esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Physicochemical Properties of Raw RS Material and Acid Hydrolysates  
The proportions of total lignin, cellulose, and hemicellulose (by mass) in the raw RS were measured at 25.0 \(\pm\) 0.2%, 25.4 \(\pm\) 0.2%, and 7.8 \(\pm\) 0.1%, respectively (Table 1). The results for cellulose and lignin in this study are consistent with previous research characterizing RS, falling within the typical ranges of 28-41% for cellulose and 5-24% for lignin [27,28]. However, the hemicellulose content observed in this study was lower than the values typically reported in the literature, which range from 15% to 30% [27,28]. This discrepancy may be due to a potential underestimation of the hemicellulose fraction, as it was determined based solely on xylose quantification, excluding other major pentoses such as arabinose. Additionally, methodological limitations could have contributed to incomplete hemicellulose hydrolysis [29]. Consequently, other pentoses might account for approximately 6.3% of the total dry matter composition [30].  
Table 2 provides details on the characterization of the acid hydrolysates, with organic matter content measured as total and soluble COD (TCOD and SCOD, respectively), recorded at 33.7 \(\pm\) 1.3 g/L and 28.5 \(\pm\) 0.9 g/L, respectively. The COD yield, determined by the ratio of TCOD in hydrolysates to the total solids (TS) concentration of 7% (\(w/w\)), was 48.2%. The observed TCOD value is lower than that reported by [PERSON] et al. [28], likely due to the higher TS concentration of 30% (\(w/w\)) used in their acid-assisted pretreatment study. The carbon-to-nitrogen ratio was found to be 22, which is within the ideal range of 20 to 30 for supporting anaerobic bacterial growth [31]. This indicates that RS can be effectively used as the sole substrate in anaerobic fermentation.
## 2 Materials and Methods  
### Governing Equations  
The flow through the RFB was numerically analyzed using a commercial CFD code, ANSYS Fluent. The governing equations considered herein include the continuity and momentum equations for incompressible flow. These equations are shown as Equations (1) and (2) below.  
\[\
abla\cdot\left(\rho\overrightarrow{v}\right)=0 \tag{1}\]  
\[\frac{\partial}{\partial t}\left(\rho\overrightarrow{v}\right)+\
abla\cdot \left(\rho\overrightarrow{v}\right)=-\
abla p+\
abla\cdot\left(\tau\right), \tag{2}\]where \(\rho\) is the density, \(v\) is the velocity, and \(p\) is the static pressure. \(\overrightarrow{\tau}\) is the stress tensor, which can be expressed as Equation (3).  
\[\tau=\mu\left[\left(\
abla\overrightarrow{v}+\
abla\overrightarrow{v}^{T} \right)-\frac{2}{3}\
abla\cdot\overrightarrow{v}I\right] \tag{3}\]  
here, \(\mu\) is the viscosity coefficient and \(I\) is the unit tensor. The pressure-based method and the SIMPLE algorithm, which are suitable for low-velocity incompressible flow analysis, were used in the numerical method herein. For the discretization of space, the second-order upwind was used to obtain the solutions in space.  
In addition, the electrode and spacer structures in the RFB were simulated using a porous media model [15]. The porous media model adds the source term (\(\overrightarrow{S}\)) into the momentum equation. These equations are shown as Equation (4) below.  
\[\overrightarrow{S}=\frac{\mu}{\eta}\overrightarrow{v}+R_{ik}\frac{1}{2}\rho \left|\overrightarrow{v}\right|\overrightarrow{v}, \tag{4}\]  
where \(\eta\) is the permeability of viscous fluid and \(R_{ik}\) is the inertia loss resistance in any direction.  
Additionally, the volume of fluid (VOF) model was used for the flow visualizations [16]. The VOF model is a multiphase flow model that predicts fluid behavior through the interfacial tracking of two or more unmixed fluids. The continuity equation for the phase q of the fluid is as shown in Equation (5), and Equation (6) is calculated for each control volume.  
\[\frac{1}{\rho_{q}}\left[\frac{\partial}{\partial t}(\alpha_{q}\rho_{q})+\
abla \cdot(\alpha_{q}\rho_{q}\overrightarrow{v}_{q})=\sum_{p=1}^{n}\left(\dot{m}_{ pq}-\dot{m}_{qp}\right)\right] \tag{5}\]  
\[\sum_{q=1}^{n}\alpha_{q}=1, \tag{6}\]  
where \(\dot{m}_{pq}\) and \(\dot{m}_{qp}\) denote the mass transfers between phases (p and q), respectively, depending on the mass transfer direction.
