esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Novel Maritime Channel Model Using Millimeter Radio Waves  
A channel characterization technique was presented in [12] by implementing an approach based on ray tracing using 5G frequencies (35 and 94 GHz). The path loss, received power, RMS delay spread, and power delay profile were considered as key parameters for evaluating channel quality. A significant improvement in channel quality is observed because the mentioned frequency bands have the least attenuation for oxygen and water. Because the two-ray model [6] works only in short ranges and the FSL model cannot properly detect the behavior of a channel model designed especially for over-the-sea wave propagation, a new propagation model was designed.  
The study authors have conducted is based on a tool called \"Wireless InSite.\" Readers can prompt [38] for further information on this tool. The tool was mainly used for ray tracing to make it easy to have a meticulous elaboration and description of the interaction of the rays in the marine environment. The results obtained on this simulator were later imported into MATLAB for further processing in order to develop a channel model that includes parameters such as path loss, received power, PDP, and RMS delay spread. The simulation was carried out for both frequency bands: 35 and 94 GHz.  
The modified two-ray model that was presented in [12] can be represented as:  
\[L_{m2-ray}=-20 log_{10}\bigg{\{}\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}\bigg{|} 1+R\ exp\bigg{(}j\frac{\alpha 2\pi\Delta d}{\lambda}\bigg{)}\bigg{|}\bigg{\}} \tag{19}\]where \(\lambda\) is the wavelength, \(d\) is the distance between the transmitter and the receiver, \(\Delta d\) is the distance between two ray paths, and \(R\) is the reflection coefficient equal to \(-1\), because the roughness considered is zero.  
The best part in [12] is the introduction of a new model that could predict the last peak of path loss better than the existing two-ray model [6], without showing any extra peak. Therefore, the channel model developed in this study can be used to deploy 5G systems over the sea surface. The biggest flaw of the work in [12] is that the value of R (reflection coefficient) is considered to be \(-1\), because the sea roughness is assumed to be zero, which is impractical.
## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.1 Pressure Estimation at Individual Anatomical Points
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **RMSLE** & **MAPE** \\ \hline Extra Trees Regressor & 23.7315 & 839.6758 & 27.1971 & \(-0.4872\) & 0.3569 & 0.3657 \\ K Neighbors Regressor & 26.994 & 1024.495 & 30.2197 & \(-0.9288\) & 0.3863 & 0.4422 \\ Dummy Regressor & 27.1484 & 1107.591 & 31.1707 & \(-0.9942\) & 0.3928 & 0.4359 \\ Light Gradient Boost. Mch. & 27.1484 & 1107.591 & 31.1707 & \(-0.9942\) & 0.3928 & 0.4359 \\ Bayesian Ridge & 27.1514 & 1107.961 & 31.1748 & \(-0.9948\) & 0.3929 & 0.436 \\ Random Forest Regressor & 27.8012 & 1089.168 & 31.2258 & \(-1.0301\) & 0.3966 & 0.4397 \\ Gradient Boosting Regressor & 27.2654 & 1064.454 & 30.9733 & \(-1.0617\) & 0.4079 & 0.4161 \\ AdaBoost Regressor & 27.9178 & 1150.729 & 32.0767 & \(-1.0725\) & 0.4172 & 0.4512 \\ Extreme Gradient Boosting & 27.5578 & 1153.395 & 31.5512 & \(-1.094\) & 0.4305 & 0.4192 \\ Orthogonal Matching Pursuit & 27.5907 & 1232.316 & 32.613 & \(-1.216\) & 0.4108 & 0.4602 \\ Elastic Net & 30.3813 & 1469.462 & 34.6957 & \(-1.3458\) & 0.4321 & 0.4989 \\ Lasso Least Angle Regr. & 31.8224 & 1645.612 & 36.0818 & \(-1.5213\) & 0.4541 & 0.5232 \\ Lasso Regression & 31.8109 & 1646.714 & 36.0897 & \(-1.5227\) & 0.454 & 0.5232 \\ Ridge Regression & 32.7574 & 1845.964 & 37.4148 & \(-1.7363\) & 0.4679 & 0.5401 \\ Decision Tree Regressor & 30.4242 & 1434.073 & 36.7814 & \(-2.6933\) & 0.4578 & 0.4362 \\ Huber Regressor & 38.303 & 2300.472 & 43.3332 & \(-2.93\) & 0.5605 & 0.5833 \\ Linear Regression & 46.8023 & 3601.207 & 57.1221 & \(-7.4216\) & 0.985 & 0.7242 \\ Passive Aggressive Regr. & 48.2434 & 3274.756 & 51.2894 & \(-9.6462\) & 0.5529 & 0.6799 \\ Least Angle Regression & 43,123.6 & \(3.15\times 10^{10}\) & 56,615.54 & \(-2.6\times 10^{8}\) & 2.0646 & 421.0547 