esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 5 Results  
### Implementation  
Alevin [12] is an open-source framework written in JAVA and used to develop, compare, and analyze VNE algorithms. The SN and VN are represented as directed or undirected graphs in which the network entities hold demands and resources that represent either consumable resources or properties, such as ID. In this work, we add type, location, domain, redundancy, latency, and VNF demands and resources. The embedding of each node/link in the VN only succeeds when there are enough resources and matching properties in SN nodes/paths. The Visitor Pattern method is used in Alevin to map demands on the respective resources and occupying (reserving) consumable resources. An extension with NFV support is available for Alevin and developed in [5]. However, we developed our own NFV extension that fits our system model and transformation methods. We added to Alevin a generic structure to define transformations that are based on an abstract transformation class and method that takes the VN and SN as input.  
Alevin implements several node and link mapping algorithms, including coordinated node and link mapping. It also implements several evaluation metrics, such as runtime, admission ratio, and cost. However, Alevin is highly flexible for adding new algorithms and metrics. It also provides an evaluation framework, in which different forms of random topologies can be defined with specific parameters. This framework also enables the definition of used mapping algorithms, metrics, and ranges of values for consumable resources and demands. Alevin supports multiple data formats, mainly XML, for representing the SN and VN and mapping the results.  
For fixed topologies that represent our use case, we use the easier JSON format to represent the AR, SN, and mapping results. For this purpose, we developed the required parsers that convert the JSON structures to the respective networks, entities, and resources and demands. The AR JSON definition format defines the application end-points and connectors. For each node, an ID, name, type, subtype, location, domain, and, if applicable, CPU demand are defined. For the connector, the source and destination nodes are defined and whether redundancy and low latency are required. The SN JSON definition format is similar but includes resources instead of demands, data resources with cycle and size, and bandwidth resources for SLis. The mapping results are also represented while using a JSON format that shows for each VNo the mapping SNo and for each simple VLi the mapping path. However, for VLis with VNF request, the node and link mappings of the FG are combined. For the FG internal VLis, the source and destination SNos with the types of the source and destination VNFs are shown, even if the source and destination SNos are the same (when multiple VNFs are mapped on one server).
## 4 Improved Routing Protocol
### Route Discovery  
#### 4.2.1 Route Request  
When the source executes a new route to a destination, it broadcasts an RREQ message containing the information of destination node and GPS position information. The RREQ can be uniquely identified by the source address and source ID number. The intermediate node receiving the RREQ firstly checks whether the request is a duplicate. If the RREQ is new, the intermediate node updates the RREQ with its GPS position information and rebroadcasts it to its neighbors. The broadcasting process is terminated when the RREQ reaches the destination.
## 3 Results
### Perceived Workload (NASA-TLX) Scores  
The NASA TLX workload questionnaire measured participants' perceived workload [64]. As shown by the arrows in Figure 13, a higher total score reflected a larger perceived load, except for the performance subscale, where a higher total score indicated a lower self-reported level of success with the task. The main effect of task complexity was significant on the mental load [F (1,26) = 939.92, \(p=0.000\), \(\eta^{2}=0.97\)], physical load [F (1,26) = 202.59, \(p=0.000\), \(\eta^{2}=0.90\)], temporal load [F (1,26) = 202.08, \(p=0.000\), \(\eta^{2}=0.90\)], performance [F (1,26) = 15.30, \(p=0.001\), \(\eta^{2}=0.40\)], effort [F (1,26) = 38.91, \(p=0.000\), \(\eta^{2}=0.60\)], and frustration [F (1,26) = 125.00, \(p=0.000\), \(\eta^{2}=0.83\)] subscales as well as overall mean NASA-TLX scores [F (1,26) = 672.45, \(p=0.000\), \(\eta^{2}=0.96\)]. Throughout the complex task, scores increased across the subscales. It was found that there was a considerable influence that instruction technique had on performance [F (1,26) = 25.46, \(p=0.000\), \(\eta^{2}=0.50\)], frustration [F (1,26) = 70.08, \(p=0.00\), \(\eta^{2}=0.73\)], and overall mean NASA-TLX scores [F (1,26) = 9.34, \(p=0.005\), \(\eta^{2}=0.26\)], with these scores being higher for paper-based instruction.  