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 5 Training and Validation  
In the neural network training, we randomly selected 1000 seismic images from an open-source dataset [15] for training, and the corresponding label data were also completed by manual marking in advance, marked as 1 in places with faults and 0 in places without faults. The purpose of network training is to optimize the parameters of the whole network. With each training, the deviation between the prediction and the actual represented by the loss function will decrease until the prediction and the actual tend to be consistent.  
Figure 7 shows randomly seismic data sets with their corresponding labels. We used another 200 images as test and validation data, which were not included in the training. In the process of training, SGD is used to optimize the network, and the number of images sent into the network is 10 each time. The network model can be trained when the number of epochs reaches 30 times. Figure 8a shows the change of training accuracy and validation accuracy of the modified U-Net with the number of epochs. After 30 epochs, the accuracy rate tends to be above 0.9. Figure 8b shows the changes of training loss and validation loss of the modified U-Net with the number of epochs. After 30 epochs, the loss value tends to 0.01. After training, save the network parameters. In the process of training and validation, in order to increase the diversity of training data sets and make the trained neural network have better classification or recognition performance, data enhancement is used to improve the diversity of training data sets. The data enhancement operation mainly includes data reversal and rotation around the time axis.
## 5 Conclusions
\begin{tabular}{l l} \(\zeta\) & magnification, \(\zeta=L/\lambda=q/q_{0}\) \\ \(L\) & length of the nominal contact surface \\ \(\lambda\) & length of asperity wavelength \\ \(A(\zeta)\) & actual contact area \\ \(A_{0}\) & nominal contact area (i.e., observed contact region is \(A_{0}\) when \(\zeta=1\)) \\ \(P(\zeta)\) & relative contact area fraction: \(P(\zeta)=A(\zeta)/A_{0}\) \\ \(E^{*}\) & equivalent elastic modulus (_MPa_) \\ \(E_{1},E_{2},\,v_{1},v_{2}\) & [PERSON]'s elastic module and respective Poisson's ratios of the two contact faces \\ \(C(q)\) & surface roughness power spectrum \\ \(\langle\mu^{2}\rangle\) & mean of the square of the contact surface height profile (\(\upmu\)m) \\ \(h_{rms}\) & The root -mean -square roughness of the contact surface height profile (\(\upmu\)m) \\ \(\langle\mu(\mathbf{x})h(\mathbf{0})\rangle\) & autocorrelation function of the surface height profiles (\(\upmu\)m) \\ \(\mathbf{q}\) & Wavevector of asperity, \(q_{0}\), \(q_{1}\) represent the minimum and maximum surface fluctuation \\  & frequency, respectively \\ \(D_{f}\) & The fractal dimension of self-affine fractal surfaces, \(D_{f}=3-H\), where \(H\) is Hurst exponent \\ \(p(\overline{u})\) & the pressure distribution at sealing interface with the average interfacial separation \(\overline{u}\) \\ \(U_{\text{el}}(\overline{u})\) & the elastic potential energy (J) \\  & relative area fraction of the contact surface with the compressive stress \(p\) and magnification \\ \(P_{p}(q)\) & \(\zeta=q/q_{0}\) \\ \(\zeta_{c}\) & critical observation scale \\ \(u_{c}\) & the average height of the critical percolation channel (\(\upmu\)m) \\ \(W(q,p)\) & elastic energy \\ \(u(\zeta)\) & the average interfacial separation \\ \(p_{1},p_{2},\,v_{1},\,v_{2}\) & the fluid pressure and the velocity on both sides of the sealing interface \\ \(z_{1},z_{2}\) & Head height on both sides of the seal interface \\ \(h_{w}\) & the energy loss \\ \(P_{w}\) & Groundwater pressure (MPa) \\ \(P_{w}^{*}\) & critical leakage pressure (MPa) \\ \(P_{ss}\) & self-sealing stress (MPa) \\ \(P_{c}\) & site percolation threshold \\ \(r\) & the radius of the percolation path, \(r=\overline{u}(\zeta)/2\) \\ \(v_{max}\) & the maximum flow rate of liquid in percolation path (m/s) \\ \(\lambda\) & Coefficient of head loss: \(\lambda=64/Re\) \\ \(Re\) & Reynolds numbers, \(\text{Re}=2\rho v\pi/\mu\) \\ \(v\) & the average fluid velocity of water (m/s) \\ \(q^{*}\) & the wavenumber where the contact area percolate when increasing the magnification \\ \(\mu\) & hydrodynamic viscosity coefficient (N\(\cdot\)s/m\({}^{2}\)) \\ \(\theta\) & the relative position of the leak point in the tunnel \\ \end{tabular}   
\(\sigma_{eff}\) the conductivity of percolation channel  
\(\mathbf{J}\) the fluid volume per unit time passing through the percolation channel  
\(\
abla p\) the pressure field distribution  
\(Lx\), \(Ly\) the geometrical dimension of sealing interface (m)  
\(Q\) total groundwater inflow (L/m\({}^{2}\)/day)
## 4 Conclusions  
An FSS Sandwiched reflectarray was designed, fabricated and measured for dual-frequency and dual-polarization applications, which exhibits wide and smooth phase ranges at both lower and upper operating frequencies. A 11 \(\times\) 7 element reflectarray using the sandwiched FSS was designed. Maximum gains of 18.9 dBi and 21.2 dBi at a 29\({}^{\circ}\) scattering angle were obtained at 12 GHz and 16 GHz, respectively. The proposed reflectarray can be used in the applications of 5G/6G base stations and point-to-point communication systems to provide fast data transfer. This concept can also be used for blind elimination in high-building downtown districts.  
