esa-sceva/satcom-chunk-collection huggingface dataset:
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 2 Structure and Method
### Optical Simulations  
The correlation between light absorption, i.e. current generation of the solar cells and the various nanophotonic grating textures, is investigated by rigorous optical simulations. We used a home-built interface to the Finite-Difference Time-Domain solver Meep [11]. The experimentally determined optical data are taken as input for the simulations. An isotropic growth model [12] is assumed to take into account the changes in the interface texture due to the coverage of the texture by the a-Si:H layer. A monochromatic plane wave source is implemented in the incident glass half space. Periodic boundary conditions are assumed in the lateral direction and open boundaries in the propagation direction. The full three-dimensional electromagnetic field distribution at steady-state condition is the output of the simulation. From this output, the optical absorptance in each layer is extracted. We consider that each photon that is absorbed in the a-Si:H slab contributes to the generated photocurrent, whereas absorption in any other layer is considered to be parasitic. A spectral resolution of 2 nm is used in order to resolve the resonances of the waveguide modes. For the perfect periodic grating textures, one lateral unit cell is taken as the calculation domain. In the case of the disordered gratings, a lateral domain of \(5\times 5\) um\({}^{2}\) was chosen. The domain size in the propagation direction was 2 um.  
An example of the simulation results is shown in the right part of Figure 1a for an incident wavelength of 700 nm. The interfaces of the absorber layer are depicted by the dashed lines. In the perfect periodic case, the excitation of a waveguide mode can be seen in Figure 1a by the well-defined intensity pattern in the high refractive index absorber medium. Such waveguide modes exhibit a resonant behavior with distinct sharp maxima in the absorptance. This resonance is achieved for the wavelength where the planar wave vector of the waveguide mode (\(k_{||}\)) equals an integer multiple \(n\) of the lattice wave vector:  
\[k_{||}=n\cdot 2\pi/g, \tag{1}\]  
where \(g\) is the grating constant, i.e., the period.  
Since the resonances related to waveguide modes are spectrally narrow, we introduce a tailored disorder to the grating structure in order to spectrally broaden the absorption enhancement. Two kinds of disorder are considered: position disorder and radius disorder. For both cases, the respective parameter is varied with respect to the perfect periodic structure according to a Gaussian distribution defined by the standard deviation \(\sigma\). This leads to disordered grating textures with particles with identical shape but a random position within the unit cell, in the case of the position disorder. For the radius disorder, the particles are located at the same position within the unit cells but their shape is varied by a randomized radius. Examples of the assumed interface textures for the periodic case and for the position and radius disorder are illustrated in Figure 1b.
###### Abstract  
This paper describes a numerically efficient method for optimizing the high power transfer efficiency (PTE) of a resonant cavity-based Wireless Power Transfer (WPT) system for the wireless charging of smart clothing. The WPT system under study unitizes a carbon steel closet intended to store smart clothing overnight as a resonant cavity. The WPT system is designed to operate at 865.5 MHz; however, the operating frequency can be adjusted over a wide range. The main reason behind choosing a resonant cavity-based WPT system is that it has several advantages over the competitive WPT methods. Specifically, in contrast to its Far-field Power Transfer (FPT) and Inductive Power Transfer (IPT) counterparts, resonant cavity-based WPIs do not exhibit path loss and significant PTE sensitivity to the distance between the Tx and Rx coils and misalignment, respectively. The non-uniformity of the fields within the closet is addressed by using an optimized Yagi-like transmitting antenna with an additional element affecting the waveguide mode phases. The changes in the mode phases increase the volume inside the cavity, where the PTE values are higher than 50% (the high PTE region). In the present study, the model decomposition method is adapted to substantially accelerate the process of finding the optimal WPT system parameters. Additionally, the decomposition method explains the mechanism responsible for extending the high PTE region. The generalized scattering matrices are computed using the full-wave simulator Ansys HFSS for three sub-models. Then, the calculated S matrices are combined to evaluate the system's PTE. The decomposition method is validated against full-wave simulations of the original WPT system's model for several different parameter value combinations. The simulated results obtained for a sub-optimal model are experimentally verified by measuring the PTE of a real-life closet-based WPT system. The measured and calculated results are found to be in close agreement with the maximum measured PTE, as high as 60%.  
wireless power transfer; cavity-based systems; remote charging +
Footnote †: journal: applied sciences
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 2 Mathematical Preliminaries
### Broad Learning System  
The BLS was proposed by [PERSON] in 2017 [13]. As an alternative to deep network structures, the input data are first mapped to mapped features before being mapped to enhancement nodes. The output layer connects both the mapped feature layer and the enhancement node layer.  
