esa-sceva/satcom-chunk-collection huggingface dataset:
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## Appendix C Proof of Theorem 2  
The Mellin transform over the service process of user \(k\) at one time slot is  
\[ \begin{split}\mathcal{M}_{\hat{\Phi}_{k}}(1-s)&= \mathbb{E}\Big{(}e^{(1-s-1)\eta_{k}\left(\hat{\eta}_{k}\ell_{k}^{d}\right)} \Big{)}\\ &=\int_{0}^{\infty}\Big{(}\ell_{k}^{d}+\left(1-\ell_{k}^{d} \right)e^{-s\eta_{k}\left(\hat{\eta}_{k}\ell_{k}^{d}\right)}\Big{)}f_{\gamma }(x)dx\\ &=\ell_{k}^{d}+\left(1-\ell_{k}^{d}\right)\int_{\gamma_{0}}^{ \infty}\Big{(}g_{\tau_{k}}\left(\hat{\gamma}_{k\cdot}\epsilon_{k}^{d}\right) \Big{)}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x)dx+\left(1-\ell_{k}^{d} \right)\int_{0}^{\gamma_{0}}\Big{(}g_{\tau_{k}}\left(\hat{\gamma}_{k},\epsilon _{k}^{d}\right)\Big{)}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x)dx\\ &\stackrel{{(c)}}{{=}}\ell_{k}^{d}+\left(1-\ell_{k}^{d} \right)\int_{\gamma_{0}}^{\infty}\Big{(}f_{\tau_{k}}\left(\hat{\gamma}_{k \cdot}\epsilon_{k}^{d}\right)\Big{)}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}} (x)dx+\left(1-\ell_{k}^{d}\right)\int_{0}^{\gamma_{0}}f_{\hat{\gamma}_{k}}(x)dx \\ &=\ell_{k}^{d}+\left(1-\ell_{k}^{d}\right)\int_{\gamma_{0}}^{ \infty}\Big{(}f_{\tau_{k}}\left(\hat{\gamma}_{k\cdot}\epsilon_{k}^{d}\right) \Big{)}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x)dx+\left(1-\ell_{k}^{d} \right)\big{(}F_{\hat{\gamma}_{k}}(\gamma_{0})-F_{\hat{\gamma}_{k}}(0)\big{)} \\ &\leqslant\ell_{k}^{d}+\left(1-\ell_{k}^{d}\right)\int_{0}^{ \infty}\Big{(}f_{\tau_{k}}\left(\hat{\gamma}_{k\cdot}\epsilon_{k}^{d}\right) \Big{)}^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x)dx+\left(1-\ell_{k}^{d} \right)F_{\hat{\gamma}_{k}}(\gamma_{0})\\ &=\ell_{k}^{d}+\left(1-\ell_{k}^{d}\right)\int_{0}^{\infty}\left(\frac{1+ \hat{\gamma}_{k}}{\exp\left(\sqrt{\frac{\hat{\gamma}_{k}^{2}+2\hat{\gamma}_{k }}{(1+\hat{\gamma}_{k})^{2}}\hat{\gamma}_{k}}Q^{-1}\big{(}\epsilon_{k}^{d}\big{)} \right)}\right)^{-\frac{s}{\ln^{2}}}f_{\hat{\gamma}_{k}}(x)dx+\left(1-\ell_{k}^{ d}\right)F_{\hat{\gamma}_{k}}(\gamma_{0}).\end{split}  \tag{12}\]  
\[\text{When }\hat{\gamma}_{k}\leqslant\gamma_{0},g_{\tau_{k}}\Big{(}\hat{\gamma}_{k },\epsilon_{k}^{d}\Big{)}= \begin{cases}f_{\tau_{k}}\left(\hat{\gamma}_{k}, \epsilon_{k}^{d}\right),\hat{\gamma}_{k}>\gamma_{0}\\ 1,\hat{\gamma}_{k}\leqslant\gamma_{0}\end{cases} ,\text{ ($c$) is established. }\]  
\[\text{When }\hat{\gamma}_{k}\gg 1,\,V(\hat{\gamma}_{k})=\frac{\hat{\gamma}_{k}^{2}+2\hat{ \gamma}_{k}}{(1+\hat{\gamma}_{k})^{2}}\to 1,\,\text{we can obtain }\sqrt{\frac{\hat{\gamma}_{k}^{2}+2\hat{\gamma}_{k}}{(1+\hat{\gamma}_{k})^{2} \hat{\gamma}_{k}}}Q^{-1}(\varepsilon)\to 0\text{ and }\frac{1+\hat{\gamma}_{k}}{\exp\left(\sqrt{\frac{\hat{\gamma}_{k}^{2}+2\hat{ \gamma}_{k}}{(1+\hat{\gamma}_{k})^{2}\hat{\gamma}_{k}}Q^{-1}(\varepsilon)}\right) }\rightarrow\hat{\gamma}_{k}\text{ \@@cite[cite]{[\@@bibref{}{}{}{}]}}.\]  