Conceptualization, J.L., L.M. and T.Z.; methodology, J.L.; software, J.L.; validation, J.L. and L.M.; formal analysis, T.Z.; investigation, T.Z.; resources, J.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 7: Scattering pattern of 11 \(\times\) 7 element reflectarray. **(a)** 12 GHz. **(b)** 16 GHz.
## 4 Classification and Features of Smartphone Apps for Solar PV Energy
### Monitoring and Control of PV Systems
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name of App** & **Develop/Company** & **Platform** & **Price \({}^{*}\)** & **Function** & **Production vs.** & **Energy Flow** & **Reference** \\ \hline Felix Solar PV Log & Felix The Paddy Tat & Android & 58.12 & & & [91] \\ Solar Watch for SolarEdge & [PERSON] & iOS & 52.99 & & & [92] \\ SunPower Customer Portal & SunPower & iOS & Free & & & [93] \\ FusionSolar & Huawei Network Energy & Android & Free & & & [94] \\ PV Monitoring & \& Management & & & & [95] \\ Application & Form Elektrik & Both & Free & & & [95] \\ Tgg SMART & Tgg Energy & Both & Free & & & [96] \\ SolarAnalyzer Prior for Android\({}^{\rm stv}\) & sunrise, Ade & Android & 53.10 & & & [97] \\ Solar Energy Monitor & Imperial Innovations & Android & Free & & [98] \\ Oday Solar Pro & Oxley Web Services & Android & 56.00 & & [99] \\ Cloud PV & MRT & Android & Free & Monitoring & & [100] \\ ReMACS Solar Monitoring FlexMC & FlexMC Solutions Private Limited & Android & Free & & [101] \\ PV Monitoring & D&E Lab & Android & Free & & [102] \\ PVOutput Pro & Corrado Bellini & Android & Free & & [103] \\ PV Output & Marlon Bunjer & Android & Free & & & [104] \\ Photovoltaic Monitor & Conena & Android & Free & & & [105] \\ Sunny Portal & SMA Solar Technology AG & Both & Free & & & [106] \\ SudarEdge Monitoring & SolarEdge Technologies & Both & Free & & & [107] \\ Emphase Enlighten & Emphase Energy, Inc. & Both & Free & & & [108] \\ Solar-Log Web Enerest\({}^{\rm stv}\) & Solare Transysteme GmbH & Both & Free & & & [109] \\ Energy Viewer & ABB Information Systems AG & Both & Free & & & [100] \\ Dzazköt Solar Kostal Piko & DazzolaSoft & Android & 53.50 & & & [111] \\ \hline \multicolumn{8}{c}{\({}^{*}\)Price in [15]} \\ \end{tabular}
\end{table} 
Table 8: Summary of smartphone applications on monitoring and control of PV systems.
# Grating-like Terahertz Metasurface for Large-Deflection-Angle Beam Manipulations
## 1 Introduction
In this paper, a reflective grating-like metasurface is developed to realize frequency-controlled beam-steering. By designing the metasurface elements, we obtain a reflective frequency-scanning metasurface operating in the optimized frequency range of 0.87-1.02 THz. The measured diffraction angles are in good agreement with the theoretical calculations and simulations.
## 4 Results and Discussion
### Output Spectra, Tumability, and Beam Stability  
Figure 3 shows the normalized experimental spectra for the SFG process in Crystal 1, Crystal 2, and the cascaded module. The intensities are normalized to the maximum detection limit of our spectrometer.  
