|z)\) is the conditional probability of the reconstructed data generated by the decoder;  
\(D_{KL}(q(z|x)||p(z))\) is the KL divergence, measuring the difference between the approximate posterior distribution \(q(z|x)\) and the prior distribution \(p(z)\).  
By maximizing the variational lower bound, VAE can optimize the representation in the latent space while maintaining the essential information of the data, thereby achieving dimensionality reduction.
## 5 Application Deployment Strategies
### Internet of Vehicle  
In intelligent transportation systems, real-time AI-based applications require optimizing every aspect for ensuring a model that has the capability to be mobility aware, engage in safe driving, be risk free, and have high efficiency. From the FL perspective, the models are constructed throughout numerous communication rounds; therefore, an optimization approach for each phase (e.g., client selection, update scheduling, and clustered aggregation) is obligatory. In [74], the authors gathered the states of vehicle position and velocity for weighting the updating participation. The problem statement was formulated with the objective of enhancing the computation, communication strength, and accuracy of collaborative FL models. The proposed scheme optimized vehicle selection based on its local data quality, which is highly significant for improving the model's performance and reliability. Moreover, in [95], a confluence between DRL and FL was made to bring efficient training data to the IoV environment and secure the state features of selected vehicles. The scheme allowed secure information-sharing between unmanned vehicles.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
## 4 Asymptotic Analysis
### RF Link \((S\to R_{1})\)  
At high-SNR regime, the CDF of the first hop, i.e., the RF link given in (1), can be re-written according to the form given in [37], as  
\[F_{\gamma RF}^{\infty}(\gamma)=\frac{1}{\Gamma(m+1)}\Big{(}\frac{m\gamma_{th}L_ {RF}}{\tilde{\gamma}}\Big{)}^{m}. \tag{9}\]
## 1 Introduction
A two-step modified orthogonal matching pursuit with fractional refinement (OMPFR) algorithm is proposed in [16], which can distinguish fractional delays and Dopplers. The OMPFR algorithm estimates target parameters iteratively. When the number of paths is large or the signals are weak, more iterations required. As a result, in dense multipath scenarios, the complexity of the algorithm becomes significantly high. The fourth category [17; 18] is a channel estimation method based on tensor decomposition. This algorithm has been proven to be applicable in LEO systems [19]. It was initially proposed for the OFDM system, but it also demonstrates an excellent performance in estimating multipath Doppler and channel coefficients. However, this algorithm requires a large number of training subframes to construct the third-order tensor model. At high mobility speeds, excessive training frames can exacerbate the phase accumulation effect caused by the Doppler shift, ultimately degrading the channel estimation performance. To further improve the accuracy of the channel estimation and reduce the complexity, we propose a joint estimation algorithm for the fractional Doppler and channel coefficients. By searching for the path with the maximum power in the DD domain and compensating for the fractional Doppler of this path, the energy is concentrated on the path of an integer Doppler point, which can enhance the estimation accuracy of the path coefficient. Finally, with these estimated parameters, the interference from the strongest path is reconstructed and iteratively canceled from the received signal until the given condition is satisfied. The main contributions of this paper are summarized as follows:
* We proposed an estimation and compensation algorithm for the fractional Doppler shift of the delayed propagation path. By optimizing the compensation value for the path, the fractional Doppler shift can be accurately estimated and the estimated fractional Doppler shift is compensated to the nearest integer value. This maximizes the detectable signal energy, thereby enabling precise estimation of the path coefficient. * We developed an iterative channel estimation scheme with interference cancellation based on the estimation and compensation algorithm of the fractional Doppler of the delay path. The multipath parameters of the channel can be reconstructed by the estimated fractional Doppler shifts and coefficients of paths. * In the estimation of the fractional Doppler, the Doppler estimation metric function is derived for the proposed algorithm and it is proven that it maintains sharp peaks even under low SNR and multipath conditions, enabling accurate estimation of fractional Doppler shifts.
# Quantum LDPC Codes Based on Cocyclic Block Matrices
## 3 Quantum Codes Based on Cocyclic Quasi-Jacket Block Matrices
### Quantum LDPC Codes Based on Cocyclic Block Matrix
For simplicity, assuming the equal error rate of two kinds of errors \(X\) and \(Z\), only the bit error rate of flip probability \(f_{m}\) is shown here. In the SNR range, it is shown that the taken two shorter lengths of quantum codes have better performance from 0.2 to 0.4 dB than the longer one. However, owing to the close cycles and their bit-flip error correction capability, the higher length of code shows its superiority with increasing length.
