esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Background  
In recent decades, transport models have significantly contributed to the decision-making process for the creation of future transportation systems and related infrastructure investments, improvements, and innovations. Macroscopic transport models are the most widely used modeling approach in transport planning. They divide a study area into zones, and the centroids of these zones represent the origins and destinations of trips taken between the defined zones. \"Travel demand\" is generally illustrated with origin-destination (OD) matrices between the defined zone centroids. Macroscopic modeling approaches rely on aggregate travel forecasts, which compile and summarize the output of simplified trips. The most common trip-based approach is the four-stage model [1], established in the 1950s in order to measure the impact of the residential and commercial development. Nevertheless, the inadequacy of trip-based models for simulating complex policy scenarioshas been noticed [1]. In real life, city inhabitants travel to participate in activities; therefore, one of the main limitations of trip-based models is reckoning without the fact that travel demand stems from people's needs to participate in various activities that are distributed through time and space. Consequently, a new modeling approach has emerged, activity-based modeling [2], in which the behaviors of transport users are simulated based on daily activity patterns. Activity-based models include the sociodemographic characteristics of each inhabitant of a study area and also incorporate non-mobility activities for more precise forecasts. Activity-based models work at a disaggregated level [3], and travel is considered only one of the activity attributes. Each simulated inhabitant is modeled at a personal level, considering household-level attributes, in order to produce detailed information across a broader set of performance metrics. Origin-destination (OD) points are usually presented as precise coordinates in the study area instead of generalized centroids in the macroscopic approach. Activity participation is influenced by spatial, temporal, interpersonal, and accessibility interdependencies that harmonize in the activity-based model. Incorporating activities distributed in space and time brings a new, more realistic dimension to transportation demand modeling. These characteristics make this approach a strong tool when modeling the impact of new transport policies and transport investments [4; 5] together with the impact of unexpected events on travel behaviors such as the COVID-19 pandemic [6; 7], which cannot be modeled using traditional approaches.
# Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
## 1 Introduction
The selected RFID chip includes a sensor front-end that is able to read out the response of the photodetector without requiring any other external component. In addition, an in-built temperature sensor is implemented in the chip, increasing the functionalities of this tag. First, we will present the design of the tag, followed by the characterization of its antenna and photodetector. Finally, the full operation capabilities of the RFID tag will be shown. This tag can be used to control the exposure of products to light, not only as a threshold sensor but also to know the absorbed light dose. Furthermore, by including the RFID tag in the inner part of a packet, any anomaly in the closure of the packet or if it has been opened is registered with high accuracy by the sensor when a reader is in its vicinity.
## 3 Mathematical Modeling of ProposedDC Architecture
### Mathematical Modeling of Wind Power  
The wind model consists of variation of wind velocity with gust and wind speed [30; 31; 32; 33; 34; 35; 36].  
\[W_{V}=V_{w}+\ V_{g}+V_{wr}, \tag{14}\]  
where, \(V_{w}\) = Base wind velocity, \(V_{g}\) = Gust wind velocity, and \(V_{wr}\) = ramp wind component.  
The gust speed is calculated by  
\[V_{wg}=\left\{\begin{array}{l}0\ t<T_{1}\\ C_{2}\Big{\{}1-\cos\pi\Big{[}\frac{t-T_{1}}{t_{2}-T_{1}}\Big{]}T_{1}\leq t\leq T _{2}\Big{\}}\\ 0\ t\geq T_{2}\end{array}\right\} \tag{15}\]  
\[V_{W}=\left\{\begin{array}{l}0\ s<T_{3}\\ C_{3}\Big{\{}\Big{[}\frac{s-T_{3}}{T_{4}-T_{3}}\Big{]}T_{3}\leq s\leq T_{4} \Big{\}}\\ 0\ s\geq T_{4}\end{array}\right\}, \tag{16}\]  
where, \(C_{2}\) = maximum value of the gust component, \(C_{3}\) = maximum wind speed caused by the ramp, and \(T_{3}\) and \(T_{4}\) are the cut-in and cut-out times of the ramp, respectively.  
