esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 4 GNSS Data
Figure 7: (**Left**) View of the roof of NGI building, with the location of control point NGB2 and (**right**) the GNSS antenna installed on the top of the pillar of NGB2. constellation combination increases significantly with the number of available satellites. With 8 GPS satellites available, the maximum number of 4-satellite combinations is 70, whereas in the case of 13 available GPS satellites, the maximum number of combinations is 1716 and occurs for GPS subsets of 6 and 7 satellites. For the case of the multi-GNSS constellation, where the available number of satellites increases, the number of possible combinations increases dramatically. Representative of this is the case of 31 available GNSS satellites, for which the maximum number of possible combinations is 300,540,195 and occurs for multi-GNSS satellite subsets of 15 and 16 satellites. Based on the methodology described in Section 3, the performance of the five optimization algorithms in optimizing GPS satellite subsets of four, five, six, and seven satellites was examined. The most suitable optimization algorithm was chosen based on its overall performance. It was then tested for optimizing multi-GNSS satellite subsets of 4, 8, 12, and 15 satellites using various algorithm parameter values.
## 3 Model Construction and Review of Traditional A* Algorithms
### Review of Traditional A* Algorithms  
The operational flow of the traditional A* algorithm is depicted in Figure 6[32], which can be summarized as evaluating nodes by integrating the cost and heuristic estimation, initiating from the starting node using a priority queue, and progressively exploring nodes within the open list until the target node is located or the open list is exhausted, followed by path reconstruction via backtracking to parent nodes. The core components primarily consist of two parts: the evaluation function and the search method. The evaluation function of the traditional A* algorithm is as follows:  
\[f(n)=g(n)+h(n)\]  
In the traditional A* algorithm, the function \(f(n)\) is employed to assess the cumulative cost of node \(n\). This cost comprises \(g(n)\), which represents the actual expenditure from the origin node to the current node \(n\), and \(h(n)\), which estimates the heuristic cost from node \(n\) to the target node. On a grid or map, \(g(n)\) is typically calculated as the straight-line or Manhattan distance from the starting point to the current position. Common heuristic functions encompass Euclidean distance, Manhattan distance, and Chebyshev distance, among others. The selection of heuristic is contingent upon the specific spatial characteristics and requirements of the problem at hand.  
The traditional A* algorithm search methods are mainly 8-domain search method or 4-domain search method, but the traditional 8-domain search may lead to more tortuous paths and higher computational complexity, and the traditional 4-domain search may not be able to capture all the possible shortest paths, especially when it is necessary to bypass obstacles [33]. Combining the two goals of path security and path length that need to be considered, we believe that traditional search methods cannot fulfill them. Based on this, this paper optimizes the above two key nodes and conducts experimental simulations in a raster environment to further verify the effectiveness of the algorithm.  
Figure 5: Raster environmental maps in this paper.
## 3 Antenna Designs and Configurations
Each supports a single feed port (ANTs #1 and #2), thereby simplifying impedance matching and facilitating mechanical integration. In this configuration, the illustration includes Separation Reducing Shared Currents, achieved by grounding separation and spatial decoupling, which effectively suppress shared current paths between the two antennas. This reduces inter-port coupling but retains effective use of the chassis ground and frame current paths. Both configurations exploit the natural resonant characteristics of the metal frame and the distributed ground plane. Radiation is both compact and efficient. There is no need for a protruding structure or a standalone radiator. Moreover, such a shared coupling approach mitigates interference from adjacent components and enhances mechanical robustness, rendering the design well suited for dense smartphone environments. These conceptual topologies form the basis of the final antennas that are integrated into the platform of Figure 1. The practical performance, including the impedance, isolation, and radiation behaviors, is analyzed in detail in the following sections. Figure 2: Conceptual topology of the proposed metal frame coupled antennas: (**a**) four-port configuration showing slot-fed stubs, capacitive coupling regions, and shared current paths; (**b**) two-port configuration illustrating separation that reduces shared currents for improved isolation.
