esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Contrast  
The mean contrast before the therapy was 7.12 (\(\pm\)2.12), while after the therapy, it was 6.36 (\(\pm\)1.86). It was shown that the contrast decreased by an average of 0.76 (\(\pm\)0.88). This reduction was found to be statistically significant (\(p\) = 0.0010) (Figure 2a) (Table 1).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Mean** & **Me** & **Min** & **Max** & **IQR** & **SD** & \(p\)**, Student’s \(t\)-Test** \\ \hline \hline  & & \multicolumn{6}{c}{Contrast in average RGB} \\ \hline before & 7.12 & 6.76 & 3.98 & 11.75 & 3.22 & 2.12 & \\ \hline after & 6.36 & 6.30 & 3.17 & 9.85 & 2.24 & 1.86 & 0.0010 \\ \hline delta & \(-\)0.76 & \(-\)0.49 & \(-\)3.59 & 0.50 & 0.88 & 0.88 & \\ \hline  & & \multicolumn{6}{c}{Contrast channel R} \\ \hline before & 5.97 & 6.18 & 1.89 & 8.73 & 1.69 & 1.72 & \\ \hline after & 5.57 & 5.76 & 1.63 & 7.61 & 1.65 & 1.53 & 0.0090 \\ \hline delta & \(-\)0.40 & \(-\)0.21 & \(-\)2.04 & 0.50 & 0.70 & 0.61 & \\ \hline  & & \multicolumn{6}{c}{Contrast channel G} \\ \hline before & 7.98 & 7.49 & 4.35 & 12.88 & 3.69 & 2.43 & \\ \hline after & 7.24 & 7.22 & 4.06 & 11.34 & 2.91 & 2.26 & 0.0067 \\ \hline delta & \(-\)0.74 & \(-\)0.58 & \(-\)4.45 & 0.78 & 1.05 & 1.08 & \\ \hline  & & \multicolumn{6}{c}{Contrast channel B} \\ \hline before & 13.17 & 12.57 & 7.86 & 21.35 & 4.94 & 3.40 & \\ \hline after & 11.70 & 11.63 & 7.20 & 16.83 & 4.10 & 2.98 & 0.0023 \\ \hline delta & \(-\)1.47 & \(-\)1.01 & \(-\)5.42 & 2.25 & 1.53 & 1.87 & \\ \hline  & & \multicolumn{6}{c}{Homogeneity in average RGB} \\ \hline before & 0.43 & 0.43 & 0.33 & 0.52 & 0.06 & 0.05 & \\ \hline after & 0.45 & 0.44 & 0.36 & 0.56 & 0.06 & 0.05 & 0.0081 \\ \hline delta & 0.02 & 0.01 & \(-\)0.04 & 0.05 & 0.03 & 0.02 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comprehensive statistical summary of texture analysis parameters before and after erbium glass laser therapy.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Mean** & **Me** & **Min** & **Max** & **IQR** & **SD** & **\(p\), Student’s \(t\)-Test** \\ \hline  & & \multicolumn{6}{c}{Homogenity channel R} \\ \hline before & 0.48 & 0.45 & 0.36 & 0.81 & 0.07 & 0.12 & \\ \hline after & 0.49 & 0.46 & 0.39 & 0.86 & 0.06 & 0.11 & 0.0377 \\ \hline delta & 0.01 & 0.01 & \(-0.04\) & 0.04 & 0.02 & 0.02 & \\ \hline  & & \multicolumn{6}{c}{Homogenity channel G} \\ \hline before & 0.41 & 0.41 & 0.32 & 0.50 & 0.07 & 0.05 & \\ \hline after & 0.43 & 0.42 & 0.34 & 0.51 & 0.08 & 0.05 & 0.0278 \\ \hline delta & 0.01 & 0.01 & \(-0.05\) & 0.05 & 0.03 & 0.02 & \\ \hline  & & \multicolumn{6}{c}{Homogenity channel B} \\ \hline before & 0.35 & 0.35 & 0.26 & 0.42 & 0.05 & 0.04 & \\ \hline after & 0.36 & 0.36 & 0.29 & 0.43 & 0.06 & 0.04 & 0.0437 \\ \hline delta & 0.01 & 0.01 & \(-0.06\) & 0.05 & 0.04 & 0.03 & \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Grayscale pictures before and after the laser procedure: (**a**) average RGB; (**b**) channel R; (**c**) channel G; (**d**) channel B.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 3 Chemical Modification and Functionalization of Nanocellulose
### Production of Hydrophobic Surface on the Nanocellulosic Materials  
The plasticization of lignocellulosic fibers occurs when cellulosic alcohols are acetylated [68]. To improve the hydrophobicity of cellulosic fibers, acetylation is often used. To catalyze the acetylation of nanocellulose, dry acetic acid and acid anhydride are gradually added, followed by sulfuric acid. [PERSON] and [PERSON] first proposed the two primary acetylation processes [69]. The two processes, fibrous and homogenous, depend on the swelling diluent's availability or lack. In the fibrous method, a diluent such as toluene is added to the reaction solution to make the acetylated cellulose insoluble. The native structure is retained while a higher level of acetylation is attained. Acetylated chains are soluble in sulfuric acid and acetic acid-containing medium in a diluent-free homogenous method. As a result, after considerable acetylation, the cellulose substrate shows significant structural alterations.  