Wind power is calculated by  
\[P_{W}=\frac{dW_{w}}{dt} \tag{17}\]  
Energy drawn by the wind turbine is  
\[\left.\begin{array}{l}W_{w}=V_{a}\times\frac{1}{2}\rho({V_{1}}^{2}-{V_{3}}^{ 2})\\ P_{W}=d\frac{V_{a}^{\frac{1}{2}}\rho({V_{1}}^{2}-{V_{3}}^{2})}{dt}\end{array}\right\} \tag{18}\]  
where, \(W_{w}\) = energy drawn by wind turbine and \(\rho\) = Air density.  
According to [PERSON], the maximum wind turbine power output is  
\[P_{M}=\frac{16}{27}A_{R}\frac{3}{2}V^{3} \tag{19}\]  
Figure 4: Solar cell characteristics curve (voltage vs. current and power).  
Equation (19) is obtained by substituting the value for \(V_{1}\), and \(V_{3}\).  
\[\begin{array}{l}V_{2}=\frac{2}{3}V_{1}\\ V_{3}=\frac{1}{3}V_{1}\end{array} \tag{20}\]  
The wind turbine model represents the output power captured by the turbine [33; 34; 35; 36]. Figure 5 shows the characteristic curve for wind speed vs. power. The power in the wind (\(P_{w}\)) in an area is obtained by  
\[P_{W}=\frac{1}{2}\rho AW_{V}{}^{3} \tag{21}\]  
\[P_{M}=P_{W}C_{p} \tag{22}\]  
\[C_{P}=\frac{1}{2}[\delta~{}-~{}0.22\beta^{2}~{}-~{}5.6)e^{-0.17\delta} \tag{23}\]  
\(\beta\) = Pitch angle of the blade in degrees, \(\delta\) = the tip speed ratio of the turbine, and \(C_{p}\) = Power coefficient.  
Wind generated power is expressed as:  
\[p_{G}=V_{G}I_{G} \tag{24}\]
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 7 Open Issues and Future Research Directions
### Future Research Directions
#### 7.4.2 Blockchain beyond 5G  
Beyond 5G, or specifically 6G, is anticipated to give superior performance to fulfill the extremely high demands from fully autonomous vehicles, by mobile, wearable, IoT networks, and by deep under water communications, etc. It can provide super high throughput, ultra-high reliability, low energy consumption, massive connection, ultra-low latency, space communications, etc. [117; 118]. However, like other technologies, 6G will have to cope with a number of stringent technical demands, particularly for tight security and full privacy. Blockchain will play a key role in promoting and enhancing security, and in transforming the future of 6G networks [119].
## 3 Results and Discussion
### Prediction of Liquor Quality  
In this study, the SPXY dataset partitioning method was used to divide the samples into a training set and a test set, which accounted for three-quarters and one-quarter of the sample set, respectively. The method used near-infrared spectrum variables and classification label variables to calculate the distance between samples to characterize the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Characteristic Wavelengths**} & \multicolumn{2}{c}{**Dimension Reduction of KPCA**} & \multicolumn{2}{c}{**Prediction Model Based**} \\ \cline{2-7}
**Preprocessing** & \multicolumn{2}{c}{**CARS**} & & & & **on H-MSVM** \\ \cline{2-7}
**Method** & \multicolumn{2}{c}{**Number of Characteristic Wavelengths**} & \multicolumn{2}{c}{**Prediction Accuracy (\%)**} & \multicolumn{2}{c}{**Number of Principal Components**} & \multicolumn{2}{c}{**Principal Component Contribution Rate**} & \multicolumn{2}{c}{**Prediction Accuracy (\%)**} & \multicolumn{2}{c}{**Improved Prediction Accuracy (\%)**} \\ \hline
1 & 52 & 89.34 & 9 & 95.21 & 92.51 & 3.17 \\
2 & 70 & 92.72 & 5 & 95.33 & 96.87 & 8.68 \\
3 & 69 & 90.59 & 7 & 95.39 & 93.66 & 3.07 \\
4 & 50 & 89.80 & 8 & 95.15 & 93.05 & 3.25 \\
5 & 52 & 90.24 & 8 & 95.09 & 92.26 & 2.02 \\
6 & 49 & 90.18 & 12 & 95.02 & 91.58 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of characteristic wavelength extraction, dimension reduction, and model prediction.  