All NASA-TLX subscales, except for effort and the overall mean, showed statistically significant interactions between task complexity and instruction technique (\(p>0.5\)). The interaction effects of task complexity and instruction method on the NASA-TLX results are presented in Figure 13. The paired \(t\)-test revealed that as task complexity increased, so did average scores of mental and physical load, performance, and frustration for both approaches of instruction. When participants were given written instructions for a complex task, they reported significantly higher levels of physical workload, performance, and frustration. There was no statistically significant difference in mental load between AR and paper-based guidance for either simple or complex tasks (\(p>0.05\)). Also, physical load did not significantly differ across groups when performing the simple task.
## 2 Results and Discussion
### Electrochemical Impedance Spectroscopy (EIS)  
The charge collection efficiencies (\(\Phi_{cc}\)) were investigated through impedence spectroscopy, carried out under 1 sun illumination, with Nyquist and Bode plots shown in Figure 7a,b, respectively, and summarised in Table 3, on the basis of the Bisquert model [32; 33]. The charge transfer resistance \(R_{ct1}\) was similar for all devices tested, which was expected, as this is known to originate from the counter electrode-electrolyte interface, which was common to all devices. Meanwhile, the SANP-S\(\,\mid\)D149 devices showed slightly lower \(R_{ct2}\) values, related to charge transport through photoanodes, compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Devices** & \(J_{sc}\) **(mA-cm\({}^{-2}\))** & \(V_{oc}\) **(V)** & **FF (\%)** & **PCE (\%)** \\ \hline SANP-S\(\,\mid\)D149 & 16.9 \(\pm\) 0.3 & 0.66 \(\pm\) 0.01 & 64 \(\pm\) 1 & 6.9 \(\pm\) 0.1 \\ SANP-T\(\,\mid\)D149 & 16.1 \(\pm\) 0.6 & 0.64 \(\pm\) 0.01 & 61 \(\pm\) 1 & 6.4 \(\pm\) 0.1 \\ SANP-S\(\,\mid\)N719 & 15.6 \(\pm\) 0.3 & 0.71 \(\pm\) 0.01 & 70 \(\pm\) 1 & 7.7 \(\pm\) 0.1 \\ SANP-T\(\,\mid\)/N719 & 13.0 \(\pm\) 0.4 & 0.70 \(\pm\) 0.01 & 71 \(\pm\) 1 & 6.8 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: J–V parameters of devices based on either SANP-S or SANP-T sensitised with D149 or N719, including short circuit current density (\(J_{sc}\)), open circuit voltage (\(V_{oc}\)), fill factor (FF) and photovoltaic conversion efficiency (PCE).  
Figure 6: **(a)** Current–voltage (J–V) characteristics of SANP-S- and SANP– based devices using D149 or N719, measured under 1 sun illumination with an active area of 0.16 cm\({}^{2}\); **(b)** incident photon to charge carrier efficiency (IPCE) of the devices in **(a)**.  
to the SANP-T\(\,\)I\(\,\)D149 devices. Although this correlates with differences seen in the FF values, it is debatable if this difference in \(R_{ct2}\) is enough to fully explain the differences in FF. On the other hand, SANP-S\(\,\)I\(\,\)N719 and SANP-T\(\,\)I\(\,\)N719 were approximately the same.  
As shown in the Bode plots in Figure 6b, the maximum values of local impedance (\(f_{max}\), low frequencies), were governed by the dye, with both D149-sensitised devices showing a peak at 16 Hz, while the two N719-containing architectures displayed a peak at 25 Hz. Electron lifetimes, obtained using \(\tau_{\text{n}}=\)1/2\(\pi\)\(f_{\text{max}}\)[34], in the D149-based devices therefore appeared longer than those in the N719-based devices. Higher chemical capacitance (\(C_{\mu}\)) values were also calculated for the D149-sensitised devices compared to the N719 ones, suggesting higher electron density resulting from more injected electrons from the photoexcited dye molecules. [35]  
Although these results show slightly better electronic properties for the SANP-S devices, this is most likely due to longer annealing times. It is conjectured that increasing the time the SANP-T electrodes are exposed to 500 \({}^{\circ}\)C should have a similar effect without increasing aggregation, as is the case with the SANP-S approach.
