esa-sceva/satcom-chunk-collection huggingface dataset:
## LVRT Control Strategy for Renewable Energy Systems  
### LVRT Control Strategy in the WPG System  
Figure 2 shows the configuration of the WPG system that uses a back-to-back converter. This system comprise a generator connected to a blade, a back-to-back converter (with a dynamic breaker), a filter, a transformer, and a three-phase grid. The generator with the blade has a large inertia; therefore, the WPG system cannot instantaneously reduce the power. The back-to-back converter is the representative topology used in WPG systems, and it comprises a generator-side converter, DC-link with a dynamic breaker, and grid-side inverter.  
When the grid voltage drops in the WPG system, the power limited by the rating current is reduced because of the low grid voltage. Therefore, surplus power exists, and it considerably increases the DC-link voltage. If the WPG system decreases the power generation for preventing the production of surplus power, the blade speed increases owing to the large inertia of the generator with the blade. Finally, the increase in the blade speed or the DC-link voltage can lead to the destruction of the WPG system. Therefore, when the grid has a low voltage, the LVRT control strategies for the WPG system should focus on the stable operation of the WPG system. In the WPG system, another device is required to consume the surplus power, comply with the LVRT requirement, and maintain stable operation. Therefore, a dynamic breaker is included in the back-to-back converter; the dynamic breaker, called crow bar, comprises a resistor and a switch.  
The LVRT control strategies for the WPG system under low grid voltage determine the injection quantity of the reactive current (\(I_{de}\)) by the LVRT requirement depending on the level of low voltage. In addition, the active current (\(I_{qe}\)) for transferring the active power allowed in the range of the rating current (\(I_{rating}\)) of the WPG system is calculated as in (1).  
\[I_{qe}=\sqrt{I_{rating}^{2}-I_{de}^{2}} \tag{1}\]  
where \(I_{de}\) and \(I_{qe}\), which represent the reactive and active powers, respectively, are the \(d\)-\(q\) axis currents in the synchronous reference frame. These are reflected in the control of the grid-side inverter. If surplus power is generated, the generator-side converter is able to reduce the power until the blade speed becomes equal to the rating speed. At the rating speed of the blade, the dynamic breaker operates to decrease the DC-link voltage.  
Figure 2: Configuration of the wind power generation system that uses a back-to-back converter.
## 3 Results
### Comparison of the Static Flow Characteristics Determined Experimentally and Numerically  
The determined static characteristics of both tracts providing the tracking and accelerating action of the prototype pneumatic brake valve are presented in Figure 15 for the main path and in Figure 16 for the additional path. Tables 1-4 present details of the MAPE and MAE indices for individual valve opening degrees, maintaining both flow directions and referring to the reference characteristic obtained by the experimental method.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tract Opening [mm]** & **0** & **0.25** & **0.5** & **0.75** & **1** & **1.25** & **1.5** & **1.75** & **2** \\ \hline
**Weighted fit** & 0 & 0.03158 & 0.06060 & 0.08708 & 0.11100 & 0.13238 & 0.15120 & 0.16748 & 0.18120 \\
**MAPE [\%]** & 0 & 11\% & 8\% & 7\% & 4\% & 1\% & 1\% & 6\% & 9\% \\
**MAE** & 0 & 0.00352 & 0.00510 & 0.00596 & 0.00445 & 0.00152 & 0.00190 & 0.00989 & 0.01640 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAPE and MAE metrics for main tract—supply from inlet to outlet.  
Figure 14: Pressure distribution of additional tract opening: (**a**,**b**)—1.75 mm; (**c**,**d**)—2.50 mm; (**e**,**f**)—3.25 mm.  
Figure 16: Experimental and numerical static flow characteristic of additional tract.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tract Opening [mm]** & **0** & **0.25** & **0.50** & **0.75** & **1.00** & **1.25** & **1.50** & **1.75** \\ \hline
**Weighted fit** & 0 & 0.04710 & 0.08564 & 0.11631 & 0.13980 & 0.15679 & 0.16796 & 0.17400 \\
**MAPE [\%]** & 0 & 6\% & 8\% & 8\% & 7\% & 11\% & 11\% & 2\% \\
**MAE** & 0 & 0.00269 & 0.00688 & 0.00911 & 0.01031 & 0.01733 & 0.01791 & 0.00316 \\ \cline{2-9}  & **2.00** & **2.25** & **2.50** & **2.75** & **3.00** & **3.25** & **3.50** & **3.70** \\ \cline{2-9}  & **2.00** & **2.25** & **2.50** & **2.75** & **3.00** & **3.25** & **3.50** & **3.70** \\ \cline{2-9}  & 0.17560 & 0.17343 & 0.16819 & 0.16055 & 0.15120 & 0.14083 & 0.13011 & 0.12176 \\
1\% & 2\% & 2\% & 0\% & 0\% & 2\% & 4\% & 14\% \\
0.00171 & 0.00419 & 0.00382 & 0.00004 & 0.00011 & 0.00327 & 0.00560 & 0.01678 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAPE and MAE metrics for main tract—supply from outlet to inlet.  
