esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison of Model Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**File 1**} & \multicolumn{2}{c}{**File 2**} & \multicolumn{2}{c}{**File 3**} & \multicolumn{2}{c}{**File 4**} & \multicolumn{2}{c}{**File 5**} & \multicolumn{2}{c}{**Mean**} \\ \cline{2-13}  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline SL & 3.202 & 2.259 & 3.179 & 2.265 & 3.175 & 2.258 & 3.143 & 2.220 & 3.188 & 2.266 & 3.177 & 2.254 \\ SVR & 3.939 & 2.833 & 3.936 & 2.831 & 3.945 & 2.848 & 3.932 & 2.838 & 3.916 & 2.816 & 3.934 & 2.833 \\ KNN & 3.774 & 2.726 & 3.764 & 2.724 & 3.741 & 2.712 & 3.747 & 2.715 & 3.781 & 2.724 & 3.761 & 2.720 \\ DT & 4.162 & 3.122 & 4.186 & 3.158 & 4.138 & 3.094 & 4.161 & 3.161 & 4.189 & 3.143 & 4.167 & 3.136 \\ RF & 3.435 & 2.472 & 3.453 & 2.490 & 3.404 & 2.461 & 3.380 & 2.439 & 3.417 & 2.454 & 3.418 & 2.463 \\ GBM & 3.319 & 2.342 & 3.313 & 2.368 & 3.264 & 2.323 & 3.231 & 2.299 & 3.315 & 2.346 & 3.288 & 2.336 \\ XGB & 3.250 & 2.303 & 3.206 & 2.281 & 3.234 & 2.307 & 3.190 & 2.249 & 3.215 & 2.289 & 3.219 & 2.286 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction performance results of each model on 5 files. Figure 7: The average performances of 2-fold cross-validation on 5 files for each model.
## 3 Results and Discussion  
This study aimed to valorize the phenolic compounds of two medicinal plants from the Algerian Sahara, _Cornulaca monacantha_ Del. and _Anabasis articulata_ (Frossk.) Moq. For this purpose, the plant _Cornulaca monacantha_ Del. <<Had>> was selected to optimize the ultrasound-assisted extraction conditions. Indeed, the Box-Behnken Design was used to create the experimental plan for this work. The main objectives that were taken into account when determining the factors to be examined in this study were not to exceed an extraction temperature of 45 \({}^{\circ}\)C to avoid the degradation of these phenolic compounds; not to consume a large amount of solvent, greater than 20 times the plant powder; and to carry out an extraction lasting no more than half an hour. Indeed, previous research has indicated that prolonged extraction time by UAE can negatively affect the structure of phenolic compounds by causing their decomposition through catalyzed oxidation and polymerization reactions [75]. In addition, it can negatively affect the mass transfer kinetics of bioactive molecules due to vaporization phenomena that result in excessive solvent loss [76].  
The choice of the hydroalcoholic mixture (ethanol/water) as the extraction solvent refers to several previous studies that have demonstrated its effectiveness in phenolic compound extraction due to its high affinity for these bioactive substances [77] and its non-toxicity, which allows it to be widely used in various food applications [38; 78; 79; 80].  
In addition, the reason for using ethanol/water (80/20 _v/t_) for extraction in this study is its efficiency in extracting antioxidant compounds, especially polyphenols [81].  
Impact of Varied Ultrasound-Assisted Extraction Parameters on the Extraction Responses of Phenolic Compounds from Cormulaca monacantha Del  
Three factors (solid-to-liquid ratio (\(X_{i}\)), extraction temperature (\(X_{j}\)), and extraction time (\(X_{k}\))) at three levels (\(-\)1, 0, +1) were selected to control for their linear, interactive, and quadratic effects on the following dependent variables: yield, TPC, and TFC (as shown in Table 1). A second-order polynomial equation was executed for each response to characterize the relationships between the independent variables (extraction factors) and the dependent variable.
## 3 Simulation and Analysis
### Experimental Simulation and Analysis
#### 3.3.3 Map 3  
Figure 9 displays the outcomes of the three RRT algorithms applied in Map 3. The figure illustrates that in this environment, the path deviation planned by the traditional RRT algorithm, the RRT* algorithm, and the enhanced algorithm discussed in this study is minimal, and they can quickly converge to the target point.  
