esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The use of zirconia for full-arch Implant-Supported Fixed Complete Dental Prostheses (ISFCDPs) is common and reliable, with different techniques available for their design and fabrication. This retrospective study investigated prosthetic and implant survival/success rates and patients' satisfaction for ISFCDPs produced with three different techniques: zirconia-on-titanium (milled zirconia arch glued to a titanium bar), progressive monolithic zirconia (entirely made of zirconia and directly screwed to the implants) and zirconia-on-zirconia (milled esthetic zirconia crowns glued to a milled high resistance zirconia frame). Fourteen patients (five males, nine females) aged 52-80 and treated with 14 ISFCDPs (86 implants) were included in this analysis. The mean follow-up at the time of recall was 36 months. Prosthetic and implant-related success rates were, respectively, 92.86% and 95.35%. No failures have been reported. One case of prosthetic chipping was observed; however, it was successfully repaired intraorally. Patients' satisfaction was high: 78.57% were completely satisfied, 14.53% very satisfied and 7.14% satisfied. In conclusion, all the patients recommend treatment with ISFCDPs as full-arch prosthetic rehabilitation. The present study demonstrates positive clinical outcomes and high patients' satisfaction. Further long-term, prospective studies with a larger cohort of patients are needed to confirm the advantages of the different prosthetic designs.  
implants; zirconia; full arch +
Footnote †: journal: Applied sciences
# Exploring CP Array Antenna Characteristics and Its Applications to a Deployable Structure
## 2 Design and Experimental Results
### Circularly Polarized Antenna Design
#### 2.2.2 Array Antenna Characteristics for Various Mounting Scenarios
As shown in the measurement results, the antenna gain with two panels does not reach the values predicted by simulations. From the measured radiation pattern in the \(\phi=0^{\circ}\) plane, it is observed that the first sidelobe unexpectedly shifts too close to the main lobe. This results in a broadened main lobe and the elimination of the first null. To address this issue, further optimization of the radiation patch arrangement along the x-axis is required when connecting the two panels using deployable hinges. Key factors to consider include element spacing, hinge size, and the gap between the two panels. Another important consideration is the input phase response at each feed point. As depicted in the  
Figure 7: Simulated CP radiation patterns at (**a**) 5.45 GHz, (**b**) 5.8 GHz, (**c**) 6.15 GHz; measured radiation patterns at (**d**) 5.45 GHz, (**e**) 5.8 GHz, and (**f**) 6.15 GHz. inset of Figure 9b, each 8 \(\times\) 8 array antenna is fed using two input coaxial cables during the measurement. The measured S-parameters of the T-junction with two input cables are provided in Figure 10. As shown in Figure 10a, there is minimal magnitude imbalance between the two cables. However, an additional loss of approximately 1 dB is introduced by the cables, and their impedance-matching performance is not optimal. More importantly, a phase imbalance was observed between the two cables, causing a deviation of approximately 50\({}^{\circ}\) between the input signals to the two panels at the center frequency. To evaluate the impact of this phase mismatch, the radiation pattern of the two-panel CP antenna was simulated with different phase excitation. Figure 10c presents the results when the two input signals have a 50\({}^{\circ}\) phase difference. The simulated radiation pattern shows an obvious tilt in the peak of the \(\phi=0^{\circ}\) plane, along with an increase of approximately 3.25 dB in the first sidelobe level. In addition, this phase imbalance may cause an additional degradation in the antenna gain performance, as shown using the solid gray line in Figure 9c. Figure 8: (**a**) Fabricated 4 units of 4 \(\times\) 4 CP antenna for configuration \(\sharp\)2 (front view), (**b**) rear view of the fabricated antenna with power divider, (**c**) front view of the power divider, (**d**) coaxial cable connection example, measured (**e**) \(S_{11}\), (**f**) axial ratio and realized gain, and radiation patterns at (**g**) 5.45 GHz, (**h**) 5.8 GHz, and (**i**) 6.15 GHz.
# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Airborne Data Pre-Processing
Specifically, this transformation is often applied to hyperspectral imagery to reduce the number of spectral bands in which two principal components transformations are used to isolate and reduce noise, likewise reducing computational requirements [38]. Using the software-recommended number of spectral bands to retain important spectral information, the reduced images resulted in 23 MNF bands for the WLR image and 10 MNF bands for the FU image. Lastly, a lidar reflectance image was generated from the bathymetry, illustrating relative seafloor reflectance at 532 nanometers, following the model developed in [PERSON] et al. [39] with improvements specific to CZMIL lidar data (e.g., radiometric calibration and optimized calibration coefficients, implementation of a radiometric balancing channel to account for inhomogeneity in the return intensity across shallow channels, and improved color and flight line balancing, Figure 2, panel d). All images and grids were resampled to 3 m spatial resolution to strike a balance between spatial detail and computational intensity, clipped to the study area, ranging from the high-water line onshore (176 m contour obtained from the elevation grid) to approximately 10 m depth offshore, and consolidated in a layer stack for use in image classification analysis. Figure 2: Airborne data products: (**a**) bathymetry, (**b**) hyperspectral water-leaving reflectance, (**c**) fusion, and (**d**) bathymetric reflectance.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 4 Our Proposed Routing Schema  
### Clustering Formation  
We partition the whole sensor field into several sectors and the number of sectors is determined by the CHs. Each node joins one cluster according to its geographical information. The angle of each sector is shown as Equation (5):  
\[\mathrm{A}=\frac{2\pi}{N_{CHs}} \tag{5}\]  
where \(N_{CHs}\) denotes the number of CHs. Then, we calculate the cluster that each node should take part in using Equation (6):  
\[CH_{id}=\left\{\begin{array}{ll}\frac{\arctan\left(\frac{x}{y}\right)}{A}&if~ {}~{}x\geq 0~{}and~{}y\geq 0\\ \frac{\arctan\left(\frac{x}{y}\right)+\pi}{A}&if~{}~{}x<0\\ \frac{\arctan\left(\frac{x}{y}\right)+2\pi}{A}&if~{}~{}x\geq 0~{}and~{}y<0 \end{array}\right. \tag{6}\]  
where \(x\) denotes the position of the sensor in coordinate axis \(x\) and \(y\) denotes the position of the sensor in coordinate axis \(y\). We set the center of the sensor field as the origin of coordinates and the network after clustering is shown as Figure 3.  
Figure 2: Energy model.
## 3 Results and Analysis
Compared with the other three algorithms, the correlation coefficient R of the TLSC is relative weak, and the values are 0.9077 and 0.9233 in the east and north directions, respectively (Figure 5a1,b1)). For the DLSC and ALSC, the value of R are 0.9383 and 0.9281 in the east direction, and are 0.9418 and 0.9548 in the north direction (Figure 5a2,a3,b2,b3); however, for ADLSC, the value of R are 0.9404 and 0.9863 in the east and north directions, respectively (Figure 5a4,b4). The above analysis shows that the positive correlation between the observed and fitting velocity calculated by the ADLSC is very strong, which is further proves that the ADLSC algorithm has the best fitting effect. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**Fitting Points RMS (mm/a)**} & \multicolumn{2}{c}{**Checking Points RMS (mm/a)**} & \multicolumn{2}{c}{**Running Time**} \\ \cline{2-5}  & **(E)** & **(N)** & **(E)** & **(N)** & **(s)** \\ \hline TLSC & 2.43 & 1.32 & 3.21 & 1.86 & 1.29 \\ DLSC & 2.00 & 1.19 & 2.61 (18.7\%) & 1.73 (6.9\%) & 0.81 \\ ALSC & 2.15 & 0.96 & 2.82 (12.1\%) & 1.24 (33.3\%) & 1.42 \\ ADLSC & 1.96 & 0.54 & 2.57 (19.9\%) & 1.03 (44.6\%) & 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Root mean square error (RMS) of the fitting points and checking points using four algorithms. Figure 3: Residual between the estimated and observed velocity of the checking points: (a) east direction; (b) north direction. Figure 4: Residual between the estimated and observed velocity of the fitting points in the (**a**) east and (**b**) north. Figure 5: \(Cont\).
