esa-sceva/satcom-chunk-collection huggingface dataset:
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 3 Vaginal Microbiome  
The vaginal microbiome is a crucial component of the female body's microbiome and plays a significant role in maintaining vaginal and reproductive health [15]. It is involved in maintaining its homeostasis and equilibrium. The species within the vaginal microbiome are mutually interdependent and controlled by various factors, such as local immunity, the  
Figure 2: Main polyphenols covered in this review.  
endocrine system, and the internal environment [16]. Disruption in this balance can lead to dysbiosis, which is associated with various adverse health outcomes, including bacterial vaginosis (BV), preterm birth (PTB), and increased risk of STIs [17; 18]. The vaginal microbiome consists of a diverse array of microorganisms, where the host provides the bacteria with nutrients needed to support their growth, and, in exchange, they play a protective role in preventing colonization of the host by potentially pathogenic organisms [19].  
Culture-independent methods primarily relying on the examination of 165 rRNA gene sequences are employed to identify microbial diversity [20]. By utilizing this approach, researchers have been able to determine that vaginal communities are primarily governed by _Lactobacillus_ spp., responsible for around 73% of its microbiota, and the more prevalent species were _L. crispatus, L. iners, L. gassert_, and L. jensii_[19].  
Lactobacillus convert glycogen from exfoliated epithelial cells into lactic acid, keeping the vaginal pH of healthy women of reproductive age acidic (pH between 4 and 5), which inhibits the growth of pathogenic microorganisms [19; 20]. They also exhibit antimicrobial properties beyond the effect of lactic acid production, such as producing target-specific bacterios. The vaginal microbiome, and consequently the pH, varies by age, menstrual cycle stage, the occurrence of infection, and sexual arousal. Menstrual, cervical, and uterine secretions, and sperm act as alkalizing agents and raise the pH. Changes in hormone levels (particularly estrogenic) during the menstrual cycle cause changes in epithelial cell layer thickness, intercellular channel width, pH, and secretions [21].  
Contrary to previous beliefs associating a \"healthy\" and \"normal\" vaginal microbiome with the predominance of _Lactobacillus_ spp. [22], recent studies have revealed that 20% to 30% of healthy women carry vaginal communities where lactobacillus are not significantly present. Instead, these communities consist of a diverse range of facultative or strictly anaerobic bacteria, such as _Atopobium_, _Corynebacterium_, _Anareococcus_, _Peptonipililus_, _Prevotella_, _Gardnerella_, _Sneathia_, _Eggerthella_, _Mobilincus_, and _Finegoldia_, among others [19; 23]. These microorganisms possess the ability to sustain functional vaginal ecosystems by preserving lactic acid production, and some of them can engage in either homolactic or heterolactic acid fermentation [24].  
Understanding the vaginal microbiome and its role in maintaining vaginal equilibrium is crucial for promoting women's health and reproductive well-being.
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
###### Abstract  
Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid [PERSON] algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid [PERSON] algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.  