Figure 15: Experimental and numerical static flow characteristic of main tract.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tract Opening [mm]** & **0** & **0.25** & **0.5** & **0.75** & **1.00** & **1.25** & **1.50** & **1.75** \\ \hline
**Weighted fit** & 0 & 0.02383 & 0.04715 & 0.06998 & 0.09230 & 0.11413 & 0.13545 & 0.15628 & 0.17660 \\
**MAPE [\%]** & 0 & 22\% & 25\% & 23\% & 19\% & 16\% & 13\% & 10\% & 10\% \\
**MAE** & 0 & 0.00524 & 0.01167 & 0.01642 & 0.01776 & 0.01880 & 0.01699 & 0.01568 & 0.01763 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MAPE and MAE metrics for additional tract—supply from inlet to outlet.
## 1 Introduction
However, the lower oxidation potential of bi- and ter-thiophene, compared to thiophene as it is, allows to avoid overoxidation phenomena on the growing polymer. Therefore, these oligomers are often used in the electrochemical synthesis of thiophene polymers. In this context, the functionalization of terthiophene (Figure 1) on the C-3\({}^{\prime}\) position is often adopted to assign new features to thiophene-based CPs. The synthesis of terthiophene oligomers with N-chelating moieties, connected to the central thiophene ring through a saturated or an unsaturated tether, has been previously reported [22; 27; 28]. Such arrangements allow to combine the polymerizing ability of the terthiophene to the coordinating ability of the N-ligand towards metal ions, mixing the conducting properties of the organic framework to the electronic and optical properties of the metal center. The resulting metal-containing species can be included in the so-called \"outer-sphere metallopolymers\" group [29]. In particular, an unsaturated (ethynyl) spacer between the polymerizing and coordinating units instead of a saturated (methoxy) one leads to a higher extension of conjugation, evidenced by a lower energy gap value. On the other hand, 5-substituted terthiophenes are reported as building blocks in photo-sensitive arrangements [30; 31]. As in the case of 3\({}^{\prime}\)-ethynyl-terthiophene, the analogue 5-ethynyl  
Figure 1: 2,2\({}^{\prime}\):5\({}^{\prime}\),2”-terthiophene. derivative could be properly modified to achieve a molecule with a double function, that is, a polymerizing and a coordinating one. In this last case, the resulting metallopolymers could be defined as \"inner-sphere metallopolymers\" [29], with conducting, electronic, and optical performances higher than in the case of outer-sphere materials. In this context, a comparison between 3\({}^{\prime}\)-ethynyl- and 5-ethynyl-terthiophene could give useful suggestions to predict the optical and electrochemical behavior of new species, including such fragments as building block. Unlike 3\({}^{\prime}\)-ethynyl-terthiophene, the behavior of the 5-ethynyl- analogue has been reported only as a part of organic [31] or metallorganic [32; 33] compounds, but its electrochemical characterization has not so far been reported to the best of our knowledge. Hence, with the aim of studying the influence of the position of an ethynyl substituent on the properties of the monomers and the resulting polymers, here we report a voltammetric and UV-Vis characterization of 5-ethynyl-terthiophene and poly (5-ethynyl-terthiophene), as well as a comparison of the corresponding 3\({}^{\prime}\)-ethynyl-terthiophene species. The experimental results are compared to calculated data by DFT-approach.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Network Slicing  
A network slicing technique is essential in accomplishing the framework for supporting the prerequisites of various applications. This technique arose with 5G mobile systems, but other application systems can also adopt this technique. The slice-specific customisation and performance isolation of resources could be done to effectively empower various applications and guarantee the effective usage of resources. Using these techniques, it is necessary to slice the network with core network resources and radio access.  