## 5 Technology and Case Studies
### Heavyweight AAC Corcretes  
#### 5.3.1 Concrete for Pavement Application  
The AAC composition with \(\rm M_{b}\) = 1.2 was used in the production of a concrete due to its high strength gain to ensure the required elasticity and durability (corrosion resistance, freeze-thaw resistance, etc.) [90; 101]. The use of properly chosen admixtures ensures the effective modification of a pore structure [102].  
The concrete mix design used in the study was, % by mass: AAC with \(\rm M_{b}\) = 1.2-380, silica sand (fineness modulus 1.94)--710, granite gravel (fr. 5/10)--330, granite gravel (fr. 10/20)--780, triamine + silicone-based defoaming agent--3, water--125.  
Properties of the resulting concrete were compressive strength class C12/15-C40/50, 28 days tensile strength in bending--7.6-7.8 MPa (28 days), modulus of elasticity--28.2\(\cdot\)10\({}^{3}\)-29.9\(\cdot\)10\({}^{3}\) MPa, grade in water impermeability--W8, grade in freeze-thaw resistance--F200,coefficient of corrosion resistance in the solutions of sodium sulphate and magnesium--0.90-1.03, and abrasion--0.53-0.54 g/cm\({}^{2}\).  
The use in the concrete mix of triamine in conjunction with polyhydrosiloxane and silicone-based defoaming agent decreased the open porosity and increased the volume of closed capillary pores. The proposed modification made the concrete based on AAC with M\({}_{\rm b}\) = 1.2 suitable for making pavements with high traffic intensity (road and airport pavement slabs, sidewalk slabs for main streets, etc.).
# Superhydrophobic Surfaces Enabled by Femtosecond Fiber Laser-Written Nanostructures
## Materials & Methods
### Laser System Setup  
There are four parameters that play an especially important role in creating hierarchical structures: pulse repetition rate (PRR), energy per pulse, scanning speed, and pitch [26]. The experiment setup allowed these parameters to be adjusted for processing (Figure 1).  
In the experiment, we used an fs fiber laser (Uranus 3000, Laser-Femto, San Jose, CA, USA), with a wavelength of 1030 nm. The pulse width was 750 fs and the maximum output energy per pulse was 100 uJ. The pulse energy was adjustable by an optical attenuator to obtain the desired energy level. An acousto-optic modulator (AOM, Gooch Housego, Ilminster, UK) was used to adjust the PRR from 100 kHz up to 1 MHz. The laser beam was guided by mirrors (Thorlab) towards an automated scanner (ScanLab, Puchheim, Germany) and focused by an F-theta lens (f = 100 mm, Thorlab, Newton, NJ, USA)onto the sample. The sample was mounted on a computer-programmed 5D translation stage with nano-precision (AeroTech, Pittsburgh, PA, USA). As the motorized stage moved the metal sample in a linear direction, the scanner directed beams at a preset speed onto the sample. The sample was scanned line by line; the distance between each line is called pitch (hatch space).  
A charge-coupled device (CCD) camera (Thorlab) was used to obtain a live view of the laser writing. The output collimated beam was a nearly symmetric Gaussian with M\({}^{2}\) < 1.3 (beam quality factor). A beam shaper (PiShaper, Berlin, Germany) was used to transform the Gaussian shape beam into a flat top shape so that a more uniform energy distribution could be achieved than the original round-shaped beam (Figure 1 inset). All the parameters were controlled by a computer, which signaled the laser to irradiate its beams accordingly. The setup was placed on a 4\({}^{\prime}\)\(\times\)6\({}^{\prime}\) optical table.
## Research of Integrity and Authentication in OPC UA Communication Using Whirlpool Hash Function  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
North China Electric Power University, No.2 Beinong Road, Changping District, Beijing 102206, China; E-Mails: [EMAIL_ADDRESS] (K.W.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (Z.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-10-6177-2747.  
Academic Editor: [PERSON]  
_Received: 21 June 2015 / Accepted: 17 August 2015 / Published: 21 August 2015_  
**Abstract:** Currently, the demand for information security of industrial control systems is becoming more and more urgent, but the security model proposed by OPC UA cannot meet the practical requirements of industrial control systems. For this reason, this paper proposes a new security communication model to provide integrity and authentication in OPC UA. This model uses the Whirlpool hash function to check integrity and generates digital signature along with RSA in message transmission. Compared to SHA-1, Whirlpool has a higher calculation speed and lower collision rate. Through this model, terminals in the upper layer can communicate with field devices via a channel with high security and efficiency.  