For the acetylation of mechanically separated NFC, [PERSON] and colleagues utilized acetic anhydride [70]. In their study, they demonstrated that nanofibers with a higher level of substitution impacted the characteristics of the polylactic acid-ac acetylated NFC composite significantly. The estimated contact angle was increased from 33\({}^{\circ}\) (for non-acetylated) to 115\({}^{\circ}\) (for acetylated) nanofibers, indicating a significant improvement in hydrophobicity. As an acyl donor, the enzyme lipase from _Aspergillus niger_ was recently used to acetylate NFC via acetic anhydride [71]. Compared to the acetylation via the chemical method, the enzyme acetylation on NFC substantially increased hydrophobicity [71].
# Effects of Cement-Mineral Filler on Asphalt Mixture Performance under Different Aging Procedures
###### Abstract  
Cement-containing mineral powder can effectively improve the moisture stability of an asphalt mixture; therefore, this study systematically summarizes the research status of cement-mineral fillers on the performance of an asphalt mixture and determines the limitations of related studies. In this study, long-term performance tests of styrene-butadiene-styrene- (SBS)-modified asphalt mixtures are designed and evaluated with different blending ratios of the cement-mineral powder under three aging conditions. Moreover, the effect of the cement-mineral composite filler on long-term performance of the asphalt mixture using different blending ratios is determined. Cement improves the high-temperature performance and water stability of asphalt mixtures, but only for certain aging conditions. Considering the regulations for the road performance of asphalt mixtures for three aging conditions, as well as long-term performance considerations, the results indicated that the mass ratio of Portland cement to mineral powder must not exceed 2:2. Low-temperature bending and splitting tensile tests confirmed that an excessive amount of cement filler will embrittle the modified asphalt mixture during long-term aging, thereby deteriorating the tensile properties. The mechanism by which the filler influences the performance of the asphalt mixture should be further studied from the perspective of microscopic and molecular dynamics.  
Keywords: asphalt mixture; cement; filler; long-term performance; aging; SBS-modified asphalt +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Contribution  
In this paper, a multi-user full-duplex VLC system is proposed which supports multi-user full-duplex communication. The main contributions of this paper are as follows:  
* A multi-user full-duplex VLC system is proposed.
* The system is capable of handling both user devices and smart devices.  
* The downlink section of the system separates the data based on the type of user. A red light is used to transmit user data while green light is used for smart device data. Asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM) is used as the modulation scheme to cater to multiple users and minimize interference.
* A resource allocation scheme is also implemented to efficiently assign resources during downlink transmission.