# On the Effect of DLC and WCC Coatings on the Efficiency of Manual Transmission Gear Pairs
## 2 Methodology  
### Model Overview  
A tribo-dynamic model of a manual six-speed transmission is developed for calculating efficiency by utilizing coated gears. Gear teeth modifications have not been considered and are out of the scope of this work. The use of coatings was examined only for the loaded gear pair under conditions provided from the selected driving cycle. This was due to the fact that previous research [17] has indicated that the loaded gear pair is the highest contributor to transmission power losses.  
The equations of motion describing the dynamics of the examined system are shown below. It has to be d that Equation (1) refers to loose gears, while Equation (2) refers to loaded gears.  
\[I_{i}\ddot{\varphi_{i}} = W_{i}R_{bui}-F_{v,i}R_{bui}-F_{p,i}R_{bui} \tag{1}\] \[I_{i}\ddot{\varphi_{j}} = W_{j}R_{buj}-F_{v,i}R_{buj}-F_{b,i}R_{buj} \tag{2}\]  
where \(i\) is the loose gear examined, \(j\) is the engaged gear examined, \(W_{i}\), \(W_{j}\) is the load on the loose and engaged gear teeth, respectively, \(F_{v,i}\), \(F_{v,j}\) is the viscous friction generated on the loose and engaged gear respectively, \(F_{p,i}\) is the friction generated between the loose gear and the shaft, \(F_{b,j}\) is the boundary friction generated on the loaded gear teeth, and \(R_{bwei}\), \(R_{bwj}\) is the base radius of the loose and engaged wheel examined. As a result, there is a set of six equations of motion, i.e., five for the unloaded gears described by Equation (1) and one for the engaged gear described by Equation (2), which are solved simultaneously.  
The dynamic model that has been developed, can identify throughout the simulated running time which of the six gears of the transmission is engaged based on data provided by the user that follow the chosen driving cycle.  
Within the same model, the gear that has been coated is traced so that the effect on the frictional losses can be identified. The contact mechanics of the loaded gear at each instant of time is the main focus, if uncoated is considered to follow Hertzian theory. On the other hand, when the engaged gear is considered coated, the contact mechanics is tackled following a specific approach that will be described in detail in the following sections. At the boundary level, the friction model for coated gears is using measurements for the coefficient of boundary shear strength of asperities, taken using an atomic force microscope (AFM) in lateral mode. Thus, it utilises realistic values for their surface properties.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.8 Workflow  
The workflow for determining the capacity factor for each profile in the dataset consists of three distinct phases, as illustrated in Figure 3.  
Figure 3: Workflow for determining the capacity factor.  
In the first phase, the synthesis of a generic profile structure is performed to establish a standardized representation of employee characteristics. Subsequently, tasks are classified based on their complexity levels, facilitating the identification of temporal dependencies in execution time. Personnel selection criteria are then defined, creating a subset of eligible employees who meet the predefined qualifications. To ensure the reliability of the analysis, time-related performance metrics are collected over a statistically valid period, enabling a comprehensive evaluation of task execution patterns.  
In the second phase, various machine learning algorithms are assessed and compared to identify the most suitable model for load distribution within the targeted public sector environment. Among the tested models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was selected due to its effectiveness in modeling the transformation of employee profiles into task execution times, referred to as the Time Factor (TF). This facilitates a more adaptive and context-aware prediction of workload distribution, improving efficiency in task allocation.  
The third phase focuses on implementing load redistribution mechanisms, including Load Control and load balancing strategies, to further enhance workforce efficiency and optimize task distribution.  
As Phases 1 and 3 fall outside the primary scope of this study, the present research remains focused on its main objective: the comparative evaluation of machine learning algorithms for load distribution.
## 3 Experimental Result  
### Failure Mode  
#### 3.1.1 Crack Mode of Specimens  
Figure 8 illustrates the distribution of cracks under various working conditions. It is observed that the concrete structure is mainly damaged near the void boundary. Moreover, some cracks appear near the support. However, these cracks mainly originate from uneven contact between the support and the model. Based on the Saint-Venant principle [24], the force near the support only affects the local stress distribution near the support and had almost no effect on the stress in the void area. Accordingly, the load-induced structural damage mainly appears near the void boundary. Under the existing bearing conditions, there is a negative correlation between the void height and the degree of damage.  