**Keywords:** industrial control system; information security; OPC UA; Whirlpool hash function
## 4 Information Fusion and Residual Life Estimation
### Posterior Distributions of Multi-Source Information
#### 4.2.3 Posterior Distribution of Degradation Data  
As has been discussed, the failure time denoted by \(D_{G}=\left\{t_{1}^{G},t_{2}^{G},\cdots t_{l}^{G}\right\}\), can be predicted using the degradation data. Then, similar to historical lifetime data, the posterior distribution of the degradation data is  
\[\pi_{3}(\lambda,\beta|D)=\frac{\tau^{a_{3}\beta}\beta^{l}(E^{G})^{\beta-1} \lambda^{l+a_{3}-1}\exp\left[-\lambda(F^{G(\beta)}+b_{3}\tau^{\beta})\right]}{ \int_{\beta_{1}}^{\beta_{2}}\tau^{a_{3}\beta}\beta^{l}(E^{G})^{\beta-1}\frac{ \Gamma(l+a_{3})}{(F^{G(\beta)}+b_{3}\tau^{\beta})^{l+a_{3}}}d\beta}, \tag{47}\]  
where \(E^{G}=\prod\limits_{i=1}^{l}t_{i}^{G}\), \(F^{G(\beta)}=\sum\limits_{i=1}^{l}\left(t_{i}^{G}\right)^{\beta}\), \(a_{3}=\left\{\begin{array}{l}0,d\
eq 0\\ 1/2,d=0\end{array}\right.\), \(b_{3}=0\), \(M=\prod\limits_{i=1}^{r}t_{i}\), and \(N^{(\beta)}=\sum\limits_{i=1}^{n}t_{i}^{\beta}\). \(l\) is the sample size of \(D_{d}\), and \(r\) is the number of failures of field data.  
Similar to Section 4.2.1, for the failure time (\(t_{j}^{G}\), \(j\leq l\)), we have  
\[\begin{array}{ll}&f(t_{j}^{G}|\pi_{3}(\lambda,\beta))\\ =&\int_{\beta_{1}}^{\beta_{2}}\int_{0}^{\infty}f(t_{j}^{G}|\lambda,\beta)\cdot \pi_{3}(\lambda,\beta)d\lambda d\beta\\ =&\frac{\int_{\beta_{1}}^{\beta_{2}}\tau^{a_{3}\beta}(t_{j}^{G})^{\beta-1}\beta^ {d+1}(c^{G^{\beta-1}}(l+a_{3})\left[F^{G(\beta)}+(t_{j}^{G})^{\beta}+b_{3} \tau^{\beta}\right]^{-(l+a_{3}+1)}d\beta}{\int_{\beta_{1}}^{\beta_{2}}\tau^{a_{ 3}\beta}\beta^{l}(E^{G})^{\beta-1}(F^{G(\beta)}+b_{3}\tau^{\beta})^{-(l+a_{3} )}d\beta}.\end{array} \tag{48}\]The marginal PDF is  
\[\begin{array}{ll}&\int_{\beta_{1}}^{\beta_{2}}\int_{0}^{\infty}\pi_{3}(\lambda, \beta)P(D|\lambda,\beta)\ d\lambda d\beta\\ =&\int_{\beta_{1}}^{\beta_{2}}\left(\int_{0}^{\infty}\pi_{3}(\lambda,\beta) \cdot\lambda^{\prime}\beta^{\prime}M^{\beta-1}\exp\Bigl{(}-\lambda N^{(\beta)} \Bigr{)}d\lambda\Bigr{)}d\beta\\ =&\frac{\int_{\beta_{1}}^{\beta_{2}}\left(\int_{0}^{\infty}\tau^{3+\beta} \beta^{\prime+}(E^{\alpha}\lambda)^{\beta-1}\lambda^{\prime+1-\alpha}\exp \Bigl{[}-\lambda(E^{(\beta)}+N^{(\beta)}+b_{3}\tau^{\beta)}\Bigr{]}d\lambda \Bigr{)}\mu_{\beta}}{\int_{\beta_{1}}^{\beta_{2}}\tau^{\alpha_{3}\beta}\beta(E ^{(\beta)}+b_{3}\tau^{\beta})^{-1}\frac{\Gamma(+\alpha_{2})}{(E^{(\beta)}+b_{ 3}\tau^{\beta})^{1+\alpha_{3}}}d\beta}\\ =&\frac{\int_{\beta_{1}}^{\beta_{2}}\left(\tau^{\alpha_{3}\beta} \beta^{\prime+}(E^{\alpha}\lambda)^{\beta-1}\frac{\Gamma(+\tau+\alpha_{3})}{( E^{(\beta)}+b_{3}\tau^{\beta})^{1+\alpha_{3}}}d\beta\right.\right.}{\left.\left. \left.\left(E^{(\beta)}+b_{3}\tau^{\beta}\right)^{1+\alpha_{3}}\right.\right) \beta}\\ =&\frac{\int_{\beta_{1}}^{\beta_{2}}\left(\tau^{\alpha_{3}\beta} \beta^{\prime+}(E^{\alpha}\lambda)^{\beta-1}\frac{\Gamma(+\tau+\alpha_{3})}{( E^{(\beta)}+b_{3}\tau^{\beta})^{1+\alpha_{3}}}d\beta\right.}{\left.\left.\left(E^{( \beta)}+b_{3}\tau^{\beta}\right)^{1+\alpha_{3}}\right.\right.}\end{array} \tag{49}\]
