esa-sceva/satcom-chunk-collection huggingface dataset:
# Regression Approach to a Novel Lateral Flatness Leveling System for Smart Manufacturing
## 3 Materials and Methods
### Machine Learning Workflow  
In this research, the training data used were the previously recorded data from flatness verification against the machine operator's input value while applying the leveling process during production. The sample data were then applied using multiple regression as a training model to predict the parameters for the leveling process. The machine learning workflow of this research was based on a flowchart, as shown in Figure 5. The measuring system was set up at the exit side of the leveler, and each of the sensors was placed at the same position as each of the backup sets. Therefore, each laser sensor could measure the flatness of the sheet at the location leveled or corrected by each of the backup sets. The system was initiated by receiving reading sample data from the laser sensor. After receiving the value, it checked whether the value was above the flatness threshold. If the value was above the threshold, the sensor reading was fed forward to the multiple regression model for the machine learning process. After the value was predicted, each backup set was adjusted based on the calculated value to achieve an optimized value for flatness. The backup set was adjusted up or down through the servo motor to deform the work roll as shown in Figure 3 according to a different application. Finally, the learned regression model was validated in this research.
## 3 Detector Simulation and Optimization  
Theoretically, the spatial resolution of the pixel-readout-type detector increases with a decrease in the pixel size of the scintillator and the SiPM array [20]. To determine the influence of the main parameters on the imaging quality and to obtain better spatial resolution, the structure and specific parameters of this detector were optimized by Monte Carlo simulation using Geant4 10.7 software [21]. The simulation results were regarded as a guideline for fabricating the detector prototype.
###### Abstract  
The features of polarization control of multiple multiresonances for quasi-trapped modes excited by synchronization of bianisotropic dipole responses in MoS\({}_{2}\) disks with a hole are considered. Using numerical calculations with analytical multipole analysis, we showed that the presence of a strong optical anisotropy of MoS\({}_{2}\) nanoparticles provides an additional degree of freedom and makes it possible to observe several resonances of electric and magnetic dipoles at once in a narrow spectral range. Based on the simulation results, we obtained the frequency dependences for the dipole polarizabilities of the MoS\({}_{2}\) disk with a hole, which can be used to distinguish the contributions of the nonlocal and bianisotropic dipole responses and possessing several features in the near-infrared range. Using the polarizability spectra of single nanoparticles and applying the tuning strategy, the design of the MoS\({}_{2}\) metasurface placed in air and supporting three resonances of quasi-trapped modes at once in a narrow spectral range was developed. One of these resonances corresponds to the telecom wavelength of about 1550 nm. The spectrum of light reflection for the MoS\({}_{2}\) metasurface is characterized by three narrowband dips corresponding to the wavelengths of the quasi-trapped modes. It was shown that a change in the polarization of a wave normally incident on the metasurface to orthogonal one leads to a change in the type of bianisotropic response excited in each MoS\({}_{2}\) disk and to the excitation of three other features in the reflection spectrum of the metasurface at wavelengths close to the initial values.  
metasurfaces; transition metal dichalcogenides (TMDCs); quasi-trapped modes (QTMs); polarizing control; bianisotropic response +
Footnote †: journal: Applied Physics Letters B  
1  
[PERSON]. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], A.V. Multiresonances of Quasi-Trapped Modes in Metasurfaces Based on Nanoparticles of Transition Metal Dichalcogenides. _Appl. Sci._**2023**, _13_, 8961. [[https://doi.org/10.3390/app13158961](https://doi.org/10.3390/app13158961)]([https://doi.org/10.3390/app13158961](https://doi.org/10.3390/app13158961))
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
###### Abstract  
A hybrid optical-acoustic underwater wireless sensor network (OA-UWSN) was proposed to solve the problem of high-speed transmission of real-time video and images in marine information detection. This paper proposes a novel energy-efficient contention-based media access control (MAC) protocol (OA-CMAC) for the OA-UWSN. Based on optical-acoustic fusion technology, our proposed OA-CMAC combines the postponed access mechanism in carrier sense multiple access with collision avoidance (CSMA/CA) and multiplexing-based spatial division multiple access (SDMA) technology to achieve high-speed and real-time data transmission. The protocol first performs an acoustic handshake to obtain the location information of a transceiver node, ensuring that the channel is idle. Otherwise, it performs postponed access and waits for the next time slot to contend for the channel again. Then, an optical handshake is performed to detect whether the channel condition satisfies the optical transmission, and beam alignment is performed at the same time. Finally, the nodes transmit data using optical communication. If the channel conditions do not meet the requirements for optical communication, a small amount of data with high priority is transmitted through acoustic communication. An evaluation of the proposed MAC protocol was performed with OMNeT++ simulations. The results showed that when the optical handshaking success ratio was greater than 50%, compared to the O-A handshake protocol in the literature, our protocol could result in doubled throughput. Due to the low energy consumption of optical communication, the node's lifetime is 30% longer than that of pure acoustic communication, greatly reducing the network operation cost. Therefore, it is suitable for large-scale underwater sensor networks with high loads.  