Figure 7: Measurement scheme. (**a**) Measurement scheme of the concrete; (**b**) Measurement scheme of the reinforcement.  
For unstrengthened specimens, the crack development can be mainly divided into two stages. In the first stage, cracks appear at the boundary of the gap on the bottom surface. In this stage, cracks gradually extend as the applied load increases. When the crack further propagates and reaches the steel bar, the crack propagation suspends. Then, the crack enters the second stage as the applied load further increases. Furthermore, original cracks in the cracked area grow, and new cracks appear on the lower surface. At this time, oblique cracks begin to appear at the specimen support.  
In the strengthened specimens, the number of cracks is significantly reduced and the length of cracks is significantly shortened after steel plate strengthening. When the void height is H/4, compared with the unstrengthened specimen, the number of cracks in the specimen strengthened by steel plate is reduced from 14 to 9, and the length of the longest crack is reduced from 13.2 cm to 8.3 cm. When the void height is H/3, the number of cracks is reduced from 10 to 1, and the length of the longest crack is reduced from 13.9 cm to 3.7 cm. When the void height is H/2, the number of cracks is reduced from eight to six, and the length of the longest crack is reduced from 12.3 cm to 6.3 cm. When the void height is 2H/3, there is no obvious crack on the lower surface of the steel plate strengthened specimen after loading. The experimental results show that steel plate strengthening can effectively reduce the damage caused by lining voids.
## 4 Results
### Predicting Goals and Assists
#### 4.2.2 Model Performance Comparison Based on RMSE  
Referring to Table 2, the modelling process begins with a baseline comparison using the Naive Model to establish a starting point. Despite its simplicity, the Naive Model yields a relatively high RMSE of 6.62, highlighting its limitations and underscoring the need for more sophisticated modelling techniques. Subsequently, we explore various models, including Decision Tree (DC_1 and DC_2), Random Forest (RF_1 and RF_2), Gradient Boosting (GB_1 and GB_2), and Ridge Regression (RR_1 and RR_2).  
Figure 10: Learning curve: Ridge Regression.  
DC_1 represents the default configuration, while DC_2 incorporates fine-tuned hyperparameters. DC_2 outperforms its default counterpart with lower a RMSE. Transitioning to Random Forest models, RF_1, with default settings surpasses the predictive accuracy of Decision Tree models. The introduction of fine-tuned hyperparameters in RF_2 further enhances predictive accuracy, resulting in a lower RMSE. Similarly, Gradient Boosting models (GB_1 and GB_2) demonstrate their predictive power, with GB_1 showcasing a lower RMSE than previous models. Fine-tuning further improves performance in GB_2, leading to increased accuracy. Finally, two Ridge Regression models, RR_1 (default configuration) and RR_2 (fine-tuned with an alpha value of 10), are presented. RR_2 emerges as the best-performing model with the lowest RMSE among all models. This could be due to the fact that Ridge Regression models perform well for datasets where predictors are highly correlated with each other [21]. In this case, it has already been observed that predictor variables like minutes played, matches played, etc., are highly correlated with each other.  
In conclusion, due to its low RMSE, the fine-tuned Ridge Regression model RR_2 stands out as the best-performing model, outperforming Decision Trees, Random Forests, Gradient Boosting, and the Naive Model. The selection of RR_2 as the favored model demonstrates its ability to produce accurate predictions about player performance regarding goals and assists.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 2 Description of Building Frame Structure Model  
The building frame structure model studied in this study is the four-story steel structure frame built by Columbia University, as shown in Figure 1[33]. It can be seen from the figure that the frame structure of the building is divided into four sides: east, south, west, and north; the structure distribution on each side is the same, and the same code is used for the same location. Fifteen accelerometers were placed on the frame structure. Starting from the first floor, three accelerometers were installed on each floor: one was in the west, one was in the east, and the other was near the central column. In addition, No.1 to No.3 accelerometers were placed on the ground floor and the rest were placed on the top of each floor. In this paper, five damage locations of the building frame structure were selected for experimental research. Since four surfaces of the building frame structure were numbered with numbers 1-12, five damage locations were selected as shown in Table 1.  