The data are transformed by a linear mapping function connecting the input weight matrices to obtain a mapping feature set. The mapped features are noted as \(Z_{i}\), the ithset of mapped features, \(i=1\), \(2\),..., \(n\), obtained after a linear mapping and activation function, i.e.,  
\[Z_{i}=\phi_{i}(XW_{e_{i}}+\beta_{e_{i}}),i=1,2,\ldots,n, \tag{5}\]  
where \(X\in R^{a\times b}\) denotes the input sample data for model training, where the number of samples is a and the dimension of each sample is b. \(W_{e_{i}}\) denotes the weight matrix of the ith feature node, the optimal input connection weight matrix obtained by sparse autoencoder fine-tuning. \(\phi_{i}\) is the activation function of the feature node. \(\beta_{e_{i}}\) denotes the input layer to the mapping feature layer with \(W_{e_{i}}\) being the corresponding bias matrix. The dimension of the weights \(W_{e_{i}}\) determines the number of nodes in the \(Z_{i}\) mapped feature group. To tackle the problems caused by randomness, a sparse autoencoder effectively reduces the linear correlation of newly generated feature nodes [22; 23]. What makes the sparse feature matrix learning model attractive is that it can explore essential features [24; 25]. The mapped feature group obtained by n transformations is denoted as follows:  
\[Z^{n}=[Z_{1},Z_{2},Z_{3},\ldots,Z_{n}], \tag{6}\]  
The enhancement nodes are obtained from the mapped nodes by a nonlinear mapping and activation function transformation. Denote \(H_{j}\) as the \(j\)th group of enhancement nodes, \(j=1\), \(2\),..., \(m\), that is the following:  
\[H_{j}=\zeta_{j}(Z^{n}W_{h_{j}}+\beta_{h_{j}}),j=1,2,\ldots,m, \tag{7}\]  
where \(\zeta_{j}\) is the activation function of the enhancement node. \(W_{h_{j}}\) denotes the random connection weight matrix of the \(j\)th group of mapped feature nodes to the enhancement node layer. \(\beta_{h_{j}}\) denotes the \(j\)th group of bias matrices, and the group of enhancement nodes obtained by m transformations is denoted as follows:  
\[H^{m}=[H_{1},H_{2},H_{3},\ldots,H_{m}], \tag{8}\]  
The mapped feature nodes together with the enhancement nodes are used as inputs for the BLS and is solved for the eigenvector using \(Y=[Z^{n}|H^{m}]W^{m}\).  
Where matrix \([Z^{n}|H^{m}]\) denotes the actual input to the BLS. Ydenotes the eigenvector of the input. \(W^{m}\)denotes the matrix of weights connecting the input to the output layer of the system, which is solved by solving a ridge regression [26], as follows:  
\[W^{m}=\left(\lambda I+A^{\top}A\right)^{-1}A^{\top}Y, \tag{9}\]  
where \(A=[Z^{n}|H^{m}]\) denotes the actual input to the BLS. \(\lambda\) denotes the regularization factor and \(I\) denotes the unit matrix.
## 2 Hybrid Wind-PV System Modeling
### Wind Turbine System Modeling
#### 2.1.2 PMSG Modeling  
The wind turbine based on PMSG was chosen in this work due its high efficiency and high torque development. The induced voltage magnitude and frequency of the PMSG varies with wind speed and the generated power will be converted to DC power using a rectifier in order to overcomes this issue [40]. The PMSG mathematical equations is given in synchronous frame by Equation (6) as follows:  
\[\rm\left\{\begin{array}{l}\rm V_{d}=-R_{s}I_{d}-L_{d}\frac{dI_{d}}{dt}+L_{d} \omega I_{d}\\ \rm V_{q}=-R_{s}I_{q}-L_{q}\frac{dI_{d}}{dt}-L_{d}\omega I_{d}+\phi_{f}\omega \\ \rm T_{em}=-\frac{2}{J}\rm\rho\phi_{f}I_{q}\end{array},\right. \tag{6}\]  
where \(\rm R_{s}\) is the stator resistance, \(\omega\) is the rotor electric speed (\(\omega=p\Omega\)), \(\rm I_{d}\), \(\rm I_{q}\), \(\rm V_{d}\), \(\rm V_{q}\), \(\rm L_{d}\), \(\rm L_{q}\) are, respectively, the direct and the quadrature axes components of current, voltage and stator inductance and \(\phi_{f}\) is the flux of the permanent magnet.  