## 5 Numerical Implementation
### Analysis of Scenario 2 (S2)  
This scenario studied the performance of the proposed optimization model and compares it against that of the BWO and VSA. These algorithms were only used for a fixed operation of the FACTS. In this scenario, no solver of the GAMS reaches convergence. As in the previous scenario, the installation of only SVC devices was considered, given that [10] only analyzes SVCs for the IEEE 69-bus system. The results of the objective function for the IEEE 69-bus test system are shown in Table 6, considering the fixed or variable operation of SVC devices during the day.  
From the results obtained in Table 6, note that:  
1. The solutions found by the BWO and VSA in [10; 26] are the global optimum of the problem as the proposed convex model achieves the same configuration. Still, it is essential to note that the proposed convex model will always find the same values, while the BOW and VSA cannot guarantee these results. Furthermore, these algorithms require tuning many parameters, which can affect their performance.
2. According to the results obtained for the IEEE 69-bus test system, the node with the highest sensitivity is node 61, as it was selected to install the SVC device with the highest capacity. This behavior is the same for the fixed and variable SVC operations.
3. The optimal integration of SVC devices reduces the annual energy losses costs. This reduction is greater for the variable operation. These devices report reductions of 13.97% and 15.79% in the objective functions for the fixed and variable operation. The latter saves USD 3778.4/year more than the former. Despite this, the total size of the SVCs increases by 24.35% for the variable operation.
# Cost-Benefit Analysis of Small-Scale Rooftop PV Systems: The Case of Dragotin, Croatia
## 3 Results and Discussion
### Payback Time
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & **From Grid** & **To Grid** & \multirow{2}{*}{**Expression**} & **The Purchase Price** & **Profit** \\  & **kWh** & **kWh** & & **\(\xi\)/kWh** & \(\xi\) \\ \hline January & 194.41 & 36.60 & 5 & 0.056 & 2.07 \\ February & 142.38 & 75.23 & 5 & 0.056 & 4.25 \\ March & 104.35 & 157.84 & 6 & 0.0374 & 5.89 \\ April & 69.00 & 199.87 & 6 & 0.020 & 3.90 \\ May & 57.28 & 230.53 & 6 & 0.0147 & 3.23 \\ June & 48.78 & 240.53 & 6 & 0.012 & 2.75 \\ July & 50.88 & 259.94 & 6 & 0.0107 & 2.87 \\ August & 59.19 & 259.93 & 6 & 0.0133 & 3.34 \\ September & 243.84 & 116.98 & 5 & 0.056 & 6.61 \\ October & 118.59 & 138.54 & 6 & 0.048 & 6.70 \\ November & 164.22 & 60.38 & 5 & 0.056 & 3.41 \\ December & 204.00 & 27.96 & 5 & 0.056 & 1.58 \\ Total & 1456.91 & 1804.32 & & & 46.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Calculation of profit for sold surplus electricity. There are several different techniques for load management, such as direct load control, load limits, and smart metering and devices [45]. Most of the papers studied the PV-battery system with a storage capacity of 0.5-1 kWh times the installed PV capacity in kW [46; 47; 48; 49; 50]. This means that battery systems are applied for short-term storage, usually shorter than one day. Although battery prices are falling, PV systems with batteries are still not cost-effective, as stated in the introduction [8; 9; 10; 11; 12]. Dynamic power control controllers can also be purchased on the market, which constantly matches the amount of electricity produced by photovoltaic panels with the amount of energy consumed in the building. They do this by changing the MPPT (maximum power point tracker) of the inverter according to the power consumption of the user. This guarantees that no excess energy is injected into the grid or that only a certain amount is injected in accordance with the legislation of each country.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
###### Abstract  
Autonomous, flexible, and human-robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.  
