esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Fragility Analysis of the Transmission Tower-Line System Considering Bol Slippage
## 6 Conclusions  
In this paper, the transmission tower-line system model, which considers bolt slippage, is established. The fragility of the tower-line system is calculated using the incremental dynamic analysis method, and the influence of bolt slippage behavior on seismic fragility is studied. In addition, the influence of foundation deformation on the seismic performance of towers is analyzed. The conclusions can be summarized as follows:  
1. Considering the bolt reciprocating slippage behavior, the seismic fragility of the TTLS without foundation deformation increases, with the failure probability increasing more obviously in both the severe damage limit state and the collapse limit state;
2. Foundation deformation leads to an increase in the failure probability of the TTLSs subjected to earthquakes and changes the weak position of the tower. However, ignoring the bolt slippage would overestimate the seismic fragility of the TTLSs with foundation deformation;
3. The limit states of the TTLSs are determined by the maximum stress ratio of the compressed diagonal members.  
In this paper, the main purpose is to study the influence of the bolt slippage effect and foundation deformation on the seismic fragility of the transmission tower-line system. Therefore, uniform ground motion analysis is commonly employed to study the seismic fragility of transmission lines [31; 32; 33]. Since the distance between the two towers is a fixed value, the influence of the wave passage effect on seismic fragility depends on the apparent wave velocity [34]. As the apparent wave velocity increases, the results considering the wave passage effect gradually converge to that of the uniform ground motion analysis. In future research, the influence of ground motion spatial variation (i.e., wave passage effect, coherency loss effect, and local site characteristics) on the seismic fragility of the transmission tower-line system should be more systematically studied.  
Figure 22: _ISDR_ values for TTLSs with foundation horizontal displacement: (**a**) RM; (**b**) JM.  
**Author Contributions:** Conceptualization, J.-X.L. and C.Z.; methodology, J.-X.L. and J.-P.C.; software, J.-P.C.; investigation, J.-X.L. and C.Z.; writing--original draft preparation, J.-X.L., C.Z., and J.-P.C.; writing--review and editing, J.-X.L. and C.Z.; visualization, C.Z. and J.-P.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. N2301012).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author/s.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 System Architecture
### Scope and Context
#### 4.1.2 Scenario 2: Services for the Management of Diets  
Scenario 2 is related to the care of food intake within the home. The primary objective of these services is to help people to maintain healthy nutrition. Special considerations for the treatment of diets related to specific diseases are beyond the scope of this work.  
The main actors of the system are the nutritionist and elderly people who follow the diet. Moreover, a third actor could exist, a housekeeping paper who helps with household chores. Hence, a minimum set of requirements was considered for the system components related to the prediction, planning, control, and monitoring processes of diets:  
1. Digitized Diet Catalog. The art of designing diets, types of food, and nutritional components is beyond the scope of this article, so the diet catalog is based on the design of nutritional intakes provided by the healthy Mediterranean-style eating pattern and healthy US-style eating pattern. These patterns are described in the dietary guidelines for Americans 2015-2020 [39]. The healthy US-style pattern includes 12 calorie levels to meet the needs of individuals across the lifespan. The pattern is used for the creation of a digitized catalog of diets by building schedules of daily intakes during each week, following the distribution of the recommended daily calories for a person;
2. Prediction of the diet. The diet prediction uses the dietary reference intakes (DRIs) that are published by the Food and Nutrition Board of the Institute of Medicine, and they are intended for healthy people in the United States and Canada. Thus, the prediction of diets is based on the estimated energy requirement (EER). The EER is defined as the average dietary intake that is predicted to maintain energy balance in a healthy adult of a defined age, gender, weight, height, and physical activity level (PAL) consistent with health [40];
3. Diet planning. The catalog and its basic plans help the nutritionist to plan a diet for a person by making minimal changes in the proportions of food. Through the system, the nutritionist can assign a weekly plan and meal schedules to dieters;
4. Monitoring and control of the diet. The monitoring and control cycle is as follows: the housekeeping chooses the diet and prepares it every day. The system provides services such as consultation of recipes and videos for the preparation of meals. Once the person completes the food intake, the housekeeping confirms the completion of the intake to the system by sending two quick response codes (QR codes), ingested food and elderly person ID, and the value of the amount ingested through the mobile crowd sensing components. Finally, automatic alerts are sent to users if dietary plans are not carried out.
