esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 5 Roles of _P. ginseng_ in the Oxidative-Stress-Related Signaling Pathways
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diseases** & **Ginseng/Ginsenosides** & **Action** & **Model** & **References** \\ \hline Intestinal I/R injury & Rg1 & \(\downarrow\)ROS, \(\downarrow\)noptosis & In vitro & [52] \\ Adriamycin-induced & & \(\downarrow\)ROS, \(\downarrow\)tunneling positive cell & In vivo & [52] \\ cardiotoxicity & & \(\uparrow\)ROS, \(\downarrow\)MDA, & & \\  & & \(\uparrow\)SOD, \(\uparrow\)ENOS & In vitro & [60] \\ Inflammation in asthma & & \(\uparrow\)Nrf2, \(\uparrow\)HO-1, \(\uparrow\)SOD, \(\uparrow\)GSH, & & \\  & & \(\downarrow\)MDA & & In vivo/Mice & [63] \\ Diabetic nephropathy & CK & \(\downarrow\)ROS, \(\downarrow\)IL-1\(\beta\) & In vivo/Mice & [61] \\ Myocardial cell injury & 20(S)-Rg3 and 20(R) & \(\downarrow\)ROS, \(\downarrow\)MDA & & In vitro & [59] \\ Myocardial & & \(\uparrow\)SOD, \(\uparrow\)GSH-Px, \(\uparrow\)CAT & & \\ ischemia–reperfusion & & \(\downarrow\)CK, \(\downarrow\)LDH, & In vivo/Rats & [54] \\ Spinal cord injury & & \(\uparrow\)HO-1 & \(\uparrow\)SHOI, \(\uparrow\)CAT, \(\uparrow\)GSH & In vivo/Rats & [57] \\ Rat SCI model & & \(\downarrow\)MDA, \(\uparrow\)SOD, \(\uparrow\)GSH & In vivo/Rats & [58] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antioxidant activity of ginseng/ginsenosides in oxidative-stress-mediated diseases.
## 3 Results
### Bacterial Community  
PCR-DGGE profiles from different variants of experiments differed concerning the absence and density of the bands. A phylogenetic dendrogram shows that bacterial communities were grouped into two main branches (Figure 7). One branch represents communities from digesters operated with OLRs at 1.0 gCOD/dm\({}^{3}\cdot\)d and 2.0 gCOD/dm\({}^{3}\cdot\)d. Under the OLR of 2.0 gCOD/dm\({}^{3}\cdot\)d, the bacterial community in the control system showed greater similarity to communities in conditions for an OLR of 1.0 gCOD/dm\({}^{3}\cdot\)d than the community operated with the same OLR, but with a different heating method. The second main branch was formed by communities from digesters operated with an OLR of 3.0-6.0 gCOD/dm\({}^{3}\cdot\)d. The band patterns showed similarities between bacterial communities from variants with the same OLR, despite different heating methods (MW4 and C4; MW3 and C3; MW5 and C5; MW6 and C6; C1 and MW1).  
Greater bacterial diversity was observed in digesters operated with a higher OLR. The Shannon diversity index (\(H\)) was used to calculate the diversity of the bacterial communities, and it ranged from 1.44 to 1.91. Higher \(H\) values were observed at 5.0 gCOD/dm\({}^{3}\cdot\)d and 6.0 gCOD/dm\({}^{3}\cdot\)d OLRs, indicating higher numbers of species (species richness) and higher relative abundances in the digesters operated with these OLRs. Some bands were common in almost all samples (e.g., bands 4 and S), while others were only present in some of the digesters (e.g., band 3--MW6; band 15--MW4) (Figure 8). Some of the bands characterized digesters with lower OLR (bands 7, 8, 9, 10), some of them characterized digesters with higher OLR (bands 18, 19, 20).  
Only bands 1, 2, and 20 had sufficient quality for the studies of phylogenetic analysis. When the 16S rDNA partial sequences from these bands were compared with the database in GenBank, bands 1 and 2 showed the highest similarity to Flavobacteriaceae (Bacteroides) (78.2% and 94.3%, respectively), and band 20 was closely related to _Tissierella_ sp. (98.3%).  
Figure 7: Phylogenetic dendrogram based on the DGGE profiles.
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 2 Invasive Methods
### Ion-Selective Field-Effect Transistor (ISFET)
However, more improvements are still required for reducing detection limits in order to produce more accurate and reliable glucose measurements. Despite the effectiveness, invasive methods face challenges such as sensor calibration, potential discomfort, and biological responses at the implantation site. As the demand for accurate, user-friendly monitoring solutions grows, numerous innovative approaches focused on minimally invasive and non-invasive technologies.