## 3 Modeling the Auditory Distance
### Binaural Spectral Magnitude Difference Standard Deviation  
In [22] a feature is introduced related to the standard deviation of the magnitude spectrum of the room transfer function. Similar to the D/R ratio, this feature, noted as BSMD STD, represents a distance-dependent behavior and is implemented to model the source-listener distance within the freely available Auditory Modeling Toolbox ([[http://amtoolbox.sourceforge.net/](http://amtoolbox.sourceforge.net/)]([http://amtoolbox.sourceforge.net/](http://amtoolbox.sourceforge.net/))). For calculating the BSMD STD, any binaural signal is sufficient.  
Binaural input signals are generated by firstly convolving the signal of each propagation path arriving at the listener with respective HRTF measurements of a KEMAR dummy-head microphone (freely accessible measurements of the MIT available at [[http://sound.media.mit.edu/resources/KEMAR.html](http://sound.media.mit.edu/resources/KEMAR.html)]([http://sound.media.mit.edu/resources/KEMAR.html](http://sound.media.mit.edu/resources/KEMAR.html))) and then summing up obtained signals for each ear respectively. The linear regression yields the same correlation as the D/R ratio (\(R^{2}=0.93\) with \(k=0.32\) and \(d=-1.52\)), although their progression along the beampattern index is qualitatively different, cf. Figure 9b.  
Figure 9: Comparison of medians and 95% confidence intervals for all conditions (thin lines) with predictors (thick lines): D/R, BSMD STD, LF, and IACC.
## Materials and Methods
### Image Feature Extraction  
#### 2.4.1 Histogram of Oriented Gradient (HOG)  
In image recognition, the histogram of oriented gradient (HOG) can be used to describe the characteristics of the image. This method calculates the statistics of the pixel distribution in the target image, and uses the gradient distribution characteristics and directional density distribution of the pixel values in the figure to describe the texture and boundary in the image. The calculation method of HOG features is as follows:  
For color images, it is necessary to first perform gray processing and gamma correction on the gray images, which is helpful to reduce the influence of uneven distribution of light intensity and noise on texture features in the image.  
1. Calculation of pixel distribution gradient. Common forms of simple convolution are [-1,0,1] and [-1,0,1]\({}^{T}\). Convolution calculation is performed on the preprocessed image to generate two orthogonal gradient components, and the gradient direction of each pixel can be calculated according to the component. The calculation formula is shown as follow: \[\left\{\begin{array}{rl}G(x,y)=\sqrt{G_{x}(x,y)^{2}+G_{y}(x,y)^{2}}\\ M(x,y)=\tan^{-1}\left(G_{y}(x,y)/G_{x}(x,y)\right)\\ G_{x}(x,y)=H(x+1,y)-H(x-1,y)\\ G_{y}(x,y)=H(x,y+1)-H(x,y-1)\\ \end{array}\right.\] (17) where \(G(x,y)\) is the gradient amplitude of the pixel, \(M(x,y)\) is the gradient direction of the pixel, \(G_{x}(x,y)\) is the horizontal gradient of the pixels, \(G_{y}(x,y)\) is the vertical gradient of the pixels, \(H\left(x,y\right)\) is the gray value of the pixel.