The Mellin transform over the service process in the SNR domain of the \(k\)th user at one time slot can be rewritten as \[
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 3 Results and Discussion
### Analysis of Seepage in Rare Earth Mine Slopes
Figure 4: Distribution of pore water pressure at intervals at different times during leaching (The contour map represents the variation of pore water pressure, the dashed line represents the zero pore pressure line, and the arc represents the critical sliding surface). The primary failure mode of the slope in Figures 4 and 5 is circle sliding, which occurs mainly in the topsoil and fully weathered granite layers. This shallow depth makes surface and mid-level landslides more likely. The physical and mechanical properties of the rare earth ore body soil deteriorate significantly during leaching. The permeability of the topsoil is markedly lower than that of the fully weathered layer. If the seepage line is not properly controlled during leaching, the leaching solution can accumulate within the topsoil, leading to saturation and a sharp decrease in shear strength. Under gravitational forces, the sliding mass experiences flow-plastic sliding along the interface between the topsoil and the fully weathered layer. This mechanism is the primary cause of topsoil landslides. As the leaching solution penetrates the highly permeable, fully weathered layer, the fine particles are stripped from the ore body by the leaching solution under dynamic water pressure and gravity, and will accumulate and rearrange within the pores formed by the coarse particles. In regions where fine particles concentrate, the ore body's pores gradually become clogged, significantly reducing permeability. This leads to a corresponding decrease in effective stress with a continuous rise in pore water pressure. With the continued injection of leaching solution, the seepage line within the ore body expands further into the surrounding area, transitioning unsaturated zones into saturated zones and markedly decreasing shear strength. With good mobility within the fully weathered layer, the leaching solution gradually connects weakened sections of the  
Figure 5: Distribution of pore water pressure at different times during the push-water operation (The contour map represents the variation of pore water pressure, the dashed line represents the zero pore pressure line, and the arc represents the critical sliding surface). ore body, forming a weak interlayer. Under the weight of the overlying soil mass, once the downward sliding force exceeds the frictional upslope force generated by the ore structure, the frictional upslope force generated by the ore structure, the slope is very likely to slide along this weak interlayer. This is the primary mechanism triggering ore layer landslides.