Table 5 represents the comparative data for the route planning of the three algorithms in Map 3. The table illustrates that the improved algorithm in this paper is compared with the conventional RRT algorithm and EP-RRT* algorithm. In terms of path length optimization, the enhanced algorithm discussed in this document has been decreased by 9.72% and 5.09%, respectively. In terms of turning points, this paper's enhanced algorithm shows a decrease of 64.71% and 55.14%, respectively, in comparison to the other two algorithms. In terms of planning success rate, the enhanced algorithm presented in this study outperforms the other two algorithms by 8.16% and 6.12%, respectively. From the table data, because the map environment is relatively simple, the three algorithms can plan the path well, and the planned path length is less different, and the success rate is also high. In terms of path tortuosity, the enhanced algorithm introduced in this study features a reduced number of inflection points, which is more conducive to the autonomous movement of the robot.  
When integrated with the evaluation of the path-planning outcomes for the three maps, the simulation experiment demonstrates that the suggested algorithm outperforms  
Figure 9: The path-planning results of Map 3. (**a**) Traditional RRT algorithm; (**b**) EP-RRT* algorithm; (**c**) FGA-RRT and PRM fusion algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Path-Planning Length** & **Inflection Points** & **Rate of Success** \\ \hline Traditional RRT & 78.15 & 13.6 & 90\% \\ EP-RRT* & 74.34 & 10.7 & 92\% \\ PGA-RRT and PRM fusion & 70.55 & 4.8 & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results of Map 3 planning efficiency.  
others in success, boasts a smoother path, and has a shorter path length, thereby confirming its practicality and superiority.
## 3 Experimental Results and Discussion
### \(\Delta D\)/\(D_{\rm o}\)-\(\kappa\) Relationships  
Figure 7a-e, respectively, depict the \(\Delta D\)/\(D_{\rm o}\)-\(\kappa\) relationships of EMT carbon steel RHTs under cyclic bending with a fixed \(\phi\) = 0\({}^{\circ}\) and different \(d\) values: 2, 4, 6, 8, and 10 mm. The controlled curvature ranges from \(-\)0.3 m\({}^{-1}\) to 0.3 m\({}^{-1}\). The experimental results show that regardless of deformation in the elastic or plastic range, the \(\Delta D\)/\(D_{\rm o}\)-\(\kappa\) curve exhibits a nonlinear trend. When the EMT carbon steel RHT is loaded, \(\Delta D\)/\(D_{\rm o}\) shows an increasing trend, reaching its peak when the curvature is 0.3 m\({}^{-1}\). Subsequently, in the unloading phase, \(\Delta D\)/\(D_{\rm o}\) shows a decreasing trend, and when the curvature reaches 0.0 m\({}^{-1}\), \(\Delta D\)/\(D_{\rm o}\) is not zero, indicating plastic deformation in the EMT carbon steel RHT. During the reverse loading phase, \(\Delta D\)/\(D_{\rm o}\) shows an increasing trend again, reaching its peak when the curvature is \(-\)0.3 m\({}^{-1}\). Then, in the unloading phase, when the curvature returns to 0.0 m\({}^{-1}\), \(\Delta D\)/\(D_{\rm o}\) increases slightly. Through repeated cyclic loading, the \(\Delta D\)/\(D_{\rm o}\)-\(\kappa\) curve exhibits an increasing, serrated, and bowtie trend. In addition, no matter what \(d\) is, the \(\Delta D\)/\(D_{\rm o}\)-\(\kappa\) relationship shows an asymmetric pattern, and the larger \(d\) is, the more serious the asymmetry is. It is observed that as \(d\) increases, the growth of \(\Delta D\)/\(D_{\rm o}\) also increases. This phenomenon is consistent with the experimental results of [PERSON] et al. (2017).  
Figure 6: \(M\)-\(\kappa\) relationship of the EMT carbon steel RHT under cyclic bending with \(\phi\) = 0\({}^{\circ}\) and \(d\) = 2 mm.  
