esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 4 Polarization Modulation in Graphene Metasurfaces
### Graphene-Based Polarizer  
In addition to engineering the phase retardation of the two eigenmodes, graphene metasurfaces can be tailored to filter selectively one eigenmode while eliminating the orthogonal one so as to be a polarizer, or in other words a polarization-selective surface. A straightforward example is an anisotropic graphene resonator such as Figure 9a supporting two spectrally-separated resonances along orthogonal directions [95]. By working at a frequency where one polarization is on resonance and the other is off resonance, the on-resonance state is strongly reflected, while reflection of the off-resonance state is fairly weak (Figure 9b). Such polarization selectivity is switched on only when the graphene is doped to support the plasmonic resonance.  
Besides the switchable function, graphene endows the polarizer with frequency tunability and the active controllable isolation depth of the polarization states with the elaborate designs in Figure 9c,e, respectively. In Figure 9c, the L-shaped trace is inserted into orthogonal-oriented graphene strips to couple the resonances in the two directions such that the whole structure exhibits the CP selectivity [96]. Left circular polarization (LCP) and right circular polarization (RCP) excitation causes different current distributions in the sandwich element with different resonance frequencies, such that one polarization is blocked by strong resonances and the other goes through with small insertion loss. Figure 9d shows that the operation frequency can be dynamically shifted with the variation of the graphene Fermi level.  
The chiral metamaterials have been studied with enhanced circular dichroism (CD) [97] and optical activity (OA) [98]. When the graphene layer is incorporated as a loss-controllable medium into the chiral resonators in Figure 9e, the radiation loss of the resonator is controlled by the voltage [99]. Underdamped to overdamped phase transition for the RCP wave is clearly observed in the experiment, while the off-resonance LCP wave experiences negligible modulation (Figure 9f). Therefore, at the critical coupling condition, a giant CD with a 45-dB isolation is experimentally achieved. Different from the plasmonic feature of patterned graphene in most of the polarization controllers, here the tunable loss of the monolayer graphene is the key to tune the CD originating from the chiral meta-atoms.
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 4 Implementation on Embedded Equipment
### Difference between Architectures
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hardware Architecture** & & **Characteristics** \\ \hline \multirow{8}{*}{NUCLEO-H753 ZI} & • & Arm Cortex-M7 Core at 480 MHz \\  & • & 2 Mbytes of Flash memory \\  & • & 1 Mbytes of SRAM \\  & • & Voltage at device input = 3.3 V \\  & • & Cost: 34 euros \\  & • & Programmed in C or C++ using tools such as STM32 CubeIDE. \\ \hline \multirow{8}{*}{ESP32 Devkit V4} & • & 32-bit Xtens dual-core 240 MHZ \\  & • & 520 KB SRAM of SRAM (16 KB for Cache) \\  & • & 448 KB of ROM \\  & • & Cost: 12 euros \\  & • & Supports programming in C or C++ using the ESP-IDF, as well as MicroPython. \\ \hline \multirow{8}{*}{Raspberry-Pi3 B+} & • & 64-bit quad-core ARM Cortex-A53 \\  & • & processor at 1.4 GHz \\  & • & 1 GB of RAM \\  & • & Cost: 59 euros \\  & • & Supports a variety of programming \\  & • & languages, including Python, C, C++, \\  & • & and others. Typically programmed using \\  & • & a Linux-based environment. \\ \hline \multirow{8}{*}{desktop with Linux OS} & • & Intel(R) Xeon(R) CPU E5-2640 \\  & • & v3 @ 2.60 GHz \\ \cline{1-1}  & • & 131,788,880 kB of total memory \\ \cline{1-1}  & • & CPU family: 6 \\ \cline{1-1}  & • & Model: 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of hardware architectures. statistical study has shown that the time delta between events is equal to 10 s for a complete configuration of sensors, while it is equal to 17 s with a minimum configuration of sensors.
# Multicriteria Decision Making and Its Application in Geothermal Power Project
## 5 Conclusions  
The selection process of determining an optimal location for a geothermal power plant project is an MCDM issue involving both qualitative and quantitative considerations. In this research work, the authors introduced a fuzzy Multicriteria Decision-Making Model, including the methods of fuzzy analytic hierarchy process (FAHP) and the Weighted Aggregated Sum Product Assessment (WASPAS), for the selection of a geothermal power plant site in Vietnam. The FAHP model is first used to determine the weights of each criterion and sub-criterion. Then, the ranking of each alternative is calculated by the WASPAS model. According to the data in Figure 4, Quang Tri (DMU5) is the optimal location.  