# Ni/Zn Layered Double Hydroxide (LDH) Micro/Nanosystems and Their Azorubine Adsorption Performance
###### Abstract  
A membranous shaped Ni/Zn layered double hydroxide based nanohybrid was obtained using a low-cost template-free hydrothermal process at optimized growth conditions of 180 \({}^{\circ}\)C for 6 h. The synthesized nanohybrid was structurally, texturally and morphologically characterized using different techniques such as X-ray diffraction, FTIR, XPS spectroscopy, BET analysis and FESEM microscopy. The adsorption performance of our product was estimated through the Azorubine dye removal from synthetic wastewater. We therefore studied the synergic effects of Ni/Zn adsorbent dosage, contact time, pH, adsorbate concentration, stirring speed and temperature on the Azorubine adsorption efficiency. In this investigation, we obtained bi-structure based nanoadsorbent with 54% crystallinity order composed of nickel hydrate and zinc carbonate hydroxides in irregular nanoflake-like mesoporous nanohybrid morphology. Interestingly, it was also revealed to have high specific surface area (SSA) of around 110 m\({}^{2}\) g\({}^{-1}\) with important textural properties of 18 nm and 0.68 cm\({}^{3}\) g\({}^{-1}\) average pore size and volume, respectively. Moreover, the adsorption results revealed that this novel Ni/Zn layered double hydroxide (Ni/Zn LDH) was an efficient adsorbent for Az molecule and possesses an adsorptive ability exhibiting a short equilibrium time (60 min) and a high Az adsorption capability (223 mg g\({}^{-1}\)). This fast removal efficiency was attributed to high contact surface area via mesoporous active sites accompanied with the presence of functional groups (OH\({}^{-1}\) and CO\({}_{3}\)\({}^{-}\)). In addition, the Langmuir and Freundlich isotherms were studied, and the results fitted better to the Langmuir isotherm.  
Ni/Zn layered double hydroxide; Azorubine; adsorption; nanohybrid; Carmoisine +
Footnote †: journal: Physics Letters B
# Integrated Module Antenna for Automotive UWB Application
## 2 Proposed Antenna Design
### Fabrication and Measurement  
The proposed antenna was verified with a fabricated structure and measurements. The proposed antenna was fabricated to verify the individual operating characteristics of the monopole and patch array, as shown in Figure 12. The antenna measurements were taken. The measurement setup is shown Figure 13. The measured return loss and radiation patterns of the proposed antenna are shown in Figure 14 and summarized in Table 5.  
Figure 14: Results of the return loss of the proposed antenna with case.  
Figure 12: Fabricated structure of the proposed antenna and antenna case.  
Figure 13: Measurement setup for S-parameter (**a**) and radiation pattern (**b**).  
The measurement of return loss is conducted by using Agilent's MS4644B Vector Network Analyzer. The measured return loss shows that the bandwidth of the monopole and patch array are 6.27\(\sim\)8.21 GHz and 6.85\(\sim\)8.61 GHz. The radiation pattern is measured in the anechoic chamber. The measured results of the beamwidth of the monopole in the yz-plane at 6.5 GHz are 172\({}^{\circ}\), and 117\({}^{\circ}\) at 8.0 GHz. The patch array has 5.22 dBi gain and 90.9\({}^{\circ}\) beamwidth in the xz-plane. The measured results of the proposed antenna are well agreed with the simulations.
## 3 Local Differential Privacy  
### Fundamental Definition  
LDP is a privacy-preserving technique in which individual users randomize their own data before submitting them to a service provider. This randomization process helps to protect the privacy of each user's data while allowing accurate computation of the aggregate statistics. LDP ensures that sensitive information remains confidential at the local level, providing a decentralized solution for data privacy. LDP is defined as follows:  
Definition 1: A randomized algorithm \(Q\) satisfies \(\epsilon\)-LDP if for all pairs of values \(v\) and \(v^{\prime}\) of domain \(V\) and for all subsets \(S\) of range \(Z\) (\(S\subset Z\)), and for \(\epsilon\geq 0\), the following inequality always holds:  
\[Pr[Q(v)\in S]\leq e^{\epsilon}Pr[Q(v^{\prime})\in S].\]
## 5 Challenges and Limitations  
AI-based acoustic systems are continually improving and are being integrated with IoT infrastructure. However, there are still numerous technical and practical issues that make it challenging to utilise intelligent acoustic monitoring. The quality of the data is a big problem. Acoustic signals are susceptible to noise, echoes, and sources that are too close together in the environment. This makes it more difficult to obtain useful features and increases the likelihood of false positives in detection systems. Advanced filtering and deep learning methods can mitigate some of these effects. Still, they often require large, labelled datasets, which are difficult to obtain and time-consuming to collect, especially in real-world applications.  