Regardless of a lot of research on network slicing, various issues related to slicing require further analysis, including productive virtualisation of the network framework, applying services to network slice, the disintegration of network function, and end-to-end network orchestration, and management mechanism [64]. Without depending on the tedious architecture and protocol-level alternations [65], radio/remote resource slicing must be accomplished dynamically, which is an essential part of any network slicing resolution.  
In addition, mission-critical IoT (mc-IoT) services have different requirements in terms of latency, availability, reliability, network resources, etc. The work done in [66; 67] has not considered the network infrastructure that caters to mc-IoT's strict requirements. To address the above challenges, a flexible network infrastructure based on NFV was designed [68]. Depending upon the need for different mc-IoT applications, the services that guarantee slicing are deployed, and the network resources are distributed based on the shared physical infrastructure.  
According to the 3 GPP releases, there are three generic services under the 5G-New Radio (NR) framework that address new verticals and scenario deployments, as shown in Figure 8. The generic services are:  
* Enhanced mobile broadband (eMBB): Mainly focusses on faster speeds for applications demanding higher rates.  
Massive Machine-Type Communication (mMTC): Mainly focusses on sensing and monitoring systems.
* Ultra-reliable low-latency communications (URLLC): Mainly focusses on providing very low latency for mission-critical systems.  
In network slicing, the 5G framework is relied upon to oblige most devices that will interface with the network with a different Quality of Service (QoS) and Quality of Experience (QoE). It incorporates round-trip time (RTT), throughput, nature of information shared between the end devices and application servers, and frequency of data transfer [69; 70]. The three generic services and their attributes are mentioned in Table 4.
## 5 Achieved Results and Performance Comparison
### Performance Comparison
#### 5.3.4 Virtex-7 Platform Comparison  
Our architecture, designed for Virtex-7, enhances computing efficiency and optimizes hardware resources. It uses 1.4 times less hardware than [20] while attaining a significant 1.27 times faster processing speed. Its effective design and potential for resource-sensitive applications are highlighted by the optimization's excellent 1.78 times greater throughput/area ratio. In a similar vein, our architectural solution uses 6.5 percent more hardware resources than [21]. Nevertheless, in terms of processing time in microseconds, it greatly outperforms the work in [21]. This results in an impressive 1.31 times greater throughput/area ratio,demonstrating its flexibility and effectiveness, especially in applications where low latency is crucial. The throughput/slice performance metric is our primary area of concern. Therefore, this performance metric is taken into consideration when visually presenting the significance of achieved results. The visual comparison of throughput/slice is shown in Figure 4. The plotted values in Figure 4 are taken from the last column of Table 4.
## 2 Thermophysical Properties
### Density  
In order to evaluate the fluid dynamics and heat transfer performances of nanofluids, the density must be known. However, limited data are available for nanofluids, specifically related to EG-W and PG-W solutions. Most authors prefer to use the classical mixture law:  
\[\rho_{nf}=\rho_{np}\varphi+(1-\varphi)\rho_{bf} \tag{1}\]  
[PERSON] et al. [36] conducted density measurements for \(Al_{2}O_{3}\), \(ZnO\) and \(Sb_{2}O_{5}:SnO_{2}\) nanoparticles dispersed separately into a 60:40 EG-water mixture with different volumetric concentrations up to \(\varphi=10\%\) and temperatures ranging between 0 \({}^{\circ}\)C and 50 \({}^{\circ}\)C. The results revealed that for all the studied nanofluids, the density augments by increasing the nanoparticle concentration \(\varphi\). For example, at \(T=10\)\({}^{\circ}\)C, the nanofluid density increases by nearly 22% when the loading of \(Al_{2}O_{3}\) nanoparticles in the base fluid increases from \(\varphi=1\) to 10 vol.% (Figure 0(a)). Density slightly decreases linearly with temperature for these sets of parameters (Figure 0(b)). They compared their results to the [PERSON] and Cho [37] Equation (1) and stated that their data agreed particularly with it within \(\pm 1\%\), except for \(ZnO\) nanofluids, which exhibited deviations from 2-8%, for increasing values of \(\varphi\). To compensate this discrepancy, they proposed a correction factor, which is a function of \(\varphi\).  