Figure 3: Turbine model scheme.
# Practical Model for Metamaterials in Wireless Power Transfer Systems
## 2 Practical Model for Low-Frequency Metamaterials  
### Lumped Impedance Parameters of the MTM Unit  
The Swiss roll, as shown in Figure 1, is the most widely-used structure for low-frequency MTM units, which can be simply fabricated using thePrinted Circuit Board (PCB) technology. Two reverse spirals are printed on both sides of the FR4 (a kind of glass fiber composite material) substrate and connected by vias in order to reduce the MTM resonance frequency [34; 35; 36]. To facilitate easy adjustment of the MTM resonance frequencies, lumped capacitors are connected in series with the reverse Swiss rolls. Moreover, it has been experimentally confirmed that the lumped capacitance dominates the resonance on low frequencies [37]. Therefore, one can use lumped parameters to characterize the MTM units.  
In order to design MTMs in a proper way in the near field, the lumped parameters are used to model the MTM unit. According to [37], the relative permeability \(\mu_{\text{r}}\) of the MTM modeled by the lumped parameters is given in (1):  
\[\mu_{\text{r}}=1+F\cdot\frac{\omega^{2}}{-\omega^{2}+j\omega(\omega_{\text{M}}/Q _{\text{M}})+\omega_{\text{M}}^{2}}, \tag{1}\]  
where \(\omega_{\text{M}}\) is the resonance frequency of MTMs (related to the effective inductance and capacitance), and \(Q_{\text{M}}\) is the quality factor of the MTMs. \(F\) is a coefficient related to the shape and the structure of the unit:  
\[F=\frac{\mu_{0}\cdot\left(\sum_{i=1}^{n}S_{i}\right)^{2}}{L_{eff}V_{unit}}, \tag{2}\]  
where \(L_{eff}\) is the effective inductance, \(V_{unit}\) is the volume of an MTM unit, and \(S_{i}\) is the area of \(i\)th turn in a unit with \(n\) turns in total. The coefficient \(F\) typically falls in the range of 0.16-0.50, according to [38]. Since the lumped capacitance is much larger than the parasitic capacitance of the unit, the latter can be neglected in this case. In other words, if an MTM unit is in a fixed shape and structured (fixed \(F\)) with a lumped capacitor, the property of its relative permeability can be decided by the resonance frequency (calculated by \(L_{eff}\) and capacitance) and the quality factor.
## 4 Motion Planning and Control in ADAS for City Buses
### State Estimation  
To estimate the states of the bus, a state estimation approach is employed. The states of the bus can be presented as \(\boldsymbol{q}_{\text{CarEst}}=\begin{bmatrix}\theta&x&y\end{bmatrix}^{T}\); depending on the presence and delay in receiving the perception data, the state estimator follows various policies. If the localisation data indicated by \(\boldsymbol{q}_{\text{CarLoc}}\) from the GNSS module are not present, the estimated localisation is computed by integrating (1) and applying odometry, resulting in \(\boldsymbol{q}_{\text{CarEst}}=\boldsymbol{q}_{\text{CarPred}}\). However, if the localisation data from GNSS are received and data are not delayed, the states are directly taken from the localisation data as \(\boldsymbol{q}_{\text{CarEst}}=\boldsymbol{q}_{\text{CarLoc}}\), and if the received data are delayed, a weighted sum estimator is utilised  
\[\boldsymbol{q}_{\text{CarEst}}=\mathbf{w}\odot\boldsymbol{q}_{\text{CarPred}}+ (1-\mathbf{w})\odot\boldsymbol{q}_{\text{CarLoc}} \tag{3}\]  
where \(\odot\) denotes the element-wise (Hadamard) product, and \(\mathbf{w}\) is a vector containing the weight parameters for each component. Here \(\mathbf{w}=[w_{\text{theta}},w_{\text{pos}},w_{\text{pos}}]\) are the weight parameters for the theta and position components, respectively.