It can be seen from Table 1 and Figure 1 that the damage cases 2-5 of the building frame structure model do not damage the main support column and beam structure, so the difference of vibration data of damage cases 2-5 is not very obvious. Because the vibration exciter is located at the top of the building model, when the frame structure of the building model is damaged, the most stable part is the bottom layer. Therefore, in this paper, the vibration data of the acceleration sensor on the ground floor were selected for the fault diagnosis research of the building model, because in this case, the collected data are less affected by noise, and it is convenient to install the acceleration sensor on the ground floor. When this method is extended to larger frame structures, fault diagnosis can be carried out quickly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cases** & **Specific Damage Locations** \\ \hline
1 & No damage \\
2 & Remove structures numbered 1–8 on the east side \\
3 & Remove structures numbered 1-4 on the east side \\
4 & Remove structures numbered 1 and 4 on the east side \\
5 & Remove structure numbered 1 on the east side \\ \hline \hline \end{tabular}
\end{table}
Table 1: Five damage locations.  
Figure 1: Building frame structure model. (**a**) Picture of frame structure, (**b**) 3D model of frame structure.
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 2 Synthesis of Hydrophobic Catalysts
### Polymeric Catalyst
The obtained resin exhibits a lower number of acidic sites if compared to the commercial Amberlyst-15 (0.76 mmol/g versus 4.6 mmol/g). However, it shows a marked hydrophobic character displaying a water contact angle of 154\({}^{\circ}\), much higher if compared with the commercial sulfonic resin [40]. Similarly, [PERSON] et al. demonstrated a synthesis method to produce a novel hydrophobic mesoporous polymeric solid acid catalyst that consists in the copolymerization of divinylbenzene with sodium \(p\)-styrene sulfonate (H-PDVB-x-SO\({}_{3}\)H's) under solvothermal conditions with an adjustable sulfur content. It is worth noting that a lower sulfur content and consequently a lower number of acidic sites correspond to a higher hydrophobicity. In fact, the H-PDVB-x-SO\({}_{3}\)H's present a lower sulfur contents (0.31\(-\)2.36 mmol/g) and acidic concentrations (0.26\(-\)1.86 mmol/g) if compared to PDVB-SO\({}_{3}\)H and commercial Amberlyst-15 (sulfur content 3.64 mmol/g and 4.30 mmol/g, respectively; acidic concentration 4 and 4.7 mmol/g, respectively). The H-PDVB-0.05-SO\({}_{3}\)H's that has the lower sulfur content (0.32 mmol/g) presents the higher contact angle of the entire series (152\({}^{\circ}\)). Moreover, all the H-PDVB-x-SO\({}_{3}\)H's catalysts present lower contact angle measures performed with organic molecule again compared with PDVB-SO\({}_{3}\)H and Amberlyst-15, demonstrating a higher affinity towards organic substrates [41]. The same authors reported later a synthesis of the strong solid acid PDVB-SO\({}_{3}\)H-SO\({}_{2}\)CF\({}_{3}\) grafting the strong electron withdrawing group -SO\({}_{2}\)CF\({}_{3}\) onto the network of mesoporous solid acid PDVB-SO\({}_{3}\)H, using HSO\({}_{3}\)CF\({}_{3}\). This method could maintain a good level of acidity of the solid together with the hydrophobicity of the surface thanks to the electron withdrawing power and the intrinsic polar hydrophobicity of the fluorinated groups [42]. In Table 4, hydrophobicity related to the sulfur content is showed. Figure 4: Conversion and selectivity of Polystyrene resins vs contact angles in the hydroxyalkylation/alkylation of 2-methylfuran (2-MF) with ketones. Adapted from Reference [38].
