esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hull Form Variation and Design Parameters
\begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Modification** \\ **Function** \\ \end{tabular} & \multicolumn{2}{c}{**Design Variable**} & **Description** \\ \hline \multirow{3}{*}{SAC Shape} & \multirow{3}{*}{\(f_{1}(X)\)} & \(X_{\text{C}}^{SAC}\) & Fixed X position \\ \cline{3-4}  & & \(\Delta X_{\text{C}}^{SAC}\) & X movement at shoulder part \({}^{*}\) \\ \cline{3-4}  & & \(\Delta X_{\text{C}}^{SAC}\) & X movement at entrance part \\ \hline \multirow{3}{*}{Section Shape} & \multirow{3}{*}{\(f_{2}(X,Z)\)} & \(X_{\text{C}}\_Z_{\text{D}}^{Sectin}\) & Reference X position at design draft (\(Z_{0}\)) \\ \cline{3-4}  & & \(\Delta Y_{\text{GC}}\_Z_{\text{D}}^{Sectin}\) & Y movement at shoulder part and design draft (\(Z_{0}\)) \\ \cline{3-4}  & & \(\Delta Y_{\text{C}}\_Z_{\text{D}}^{Sectin}\) & Y movement at entrance part and design draft (\(Z_{0}\)) \\ \cline{3-4}  & & \(\Delta Y_{\text{C}}\_Z_{\text{D}}^{Sectin}\) & Reference X position at lower draft (\(Z_{1}\)) \\ \cline{3-4}  & & \(\Delta Y_{\text{C}}\_Z_{\text{D}}^{Sectin}\) & Y movement at shoulder part and lower draft (\(Z_{1}\)) \({}^{*}\) \\ \cline{3-4}  & & \(\Delta Y_{\text{C}}\_Z_{\text{D}}^{Sectin}\) & Y movement at entrance part and lower draft (\(Z_{1}\)) \\ \hline \multirow{2}{*}{Bulb Shape} & \(f_{3}(X,Z)\) & \(\Delta Z^{Bulb}\) & Z movement of bulb height \\ \cline{3-4}  & \(f_{4}(X,Z)\) & \(\Delta Y^{Bulb}\) & Y movement of bulb breadth \\ \hline \hline \end{tabular}  
* Variables to be adjusted to maintain constant displacement.  
\end{table} 
Table 2: Design variables of hull form variation. \(\Delta Y_{0C}\_Z_{D}^{Section}\), \(\Delta Y_{C1}\_Z_{D}^{Section}\), \(X_{C}\_Z_{L}^{Section}\), \(\Delta Y_{0C}\_Z_{L}^{Section}\), and \(\Delta Y_{C1}\_Z_{L}^{Section}\). As pointed out by [PERSON] et al. [43], the utilization of two waterline modification functions defined at two different drafts enabled one to achieve the hull form variation with higher degree of freedom in terms of design variables as well as higher accuracy in constraint satisfaction. This is the central idea of the multiple parametric modification curves method. Regarding the bulb height modification function \(f_{3}(X,Z)\) and breadth modification function \(f_{4}(X,Z)\), each function was given one design variable. Among the eleven design variables listed in Table 2, the values of \(\Delta X_{0C}^{SAC}\) for the SAC variation and \(\Delta Y_{0C}\_Z_{L}^{Section}\) for section shape variation were adjusted to maintain the displacement of the modified hull unchanged. Thus, the number of independent design variables used in this study was nine. The change in the displacement was kept below 0.015% of the original hull displacement.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 3 Proposed Approach
### Overview of Bison Algorithm  
The Bison Algorithm is inspired by the collective behavior of bison herds, which exhibit efficient exploration and exploitation strategies during migration. The algorithm divides the bison herd into elite, swarmer, and runner groups, each contributing uniquely to the optimization process.  
The following is the overview description of the Bison Algorithm.  
1. Initialization: A population of bisons is initialized with random positions in the search space.
2. Fitness Evaluation: The fitness of each bison is evaluated using a predefined fitness function.
3. Elite Group: The best-performing bisons are selected as the elite group, representing the best solutions found so far.
4. Swarmers: The positions of swarmer bisons are adjusted towards the elite center, promoting exploitation.
5. Runners: Runner bisons explore the search space randomly, promoting exploration.
6. Iteration: The process is repeated for a predetermined number of iterations or until convergence criteria are met.  
The detailed algorithm description is the following applied to the classification task:  
1. Initialization:  
* Generate a random population of bisons within the defined search space bounds.