## 3 Results  
### Changes in Physico-Chemical Properties During the Treatment Process  
Analysis of the samples' water quality parameters throughout the swine wastewater treatment plant revealed distinct trends in SS, BOD, TOC, TN, and TP at each treatment stage (Table 1, Figure 2). SS reached 5700 mg/L in the raw wastewater, while BOD and TOC measured 3740 mg/L and 6100 mg/L, respectively. TN and TP were also elevated, at 2520 mg/L and 90.4 mg/L, respectively. During flocculation, SS dropped sharply to 440 mg/L, indicating the effective removal of suspended particles via coagulation and settling of flocs. This corresponded to a removal efficiency of 92.2%. BOD decreased by 33.6% to 2480 mg/L, while TOC showed a 69.8% reduction. TP plummeted to 2.76 mg/L, reflecting a 96.9% reduction, whereas TN removal (3.1%) was comparatively minimal. In the subsequent sedimentation and sand filtration step, SS slightly increased to 468 mg/L, while BOD rose marginally to 2620 mg/L. TOC remained at 1910 mg/L. TN decreased to 2250 mg/L, although this reduction was not substantial compared to previous changes in other parameters. With the introduction of ozone oxidation in the first and second ozone reactors, SS continued to decrease, from 468 mg/L to 414 mg/L, and then to 304 mg/L. BOD remained within the range of 2470-2620 mg/L, indicating partial stabilization of the organic load. TOC declined slightly, from 1910 mg/L to 1840 mg/L in the first reactor and 1810 mg/L in the second, suggesting ozone-mediated oxidation or transformation of organic matter. However, these changes were not pronounced. TN values hovered around 2190-2200 mg/L through these stages. Finally, activated carbon filtration further lowered SS to 253 mg/L, accompanied by more notable drops in BOD, to 2060 mg/L, and TOC, to 1750 mg/L. In contrast, TN rose slightly to 2240 mg/L before decreasing sharply to 102 mg/L in the deaeration unit. This final stage thus contributed to a more significant reduction in TN compared to earlier processes.
# Open Bite Treatment with Combined Aligners and Myofunctional Appliances: A Case Report
## 2 Case Description and Results
### Treatment Alternatives  
To achieve all the aforementioned treatment objectives, several treatment options were presented to the patient:  
1. Orthognathic surgery was presented as the ideal treatment option to improve the profile aesthetics, to correct the mandibular laterodeviation, and to resolve the gummy smile.
2. Alternatively, skeletal maxillary expansion was proposed through surgically-assisted or miniscrew-assisted rapid palatal expansion--with an explanation of the pros and cons of both procedures, as well as the difference in scientific evidence supporting both options--followed by fixed appliance treatment with asymmetric elastics, with the purpose of correcting the mandibular laterodeviation, at least from a dentoalveolar point of view.
3. To correct the patient's malocclusion while embracing the patient's request to have an aesthetic treatment, a lingual fixed appliance was proposed.
4. The last treatment option proposed to the patient was orthodontic therapy with clear aligners and elastodontic devices (Nuvola(r) OP System), combined with miniscrews and a transpalatal arch (TPA) to allow controlled intrusion of the posterior molars.  
Since the patient refused any orthognathic surgery, palatal expansion with miniscrew-supported devices, and lingual fixed appliances, the latter treatment option was chosen. The Nuvola(r) OP System involves a combination of clear aligners and myofunctional devices as a complete system to properly reconfigure the dental physiology as well as the occlusal planes.  
Anatomical reinforcements and pins are inserted into the aligners and should allow the transmission of axial forces to dental groups, with the purpose of helping the expansion of the dental arch, even in asymmetric arches.  
The aligners also have two lingual pins placed on both sides of the retro-incisive papilla, which are designed to generate proprioceptive stimulation of the tongue according to the principles of myofunctional therapy. The patient must use the myofunctional appliance (Freedom) for 30 min a day, performing 3-s-long clenches, alternated with 3 s of relaxation.  
