esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Tests
### Plate Shear Flow
#### 3.2.1 Effect of Degree  
According to Equation (23), three hard constraints with different degrees (i.e., \(k=1.0\), \(2.0\) and \(3.0\)) and the same scaling factor (i.e., \(a=1.0\)) are generated to examine the effect of degree on the prediction accuracy of PINN for the plate shear flow.  
In order to quantitatively measure the prediction accuracy of PINN for the plate shear flow, a relative \(L_{2}\)-norm error is defined as follows, i.e.,  
\[\varepsilon=\frac{\sqrt{\sum\limits_{i=1}^{i=n}\left(\hat{u}_{i}-u_{i}\right)^ {2}}}{\sqrt{\sum\limits_{i=1}^{i=n}\left(u_{i}\right)^{2}}}\times 100\% \tag{24}\]  
where \(u_{i}\) is the analytic solution of axial velocity at the \(i\)-th node, and \(n\) is the number of the computational node. The relative \(L_{2}\)-norm error of axial velocity predicted by the PINN and the analytic solution at different degrees and Reynolds numbers are shown in Table 3. It can be observed that, with the increase in Reynolds number, the PINN-prediction results exhibit an increasing trend. However, across all ranges of Reynolds numbers, both second- and third-degree hard constraints result in obviously smaller relative errors than the first-degree hard constraint, despite the errors of second-degree hard constraints being slightly larger than those of third-degree ones.  
Figure 4 compares the distributions of axial velocity by PINN against the analytic solution at a Reynolds number of \(60.0\). From the figure, significant deviations are observed for PINN-predicted results with first-degree hard constraints, while the predicted axial velocity distributions by PINNs with second- and third-order hard constraints exhibit good agreements with the analytic solutions, corresponding to the errors shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree** & **Reynolds Number** & \(\boldsymbol{L_{2}}\)**-Norm Error** \\ \hline \multirow{4}{*}{First} & 60 & 1972\% \\  & 120 & 1602\% \\  & 240 & 958.5\% \\  & 480 & 360.7\% \\  & 960 & 225.4\% \\ \hline \multirow{4}{*}{Second} & 60 & 0.005\% \\  & 120 & 3.350\% \\  & 240 & 1.369\% \\  & 480 & 18.03\% \\  & 960 & 24.02\% \\ \hline \multirow{4}{*}{Third} & 60 & 0.020\% \\  & 120 & 2.210\% \\  & 240 & 0.290\% \\  & 480 & 3.010\% \\  & 960 & 19.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The \(L_{2}\)-norm errors of axial velocity of plate shear flow with different degrees of hard constraints.
## 1 Introduction  
Conventional alginate impression techniques have been challenging for patients with sensitive gag reflexes, causing feelings of irritation and discomfort [1]. The first steps in computer-aided design/computer-aided manufacturing (CAD/CAM) systems in dentistry were taken by [PERSON] and coworkers in 1987 [2]. Further, Dr. [PERSON] introduced CAD/CAM concepts into dentistry in 1989 [3; 4; 5], and several intraoral scanners have been introduced recently [6; 7]. Imaging technology is now developing, and intraoral scanners have been steadily upgraded. Since the plaster model and intraoral scans are used together in clinics, it is necessary to evaluate the difference between alginate impression and intraoral scanners. In orthodontic clinics, alginate impression is still being used to fabricate orthodontic diagnostic models or orthodontic appliances. In the present study, the two intraoral scanners were used, TRIOS and iTero. The scanning technology of the two scanners is confocal microscopy; however, the light sources of the two scanners are different. TRIOS is based on a structured-light scanner with an infrared light inside, whereas iTero uses laser as its light source.  
Since the introduction of digital impressions with intraoral scanners, the advantages of digital impressions and scanning systems have been fixing the shortcomings of conventional impression using a tray and materials [8]. Intraoral scanners can now take full arches, and intraoral scanning is available for both prosthetic and orthodontic diagnosis and treatment plans [9].  
The accuracy of intraoral scanners has been evaluated for single teeth [10; 11; 12; 13; 14; 15; 16] and short-span fixed dental prostheses [17; 18; 19; 20]. In order to determine the accuracy of intraoral scanners, researchershave performed in vitro studies using reference models [21; 22; 23; 24; 25; 26]. Although short-span intraoral scans have exhibited excellent accuracy, few studies have investigated the accuracy of in vivo intraoral scans for full arch scans. The validity of intraoral scans was limited to tooth size measurements [27], and three-dimensional (3D) superimpositions were not performed with conventional impression [28]. While recent studies have compared conventional impression and intraoral scanning [29; 30; 31], there are still limitations in their in vitro studies [29; 30] and small sample size [30; 31]. The purpose of the present study was to compare in vivo full arch intraoral scans and conventional impressions using 3D surface analysis.