* Define the number of iterations (\(max\_iter\)) and the size of the bison population (\(swarm\_size\)).
2. Fitness Function:  
* The fitness function evaluates the accuracy of the classification model. In this case, it is defined as the negative accuracy to facilitate minimization.
3. Iteration Loop:  
* For each iteration:  
* Evaluate the fitness of each bison.
* Update the best-known positions (elite group).
* Calculate the elite center as the mean position of the elite group.
* Adjust the positions of swarmer bisons towards the elite center with a random step size.
* Allow runner bisons to explore randomly within the search space bounds.
4. Convergence:  
* The process continues until the maximum number of iterations is reached or convergence criteria are met.
## 2 Antenna Design Procedure and Methods  
Figure 1 illustrates the three-dimensional structure of the proposed 4 \(\times\) 4 dual-band, dual-CP antenna array. The Ka-band left-handed circularly polarized (LHCP) array is at the bottom layer of the antenna structure and the K-band right-handed circularly polarized (RHCP) array is stacked above the Ka-band array. A waffle grid is placed on top of the whole array structure to increase the effective area of apertures and reduce grating lobes. Two waveguide transitions were designed to connect the input ports of the K- and Ka-band arrays to corresponding standard waveguide interfaces to facilitate antenna measurement and system integration.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 4 Simulation Results
### Case C  
In this scenario, eight nodes are moving while four are stationary. The average BER, as observed at each gateway, is shown in Figure 10a,b for SCA and RL-CAA, respectively.  
Figure 9: **(a)** Individual nodes dissatisfaction probabilities for the SCA algorithm; **(b)** Individual nodes dissatisfaction probabilities for the RL-CAA algorithm; **(c)** Comparison of the RL-CAA and SCA based total dissatisfaction probabilities averaged over time.  
The RL-CAA is giving a below 10% error rate on average and a few fluctuations giving an error rate over 10%. The SCA's BER is over 10% for all gateways. The RL-CAA is providing better throughput per gateway as compared to SCA. For a short period, G4 experiences a throughput over 7 Mbps (Figure 11a,b). From Figure 12a,b, it is clear that RL-CAA provides better QoS satisfaction and little dissatisfaction is observed. From Figure 12c, it can be observed that there are short periods when the total system faces dissatisfaction. The RL-CAA in general provides better user QoS satisfaction.  
Figure 11: (**a**) Average throughput for the SCA algorithm; (**b**) Average throughput for the RL-CAA algorithm.  
Figure 10: (**a**) Average BER for the SCA algorithm; (**b**) Average BER for the RL-CAA algorithm.
## 4 The System Framework
### Data Visualization Module  
The Data Visualization Module includes a dashboard created within the Power BI platform for displaying the QC results following each update. The developed dashboard comprises six sections, a summary statistics section and five sections for the five disciplines. The summary statistics section contains the total passing and failing elements for each discipline to provide an overall condition of the BIM models' data quality. Sections for each discipline comprise various components such as \"Percentage Pass Organized by Name\", \"Percentage Fail Organized by Name\", \"Quality Score\" for each Category of QC, \"Total Number of Fails\", \"Total Number of Pass\", \"Total Number of Objects\", and a \"comparison chart\" to compare and track the progress of corrections for every month.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Position Aware Routing Protocols
#### 4.2.1 Robust and Reliable Predictive Routing (RARP)  
[PERSON] et al. introduced a robust and reliable routing protocol (RARP) for FANETs [42], which consists of six parts, as shown in Figure 6. RARP is characterized by the employ of uni-cast and geolocation routing which using node position and trajectory information, using three-dimensional estimation and directional transmission of predicted positions to predict the position of intermediate nodes, making the transmission range larger and tracking topology changes, to ensure the reliability of the protocol. What is more, reducing link rebuild and service interruption time to achieve high path lifetime and success rate of packet transmission. When calculating the expected connection time of the source node and the target node, the modified RREQ package is used to obtain the speed and location information of the node. The utility function of intermediate nodes are evaluated by \(U=\omega_{CT}\times minCT-\omega_{H}\times Hop_{count}-\omega_{R}\times maxR\), where \(CT\) is the expected connection time, \(Hop_{count}\) is the number of hops, and \(maxR\) is the max transmission range. In addition, directional antenna is used to increase transmission distance. To overcome the far-and-near node problem, the author proposed a dynamic angle adjustment method according to the estimated angle error.  