The extraction of the lower third molars was proposed to the patient, who refused, despite being informed about the possible sequelae, because she was afraid of surgical procedures. It was then decided to regularly monitor the conditions of the lower third molars.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### EWT Decomposition  
EWT is a nonstationary time series processing method that combines the concept of EMD adaptive decomposition and wavelet transform theory. The purpose of EWT is to  
Figure 4: (**A**) Monthly runoff depth mutation, (**B**) Monthly runoff depth trend, (**C**) Monthly runoff depth real part time–frequency diagram, and (**D**) Monthly runoff depth variance diagram.  
decompose the original data series into different scales, so the IMF components represent the amplitude change and frequency change of the original runoff series, and each IMF component corresponds to a different physical background. Because the results of the superposition of each IMF component is the original data series, it includes the original information of the data series, and intuitively shows the features on different characteristic scales of the original series. As a result, the periodicity and regularity of the runoff series can be analyzed more accurately, and a model that is more consistent with actual runoff patterns can be more easily established. The subseries results obtained by decomposing the original runoff series using EWT are shown in Figure 5. The EWT decomposition results don't contain false modes, and there is no trend component. The absence of false modes can effectively increase the precision of forecasting results. Figure 5 shows that the subseries decomposed by EWT can demonstrate the frequency, amplitude, and wavelength patterns more intuitively. Among them, the IMF\({}_{1}\) component has the smallest frequency and amplitude compared with other components, and the fluctuation is relatively gentle. The fluctuation range of IMF\({}_{1}\) is 30-75, which is positive. The fluctuation ranges of IMF\({}_{2}\), IMF\({}_{3}\) and IMF\({}_{4}\) are \(-\)50-38, \(-\)110-98, and \(-\)55-124, respectively. The frequency and amplitude of each subseries gradually increase, while the wavelength gradually decreases.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 3 Results
### Bimodal vs. EMG vs. FMS Efficacy  
In this section, we explore the impact of combining EMG and FMG characteristics on the performance of classifiers. To this end, the bimodal approach was contrasted with the more common practice that exclusively uses EMG characteristics. Table 7 details the performance of the six classifiers indicated above when they use all characteristics, only EMG characteristics, and only FMG characteristics.  
The results are based on the initial evaluation of the classifiers, presented in Table 3, without the selection of features or tuning of hyperparameters. The results of Table 7 show that the effectiveness of most classifiers is greater when using a combination of EMG and FMG characteristics, rather than just one of them. This is true for all top 5 classifiers in which it is also found that better results are obtained using only features of EMG signals than only those of FMG. But regarding test accuracy, it is clear the benefit of using both EMG and FMG characteristics, p.i., in Weighted KNN and Cubic SVM accuracy raises from 79.63% to 87.96% and from 77.31% to 88.43%, respectively.  
However, there are notable exceptions to this trend. For example, for the Fine Gaussian SVM classifier, the effectiveness obtained with the exclusive use of EMG characteristics is slightly higher than that obtained with all data. This indicates that for certain classifiers, EMG information may be sufficient to achieve good performance, eliminating the need to include FMG features. Interestingly, for the Efficient Logistic Regression classifier, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Validation** & **Test** & **Validation** & **Test (EMG)** & **Validation** & **Test (FMG)** \\  & **(EMG + FMG)** & **(EMG + FMG)** & **(EMG)** & **Test (EMG)** & **(FMG)** & **Test (FMG)** \\ \hline Efficient Logistic Regression & 38.77 & 31.02 & 34.72 & 26.85 & 47.92 & 50.93 \\ Quadratic SVM & 89.35 & 87.96 & 79.28 & 75 & 65.16 & 68.06 \\ Cubic SVM & 89.24 & 87.96 & 79.4 & 79.63 & 70.49 & 70.37 \\ Fine Gaussian SVM & 74.88 & 72.69 & 75.93 & 75.46 & 66.32 & 68.06 \\ Fine KNN & 86.69 & 86.57 & 74.88 & 80.09 & 65.05 & 63.43 \\ Weighted KNN & 86.92 & 88.43 & 75.58 & 77.31 & 67.25 & 64.81 \\ Wide NN & 88.43 & 82.41 & 78.47 & 79.17 & 67.25 & 68.06 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the accuracy of the classifiers between the use of all data, EMG only, and FMG only.  
Figure 8: Confusion matrices for the Cubic SVM model. (**a**) validation (**b**) test.  
performance when using only FMG features outperforms that when using all features or only EMG. This is a peculiar case that highlights how certain classifiers may be better suited to specific sets of features. Besides the five models with the best results, these two exceptions are presented in Table 7. In any case, these two models, and in particular the last mentioned above, have a very low accuracy.