* The uplink section of the system uses blue light to transmit both user and smart device data. The modulation scheme used is on-off keying (OOK).
* Pulse amplitude modulation (PAM)-OOK is used to efficiently assign resources during uplink transmission.
* The performance of the system is analyzed and an in-depth cost-power evaluation is provided to test for feasibility.
* The proposed system is compared with other available systems such as traditional Wi-Fi and hybrid VLC-Wi-Fi implementations.  
The rest of the paper is outlined as follows. Section 2 discusses the VLC system model. Section 3 describes in detail the proposed multi-user full-duplex VLC system designed for a smart home. In Section 4, the resource allocation scheme is explained. In Section 5, the cost and energy evaluation is presented. In Section 6, results of the proposed system along with the evaluation of the cost and power consumption is discussed. In Section 7, conclusions based on the results are provided.
## 3 The Extreme-Points-Distribution-Based Neural Network (EDNN)
### Loss Function Containing Extreme Points Distribution Feature Errors  
The number of extreme points of the real physical process is the reference for calculating the extreme points distribution feature error.  
If the number of extreme points of derivative \(D^{m}\big{(}\mathbf{a}_{i}^{O}(\mathbf{x}),\mathbf{x}_{n}^{I}\big{)}\) is \(R_{m,j,n}\) and the number of extreme points of the real physical process is \(TCE_{n,i,m}\), then the extreme points distribution feature error of derivative \(D^{m}\big{(}\mathbf{a}_{i}^{O}(\mathbf{x}),\mathbf{x}_{n}^{I}\big{)}\) is defined as:  
\[E_{m,i,n}=R_{m,i,n}-TCE_{n,i,m} \tag{21}\]  
The extreme points distribution feature error loss function \(LossEV\) is defined as:  
\[LossEV=\sum_{n=1}^{N_{l}}\sum_{i=1}^{N_{o}}\sum_{m=0}^{M_{d}}\Big{\{}(E_{m,i, n})^{2}\Big{\}} \tag{22}\]  
where \(N_{o}\) is the number of nodes in the output layer, \(N_{l}\) is the number of nodes in the input layer and \(M_{d}\) is the highest order of the partial derivatives.  
The sample error loss function \(LossSp\) is defined as:  
\[LossSp=\sum_{s=1}^{N_{l}}\sum_{i=1}^{N_{o}}\Big{[}\mathbf{t}_{i,s}-\mathbf{a}_{i,s}^{O }(\mathbf{x})\Big{]}^{2} \tag{23}\]  
where \(\mathbf{t}_{i,s}\) is the \(i\)th component of the \(sth\) training sample; \(\mathbf{a}_{i,s}^{O}(\mathbf{x})\) is the \(i\)th node's output of the output layer corresponding to the \(s\)th training sample; \(N_{l}\) is the number of training samples.  
The total loss function of the \(EDNN\) is defined as:  
\[Loss=S_{S}\cdot LossSp+S_{E}\cdot LossEV \tag{24}\]  
where \(S_{S}\) is the weight of the sample error loss function \(LossSp\); \(S_{E}\) is the weight of the extreme points distribution feature error loss function \(LossEV\).  
The optimization criterion is to minimize the total loss function. Additionally, the stopping criterion is when the total loss function is less than \(10^{-4}\) or the gradient is less than \(10^{-7}\).
# Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples
## 3 Results and Discussion
### Influence of Laser Energy Density  
Figure 10 clearly shows that the reduction in the surface roughness is related to the energy density of the polishing laser, as determined by the laser power, scanning speed and hatch space. As the energy density increases, the effect of laser polishing becomes more obvious. The low LED is not sufficient to penetrate the sample surface, causing that the metal surface melts incompletely and the molten pool diffuses inadequately. In this case, the solidification time is transient, and the polishing effect is not obvious. With the increase of energy density, the solidification time becomes sufficiently long to make the surface molten pool and surrounding material wet and diffused sufficiently. Under the influence of gravity and surface tension, the molten pool flows, and the surface is redistributed. The larger the energy density, the larger the molten pool, and the internal flow becomes more intense, causing the resulting surface to be smoother after solidification. In the present study, the surface roughness was about 3 \(\upmu\)m with the best polishing parameters.  