Figure 8a-d, respectively, illustrate the \(\Delta D/D_{\rm o}\)-\(\kappa\) relationships of EMT carbon steel RHTs under cyclic bending with different \(\phi\) values: \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\) and a fixed \(d\) = 10 mm. It can be observed that a smaller \(\phi\) leads to a larger \(\Delta D/D_{\rm o}\). In addition, it can be observed that \(\Delta D/D_{\rm o}\)-\(\kappa\) relationships are asymmetrical for small values of \(\phi\), but \(\Delta D/D_{\rm o}\)-\(\kappa\) relationships are symmetrical for large values of \(\phi\). This phenomenon is consistent with the experimental results of [PERSON] et al. [27].
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 4 Experiment and Result Analysis
### Results on RESIDE Dataset
Traditional single-image dehazing algorithms, when dealing with this issue, often lead to problems such as color deviation, loss of detail, and poor edge handling in the recovered images. Moreover, some deep-learning-based dehazing algorithms demonstrate insufficient generalization capability when dealing with real foggy images. Figure 11 shows the visual comparison of dehazing results on real outdoor foggy images between classical image dehazing algorithms and MFID-Net. It can be clearly seen that the results of the DCP algorithm are overly saturated in color overall, which easily leads to information loss in the image. AOD's performance is not satisfactory when dealing with dehazing tasks in distant areas. As for GCA-Net, GridDehazeNet, and FFA-Net, their dehazing effects are not pronounced, only capable of removing surface fog, with FFA-Net even possibly leading to loss of image detail information. In contrast, the algorithm proposed in this paper, when dealing with these real foggy images, can not only effectively remove most of the fog but can also maintain a good preservation of image details and color authenticity. Finally, Table 3 presents a comparison between LGCA and other attention mechanisms in terms of the number of parameters and computational cost. Through this comparison, it can be demonstrated that LGCA possesses characteristics of being lightweight. Figure 11: Real hazy image results. (**a**) Hazy, (**b**) DCP, (**c**) AOD-Net, (**d**) GCA-Net, (**e**) GridDehazeNet, (**f**) FFA-Net, (**g**) MFID-Net. Figure 10: Large area color block results. (**a**) Hazy, (**b**) DCP, (**c**) AOD-Net, (**d**) GCA-Net, (**e**) GridDehazeNet, (**f**) FFA-Net, (**g**) MFID-Net, (**h**) Clear.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### MISO Systems  
In this section, we present the optimized algorithms, which use different techniques for the channel estimation in MISO systems.
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 5 Implementation Strategies
### Spectrum Allocation and Management  
Spectrum allocations are essential for a country's digital transformation. This directly and indirectly impacts the region, benefiting users and the economy. However, restricting, delaying, or increasing the cost of spectrum access can hinder infrastructure deployment or diminish its quality. In 2021, 1920 MHz of spectrum was allocated for national coverage licenses [46].
## 2 Seismic Vulnerability Analysis  
### Seismic Vulnerability Analysis Process  
For the accurate estimation of the complete range of structural earthquake response, incremental dynamic analysis (IDA) [21] is used as one of the most powerful analysis methods available. Based on the non-linear dynamic time history analysis, IDA analyzes the seismic performance level of the structural system through a large number of seismic calculation results. The method can be widely used in the process of structural seismic performance evaluation and vulnerability analysis, while considering factors such as the different seismic requirements of structures and the uncertainties of various parameters. As a nonlinear analysis method, IDA analysis can reflect the changes in a structural system with the change in ground motion intensity.  
Using this series of seismic actions of different intensities for the structure and time-history analysis, we can calculate the relationship between the seismic response parameters and the ground motion intensity under the action of single ground motion, namely the IDA curve. An IDA curve includes the entire process of structural failure: the initial elastic phase, the intermediate elastoplastic deformation phase, and the final phase up until the structural instability collapses, so the IDA curve can very comprehensively reflect the relationship between ground motion and the structural response of different strengths. The incremental dynamic analysis method has two characteristics: predicting the responses of the structure under the influence of ground motion and accurately reflecting the change process of the structure's strength, stiffness, and deformation under earthquake action. Therefore, as an effective way to simulate the seismic collapse performance of structures, incremental dynamic analysis methods have been widely used worldwide.  