Another problem is that sensors are different and are placed in various locations. Distributed acoustic sensor networks require calibration and synchronisation to achieve consistent results. Different hardware settings (microphones, cases, or gain settings) can bias the data and slow the system down. Additionally, edge devices have limited computing power, which makes utilising complex models challenging, particularly in applications that require real-time analyses.  
Scalability is also a big problem. Many systems developed in research settings cannot be easily scaled up for use on a large scale due to cost, infrastructure issues, or concerns about data privacy. Finally, in areas such as health and safety, regulatory and ethical issues (such as the right to acoustic privacy or being responsible for missed detections) create limits that are often overlooked in the technical literature but are crucial for acceptance in the real world.  
Smart sound processing has come a long way, but numerous issues remain with acoustic monitoring systems that utilise AI and the IoT. The accuracy of the classification and the likelihood of false alarms are two key factors to consider. When security or medical systems generate false alarms, they can cause unintended consequences. Detection must be highly reliable and capable of functioning even in the presence of significant noise or movement. Ethics and privacy are also problems. When devices are recording in private areas, it can be hard to tell where proper monitoring ends and possible surveillance begins. Questions about the acceptability of such technology and the need for its social familiarisation arise.  
Issues related to the security of audio data, especially those sensitive to health or legal contexts, are no less important. Effective encryption, anonymisation, and access control mechanisms are necessary. At the same time, the development of distributed systems that collect data from many sources creates a challenge in managing large datasets, from their transmission and storage to the optimisation of processing and analysis in real time. In particular, the need for efficient edge computing architectures and cloud integration becomes crucial for the scalability of such solutions.
## 4 Mobile Head CT Scanner and Rotorcraft Retrofitting  
### Types of Portable Head CT Scanners  
There are several types of commercially available CT scanners depending on the diagnostic purposes, body organs to be examined, and sensitivity and specificity. As the MSU and Air-MSU are designed to diagnose and distinguish between ischemic stroke (stroke where blood flow to the brain is blocked by a clot) and hemorrhagic stroke (stroke where a blood vessel bursts) from a suspected stroke patient, a CT scanner that can perform brain scans via non-contrast CT is sufficient. Therefore, full-body scanners are neglected since they generally have a larger build than head CT scanners and are unnecessary for head scans. Portable head CT scanners are designed to deliver head CT scans to stroke patients by the bedside; thus, they are designed to be as lightweight and compact as possible to maximize portability. While the image quality is lower than that of a conventional hospital floor-fixed CT scanners, portable CT scanners are shown to have sufficient diagnostic quality [26]. Table 1 shows four commercial mobile head CT scanners with their respective technical specifications (Table 1).  
Although the technology of each machine varies between one and another, they are all capable of performing head CT scans with sufficient diagnostic quality; hence, the imaging-quality aspect will not be compared and analyzed between each model. All four mobile head CT scanner models share the similarity of having a built-in trolley system which allows the scanner to be easily maneuvered. They also have a computer unit built-in which is used for controlling various functions of the mobile head CT scanners. As each mobile head CT scanner has a unique mechanical and structural design, the retrofitting process will vary between each model. Therefore, it is important to generalize and identify the key design constraints and criteria. An example of the CereTom and xCAT models are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CT Scanner** & **Manufacturer** & **Weight** & \multicolumn{3}{c}{**Dimensions (cm)**} \\  & **Height** & **Width** & **Depth** \\ \hline CereTom [26] & Neurologica (Samsung) & 438 kg & 153 & 134 & 73 \\ OmniTom [27] & Neurologica (Samsung) & 726 kg & 152.4 & 137.7 & 73.9 \\ xCAT [26] & Xoran Technologies & 236 kg & 170 & 119 & 81 \\ SOMATOM On.site [28] & Siemens & 998 kg & 155 & 160 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of mobile head CT scanners.