We further established the tunability of the orange output by increasing the temperature of Crystal 1 from 25 to 90 \({}^{\circ}\)C, while simultaneously tuning the pump laser from 1527 to 1565 nm (upper horizontal axis) as seen in Figure 4. The corresponding output wavelengths are shown in the lower horizontal axis with a spectral resolution of \(\sim\)0.75 nm. As the input pump wavelength was tuned, the SFG intensity exhibited oscillations as seen in Figure 5. While Crystal 1 showed a single phase matching point at 1544.9 nm, Crystal 2 revealed two phase matching points at 1545.7 nm and 1556.5 nm. The cascaded module has a broader input wavelength acceptance range compared to Crystals 1 and 2. This technique allows the pump wavelengths ranging from 1527 to \(\sim\)1545 nm to be utilized for SFG, which is not achievable with individual crystals. It is worth noting that the orange intensity dependence on the pump wavelength shown in Figure 5 (green curve) for the cascaded case does not fit to Equation (3) as expected, from which we can conclude that the theory is not valid for this case.  
Figure 3: Normalized spectrum of the generated orange light for Crystal 1 (black), Crystal 2 (red), and the cascaded module (green).  
Figure 2: (**a**) Power of the generated orange light as a function of the total input fundamental power. (**b**) Beam profiles at 595.9 nm, 597.3 nm, and 596.9 nm for the three cases studied taken when \(P_{1}\) and \(P_{2}\) were 418 mW and 229 mW, respectively.  
Thus, by temperature tuning, the SFG quasi phase-matching condition can be shifted owing to the fact that the refractive index of MgO:PPLN is temperature-dependent according to the Sellmeier equation. In particular, the phase-matching condition shifts to longer wavelengths with an increase in temperature. According to Figure 6, the orange light output as a function of crystal temperature can be tuned by \(\sim\)0.05 nm/\({}^{\circ}\)C without affecting the SFG conversion efficiency for the PPLN crystal used in this work. We therefore benefit from a temperature-assisted tunable orange light source.  
Figure 4: Spectra of orange light tuned across the 595.9–598.9 nm wavelength range.  
Figure 5: Measured orange laser intensity as a function of pump wavelength.  
In addition, the stability of the orange light output generated by the cascaded module for one hour was measured using a power meter, showing <2.5% fluctuation of the output power around an average of 10 mW (Figure 7). We attribute this fluctuation to the stability of both fundamental lasers.
## 2 Results and Discussion  
In this section, we show numerical results for PLAs made of W and SiN. Periodic structures are introduced to absorb incident light efficiently in the very thin membrane structures of subwavelength thickness. Below, we confirm the basic optical properties of the constituent materials.  
Figure 2 shows the basic optical properties of W and SiN. Figure 2a presents R and T spectra of W membrane with red solid and dashed lines, respectively. The W membrane is assumed to be free standing in air. The spectra are shown in the \((0,1)\) scale. Note that the T spectrum is close to 0 in the whole wavelength range shown. Figure 2a also shows R spectrum of bulk W of 1 mm thickness with blue curve, which is almost identical to the R spectrum of the W membrane. This means that the W membrane of 100 nm thickness is optically thick enough, being almost equivalent to bulk W in terms of the optical properties.  
As for the spectral shapes of the W membrane in Figure 2a, R decreases as the wavelength becomes shorter. For example, at 500 nm, R is 0.48 and, at 1000 nm, R is 0.58. In comparison with a noble metal Ag, the R values are quite small. This means that W is associated with substantially large optical loss even at the near-infrared range.  
Figure 2b shows R and T spectra of SiN membranes of thickness \(t=100\), 200, and 300 nm with red, green, and blue curves, respectively. The R and T spectra are shown with solid and dashed curves, respectively. The SiN membranes were assumed to be free-standing in air. The spectral shapes manifest the Fabry-Perot-type interference that is determined by the refractive index and thickness.  
Since we are assuming loss-less refractive index, light absorption does not take place at all in the SiN membranes.
## 4 Experimental Results and Discussion
### Evaluation of the Objective Color Correction Network
#### 4.2.3 The Consistency of Inter-Device  
Consistency between devices is critical to the usability of the device. The inter-device variations are mainly caused by fluctuations in illumination, which are random errors. This random error cannot be completely eliminated. Moreover, in addition to random errors, another factor that causes variations in the two devices is the difference in imaging characteristics of the cameras. Regardless of the fact that the calibration procedure can reduce this kind of disparity, it is a systematic error and also cannot be eliminated completely. Therefore, color correction methods can be used to reduce the inter-device inconsistency.  
We evaluated our method on two tongue image capture devices. After calibration, the two devices should have very similar image rendering capability. In this experiment, the two devices first captured the same Color Checker SG. Then, the color difference is calculated to measure the accuracy between the devices. We have captured the image groups of these two devices and calculated the color difference between them.  