## 2 Evaluating Pump Energy Consumption
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & \(P_{rate}\)**, **W** & **\(n_{rate}\)**, **rpm** & **Q\({}_{BEP}\)**, **m\({}^{3}\)/h** & **\(H_{BEP}\)**, **m** & **Pump Efficiency (BEP), \%** \\ \hline B-NM4 65/25B/B & 4000 & 1450 & 60 & 15.4 & 75.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Published characteristics of pump from manufacturer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & **Rated Mechanical** & **Power, W** & **Poles** & **Frame Size** & **Frame Material** & **Wight, kg** & **Rated Voltage, V** \\ \hline DOL SynRM & 4000 & 4 & IEC 112 & No data & No data & 400 \\ DOL PMSM & 4000 & 4 & IEC 112 & Cast iron & 49 & 400 \\ IE2 IM & 4000 & 4 & IEC 112 & Cast iron & 45.1 & 400 \\ IE3 IM & 4000 & 4 & IEC 112 & Cast iron & 42 & 400 \\ IE4 IM & 4000 & 4 & IEC 112 & Cast iron & 58 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Motor characteristics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & \multicolumn{3}{c}{**Motor Efficiency, \%**} & \multicolumn{3}{c}{**Motor Power Factor**} \\ \cline{2-7}
**Motor** & **50\% Load** & **75\% Load** & **100\% Load** & **50\% Load** & **75\% Load** & **100\% Load** \\ \hline DOL SynRM & 91.6 & 92.4 & 91.9 & 0.607 & 0.713 & 0.755 \\ DOL PMSM & 89.0 & 91.0 & 91.7 & 0.68 & 0.81 & 0.88 \\ IE2 IM & 86.0 & 86.7 & 86.7 & 0.62 & 0.74 & 0.8 \\ IE3 IM & 88.7 & 89.1 & 88.8 & 0.6 & 0.72 & 0.78 \\ IE4 IM & 91.0 & 91.6 & 91.1 & 0.63 & 0.75 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Motor characteristics. In Figure 3a, it can be seen that DOL SynRM has the highest efficiency over the whole loading range. The efficiency of the DOL PMSM in the rated loading point (4000 W) is very close to that of the DOL SynRM, but at partial loads, the DOL PMSM's efficiency decreases much faster than that of DOL SynRM. In Figure 3b, it can be seen DOL PMSM has the highest power factor over the whole loading range. The power factors of IE3 IM and DOL SynRM are close and significantly lower than the one for PMSM. The power factors of IE2 IM and IE4 IM are slightly higher than those of IE3 IM. The rate of power factor decrease with a decrease in load is approximately the same for all studied motors: When the load was decreased from 100% to 50% the power factor decreased by approximately 0.15.
## 4 SMRRPM Evaluation
On the private Internet networks, the SMTRPM established communication without problems, obtaining better performance by the Android application. Considering these results, it is important to connect the mobile device and the last patient node to a private Internet network to obtain better data transmission performance. Figure 15: Tests of HR measurements in patients without previous physical activity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transmission Distance in _Ad Hoc_ Network** & **Packet Loss Rate** & **Internet Network** & **Packet Loss Rate** \\ \hline
6 m & 0.13\% & Private one & 0.11\% \\
12 m & 0.19\% & Private two & 0.14\% \\
20 m & 0.8\% & Public one & 48\% \\
23 m & No comm & Public two & No comm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Communication test (packet loss rate values). Finally, the SMRRPM was tested for diagnosing basic conditions related to the BT and HR variables by comparing 50 diagnoses both generated by the system and provided by nurses. To determine the reliability of SMRRPM diagnostics, Table 6 shows the false acceptance and false rejection rates that were found. The results of the comparative test show that the SMRRPM provided correct BT diagnoses in most of the cases. However, for the HR conditions, there was a small rate of false diagnoses. One of the main reasons for this was that the HR sensor prototype was often not stably fixed to the patient's thumb. Consequently, a restraint mechanism should be developed for the final product design to obtain fewer false diagnoses.
