esa-sceva/satcom-chunk-collection huggingface dataset:
# NOMA-Based VLC Systems: A Comprehensive Review
## 1 Introduction
### Background
#### 1.1.1 NOMA for VLC
* Cooperative NOMA is basically introduced in RF networks for the exploitation of redundant data in NOMA systems, and for the compensation of weak user facing co-channel interference. The cooperative NOMA can be integrated in VLC systems or through relaying systems. While assuming a VLC system of two users, all users can collect data from RF or VLC networks simultaneously. In such cases, the strong user can decode the weak user's signal and forward it to the intended user through Bluetooth or Wi-Fi. The weak user can then mix the VLC and RF signals through combining methods. * In the literature, several studies have reported modulation and coding schemes for RF-NOMA systems. Since the modulation and coding schemes for NOMA-VLC are different from RF-NOMA, finding the novel modulation and coding schemes for NOMA-VLC systems is worthy for successful deployment of these systems. * As VLC systems are prone to SNR fluctuation, some users receive poor QoS due to handover overhead, inter-cell interference, and LoS blockage, while other users get a high QoS. A cooperative NOMA-based scheme can be adopted where good-serviced users can support weak users through RF links. Thus, a hybrid system can be easily established to provide good service for all users.
# Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning
## 2 Power Generation Technology of VIVACE Converter Using FIVs
An optimal harnessed power curve is created by superimposing the results of the harnessed power calculated from different combinations of the damping and spring stiffness values, as shown in Figure 5.  
Figure 4: Flow induced motions (FIM) zone nomenclature for a circular cylinder with PTC [1]. Figure 5: Optimal harnessed power envelope of VIVACE converter with and without PTC [14]. The optimal harnessed power of a cylinder with PTC is larger than that of a smooth cylinder in the synchronization region of VIV. Further, when the flow velocity exceeds 1.2 m/s, the harnessed power curve increases sharply, reaching 49.35 W at a flow velocity of 1.45 m/s for K = 2000 N/m and \(\zeta_{\text{harness}}\) = 0.16; however, the smooth cylinder is asynchronous and can no longer supply power. The power calculated at 1.45 m/s is more than three times the maximum harnessed power of the smooth cylinder.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Key Space
#### 4.2.3 Remarks on DRPE Phase Mask  
Let us recall that we have the key, which is a vector containing the permutations of the pixels, and a vector in which the permuted phases are stored. Now we will modify these values as follows: Another vector will be constructed to store the modifications of the phase values, based on the vector containing the pixel permutations. Since \(Z\) is a number greater than the phase range encoded in the CGH [0-255], the proposed procedure is to truncate (modulo 256) each component of the pixel shuffling vector (the key) and add it to the phase in the same position in the phase vector. In this way, for each pixel in the CGH, a new phase value is obtained, which can be within the range [0-255]. A brute force attack would involve modifying the positions and values of the pixels.  
Following a similar approach to the previous subsection, we assume that we have \(Z\) elements of a matrix with values in the range [0-255]. Adding a random phase implies that each of these \(Z\) elements can have any value within the mentioned range. The probability of recovering the original phase of pixel \(i\) in the CGH is  
\[P_{phase_{i}}=\frac{1}{256} \tag{13}\]  
Since each pixel is an independent case, the probability of correctly recovering all the phases would be \[P_{phase_{SLM}}=\prod_{i=1}^{Z}P_{phase_{i}}=\frac{1}{256^{2}}=\frac{1}{256^{(1920 \times 1080)}} \tag{14}\]  
The probability of finding the correct phase match is also very small (the denominator has nearly 5 million digits) and adds another layer of security to the CGH encryption, breaking any pattern that could have existed in the phase histogram.
## 5 Prediction Model of Slope Flow Value and Test Time
### Slope Flow Value and Time Prediction Model of Different Bitumen Aggregate Ratio  
From Figure 7, the relationship between slope flow value and time at different bitumen aggregate ratios with 11% filler content was obtained. The exponential function BoxLucas1 model was used to describe the law of slope flow value (\(U_{B}\)) changing with time (\(T\)):  
\[U_{B}=a_{3}[1-exp(-b_{3}T)] \tag{12}\]  
In the formula: \(a_{3}\) and \(b_{3}\) are material parameters.  