MAC protocol; OA-UWSN; high throughput; low energy; contention-based 2019/183; doi:10.3390/s19010183
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method  
With the advancement of hardware and algorithms and the exponential growth of data, fault diagnosis has shifted from machine learning to deep learning [115]. Deep learning, a subfield of artificial intelligence, is the newest data analysis and image processing technology. Compared with traditional statistical learning models, deep learning models have a stronger ability to extract underlying information from data. The structure of deep learning models is usually formed by multi-layer neural networks, which can be flexibly changed to meet different practical needs. In addition, multiple layers of data processing units are assembled to form a deep architecture. It has powerful learning capabilities and can extract features automatically from input data. Hierarchical features are represented by low-level features, which can efficiently and quickly solve complex problems and have good portability, making them suitable for sensor fault diagnosis. Autoencoders (AEs), Deep Belief Networks (DBNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are the current deep learning models for sensor fault diagnosis.
## 5 Smart Grid Reliability Assessment
### Simulation Results  
The simulations of an integrated smart grid system were run with the structure illustrated in Figure 10. In the simulations, component failures and the existence of communication were considered, and generation parameters were omitted. In order to calculate the relevant reliability indicators, the number of customers has been taken into account.  
Simulations of N = 100 years with the step-in sequence of one year (8760 h) with the resolution of one hour were run using the input data presented in Table 13. Faster responses to failures, shorten the time to repair for the smart grid. The results of reliability parameters obtained with time sequential simulation are strongly influenced from the failure, repair rates and system structure. The distribution of reliability indices significantly depends on the number of simulated years.  
Simulation results are presented in Table 14 and Figure 7. The average system indices show that the electric power system with a communications infrastructure is more reliable. The presence of ICT shortens interruption times, represented by the SAIDI index.  
Moreover, the range of index distributions shifts toward zero in systems with communications, reflecting improved reliability. Distributions of the SAIFI index representing the number of interruptions in one year are identical for systems with and without communications because only the durations of interruptions change in both scenarios but the number of interruptions in one year remain constant.  
The CAIDI index representing the average interruption duration is significantly smaller in electric power systems with ICT than in systems without communications. This is due to the shorter break times.
## 6 Results and Discussion  
Under very challenging conditions, namely partial shading of the PV array, step changes in load, and step changes in irradiance, MATLAB simulations are carried out to evaluate the effectiveness of PSO method. Figure 14a,b represent the output power from PSO based MPPT. Figure 15a,b shows the comparison of PSO with conventional P&O algorithm. From Figures 14 and 15a the output power settled after 0.4 s in PSO algorithm, but in P&O 0.75 s. The outputs of PSO and conventional P&O methods are compared to see the effectiveness of presented PSO based MPPT. From Figure 15, PSO based MPPT has less tracking time and steady state oscillations.  
Figure 13: Equivalent Circuit of the ZSI in traditional zero states.  
Figure 14: Output power from PSO for (**a**) 1000W/m\({}^{2}\); (**b**) irradiance is varied from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\) at 1 s then increased to 1000 W/m\({}^{2}\) at 2 s.  
Figure 15: Comparison of output power from PSO based MPPT and P&O based MPPT for (**a**) 1000 W/m\({}^{2}\); (**b**) irradiance is varied from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\).  