# SPH Simulation of Hydraulic Jump on Corrugated Riverbeds
## 3 Benchmark Study
### Flow Rate Through Reservoir Gate  
The constant inflow velocities in different cases of this study are obtained by horizontally moving a vertical plate [45] that is installed at the left of the flume. The height of the plate is equal to the water depth \(H\) in front of the reservoir gate. The velocity of the plate is equal to the \(U_{0}\) in Equation (6). The rate of the flow through reservoir gate is controlled by the height and velocity of the pushing plate, so that the water level near reservoir gate remains constant and thus the constant flow in the flume can be obtained. The flow rates in the reservoir (the cross-sectional mean flow rate at 0.5 m away from the reservoir gate) and through the reservoir gate of Case A predicted by the SPH model are compared with the experimental results (see Figure 3). The flow rate in the reservoir fluctuates in the first eight seconds and then becomes stable. The standard deviation of the flow rate history is 0.008 m\({}^{2}\)/s. In addition, it can be seen that the simulated flow rate through the reservoir gate becomes stable very quickly and only shows very minor fluctuation during the whole simulation process. The simulated flow rates in the other cases follow a similar trend in terms of the comparison with the experimental data. For better illustration, the numerical and experimental flow rates through the reservoir gate in all the studied cases are presented in Table 2. The relative deviations between the simulated and experimental flow rates in these cases are from 2.0% to 4.5%. It indicates that the pushing plate model has a good performance in controlling the flow rate and thus the constant inflow condition in the experiment can be simulated.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cases** & \(S\)**(mm)** & \(t\)**(mm)** & \(Fr\) & \(U_{1}\)**(m/s)** & \(q\)**(m\({}^{2}\)/s)** & \(y_{1}\)**(mm)** & \(y_{2}\)**(m)** & \(L_{j}\)**(m)** \\ \hline A & 68 & 13 & 4 & 2 & 0.051 & 25.4 & 0.104 & 0.41 \\ B & 68 & 13 & 5 & 2.5 & 0.063 & 25.4 & 0.128 & 0.48 \\ C
## 3 Methods  
Radar backscattering responds to surface roughness and soil moisture; however, in a vegetated area, it can also be affected by vegetation cover and plant water content [18, 33, 34, 35]. Therefore, the backscattering from the vegetated area consists of the volume scattering from the vegetation and the surface scattering from the underlying soil. In addition, the signal scattering from the underlying soil is attenuated by the vegetation layer.  
In this study, a microwave/optical synergistic model to simulate the radar backscattering from a vegetated surface based on the WCM [26] and IEM [6] was developed. The IEM was used to simulate the scattering from a bare soil surface, and the WCM was used to calculate the volume scattering and the two-way attenuation from the vegetation. In addition, the synergistic model included usage of the vegetation cover fractions derived from optical remote sensing data for the vegetation gap information. Then, the soil moisture was estimated by applying the inverted model.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### External Decoupling
#### 4.1.4 Grounding Branch Technique  
The grounding branch blocks the surface current by inserting the metal strip between two compact elements for coupling reduction. The larger the physical dimensions of added grounding branches mean the stronger the introduced inductance, which can create the mutual coupling effect minima.  
By sharing a common rectangular grounding branch between two neighboring antenna elements, a self-decoupled four-port MIMO system was established [47]. As shown in the current distribution diagram, the characteristic of the grounding branch structure can be observed clearly. The current almost did not spread from one antenna element to another, although there was a 1.2 mm distance between them only. The MIMO system was optimized at a working frequency of 3.5 GHz, and isolation was better than 17 dB. Furthermore, the author upgraded this system to a dual-band system as shown in Figure 4, and the common ground branch was lengthened accordingly [48]. This design can provide isolation of better than 17.5 dB. A multi-branch T-shaped grounding branch was inserted in the ground plane to realize the decoupling of a UWB MIMO [49]. With adding the branches of the decoupling structure, the input impedance increased and the isolation improved to \(>\)15 dB. The T-shaped grounding branches were placed on the opposite side from antenna elements in [PERSON] et al. [50]. More than 15 dB isolation was obtained between the eight-port MIMO. The two meandering grounding branches were introduced between two antenna pairs [51]. Additionally, the optimized length of the proposed decoupling structure was one-quarter wavelength at the resonance frequency. The isolation was achieved at more than 12 dB in this ten-port MIMO.  
Figure 3: MIMO antenna with DGS and CSRR: (**a**) geometry of proposed MIMO; and (**b**) the current distributions with hybrid decoupling structures (Results from [42]).
# Network Visualization of Cooperative Games
## 2 Preliminaries
### Communication Situations  
A communication situation as introduced in [31] can be modeled using a triplet \((N,v,\gamma)\). Here, \((N,v)\) represents a transferable utility (TU) game and \((N,\gamma)\) signifies a communication network.  