## 3 Joint Optimization Algorithm Design
### Complexity Analysis  
The complexity of a neural network usually depends on the state dimension of the input layer, the number of neural network layers, and the number of neurons in the hidden layers and output layer. Since the MAHPPO algorithm contains \(U\) actor networks and a critic network, the complexity of the MAHPPO algorithm can be expressed as \(\mathcal{O}\Big{(}E_{p_{\max}}K^{\max}\Big{(}U\sum_{l=1}^{L^{a}-1}e_{l}^{a}e_{l+ 1}^{a}+\sum_{l=1}^{L^{c}-1}e_{l}^{c}e_{l+1}^{c}\Big{)}\Big{)}\), where \(L^{a}\) and \(L^{c}\) represent the number of neural network layers of the actor and critic networks, respectively, and \(e_{l}^{c}\) and \(e_{l}^{c}\) represent the number of neurons in the \(l\)-th layer of the actor and critic networks, respectively. Since the maximum value of \(U_{\text{co}}\) and \(U_{\text{of}}\) is \(U\), the highest algorithm complexity of Algorithm 2 and Algorithm 3 is \(\mathcal{O}(U(U+M))\) and \(\mathcal{O}(U)\). Obviously, the complexity of Algorithm 2 is higher than that of Algorithm 3. In order to reduce the complexity of Algorithm 4, Algorithm 3 is executed with priority so that Algorithm 2 only needs to be executed once. In summary, the computational complexity of Algorithm 4 is \(\mathcal{O}\Big{(}E_{p_{\max}}K^{\max}\Big{(}U\sum_{l=1}^{L^{a}-1}e_{l}^{a}e_{l +1}^{a}+\sum_{l=1}^{L^{c}-1}e_{l}^{c}e_{l+1}^{c}+U(U+M)+U\Big{)}\Big{)}\).
## 3 Results
### Trends in NPP
Panels (**f-j**) are similar to (a–e) except that the variables NPP, temperature, precipitation and CO\({}_{2}\) are interannual anomalies (the linear trends are removed from the original data). In each panel, R\({}^{2}\) (coefficient of determination) values for the regression model is also shown. Over all, this analysis indicates that increasing atmospheric CO\({}_{2}\) is probably the main driver for the positive trend in NPP, although other causes such as land use change cannot be ruled out. In the absence of other limiting factors, increasing atmospheric CO\({}_{2}\) concentration has the potential to stimulate photosynthesis (or Gross Primary Productivity when scaled up) and, hence, NPP. This is an effect known as CO\({}_{2}\) fertilization [24, 29, 30]. Experimental studies provide evidence for CO\({}_{2}\) fertilization: for example, free-air CO\({}_{2}\) enrichment experiments in forest stands show \(\sim\)23% median increase in NPP for a CO\({}_{2}\) change from 376 to 550 ppm [31]. Long-term tree ring studies suggest a more complex picture and perhaps a lack of universality of the CO\({}_{2}\)-fertilization effect [32]. Coupled climate-carbon models all show increases in terrestrial carbon uptake as a result of CO\({}_{2}\) fertilization [33, 34, 35, 36]). In a recent paper, the bounds on terrestrial carbon uptake over India that arises solely due to CO\({}_{2}\) fertilization were investigated [37]. It was found that NPP increased by 84% solely due to CO\({}_{2}\) fertilization by the year 2100 relative to 1975 and decreased by 13% due to climate change for the SRES A2 emissions scenario. Since the CO\({}_{2}\)-fertilization effect was about 4 to 5 times stronger than climate effect in that modeling study, the net effect was an enhancement in NPP by about 71% between 2100 and 1975. Global carbon budgets studies have also indicated that the global land is a sink for carbon [38, 39]: in spite of deforestation in the tropics, about 20%-25% of the fossil emissions have been taken up by land in past decades. Therefore, it is likely, as shown by our analysis, that CO\({}_{2}\) fertilization is the main driver for the increase in NPP in India and also, possibly, for the positive trend in global mean NPP [8]. More research is needed to find out the extent to which CO\({}_{2}\) fertilization is responsible for present terrestrial carbon uptake and potential future terrestrial ecosystem carbon sink.