velocity calculation; optical flow; pyramid LK; Mask-R-CNN; clustering algorithm
## Appendix A Derivation of the Precision Measurement Optimization Model
### Derivation of Equations (1) and (2)
Since plane \(B\) passes through the origin, the point-normal form of the equation for plane \(B\) is:  
\[x\cdot\cos\beta+y\cdot\sin\beta=0\]  
As depicted in Figure A1c, the projection of plane \(C\) onto the \(XOY\) plane is represented by \(n_{2}\), and \(n_{3}\) is the normal vector of plane \(C\) passing through the origin. It follows that\(n_{3}=(\sin\beta,-\cos\beta,0)\). Since plane \(C\) passes through the origin, the point-normal form of the equation for plane \(C\) is:  
\[x\sin\beta-y\cos\beta=0\]  
To find the line of intersection \(l_{1}\) between plane \(A\) and plane \(B\), consider their normal vectors:  
\[n_{1} =(\sin\alpha,0,\cos\alpha),\] \[n_{2} =(\cos\beta,\sin\beta,0).\]  
According to Theorem A1, the direction vector \(\mathbf{w}\) of the line of intersection \(l_{1}\) is given by the cross product of the normal vectors of planes \(A\) and \(B\):  
\[\mathbf{w}=(-\cos\alpha\sin\beta,\cos\alpha\cos\beta,\sin\alpha\sin\beta).\]  
Subsequently, the coverage distance at the central point of the marine area is determined. Figure A2 illustrates the triangle \(PQH\) formed by the multibeam echosounder from the survey ship at the center of the marine area and the seabed slope. Given the vectors \(\mathbf{e}_{1}=(0,0,1)\) and \(\mathbf{w}\), the angle \(\mu\), which is the angle between \(EH\) and \(PE\), is computed as follows:  
\[\cos\mu=\frac{\mathbf{e}_{1}\cdot\mathbf{w}}{|\mathbf{e}_{1}|\cdot|\mathbf{w }|}=\frac{\sin\alpha\sin\beta}{\sqrt{(\cos\alpha\sin\beta)^{2}+(\cos\alpha \cos\beta)^{2}+(\sin\alpha\sin\beta)^{2}}}\]  
Therefore, the angle \(\mu\) is given by:  
\[\mu=\arccos\left(\frac{\sin\alpha\sin\beta}{\sqrt{(\cos\alpha\sin\beta)^{2}+( \cos\alpha\cos\beta)^{2}+(\sin\alpha\sin\beta)^{2}}}\right)\]  
Based on the sine rule, we obtain:  
\[|EH|=\frac{D\cdot\sin\frac{\theta}{2}}{\sin\left(180^{\circ}-\frac{\theta}{2} -\mu\right)}\]  
\[|EQ|=\frac{D\cdot\sin\frac{\theta}{2}}{\sin\left[180^{\circ}-\frac{\theta}{2} -(180^{\circ}-\mu)\right]}\]Therefore, the coverage width \(W=|EH|+|EQ|\) is given by:  
\[x\cdot\cos\beta+y\cdot\sin\beta=0\]0  
Thus, Equations (1) and (2) have been proven, establishing the relationship between \(W\) and \(\beta\).
## 2 Materials and Methods
### Expression of Multiscale Features  
Point-cloud data comprise a mass point set representing the surface of the target and do not contain the geometric topology information of traditional entity grid data. The most important problem in point-cloud processing is to establish the topological structure between discrete points and realize fast searches based on neighborhood relationships, which becomes very important in real-world applications. In this paper, kD-Tree is used to efficiently compress the storage and management of massive point clouds and perform quick searches based on neighborhood relationships. There are several common methods used to perform searches in a kD-Tree: neighbors with voxels search, K nearest neighbors search, and neighbors within radius search. The radius search method is used in this paper. It gives the threshold value of the target point and the search distance, takes the target point as the center of the circle and the search distance as the radius, and finds all the data within the dataset whose distance from the target point is less than the threshold value. The search radius of the selected region is taken as the scale parameter, and the multiscale is obtained by changing its value to form the multiscale and multi-neighborhood features. Moreover, the point-cloud features calculated under different spherical radii of the neighborhood are not consistent, and different scales of the point cloud can extract the features of different dimensions [28]. According to the average point spacing of the dataset, we set the radius of two scales, R\({}_{1}\) and R\({}_{2}\), to be 0.8 m and 1.2 m, respectively.  
The multiscale spherical neighborhood was used for feature calculation. When the radius value r of the neighborhood is small, there will be some discrete points, and the effective eigenvalues cannot be calculated because there are not enough points in the neighborhood to generate the fitting surface. In this study, these outliers were replaced by \(-1\).  
When R = 0.8, the estimated values of roughness, omnivariance, planarity, linearity, and verticality are as shown in Figure 1.  
Figure 1: Estimated values of each feature.
## 4 Simulation
### Simulation Results  
Figure 11 shows a series of histograms relevant to the PU traffic model created in the learning engine. The histograms present the distribution of the PU occupancy probability. In Figure 11, when the value of \(P_{on}^{Avg}\) is 0.1, 0.2, 0.3, or 0.4, a channel's PU occupancy state probability value \(P_{on}^{n}\) is generally found in positions that have small values. When the \(P_{on}^{Avg}\) value is 0.5, a histogram is distributed with left-right symmetry. Further, when the \(P_{on}^{Avg}\) value is 0.6, 0.7, 0.8, or 0.9, the PU occupancy probability \(P_{on}^{n}\) seems to be biased toward positions of large values.  