## 3 SPSA Learning-Based Three-Dimensional Fuzzy Modeling
### SPSA Learning for Resulting Components of Three-Dimensional Fuzzy Rule
#### 3.3.3 Three-Dimensional Fuzzy Modeling Flowchart  
The proposed three-dimensional fuzzy modeling is designed in seven stages as illustrated in Figure 3.  
Stage 1: Set up the elements of a three-dimensional fuzzy model, consisting of the structure of three-dimensional fuzzy rules, the method of three-dimensional fuzzification, the method of defuzzification, various types of fuzzy operators, and other related components.  
Stage 2: Assign the Fourier space base functions, as depicted in Equation (9), to the resulting components of the three-dimensional fuzzy rules.  
Stage 3: Create a collection of spatial and temporal input/output data from a DPS.  
Stage 4: Cluster the spatiotemporal data using the AP clustering algorithm.  
Stage 5: Create the preceding components of the three-dimensional fuzzy rules based on the clusters identified in Stage 4.  
Stage 6: Utilize the SPSA technique to determine the parameters of the Fourier space base functions in the resulting components of the three-dimensional fuzzy rules.  
Stage 7: Combine the preceding components from Stage 5 with the resulting components of the three-dimensional fuzzy rules to form the entire three-dimensional fuzzy rule foundation.  
A three-dimensional fuzzy model is obtained after the completion of a seven-stage design process.
## 7 Clinical Implications in OSA Treatment
### Considerations in Treatment with Continuous Positive Airway Pressure  
Continuous positive airway pressure (CPAP) therapy also necessitates regular follow-up due to its potential impact on oral health. The existing literature on the subject is limited, with published findings often being inconsistent and controversial. Side effects such as dry mouth, mouth breathing, and plaque accumulation are common and may contribute to gingival inflammation and an increased risk of PD progression. A questionnaire-based survey showed that approximately 40% of CPAP users exhibit oral symptoms, with OSA patients with diabetes being particularly susceptible to oral diseases [76]. Despite these concerns, a population-based study comparing OSA patients using CPAP with age-, gender-, and body mass index-matched controls found no significant differences in terms of amount of plaque, gingival inflammation, and masticatory function. These findings suggest that CPAP therapy, possibly through its role in improving sleep quality, may exert a protective effect on oral and periodontal health. However, the cross-sectional nature of the study limits definitive conclusions [77].  
To mitigate the potential adverse effects of CPAP on oral health, dentists should closely monitor patients undergoing this therapy. Interventions such as the use of humidifiers or saliva substitutes may help alleviate dry mouth symptoms and protect periodontal health. Regular dental check-ups and tailored oral hygiene regimens should be integral to the management plan for CPAP users, ensuring long-term oral health stability [78].
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 1 Introduction
Consequently, accurately distinguishing the polyp foreground from its edges within a similar background remains a significant challenge. This is particularly critical in extracting high-frequency edge details and seamlessly integrating them with contextual features to enhance segmentation performance. To address the aforementioned challenges, we propose the Multi-Scale Selective Edge-Aware Network (MSEANet), a novel framework that selectively fuses edge information and multi-scale contextual information via an edge-aware module for polyp segmentation. The proposed method effectively integrates global contextual information with high-frequency edge features of polyps, thereby enhancing segmentation performance. Furthermore, MSEANet operates in a fully end-to-end manner, eliminating the need for additional manual annotations or separate training stages. The main contributions are summarized as follows:  
1. An enhanced Edge Feature Extractor (EFE) is introduced to capture high-frequency edge information of polyps during the early stages of the encoder, ensuring precise delineation of polyp boundaries. 2. The Cross-layer Context Fusion (CCF) block is designed to effectively merge local structural features with global contextual information, improving the model's ability to understand target characteristics and accurately localize polyps in complex scenes. 3. Through the proposed Selective Edge Aware (SEA) module, edge information and contextual features extracted by the CCF block are integrated and preserved across multiple scales. This design significantly enhances segmentation accuracy by maintaining the fidelity of polyp boundaries. 4. Our proposed MSEANet was rigorously tested on the Kvasir-SEG [12], Kvasir-Sessile [13], and BKAI [14] datasets, achieving mean Dice scores of 91.92%, 80.63%, and 91.50%, respectively. These results demonstrate the effectiveness of our method in diverse datasets. In the following sections, we provide a detailed description of the proposed algorithm, including related work, methods, experiments, and conclusion.
# Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerlycerols and Volatiles Analysis
## Results and Discussion
### Volatile Fraction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Compound** & **Dolce Agogia** & **Frantoio** & **Leccino** & **Moraiolo** \\ \hline
1 & ethanol & 0.43 \(\pm\) 0.03 & 0.61 \(\pm\) 0.04 & 0.98 \(\pm\) 0.28 & 4.35 \(\pm\) 0.45 \\
2 & pentanal & 0.90 \(\pm\) 0.14 & 0.68 \(\pm\) 0.05 & 0.85 \(\pm\) 0.14 & 2.75 \(\pm\) 0.45 \\
3 & _n_-decane & 0.81 \(\pm\) 0.06 & 0.01 \(\pm\) 0.01 & - & 0.98 \(\pm\) 0.11 \\
4 & 3-ethyl-1,5-octadiene & 0.81 \(\pm\) 0.07 & 2.17 \(\pm\) 0.21 & 4.02 \(\pm\) 0.45 & 1.92 \(\pm\) 0.28 \\
5 & 1-penten-3-one & 2.18 \(\pm\) 0.15 & 5.33 \(\pm\) 0.27 & 4.01 \(\pm\) 0.45 & 2.74 \(\pm\) 0.45 \\
6 & decadiene & 0.53 \(\pm\) 0.03 & 1.09 \(\pm\) 0.15 & 0.92 \(\pm\) 0.16 & 1.07 \(\pm\) 0.12 \\
7 & hexanal & 3.01 \(\pm\) 0.04 & 4.82 \(\pm\) 0.30 & 3.74 \(\pm\) 0.12 & 4.55 \(\pm\) 0.37 \\
8 & _trans_-2-pentanal & 0.30 \(\pm\) 0.03 & 0.37 \(\pm\) 0.04 & 0.31 \(\pm\) 0.03 & 0.57 \(\pm\) 0.09 \\
9 & _cis_-2-hexenal & 0.71 \(\pm\) 0.02 & 0.68 \(\pm\) 0.11 & 0.80 \(\pm\) 0.07 & 0.78 \(\pm\) 0.12 \\
10 & _trans_-2-hexenal & 74.45 \(\pm\) 1.31 & 79.47 \(\pm\) 1.52 & 75.74 \(\pm\) 1.35 & 66.07 \(\pm\) 1.48 \\
11 & _o_-cymene & 0.40 \(\pm\) 0.03 & 0.01 \(\pm\) 0.00 & - & 0.10 \(\pm\) 0.05 \\
12 & _n_-hexyl acetate & 0.15 \(\pm\) 0.01 & 0.34 \(\pm\) 0.02 & 0.12 \(\pm\) 0.02 & 1.10 \(\pm\) 0.02 \\
13 & _cis_-2-pentenol & 1.02 \(\pm\) 0.03 & 1.84 \(\pm\) 0.04 & 1.07 \(\pm\) 0.06 & 1.19 \(\pm\) 0.08 \\
14 & 2-heptenal & 0.10 \(\pm\) 0.01 & 0.20 \(\pm\) 0.01 & 0.08 \(\pm\) 0.01 & 0.25 \(\pm\) 0.03 \\
15 & 1-hexanol & 3.69 \(\pm\) 0.07 & 0.86 \(\pm\) 0.05 & 2.45 \(\pm\) 0.32 & 4.58 \(\pm\) 0.22 \\
16 & _cis_-3-hexen-1-ol & 0.09 \(\pm\) 0.01 & 0.03 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.23 \(\pm\) 0.00 \\
17 & _trans_-3-hexen-1-ol & 0.79 \(\pm\) 0.02 & 0.98 \(\pm\) 0.05 & 1.32 \(\pm\) 0.72 & 1.54 \(\pm\) 0.43 \\
18 & _trans_-2-hexen-1-ol & 9.05 \(\pm\) 0.01 & 1.48 \(\pm\) 0.16 & 3.69 \(\pm\) 0.68 & 11.98 \(\pm\) 0.87 \\
19 & 2,4-hexadienal & 1.89 \(\pm\) 0.09 & 0.34 \(\pm\) 0.50 & 1.68 \(\pm\) 0.97 & 0.35 \(\pm\) 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile composition of monovarietal EVOO samples (% areas, mean values \(\pm\) SD, \(n=3\)). on the results obtained, it could be affirmed that the activity of the enzyme alcohol dehydrogenase is influenced by the variety. EVOO cultivar discrimination by volatile analysis has been addressed by numerous other authors. In this regard, SPME-GC-MS technique has also been used to discriminate Italian monovarietal EVOO [34; 35]. Moreover, in a recent paper several approaches for the varietal differentiation of monovarietal virgin olive oils are overviewed [36].