As shown in Table 6, the mean of color differences without color corrected is 5.03. There will be perceptual differences. Compared with other methods, our proposed OCC network achieved the best objective results of 1.32.  
For more details, we will show the difference between the corrected color checker and Color Check SG in the CIE Lab color space. The objective result is shown in Table 7. The OCC network achieved the best results in terms of mean color differences, and it almost achieves the best results in the maximum color differences among the four methods.  
Figure 8 shows the tongue images captured with device 01 and device 02. Comparison results of tongue and face images captured in the two imaging systems before and after correction are listed. Subjectively, our method makes the corrected tongue and face images render consistently.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cameras** & & **Uncorrected** & **POLY [7]** & **SVR [12]** & **K-PLSRc [16]** & **OURS** \\ \hline \multirow{2}{*}{Logitech C920} & \(\Delta E_{mean}\) & 19.91 & 7.63 & 3.07 & 2.89 & **2.05** \\  & \(\Delta E_{max}\) & 27.84 & 19.06 & 16.53 & 7.47 & 7.56 \\ \hline \multirow{2}{*}{Cannon} & \(\Delta E_{mean}\) & 8.89 & 6.92 & 3.58 & 2.96 & **1.98** \\  & \(\Delta E_{max}\) & 27.03 & 14.44 & 12.22 & 9.40 & **7.73** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of the color differences using the four methods with different cameras.
# Virtual Zirconia Implants Replace Titanium Implants?
## 5 Soft Tissue Reaction
Currently, several in vitro studies show that zirconia implants have better biological reactions in periodontal tissues when compared with titanium [13]. For instance, fibroblasts proliferate faster and are more equally distributed in zirconia disks compared with titanium [86]. Moreover, this material enhances collagen and extracellular matrix proteins' release [87; 88]. Furthermore, analyzing the results of zirconia interaction with epithelial cells, it looks like zirconia increases the differentiation and proliferation of these cells, facilitating the healing process and protective scarring around the implants [13]. Moreover, the zirconia implants present longer junctional epithelium and a higher density of collagen fibers, which might increase the soft-tissue closure and decrease the inflammatory infiltration around zirconia implants [56]. Regarding the orientation of collagen fibers around implants, the same way as in titanium pieces, the adherence of collagen fibers into zirconia implants occurs in a parallel and parallel-oblique way [89]. Furthermore, the supracreal tissue adhesion (also formerly known as biology width or supracreal tissue attachment) is more prolonged in titanium implants (mean of 5 mm) compared with zirconia implants (4.5 mm), meaning that zirconia implants have a significantly lower sulcus depth. Nevertheless, the epithelium length mean was 2.9 mm in both groups. On the other hand, a significant difference was found in the connective tissue of both materials (2.4 mm around zirconia implants and 1.5 mm around titanium implants), demonstrating a higher organization of collagen fibers. These results suggest that a more mature and pronounced soft tissue integration happens with zirconia implants compared with titanium [53].
## 4 Efficiency of Power Packet Transmission  
In the power packet dispatching system, the electrical power is transferred by a pulse-shaped voltage as shown in Figure 1. The efficiency of the power packet transmission can be estimated for the pulse wave transfer in the power line channel. For the estimation, two types of pulse are introduced. One is a rectangular pulse applied to the power transmission, because of hard switching of voltage or current. The duty cycle \(D\) is set at \(1/3\). Positive and negative voltage pulses are transmitted randomly through the power line channel. The other is a sinusoidal-wave for power transmission. To estimate the efficiency of the power transmission, we calculated the ratio of the power consumed at the destination load to the input power into the power line channel described in Section 3. Here, the impedance of the load is matched to the characteristic impedance of the power line cable.  
Figure 8 shows the power ratio arriving at the load as functions of frequency. Compared with the branch-less channel, the branched channel shows the less efficiency at the load than the expected in Figure 7. The rectangular pulse displays slightly poorer power transmission characteristics than the sinusoidal-wave. However, around the resonance frequency in the branched configuration, the rectangular pulse can dispatch much more power than the sinusoidal-wave. This is because the  
Figure 6: Power line configuration.  
Figure 7: Voltage gain for the power line channel.  
rectangular pulse consists of broad spectrum components. At frequencies of \(1\) MHz or less, both the rectangular pulse and the sinusoidal wave seem to retain more than \(80\%\) of the incoming power. It is worth noting that conventional power distribution from dispersed power sources to loads wastes more than \(20\%\) of generated power during power conversions.