From Figures 14 and 15, PSO based MPPT produces lesser oscillations and tracking time when compared to P&O algorithm. The tracking time of PSO algorithm is 0.2 s, but for the P&O algorithm, is 0.35 s. PSO algorithm decides the shoot through duty cycle for the Z-Source inverter, according to the PV voltage. Figure 16 shows the output voltage V\({}_{\rm L}\), current i\({}_{\rm L}\), power P\({}_{\rm L}\) and shoot through duty cycle D of the Z-Source inverter. Figure 17 represents the phase to phase voltage of the Z-Source inverter.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
## 3 Design and Optimization of The Rotating Element of the Reflectarray
Note that two pairs of them provided a 180 deg difference of phase for a rotation angle of 45 deg (i.e., for significant change of the periodic lattice). Therefore, we have for the (\(-\)52.5, 52.5) deg range of the rotation angle samples each 22.5 deg for the lengths of the cross arms  
Figure 4: Electromagnetic response provided by the unit cell at 19.7 GHz with fixed values of L\({}_{2}\) = 4.9 mm and L\({}_{1}\) = 6.025 mm as a function of rotation angle. (**a**) Phases of the reflected electric fields of the co-polar circularly polarized waves. (**b**) Amplitude of the reflected electric field of the cross-polar circularly polarized wave for a right-handed circularly polarized (RHCP) incident wave. Figure 5: Aspect of the periodic lattice: (**a**) cross element on its original position with its arms parallel to the vertical and horizontal lines of the paper; (**b**) cross element rotated 45 deg; (**c**) cross element rotated 45 deg shown when the paper was also rotated 45 deg. that were optimized for the VRT. These samples defined six angular intervals in the (\(-\)52.5, 52.5) deg rotation range. So, the pairs of lengths were obtained for any rotation angle by linear interpolation in the corresponding angular interval. The interpolation in these six angular intervals would provide few calls to a full-wave analysis routine in the design process of the parabolic reflectarray and the CPU time of the design process would be reduced. Figure 6a shows the value of the phase error \(\Delta\varphi\) produced in the 180\({}^{\circ}\) of difference of phase between the reflection coefficients \(R_{x^{\prime}x^{\prime}}\) and \(R_{y^{\prime}y^{\prime}}\) at 19.7 GHz. These values of phase error are shown as a function of the rotation angle. A maximum phase error of 7\({}^{\circ}\) was obtained. This phase error predicts \(-\)24 dB of amplitude of the reflected electric field of the cross-polar circularly polarized wave (see (6) with E\({}_{0}\) = 1). Figure 6b shows the phase of the reflected electric field of the co-polar circular polarized wave as a function of the rotation angle. Very linear dependence with respect of rotation angle was obtained. These dependences were roughly \(+\)2\(\alpha\) for RHCP and -2\(\alpha\) for LHCP reflected waves, as expected from (5). The amplitude of the reflected electric field of the cross-polar circularly polarized wave for RHCP incident wave is shown in Figure 6c. Similar cross-polarization results were obtained for an LHCP incident wave. We see that the cross-polarization level was between \(-\)30 and \(-\)20 dB, as predicted from (6).
## 3 Results and Discussion
### Monovalent Selectivity  
For the separation of monovalent and divalent cations, a comparison of monovalent and multivalent cations fluxes is presented in Figure 9. In the binary mixtures, MPD/TMC modified membranes show a much lower Na\({}^{+}\) and Mg\({}^{2+}\) flux than the untreated membrane, demonstrating the improvement of the steric hindrance effect. Simultaneously, the monovalent selectivity of the modified membrane increased from 1.77 to 3.66. Particularly, with incorporating ZIF-8 nanoparticles, the monovalent selectivity notably increased to 4.03. However, the flux of Na\({}^{+}\) was reduced, which is different from the observations in single salt desalination. The larger affinity of Mg\({}^{2+}\) for the ion exchange groups inside the membrane matrix would allow for them to occupy more ions exchange transfer sites; as a consequence, a strong suppression was imposed on the transfer of Na\({}^{+}\) ions. Furthermore, the more hydrophobic membrane surface could reduce the permeation of strongly hydrated cations, while facilitating the less hydrated ones [35]. Combining the contribution of the  
Figure 8: (**a**) Conductivity change of diluate compartment for Sodium chloride (NaCl) system; (**b**) Conductivity change of diluate compartment for the magnesium chloride (MgCl\({}_{2}\)) system; (**c**) Flux of ions in 15 min; and, (**d**) Energy consumption for different systems.  
dense MPD/TMC layer as well as the size sieving effect of ZIF-8, the monovalent selectivity was greatly enhanced. Further increasing the ZIF-8 content has no obvious effect on the monovalent selectivity; however, in this case, the both the Na\({}^{+}\) and the Mg\({}^{2+}\) permeance increased. In this condition, the increased amount of ZIF-8 reduced the thickness of the dense surface, which would also contribute to the increase of the ion flux. For M-5, although the transfer resistance was further decreased, the voids facilitated both the Na\({}^{+}\) and Mg\({}^{2+}\) migration from the membrane matrix to the solution. In this case, a lower monovalent selectivity was obtained.