**Advantages:** (1) The directional beam method expand the transmission range, improved path transmission efficiency, and reduced probability of packet collisions. (2) Through the flexible angle adjustment of the transmission node, the bad influence of the near and far nodes is effectively solved in the directional transmission process and prolong the average path life. (3) Setting the alternate path  
Figure 6: Robust and Reliable Predictive Routing (RARP) routing strategy.  
reduces the path reconstruction and service interruption time and increases the successful transmission rate of the data packet.  
**Disadvantages:** A number of control packets are set up to maintain forward and backward tables but load balancing between nodes is not considered.
## 3 Methodology
### MLP Modelling Parameters and Search Space
#### 3.3.1 The Hidden Layers  
Deciding the number of the hidden layers is one of the most important issues while investigating the neural network architecture for predictive modelling and data mining. Using too many hidden layers can result in poor generalization and complex neural network training. According to authors in [44; 45; 46; 47], two hidden layers, combined with m output neurons, are adequate for a neural network to learn N data samples and produce negligibly minor errors.  
Previous studies have examined the suitability of several machine models for path loss predictions as contained in [48; 49; 50]. The need to overcome the problems of empirical models when used for path loss predictions led to an artificial neural network [49]. ANN path loss prediction models were also more efficient and easier to deploy than deterministic models [51]. In [52; 53], analyses of empirical models with different propagation features were performed, and the model with the lowest RMSE value was then compared with the prediction from ANN. The ANN-based path loss prediction model produced a much lower value of MSE upon validation. In [54], a multi-layer perceptron neural network was introduced for path loss prediction. The MLP network was then trained with a backpropagation algorithm. The MLP-based prediction was compared with predictions from analytical models, and the results indicated the former to be efficient for radio network planning and optimization.  
Figure 3: ANN MLP Model Structure for Path Loss Prediction.  
ANN was also used for path loss prediction in urban areas [55]. The work explored the effect of the various input parameters and the environmental terrains on the robustness of the path loss prediction. One key finding from the study is that the accuracy of the signal prediction model increases with more input parameters: the greater the number of features, the greater the system's accuracy. This trend is because machine learning algorithms thrive well with the availability of large datasets. The model is trained with the help of the data and, in this case, the input features. An ANN-based path loss model at 800 MHz and 1800 MHz introduced in [47; 48] were input for longitude, latitude, distance, elevation, clutter height and elevation. The ANN method in [56] outperforms the Support Vector Machine (SVM)- and the Random Forest (RF)-based predictions.  
In [57], an artificial neural network was used for path loss prediction in a smart campus environment at 1800 MHz. There were two hidden layers for this network, and the performance of the network outperforms the prediction made by using RF. Moreover, in [58; 59; 60], several machine learning-based prediction models were introduced for signal predictions for wireless sensor networks. The machine learning-based prediction model in [61; 62] also produced the lowest values of RMSE when compared to the other analytical models in a wireless sensor network.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results  
APP underwent verification and validation through a finite set of use cases, many of which were inspired by the LEADER project [15]. These use cases included scenarios such as smog distribution in Poland and BC measurements in Svalbard.  
Smog poses a significant issue in Poland, particularly during winter months when emissions from hard coal heating systems increase. While previous studies have examined smog concentrations at lower altitudes, the three-dimensional distribution of aerosols in urban and rural environments remains unexplored. To address this, low-altitude flights (1-2 km) are planned to measure vertical and horizontal gradients. These flights will primarily take place over the Central European Drone Demonstrator (CEDD) area, adhering to Poland's legal regulations. Arctic missions under the LEADER project aim to study the impact of Short-Lived Climate Forcers (SLCFs) on the climate. A significant gap in SLCF data, particularly in the eastern Arctic, has been highlighted by the AMAP assessment [37]. These missions will take place in Svalbard, Norway, with a primary focus on areas around Ny-Alesund, the Holtedahlfonna and Kongsvegen glaciers, and the eastern parts of Svalbard [15].