## 5 Evaluation
### Further Analysis of Our Proposed Framework  
To further analyze the performance of the proposed framework, more evaluations were performed with more event logs and redundant activity labels. Apart from running experiments on our multi-view framework, we also implemented two baselines to prove the effectiveness of our framework: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Event Log**} & \multirow{2}{*}{**Number of Redundant Activity Labels**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F-score**} \\ \cline{3-8}  & & **Ours** & **Baseline** & **Ours** & **Baseline** & **Ours** & **Baseline** \\ \hline \multirow{8}{*}{Hospital Billing} & 5254 (1\(\%\)) & 0.97 & 0.05 & 0.89 & 0.20 & 0.93 & 0.08 \\  & 21,693 (5\(\%\)) & 0.94 & 0.17 & 0.90 & 0.73 & 0.92 & 0.28 \\  & 44,890 (10\(\%\)) & 0.88 & 0.5 & 0.80 & 0.18 & 0.84 & 0.26 \\  & 66,368 (15\(\%\)) & 0.85 & 0.19 & 0.89 & 0.90 & 0.87 & 0.31 \\  & 90,273 (20\(\%\)) & 0.87 & 0.24 & 0.86 & 0.92 & 0.86 & 0.38 \\  & 112,840 (25\(\%\)) & 0.80 & 0.30 & 0.94 & 0.85 & 0.86 & 0.44 \\  & 135,480 (30\(\%\)) & 0.80 & 0.42 & 0.96 & 0.95 & 0.87 & 0.58 \\ \hline \multirow{8}{*}{Sepsis} & 180 (1\(\%\)) & 0.76 & 0.39 & 0.90 & 0.23 & 0.82 & 0.29 \\  & 745 (5\(\%\)) & 0.75 & 0.47 & 0.89 & 0.42 & 0.81 & 0.44 \\  & 1569 (10\(\%\)) & 0.93 & 0.52 & 0.77 & 0.45 & 0.84 & 0.47 \\  & 2327 (15\(\%\)) & 0.93 & 0.33 & 0.71 & 0.25 & 0.81 & 0.29 \\  & 3086 (20\(\%\)) & 0.90 & 0.48 & 0.76 & 0.46 & 0.83 & 0.47 \\  & 3844 (25\(\%\)) & 0.80 & 0.55 & 0.85 & 0.49 & 0.82 & 0.51 \\  & 4605 (30\(\%\)) & 0.86 & 0.52 & 0.77 & 0.58 & 0.82 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of our framework with SynonymousLabelRepair. Figure 6: Comparison of our framework with the baseline using the Hospital Billing event log. Figure 7: Comparison of our framework with the baseline using the Sepsis event log. * Control-Flow Only: the baseline only relies on the control-flow similarity to detect redundant activity labels. * Attribute Value Only: the baseline only relies on the attribute similarity to detect redundant activity labels. In total, all four event logs are used to evaluate our framework. For each event log, we randomly rename 1%, 5%, 10%, 15%, 20%, 25%, and 30% of the activity labels. Table 3 and Figure 8 present the f-score comparisons between the baselines and our framework. All f-scores were averaged by 5 repeats. In total, 140 event logs were generated and evaluated in the experiment. The f-scores of our framework are much higher than the two baselines, which only detect from a single perspective. The results suggest that it is important to consider information from multiple views when detecting redundant activity labels. We also notice that, except for the BPI Challenge 2012 event log, the control-flow view usually plays a more important role than the attribute value view.