path planning; Gaussian Mixture Model (GMM); Gaussian Mixture Regression (GMR); Modified Probabilistic Roadmap (MPRM) +
Footnote †: journal: Applied Sciences
## 5 Model Experiment
### Parameter Settings
#### 5.2.2 Optimization Algorithms  
The Adam optimizer, used in the CvT, Bi-LSTM, and multimodal fusion models, updates the weights according to the following equations:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{13}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{14}\]  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{15}\]  
\[\theta_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{16}\]  
\[\theta_{t}=\theta_{t-1}-\alpha\frac{\hat{m}_{t}}{\sqrt{\hat{\phi}_{t}+\epsilon}} \tag{17}\]  
where \(m_{t}\) and \(v_{t}\) are the estimates of the first and second moments of the gradients respectively, \(\beta_{1}\) and \(\beta_{2}\) are the decay rates, \(g_{t}\) is the gradient at time step \(t\), \(\alpha\) is the learning rate, and \(\epsilon\) is a small constant used to prevent division by zero. In our implementation, we used the default values \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\). For the ViT model, we employed the Stochastic Gradient Descent (SGD) optimizer with momentum, which updates the weights as follows:  
\[v_{t}=\gamma v_{t-1}+\alpha g_{t} \tag{18}\]  
\[\theta_{t}=\theta_{t-1}-v_{t} \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Module Type** & **Optimizer** & **Lr** & **Loss Function** & **Epochs** & **Batch Size** & **Random Shuffle** \\ \hline ViT-Based Facial Expression Recognition Model & SGD & \(1\times 10^{-3}\) & Cross Entropy Loss & 500 & 32 & True \\ CvT-Based Facial Expression Recognition Model & [PERSON] & \(1\times 10^{-5}\) & Cross Entropy Loss & 500 & 32 & True \\ CvT-Based Mel Spectrogram & [PERSON] & \(1\times 10^{-4}\) & Cross Entropy Loss & 300 & 32 & True \\ Recognition Model & [PERSON] & \(1\times 10^{-5}\) & MSE & 500 & 64 & False \\ Expression and Speech Feature Fusion Model Based on the Attention Mechanism & [PERSON] & \(1\times 10^{-6}\) & Cross Entropy Loss & 30 & 8 & False \\ \hline \end{tabular}
\end{table}
Table 6: Parameter settings for different models.  
where \(v_{t}\) is the velocity at time step \(t\), \(\gamma\) is the momentum coefficient (set to 0.9 in our experiments), \(\alpha\) is the learning rate, and \(g_{t}\) is the gradient at time step \(t\).
# Performance and Control Strategy of Real-Time Simulation of a Three-Phase Solid-State Transformer
## 3 Control Strategy  
One advantage of the RTS is the capability to test control strategies in similar conditions of a physical application, incorporating effects to the control schemes like saturation, data acquisition delays, sampling process and discretization. For this reasons RTS simulations have a high acceptance level for technicians and engineers, allowing to evaluate performance and stability conditions previously to use an experimental or real system.  
The Figure 2 shows the \(dq\) reference frame control strategy for the SST [18], each one of the control stages achieves its own control task independently without feedback from the other stages due to the dynamic decoupling caused by the proper selection of the capacitors \(C_{1}\) and \(C_{2}\). For \(VSC_{i}\) the control tasks are the regulation of the voltage across the capacitor \(C_{1}\) (\(V_{\text{C1}}\)), the reactive compensation to the input feeders and to provide the operating VARs for the input LCL filter. The DAB converter regulates the voltage in the capacitor \(C_{2}\) (\(V_{\text{C2}}\)); and the \(VSC_{o}\) converter provides the total active power transfer for the entire topology, the reactive compensation to the output AC source, as well as supplying the operating VARs for the output LCL filter.  
For \(VSC_{i}\) the control scheme operates in two separate levels, one for each control component (\(U_{i}^{d}\), \(U_{i}^{q}\)). The upper level has two interconnected loops, the outer loop regulates the voltage \(V_{\text{C1}}\) providing the inner superior loop the reference \(I_{d}^{*}\), for the reference for the inner inferior loop Equation (1) is used. The control for \(VSC_{o}\) operates in a similar way and have the same structure; however, only the inner loops are required and their references are also calculated by (1). The DAB control operates as a single-loop PI voltage control, and the processing of the voltage error gives the \(\theta\) angle to the modulation strategy.  
Figure 1: Three-Stage SST.  