It can be seen from Table 9 that the variation range of \(b\) value was small and there was no obvious rule. Therefore, the \(b\) value can be replaced by the mean value \(b\) in the calculation. The bitumen aggregate ratio (\(B\)) and \(a\) value were fitted. The results showed that \(a\) and \(B\) had an exponential function relationship, as shown in Figure 12.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Filler Content (F)/\%**} & \multicolumn{2}{c}{**Fitting Parameters**} & \multicolumn{1}{c}{\multirow{2}{*}{**Error Analysis R\({}^{2}\)**}} \\ \cline{2-2} \cline{4-4}  & \(a\) & \(b\) & \\ \hline
9 & 0.45 & 0.38 & 0.968 \\
11 & 0.51 & 0.42 & 0.973 \\
13 & 0.73 & 0.36 & 0.989 \\
15 & 1.05 & 0.41 & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fitting results of different filler content.  
Figure 10: Fitting parameter \(a\) and filler content relationship curve.  
After fitting, it was found that the relationship between \(a\) value and \(B\) function was as follows:  
\[a=exp\Big{(}\alpha_{3}+\beta_{3}F+\gamma_{2}F^{2}\Big{)} \tag{13}\]  
In the formula: \(\alpha_{3}\), \(\beta_{3}\), \(\gamma_{2}\) are fitting parameters. By substituting Formula (13) into (12), we obtain:  
\[U_{F}=exp\Big{(}\alpha_{3}+\beta_{3}F+\gamma_{2}F^{2}\Big{)}[1-exp(-b_{3}T)] \tag{14}\]  
Formula (14) was the BoxLucas1 correction model, and the calculation results were in good agreement with the experimental data. The formula can be used as the correction curve model of slope flow value and test time for different bitumen aggregate ratios. The correction model had four parameters, and \(\alpha_{3}\) = 49.331, \(\beta_{3}\) = \(-\)14.783, \(\gamma_{2}\) = 1.1, \(b_{3}\) = 0.275 can be obtained by calculation.  
According to the experimental calculation parameters and the calculation model of Formula (14), the prediction model of slope flow value with different bitumen aggregate ratios was obtained, and it was fitted with the experimental data. As shown in Figure 13, it was found that the calculation results of this formula were in good agreement with the experimental data.  
Figure 11: Slope flow value and time calculation curve of different filler content.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Bitumen Aggregate Ratio (B)/\%**} & \multicolumn{2}{c}{**Fitting Parameters**} & \multirow{2}{*}{**Error Analysis R\({}^{2}\)**} \\  & \(a\) & \(b\) & \\ \hline
6.6 & 0.73 & 0.29 & 0.975 \\
6.9 & 0.83 & 0.23 & 0.986 \\
7.2 & 0.91 & 0.34 & 0.995 \\
7.5 & 1.47 & 0.24 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Fitting results of different asphalt content.
## 4 Evaluation Results and Discussion
### Performance Analysis
#### 4.2.5 Adaptivity of QUAKE  
The adaptive behavior of the developed framework can prove to be decisive for satisfactorily serving critical performance-driven VNRs, regardless of their orientation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**QUAKE Value Groups**} & \multicolumn{8}{c}{**Path Length (in Hops)**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
0.041–0.141 & 8 & 9 & 1 & 0 & 0 & 0 & 18 \\
0.141–0.241 & 4 & 10 & 11 & 10 & 1 & 0 & 0 & 36 \\
0.241–0.341 & 0 & 7 & 32 & 32 & 13 & 8 & 0 & 92 \\
0.341–0.441 & 0 & 0 & 2 & 28 & 68 & 28 & 0 & 126 \\
0.441–0.541 & 0 & 0 & 0 & 0 & 6 & 44 & 35 & 85 \\ \hline
**Total** & 12 & 26 & 46 & 70 & 88 & 80 & 35 & 357 \\ \hline \hline \end{tabular}
\end{table}
Table 14: \(VNR_{2}\) use case—QUAKE classification of all paths per path length category.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**QUAKE Value Groups**} & \multicolumn{4}{c}{**\(L\) Value Groups**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-7}  & **0.026–0.126** & **0.126–0.226** & **0.226–0.326** & **0.326–0.426** \\ \hline
0.041–0.141 & 18 & 0 & 0 & 0 & 18 \\
0.141–0.241 & 24 & 12 & 0 & 0 & 36 \\
0.241–0.341 & 21 & 63 & 8 & 0 & 92 \\
0.341–0.441 & 1 & 81 & 44 & 0 & 126 \\
0.441–0.541 & 0 & 6 & 76 & 3 & 85 \\ \hline
**Total** & 64 & 162 & 128 & 3 & 357 \\ \hline \hline \end{tabular}
\end{table}
Table 15: \(VNR_{2}\) use case—QUAKE classification of all paths against \(L\) value groups.  