## 3 Results
### Fresh Concrete Properties
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fresh Concrete Properties**} & \multicolumn{8}{c}{**Concrete Mixture**} \\ \cline{2-10}  & **NC1** & **NC2** & **SCC0** & **SCC10** & **SCC20** & **SCC30** & **SCC40** & **SCC50** & **SCC60** \\ \hline \multirow{2}{*}{Density (kg/m\({}^{3}\))} & 2330 & 2295 & 2460 & 2365 & 2299 & 2259 & 2392 & 2378 & 2163 \\  & (\(\pm\)10) & (\(\pm\)9) & (\(\pm\)1) & (\(\pm\)31) & (\(\pm\)40) & (\(\pm\)20) & (\(\pm\)40) & (\(\pm\)7) & (\(\pm\)22) \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & 19.8 & 20.1 & 23.7 & 23.1 & 23.1 & 22.2 & 23.2 & 23.1 & 21.7 \\  & (\(\pm\)1.6) & (\(\pm\)2.1) & (\(\pm\)0.3) & (\(\pm\)1.4) & (\(\pm\)0.6) & (\(\pm\)0.5) & (\(\pm\)0.6) & (\(\pm\)1.5) & (\(\pm\)0.2) \\ \hline \multirow{2}{*}{Air content (\%)} & 3.5 & 2.8 & 1.6 & 2.2 & 3.0 & 1.5 & 1.8 & 2.3 & 3.1 \\  & (\(\pm\)0.8) & (\(\pm\)0.1) & (\(\pm\)0.3) & (\(\pm\)0.7) & (\(\pm\)0.9) & (\(\pm\)0.2) & (\(\pm\)0.2) & (\(\pm\)0.6) & (\(\pm\)0.5) \\ \hline \multirow{2}{*}{Slump (mm)} & 90 & 95 & - & - & - & - & - & - & - & - \\ \hline \multirow{2}{*}{Slump flow (mm)} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & 745 & 735 & 690 & 715 & 800 & 755 & 725 \\  & & & (SF2) & (SF2) & (SF2) & (SF3) & (SF3) & (SF3) & (SF2) \\ \hline \multirow{2}{*}{Slump flow time, \(t_{500}\) (s)} & \multirow{2}{*}{-} & 1.7 & 0.5 & 1.4 & 0.6 & 1.0 & 1.9 & 2.4 \\  & & & (VSI) & (VSI) & (VSI) & (VSI) & (VSI) & (VSI) & (VSI) \\ \hline \multirow{2}{*}{L-box (3 bars) (-)} & \multirow{2}{*}{-} & 0.81 & 0.82 & 0.75 & 0.72 & 0.71 & 0.78 & 0.80 \\  & & & (PL2) & (PL2) & (\(-\)) & (\(-\)) & (\(-\)) & (\(-\)) & (PL2) \\ \hline \multirow{2}{*}{V-funnel (s)} & \multirow{2}{*}{-} & 5.6 & 2.5 & 2.7 & 3.1 & 7.4 & 11.8 & 9.4 \\  & & & (VF1) & (VF1) & (VF1) & (VF1) & (VF1) & (VF2) & (VF2) \\ \hline \multirow{2}{*}{GTM sieve (\%)} & \multirow{2}{*}{-} & 5.2 & 11.6 & 6.0 & 8.8 & 11.0 & 3.5 & 0.0 \\  & & & (SR2) & (SR2) & (SR2) & (SR2) & (SR2) & (SR2) \\ \hline \hline \end{tabular} For the density, the temperature and air content numbers in parentheses represent the half-span of two measurements. Classification of SCC mixes according to standard EN 206 is given in parentheses. For the L-box test, (\(-\)) means that the criteria are not met.  