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regression model metrics: hallux average peak pressure. For the heel (Table 4), the Extra Trees Regressor again performed the best among the models but with a negative R\({}^{2}\) value (\(-0.8203\)) and significant errors (MAE = 42.6667; RMSE = 48.1738). The Linear Regression and Least Angle Regression models were particularly ineffective, with extremely high error metrics and highly negative R\({}^{2}\) values, indicating a complete failure to predict the pressure values accurately in this region. Overall, the results across all the anatomical points suggest that the regression models struggle to accurately predict plantar pressure based solely on thermal data. The consistently high errors and negative R\({}^{2}\) values across the models raise questions about the feasibility of using thermal time series data as a standalone predictor for plantar pressure. Further research is needed to explore alternative features or combinations of data that may improve the predictive accuracy of these models.
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 3 Optimization of Algorithm Design
### Complexity Analysis  
Following the AO framework, problem (5) can be solved by solving problems (14) and (33) in an iterative manner. It can be observed that these resulting convex problems contain linear constraints; thus, all problems can be solved by the interior point method [24]. Specifically, by ignoring the non-dominated linear constraints, the general expression for complexity is provided in [25]. Then, the complexity of solving problems (14) and (33) is given by \(O_{\rm F}=O(M^{3.5}N^{4.5})\) and \(O_{\rm U}=O(M^{4.5}N^{3.5})\), respectively. Furthermore, let \(I_{iter}\) denote the number of iterations needed for the convergence of the proposed AO algorithm; the overall complexity is given by \(O(I_{iter}(O_{\rm F}+O_{\rm U}))\).
## 3 System Model  
Consider a hybrid clustered multi-tier network with a single macro base station (MBS) and randomly deployed clustered small-cell base stations (SBSs) under a Poisson distribution. The SBSs are laid as high-power small-cell base stations (HSBS) and low-power small-cell base stations (LSBS) over tier-2 and tier-3, respectively. The SBSs are located at the center of the cluster with radius '\(r\)', with '\(n\)' number of users deployed within each SBS cluster. It was assumed that users with considerable correlations were grouped into a cluster. Figure 2 illustrates the system model considered in this study, which is comprised of a 3-tier heterogeneous network. MBSs are deployed at tier-1, pico BSs are deployed at tier-2, and femto BSs are deployed at tier-3. Hybrid clustering is performed at tier-2 and tier-3 to manage interference and improve the system throughput [1]. PD-NOMA is implemented at the user side _UE_ at tier-2 and tier-3.  
Figure 2: System model of clustered PD-NOMA implemented at an SBS-tier on randomly deployed ultra-dense SBSs with the magnified view of PD-NOMA implementation at tier 2 and tier 3.  
According to the _Interference Managed Hybrid Clustering (IMHC)_ algorithm proposed in [1], the system interference is reduced by deploying the SBSs using a hybrid clustering technique, where SBS are deployed in a more decentralized manner. In addition, a power control algorithm is implemented on the clustered SBSs based on the threshold interference value and are categorized as either a low-power small-cell base station (LSBS) or high-power small-cell base station (HSBS). Therefore, LSBSs and HSBSs will form clusters to serve their relative associated users. The author has considered the Pico BSs as HSBS and Femto BSs as LSBSs. The results in [1] show that higher data rates are achieved with HSBS than with the LSBS.
## 3 Results  
AutoRIFT extracts glacier motion velocity map data from Sentinel-1 satellite in two regions, while InSAR extracts coherence coefficient map data from two regions. For the measured data of the ground-based radar system, the phase temporal variation results were obtained by imaging the sampled point signals and performing time-series interferometry processing based on MATLAB R2020b and then fitted, as shown in Figure 8.  