The contribution of this research is to determine a decision support system for geothermal power in Vietnam. First, the studied approach is the first geothermal power plant site selection model in Vietnam consulting expert opinions and the literature. Second, this is the first work to introduce a case study on judging potential locations for the geothermal power plant site selection that uses a combination of FAHP and the WASPAS model. The findings also provide a useful guideline for selecting optimal locations in other types of renewable energy resources.  
In the case that AHP fails to apply in methodology and does not compute a suitable answer, the decision makers can use other fuzzy integrated models such as F-TOPSIS to rank the weight of each criterion suitably.  
In the future, this research can be opened to other MCDM techniques, such as Aggregated Indices Randomization Method (AIRM), Base-criterion method (BCM), the Dominance-based rough set approach (DRSA), etc.  
Conceptualization, T.H.T.N., C.-N.W. and N.V.T.; Methodology, C.-N.W. and N.V.T.; Software, N.V.T.; Formal analysis, C.-N.W. and N.V.T.; Investigation, T.H.T.N., C.-N.W. and N.V.T.; Resources, C.-N.W.; Data curation, T.H.T.N., C.-N.W. and N.V.T.; Writing--original draft, T.H.T.N. and N.V.T.; Writing--review & editing, N.V.T.; Project administration, T.H.T.N. and C.-N.W.; Funding acquisition, T.H.T.N., C.-N.W. and N.V.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Van Lang University, Vietnam.  
The authors wish to express their gratitude to Van Lang University, Vietnam, for financial support for this research.  
The authors declare no conflict of interest.  
Figure 5: Ranking results when changing \(\lambda\) value.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Big Vehicle Shadowing Effect
#### 4.3.2 Connectivity with Number of Vehicles  
In Figure 10 illustrates the effect of the number of communicating vehicles in a network, at transmitted power of 1 W and \(\alpha=2\). With only a small proportion of big vehicles (\(P_{k}=0.1\)) and a vehicle density of \(\rho=0.2\), connectivity decreases depending on the traffic model used, with a more significant drop in environments with extreme fading, as represented by Nakagami-\(m\) and Weibull distributions. As discussed in the case presented in Figure 9, an increase in the number of big vehicles further degrades communication.  
Figure 10: Total number of vehicles effect on overall connectivity at \(P_{k}=0.1\), \(\alpha=2\).  
Figure 9: Big vehicle effect on connectivity probability. (**a**) Connectivity probability at \(\alpha=2\). (**b**) Connectivity probability at \(\alpha=3\).  
Lastly, in Figure 11 we see the combined effect of path-loss coefficient with [PERSON]-\(m\) factor \(m\) and Weibull factor \(C\) on the connectivity. From the plots, it is evident that as the channel conditions worsen (increasing \(\alpha\) or \(m\) for Nakagami-\(m\) channel) in Figure 11a, the connectivity decreases; Further more, as the channel improves (increasing \(C\)) as seen in Figure 11b, the connectivity probability increases.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 2 Unmanned Ship-Wind and Wave Coupling CFD Model
### Governing Equations and Numerical Methods  
The process of ship berthing involves the simulation of the two phases of water and air and has to take into account the existence of free liquid surface [31]. The multiphase flow VOF model was used to track the interface on the water surface [34; 40]. Because air and water flow cannot dissolve into each other, and an interaction force is established between molecules on the two phases, a surface tension coefficient should be considered. Because the surface tension coefficient has different values at different temperatures, we adopted the tension coefficient at a natural temperature of 15 \({}^{\circ}\)C, which is 0.0073.  
In calm water conditions (the ship's eigen wave occurs), the turbulence model adopts the renormalization group (RNG) \(k\)-\(\epsilon\) model to ensure calculation accuracy. The RNG \(k\)-\(\epsilon\) model equations are shown below, as Equations (1) and (2) [6].  