\end{table} 
Table 4: Fresh concrete properties.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Polymorphism**} & \multicolumn{2}{c}{**CS\&nHT (\(n=16\))**} & \multicolumn{2}{c}{**CS\&HT (\(n=101\))**} & \multicolumn{2}{c}{**CS\&nHT vs. CS\&HT**} \\ \cline{2-9}  & \(n\) & \% & \(n\) & \% & \(p\)\({}^{\text{a}}\) & \begin{tabular}{c} **Compared** \\ **Genotypes or** \\ **Allees** \\ \end{tabular} & \(\mathbf{OR}\) (95\% CI) & \(p\)\({}^{\text{b}}\) \\ \hline \multicolumn{9}{c}{Th1111I genotype} & \multicolumn{9}{c}{0.397} \\ \hline CC & 5 & 31.25\% & 49 & 48.51\% & CC + CT vs. TT & \begin{tabular}{c} 1.116 \\ (0.128–53.657) \\ \end{tabular} & 1 \\ \hline CT & 10 & 62.50\% & 45 & 44.55\% & CC vs. CT + TT & \begin{tabular}{c} 0.485 \\ (0.123–1.65) \\ \end{tabular} & 0.281 \\ \hline TT & 1 & 6.25\% & 7 & 6.93\% & CC vs. TT & \begin{tabular}{c} 0.719 \\ (0.064–38.552) \\ \end{tabular} & 0.58 \\ \hline \multicolumn{9}{c}{Tth111I allele} & \multicolumn{9}{c}{} \\ \hline C & 20 & 62.50\% & 143 & 70.79\% & C vs. T & \begin{tabular}{c} 0.689 \\ (0.298–1.65) \\ \end{tabular} & 0.408 \\ \hline T & 12 & 37.50\% & 59 & 29.21\% & & & \\ \hline \multicolumn{9}{c}{N363S genotype} \\ \hline AA & 15 & 93.75\% & 95 & 94.06\% & \begin{tabular}{c} AA vs. AG + \\ GG \\ \end{tabular} & 0.948 & \multirow{2}{*}{1} \\ \hline AG & 1 & 6.25\% & 5 & 4.95\% & & \\ \hline GG & & & 1 & 0.99\% & & \\ \hline N363S allele & \multicolumn{9}{c}{} \\ \hline A & 31 & 96.88\% & 195 & 96.53\% & A vs. G & \begin{tabular}{c} 1.112 \\ (0.135–51.751) \\ \end{tabular} & 1 \\ \hline G & 1 & 3.12\% & 7 & 3.47\% & & \\ \hline ER22/ER23 EK & \multicolumn{9}{c}{} \\ genotype & \multicolumn{9}{c}{} \\ \hline AA & \multicolumn{9}{c}{} \\ \hline AG & \multicolumn{9}{c}{} \\ \hline GG & 16 & 100\% & 100 & 99.01\% & \\ \hline ER22/ER23 EK allele & \multicolumn{9}{c}{} \\ \hline A & \multicolumn{9}{c}{} \\ \hline A & \multicolumn{9}{c}{} \\ \hline G & 32 & 100\% & 201 & 99.50\% & & \\ \hline Tth111/N363S/ & \multicolumn{9}{c}{} \\ ER22/23 EK & \multicolumn{9}{c}{} \\ haplotype & \multicolumn{9}{c}{} \\ \hline CAG & 19 & 59.38\% & 137 & 67.82\% & CAG vs. other & \begin{tabular}{c} 0.695 \\ (0.304–1.631) \\ \end{tabular} & 0.42 \\ \hline TAG & 12 & 37.50\% & 57 & 28.22\% & TAG vs. other & \
\begin{tabular}{c} 1.523 \\ (0.635–3.524) \\ \end{tabular} & 0.301 \\ \hline CGG & 1 & 3.13\% & 6 & 2.97\% & CGG vs. other &
\begin{tabular}{c} 1.054 \\ (0.022–9.15) \\ \end{tabular} & 1 \\ \hline TAA & \multicolumn{9}{c}{} \\ \hline TGG & \multicolumn{9}{c}{} \\ \hline \hline \end{tabular}  
* CS&HT—patients with CA stenosis and arterial hypertension; CS&nHT—normotensive patients with CA stenosis.
* \(p\)-values were calculated with the \(\chi^{2}\) test.
* \(p\)-values were calculated with [PERSON]’s exact test.  
\end{table} 
Table 4: Th111I, N363S, and ER22/23 EK genotypes in CS&HT and CS&nHT. well as women with and without CA stenosis (Table 6). No association reaching statistical significance was found.