Figure 5: (**A**) The 3D distribution map of principal components, and (**B**) principal component contribution rate.  
sample distribution and effectively cover the vector space. By increasing the diversity and representativeness of sample division, the stability of the model can be improved.  
The principal components obtained based on KPCA were used as input parameters for H-MVSM model learning. The accuracy of the model for liquor quality identification was verified through the test set, as shown in the \"Prediction Model based on H-MSVM\" section of Table 2.  
The higher the number of characteristic wavelengths obtained based on the improved CARS, the fewer the number of principal components with the cumulative contribution rate of KPCA reaching more than 95%, and the higher the prediction accuracy of the model. Therefore, characteristic wavelength extraction did not indicate that lowering the dimension improved the model; instead, as much as possible the feature information in the original NIR spectrum should be retained. Through KPCA dimensionality reduction, the complexity of the model was reduced, and discrimination accuracy was improved. Using the second set of NIR spectroscopy preprocessing parameters as an example, when the number of characteristic wavelengths was 70, the accuracy of the model trained by the first five principal components improved by 8.68%, reaching a high of 96.87%.
## 2 Proposed Cone Cell Identification Method
### Merging of Closed Identification Results  
After threshold segmentation, some cone cells are identified more than once. To mitigate duplicate identification, we merge closed identified cone cells by using the method in [16], which combine several closed identified results into one identified result. Specifically, morphological dilation is applied to the identification results (function morphologyEx of OpenCV), and the centroid of each region is obtained. Then, closed identified cone cells are combined, obtaining refined identification around the middle of the previously identified closed cone cells.  
Figure 6: Duplicate identification removal. Identified cone cells are marked in red on the denoised AO-SLO image patch (**a**) before and (**b**) after duplicate identification removal.  
Figure 7: Cone cell densification based on threshold segmentation. (**a**) Denoised AO-SLO image patch. Identification results from (**b**) the method in Section 2.4 (red dots), (**c**) threshold segmentation (green dots), and (**d**) both methods on the denoised AO-SLO image patch.  
Figure 8 shows an example of merging closed identified cone cells on a representative image patch. Closed cone cells are merged, thus improving the identification results.
## 3 Results and Discussion
### Multivariate Statistical Analysis
The PLS regression is performed using the plsr function provided by R pls package. The classification and cross-validation are performed using the corresponding wrapper function offered by the caret package provided by MetaboAnalyst. To assess the significance of class discrimination, a permutation test was performed. In each permutation, a PLS-DA model was built between the data (\(X\)) and the permuted class labels (\(\gamma\))  
using the optimal number of components determined by cross validation for the model based on the original class assignment. In addition, test statistics for measuring the class discrimination were performed based on prediction accuracy during training. (**B**) Selected compounds based on VIP scores. Variable importance in projection (VIP) is a weighted sum of squares of the PLS loadings taking into account the amount of explained _Y_-variation in each dimension. VIP scores are calculated for each component. The colored boxes on the right indicate the relative concentration of the corresponding metabolites and gene expressions in each group under study. (**C**) Correlation analysis of metabolites and gene expression levels. G2: Gaoshan #2, G4: Gaoshan #4, XZZ: Xiangzhenzhu, BY: Baiyu.