## 3 Methods
### Road Network Processing  
To monitor the damage to roads in the main urban area caused by the July 20 rainstorm, the author obtained the road network data of the main urban area of Zhengzhou in 2021 using the Python web crawler tool. Road information includes ten categories of urban first-grade roads, urban second-grade roads, urban third-grade roads, urban fourth-grade roads, expressways, national roads, provincial roads, railways, county roads, and township roads in the main urban area of Zhengzhou. The processing of road network data mainly includes:  
1. Culling small roads. The rich variety of roads in the Gaode road network data contains some redundant roads with too short lengths. Fine roads less than 5 m were excluded from this study to reduce data redundancy. \[RT=\{L|L>5\}\] (4) where \(RT\) is the set of road elements and \(L\) is the length of the road.
2. Topology check. The original road network data is intricate and overloaded with details, so it is necessary to perform topological checks on the roads to avoid errors in the subsequent analysis. We check each line element for topological errors such as line segment self-intersection, line overlap, hanging points, and pseudo-nodes. Finally, we obtain the corrected road network data for the study area shown in Figure 5.  
Three feature areas with different spatial distributions were selected to verify whether the processed road network data fit the actual road network. Their processed road network information was confirmed with the corresponding Google images. The three areas have different road network characteristics due to different geomorphological and spatial distribution characteristics. Area A is located near the intersection of Science Avenue and Xushui River Road, where the road network is dominated by Xushui River Road on both sides of the river, and the road network information of Science Avenue and Xushui River Road can be seen from the enlarged map of area A in Figure 5. The B area is located near the Longhubei subway station. The road network information in this area is relatively simple, and the comparison is also apparent. C area is located at the intersection of West Haoheng Road and West Fourth Ring Road, which is dominated by elevated bridges and has more accurate road network information. In summary, this paper's processed road network information is reliable after comparing it with Google images.
## 5 Parametric Study on the Thermomechanical Performance
### Influence of Emittance  
The surfaces of the plates were assumed to be coated with a material that has the desired value of emissivity before it was brazed into the skeleton of the TPS panel. The purpose of the surface coating was to prevent the skin of the vehicle from oxidation and increase the surface emissivity so that a large amount of heat flux could be emitted to the environment. Details of the development and the use of coatings to control thermal characteristics for high-temperature metallic materials are discussed in the literature [55; 56; 57]. In this study, the emissivity of the outer face sheet was varied with values of 0.7 and 0.8 and a referred value of 0.86 from the literature [1; 4; 12]. The maximum deflections of the UCS and BCS under the high heating rate are shown in Figure 23. The maximum deflections decreased with an increase in the emissivity. As shown in Table 7, as the emissivity increased, the temperature at the outer face sheet decreased owing to the decrease in the amount of heat entering the sandwich structure through the outer face sheet. The deflection was caused by the difference in the thermal expansion between the face sheets or the top and bottom of the cores; thus, as the peak temperature decreased, the corresponding coefficient of thermal expansion also decreased. Consequently, it resulted in a decrease in the deflection. Note that, from the original surface emissivity (0.6), the maximum deflection of 2.04 mm (0.09% of the deflection limit) of the stainless steel BCS under a high heating rate was asymptotic to the deflection limit (1%). After the emissivity was increased, the maximum deflection decreased significantly. The sandwich structure made of stainless steel withstood the high heating rate of 5.5 W/cm\({}^{2}\) well. In addition, the deflection results of the increase of the emissivity corresponded to the deflection results of the increase in the core thickness (0.3 mm), which means that the coating surface reduced not only the deflection limits, but also the significant weight of the sandwich structures (14.4%). Thus, we recommend that the surface of the sandwich structures for the TPS panel should be coated with the material that has as high emissivity as possible.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Sandwich Panel**} & **Thickness of Face** & **Area Weight** & **Max Deflections** & **Diagonal Length** \\  & **Sheet (mm)** & **(kg/m\({}^{2}\))** & **(mm)** & **(mm)** \\ \hline \multirow{3}{*}{Unidirectional corrugated core} & 0.15 & 4.4 & 1.58 & 240 \\  & 0.3 & 6.8 & 1.74 & 240 \\  & 0.4 & 8.3 & 1.86 & 240 \\ \hline \multirow{3}{*}{Bidirectional corrugated core} & 0.15 & 4.4 & 2.05 & 240 \\  & 0.3 & 6.8 & 2.04 & 240 \\  & 0.4 & 8.3 & 2.1 & 240 \\ \hline \multirow{2}{*}{Honeycomb core [4; 12]} & 0.15 & 4.4 & 6.4 & 646 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum deflections of the unidirectional core structure (UCS) and bidirectional core structure (BCS) sandwich structures under a high heating rate varied with the thicknesses of the face sheet.
