esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning Query Strategy
#### 3.2.1 Uncertainty Sampling  
Uncertainty sampling is a typical informative-based strategy and is the most popular query strategy for active learning. It assumes that the uncertainty samples provide more information for training a machine learning model if they are labeled. The rationale for this  
Figure 2: The workflow cycle of active learning in pool-based active learning scenarios.  
is that instances with lower certainty are typically located near the decision boundary of the classification, while highly certain instances are usually far from the decision boundary. Therefore, instances that are distant from the decision boundary are often considered redundant. The uncertainty sampling strategy selects an instance with the lowest confidence predicted by the current machine learning model as the query instance.  
Common criteria for evaluating the uncertainty include least confidence, uncertainty margin, and entropy.  
Least confidence is a strategy based on the prediction uncertainty. It measures uncertainty as the level of confidence in the most likely label. It is based on the probability of the top-class label with the highest posterior probability for a given instance. The uncertainty of an unlabeled instance is defined by Equation (1):  
\[x_{s}=\arg\underset{x\in D_{U}}{max}\{1-P_{\theta}(\hat{g}|x)\}=\arg\underset {x\in D_{U}}{min}\,P_{\theta}(\hat{g}|x) \tag{1}\]  
where \(P_{\theta}(\hat{g}|x)\) is the probability of the top-class label \(\hat{g}\) with the highest posterior probability (for instance, \(x\)), \(D_{U}\) represents the unlabelled data pool, and \(x_{s}\) is the uncertainty score of the query instance. The least confidence criterion strives to find the most indistinguishable instance of the current model as the query instance.  
Least confidence only considers the probability of the best prediction class label, ignoring the information from other class labels. As an improved query strategy, margin sampling can calculate the difference between the two most confident posterior probabilities, as defined by Equation (2):  
\[x_{s}=\arg\underset{x\in D_{U}}{min}\,\{P_{\theta}(\hat{g}_{1}|x)-P_{\theta}( \hat{g}_{2}|x)\}, \tag{2}\]  
where \(P_{\theta}(\hat{g}_{1}|x)\) and \(P_{\theta}(\hat{g}_{2}|x)\) are the top-1 and top-2 posterior probabilities. The instance with the smallest difference is defined as a hard-to-classify instance for labeling.  
In order to further consider the information of all class, another more typical measure of uncertainty is entropy, which is defined by Equation (3):  
\[x_{s}=\arg\underset{x\in D_{U}}{max}-\sum_{i}^{C}P_{\theta}(\hat{g}_{i}|x)\, \log P_{\theta}(\hat{g}_{i}|x)=\arg\underset{x\in D_{U}}{min}\sum_{i}^{C}P_{ \theta}(\hat{g}_{i}|x)\,\log P_{\theta}(\hat{g}_{i}|x), \tag{3}\]  
where \(C\) is the number of classes. The entropy measures the purity of a class for one sample, with larger entropy denoting higher uncertainty. The instance with the largest entropy is selected as query instance.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 4 Analytical Program  
### Geometric Model  
In this study, SPH was used to simulate the response of beams to impact. SPH is a numerical technique and belongs to the category of mesh-free discretization techniques that could be used in continuum mechanics to simulate solid dynamics, including elasticity and plasticity. The way the spatial domain is discretized in SPH approaches is different from the FEM. A collection of points, known as particles, are used in the SPH approach to discretize the domain. The advantage of SPH over the FEM is that SPH can predict similar response behavior with more accurate and realistic failure patterns [27].  
The SPH analytical model is presented in Figure 11. The analytical model using a self-coded Java program comprised two parts: the 100 kg falling weight and the RC beam. According to the objective of acquiring a detailed crack pattern, the RC beam in the analytical models was discretized with a particle size of 6.0 mm. According to the desktop capacity and to minimize the analysis time, only half of the beam was included in the analysis area, taking the center of the span as a plane of symmetry that was controlled by preventing movement in the x-direction. Furthermore, the RC beam was constrained using top and bottom hinges at the fulcrum position, similar to the experimental conditions, to prevent the beam from bouncing or z-direction movement.  