## 4 Fault Experiment Signal Analysis
### Case Western Reserve University (CWRU) Dataset
#### 4.1.3 Experiment Validation in Noisy Environments
While parameter-optimized VMD is less robust compared to VME, and EEMD shows minimal improvement in terms of accuracy, both methods have longer running times than VME. These results indicate that preprocessing the noisy original vibration signal with parameter-optimized VME can enhance accuracy and save time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter Optimized** & **No Noise** & **0 dB** & \(-4\) dB & \(-8\) dB & \(-12\) dB & \(-16\) dB \\ \hline Yes & \(\mathbf{100\pm 0}\) & \(\mathbf{100\pm 0}\) & \(\mathbf{99.9\pm 0.1}\) & \(\mathbf{99.7\pm 0.4}\) & \(\mathbf{97.7\pm 0.93}\) & \(\mathbf{97.4\pm 0.74}\) \\ No & \(99.9\pm 0.2\) & \(99.1\pm 0.2\) & \(98.0\pm 0.5\) & \(95.9\pm 1.0\) & \(84.8\pm 1.3\) & \(82.8\pm 2.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of parameter-optimized VME and empirical VME within the MRCNN model. Figure 16: Visualization results of different layers under t-SNE. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & \(\mathbf{\omega/Hz}\) & **Fault Type** & \(\mathbf{\omega/Hz}\) \\ \hline Nor & 1000 & ORF14 & 3400 \\ IRF07 & 3500 & ORF21 & 3400 \\ IRF14 & 3500 & BF07 & 3400 \\ IRF21 & 2800 & BF14 & 3400 \\ ORF07 & 2800 & BF21 & 3400 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The center frequency of the bearing vibration signal for different fault types. Figure 17: Confusion matrixes at \(-16\) dB: (**a**) parameter-optimized VME; (**b**) empirical VME. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **VME** & **VMD** & **EEMD** \\ \hline Time(s) & 0.02 & 4.32 & 4.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average time of each decomposition using different signal preprocessing methods. Figure 18: Results for the MRCNN model using different signal preprocessing methods.
# Translational Echocardiography: The Dog as a Clinical Research Model of Cardiac Dysfunction
## 6 Dilated Cardiomyopathy (DCM), a Human and Canine Common Disease
### Echocardiographic Measurements on DCM
1.2 E-Point to Septal Separation or EPSS: An Echocardiographic Parameter for Accurate Assessment of Left Ventricular Performance  
The separation between the E point and the septum should be measured in the right parasternal long axis or the parasternal short axis at the level of the tip of the mitral septal leaflet. The separation between the E point and septum refers to the distance between the mitral septal leaflet's peak early diastolic motion (E point) and the interventricular septum. A recent study in dogs evaluated EPSS as a technique for detecting occult dilated cardiomyopathy in Doberman canine patients. It demonstrated that EPSS greater than 6.5 mm is a valuable additional variable for diagnosing DCM. Incorporating the E point in the septal separation (EPSS) measurement, in addition to M-mode measurements, can enhance the sensitivity and specificity of cardiac evaluations, comparable to volume measurements obtained using the [PERSON]'s biplane method of disk summation or SMOD [96], as seen in Figure 15.  
Figure 13: Left ventricular M-mode measurements in a dog.
## 3 Methodology
### Random Forest and Ridge Regression Ensemble Algorithm (RFRR)  
#### 3.4.1 Random Forest (RF)  
Random Forest is a machine learning method that makes predictions by constructing multiple decision trees. Its key feature is that each tree is built by randomly selecting a subset of features, which helps to reduce the model's variance and improve its generalization ability. Random Forest performs well in handling large datasets and problems with complex data structures. Additionally, it is effective in dealing with missing values and outliers.  