For \(VSC_{i,o}\) the control bandwidth response is decoupled from the filter resonance frequencies \(f_{1}\) and \(f_{2}\) in order to avoid resonance to the grid; the parameters used in the simulation are shown in Table 1.  
\[\mathbf{I}=\begin{bmatrix}I_{i,o}^{d_{o}}\\ I_{i,o}^{q*}\end{bmatrix}=\frac{-2}{\left(V_{i,o}^{d}\right)^{2}+\left(V_{i,o}^{d }\right)^{2}}\begin{bmatrix}V_{i,o}^{d}&V_{i,o}^{q}\\ V_{
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
## 3 Modeling Electromagnetic Propagation in a Rotating Machine
### Design of RN 171 Antennae  
The underlying hardware architecture depends on the RN-171 module, incorporating a 32-bit SPARC processor (scalable processor architecture), a real-time clock, a 2.4 GHz radio, a CPU, a crypto accelerator, power management, analog sensor interfaces, and a TCP/IP stack [36].  
We chose the RN171-EK specifically for our system's mobile wireless applications, given its exceptionally minimal energy usage.  
The positioning and alignment of the two antennas remained consistent across the four scenarios. Matching parameters were established at the zero position and maintained constantly during rotor rotation. Subsequently, the radiation efficiencies and S-parameters were computed, and the propagation factor \(F_{p}\) was determined by Equation (1).  
Figure 4 represents the HFSS RN-171 design. The characteristics of the RN-171 module are shown in ref. [8].  
Figure 5 represents the HFSS simulation of two RN-171 antennas with an electrical machine.  
Table 2 presents the computed S-parameters and propagation factors for electromagnetic propagation/reception in two scenarios.  
The propagation factor closely approaches the maximum achievable when the transmitter is positioned in free space. Clearly, the electrical machine bodies cause significant attenuation in signal propagation.  
Figure 4: The RN171 antenna design.  
Figure 5: The HFSS simulation of two RN171 antennas with an electrical machine.
## 2 Methodologies
### Sound Source Localization
\begin{array}{ll}\mathbf{R}&=\mathbf{U}\mathbf{D}\mathbf{U}^{H}\\ &=\mathbf{U}_{sn}\mathbf{D}_{sn}\mathbf{U}_{sn}^{H}+\mathbf{U}_{n}\mathbf{D}_ {n}\mathbf{U}_{n}^{H}\end{array} , \tag{8}\]  
where \(\mathbf{U}_{sn}\in\mathbb{C}^{m\times x}\) and \(\mathbf{D}_{sn}\in\mathbb{R}^{x\times x}\) represent the signal subspace corresponding to the \(\kappa\) largest eigenvalues of \(\mathbf{R}\), and \(\mathbf{U}_{n}\in\mathbb{C}^{m\times(m-\kappa)}\) and \(\mathbf{D}_{n}\in\mathbb{R}^{(m-\kappa)\times(m-\kappa)}\) represent the noise subspace [20]. Because \(\mathbf{U}_{n}\) is orthogonal to the basis vectors of the signal subspace, the beamforming power calculated from the correlation matrix consisting of these eigenvectors is minimal for a scan vector \(\mathbf{w}(r_{m})\) belonging to the signal subspace. The inverse of this beamforming power indicates that the source location has a maximum value when the scan vector \(\mathbf{w}(r_{m})\) belongs to the signal subspace:  
\[\beta_{MUl}(r_{m})=\frac{1}{\mathbf{w}(r_{m})^{H}\mathbf{U}_{n}\mathbf{U}_{n} ^{H}\mathbf{w}(r_{m})}. \tag{9}\]  
Considering the different contributions of each frequency in the spectrum, a weighted broadband MUSIC output can be expressed as  
\[\beta_{WBM}(r_{m})=\frac{1}{N}\underset{f=f_{0}}{\overset{f_{N}}{\sum}}\ F(f)\beta_{MUl}(r_{m},f), \tag{10}\]  
where the narrowband MUSIC output is averaged and weighted by the different contributions in the spectrum for each frequency bin, \(f\)[34]. Therefore, the interference of  
Figure 2: Spherical wave-front model. the non-dominating frequency band can be eliminated during the computation of the beamforming maps.