The glacier movement velocity data at the sampling point at the glacier termini can be extracted from the Sentinel-1 satellite radar remote sensing data velocity map. The phase accumulation of the sampling points measured by GB radar interferometry is calculated using Equation (13), and the average of multiple measurements is taken to obtain the results of the glacier termini movement velocity in the direction of the radar line of sight, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Image File Name**} & \multicolumn{2}{c}{**Time**} & \multirow{2}{*}{ \begin{tabular}{c} **Motion Speed** \\ **by GB Radar** \\ **(cm/day)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Motion Speed** \\ **by Sentinel-1** \\ **(cm/day)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Motion Speed** \\ **by Sentinel-1** \\ **(cm/day)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Sentinel-1** \\ **(cm/day)** \\ \end{tabular}  } \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{**Master Image**} & & & & & & & & \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{**SIA**} & \multicolumn{1}{c}{_IW\_SLC\_1 SDV_} & \multicolumn{1}{c}{_SIA\_IW\_SLC\_1 SDV_} & \multirow{2}{*}{5 June 2023} & \multirow{2}{*}{17 June 2023} & \multirow{2}{*}{64 m} & \multirow{2}{*}{VV} & \multirow{2}{*}{Descending} & \multirow{2}{*}{\ \begin{tabular}{c} Rongbuk \\ Glacier \\ \end{tabular}  } \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{**SIA**} & \multicolumn{1}{c}{_IW\_SLC\_1 SDV_} & & & & & & \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{**SIA**} & \multicolumn{1}{c}{_IW\_SLC\_1 SDV_} & \multirow{2}{*}{22 June 2023} & \multirow{2}{*}{4 July 2023} & \multirow{2}{*}{\(-\)64 m} & \multirow{2}{*}{VV} & \multirow{2}{*}{Ascending} & \multirow{2}{*}{ \begin{tabular}{c} Yangbulake \\ Glacier \\ \end{tabular}
# Inter-UAV Routing Scheme Testbeds
## 4 Findings and Discussion  
The Findings and Discussion section is dedicated to the explanation of the three main modes in which the proposed module shall operate. The first mode is a result of direct usage of the proposed module as both a flight controller and a routing evaluation medium. As for the second mode, it likely constitutes the most directly usable mode of operation: the module only implements routing (and the evaluation thereof), while an external flight controller, native to the UAV, handles all aircraft control, PID tuning and external sensor interfacing. Similarly, the last mode of operation is the one in which no aircraft is utilized for scheme evaluation, rather a stationary set of networked modules. The last mode is suitable for both VANET deployments as well as stationary (e.g., sensor network) networks. Figure 4 showcases a 3D render of the design.  
The design files for the proposed testbed module have been made publicly available in GitHub: [57]. The design software used is KiCAD. Along with the design files are the production file, which can be used for fast prototyping and testing. The work of engineers in [58] was utilized as a PC/104 template.
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 2 Literature Review and Study Overview
### Research Gaps and Study Overview  
Despite the growing use of machine learning in sports analytics, salary prediction studies often rely on general performance metrics (e.g., goals, assists) without addressing mismatches between player contributions and compensation ([PERSON], 2007; [PERSON], 2007; [PERSON], 2007). This study fills this gap by classifying players as underestimated or overestimated based on salary-performance alignment, highlighting compensation imbalances across leagues.  
Additionally, previous research tended to overlook positional effects, favoring attacking players due to their visibility. We introduce a positional ratio feature to balance salary modeling across roles, ensuring the fair evaluation of defenders and midfielders and enhancing model robustness. The study also compares traditional ML models with AutoML frameworks. While AutoML automates tuning and selection [28, 29, 30], traditional models outperform AutoML in accuracy and interpretability, providing clearer insights into salary-performance relationships vital for club's financial decisions. Finally, key salary predictors include performance metrics (e.g., xGg, aGg), market features (league and position ratios), and engineered variables, offering both academic and practical values for fair contract negotiations.