Table 5 shows the parameter type and their corresponding values used to verify the performance of the case-based reasoning method proposed in this paper deployed in the cognitive radio engine platform. Further, Figure 12 shows PU reference traffic models in which the average of the channel occupancy probability \(P_{on}^{Avg}\) was enforced to be incremented from 0.1-0.9. As a result, nine PU traffic models are consecutively generated for every time slot interval, which was set to 300 slots per model. The overall time slot interval \(T\) was set to 2700 slots, and the total number of channels \(N\) was set to 500. In Figure 12, the OFF state is shown in blue for the channels of the cognitive radio nodes that are available. The ON state shown in red implies that the cognitive radio nodes could not find available channels. Overall, as the value \(P_{on}^{Avg}\) (the average probability of the PU channel occupancy) increases, it is evident that the frequency of PU occupancy also increases.  
Figure 10: Definition of collision and an example of a periodic operation of the CR engine.  
Figure 11: Results of PU traffic model generation based on the exponential probability distribution \(M\) = 2700).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter Type** & **Setting Value** \\ \hline Overall number of channels, \(N\) & 500 \\ Overall time slot interval, \(T\) & 2700 slots \\ PU traffic model’s unoccupied channel probability mean value, \(P_{on}^{Avg}\) & 0.1, 0.2,..., 0.9 \\ Number of PU traffic models, \(I\) & 9 \\ Time slot interval for each traffic model & 300 slots \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation test parameter types and setting values.
## 2 Materials and Methods
### Testing of the Cells' Metabolic Activity  
Human melanoma cell line WM-266-4 (ATCC(r) CRL1676(tm), USA) was cultured in Eagle's Minimum Essential Medium (EMEMEM, ATCC(r) 30-2003(tm)) containing 1% fetal bovine serum (FBS, ATCC(r) 30-2021(tm)) and 0.02% MycoZap(tm) Plus-CL (Lonza, Portsmouth, NH, USA). Cells were cultured at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\)\(\geq\)90% RH. The cells were plated at a density of \(2\times 10^{4}\) viable cells per well in 96-well culture plates. The cells were cultured in complete medium for 24 h to allow cell attachment. In order to measure the cells' metabolic activity, the cells were exposed to selected concentrations of extracts and cultured for 24 h. Concentrations of extracts were 0.001, 0.01, 0.1, 1, 5, 10, 20, and 100 mg/mL. Cells for controls were cultured for 24 h in the complete medium only, no extracts added. The WST 8 Colorimetric Cell Viability Kit I (PromoKine, PromoCell, Heidelberg, Germany, EU) was used, according the manufacturer's instructions. Absorbance (\(A\)) was measured spectrophotometrically at 570 nm (background absorbance at 630 nm) in pentaplicates for all samples. The percentage of the cells' metabolic activity was calculated with the following equation: (A570-A630) test sample value/(A570-A630) control value \(\times\) 100, where \(A\) represents average value calculated from pentaplicates.  
In addition, cell morphology was observed with an inverted microscope (DM16000B, Leica) using a digital camera (DFC365 FX Leica, Buffalo Grove IL, USA).