Before the IDA analysis, in order to ensure the accuracy of the subsequent analysis results, it is necessary to determine a reasonable and effective ground motion intensity measure (IM), which is used to reflect the scaling factor of the record, and a damage measure (DM), which is used to evaluate the output of the corresponding non-linear dynamic analysis.
## 5 Implementation and Simulation Setup
### Multi-Interface Support for Contiki-NG  
We chose Contiki-NG [12] as our OS because it supports RPL, allows for (future) integration into a 6 TiSCH-compliant stack, and comes with a simulator that runs binaries compiled from source. Since Contiki-NG does not support multiple radio interfaces, many modifications to the codebase were made (\(>\)12,000 additions and \(<\)300 deletions). These modifications are provided open-source, allowing developers to use the code for future multi-interface research. For an in-depth look, we kindly refer you to Appendix B, the Supplementary Materials, and our GitHub repository. In summary, our main contributions to Contiki-NG consisted of:  
* A multi-interface radio driver for Zolertia Zoul-based [32] platforms, which serves as an abstraction of underlying radio drivers, as well as a reworked Cooja radio driver based on the same abstraction principles.
* A MAC layer adapted from the Contiki-NG Carrier-Sense Multiple Access (CSMA) MAC layer to accommodate the newly developed radio-driver semantics. We did not  
Figure 6: Comparison of upward topology formation between the Parent Oriented Objective Function (POOF) [9,10] and DRiPLOF. All nodes have a sub-GHz interface (green) and a 2.4 GHz interface (blue). Inferred metrics are displayed in the same color as their corresponding interface; normalized metrics are written in black; the arrows indicate which interface is preferred. **(a)** Upward topology formation under POOF. **(b)** Upward topology formation under DRiPLOF. Note that \(I_{node}=2\), \(IL_{max}=1\), \(IL_{div}=4\), and \(S=8\).  
alter the original MAC protocol as such, but rather, enabled (near) independent MAC layer functionality for every interface.
* Source files for POOF and DRiPLOF.
* An extension to the Contiki-NG link stats module, which enables multi-interfaced operation. The link stats module is now also responsible for: (1) tracking inferred metrics/interface availability; (2) performing preferred interface selection; (3) performing metric normalization and presenting the result to an OF source file; and (4) tracking the freshness (\(\
eq\)availability) of interfaces.
* A major rework of Contiki-NG's RPL-Classic routing layer such that it complies with RFC 6719 [11] (Section 3.3) when used with MRHOF and MRHOF-derived OFs (such as DRiPLOF).
* A new probing target selection algorithm and parent selection algorithm such that they now factor in the freshness of interfaces when deciding which neighbor to probe/select as a preferred parent. You can read more about probing and its relation to interface freshness tracking in Appendix B.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 2 Introduction to IoT
### Applications of IoT
#### 2.1.2 IoT in Process Automation  
To increase the efficiencies and the reduce costs of the different processes in warehouses, plants, and factories, the world is moving towards automating the mundane and repetitive tasks to free humans, so they can think for high-level activities. Different industries use different ways to automate the process, such as robots, IoT, and software [11]. In industrial processes, IoT is used to track and monitor the analytics of different machines. In production plants, IoT monitors the temperature and pressure and switches different processes on and off, based on the conditions. It also monitors harmful gas leaks and rings alarms to inform the industrial managers about danger.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 4 Computational Experiments
### Dynamic RID Scheduling
#### 4.2.3 Dynamic Scheduling with Varying Priority  
To incorporate a more realistic distribution, multiple priorities are included in this dynamic RID model. Causality index and wait-time index are applied for priority setting of individual disaster regions. The causality index reflects the percentage of the population who have been severely affected by the disaster, i.e., the causality index reflects the population with high risk; whereas, the wait time is calculated by applying a timestamp at each disaster region when it gets the relief items supplied. At first, the wait-time for each disaster region is set to 0. At each time slot, the disaster region's wait time is calculated based on the time when the region last received the relief items. Normalisation on each factor is applied in the range [0 1] where the maximum value is set to 1 and other values are divided by the corresponding maximum value.  