## 9 Results and Discussions
Figure 13a exhibits VD\({}_{7}\), and Figure 13b shows Vsw\({}_{1}\), which was found to be a small value. In Figure 13c, Vsw2 is illustrated for the time interval (D < t < D1), which is a very short duration, and (D1 < t < Ts), where it equals V\({}_{3}\). In Figure 13d, VD\({}_{5}\) and VD\({}_{6}\) are reduced when the converter supplies Vo = 500 V at 325 W. Figure 13e shows the load current Io = 1 A at Vo = 325 V and 325 W. Furthermore, Figure 13f displays a load current of 0.65 A at Vo = 500 V and 325 W. Additionally, in Figure 13f, it can be observed that the input current (Iin) has no pulsation after adding a very small input capacitor (Cin). In Figure 13g, the current through D\({}_{1}\), D\({}_{2}\), D\({}_{3}\), and D\({}_{4}\) is depicted, while Figure 13h represents the current through the capacitors C\({}_{1}\) and C\({}_{4}\). Figure 9: (**a**) Experimental 325 W prototype of the proposed converter; (**b**) 325 W PCB prototype of the PC. Figure 12: Cont. Figure 13: Cont. It is evident that the converter can step up a low input voltage to a high output voltage from (30 to 500 V) at a very low duty cycle. Moreover, a low duty cycle at high voltage gain (Mdc) significantly reduces the total power losses of the converter, enabling it to operate with high efficiency and performance. Additionally, the MHSLCP inductors operate in DCM when the converter supplies 500 V. Furthermore, the voltage on diodes, inductors, and MOSFETs is greatly reduced at high voltage gain when the MHSLCP inductors operate in DCM. In addition, the proposed converter operates in DCM at high voltage gain under high load power to increase efficiency and performance, following voltage stress reduction on power devices. In Figure 14a, it is evident that the proposed converter achieves high efficiency, reaching 96.2% at a very low duty cycle at Vo = 500 V. Figure 14b shows that the converter at a very low duty cycle can supply a high load current at the high efficiency of 96%. In Figure 14c, the total power losses of the proposed converter at Vo = 500 V and 325 W load are depicted. Notably, high losses are attributed to diodes, which are significantly reduced after implementing SiC diodes with very low forward voltage and low internal resistance. Additionally, lower losses were observed in inductors after using flat wire inductors with very low internal resistance at a very high switching frequency. Figure 14d illustrates the percentage of losses of elements of the converter at 325 W and Vo = 500 V. Diodes contributed the highest losses, accounting for almost 43% of all losses. Figure 14: Efficiency of the proposed converter (**a**) vs. duty cycle and (**b**) vs. load power watts (**c**) (T\({}_{\rm{THC}}\)) at 325 W and 500 V output voltage; (**d**) percentage losses of the elements in the converter at 225 W.
## 3 Methodology
### Data Processing and Uncertainties  
Regarding the accuracy of the data on the map, it has to be noted that we used only affected elements (UFIEs) where the location was accurately known. Impact data lacking precise spatial placement were omitted from further analysis.  
In terms of the UFIEs acquired through field observations, they were obtained utilizing handheld GPS devices characterized by a horizontal accuracy of 3 m. Conversely, the UFIEs extracted from official damage records were projected based on the street addresses associated with the affected structures. Specifically, the projection positioned the UFIEs at the centroid of the building's footprint shape, except in cases where a building spanned multiple cells. In such cases, multiple points were added to delineate the extent of the damage. The same practice was used in all the impacted elements that expanded geographically beyond a single cell. The process kept the accuracy standards described by [PERSON] et al. [18].  
Concerning uncertainties associated with pollution phenomena, it is important to acknowledge that due to the multifaceted pathways through which pollution may propagate and the diverse receptors, precise determination of its extent remains very difficult in the context of an impact severity study. To address this challenge, we used visible pollution elements and the spatial spread of pollutant plumes, debris, or observable contaminants deposited on surfaces (e.g., hydrocarbon residues) to signify the point or area of the pollution source, rather than delineating its complete extent. Non-visible pollution information was not incorporated into this application; thus, the findings related to this aspect should be considered with caution.  
In summary, since the final outcome of the approach is presented in a grid format, inaccuracies in the UFIE placement only become significant when they exceed the grid's resolution, which is determined by the cell size. In this study, we used a cell size of 100 m.
## 3 Results
### Sensitivity Analysis (SA)  
A sensitivity analysis was performed using the multilayer perceptron models detailed in Table 4. The relative importance of electrical parameters is presented as their percentage of influence on certain output parameters (Figure 2).  