Density measurements were performed also by [PERSON] et al. [38] on several nanofluids containing nanoparticles of aluminium oxide, zinc oxide, copper oxide, titanium oxide, silicon dioxide, and carbon nanotubes individually dispersed in a base fluid of 60:40 propylene-glycol and water over a temperature range between 0 and 90 \({}^{\circ}\)C. The results indicated that the nanofluid densities were independent of the nanoparticle size, but they increased with an increase in the nanoparticle  
Figure 1: Comparisons between the density measurements of [PERSON] et al. [36] and the Pak and Cho [37] equation for \(Al_{2}O_{3}\) nanofluids: (**a**) influence of concentration at three temperatures and (**b**) influence of temperature for six nanoparticle concentrations. The base fluid is a mixture of 60:40 ethylene-glycol/water by mass. After [36], with permission from [PERSON] and [PERSON].  
concentration and decreased as the temperature increased. They compared their experimental data with the values predicted by the theoretical Equation (1) of [PERSON] and [PERSON] [37] for nanoparticle concentrations up to 6 vol.% and nanoparticle sizes ranging from 10-76 nm. They found that the experimental results were in a good agreement with the available theory with a maximum deviation of \(-3.8\%\) for \(CuO\) nanofluid and an average deviation of \(-0.1\%\) for all the nanofluids tested.  
As shown in Table 3, the densities of EG and PG are only 12.7% and 3.6% higher than that of liquid water at 20 \({}^{\circ}\)C. From that point of view, none of these base fluids may be recommended preferably in regards to the sedimentation phenomenon.
## Materials and Methods
### Obtaining the Gaze Direction \(\varphi_{g}\)  
Since we wish to integrate information concerning gaze direction at a later point along with the environmental information acquired by the LRF, this step describes the method to obtain gaze direction \(\varphi_{g}\) with the front of the wheelchair coordinate system aligned at \(0^{\circ}\). The eye tracker is attached to the head of the passenger and moves freely against the wheelchair. This facilitates use of not only the information recorded by the eye tracker but also that captured by the RGB camera fixed to the wheelchair. First, the gaze point in the world camera coordinate system \((x_{g}^{e},y_{g}^{e})\) is converted to the RGB camera coordinate system \((x_{g}^{e},y_{g}^{e})\). Subsequently, the gaze point in the RGB camera coordinate system is converted into gaze direction \(\varphi_{g}\) relative to the wheelchair coordinate system. The gaze direction is then successively obtained and filtered. This makes it possible to acquire gaze information with higher reliability by excluding those movements of the passenger that do not qualify as gaze motion.
## 3 Preliminaries
### Joint-Shamir Random Secret Sharing  
Joint-Shamir Random Secret Sharing (Joint-Shamir RSS) [33] enables the generation of the master secret value \(\sigma\) without relying on a trusted third party. The master secret value is computed as the sum of the sub-secret values \(\sigma_{i}\), which are independently generated by each participating entity. Each sub-secret value is calculated through the sub-shared value \(s_{ij}\). Notably, individual entities are unable to access the sub-secret values generated by other participants. This subsection outlines the steps involved in the process.  
1. Each entity \(P_{i}\) chooses a random sub-secret value \(\sigma_{i}\in\mathbb{Z}_{q}\). Then, \(P_{i}\) chooses a polynomial \(f_{i}(z)\) over the \(\mathbb{Z}_{q}\) of degree \(t\). Let denote sub-secret value \(\sigma_{i}=f_{i}(0)\). \(P_{i}\) distributes to every entity \(P_{j}\) a sub-share value \(s_{ij}=f_{i}(j)\), where \(i,\ j=1,\ 2,\ \ldots,\ n,\ i\
eq j\).
2. Each entity \(P_{j}\) received \(n\) sub-share value \(s_{ij}\), where \(i,\ j=1,\ 2,\ \ldots,\ n,\ i\
eq j\). Then, \(P_{j}\) computes the master-share value \(s^{\prime}_{j}\). Let \(s^{\prime}_{j}=\sum_{i=1}^{n}s_{ij}\ mod\ q\).  
3. Each entity \(P_{j}\) computes the final random master-secret value \(\sigma\). With [PERSON] [34] knowledge, any \(k\) master-share value \(s^{\prime}_{1},~{}s^{\prime}_{2},~{}\ldots,~{}s^{\prime}_{k}\) can compute a random master-shared value \(\sigma\). Using the Lagrange interpolating formula, \[\begin{array}{ll}\sigma&=\sum\limits_{j=1}^{k}s^{\prime}_{j}L_{j}\\ &=\sum\limits_{j=1}^{k}\bigg{(}\sum\limits_{i=1}^{n}s_{i,j}~{}mod~{}q\times L _{j}\bigg{)}\\ &=\sum\limits_{i=1}^{n}s_{i},\end{array}\] (1) where \(L_{j}=\prod_{i\
eq j}\frac{-j}{i-j}\).