The working principle of Random Forest is to first extract multiple sample sets from the original dataset using bootstrap sampling (sampling with replacement). Then, a decision tree is built for each sample set. During the construction of each decision tree, only the best feature from a randomly selected subset of features is considered when splitting each node. Finally, the Random Forest prediction result is obtained by voting on the results of all decision trees (for classification problems) or averaging them (for regression problems). Given the complex relationships between surface temperature and numerousenvironmental variables, the Random Forest algorithm can effectively handle missing values and outliers while performing the nonlinear mapping of data features.  
When using the Random Forest algorithm for model training, it is essential to configure the software environment and training parameters properly. In this study, the Random Forest machine learning library was used to construct the model within the software environment of the Windows 11, PyCharm 2023.2.1 Integrated Development Environment (IDE), and Python 3.11.5. Table 3 lists the main hyperparameters of the Random Forest model, while the other parameters not listed were set to their default values.  
Due to the large-scale 1 km product over 1062 days, a substantial number of RF evaluations are required, leading to high computational demands. As a result, multiple experiments were conducted to determine the optimal selection of n_estimators. Ultimately, a value of 10 was chosen for the experiment, as this setting mitigates the risk of memory overflow, while maintaining a similar level of accuracy compared to larger models. Moreover, this configuration significantly improved operational efficiency. The following Table 4 outlines the hardware specifications.  
Under these hardware conditions, the model training time per year is approximately 9 h.
# Adaptive Model Output Following Control for a Networked Thermostat +
## 3 Modelling
### Thermostat Model  
The configuration of the thermostat process is shown in Figure 4 and Table 1. Liquid flows from one side to the other side. Assume heat is conducted from top to bottom, the heat conduction \(Q_{c}\), based on [PERSON]'s law of heat conduction, is computed as  
\[Q_{c}=-\lambda(T_{0}-T(t))d_{6}(\frac{d_{2}}{d_{1}-d_{4}+2d_{3}}+\frac{d_{1}} {d_{3}+2d_{2}}), \tag{6}\]where \(T_{0}\) and \(T(t)\) are the initial temperature and current temperature, respectively. The constant \(\lambda\) is the thermal conductivity of the thermostat. As the temperature of the thermostat drops, the released heat is given by  
\[Q_{d}=\frac{\text{d}(T_{0}-T(x))m_{a}c_{a}}{\text{d}t}, \tag{7}\]  
where \(m_{a}\) is the quality of the thermostat and \(c_{a}\) is its specific heat coefficient. Based on [PERSON]'s law of cooling, the convective heat from the thermostat to the air is  
\[Q_{c}=\alpha(T_{0}-T(x))d_{4}(d_{2}-d_{5}). \tag{8}\]  
The released heat of the temperature drop from the liquid is given by  
\[Q_{l}=\frac{\text{d}(T_{0}-T(x))m_{l}c_{l}}{\text{d}t}, \tag{9}\]  
where \(m_{l}\) is the quality of liquid and \(c_{l}\) is its specific heat coefficient. The heat released by the temperature drop from the air temperature in the thermostat is  
\[Q_{a}=\frac{\text{d}(T_{0}-T(x))V_{a}c_{p}}{\text{d}t}, \tag{10}\]  
where \(V_{a}\) is the volume of the air in the thermostat and \(c_{p}\) is the specific heat coefficient of air. According to the conservation of energy, the thermostat can be modeled as  
\[(m_{a}c_{a}-m_{l}c_{l}-V_{a}c_{p})\frac{\text{d}(T_{0}-T(x))}{ \text{d}t}= -\lambda(T_{0}-T(x))d_{6}(\frac{d_{2}}{d_{1}-d_{4}+2d_{3}}+\frac{ d_{1}}{d_{3}+2d_{2}})\] \[-\alpha(T_{0}-T(x))d_{4}(d_{2}-d_{5})+Q_{p}. \tag{11}\]  
Define \(y(t)=T_{0}-T(x)\), and Equation (11) can be rewritten as  
\[a_{0}\frac{\text{d}y(t)}{\text{d}t}+a_{1}y(t)= (S_{p}T_{1}l_{c}-\frac{1}{2}R_{p}l_{c}^{2}+Kk_{1}l_{c})u_{p}+K(k _{2}\text{e}^{-k_{3}t}-k_{4}\text{e}^{-k_{3}t}), \tag{12}\]  