## 4 Results
### Statistical Significance of the Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison** & **Accuracy of Model 1 (\%)** & **Accuracy of Model 2 (\%)** & **Wilcoxon \(p\)-Value** & **Significance** \\ \hline GPT-2 + SVM (Real) vs. GPT-2 + RF (Real) & 90.84 & 88.14 & 0.056 & NS \\ GPT-2 + SVM (Real) vs. WGAN-GP + CNN (Real) & 90.84 & 92.0 & 0.042 & S \\ GPT-2 + RF (Real) vs. WGAN-GP + CNN (Real) & 88.14 & 92.0 & 0.035 & S \\ GPT-2 + SVM (Synthetic) vs. GAN + CNN (Synthetic) & 66.88 & 85.78 & 0.018 & S \\ GPT-2 + SVM (Synthetic) vs. WGAN-GP + CNN & 66.88 & 98.42 & 0.005 & S \\ GAN + CNN (Synthetic) & 85.78 & 98.42 & 0.011 & S \\ GPT-2 + SVM (Real+Synthetic) vs. GAN + CNN & 93.71 & 94.11 & 0.123 & NS \\ (Real+Synthetic) & 93.71 & 98.45 & 0.007 & S \\ GPT-2 + SVM (Real+Synthetic) vs. WGAN-GP + CNN & 94.11 & 98.45 & 0.004 & S \\ (Real+Synthetic) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of model performances based on accuracy and Wilcoxon _p_-value significance testing, where “S” means significant, and “NS” means not significant in the _Significance_ column. Figure 10: Bar chart of model accuracies with significance annotations. The label “_ns_” stands for no statistical significance and the label “_s_” presents comparisons with statistical significance.
# Topological and Fractal Analysis of Nanostructured Metal-Dielectric Films
## 3 Results and Discussion
Reference [61] demonstrates that the power spectrum of a fractal surface follows a power-law relationship for spatial frequencies:  
\[S(\
u_{x})=\frac{K_{\alpha}}{\
u_{x}^{\alpha}}, \tag{7}\]  
where \(K_{\alpha}\) represents the spectral strength and \(\alpha\) signifies the spectral index. These parameters are considered fractal characteristics that define surface roughness in the spatial frequency domain. As evident from the equation, the value of \(\alpha\) can be determined from the slope observed in a double logarithmic representation of the power spectrum. The Blackman-Tuke method [62] is employed to calculate the power spectrum in one dimension as a function of spatial frequencies, as presented in Figure 18 using a double logarithmic scale. The spatial index \(\alpha\) was obtained as 2.52 by fitting these data with a linear model (solid curve). Figure 18: Relationship between spectral power (crosses) and spatial frequencies on a double logarithmic scale. The solid line represents the linear fitting of the data points. The relationship between the \(\alpha\) and \(H\) is provided by \(\alpha=2H+1\)[63]. Furthermore, the correlation fractal irregularity \(D_{c}\) is associated with the fractal co-dimension by the equation \(D_{c}=3-H\)[61]. Figure 19 depicts dependencies of the Hausdorff fractal dimension (\(D_{H}\)), assessed using the box-counting approach, as well as correlation dimension (\(D_{c}\)) on mass thickness for both Ag and Au films. Each data point represents the average value calculated from the smallest five AFM visuals of the corresponding samples. It was shown that the \(D_{c}\) consistently remained lower than \(D_{H}\) (\(D_{c}<D_{H}\)) for both Ag and Au films within the investigated thickness range. It is noteworthy that the rate of change of these fractal dimensions changes as the thickness of the mass increases. In the case of Ag films, the increase is 0.028 nm\({}^{-1}\) for \(D_{H}\) and 0.008 nm\({}^{-1}\) for \(D_{c}\). Similarly, Au films exhibit an increase of 0.047 nm\({}^{-1}\) for \(D_{H}\) and 0.027 nm\({}^{-1}\) for \(D_{c}\). The observed inequality (\(D_{c}<D_{H}\)) arises from the inherent definitions of these dimensions. The correlation dimension is defined by the following equation [61]:  
\[D_{q}=\underset{r\to 0}{\lim}\frac{1}{q-1}\frac{\ln\sum_{k}p_{k}^{q}}{\ln r},- \infty\leq q\leq\infty \tag{8}\]  
where the summation extends over all boxes of size \(r\) that partition the space, and \(p_{k}\) represents the probability of particle fractal structure occupying to the \(k\) box. When \(q\) approaches zero (\(q\to 0\)), this equation reduces to the dimension determined by the box-counting approach (Hausdorff dimension). In this case, boxes with at least one fractal point are considered during the summation. Studied correlation dimension relates to a specific value of \(q\) equal 2 (\(D_{c}=D_{2}\)). Due to \(D_{q}\geq D_{q^{\prime}}\) for \(q^{\prime}\geq q\) of multiple distinct fractal dimensions, (\(D_{H}\) and \(D_{c}\)) signify the multifractal nature of the ultrathin film structures.