## 6 Conclusions  
In this paper, a dual-hop DF relaying system was studied using different adaptive transmission techniques, where multiple antennas were only employed at the \(S\) and \(D\) nodes. The system performance for different adaptive techniques were evaluated for \(\kappa-\mu\) shadowed fading channels. The new and exact analytical expressions for the OP and average capacity using the ORA, OPRA, TIFR, and CIFR techniques were obtained. With the aid of the obtained analytic expressions, the performance was analyzed for different parameters, such as shadowing (i.e, \(m_{1}\) and \(m_{2}\)), fading (i.e., \(\kappa\) and \(\mu\)), and the number of antennas (i.e., \(N_{1}\) and \(N_{2}\)). The parameter \(\mu\) and number of antennas have a positive relationship with the system performance. The ORA technique performs better, as compared to the OPRA, CIFR, and TIFR techniques. The improvement in the capacity performance is observed under light shadowing conditions (larger \(m\)) than under strong shadowing conditions (lower \(m\)). Additionally, the capacity performance decreases in a strong shadowing environment as \(\kappa\) increases, and _vice versa_. The main contribution of our paper was to derive new and exact analytical expressions in the general form for a dual-hop DF relaying system with beamforming using adaptive transmission techniques, therefore, the derived analytical results are useful for evaluating system performance under different fading and shadowing environments. Our results are helpful to system design in the sense that the system performance can efficiently be analyzed for arbitrary system parameters without any need for an extensive and complex Monte Carlo simulation.  
[PERSON] carried out formal analysis, provided the research methodology, performed numerical analysis and Monte Carlo simulations of the proposed system model, [PERSON] supervised and reviewed the manuscript.  
This work was supported by the Dong-A University Research Fund.  
The authors extend their gratitude to the anonymous reviewers for their valuable and constructive comments, which helped us to improve the quality this manuscript.  
The authors declare no conflicts of interest.  
Figure 8: Average channel capacity using ORA technique of dual-hop relaying systems operating over \(\kappa-\mu\) shadowed fading links and its equivalent Nakagami-\(q\) fading links for different number of antennas.
###### Abstract
However, compared with the above methods, the analytical method is more intuitive in terms of solving the seepage field, and the seepage characteristics of a foundation pit can be directly expressed in the form of a function. Therefore, the analytical solution has important theoretical significance. [PERSON] et al. [24] deduced the calculation formula of water inflow in a circular foundation pit of a suspended waterproof curtain and put forward a calculation method of surface settlement considering a suspended waterproof curtain by referring to the improved resistance coefficient method. [PERSON] et al. [25] obtained the explicit analytical solution of a two-dimensional seepage field of a foundation pit by using the superposition method and the separation variable method, combined with the continuous conditions between regions, and obtained the position of the phreatic surface outside of the pit in the solution of a seepage field considering the phreatic surface outside the pit. Based on the barrier theory and numerical simulation, [PERSON] et al. [26] proposed a formula for calculating the hydraulic head difference between the inside and outside of a suspended curtain. However, solving the seepage field of a foundation pit using numerical methods requires significant time, and the results are not universal, which is limited in terms of practical engineering applications. In addition, the analytical theory relating to seepage type from phreatic water to incomplete wells is still not sufficiently mature. With the gradual increase in aquifer thickness, the seepage characteristics of a foundation pit will change dramatically. The assumption that the hydraulic head from the bottom of a waterproof curtain to the top of the aquiclude is a fixed value adopted by many scholars in the solution process will be quite different from real situations. Based on the current state of the research, this paper assumes that the horizontal hydraulic gradient of the seepage field above the aquiclude below the bottom of a waterproof curtain decreases linearly. The separation variable method is used to solve the seepage field in the regular area of the foundation pit, and the calculation expression of the water inflow of the foundation pit is further deduced. On this basis, the improved resistance coefficient method is used to establish the relationship between the water level outside the curtain and the solved hydraulic head in the pit. The calculation results are compared with the calculation results of the existing model and the measured data of the foundation pit project of Yangwan Station. The rationality of the calculation results is verified.