The diversity of the values of QUAKE is evidently illustrated in Figure 9, where a number of representative Wii combinations of Table 6 are plotted.  
All 357 calculated possible paths of the simulated topology demonstrate either milder or sharper fluctuations in their QUAKE-based profile, depending on the applied Wii combination. This is a desirable feature to be exhibited by a modern performance-oriented path evaluation mechanism, to facilitate appropriate resource allocations to the vast variety of user-centric NG applications. Moreover, the flexibility of QUAKE can be further enhanced by considering that Figure 9 depicts 6 of the 24 weighted groups calculated for the purpose of the designed simulated experiments. Depending on the examined situation, an orchestration authority can calculate as many Wii combinations as needed for customized performance-oriented solutions.
## 2 Materials and Methods
### Quantification Method Based on the Carbon Number Approach Without Standards  
Twelve CLASS compounds (compounds lacking authentic standards or surrogates) were quantified without using standards (e.g., \(\alpha\)-pinene, \(\beta\)-pinene, \(\beta\)-myrrene, limonene, 3-octanol, (+)-isomenthone, (-)-isomenthone, menthyl acetate, neomenthol, menthol, piperitone, and m-eugenol). Although standards for these compounds are not available, their presences were detectable in the form of large peaks in the chromatograms obtained by the vapor sample analyses. These 12 CLASS species were thus quantified based on the effective carbon number theory [16; 17], which is defined in relation to the sum of the carbon number in each compound's molecular structure. First, \"predicted response factor (RF) values\" were assigned to each of the 25 standard compounds; predicted RF is based on the theoretical RF value but quantified using the total mass spectrum (35-400 m/z) applied in this experiment. Then, an empirical equation was developed to predict the RF values of the 25 (CLASS) VOCs based on the relationship between carbon numbers of 25 standards and their RF values (Figure 1). The RF values for 12 CLASS compounds were thus estimated using the relationship derived from 25 standard compounds. For example, the RF value of \(\alpha\)-pinene was predicted as 108,8703 by putting its carbon number (10) into the relationship.
## 3 Study Framework
### Traffic Conflict Ratio  
To evaluate the 2+1 designs against the conventional TLTW design for different traffic conditions, the simulated conflicts in the before period (i.e., with the conventional TLTW design) and the after period (i.e., with the 2+1 design) are estimated, and then the conflict ratio (\(\rho\)) is obtained as follows [26; 27]:  
\[\rho=\frac{C_{a}}{C_{b}} \tag{1}\]  
with a variance of:  
\[Var(\rho)=(C_{a}/C_{b})^{2}\times\left[\left(Var(C_{a})/{C_{a}}^{2}\right)+ \left(Var(C_{b})/{C_{b}}^{2}\right)\right] \tag{2}\]  
where,  
\(\rho\) = Conflict ratio;  
\(C_{a}\) = Average number of conflicts for the 2+1 design (i.e., after);  
\(C_{b}\) = Average number of conflicts for the conventional TLTW design (i.e., before);  
\(Var(C_{a})\) = Variance of the conflicts in the after;  
\(Var(C_{b})\) = Variance of the conflicts in the before.  
The \"\(\rho\)\" value can be viewed as a modification factor based on conflicts [28]. When the value of \"\(\rho\)\" is higher than \"1.0\", it indicates that the 2+1 design would result in more  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Speed-Limit Strategy**} & **Traffic Volume** & **HVs(\% from Total Volume)** & \multicolumn{2}{c}{**Speed Limit**} \\ \hline \multirow{4}{*}{USL} & 250 & & - & 50 km/h \\  & 500 & & - & 60 km/h \\  & 750 & 2.50, 5.0, 10.0 and 15.0 & - & 70 km/h \\  & 1000 & & - & 80 km/h \\  & 1250 & & - & 90 km/h \\ \hline \multirow{4}{*}{DSL} & 250 & & PCs: 90 km/h \\  & 500 & & - & HVs: 60 km/h \\ \cline{1-1}  & 750 & 2.50, 5.0, 10.0 and 15.0 & - & PCs: 80 km/h \\ \cline{1-1}  & 1000 & & - & HVs: 60 km/h \\ \cline{1-1}  & 1250 & & - & HVs: 60 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different combinations of traffic volumes, %HVs, and speed limits.  