## 3 Results and Analysis
### DPRS Load Distribution Analysis
#### 3.2.3 Influence of the Number of Screw Thread Sections at Different Ambient Temperatures  
The number of screw thread knots in a roller directly affects the load distribution on the screw threads. Figure 15 shows the load distribution of a DPRS with 10, 13, and 18 thread sections and a 30 kN load at different ambient temperatures. It can be seen that the trend of the load distribution does not vary with the number of thread sections, but the load decreases as the number of thread sections increases because the greater the number of thread sections, the smaller the load assigned to each threading, while the load remains the same. Comparing the DPRS load distribution at different temperatures shows that as the ambient temperature increases, the difference in the load distribution due to the different number of thread sections decreases.  
The results show that the number of thread sections does not affect the trend of the load distribution but has a large effect on the size of the load distribution and that the difference in the load due to different numbers of thread sections is somewhat reduced by an increase in the ambient temperature.
## 6 Milling Test  
### Milling Force Test Analysis  
The T2, T3, and T4 tools in Figure 13 were fabricated via fiber laser machining. The simulation-derived tool parameters were applied to the experimental tools, which underwent milling tests on a three-axis vertical machining center using unidirectional climb milling. The milling parameters matched simulation settings to ensure accuracy: spindle speed = 5000 r/min, feed rate = 0.5 m/min, milling path length = 120 mm, and cutting depth = 0.35 mm. The workpiece was clamped at a 0\({}^{\circ}\) angle relative to the machine tool guide rail horizontal plane. Milling forces, tool wear amounts, and workpiece surface roughness were analyzed for all three tool configurations.  
Milling tests were conducted on four tool configurations. During stable milling phases, force dynamometers were used to capture milling forces, as shown in Figures 14 and 15.  
Figure 12: Comparison diagram of milling forces for four types of cutting tools.  
Figure 13: Laser preparation of microtextured cutting tools.  
The milling forces for the four tool configurations are shown in Figures 16-19. The resultant milling force calculation is presented in Equation (22).  
\[F_{Resultant}=\sqrt{Fx[N]^{2}+Fy[N]^{2}+Fz[N]^{2}} \tag{22}\]  
Figure 16: T\({}_{1}\) milling force (336.4 N).  
Figure 14: Milling force test.  
Figure 15: Milling force acquisition device.  
As shown in Figures 16-19 and Equation (22), the resultant milling forces for the four tool configurations are as follows: T\({}_{1}\) (336.4 N), T\({}_{2}\) (319.3 N), T\({}_{3}\) (288.1 N), and T\({}_{4}\) (274.3 N). Compared to T\({}_{1}\), T\({}_{2}\), T\({}_{3}\), and T\({}_{4}\) demonstrated varying degrees of milling force reduction (5%, 14.3%, and 19.5%, respectively). This reduction is attributed to the microstructures' ability to store and reduce the chip volume, thereby decreasing cutting  
Figure 17: T\({}_{2}\) milling force (319.3 N).  
Figure 18: T\({}_{3}\) milling force (288.1 N).  
forces and minimizing tool-workpiece friction. Enhanced chip storage lowers contact pressure between chips and tool surfaces, further reducing vibration during machining and improving stability. Consequently, the microstructured tools exhibited significant force reductions compared to the non-microstructured tool.  