## 3 Results
### Effect of Area Ratio  
In a stable hydrodynamic environment, the TCC will take on a specific shape. In general, as the inflow velocity increases, the TCC deformation increases, and the area ratio increases. When the water blocking rate is constant, the curtain load can be accurately predicted by analyzing the influences of different area ratios on the drag coefficient. The range of area ratio is 1.0-1.5, and the step size is 0.1. Assuming that when area ratio (\(Pr_{a}\)) varies, the other main parameters remain constant. According to Table 3, the inflow velocity (\(v\)) is uniform and the value is 0.05 m/s, the water blocking rate (\(Pr_{h}\)) is 0.6, 0.7, and 0.8, respectively, the inclination ratio (\(Pr_{l}\)) is 0, and the water temperature stratification ratio (\(\beta\)) is 0. For the convenience of comparison, the calculated drag coefficient is normalized; that is, the calculated drag coefficient is divided by the basic working condition drag coefficient. As the area ratio increases, the drag coefficient first increases and then decreases (Figure 10). When the area ratio is 1.3, the drag coefficient reaches a maximum value of 1.35. A fitting formula is investigated to quantify the effect of the area ratio on the drag coefficient. The results of the fitting formula show that the change in the drag coefficient obeys a quadratic function at different area ratios. When the inflow velocity remains constant, the relationship between the TCC load and the area ratio also follows a quadratic function. The area ratio mainly influences the drag coefficient, thus affecting the load.
## 2 Materials and Methods  
The present study was conducted in the Oral Radiology Section of College of Dentistry, Jouf University, Sakaka in the Kingdom of Saudi Arabia. Ethical clearance was obtainedfor the execution of the study from the Local Committee for Bioethics (LCBE no: 9-16-8/39). The study was conducted from the period June 2020 to January 2021. The study population included local Saudi subjects, aged between 15-30 years, attending the dental outpatient department (OPD) for various dental treatment purposes. The demographic details were obtained from the dental OPD. Over 780 high volumetric CBCT data sets were analyzed, and 252 volumetric data sets were shortlisted as per the set inclusion and exclusion criteria. The subjects with good quality CBCT images having EET and matched control, no history of orthodontic or craniofacial surgical treatment, and subjects with no anatomical variation in the ST and spinenoidal regions matched with healthy control without any EET were recruited into the study. Subjects using hormonal medications or corticosteroids were excluded from the study. Through a convenient sampling method, 252 subjects were distributed into different EET groupings as follows: 62 in (unilateral 13), 59 in (unilateral 23), 76 (bilateral 1323), and 55 in control groups. The sample size was decided based on 80.16% power (1-8 err prob), \(\alpha\) = 0.05, critical f = 1.87, and effect size (d) = 0.259 effect size (G*Power software version 3.0.10; Franz Faul Universitat, Kiel, Germany). Hence, the appropriate total sample size for this study (all 4 groups) was 252. The high volumetric CBCT images were acquired from Soredex (scanora 3D) CBCT machine manufactured by Soredex Imaging Systems (Tuusula, Finland). The data sets were analyzed using the On-Demand 3D software (Seoul, Korea). All of the CBCT scans were captured by the single radiographer from the period 2018-2020, with the subjects seated in upright position with teeth in centric occlusion. All of the scans were captured under the standard exposure settings of 70 kV, 10 mA, 2.4-6 s exposure time, with field of view of (FOV-XL + 240 \(\times\) 165 mm). A total of seven parameters, a-g, were employed for the assessment of STM morphometrics [11; 12; 14; 16; 17]. SOS evaluation was performed based on SOS scoring followed from [PERSON] et al. [12]. The details of both evaluations are presented in Figure 1 and Table 1.  
Figure 1: Showing different groups of study population. **(A)**: 1323 (Bilateral), **(B)**: 13 (Unilateral), **(C)**: 23 (Unilateral), **(D)**: Buccal, **(E)**: Palatal, **(F)**: Spheno-Occipital suture (open), **(G)**: Sella turcica (H): Sella turcica morphometrics.