Figure 2: Traffic network: (**a**) convectional TLTW, (**b**) 2+1 (prohibited overtaking in opposite direction), and (**c**) 2+1 (allowed overtaking in opposite direction).  
simulated conflicts and, hence, more accidents than the TLTW design. On the contrary, when the value of \"\(\rho\)\" is lower than \"1.0\", it indicates that the \"2+1\" design would result in fewer simulated conflicts. Consequently, fewer accidents would occur compared to the TLTW design. A \"\(\rho\)\" value of \"1.0\" indicates no significant safety changes between the two designs. The statistical significance of the \"\(\rho\)\" can be estimated in the same fashion as the collision modification factors (CMFs) [21; 26; 27; 54]. For the 5% significance level, it can be estimated as follows) [55; 56]:  
\[CI_{95\%}=\rho\pm 1.96\times SE \tag{3}\]  
where  
\(CI_{95\%}=\) the confidence interval at the 5% significance level;  
\(SE=\) the standard error from the 10 simulations.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Results and Discussion
#### 3.2.2 Echo Sounder  
The SRA images acquired by the MBES showed corrugated profiles compared with the surrounding seafloor. Unlike the images acquired by the SiSS, no distortion of the overall shape was visible due to the post-correction of the elements that may have caused distortion, as detailed in Section 3.2.4. However, the topographical features within the SRA were not expressed as clearly as expected, and the results had a similar resolution to the 400 kHz SiSS images.
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
###### Abstract  
In the construction of high dams, many high rock slope failures occur due to flood discharge atomized rain. Based on the steel frame lifting technique and strength reduction materials, a comprehensive method is proposed in this paper to study the stability of high bedding rock slope subjected to atomized rain. The safety factor expression of the comprehensive method and the evaluation method for deformation instability were established according to the similarity theory of geomechanical model, failure criterion, and mutation theory. Strength reduction materials were developed to simulate the strength reduction of structural planes caused by rainfall infiltration. A typical test was carried out on the high bedding rock slope in the Baihetan Hydropower Station. The results showed that the failure modes of the bedding rock slope were of two types: sliding-fracturing and fracturing-sliding. The first slip block at the exposed place of the structural plane was sliding-fracturing. Other succeeding slip blocks were mainly of the fracturing-sliding type due to the blocking effect of the first slip block. The failure sequence of the slip blocks along the structural planes was graded into multiple levels. The slip blocks along the upper structural planes were formed first. Concrete plugs had effective reinforcement to improve the shear resistance of the structural planes and inhibit rock dislocation. Finite element method (FEM) simulation was also performed to simulate the whole process of slope failure. The FEM simulation results agreed well with the test results. This research provides an improved understanding of the physical behavior and the failure modes of high bedding rock slopes subjected to atomized rain.  
high bedding rock slope; atomized rain; slope stability; comprehensive method +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results  
### Experimental Setup  
The system setup used in the experiment is as follows. To test the performance of the developed detection model, experiments were carried out on a system with a built-in GPU. Python was used as the programming language.  
* CPU: Intel(R) Core(TM) i7-10900k CPU 2.90 GHz
* RAM: 96 GB
* GPU: NVIDIA GeForce RTX3090
## 3 Numerical Results and Discussion
### Incident Angle
As the angle increases to \(\theta=30^{\circ}\) and \(\theta=50^{\circ}\), the lower edges occur at frequencies 3.80 THz and 4.09 THz, respectively. It is obvious that the lower edges moved to the higher frequencies, but the movement is faster than the TE polarization. Also, the thermal radiation spectrum shifts to a higher frequency, and in contrast to the TE mode, as the angle increases, the width of the thermal radiation spectrum reduces. For example, the maximum radiation width is about 1.02 THz at \(\theta=30^{\circ}\), and it lessens to about 0.64 THz at \(\theta=85^{\circ}\). The width of the omnidirectional spectrum of thermal radiation in this case occurs at frequencies of about \(f_{a}=4.26\) THz and \(f_{b}=4.39\) THz, which remained invariant in the related frequency range for TM polarization, meaning the thermal radiation spectrum will be always in its maximum magnitude. However, it is clear that a narrower width (\(\Delta f=0.13\) THz) occurred compared to the TE mode. Generally, the impacts of TE and TM modes on the transmission, reflection, radiation quantities, and PBG depend on the proposed structure, materials, and incident angle of the THz waves. In the case of our study, according to our calculations, the proposed structure shows more tunability towards the TM polarization mode for some parameters. For instance, the radiation PBGs in the TE mode considerably shift faster towards higher frequencies than in the TE mode by increasing the angle of incidence. Figure 3: The thermal radiation power spectrum (dashed rectangle depicts the omnidirectional spectrum) of the 1D MQP PC (with FB and ML sequences) as a function of frequency for TE polarization at T = 300 K and \(M=1\), under several incident angles \(\theta=0^{\circ}\), \(30^{\circ}\), \(50^{\circ}\), and \(85^{\circ}\). Here, the lattice constants are considered as \(d_{A}=12\) μm, \(d_{B}=6\) μm, \(d_{C}=8\) μm, and \(d_{D}=6\) μm.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 1 Introduction  
Digital displays are an integral part of our everyday life, and they are still evolving in multiple directions. The most obvious branch is evolution towards larger screen size, higher resolution and improved efficiency. Nevertheless, the new revolution in display technologies is brewing within the segment of personalized wearable displays. Currently this niche is dominated by virtual reality (VR) head-mounted displays mainly for gaming and entertainment purposes. Nevertheless, it is expected that in the next few following years augmented reality (AR) displays will rapidly catch up and overtake the dominant position in the wearable head-mounted display segment [1].  