In the analytical model of the strengthened RC beams, different materials were defined, namely, concrete, reinforcing steel, CFC panels, and the adhesive between the CFC panels and concrete. The mechanical properties of these four materials are listed in Table 1.  
Figure 12 shows the adhesion model used in this analysis. Virtual adhesive particles were placed between the concrete and CFC particles. In the analysis, the strains of the concrete and CFC panel particles were calculated based on the conventional SPH method, and their average value was used as the strain of the adhesive particles. The stress of the adhesive particles was then calculated using the constitutive law of the adhesive material and controlling failure through the von Mises equivalent failure criterion. When the adhesive particles reached the failure state, the interaction between the adjacent CFC and concrete particles was excluded from the SPH analysis (excluded from the mutual influence domain) to account for the delamination of the CFC panel.  
Figure 11: Geometry of the numerical model in this study (unit: mm).
# Kinetic Comparison between Drop Jumps and Horizontal Drop Jumps in Elite Jumps and Sprinters
## 2 Materials and Methods
### Testing Procedures  
The testing procedures and instrumentation followed previously applied criteria in the first part of this study, which analyzed the spatiotemporal variables [17].  
The height, body mass, and age of each participant was recorded prior to the assessment session. Height was determined using a stadiometer with a precision of 0.1 cm (Bodymeter 206, SECA, Hamburg, Germany). Body mass was measured using a SECA scale (Hamburg, Germany), and body mass index was computed following established protocols [26]. Each athlete underwent a warm-up tailored to their individual needs. The warm-up lasted an average of 50 min and consisted of two segments. The initial segment, termed the general warm-up, included activities such as joint mobility exercises, approximately 5 min of running, and dynamic flexibility work. The subsequent segment, known as the specific warm-up, involved exercises relevant to each athlete's respective specialty sport. Following the warm-up, all participants had a 5-min recovery period.  
Participants executed two DJ attempts from three distinct FHs to assess the dependent variables. All athletes were accustomed to the procedure. They completed two DJ attempts at 0.3 m (DJ30), two at 0.4 m (DJ40), and two at 0.5 m (DJ50). Following an active rest period, comprising dynamic stretching exercises, a 30-m progressive sprint, and an SLJ ensuring full recovery, they performed two HDJ attempts at 0.3 m (HDJ30), two at 0.4 m (HDJ40), and two at 0.5 m (HDJ50). The best jump from each height was selected for data analysis. The DJ was executed with rebounding [15], and both DJs and HDJs retained consistent arm swinging. For HDJ attempts, it was stipulated that athletes maintain a vertical displacement during the eccentric phase, transitioning to a horizontal movement only after reaching the lowest point. This criterion ensured that the eccentric phase of both exercises was determined based on zero velocity parameters. Jumps were eliminated if they displayed asymmetric contacts, had a Ground Contact Time (GCT) exceeding 250 ms during the DJ (excluding HDJs), or lacked contact with the force platform. This resulted in the elimination of four jumps. The SLJ was performed with both feet to maximize horizontal jump distance. A reference line was placed on the force platform for initial alignment, and jump length was measured using a metallic tape measure, with the distance recorded from the line to the point where the heel landed closest to the starting line, as previously outlined [27]. During HDJs, athletes were instructed to contact the force plate as close to the SLJ reference line as possible. The covered jump distance was measured following the same guidelines as an SLJ. A rest interval of 1 min was provided between jumps of the same drop height, and 4 min between different drop heights.