Figure 10: Relationship between energy density and roughness reduction rate: (**a**) Ti6 Al4V, (**b**) AlSi10 Mg, (**c**) 316L and (**d**) IN718.  
Meanwhile, for any given energy density, the laser power and scanning speed will vary (\(P=100\) W, \(v=0.5\) m/s; \(P=200\) W, \(v=1\) m/s; \(P=200\) W, \(v=0.5\) m/s; and \(P=400\) W, \(v=1\) m/s). At this time, the results of laser polishing will also vary slightly. As the laser power is increased, the polishing effect often becomes more obvious such that the surface roughness decreases with an increase in the laser power. That is to say, for a given energy density, the effect of the laser power is more important than the scanning speed.
## 2 Methodology
### Three-Dimensional Numerical Simulation  
#### 2.2.1 Numerical Methods in Simulation  
Finite element mesh generation followed the following principles: (1) Meshing should be sparse in smooth parts and dense in regions of stress concentration. (2) Manual meshing operations should be employed in complex regions to avoid generating singular elements. This study utilized the 10-node tetrahedral elements, as they demonstrated numerical stability in handling complex geometries and nonlinear scenarios. Several refinements were made on the basis of the coarse mesh, and the performance of flow field under different grid counts was compared, as shown in Figure 4. When the grid count reached 3.45 million, the performance of the flow field stabilized. Consequently, the grid refinement process was terminated.  
An implicit numerical method was employed to discretize the equations. The discretized governing equations for motion and energy were solved until preset convergence-criteria were met. The time step must be properly chosen to obtain trustable results. This issue leads to the widely known Courant-Friedrichs-Lewy (CFL) criterion for the numerical stability of difference schemes. For the solution of the discretized equations, the Jacobi conjugate gradient method was employed.  
The Reynolds number in the external flow field of the turbine blades exceeded 100,000, indicating a typical turbulent flow. The choice of turbulence models directly affects the surface temperature distribution of the turbine blades in the finite element software. The performance of the standard k-\(\varepsilon\) model was limited in accurately simulating complex flows characterized by high-pressure gradients. As a result, the Renormalization Group (RNG) k-\(\varepsilon\) was developed instead of the standard k-\(\varepsilon\) model in this paper, and the added corrective term is shown in Equation (1). The RNG k-\(\varepsilon\) model enhanced its ability in capturing the intricacies of turbulent flows, especially the presence of significant pressure gradients.  
\[R_{\varepsilon}=\frac{C_{\mu}\rho\eta^{3}\Big{(}1-\frac{\eta}{\eta_{0}}\Big{)} }{1+\beta\eta^{3}}\frac{\varepsilon^{3}}{k} \tag{1}\]  
\(k\) and \(\varepsilon\) represent the turbulent kinetic energy and turbulent dissipation rate, while \(\eta\) is the turbulent shear stress ratio. \(C_{\mu}=0.0845\), \(\eta_{0}=4.38\), \(\beta=0.012\).  
Figure 4: Grid independence verification of the blade model.
## 3 System Identification
### System Identification Process Overview  
Experiments were carried out to create a training dataset consisting of system input-output data pairs. As for all black box identification approaches, the design of the experiment was carefully planned and the sampling time was carefully chosen. The aim of the data collection is to fully capture the system dynamics, since a model-based approach is proposed. The collected dataset was preprocessed (normalization, mean removal) to cancel the influence of different measuring scales and to reduce the weight of outliers.  
The model structure selection was addressed by choosing the regressor vector form, basis functions, and their parameters. For the WANARX model, orders \(n_{a}\), \(n_{b}\) and delay \(n_{k}\) were selected. The covariance function and its hyperparameters were instead set for the GP-NARX model.  