## 3 Choosing Right Component for MIP
### Cross-Linker  
The cross-linker, as a reagent of MIP, has a significant role in the formation of MIP. The role of the cross-linker is to participate in the formation of the physical characteristics of polymers in MIP. The physical characteristics of the polymer affected by the presence of the cross-linker are the morphological characteristics of the absorbent structure, three-dimensional structure, optimal stiffness, and the durability of the MIP [59; 65].  
There are several important factors in the use of cross-linkers, including the type and number of cross-linkers used for polymerization. The higher number of cross-linkers makes it possible to obtain a stable porous material [17]. A study was carried out by [PERSON] et al. [61] to see the effect of cross-linker amount on adsorption performances of a MIP. Their research proved that the adsorption performance of a MIP would increase to a certain concentration of cross-linker. After reaching optimum conditions, the further addition of the cross-linker will not increase the adsorption performance; it will even decrease the adsorption performance. The chemical compounds most often used as cross-linkers are trimethyllopropate trimethacrylate (TRIM), divinylbenzene (DVB), and ethylene glycol dimethacrylate (EGDMA) [59; 66].  
The presence of a cross-linker also affects the morphology of MIP that will affect the binding capacity of MIP. Studies conducted by [PERSON] et al. [67] and [PERSON] et al. [68] showed similar results that lower cross-linker concentrations provide greater binding capacity. This phenomenon occurs because the mean pore diameter decreased with increasing EGDMA, which may have resulted in a greater sieving ability of these polymers, which leads to lower overall capacity. Several studies have shown a comparison of the cross-linkers commonly used in MIP. [PERSON] et al. [66] compared the use of TRIM and EGDMA in MIP with methacrylic acid (MAA) as functional monomer and carbamazepine (CBZ) as the template. Their study showed that MIP using TRIM (MAA-TRIM) as a cross-linker showed better binding capacity and had more carbamazepine recognition sites compared to MAA-EGDMA, with MAA-TRIM binding capacity > 300 (around 300-500) micrograms CBZ while MAA-EGDMA binding capacity was around 300 micrograms CBZ, both measured at 180 min. Another study from [PERSON] et al. [69] also, MIP with TRIM as a cross-linker showed a higher binding capacity percentage of 45-50% than MIP with EGDMA as cross-linker, which only shows 40-45%. The structure of TRIM and EGDMA possibly influences this. In the TRIM molecule, three-branched chains contain three vinyl groups, making TRIM more preferable in the polymerization process and resulting in more rigid complementary recognition sites for the template than the EGDMA molecule only has two vinyl groups. The contribution of the cross-linker, of course, also depends on other components, such as using the suitable functional monomer for the template or selecting the right solvent in the polymerization process [66; 69].
## 6 Conclusions  
1. In this paper, a simulation model of the operating overvoltage of a dropping pantograph based on the pantograph-catenary arc and variable capacitance is proposed. The error between the simulation and test overvoltage amplitudes under the same conditions is less than 4.0%, which proves the credibility of the model.
2. The duration of the arc is tens of nanoseconds, which is similar to the duration that is characteristic of VFTO. This shows that the operating overvoltage of a dropping pantograph has ultra-fast transient characteristics.
3. The amplitude of the operating overvoltage of a dropping pantograph increases with the decrease in pantograph dropping speed. When the speed is 0.30 m/s, the amplitude of the operating overvoltage of the dropping pantograph reaches the maximum value of 298.20 kV. When the speed is 0.90 m/s, the operating overvoltage of the dropping pantograph reaches the minimum value of 39.48 kV, and almost no overvoltage is observed at this time.  
Figure 14: Relationship between the amplitude of the dropping pantograph’s operating overvoltage and the catenary voltage phase.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \
\begin{tabular}{c} **The Phase of Catenary Voltage (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Operating Overvoltage of** \\ **Dropping Pantograph (kV)** \\ \end{tabular} & **Arc Duration (\(\mu\)s)** \\ \hline
30 & 79.33 & 4.98 \\
45 & 100.32 & 5.12 \\
60 & 130.60 & 2.58 \\
75 & 176.85 & 3.33 \\
90 & 298.20 & 3.02 \\
105 & 141.56 & 2.82 \\
120 & 99.63 & 5.57 \\
135 & 59.50 & 3.34 \\
150 & 46.06 & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amplitude of pantograph dropping overvoltage at different catenary phases.  
* The amplitude of the operating overvoltage of a dropping pantograph varies sinusoidally with the phase of catenary voltage. When the phase of catenary voltage is 90\({}^{\circ}\), the amplitude of the operating overvoltage of the dropping pantograph reaches the maximum value of 298.20 kV. When the phase of the catenary voltage is 150\({}^{\circ}\), the amplitude of the operating overvoltage of the dropping pantograph reaches the minimum value of 46.06 kV.  