## Materials and Methods
### Methodologies  
The first phase of the research study involved determining the properties of the materials used, and the results were compared with the relevant specifications. Water content, penetration, softening point, ductility, flash, and fire point tests were the tests carried out on the binders. In the second phase, the optimal binder content was determined and the value was used as a control for the binder content of the mix. The bio-asphalt binder mix containing 5%, 10%, 15%, and 20% by weight of molasses as a partial placement for the BB was prepared. The study sample range matrix is presented in Table 2. Then; the Fourier transform infrared (FTIR) spectrometer test was carried out on the molasses, BB, and MMBs to know the functional group and the change in the chemical composition of the parent materials before and after blending.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sieve Size (mm)** & **Properties** & **Result** & **Specification** & **Test Method** \\ \hline
25–4.75 & Bulk specific gravity (SG) & 2.74 g/cm\({}^{3}\) & * & ASTM C-127–68 \\  & Apparent SG & 2.85g/cm\({}^{3}\) & * & ASTM C-127–68 \\  & Water absorption & 1.1\% & \textless{2}\% & ASTM C-127–68 \\  & Soundness & 4.63\% & 10–20\% & ASTM C-88 \\  & Flakiness index & 4.00\% & \textless{10}\% & BS 812 \\  & Elongation index & 6.50\% & \textless{10}\% & BS 812 \\  & Impact value (IV) & 6.59\% & \textless{30}\% & BS 812:112 \\  & Crushing value (CV) & 12.65\% & \textless{35}\% & BS 812:110 \\  & Los-Angeles Abrasion LAT & 9.46\% & 35–45\% & ASTM C-131–69 \\
4.75–0.075 & SG & 2.71g/cm\({}^{3}\) & * & ASTM C-127–68 \\  & Apparent SG & 2.64 g/cm\({}^{3}\) & * & ASTM C-127–68 \\  & Water absorption & 1.2\% & \textless{2}\% & ASTM C-127–68 \\  & Angularity & 78\% & \textgreater{}45\% & AASHTO T33 \\
0.075 & SG & 2.706 g/cm\({}^{3}\) & * & ASTM C-127–68 \\ \hline \end{tabular}  
* Not applicable. ASTM (American Society for Testing and Material), AASHTO (American Association of Highway and Transportation Officials), BS (British Standard).  
\end{table}
Table 1: Characteristics of the aggregate.  
Figure 1: Particle size distribution curve.  
In the third phase, binder specification tests such a rotational thin film oven (RTFO) test, dynamic shear rheometer (DSR) test, frequency sweep test (FST), amplitude sweep test (AST), multiple stress creep recovery (MSCR) test, and PG determination test were carried out to evaluate the high, intermediate, and low-temperature rheological properties and performances of the BB and MMB's. The entire sequence of the experimental program is presented in Figure 2.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 5 Conclusions  
In this paper, the feasibility of a CubeSat-based LEO PNT system that could offer services to multiple country-sized areas in the same latitude bounds was assessed. A preliminary design of the system that is based solely on COTS components is presented and accounts for every aspect of such a mission. The design is based on the ERMIS mission of the Greek CubeSat In-Orbit Validation program, and it concluded that it is possible to provide the PNT services mentioned above with 100 SU CubeSats without the need for a ground segment dedicated to timekeeping. Instead, the CubeSats shall operate by relying on the MEO GNSS and relaying the PNT signals on S-Band.  
Since such a mission is feasible, a more detailed system analysis is recommended to lead to the CDR (Critical Design Review) of the system. However, in order to proceed, additional information is required for the subsystems and the payload. It is more probable than not that this can only be obtained after an NDA (non-disclosure agreement) with the respective provider.  
Overall, the proposed system's design is fully compatible with the CubeSat standard and holds the potential to revolutionize PNT systems in LEO. Rather than replacing GNSS services, it complements and enhances them, transforming them into a multi-layered system-of-systems [3]. This approach lays the groundwork for significant advancements in navigation technologies, such as autonomous vehicle systems, asset tracking, and a wide range of other applications. In EUSPA EO and GNSS Market Report, it was concluded that GNSS cumulative revenues are forecast to reach EUR 4.6 trillion in the next decade with the service revenues accounting for almost 80% in 2033 [1]. The current and the predicted economic growth of the PNT market along with the success story of the New Space and the CubeSats suggest that a business offering commercial PNT services via a CubeSat-based PNT system in LEO is positioned for success in an emerging market.  