where  
\[a_{0} =m_{a}c_{a}-m_{l}c_{l}-V_{a}c_{p}, \tag{13}\] \[a_{1} =\lambda d_{6}(\frac{d_{2}}{d_{1}-d_{4}+2d_{3}}+\frac{d_{1}}{d_{3 }+2d_{2}})+\alpha d_{4}(d_{2}-d_{5}). \tag{14}\]  
Because the mass of liquid \(m_{a}\) and the volume of air in the thermostat are not constant, \(a_{0}\) varies with time. Define one time-varying variable \(\beta\), which satisfies  
\[\beta u_{p}=K(k_{2}\text{e}^{-k_{3}t}-k_{4}\text{e}^{-k_{5}t}). \tag{15}\]  
Then the model becomes  
\[a_{0}\frac{\text{d}y(t)}{\text{d}t}+a_{1}y(t)=b_{1}u_{p}, \tag{16}\]  
where \(b_{1}\) is also time-varying and given by  
\[b_{1}=S_{p}T_{1}l_{c}-\frac{1}{2}R_{p}l_{c}^{2}+Kk_{1}l_{c}+\beta. \tag{17}\]  
According to Laplace transform, the transfer function of the equation above can be represented by  
\[G(s)=\frac{b_{1}}{a_{0}s+a_{1}}. \tag{18}\]
# Evaporative Mist Cooling as Heat Dissipation Technique: Experimental Assessment and Modelling
## Conflicts of Interest: The authors declare no conflict of interest.  
### Nomenclature  
Variable Description Unit  
vx Droplet velocity in x direction  
vx Droplet velocity in y direction  
f Darcy friction factor  
v Droplet velocity  
m/s  
\(\rho\) Air density  
kg/m\({}^{3}\)  
\(\rho_{w}\) Water density  
kg/m\({}^{3}\)  
r Droplet radius  
m  
g Gravitational acceleration  
m/s\({}^{2}\)  
Ff Friction or drag force  
N  
S Frontal area of drop  
m\({}^{2}\)  
Ki Kinetics energy of drop  
J  
\(CP_{w}\) Specific heat of water  
kJ/kg-K  
\(T_{w}\) Droplet temperature  
\({}^{\circ}\)C  
h Film coefficient  
W/mK  
\(T_{a}\) Air temperature  
\({}^{\circ}\)C  
\(k_{y}\) Global heat transfer coefficient  
m/s  
\(h_{lg}\) Latent heat of vaporization  
KJ/kg  
\(Y_{A0}\) Air mass fraction  
kg/kg  
\(Y_{A0\infty}\) Saturated air mass fraction  
kg/kg  
\(\varepsilon\) Efficiency  
mwt Return water flow rate  
kg/s  
mwi Initial water flow rate  
kg/s  
Twi Initial water temperature  
\({}^{\circ}\)C  
Twt Return water temperature  
\({}^{\circ}\)C  
Twb Wet bulb temperature  
\({}^{\circ}\)C  
Yai Absolute humidity  
g/kg  
H Spray tower height  
m  
Fc Model-empirical correction factor  
\(T_{ip}\) Initial evaporative pond temperature  
\({}^{\circ}\)C  
\(T_{qp}\) Final evaporative pond temperature  
\({}^{\circ}\)C  
\(T_{ipr}\) Initial reference pond temperature  
\({}^{\circ}\)C  
\(T_{qpr}\) End reference pond temperature  
\({}^{\circ}\)C  
Qcool Cooling power  
W
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## Appendix A
### LES and Snow Properties  
LES is one of the most important CYGNSS observables. Figure 11 presents the relationship between LES and snow depth. Figure 12a-c are the corresponding relationships for high-, moderate-, and low-vegetation-covered area, while subfigure 12 is for the barren or desert area. At present, from the relationship shown in Figure 11, we can see that LES has a poor relationship with the snow depth. Since LES is one of parameters that reflects the surface roughness condition, it cannot present the snow depth information in the CYGNSS pixel, which has a spatial resolution of 7 \(\times\) 2.5 km. However, it is potential good indicator of snow depth for complex mountain terrain.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 6 Results
### Identification of Unknowns by Temporal Segmentation for Other Audio Data
#### 6.2.3 Sound-Event Detection with Synthetic DCASE2019 Task 4  
We considered the task of sound-event detection of DCASE2019 Task 4, which typically encompasses the detection of multiple simultaneous sound events. However, our analysis was restricted to identifying the \"dominant\" sound event and comparing it with the ground truth. Table 9 presents the unknown detection results, where the HRNet48 backbone network notably achieved the highest performance, with AUROC and AUPRC scores of 73.0% and 68.9%, respectively.  