In the case of soil compaction (assessed at depths of 0-0.5 m and between 0.4 and 0.5 m), the most influential electrical parameter was the electrical conductivity measured at the depth of 1 m (60.72% and 33.01%, respectively). The smallest impact on these parameters was observed for magnetic susceptibility. The higher influence on shear stress was calculated for the electrical conductivity measured at the depth of 0.5 m (41.23%). Other electrical parameters affected the shear stress at a similar level of about 20%.
## 4 Conclusions  
Catalyst SBA-15-PSSA was successfully prepared implementing a four-step protocol: production of mesoporous and high surface area SBA-15, grafting of APTS as a source of the amine functionality used for the subsequent immobilization of BMPB acting as the ATRP initiator intermediate, and 4-styrenesulfonate polymerization to produce the target catalyst. The catalyst's structural properties, determined by FTIR, TGA/DTA, BET, and TEM, confirmed its potential as an effective catalyst. The catalytic performance was assessed performing the esterification of acetic acid and \(n\)-heptanol as a model reaction. Results indicated the catalytic activity to steadily increase with increasing temperature, catalyst dose, and reaction time. The esterification kinetic was initially fast with curvature behavior steadily progressed afterward. At high temperature (110 \({}^{\circ}\)C), almost 100% of acetic acid underwent conversion within the first 20 min of the reaction. The acetic acid conversion percentage observed for the catalyzed reaction was 6.4 times larger than that observed for the uncatalyzed counterpart. Reusability experiments indicated the catalyst to display low stability; however, regeneration conditions have to be optimized further. In summary, SBA-15-PSSA, as prepared in this work, is an esterification catalyst with high application potentials. Furthermore, it could be exploited as a green and highly efficient solid acid catalyst in, for example, the production of biodiesel from the inexpensive and widely available waste biomass; therefore, additional efforts should be dedicated to fully understand its properties.  
Conceptualization, A.A.A. and F.A.A.; Data curation, A.A.A.; Formal analysis, Y.M.M. and A.-B.A.-O. Funding acquisition, A.A.A. and F.A.A.; Investigation, Y.M.M. and A.-B.A.-O.; Methodology, Y.M.M.; Project administration, A.A.A.; Software, A.-B.A.-O.; Supervision, A.A.A.; Visualization, A.-B.A.-O.; Writing--original draft, A.-B.A.O.; Writing--review & editing, A.-B.A.-O. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the Researchers Supporting Project (RSP-2020/155), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### FSS in Wireless Communications
#### 2.3.1 Beamwidth Control  
Beamwidth refers to the angular spread of the main lobe of an antenna radiation pattern. An FSS enhances beamwidth control by modifying the geometry and periodicity of the surface elements. Research has shown that by optimizing the shape and size of FSS elements (e.g., hexagonal, fractal, or cross-dipole geometries), the beamwidth can be reduced by 10-30%, which tightens the radiation pattern and improves signal directionality. For instance, using cross-dipole FSS structures can narrow the beamwidth from approximately 70\({}^{\circ}\) to 50\({}^{\circ}\)[104].
## 4 Primary Market Design
### Analyzing Buyers' Behaviors
#### 4.2.2 Simulating Online Markets with Naive DA Mechanism  
As a next step, we investigate buyers' utility in seven types of online markets with the naive DA mechanism.  
Figure 3a-c show buyers' surplus, resale profit, and total utility, respectively. Vertical lines in the graphs show standard deviations of the obtained results.  
Figure 2: Simulation results in static markets with naive DA mechanism. (**a**) Buyers’ surplus; (**b**) Buyers’ resale profit; (**c**) Buyers’ utility.  
In online markets, where demand and supply are not always balanced due to dynamically changing agents' population, low-valued buyers' bids have a high possibility of not being able to find sellers' asks that satisfy the matching condition (1), which is not the case with static markets when sellers adopt a MONO bidding strategy. Therefore, increases of buyers' surplus by misreporting their value saturate at certain points, as shown in Figure 3a, and buyers' misreporting deteriorates resale profits, as shown in Figure 3b. Adding the results of buyers' surplus and resale profit, Figure 3c shows that modestly untruthful buyers still have higher utility than the truth-telling buyers in any type of online markets.  
From the experimental results in this section, we find that the naive DA mechanism cannot satisfy quasi incentive compatibility for buyers in perishable goods markets.