Contribution, G.T. and N.A.-M.; methodology, G.T.; software, G.T.; validation, G.T., N.A.-M. and V.L.; formal analysis, G.T.; investigation, G.T.; resources, G.T.; data curation, G.T.; writing--original draft preparation, G.T.; writing--review and editing, G.T.; visualization, G.T.; supervision, N.A.-M. and V.L.; project administration, G.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 4 Results and Discussion
### Effects of Using Vertex Attributes Other than Position
Figure 8: Comparison of the effect of using a different number of LSBs of each normal vector coordinate on HMAC code placement. The graphical artefacts that occurred are highlighted with a red outline. Figure 7: Comparison of the effect of using a different number of LSBs of each normal vector coordinate on HMAC code placement at different light settings. The graphical artefacts that occurred are highlighted with a red outline. Figure 9: An example where changes in the coordinate bits of the normal vector for certain vertices do not cause visible changes. The graphical artefacts that occurred are highlighted with a red outline. Analysis of the effect of the number of modified bits in the texture coordinates on the occurrence of artefacts demonstrated that modifying too many bits (more than 12) always causes visible artefacts. However, their visibility depends on the geometry of the model and the position of the vertices. Changes in the gate.glb or pinecone.glb models are minor or even imperceptible. As a final evaluation of the method, a visual perception test (an online study) was conducted involving seventeen participants who were oblivious to which vertex attributes had been modified and the number of bits used in the watermarking process. Participants were asked to identify differences between pairs of models, where one model was always the original and the other was either the original or a watermarked version. Responses were quantified using the following rating scale: 1--no difference; 2--uncertain; 3--difference correctly identified. Table 6 shows the average results. Columns represent the results of comparing the original model with watermarked versions, where the column headers indicate the number of least significant bits (LSBs) used in the watermarking process \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Attribute** & **Original** & **8b** & **16b** & **24b** & **32b** \\ \hline \multirow{3}{*}{duck} & normal & 1.06 & 1 & 1.12 & 3 & 3 \\  & position & 1.12 & 1.06 & 3 & 3 & 3 \\  & texcoord & 1 & 1 & 1.06 & 3 & 3 \\ \hline \multirow{3}{*}{troll} & normal & 1 & 1 & 1.06 & 1 & 1 \\  & position & 1.06 & 1.18 & 3 & 3 & 3 \\ \cline{1-1}  & texcoord & 1 & 1.06 & 1 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Visual perception test average results comparing two models. Rating scale: 1—no difference; 2—uncertain; 3—difference correctly identified. Figure 11: Comparison of the effect of using a different number of LSBs of each texture coordinate on HMAC code placement. The red outline indicates a location on the surface of the original model where an artifact occurred when viewed from a distance. Figure 10: Comparison of the effect of using a different number of LSBs of each texture coordinate on HMAC code placement. The red outline indicates a location on the surface of the original model where an artifact occurred when viewed from a distance.