\[\rho\frac{\mathrm{d}k}{\mathrm{d}t}=\frac{\partial}{\partial x_{i}}\left[( \alpha_{k}\mu_{\mathrm{eff}})\frac{\partial k}{\partial x_{i}}\right]+C_{k}+G_ {b}-\rho\epsilon-Y_{M} \tag{1}\]  
\[\rho\frac{\mathrm{d}\epsilon}{\mathrm{d}t}=\frac{\partial}{\partial x_{i}} \left[(\alpha_{k}\mu_{\mathrm{eff}})\frac{\partial\epsilon}{\partial x_{i}} \right]+C_{1\epsilon}\frac{\epsilon}{k}(G_{k}+C_{3\epsilon}G_{b})-C_{2\epsilon }\rho\frac{\epsilon^{2}}{k}-R \tag{2}\]  
\(G_{k}\) is the turbulent kinetic energy produced by the laminar flow velocity gradient, \(G_{b}\) is the turbulent kinetic energy produced by buoyancy; \(Y_{m}\) is the fluctuation produced by transitional diffusion in compressible turbulence; \(C_{1}\), \(C_{2}\), and \(C_{3}\) are constants; \(\alpha_{k}\) and \(\alpha_{cr}\) are the reciprocal of the effective turbulent Prandtl number of turbulent kinetic energy \(k\) and dissipation rate \(\epsilon\).  
The pressure and velocity coupling of the discrete equations in the steady state adopts the semi-implicit method for pressure linked equations (SIMPLE) algorithm. The second-order upwind equation was used to solve the equations, the face-to-face iteration of time division multiple access (TDMA) and the low relaxation factor were used, and according to the convergence criterion, the remainder had been less than \(10^{-3}\). The pressure-velocity coupling method in unsteady state adopts the pressure-implicit with splitting of operators (PISO) algorithm [41; 42], and the calculation starts after the setting is completed and the flow field is initialized. We set the time step to 0.001 s, and 20 iterations in each time step.  
For research under wave conditions, the turbulence model adopts the standard \(k\)-\(\epsilon\) model. Although this model is not as accurate as the RNG \(k\)-\(\epsilon\) model, its form is simple, the calculation is convenient, and the accuracy is reasonable. The wave condition is set to the 5 th order VOF wave.
## 3 Diagnostic and Prognostic Framework Modelling
### Battery Discharge Model  
The [PERSON]'s circuit model is used to develop the End of Discharge (EOD) model. The voltage drop is expressed as:  
\[E=E_{0}-I\cdot(R_{E}+R_{CT}+Z_{w}), \tag{4}\]  
where \(E^{0}\) is the theoretical open circuit voltage; \(I\) is the current flowing out of the battery; and \(R_{E}\), \(R_{CT}\) and \(Z_{w}\) are the electrolyte resistance, charge transfer resistance, and Warburg impedance, respectively.  
This expression can also be rewritten to include the internal resistances of the battery:  
\[E(t)=E^{0}-\Delta E_{sd}(t)-\Delta E_{rd}(t)-\Delta E_{mt}(t), \tag{5}\]  
Figure 1: [PERSON]’s circuit model [14].  
where \(\Delta E_{sd}\) is the voltage loss due to battery shelf discharge, \(\Delta E_{rd}\) is the drop caused by the cell reactant depletion, and \(\Delta E_{mt}\) represents the drop due to internal resistance to mass transfer.  
The individual terms of Equation (4) are expressed as:  
\[\Delta E_{sd}(t)=\alpha_{1}\cdot\exp\left(\frac{-\alpha_{2}}{t}\right) \tag{6}\]  
\[\Delta E_{rd}(t)=\alpha_{3}\cdot\exp(\alpha_{4}\cdot t) \tag{7}\]  
\[\Delta E_{mt}(t)=\Delta E_{init}-\alpha_{5}\cdot t. \tag{8}\]  
Model parameters \(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4},\alpha_{5}\) are to be calculated and \(\Delta E_{init}\) is the voltage drop as the current, \(I\), passes through the internal resistance, \(R_{E}\), at the start of the discharge cycle. This is calculated by \(\Delta E_{init}=I\times R_{E}\). The effect of each term and the whole EOD model is shown in Figure 2. It can be observed that from the three components given in Equations (6)-(8), the cell reactant depletion (\(\Delta E_{rd}\)) is the main term when determining the EOD. For this reason, \(\alpha_{3}\) and \(\alpha_{4}\) will be the most important model parameters affecting the EOD prediction.