## 3 Results and Discussion
### Calibration Model for Quantitative Estimation  
The individual phases present in the binary samples system were estimated using linear calibration models to establish the relationship between the relative intensities of the Raman shift (band) and the phase fractions. The sample phase content or fraction analyzed using XRD for phase identification and XRF for elemental evaluation were used as the dependent variable for the calibration model. Furthermore, the average values of the coefficient of determination, the mean absolute error values (MAE) and the relative stabilities of the calibration variable candidates were estimated as presented in Tables 4-8. The relationship between the relative stabilities of the calibration variable and the mean absolute error values (MAE) is shown in Figures 9, 11, 13, 15 and 17. Section 2.3 provides a detailed description of how these parameters were evaluated.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & & \multicolumn{3}{c}{**Training Data**} & \multicolumn{3}{c}{**Validation**} & \\ \cline{3-8}
**Phases** & **Relative Intensity** & **Mean (R\({}^{2}\))** & **Mean (MAE)** & **Mean (R\({}^{2}\))** & **Mean (MAE)** & **Sum MAE** & **Relative Stability** \\ \hline CaS-CA & 157/524 & 0.97 & 3.49 & 0.98 & 3.71 & 7.20 & 0.48 \\ CaS-CA & 157/790 & 0.92 & 6.08 & 0.93 & 6.12 & 12.20 & 0.15 \\ CaS-CA & 200/524 & 0.94 & 5.30 & 0.94 & 5.50 & 10.80 & 0.17 \\ CaS-CA & 200/790 & 0.83 & 9.27 & 0.84 & 9.49 & 18.76 & 0.07 \\ CaS-CA & 478/524 & 0.75 & 11.72 & 0.76 & 12.12 & 23.84 & 0.05 \\ CaS-CA & 478/790 & 0.84 & 9.22 & 0.84 & 9.36 & 18.58 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of the coefficient of determination (R\({}^{2}\)) and mean absolute error (MAE) for the prediction and validation between the phase content and relative intensity of the Raman peaks and phase content for CaS-CA.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & & \multicolumn{3}{c}{**Training Data**} & \multicolumn{3}{c}{**Validation**} & \\ \cline{3-8}
**Phases** & **Relative Intensity** & **Mean (R\({}^{2}\))** & **Mean (MAE)** & **Mean (R\({}^{2}\))** & **Mean (MAE)** & **Sum MAE** & **Relative Stability** \\ \hline MA-CaS & 412/157 & 0.96 & 4.35 & 0.96 & 4.52 & 8.87 & 0.56 \\ MA-CaS & 776/157 & 0.72 & 11.00 & 0.72 & 11.92 & 22.92 & 0.06 \\ MA-CaS & 412/200 & 0.87 & 7.53 & 0.87 & 7.84 & 15.37 & 0.14 \\ MA-CaS & 412/478 & 0.76 & 11.01 & 0.77 & 11.47 & 22.48 & 0.13 \\ MA-CaS & 776/478 & 0.75 & 10.65 & 0.74 & 11.29 & 21.94 & 0.07 \\ MA-CaS & 776/200 & 0.63 & 12.68 & 0.62 & 13.57 & 26.26 & 0.04 \\ \hline \end{tabular}
\end{table}
Table 5: Evaluation of the coefficient of determination (R\({}^{2}\)) and mean absolute error (MAE) for the prediction and validation between the phase content and relative intensity of the Raman peaks and phase content for MA–CaS.
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 3 Simulation Conditions and Model Validation
### Parameter Calibration  
The simulation parameters of Ti65 powder are calibrated by comparing the numerical and experimental measurements of the angle of repose (AOR) and the packing density of the formed Ti65 powder pile. Both AOR and packing density are widely used to characterize the effects of particle friction, restitution coefficient, adhesive force, and particle size [35]. Figure 2a illustrates the physical setup. The left image shows the experimental configuration, where Ti65 powder is discharged from a hopper positioned above a fixed platform under gravity, forming a powder pile. The right image depicts the corresponding DEM simulation of the same process. Notably, the particle size distribution of T65 powder in the simulation replicates that of the experimental powder, as detailed in the following section. Figure 2b compares the numerical and experimental results, where the AOR is defined as the angle between the horizontal base and the slope of the powder heap [36]. The deviations in both AOR and packing density are within 5%, indicating that the simulation parameters are sufficiently calibrated. The calibrated parameters used in the simulation are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Particle size, \(D\), \(\mu\)m & \(D_{50}\) = 38.7, \(D_{90}\) = 61.5 \\ [PERSON]’s ratio, \(\upsilon\), - & 0.28 \\ Density, \(\rho\), kg/m\({}^{3}\) & 4540 \\ [PERSON]’s modulus, \(E\), GPa & 117 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters used in DEM model.  