2. Cell unit division and gradient direction histogram calculation. First, we divide the image into cells of the same size, generally set to \(8\times 8\) pixels, and limit the gradient direction to [0, \(\pi\)]. The gradient direction is evenly divided into 9 intervals, and the gradient value of each pixel in the cell unit is weighted and projected in the histogram. The gradient direction histogram of the cell unit is generated, and each interval corresponds to a one-dimensional feature vector.
3. HOG feature vector calculation. In the image, the four cells in the cube are combined into a block, and the block corresponds to a total of 36-dimensional feature vectors. In order to reduce the influence of the local illumination on the image on the feature vector, all the calculated feature vectors are normalized and combined to generate the HOG feature vector of the entire image.
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 5 Roles of _P. ginseng_ in the Oxidative-Stress-Related Signaling Pathways  
It has been reported that _P. ginseng_ and its derivatives are some of the antioxidant-rich sources involved in the regulation of many oxidative-stress-related pathways [20]. Ginseng and ginsengides have antioxidant activity, inhibiting oxidative stress, and their preventive effects are explained by their ability to scavenge ROS. Several in vitro and in vivo experiments have depicted that _P. ginseng_ and ginsengides increase antioxidant enzymes activity to inhibit ROS. _P. ginseng_ showed a barrier role against oxidative stress in several cell lines. Furthermore, the supplementation of _P. ginseng_ increases antioxidant enzymes and reduces the generation of ROS and MDA in various tissues such as heart, lung, kidney, and liver in animal models. Several studies have shown that ginseng IgG1 can stimulate the Nrf2/ARE pathway and mitigate neuronal injury prompted by ischemia reperfusion (I/R) [52]. Rg1 prevents the inflammation and oxidative stress of diabetic rats by inducing the Keep1/Nrf2 pathway, increases the survival rate of cells, and also minimizes the excessive ROS and cell apoptosis by increasing the Nrf1/HO-1 pathway [53]. Similarly, the administration of ginseng Rd reduces serum CK and LDH and induces HO-1 and Nrf2 expression to protect against myocardial I/R injury [54]. In addition, by activating the Nrf2 pathway, ginside Rb1 increases GSH levels and decreases MDA content. This antioxidant regulatory activity of Rb1 relieves diabetic retinopathy [55; 56]. Moreover, Rb1 enhances antioxidant enzymes activity that reduce the MDA level in spinal-cord-injured (SCI) rats by stimulating the Nrf2/HO-1 signaling pathway [57]. In addition, ginsengide Rg1 remarkably improves SOD and GSH contents, inhibits MDA production and ROS levels, and exerts antioxidant and anti-inflammatory activity via the activation of the Nrf2/HO-1 signaling pathway [58]. 20(S)-Rg3 and 20(R) compounds induce the expression of antioxidant enzymes and protect H\({}_{2}\)O\({}_{2}\)-mediated myocardial cell injury through the activation of the Nrf2/HO-1 pathway [59]. Along with these reports, ginsengide Rg3 inhibits oxidative damage and apoptosis via activating Nrf2/ARE through Akt activation [60]. Ginsenoside compound K (CK) inhibits diabetic nephropathy via inhibiting ROS generation and inflammatory cytokines IL-1\(\beta\) through the upregulation of the NF-kB/p38 signaling pathway [61]. Both ginsengide Rb1 and Rg1 increase the activity of catalase and reduce ROS formulation in case of inhibiting myocardial oxidation [62]. Ginsenoside Rg1 induces SOD and GSH-x enzyme activity and reduces the ROS and MDA level to attenuate oxidative-stress-mediated aging and the Wnt/\(\beta\)-catenin pathway. Similarly, red ginseng (RG) also provides significant antioxidant activity in several disease cases such as diabetes, skin cancer, hepatic and nerve disorder, etc. RG enhances the antioxidant activity and reduces the oxidative stress that is exposed in Table 1.