## 2 Optimized RANSAC Algorithm Using IWOA
### Realize Point Cloud Initial Registration Process  
In the realization process of the point cloud coarse registration by RANSAC, an appropriate sample subset can improve the registration efficiency, while the distance error determines the registration accuracy. Six sampling points are selected from \(P\), the point cloud to be registered, and the distance between every two points is greater than the preset minimum threshold \(d\). In order to obtain a minimal value by the error function, optimal ones should be found from all the transformations to complete the initial registration. In this paper, the improved WOA was used for optimization. However, the problem needs to be first elaborated by a mathematical model--fitness function, i.e., the target of the optimization algorithm. The corresponding point cloud registration error of the algorithm used in this paper and \(l\) are defined as:  
\[l=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left[{{{\left({x_{i}^{p}-x_{i}^{q} \right)}^{2}}+{\left({y_{i}^{p}-y_{i}^{q}}\right)}^{2}}+{\left({z_{i}^{p}-z_{ i}^{q}}\right)}^{2}}\right]}}. \tag{14}\]  
The fitness function \(f(t)\) is defined as:  
\[f(t)=\text{arg}min{\sum\limits_{i=1}^{n}{H(l)}}=\left\{{\begin{array}{*{20}{c.5l^{2}},{\left|{l}\right|}<{m_{l}}\\ 0.5{m_{l}}(2{\left|{l}\right|}-{m_{l}}),{\left|{l}\right|}>{m_{l}}\end{array}}}\right. \tag{15}\]  
where \(H(l)\) indicates \(Huber\) penalty function, \(m_{l}\) denotes the preset threshold, and \(l\) is the distance difference after the transformation of the corresponding points in the \(i\) th group. The transformation satisfying the minimum value of the error function is considered optimal when the transformation matrix is the initial registration and is used for completing the initial registration of the residual point cloud. The realization flow of \(RANSAC\) registration by IWOA is shown in the figure below (Figure 1):
# Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
## 4 Parameter Tuning of ADRC Using the QPSO Algorithm
### Parameter Tuning
The combination of these two distributions significantly enhances the overall performance of the algorithm. Figure 6 shows the flowchart of the QPSO optimization algorithm. The specific steps of the algorithm are as follows. Step 1: Initially, all particles and their corresponding positions are initialized. Each particle represents a potential solution, with its position indicating the coordinates within the solution space. Step 2: For each particle, the fitness value is calculated based on the objective function. The fitness value serves to evaluate the particle's quality, with higher values indicating better solutions. Using these fitness values, the individual best position for each particle and the global best position for all particles are determined. Step 3: The average best position is computed across all particles, reflecting the mean of their best positions. This serves as a reference for updating positions in subsequent iterations. Step 4: Based on the calculated average best position, the individual best position for each particle and the global best position are updated. If a particle's fitness surpasses its previous best value, its individual best position is updated. Similarly, if a particle's fitness surpasses the global best value, the global best position is updated. Step 5: To enhance the algorithm's diversity, random positions are introduced, allowing particles to explore a broader solution space and preventing the algorithm from becoming trapped in local optima. Step 6: Based on the random position and the particle's velocity (which combines both the individual best and global best positions), the particle's position is updated, gradually guiding it closer to the optimal solution. Step 7: The termination condition is then verified, typically including either reaching the maximum number of iterations or achieving minimal improvements in the global  
Figure 6: QPSO Optimization Algorithm Flowchart. best solution. If the condition is met, the algorithm terminates; otherwise, the next iteration begins.
## 6 Simulation Results and Discussion
### Case 1: BESS versus PHES without DRP  
Figure 4a,b shows the trade-off Pareto front plots for economic and reliability criteria with BESS and PHES, respectively, under case 1. From Figure 4, as expected, the system reliability condition improves (LPSP value decreases) as the total cost increases and vice visa. Hence, the cost-benefit relationship at different LPSP values is analyzed and discussed using the investment cost-savings approach. Table 3 summarizes the details of the cost-benefit analysis for case 1. The optimal selected points are derived after multiples execution of the optimization program for LPSP values in the range of 0% to 15%.  
A comparison of the two systems based on the ESS technology at maximum reliability condition i.e., LPSP = 0%; it can be seen that the choice of PHES instead of BESS results in a total investment cost reduction of about 34.28% from US $ 1.38 \(\times\) 10\({}^{7}\) to the US $ 9.06 \(\times\) 10\({}^{6}\). This a significant cost saving in the microgrid planning. Hence, PHES has been shown more economical compared to BESS.
## 4 Anti-Cancer Activities of Tanshinones
### Inhibition of Telomerase  
Telomerase is a ribonucleoprotein complex that synthesizes the repetitive G-rich DNA at each 3'-end of the chromosome [238]. Telomerase can promote carcinogenesis and progression via maintaining telomeric DNA length to overcome replicative senescence and inhibiting apoptosis by mechanisms independent of telomeric DNA synthesis [239; 240], and promote proliferation via cross-talking with other pathways [241]. Telomerase is up-regulated in the majority of cancer cells and is essential for their survival. Telomerase has been an important therapeutic target for developing novel anti-cancer drugs in the past 2 decades.  