Compared to T\({}_{1}\), T\({}_{2}\), T\({}_{3}\), and T\({}_{4}\) demonstrated varying degrees of milling force reduction (5%, 14.3%, and 19.5%, respectively). This reduction is attributed to the microstructures' ability to store and reduce chip volume, thereby decreasing cutting forces. Enhanced chip storage minimizes contact between chips and tool surfaces, reduces tool-chip friction, further mitigates vibration during machining, and improves stability. Consequently, the resultant milling forces of microstructured tools were significantly lower than those of the non-microstructured tool. Figure 20 compares experimental and simulation results for T\({}_{1}\) and T\({}_{4}\). Despite numerical discrepancies, the trend consistency between experimental and simulated milling forces is evident.
## 3 Result and Discussion
### Simulation Results of Water Quality  
The water quality was determined as the ratio of DO to BOD. [PERSON] et al. (2019) indicated that increasing streamflow could decrease BOD value (\(\leq\)6 mg/L) and increase DO value (\(\geq\)6 mg/L) [(51)]. However, they did not consider the dynamic waste generation and BOD loads that enter the river. In the current study, we considered a combination of pollution loads, waste generation due to increasing population, and increases in river flow to predict the DO and BOD value.  
Scenarios 1 and 2 produced a significantly lower DO (average 3.28 mg/L) and higher BOD (average 60.85 mg/L) than the standard values (DO \(\geq\) 6 mg/L and BOD \(\leq\) 6 mg/L for Bangladesh) over the simulation period. For example, the simulation results of changes in DO values in 2041 were 2.05 mg/L in Scenario 1 and 3.4 mg/L in Scenario 2 (Figure 8a). Similarly, BOD values in 2041 were 86.3 mg/L and 47.4 mg/L in the two scenarios, respectively (Figure 8b). Thus, the scenarios indicated that the river would become an extremely polluted ecosystem. However, the Turag-Buriganga River Restoration Project will not positively impact water quality improvement, as it primarily aimed to increase river flow from 5 m\({}^{3}\)/s to 160 m\({}^{3}\)/s. This situation occurs mainly because of the heavy pollution loads, and sediment pollutants consume considerable DO from the river water [(70)].  
However, Scenario 3 is a sustainable condition for maintaining standard river water DO (\(\geq\)6 mg/L) and BOD (\(\leq\)6 mg/L) given the introduction of pollution load control (BOD) (optimum threshold: 87.78%), migration control (\(\geq\)6%), and increased river flow (\(\geq\)160 m\({}^{3}\)/s) (Figure 8a-c). From the simulation result, this strategy could improve DO concentration by 248% and reduce BOD concentration by 90.8% in 2041, compared to their concentrations in 2020. These findings supplement those of [PERSON] and [PERSON] (2019), who found that the combined strategy (waste treatment and streamflow) could improve DO by 19.7% and reduce BOD by 89.92% [(42)].  
The DO/BOD ratio should be considered as the indicator of water quality instead of only DO value because a higher DO value can co-exist with a higher BOD value. For example, [PERSON] (2012) estimated DO and BOD in the same river system and found values of 6.5 mg/L and 30 mg/L, respectively, at Ashulia station [(71)]. Thus, to produce standard water over time, the DO value must be higher than 6 mg/L, and the BOD value must be lower than 6 mg/L.  
Scenarios 1 and 2 generated significantly lower ratios (0.024 and 0.07, respectively) (Figure 8d) than the standard values because they had lower DO (2.05 mg/L and 3.4 mg/L) and higher BOD (86.3 mg/L and 47.4 mg/L) values. However, Scenario 3 maintained a standard ratio (2.35) (Figure 8d) with a higher DO (8.3 mg/L) and lower BOD (3.1 mg/L) values. Thus, Scenario 3 will maintain a sustainable river ecosystem in the future.