Figure 10 illustrates the visualization of results with a normalized threshold of 0.85, demonstrating the network's effectiveness in accurately detecting sound events in most scenarios. However, capturing precise event boundaries proved challenging, primarily due to the short durations of sound events. A specific difficulty was noted with the boundary of the unknown event \"Electric_shaver_toothbrush\", which appears quite ambiguous in Figure 10g. We hypothesize that the diminished performance for this event stems from its frequent co-occurrence with other sounds, such as speech and running water, which complicates the audio signal and impedes the network's ability to confidently discern relevant information. Moreover, Figure 10 shows that while the accuracy for known sound events stood at 44.3%, the precision and recall rates were relatively low at 0.42 and 0.17, respectively. These findings indicate that the task of detecting unknown sound events poses significant challenges, as exemplified in Figure 10d,g, suggesting a greater complexity compared to other tasks in the study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**w/o. Center**} & \multicolumn{2}{c}{**w. Center**} & \multicolumn{2}{c}{**w. Center, Contrast Loss**} \\
**Backbone** & **AUROC** & **AUPRC** & **AUROC** & **AUPRC** & **AUROC** & **AUPRC** \\ \hline HRNet32 & 0.606 & 0.625 & 0.608 & 0.620 & 0.690 & 0.671 \\ HRNet48 & 0.681 & 0.658 & 0.704 & 0.680 & 0.730 & 0.689 \\ HRNet64 & 0.664 & 0.635 & 0.664 & 0.655 & 0.730 & 0.685 \\ ResNet101 & 0.647 & 0.639 & 0.659 & 0.662 & 0.667 & 0.665 \\ WideResNet101 & 0.624 & 0.611 & 0.657 & 0.655 & 0.677 & 0.658 \\ ResNet152 & 0.664 & 0.647 & 0.706 & 0.680 & 0.705 & 0.659 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The performance of sound event-detection dataset in various encoder networks.  
Figure 9: The visualization result of the temporal GTZAN dataset. The sequences (**a**–**e**) illustrate the segmentation results, with each sequence mixing different styles.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 4 Direct Method for the Estimation of the Rain Rate
### Experimental Derivation of the Analytical Regression Formula
Figure 8: Scatterplot between rain rate \(R\) (in mm/h) and specific attenuation \(k\) (in dB/km) for the forward link (blue dots) and for the return link (red dots), both in the Ku-band. The two black lines represent the nonlinear regression of the data.