Software, Y.G.; Validation, H.L.; Formal analysis, D.J.; Resources, Y.Y.; Writing--original draft, Y.G.; Writing--review & editing, F.T.; Supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive Open Fund Project (Grant No. GZKFFKT2022-002).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the Baoshen Railway Group, CHN ENERGY. [PERSON] was employed by the CRRC Zhuzhou Locomotive CO,LTD. [PERSON] was employed by the Beijing Jiaotong University.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction  
Oyster mushrooms (genus _Pleurotus_) are among the most cultivated and consumed mushrooms in the world [1, 2]. Their fruiting bodies (basidiocarps) are low in calories, fats and essential fatty acids,but contain large amounts of proteins with almost all of the essential amino acids, polysaccharides including soluble and insoluble glucans, dietary fibers, vitamins and minerals [3; 4; 5]. Basidiocarps and compounds derived from them have demonstrated evident medicinal impacts [6]. Polysaccharides, especially \(\beta\)-b-glucans, have positive effects on human health [7; 8]. Various polysaccharides, mainly mannogalactans and glucans, have been isolated from _Pleurotus_ mushrooms and characterized as biologically active compounds [9; 10; 11; 12; 13; 14].  
The specie _Pleurotus ostreatus_ is the most common among all oyster mushrooms [15]. Basidiocarps of this mushroom had hypocholesterolem effects in animal models and patients [16; 17] as well as potent antinociceptive effect in rats fed with it [18]. Antioxidant and anti-inflammatory activities of these basidiocarps were also reported [19; 20]. A water-insoluble (1-3)-\(\beta\)-d-glucan, named \"pleuran\", obtained from _P. ostreatus_ was the most studied polysaccharide of oyster mushrooms [21]. This glucan, as well as other polysaccharides isolated from this source, demonstrated many medicinal activities including immunomodulating [13; 22], antitumor [23], antioxidant [10] and prebiotic [14] effects. Various strains of _P. ostreatus_ with specific biopolymer compositions have been obtained for cultivation on various substrates [24].  
Screening of crude basidiocarps originating from different strains of _P. ostreatus_ is essential to evaluate the nutritional quality and prospects of these mushrooms for culinary use or as the source for preparation of food supplements. Methods of vibration spectroscopy have proved to be very useful in this respect [25]. These methods themselves and together with multivariate analyses have been applied to detect proteins, polysaccharides, proteins, sterols and aromatic compounds in fractions derived from basidiocarps of oyster mushrooms [14; 26; 27; 28; 29]. In addition, the combination of FTIR spectroscopy and multivariate analysis has been used for the discrimination of fungal samples of various origin, location and treatment [29; 30; 31].  
The main purpose of this work was to evaluate the methods of vibration spectroscopy in the screening analysis of edible and medicinal mushrooms, primarily to characterize the biopolymer composition of fruiting bodies. The subject of the study was 16 samples of basidiocarp of various _P. ostreatus_ strains promising for cultivation and gastronomic use. Sample discrimination was performed using principal component analysis (PCA) of spectroscopic data obtained by Fourier-transformed mid- and near-infrared (FT MIR, FT NIR) and FT Raman spectroscopic measurements. Partial least squares regression (PLSR) models were created to predict protein and total glucan contents based on the mentioned spectroscopic data.
## 1 Introduction
In a study by [PERSON] et al. [10] rainbow trout samples were subjected to 150, 300, 450 and 600 MPa pressure for 15 min in room temperature. Two latter pressures induced higher hardness and cohesiveness of fish meat. Similar results were obtained by [PERSON] et al. [11], when they studied salmon (_Salmo salar_) samples processed at 150 and 300 MPa for 15 min at 4 \({}^{\circ}\)C. Pressure increased hardness, gumminess, and chewiness, and decreased adhesiveness when compared to the control. No significant differences in hardness, adhesiveness, gumminess, and chewiness were observed for samples subjected to 150 MPa and the control. This was further demonstrated in [PERSON] et al. [12] when testing cutting strength with salmon. They found no difference in toughness between the control and the pressurized samples (400-900 MPa for 20 s), but on the other hand, samples subjected to 900 MPa for 10, 30 or 60 s were tougher than the samples processed at 400 MPa. While the previous literature indicates that at least 400-600 MPa might be needed to inactivate spoiling and pathogenic microorganisms in fish products, pressures higher than 300 MPa may risk sensory quality; yet, the pressure thresholds might be affected by the fish species in question [13]. All these findings reflect the importance of testing the appropriate processing parameters case by case, as the amount of pressure, holding time and temperature all influence the food item itself and thus, the consumer acceptance. Rainbow trout is among the most important farmed fish species [14]; yet, only a limited number of papers have been published on the effects of HPP parameters on rainbow trout quality. The aim of this study was to investigate how HPP with different treatment intensities in regards to pressure level affects protein solubility, protein digestibility, and WHC of cold smoked and hot smoked rainbow trout. In addition, we wanted to examine how these treatments alter the consumer perceived sensory properties and whether the protein-related variables contribute to the observed changes in sensory quality.