Augmentation of reality has been known since the late 16 th century, when the principles of what is known today as the Pepper's ghost illusion were first described by [PERSON]. With the help of an angled large glass pane and a hidden projection system, an observer can be made to perceive the presence of objects which are not actually there. In essence, the very same principle isstill used nowadays in all kinds of AR displays, where also an optical image combiner is used to fuse the light from the ambient surrounding with digital projected information. Until recently AR head-up display systems mainly could be found in military aircraft; nevertheless, as there is a virtually limitless potential for augmenting reality, head-up systems have creeped also into the consumer automotive segment but it does not stop there. With miniaturization, growth of computational power, and perfection of other key components essential to true AR (mainly positional tracking and spatial awareness), individual head-mounted AR displays are also starting to slowly appear on the market. The vision for AR applications is huge--it is foreseen as a personal assistant and guide in various workflows--starting from image-guided surgery, to manufacturing and maintenance, among many other things. Nonetheless, existing display technologies have been initially designed to show relatively rudimentary information--such as pictograms, limited text, geometric shapes, etc., which is not up to the envisioned task. In this work we review the approach that has been taken until now in development of near-to-eye AR displays, what are the challenges, and how manufacturers and scientists attempt to overcome them. We briefly analyze the future prospects in the development of AR display technologies and examine our own solution to yield a practical AR near-to-eye display hardware oriented towards professional work.
## 3 Major Routes of Xylose Transport and Metabolism in Bacteria
### Metabolic Pathways to Xylitol and Xylonic Acid
#### 3.3.2 Xylonic Acid
Besides being a good producer, _Gluconobacter_ has a higher resistance to the inhibitors present in lignocellulosic biomass hydrolysates, being a good candidate to use these residual materials as a source of xylose. _P. fragi_ also produces xylonic acid at similar volumetric rates to _G. oxydans_ but at lower specific rates [124]. _P. sacchari_ has recently been reported to be a good xylonic acid producer. In _P. sacchari_, the genes involved in the Weinberg pathway were found to have homologous genes. However, the gene encoding for 2-keto-3-desoxy-D-xylonate dehydratase (KDY), responsible for converting 2-keto-3-desoxy-D-xylonate into \(\alpha\)-ketoglutarate semialdehyde was not found. This finding could explain the accumulation of xylonic acid by _P. sacchari_ reported by [PERSON] et al. [60]. This bacterium has shown a promising capacity to produce xylonic acid from xylose, as productivities in lab-scale reactor reached 1.5 g L\({}^{-1}\) h\({}^{-1}\) using xylose as the sole carbon source and over 6 g L\({}^{-1}\) h\({}^{-1}\) in fed-batch cultivations using glucose for cell growth and xylose in the feed for xylonic acid production. Xylonic acid titers reached 390 g L\({}^{-1}\) and yields of 1.1 g\({}_{\text{xyl acid }}\)g\({}^{-1}\)\({}_{\text{xylose }}\)[80]. In _P. sacchari_, the genes involved in the Weinberg pathway were found to have homologous genes. The assays with _P. sacchari_ showed that it is necessary to maintain a high dissolved oxygen concentration to promote a high xylonic acid productivity titer and yield on xylose. This is explained by the need to restore reduced cofactors (NADH/NADPH) through oxidation and to maintain a continuous bioconversion process. Particularly in the case of _P. sacchari_, a high oxygen concentration in the medium also discouraged the formation of other xylose-derived metabolites such as xylitol (see Section 3.3.1), thus increasing the yield of xylonic acid on xylose. These authors have also observed that, if cells were previously grown on glucose and then fed with xylose, xylonic acid productivity increased by a factor of 4 or 5 [80]. Table 2 gathers information on various examples of bacterial strains able to convert xylose into xylonic acid.