Fitting models to training data allows estimating the unknown parameters and obtaining the system model.The model quality must be tested through suitable validation criteria applied to a testing dataset, i.e., a set of independent input-output data collected during experiments and not used for training. It is common practice to split the collected dataset into two parts in order to have a training and a testing dataset and perform a model cross-validation.  
In this paper, the root mean squared error (RMSE) between the predicted and measured output as well as the FIT percentage value were selected as quality indicators:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{N}} \tag{19}\]  
Figure 2: WANARX scheme.  
\[\text{FIT}=\left(1-\sqrt{\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N }(y_{i}-\hat{y}_{i})^{2}}}\right)\times 100 \tag{20}\]  
where \(\hat{y}_{i}\) is the predicted output, \(y_{i}\) is the measured output, \(N\) is the number of samples and  
\[\bar{y}_{i}=\frac{1}{N}\sum_{i=1}^{N}y_{i} \tag{21}\]  
The FIT is derived from the normalized root mean square error (NRMSE) using the relationship \(\text{FIT}=(1-\text{NRMSE})\times 100\), where \(\text{NRMSE}=\sqrt{\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N} (y_{i}-\hat{y}_{i})^{2}}}\). The best model has the smaller RMSE and the higher FIT.
# Thermodynamic Analysis of Partitioned Combined Cycle using Simple Gases
## 3 Model Description
### Heat Transfer and Efficiency Relationship  
Before the analysis of working fluids and equipment design, an optimization function was required to set parameters objectively. The Rankine cycle has an inherent latent heat efficiency loss. In a conventional CCGT, the higher the temperature and pressure of the gas entering the turbine, the higher the Brayton cycle/Rankine cycle efficiency. However, this does not necessarily result in higher CCGT overall efficiency. Equation (1) describes the relationship of overall CCGT efficiency (\(\eta_{OA}\)) as a function of Brayton cycle efficiency (\(\eta_{B}\)), Rankine cycle efficiency (\(\eta_{R}\)), heat exchanged in HRSG (\(Q_{R,Ex}\)), and fuel latent heat as input (IP). This relationship graphically shows a volcano type pressure ratio curve. Heat transfer in waste heat boiler between flue gasses and water/steam is a major factor, which is a function of flue gasses' outlet temperature (temperature approach), overall heat transfer coefficient, and exchanger area. The design of HRSG is therefore critical in maximizing the efficiency function.  
\[\eta_{OA}=\eta_{B}+\eta_{R}\bigg{(}\frac{Q_{R,Ex}}{IP}\bigg{)} \tag{1}\]  
A similar relationship for PCGT can be derived as Equation (2). Subscript 'p' indicates the quantities related to PCGT. In addition to partitioned HRSG heat transfer (\(Q_{R,Ex,p}\)), heat transfer in the combustion chamber (\(Q_{C,Ex}\)) also becomes significant. It is evident from this equation that if the Rankine cycle is not partitioned then the partitioned Brayton cycle efficiency (\(\eta_{B,p}\)) must be greater than the original Brayton cycle (\(\eta_{B}\)) to yield any benefit from the proposed scheme. However, as the exhaust temperature in PCGT can be lowered substantially (because of there being theoretically no water dew point), thus improved heat recovery in the partitioned Rankine cycle will positively affect the overall efficiency. The individual/local efficiencies are the function of temperatures and pressure ratios, which are directly affected by the heat exchangers. Therefore, any gain is directly related to the exchanger design. This argument is valid for almost any heat recuperation. However, the factual benefit of the proposed approach is the improvement in temperature gradient, which also reduces the pressure drop. Hence, Equation (2) may be used theoretically to maximize the efficiency, provided the entropy loss of the working fluid is comparable:  
\[\eta_{OA,p}=\eta_{B}+\eta_{R}\bigg{(}\frac{Q_{R,Ex}}{IP}\bigg{)}+\bigg{(} \frac{Q_{C,Ex}}{IP}\bigg{)}(\eta_{B,p}-\eta_{B})+\eta_{R,p}\bigg{(}\frac{Q_{R, Ex,p}}{IP}\bigg{)} \tag{2}\]
## 3 Calculation of Propagation Time and Attenuation Rate Using the Amiet Method
### Attenuation Rate
#### 3.3.1 Derivation of \(\text{d}A_{\theta}^{\prime}\)  
The distance \(R^{\prime}\) from the sound source to the shear layer without the flow velocity is given by the geometric relationship shown in Figure 3:  
\[R^{\prime}=\frac{R_{t}}{\sin\theta^{\prime}}. \tag{10}\]  
Here, \(\text{d}A_{\theta}^{\prime}\) can be derived when considering Equation (10).  