# Application of Direct Shear Test to Analysis of the Rate of Soil Improvement with Polyester Fibres
## 3 Results  
### Improvement of Soil Shear Strength Parameters  
Maximal values of shear stress for various amounts of fibres can be seen in Table 3. Dependence of shear stress on normal stress in soil CI, CS1, and CS2 is introduced in Figures 4-6. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **0\%** & & **0.5\%** & & **1.0\%** & & **1.5\%** \\ \cline{2-9}
**Soil** & **Normal** & **Shear** & **Normal** & **Shear** & **Normal** & **Shear** & **Normal** & **Shear** \\  & **Stress \(\sigma\)** & **Stress \(\tau\)** & **Stress \(\sigma\)** & **Stress \(\tau\)** & **Stress \(\sigma\)** & **Stress \(\tau\)** & **Stress \(\sigma\)** & **Stress \(\tau\)** \\  & **(kPa)** & **(kPa)** & **(kPa)** & **(kPa)** & **(kPa)** & **(kPa)** & **(kPa)** & **(kPa)** \\ \hline \hline \multirow{4}{*}{CI} & 50.1 & 85.667 & 50.1 & 94.667 & 50.1 & 91.111 & 50.3 & 110.333 \\  & 100.3 & 125.889 & 100.2 & 123.778 & 100.2 & 143.889 & 100.3 & 168.889 \\  & 200.3 & 177.444 & 200.1 & 183.778 & 200.1 & 218.111 & 200.3 & 215.778 \\  & 300.4 & 246.778 & 300.1 & 241.556 & 300.2 & **274.222** & 300.3 & 257.444 \\ \hline \multirow{4}{*}{CS1} & 50.2 & 74.556 & 50.1 & 137.556 & 50.3 & 131.000 & 50.3 & 125.444 \\  & 100.2 & 143.889 & 100.2 & 182.444 & 100.2 & 173.667 & 100.0 & 146.444 \\ \cline{1-1}  & 200.1 & 171.111 & 200.1 & 248.111 & 200.1 & 207.556 & 200.2 & 183.778 \\ \cline{1-1}  & 300.2 & 249.889 & 300.4 & 319.556 & 300.3 & 261.778 & 300.2 & 192.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximal values of shear stress for various amounts of fibres. Figure 4: Shear stress versus normal stress in soil CI. Figure 5: Shear stress versus normal stress in soil CS1. Values of the shear strength parameters of soils and soils reinforced by fibres as well as their expanded uncertainty are introduced in Table 4 (soil CI), Table 5 (soil CS1), and Table 6 (soil CS2).
## 4 Conclusions
Also, the performance of the model was found to be better for countries with well-distributed solar radiation. * Finally, the performance of the ANN model developed in this study is also very good, however, the large number (2500) neurons in the hidden layer and the lengthy training period make it undesirable for developing nations. On the other hand, the novel hybrid neural network models presented in this study can achieve a better result with the use of a lesser number of neurons and this makes it more suitable for application in any part of the world. Overall, it can be concluded that CNN-ANN has the best prediction performance of the two-hybrid neural network due to its simplified methodology. Although the hybrid models in this study have been tested on data from Africa, it is noteworthy that this can be applied for a similar purpose in other continents. In future studies, the application of these hybrid models for other renewable energy resources forecasts will be considered. Additionally, the development of hybrid models with traditional learning and machine learning tools will be studied. The proposed approach in this study can also be considered for PV-related systems modeling including the single diode modeling of PV systems. Conceptualization, M.M., A.O., N.Y., Z.Q., C.C.U., B.E. and O.B. ; methodology, M.M., A.O., N.Y., Z.Q., C.C.U., B.E. and O.B. ; software, M.M., A.O., C.C.U. and O.B. ; validation A.O. and O.B. ; formal analysis, A.O. and O.B. ; investigation, M.M., B.E. and O.B. ; resources, O.B. ; data curation, O.B. ; writing--original draft preparation, M.M. and O.B. ; writing--review and editing, M.M. and O.B. ; supervision, O.B. ; project administration, M.M., A.O., N.Y., Z.Q., C.C.U., B.E. and O.B. ; funding acquisition, M.M. and Z.Q. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data used for this study is available upon reasonable request from the corresponding author. We acknowledge the support of Sichuan Provincial Lab for Power Systems Wide-Area Measurement and Control during the cause of this research. The authors declare no conflict of interest.
# Atomic Receiver by Utilizing Multiple Radio-Frequency Coupling at Rydberg States of Rubidium
## 2 Methods
### Performance Test
The same investigation was also carried out for AM communication and a similar conclusion has been reached. Traditional electronic-based receivers often produce spurious, harmonic, intermodulation, and other unnecessary signals, which will cause interference within the frequency band, whereas the atomic receiver will not cause cross-talk between signals while ensuring high SNR, so it has great potential in many applications.