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 4 Performance Evaluation
### Evaluation Criteria
In anti-narrowband interference, the terms demodulation anti-jamming narrow-band merit factor (Dem and AJNM), anti-matched-spectrum-jamming merit factor (Dem and AJMS), anti-narrowband-jamming merit factors (CT and AJNM), and anti-matched-spectrum-jamming merit factor (CT and AJMS) can be expressed as:  
\[Q_{DemAJNM}=10\times\text{log}_{10}\bigg{(}\frac{1}{R\times\text{max}[G_{s}(f )]}\bigg{)}[dB] \tag{15}\]\[Q_{DemAJMS} =10\times\log_{10}\left(\frac{1}{R\times\int_{-B/2}^{B/2}G_{s}^{2}(f) df}\right)[dB] \tag{16}\] \[Q_{CTAJNM} =10\times\log_{10}\left(\frac{\int_{-B/2}^{B/2}f^{2}G_{s}(f)df}{ \max[f^{2}G_{s}(f)]}\right)[dB]\] (17) \[Q_{CTAJMS} =10\times\log_{10}\left(\frac{\int_{-B/2}^{B/2}f^{2}G_{s}(f)df}{ \max[f^{2}G_{s}^{2}(f)]}\right)[dB] \tag{18}\]  
From the perspective of communication payload, the satellite communication link budget is defined as:  
Figure 7:0  
where \(C\) denotes the carrier power and can represent the product of the information bit rate transmitted by the carrier \(R_{b}\) and energy of the transmission each bit \(E_{b}\)--that is, \(C=R_{b}*E_{b}\). Symbol \(T\) is the equivalent noise temperature at the input of the receiver. \(EIRP\) denotes the equivalent isotropic radiation power emitted by the transmitter and \(EIRP=G_{t}+P_{t}\), where \(G_{t}\) is the antenna gain and \(P_{t}\) is the transmit power. \(L\) is the total loss, \(L=L_{0}+L_{s}\), \(L_{0}\) is the propagation loss, and \(L_{s}\) is the system loss. \(G_{r}\) denotes the receiver antenna gain. For the noise power spectral density, \(N_{0}=K\cdot T\)--that is:  
Figure 7:1  
In Formula (20), \(k\) denotes the Boltzmann constant and \(k=-228.6\) dB\(/K\). Thus, the energy per bit to noise power spectral density ratio (\(E_{b}/N_{0}\)) can be obtained as below:  
\[ \begin{array}{ll}E_{b}/N_{0}&=(C/T)_{dB}-10\cdot\log(R_{b})\\ &=(C/N_{0})_{dB}-(k)_{dB}-10\cdot\log(R_{b})\\ &=EIRP-(L_{0}+L_{s})+(G_{r}/T)_{dB}-(k)_{dB}-10\cdot\log(R_{b})\end{array}  \tag{21}\]
## 3 Results and Discussion
### Thiosulfinates Content
[8] observed the same allicin level in garlic drying at 50 \({}^{\circ}\)C for 240 min compared to that obtained at 70 \({}^{\circ}\)C for 120 min. A study by [PERSON] et al. [28] demonstrates even a higher loss of allicin content at 50 \({}^{\circ}\)C drying temperature compared to 60 \({}^{\circ}\)C drying, likely due to the longer drying time at 50 \({}^{\circ}\)C. [PERSON] et al. observed an increase in allicin content during hot air drying with increased temperature [42]. Explaining the results, the authors suggested the possibility of the decomposition and rearrangement of allyl-S-cysteine sulfoxide to a larger number of daily thiosulfinates (allicin). A similar conclusion was reached in another study, which showed that heating alliin solutions at temperatures of 60 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, and 89 \({}^{\circ}\)C resulted in the generation of significant numbers of organosulfur compounds [29]. The above-mentioned finding aligns with our observation of comparable TTC in garlic samples raw and dried at different temperatures. The lack of changes in TTC levels observed in the tested garlic extracts could also be attributed to the correlation between drying temperature and duration. It was noted that as the drying temperature increased, the time needed to reach the desired final moisture content of the tested samples of garlic significantly decreased (550 min at 50 \({}^{\circ}\)C, 170 min at 70 \({}^{\circ}\)C, and 80 min at 90 \({}^{\circ}\)C). These observations indicate that drying garlic at higher temperatures accelerates the drying process and yields a product of comparable quality to that achieved at lower temperatures. This was reflected in similar antimicrobial activity recorded for samples with the same level of extract based on raw and dried garlic. Figure 4: The effect of the drying process at different temperatures on the content of thiosulfinates in garlic slices. Bars represent standard deviation (SD) of six measurements. Variables having the same letter a are not significantly different (\(p>0.05\)).