Figure 1: (**a**) Size distribution and SEM morphology (inset image); (**b**) chemical composition of Ti65 powder.
## 1 Introduction
Finally, the scalability of the GA-based optimization method can be constructed to address its applicability to higher frequencies and varying operating conditions. Specifically, the flexibility of the GA allows for seamless adaptation to different frequency bands, including those relevant to emerging technologies such as 5G and 6G. This is achieved by tuning the design constraints to account for higher operating frequencies and optimizing the metasurface parameters to support broader bandwidths and enhanced performance metrics. Furthermore, the inherent adaptability of the GA enables it to address varying operational scenarios, such as changes in beamforming requirements or environmental conditions, without significant modifications to the optimization framework. These features make the proposed method suitable for dynamic and scalable antenna system designs, ensuring its utility for future wireless communication systems. The structure of this paper is as follows: Section 2 provides a detailed explanation of the HMS design process, focusing on the use of GA optimization. Specifically, it examines the mechanism behind beam- and null-steering and utilizes the developed model to optimize cylindrical HMSs for wire antenna coatings. Section 3 showcases various configurations designed and modeled using the proposed approach, with the corresponding results compared against those obtained through full-wave numerical simulations. Finally, the conclusions summarize the main findings and outline potential future research directions.
## 5 Implementation and Results
### Experiment 2
#### 5.2.3 Comparison between the Proposed Models and the DL Based Model  
In this section, we compared the highest results of the 16 proposed models with the CNN model that was employed for ASD classification. According to the obtained results, for the PBC dataset from Table 10, we observed that the GWO-SVM gave better accuracy results of 99.66% compared to the CNN model (98.64). This can be attributed to the small size of the PBC dataset.  
Based on the results of the GE dataset from Table 10, we observed that the CNN model achieved better accuracy of 99.98% compared to the accuracy obtained by GWO-SVM, which was 99.34%.
# A Learnheuristic Algorithm for the Capacitated Dispersion Problem under Dynamic Conditions
## 3 Problem Definition  
The problem addressed in this article is an extension of the CDP, thereby considering the possibility of selected nodes not meeting their capacity. This probability of not utilizing the capacity is determined by dynamic contextual conditions.  
In a more formal way, the CDP can be defined on a complete, weighted, and undirected graph \(G(V,E)\), in which \(V\) is the set of facilities, and \(E\) is the set of edges connecting these facilities. If \(i,j\in V\), with \(i\
eq j\), each edge \((i,j)\in E\) has a distance \(d_{ij}\geq 0\) that satisfies the triangle inequality. All distances are symmetric, i.e., \(d_{ij}=d_{ji}\). Each facility \(i\in V\) has a known deterministic capacity \(c_{i}>0\). An aggregated servicing capacity \(b>0\) is required as a threshold. Then, the CDP consists of finding a subset \(O\subset V\) of facilities to open, with the aggregated capacity exceeding \(b\) and such that the minimum distance between any pair of facilities \(i,j\in S\) is maximized. In this context, \(x_{i}\) is a binary variable that takes the value one if facility \(i\in V\) is included in \(O\), and it takes the value zero if otherwise. Then, an integer programming model for this problem can be formulated as follows:  
\[\max \min_{i,j\in V,i<j} \{d_{ij}:x_{i}\cdot x_{j}=1\}\] (3) s.t. \[\sum_{i\in V}c_{i}\cdot p_{i}\cdot x_{i}\geq B \tag{4}\] \[x_{i}\in\{0,1\} \forall i\in V \tag{5}\]The objective function (3) maximizes the minimum distance between any pair of open facilities. Constraint (4) guarantees that the minimum required capacity is met where the probability of obtaining a capacity for a node \(i\) also depends on the weather \(w\), the congestion of the node (\(c_{i}\)), and the type of nodes already opened, which are determined by the dynamic context condition, i.e., \(p_{i}\sim Bern(\phi(w,\ c,\ o))\) for an unknown function \(\phi\), for a black box emulating reality. Finally, Constraint (5) defines the values that decision variables can take. Figure 1 shows an illustrative example of the dynamic CDP. It can be seen that the selection of the top right node has resulted in failure and a capacity of zero, but it still remains part of the solution, thus affecting the objective function and forcing the search for other nodes.