## 4 Review of Existing Methods to Track Bio-Fraction of Co-Processed Fuels
### Radiocarbon Methods
#### 4.3.1 Accelerator Mass Spectrometry  
An Accelerator Mass Spectrometer detects atoms of specific elements according to their different masses. In essence, it converts atoms in the test sample into a beam of fast-moving ions. It is this conversion into fast moving ions which gives the system the 'accelerator\" part to its name. The mass of these ions is then measured by applying magnetic and electric fields and recording their retention time.  
The method is highly sensitive (or accurate), and as such AMS is successful at detecting 14C in a sample as it can ignore the other more abundant carbon isotopes (i.e., 12C and 13C) that would otherwise \"swamp\" or mask the 14C signal (which needs to be seen clearly in order for its measurement to occur). More details on AMS can be found in [38; 39] While AMS can be used to detect the 14C and hence bio-content in a fuel, it is useful to have some perspective on this method. AMS has been used traditionally by archeologists to estimate the age of archaeological samples such as bones. It is able to test for 14C content using very small samples (and can also measure 14C in gas samples), but this is no advantage in fuel testing. In summary, its application in fuel testing is a relatively new development.
## 6 Conclusions  
This paper introduces a novel data hiding scheme, Selective Redundancy Removal (SRR), based on the selective application of a subset of semantically neutral transformations, chosen among those commonly used when optimizing data for reducing size and increasing processing speed. The receiver may decode the message by looking at the optimizations that the sender could have carried out but had not. An example of the application of the SRR framework is hiding of information in a HTML document. Visual web page editors are known to produce, for the sake of their own efficiency and speed when being run, bulky and inefficient HTML markup, with repetitions and code having no effect. Removal of this unneeded markup, if performed selectively, can be used to hide a message. An adversary would have to prove that the selectively optimized code would unlikely have been produced by a HTML generation system, by an optimization system, by hand, or a combination thereof. The proposed technique has an admittedly low relative capacity as compared to LSB-like approaches. Differently from those encodings, SRR does not require an encoding sequence that determines the locations in the carrier where encoded bits must be found. This would be especially noticeable with non-textual supports. Finally, LSB (Least Significant Bit) steganographic techniques traditionally employed on images and audio affect the source with tiny alterations, relying on the perceptual error margin. In contrast, like lossless data embedding methods (see for example [15]) SRR does not destroy any semantically-influent information in the source, as only optimizing transformations are used.
## 4 Simulations and Discussion  
In this section, the USV model, controller, and disturbance observer are written in MATLAB functions based on the Simulink environment, and numerical simulations are performed using a variable step size to verify the effectiveness of the designed control strategy. First, the trajectory tracking performance was validated under ideal conditions. Under non-ideal conditions, the disturbance caused by the external environment is unknown and cannot be directly measured. Next, constant environmental disturbances were introduced, disturbance observers were used to measure disturbances, and NTSM controllers with disturbance compensation were employed to achieve trajectory tracking. The inclusion of constant environmental disturbances alone is insufficient to prove the observation effect of the disturbance observer for unknown disturbances and cannot reflect real-world scenarios. Therefore, time-varying environmental disturbances were introduced to verify the compensation for the disturbance and the trajectory tracking. The model parameters of the USV can be found in [41] and are shown in Table 1.  
The controller's control performance was verified using the following two trajectories under three different conditions.
## 5 Experiment Verification and Analysis  
In order to test the performance of the fault diagnosis method proposed in this paper and verify its effectiveness, experiments are carried out with two different datasets: the bearing dataset of CWRU and the worm gear reducer dataset of Shanghai University of Technology, which are compared with other traditional intelligent diagnosis methods.