Several studies reported that TIIA treatment (1.7 \(\upmu\)M, 2-6 days) induced apoptosis and differentiation in human HL-60 and K562 leukemia cells, accompanied by inhibition of both the expression and activity of human telomerase reverse transcriptase (hTERT) [242; 243; 244]. Similarly, [PERSON] _et al._[245] reported that TI at 10-50 \(\upmu\)M concentration-dependently induced caspase-3-mediated apoptosis, in association with a decrease of hTERT expression and activity in U937, THP-1 and SHI 1 leukemia cells. [PERSON] _et al._[246] delineated the direct effect of tanshinones on telomerase using an _in vitro_ telomerase assembly assay, which allowed identification of small molecules directly affecting telomerase through inhibiting enzymatic activity, blocking specific protein-RNA interaction [247] or suppressing necessary chaperone HSP90 interaction [248; 249]. They showed that tanshinones (TI, TIIA and CT) exert direct effect on telomerase, possibly in association with reactive oxygen species (ROS) generation in a test tube [246]. Furthermore, they demonstrated that the ortho-quinone C ring (See Figure 1) of the tanshinones is essential for telomerase inhibition in that replacement of the C rings with a lactone ring caused significant loss of that activity, whereas the aromatic A ring and heteroaromatic D ring seemed dispensable for telomerase inhibition [246].
## 4 Analysis of the Influence of Tunnel Arch Subsidence
### Influence of the Surrounding Rock Class on Arch Settlement  
The stability of a tunnel is primarily influenced by the nature of the surrounding rocks. Therefore, in order to investigate the relationship between the closure distance of the inverted arch and the maximum settlement of the initial support arch under different surrounding rock classes, the strength of the surrounding rocks was varied based on the aforementioned model. The Burgers creep model parameters for different levels of surrounding rocks are presented in Table 2.  
The results obtained using the Burgers creep model are shown in Figure 10 and Table 5. It is observed that the maximum settlement value of a V-grade surrounding rock vault is larger than that of III and IV surrounding rocks, and the maximum settlement of a IV surrounding rock vault is slightly higher than that of a III surrounding rock vault at each closure distance. Furthermore, with an increase in the closure distance, the maximum settlement value of the vault also increases.  
According to the data presented in Table 5, an increase in the grade of surrounding rocks results in a significant reduction in the displacement value of the tunnel vault, indicating a strong sensitivity of the vault settlement to changes in the grade of surrounding rocks. These observations suggest that the stability of initial support is closely linked to the grade of surrounding rocks. Specifically, when the surrounding rock conditions are good, the inverted arch closure distance can be appropriately increased, and the exposed distance of the inverted arch can also be increased to facilitate the operation of large machinery.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Closing Distance (m)**} & **Unenclosed Inverted Arch 6 m Maximum Displacement of Vault (mm)** & **Unenclosed Inverted Arch 5 m Maximum Displacement of Vault (mm)** & **Unenclosed Inverted Arch 4 m Maximum Displacement of Vault (mm)** & **Unenclosed Inverted Arch 3 m Maximum Displacement of Vault (mm)** \\ \hline
30 & 13.33 & 12.3 & 12.2 & 11.98 \\
35 & 14.98 & 13.1 & 13 & 12.7 \\
40 & 16.38 & 15.85 & 15.55 & 15.1 \\
45 & 17.93 & 17.13 & 16.47 & 15.94 \\
50 & 21.71 & 20.7 & 19.78 & 18.64 \\
55 & 23.9 & 22.78 & 21.77 & 20.54 \\
60 & 25.1 & 23.96 & 22.95 & 21.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum displacement of an inverted arch without closure of 3–6 m in a V-grade rock mass.  
Figure 9: Variation curve of vault settlement.  
Conversely, when the surrounding rock conditions are poor, the inverted arch closure distance should be shortened, and the exposed distance of the inverted arch reduced, while initial support should be promptly applied to ensure the stability of the surrounding rock. Additionally, increasing the length of circular footage and reducing the time of circular footage under good surrounding rock conditions can significantly enhance the stability of the surrounding rock and the tunnel.