## 3 Method Implementation and Application
### Definition of a Landscape Subdivision Criterion and Identification of Symbols or Shapes
#### 3.2.1 Subdivision by Historical Aspects  
Since there was no political center, the numerous cities of Abruzzo, each of ancient origins and brilliant civilization, followed their own history independently:  
* The origin of Touta Marouca (later called Teate and then Chieti) is lost in the mists of time.
* Marruvium and Corfinium were pre-Roman centers.
* Atri, Alba Fucens, and Amiternum flourished under the Roman Empire.
* Sulmona and L'Aquila flourished in the Middle Ages.  
Pescara and Avezzano developed in an increasingly important way from the end of the 19 th century.  
The symbolism requires truly identifying the areas, therefore, each city was represented by a symbol belonging to the period in which it was most flourishing, both economically and socially (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Criterion**} & \multicolumn{2}{c}{**Description**} & \multicolumn{2}{c}{**Judgements**} \\ \cline{2-5}  & **Area** & **\#** & **Symbol** & **Renowned** & **Shape/Area** \\  & & & & **Shape** & **Consistency** \\ \hline \multirow{10}{*}{History} & Marsica & A1 & [PERSON] statue & 7 & 8 \\  & Provincia Chieti & A2 & [PERSON] statue & 10 & 8 \\  & Pescara Popoli & A3 & [PERSON] statue & 9 & 10 \\  & Teramo Atri & A4 & Roman statue & 7 & 7 \\  & Amiterno & A5 & [PERSON] statue & 7 & 7 \\  & Alba Fucens & A6 & [PERSON] statue & 9 & 7 \\  & Peligna—Crifinium & A7 & [PERSON] statue & 9 & 8 \\  & L’Aquila & A8 & Medieval emblem on shield & 7 & 7 \\  & Peligna—Scanno & A9 & Medieval emblem on shield & 6 & 6 \\  & Avezzano e Valle del Liri & A10 & Torlonia monument & 6 & 6 \\  & Pescara & A11 & Fishing boat & 8 & 7 \\ \hline \multirow{10}{*}{Province} & L’Aquila province & B1 & Province emblem & 9 & 9 \\  & Teramo province & B2 & Province emblem & 5 & 5 \\  & Chieti province & B3 & Province emblem & 7 & 6 \\  & Pescara province & B4 & Province emblem & 7 & 6 \\ \hline \multirow{10}{*}{Geography} & Mountains & C1 & Hiker & 8 & 8 \\  & Hills & C2 & Grape bunch & 8 & 6 \\  & Coasts & C3 & Sail boat & 9 & 9 \\ \hline \multirow{10}{*}{Vegetation} & Rdp1 & D1 & Oak tree & 7 & 6 \\  & Rdp2 & D2 & Beech tree & 7 & 6 \\  & Rdp3 & D3 & Beech tree & 7 & 7 \\ \hline \multirow{10}{*}{Fauna} & Abruzzo Lazio and Molise & E1 & Sitting bear & 9 & 9 \\  & National park & E2 & Chamois & 9 & 8 \\ \cline{1-1}  & Gran Sasso and Monti della & & & \\ \cline{1-1}  & Laga National park & E3 & Wolf & 9 & 8 \\ \cline{1-1}  & Møiella National park & E4 & Eagle & 9 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria for the subdivision in areas.  
The criterion produced 11 symbols (see Table 2). Table 3 shows the performance of the criterion, which is unsuitable as it does not fully respond to all the requirements.