In this context, \(\gamma\) represents the communication network, which is a graph indicating how players can communicate with each other. The graph \(\gamma\) is a subset of the complete graph \(\gamma_{N}\), which includes all possible communication links among players. The set of all communication situations with \(N\) as the set of nodes/players is denoted by Equation (3).  
\[\mathcal{CS}^{N}=\{(N,v,\gamma)\mid\gamma\subseteq\gamma_{N}\} \tag{3}\]  
Refs. [31; 40] defined the concept of a graph-restricted game \((N,v^{\gamma})\). In this model, the value of a coalition \(S\) under the communication constraints of \(\gamma\) is given by Equation (4), where \(S/\gamma\) represents the partition of the coalition \(S\) into connected components according to the graph \(\gamma\).  
\[v^{\gamma}(S)=\sum_{C\in S/\gamma}v(C) \tag{4}\]  
Given \(\mathcal{CS}^{N}\), the set of all communication situations in a game \((N,v)\), Ref. [31] defined a value allocation rule \(\mu\) for these situations. This rule assigns a value to each player in a communication situation, and is defined in Equation (5).  
\[\mu_{i}(N,v,\gamma)=Sh_{i}(N,v^{\gamma}) \tag{5}\]
## 2 Materials and Methods
### Data Analysis
The variation under different outlines was calculated and included in the total uncertainty for \(D_{\rm{ROI1}}\) and \(D_{\rm{ROI2}}\). A similar approach was implemented for the calculation of \(H_{RMS}\), but the calibration error was not taken into account because \(H_{RMS}\) shows the relative spread of the dose in ROI1 with respect to its mean value. For the GPRs, the ROI delineation was not used, but the error associated with the possible shift of the compared dose distributions relative to each other was also calculated based on five scenarios as for the previous metrics. An additional uncertainty could be caused by human error in outlining the 4D-WPh. To minimize the influence of this factor on the results of our study, several solutions were considered. First, when designing the 4D-WPh, we used the simplest rectangular shapes for its body and holders to make them easier for a medical physicist to recognize and outline. Second, 3D printing was used to produce the holder with a low filling density, which increased the contrast of the holder details relative to the water. Third, the PTV had a simple cubic shape, and the dose was measured by the film placed in the central slice of the PTV, ensuring that the film always fell in a uniform area of a spread-out Bragg peak. Finally, in the gamma index calculation, the reference and measured distributions fell in the same slice of the PTV, minimizing the error that could occur due to a mismatch between the central slice in the planned distribution and the slice in which the dose distribution was measured.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### Data Transmission Technologies
#### 3.2.4 Comparison of LPWANs  
The information in Table 6 indicates that the individual technologies differ, although they are all specific to LPWANs. The key advantage of Sigfox over its competitors is the size of the area covered by a single base station, up to 40 km in rural areas. Under the same conditions, the main competitors achieve a coverage of approximately 20 km with LoRaWAN and 10 km with NB-IoT [95]. NB-IoT is also limited to areas with LTE networks. In terms of deployment, LoRaWAN allows the creation of private networks, while the other two technologies rely only on the provider deployment model [96]. NB-IoT is aligned with applications that require the transfer of large amounts of data or that need a low-latency network. Sigfox offers a payload volume approximately 100 times lower than NB-IoT (maximum 1600 B). LoRaWAN provides a payload of 242 B, and latency can be reduced using class C devices [86]. All of these technologies offer sleep modes to reduce the energy consumption of a transmitter to a minimum when the end device is not transmitting. NB-IoT nodes have higher power requirements than LoRaWAN and Sigfox, drawing the highest peak currents during transmission through the use of QPSK modulation in combination with OFDM-FDMA and QoS handling [85; 97]. LoRaWAN and Sigfox are ALOHA-based and operate on unlicensed frequencies. NB-IoT relies on licensed LTE bands, which ensure higher QoS through regulation, but a consequence is a higher price for the end customer due to license fees [95].
## 3 P-M Model
### C-P Sub-Model
#### 3.1.2 Algorithm of C-P Sub-Model  
However, obtaining the energy cost price information at time \(t\) only is not sufficient. It is necessary to calculate the hourly energy cost price of the PIES through a loop iteration over the entire year. In addition, the control index of the loop iteration is expressed in Equation (8), where \(s\) represents the number of iteration steps, and \(fval_{total}^{s}\) represents the total cost during the entire year.  