Figure 3: The two adjacent emitted acoustic rays.  
\[\mathrm{d}A^{\prime}_{\theta}=2\pi\sin\theta^{\prime}R^{\prime 2}\mathrm{d} \theta^{\prime}. \tag{11}\]
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 4 Discussion
### Methodological Limitations  
The present model operates at a single speed-load point with rigid wheels and uses digitized (not exported) TE data. Hence, the following are true:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Key TE Metrics** & **Ideal** & **Pitch-Error** &
\begin{tabular}{c} **Measured** \\ **Microgeometry** \\ \end{tabular} \\ \hline Mean TE (0.35–0.40 s) [deg] & \(-5\times 10^{-5}\) & \(-9\times 10^{-5}\) & \(-3\times 10^{-4}\) \\ Peak-to-peak TE [deg] & 1.2 \(\times\) 10\({}^{-5}\) & 1.6 \(\times\) 10\({}^{-5}\) & \(3.1\times 10^{-4}\) \\ Shaft-order tone 16.66 Hz [deg] & \textless{}2 \(\times\) 10\({}^{-6}\) & \(4\times 10^{-6}\) & \(3\times 10^{-6}\) \\ Mesh-peak amplitude 383.3 Hz [deg] & 7.5 \(\times\) 10\({}^{-6}\) & 6.8 \(\times\) 10\({}^{-6}\) & \(6.5\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key TE metrics.  
Speed sensitivity of the shaft order tone remains unquantified [20];  
Local compliance effects are approximated by generic stiffness functions;  
Statistical dispersion in pitch error is not propagated;  
Lifecycle changes (wear, thermal growth) are neglected.
## 5 Discussion
### The Performance of kNDVI Is Affected by Hyperparameters  
The kNDVI leverages the RBF kernel to enhance the sensitivity and accuracy of vegetation monitoring. A key feature of this approach is the \(\sigma\) parameter, which plays a pivotal role in modulating the index's sensitivity to vegetation density. For densely vegetated regions, increasing the \(\sigma\) value lowers the kNDVI's derivative with respect to the NIR and red bands. This adjustment enhances its sensitivity to dense vegetation, enabling the index to capture the nuances of vegetation health that might be missed by traditional indices due to saturation effects. Conversely, reducing the \(\sigma\) value increases kNDVI's sensitivity to changes in sparsely vegetated areas. This adjustment allows for the detection of subtle vegetation dynamics in environments where traditional vegetation indices may not perform well. The kNDVI effectively captures the spatial characteristics of vegetation drought through the application of a spatial heuristic algorithm. However, it exhibits a low correlation with the GPP of vegetation during drought periods. This correlation with GPP during drought can be enhanced by employing the \(\sigma\) parameter, which is estimated using a temporal heuristic algorithm. This approach underscores the potential for improving kNDVI's sensitivity to the productivity dynamics of vegetation under drought stress by integrating temporal heuristics. The adjustment of kNDVI's sensitivity through the \(\sigma\) parameter is underpinned by the RBF kernel's mathematical properties. The kernel effectively models spatial relationships within spectral data, emphasizing the influence of spectral points that are close in the feature space while reducing the impact of more distant points. This local sensitivity is crucial for adapting the index to specific vegetation conditions, ensuring accurate monitoring across varied ecological landscapes.