In this model, the priority factor is calculated for each time slot as a weighted sum of casualty index and wait-time index as below:  
\[\text{Priority Factor}=0.75*\text{Casualty Index (normalised)}+0.25*\text{Wait-time Index (normalised)}\]  
Each distribution sequence is evaluated based on total distribution time, operation cost and priority factor. These three parameters are first normalised and then combined as a weighted sum to find a weighted fitness value. Based on the weighted fitness value, each distribution schedule is evaluated. The distribution schedule with the minimum weighted fitness value is selected as the best distribution schedule.  
Weighted fitness value = (distribution time (normalised) + operational cost (normalised) + priority factor (normalised))/3
## 5 Global Design Variable  
Sensitivities with respect to the global input design parameters were calculated. The material parameters, such as \(E\) and \(\rho\), and the geometry parameters \(L\) and \(b\) were considered global design variables. Third-order sensitivities were calculated with respect to \(b\). In this case, the variable \(b\) is perturbed the same amount in both directions (\(y\) and \(z\)) to achieve a global sensitivity (no breakage of symmetry). Thereby, \(b\) is perturbed to become \(b^{*}=0.01+e_{1}+e_{2}+e_{3}\). In addition, third-order mixed partial derivatives were calculated with respect to \(\rho\), \(L\) and \(b\). In this case, perturbations must be added to the three variables, these becoming \(\rho^{*}=2770+e_{1}\), \(L^{*}=5+e_{2}\), and \(b^{*}=0.01+e_{3}\). Tables 6 and 7 show the results for the sensitivity analysis. Good agreement was found between the numerical results and the analytical solution, with a maximum RE of \(2.139\times 10^{-6}\%\) for the eigenvalues. In this case, the same behavior of the error observed for the distinct eigenvalues case is present. These results validate, again, the inexistence of truncation and subtractive cancellation errors when differentiating using HYPAD with multidual numbers because the order of magnitude of the errors is preserved.  
Figure 8 shows the results for the eigenvector sensitivities; a good agreement was found between the numerical results and the analytical solutions. The maximum average RE was found to be equal to \(0.199\%\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode i.** & \(\lambda_{i,pb}\times 10^{4}\) & \(\lambda_{i,bb}\times 10^{6}\) & \(\lambda_{i,bbb}\) \\  & (\%Error\times 10^{-8}\) & (\%Error\times 10^{-8}\) & (\%Error\times 10^{-8}\) \\ \hline
1, 2 & \(0.0819\) (67.996) & \(0.0819\) (213.889) & \(0.00\) \\
3, 4 & \(3.220\) (0.250) & \(3.220\) (0.207) & \(0.00\) \\
5, 6 & \(25.249\) (5.231) & \(25.249\) (5.502) & \(0.00\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensitivities for a system with repeated eigenvalues. The subscripts imply derivation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode i** & \(\lambda_{i,pb}\) & \(\lambda_{i,bL}\times 10^{4}\) & \(\lambda_{i,\rho,lb}\) \\  & (\%Error\times 10^{-8}\) & (\%Error\times 10^{-8}\) & (\%Error\times 10^{-8}\) \\ \hline
1, 2 & \(-0.296\) (68.632) & \(-0.0655\) (19.942) & \(0.236\) (19.657) \\
3, 4 & \(-11.626\) (0.247) & \(-2.576\) (0.619) & \(9.301\) (0.621) \\
5, 6 & \(-91.150\) (5.231) & \(-20.189\) (5.280) & \(72.920\) (5.280) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mixed higher-order sensitivities for a system with repeated eigenvalues. The subscripts imply derivation.  
Figure 7: Natural Modes for a Cantilever Beam with a Squared Cross-Sectional Area (Repeated Eigenvalues). Solid black lines represent the analytical solution, red represents the first and second mode, magenta the third and fourth mode, and green the fifth and sixth mode.