\[\sigma_{s}=\frac{fval_{total}^{s}-fval_{total}^{s-1}}{fval_{total}^{s-1}} \tag{8}\]  
On this basis, Algorithm (1) can be used to perform the loop iteration of the C-P sub-model over the entire year, as illustrated in Figure 2. For starting time \(t=1\), \(max\big{(}Q_{t,i/j/k}\big{)}=\sum_{\tau=1}^{8760}\big{|}Q_{\tau,i/j/k}\big{|}=Q _{t,i/j/k}\), and Equation (6) is called to calculate \(Q_{1,i/j/k}\). When \(t=2\), \(max\big{(}Q_{t,i/j/k}\big{)}=Q_{1,i/j/k}\) or \(Q_{2,i/j/k}\), \(\sum_{0}^{8760}\big{|}Q_{\tau,i/j/k}\big{|}=Q_{1,i/j/k}+Q_{2,i/j/k}\), and Equation (6) is called to calculate \(Q_{2,i/j/k}\). Similarly, when \(t=t+1\), \(Q_{t+1,i/j/k}\) can be calculated until \(t=8760\). When the second loop starts, namely \(t=1\) again, Equation (8) is the determinator of iterative convergence. The iterative process continues until \(\sigma_{s}\leq 0.04\).  
The C-P sub-model and loop iterative method are applied in practical engineering, and the annual hourly cost price of PIES is captured under different load profiles, which provides support for the construction of the S-P sub-model.
## 3 Results and Discussion
### Volatile Compounds Analysis  
The volatile compounds and their semi-quantification in avocado oil are expressed as average values and are summarized in Table 1. The identified fraction was characterized by at least 44 components, including terpenoids, hydrocarbons, aldehydes, and ketones. Terpenoids were the dominant fraction of volatiles, with an average relative abundance of 61.7%, whilst hydrocarbons (non-terpenoids) were 35.9%. Thus, the avocado oil fragrance from the Zutano cultivar was characterized by many terpenoids with high abundance. The results show that the volatile fraction of this variety of oil is rich in terpenoids.  
As reported previously by [PERSON] [5], the quality and quantity of volatile compounds detected in avocado oil are affected by several factors such as variety, extraction conditions (e.g., organic solvent, temperature, time), and analytical technique (isolation or analysis parameters). Avocado oil obtained from _P. americana_ Mill. sourced from a Mexico City regional market was studied by [PERSON] [25], who identified 36 volatile substances using four diverse extraction methods. It should be noted that the largest number of compounds (15 volatiles) were identified using microwaves and Soxhlet and hexane as extractors. Furthermore, in a recent study by [PERSON] [37], 40 volatile compounds were detected using different extraction methods (squeezing, supercritical carbon dioxide, and aqueous). Nineteen volatile materials were identified by [PERSON] [33] in cold-pressed avocado oil grown in regions of Turkey.  
A typical chromatogram of avocado oil's ultrasound technique is presented in Figure 2. However, no qualitative changes were observed among the chromatograms that emerged from different extraction techniques or ripening stages. The total ion chromatograms reveal the capture of seven peaks that characterize the dominant volatile profile of avocado oil from Zutano cultivar. These peaks include 2,6,6-trimethylbicyclo[3.1.1]hept-2-ene (\(\alpha\)-pinene); 1-methyl-4-(prop-1-en-2-yl)cyclohex-1-ene (D-limone); 4,10-dimethyl-7-propan-2-yltricylcolq[4.4.0.01.5]dec-3-ene (\(\alpha\)-cubebe); (15,65,75,85)-1,3-dimethyl-8-propan-2-yltricylcolq[4.4.0.0.2.7]dec-3-ene (a-copane); (1R,9S,2)-4,11,11-trimethyl-8-methylenebicyclo[7.2.0]undec4-ene (\(\beta\)-caryophyllene); 2,6-dimethyl-6-(4-methylpent-3-enyl)bicyclo[3.1.1]hept-2-ene (\(\alpha\)-bergamotene); and (1\(E\),_AE_,_8_E_)-2,6,6,9-tetramethylcycloundeca-1,4,8-triene (humulene). Similarly, [PERSON] [33] identified D-limonene, \(\alpha\)-cubebene, \(\beta\)-caryophyllene, and \(\beta\)-curcumene as the most abundant compounds in avocado oil, while [PERSON] [25] confirmed the above results.
## 3 Voltage Stability of a Multi-Infeed LCC-HVDC System
### Maximum Available Power (MAP)  
In the maximum achievable power (MAP)-based approach, the maximum DC power achieved by an inverter at a constant extinction angle is taken as the stability limit [16]. For this reason, the maximum power curve (MPC) is observed, and the system strength at the maximum point is said to be the critical system strength. An analysis of MAP is provided in [17]. For MAP, only the direct current is varied, and the other parameters are kept constant. If the direct current is further increased at the MAP point, the maximum achievable power decreases. The power derivative with respect to the direct current is used as an index to assess stability. If the derivative is positive, the system is stable, and if the derivative is negative, then it will cause a reduction in DC power.