## 6 Experimental Results
### Performance of Baseline Drift Adaptation Models for Anomaly Classification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title 1** & **Metrics** & \multicolumn{3}{c}{**CPS Datasets**} \\ \cline{3-5}  & & **Fumehood** & **Vacuum Pump** & **Lithography Unit** \\ \hline \multirow{4}{*}{ARF-ADWIN} & Accuracy (\%) & 95.72 & 64.89 & 65.73 \\  & F1 Score & 0.957 & 0.646 & 0.649 \\  & Precision & 0.957 & 0.643 & 0.645 \\  & Recall & 0.957 & 0.649 & 0.6572 \\ \hline \multirow{4}{*}{ARF-DDM} & Accuracy (\%) & 95.47 & 66.52 & 67.01 \\  & F1 Score & 0.955 & 0.663 & 0.664 \\  & Precision & 0.955 & 0.662 & 0.661 \\  & Recall & 0.954 & 0.665 & 0.67 \\ \hline \multirow{4}{*}{SRP-ADWIN} & Accuracy (\%) & 95.98 & 70.21 & 67.30 \\  & F1 Score & 0.959 & 0.691 & 0.668 \\  & Precision & 0.958 & 0.690 & 0.666 \\  & Recall & 0.959 & 0.702 & 0.673 \\ \hline \multirow{4}{*}{SRP-DDM} & Accuracy (\%) & 97.34 & 70.53 & 67.84 \\  & F1 Score & 0.973 & 0.692 & 0.671 \\  & Precision & 0.974 & 0.693 & 0.668 \\  & Recall & 0.973 & 0.705 & 0.678 \\ \hline \multirow{4}{*}{EFDT} & Accuracy (\%) & 94.52 & 61.33 & 58.75 \\  & F1 Score & 0.948 & 0.544 & 0.569 \\  & Precision & 0.955 & 0.548 & 0.56 \\  & Recall & 0.945 & 0.613 & 0.588 \\ \hline \multirow{4}{*}{HT} & Accuracy (\%) & 94.83 & 66.36 & 52.75 \\  & F1 Score & 0.951 & 0.559 & 0.516 \\  & Precision & 0.96 & 0.672 & 0.674 \\  & Recall & 0.948 & 0.663 & 0.527 \\ \hline \multirow{4}{*}{LB} & Accuracy (\%) & 97.19 & 70.0 & 65.28 \\  & F1 Score & 0.972 & 0.638 & 0.629 \\ \cline{1-1}  & Precision & 0.973 & 0.719 & 0.628 \\ \cline{1-1}  & Recall & 0.972 & 0.70 & 0.653 \\ \hline \multirow{4}{*}{TWIN-ADAPT} & Accuracy (\%) & 96.97 & 71.92 & 69.26 \\ \cline{1-1}  & F1 Score & 0.97 & 0.695 & 0.693 \\ \cline{1-1}  & Precision & 0.973 & 0.710 & 0.695 \\ \cline{1-1}  & Recall & 0.97 & 0.719 & 0.692 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance measures for different drift adaptation models. Figure 16: Average accuracy comparison for baseline drift adaptation models on Fumehood dataset. Figure 17: Average accuracy comparison for baseline drift adaptation models on Vacuum Pump dataset.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 2 Mathematical Modeling
### Review of Probability Density Functions
#### 2.2.1 Gaussian Normal Distribution  
The normal probability distribution function is represented by a function of \(x\), and requires two parameters: the mean, denoted as \(x_{o}\), and the standard deviation, represented by \(\sigma\) (3). This function is symmetrical, with the mean serving as the offset coefficient and the standard deviation determining the scale.  
\[F_{x_{o},\sigma}^{Gauss}(x)=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(x-x_{o} )^{2}}{2\sigma^{2}}} \tag{3}\]  
Two first moments exist for the normal distribution, i.e., \(x_{o}\) and \(\sigma\), and can be derived analytically. Equation (4) presents formulations for sample mean and standard deviations for a sample \((x_{1},\ldots,x_{N})\), where \(N\) is a number of datapoints.  
\[mean(x)=x_{o} =\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{4}\] \[std(x)=\sigma =\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-x_{0})^{2}}{N-1}} \tag{5}\]  
It should be remembered that the scale factor \(\sigma\) is equivalent here to the measure of the sum of squared deviations, often denoted as the Integral Square Errors (ISE).
## 3 Results and Discussions
### Effects of Extracting Solvent  
To find an effective solvent for the extraction of total triterpenoids, various solvents were tested. The use of 96% ethanol produced the highest yield of total triterpenoids (2.2 g) from 100 g of dry _D. heterophyllum_ plant, while yield of total triterpenoids from 90%, 80%, 70%, 65%, 55% ethanol and chloroform was 2.0 g, 1.62 g, 1.10 g, 0.62 g, 0.15 g, and 2.1 g, respectively, from 100 g of dry _D. heterophyllum_. In our experiments, 96% ethanol was used due to high yield of extract and the lower toxicity of ethanol compared to the other solvents. Pentacyclic triterpenoids are practically insoluble in water and ethanol in a low concentration but they are soluble in chloroform, and sparingly soluble in 96% ethanol. Nevertheless, we used 96% ethanol as extracting solvent, which is the more affordable solvent as well as lower toxicity compared to chloroform.
# Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China
## 3 Results and Analysis
### Analysis of Volatile Flavor Compounds in the Two Types of Douchi  
#### 3.2.1 Determination of Volatile Flavor Compounds  
With the using of headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC /MS), the volatile compounds in the two kinds of traditional fermented douchi from Longnan and Qingyang were determined, and the relative content of flavor compounds was analyzed for significance. The results are shown in Table 4. A total of 77 volatile flavor compounds, including esters, alcohols, ketones, aldehydes, and terpenoids, were detected in the two types of douchi. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Compound Name**} & \multicolumn{2}{c}{**Relative Content (\%)**} \\ \cline{3-4}  & & & **LN Douchi** & **QY Douchi** \\ \hline \multicolumn{4}{c}{**Esters**} \\ \hline
1 & Ethyl acetate & 3.93 \(\pm\) 0.29 & - \\
2 & Ethyl isobutyrate & 4.36 \(\pm\) 0.31 & - \\
3 & Ethyl Butyrate & 4.90 \(\pm\) 0.30 & - \\
4 & Ethyl 2-methylbutyrate & 2.59 \(\pm\) 0.14 & - \\
5 & Ethyl isovalerate & 8.37 \(\pm\) 0.47 & - \\
6 & Isoamyl acetate & 1.35 \(\pm\) 0.07 & - \\
7 & Ethyl hexanoate & 2.57 \(\pm\) 0.13 & - \\
8 & Isoamyl butyrate & 0.27 \(\pm\) 0.04 & - \\
9 & 2-Methylbutyrate-3-methylbutyl ester & 0.32 \(\pm\) 0.03 & - \\
10 & 3-Methyl-2-methylbutyl butyrate & 0.47 \(\pm\) 0.07 & - \\
11 & Ethyl heptanoate & 0.36 \(\pm\) 0.04 & - \\
12 & Ethyl caprylate & 0.29 \(\pm\) 0.02 a & 0.05 \(\pm\) [PERSON] b \\
13 & & Linalyl acetate & 1.89 \(\pm\) 0.10 & - \\
14 & Methyl benzoate & 0.55 \(\pm\) 0.02 a & 0.12 \(\pm\) 0.01 b \\
15 & & \(\gamma\)-butyrolactone & 0.10 \(\pm\) 0.06 & - \\ \hline \multicolumn{4}{c}{**Alcohols**} \\ \hline
16 & Ethanol & 5.75 \(\pm\) 0.52 & - \\
17 & 2-Methyl-1-propanol & 0.19 \(\pm\) 0.04 & - \\
18 & 1-Pentent-3-ol & - & 0.13 \(\pm\) 0.01 \\
19 & Isoamyl alcohol & 3.17 \(\pm\) 0.08 b & 7.87 \(\pm\) 0.03 a \\
20 & 2-Heptanol & 0.09 \(\pm\) 0.01 & - \\
21 & 3-Nonyl alcohol & - & 0.30 \(\pm\) 0.02 \\
22 & Hexanol & 0.11 \(\pm\) 0.01 & - \\
23 & 3-Octanol & 0.37 \(\pm\) 0.02 & - \\
24 & 2-Octanol & 0.02 \(\pm\) 0.01 & - \\
25 & Anti- \(\alpha\),\(\alpha\)- 5-Trimethyl-5-vinyltetrahydro-2-furan methanol & 0.11 \(\pm\) 0.02 & - \\
26 & 1-octen-3-ol & 1.38 \(\pm\) 0.02 a & 0.18 \(\pm\) 0.01 b \\
27 & 3-Methylcyclohexanol & - & 0.06 \(\pm\) 0.00 \\
28 & 1-Octanol & 0.09 \(\pm\) 0.01 & - \\
29 & 2,3-butanediol & 1.22 \(\pm\) 0.07 & - \\
30 & 4-Terpeneol & 1.31 \(\pm\) 0.04 & - \\
31 & 2-ethylhexanol & - & 0.18 \(\pm\) 0.01 \\
32 & furfuryl alcohol & 0.48 \(\pm\) 0.01 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of volatile flavor compound analysis in Longnan and Qingyang douchi.