###### Abstract
Some researchers have investigated the circadian effects of self-luminous tablets, but their studies have led to mixed results and debate. One study found that reading on an iPad for 30 min before bed resulted in decreased subjective sleepiness and delayed electroencephalography (EEG) dynamics of slow-wave activity compared with reading a paper book [21]. [PERSON] and colleagues [22] examined 13 participants' melatonin levels after using self-luminous tablets for two time conditions: one hour and two hours. Suppression of melatonin levels after one hour of exposure was not significant, but it was significant after two hours of use [22]. [PERSON] et al. reported that exposure to an LED-backlit screen for five hours in evenings resulted in reduced salivary melatonin and sleepiness levels, but increased cognitive performance, including improved sustained attention, as well as working and declarative memory [23]. [PERSON]'s team [24] measured evening concentration of melatonin in the saliva of 14 subjects after exposure to bright light (\(-\)569 k) over 6.5 h followed by reading on a tablet or from a book for two hours (21:00-23:00). No differences in sleepiness and salivary melatonin levels were found between the tablet and book conditions. They explained that the exposure to bright light during the day might combat sleep disturbances associated with the use of blue-light-emitting electronic devices in the evening [24]. [PERSON] et al. reported that reading on light-emitting devices suppressed evening levels of melatonin by 55.12% \(\pm\) 20.12%, while reading on a book was associated with no suppression [25]. These researchers further concluded that it took longer for participants to fall asleep after reading on light-emitting devices and that their evening sleepiness, melatonin secretion and next-morning alertness were all reduced [25]. In [PERSON] and colleagues' study, participants lived in a controlled environment (in the Intensive Physiological Monitoring Unit of the Centre for Clinical Investigation of Brigham and Women's Hospital) for 14 days. Thus, [PERSON] argued that the effect of light-emitting devices has been overestimated because \"real-life light exposure desensitizes the non-image-forming system to the intensity of evening light exposure that was examined in this study\" [26]. He suggested that the results of this study cannot be generalised to answer the question of whether the use of self-luminous devices at night negatively impacts sleep [26]. Though the experimental protocol of [PERSON] et al. was well-designed as a mechanistic study and greatly contributed to the growing literature, [PERSON]'s argument did highlight the lack of field studies on light exposure in real applications. The circadian effects of light from daylight, electric light sources and self-luminous displays in real-world settings are unclear because little empirical data is available. The present study aims to understand typical light exposure and compare the range and distribution of the circadian effects of light from these three sources in real commercial and residential office spaces.
## 4 Results and Discussion  
The sealing performance of the O-ring depends on the contact pressure that develops between the O-ring and the surfaces with which it comes into contact, and the initial leakage appears once the maximum contact pressure is lower than the pressure difference across the seal [28; 29]. In this paper, the difference between the inflation and deflation state of the O-ring is that the O-ring is stressed on the upper side or the lower side, so the criterion of sealing performance in the inflation state and the deflation state remains unchanged. At 15 \({}^{\circ}\)C and 70 MPa, both in the inflated and deflated states, the maximum contact pressures on contact surfaces I and II exceed 70 MPa. At 130 \({}^{\circ}\)C and 87.5 MPa in the inflated state, the maximum contact pressures on contact surfaces I and II exceed 87.5 MPa. Additionally, at \(-60\)\({}^{\circ}\)C and 56 MPa in the deflated state, the maximum contact pressures on contact surfaces I and II exceed 56 MPa. In all these cases, the O-ring can achieve effective sealing. In addition, when the von Mises stress is increases, the possibility that a crack or the loss of elasticity appears in the O-ring is also increased [30; 31].  
To comprehensively investigate the sealing performance of the combined sealing structure, this study simulates the installation, inflation, and deflation state of the combined sealing structure at 15 \({}^{\circ}\)C and 70 MPa, the inflation state at 130 \({}^{\circ}\)C and 87.5 MPa, and the deflation state at \(-60\)\({}^{\circ}\)C and 56 MPa. The von Mises stress and contact pressure obtained from the simulation results are analyzed to gain insights into the sealing performance. Moreover, the influences of the gas pressure, gasket surface friction coefficient, gasket fillet, and gasket thickness on the sealing performance of the combined sealing structure are investigated.