esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Study of the Lattice Structure
#### 3.3.1 Convection Coefficient Determination  
The low thermal inertia of the lattice structures produced by their low mass causes to rapidly decrease the temperature when they exit the oven. Consequently, although the sample's positioning on the refractory surface and the thermocouple's installation are performed as quickly as possible, the initial temperature for the cooling tests is between 60 and 100 \({}^{\circ}\)C. In Figure 7, the experimentally measured cooling stages of the four analyzed lattice structures are shown. Afterwards, employing the Matlab R2024b software, the cooling temperatures are approximated to an exponential negative function with a minimal error, R\({}^{2}\), close to the unit value, named the _Adjusted T\({}_{lattice}\)_ and represented by the red line in Figure 7.  
Once the _Adjusted T\({}_{lattice}\)_ and the experimentally measured _T\({}_{room}\)_ are obtained, they are introduced into Equation (3) to obtain the temperature-dependent convection. The value of the temperature-dependent convection coefficient is shown in the lower diagrams in Figure 7. Finally, the convection coefficient is approximated to a linear function for temperatures above 30 \({}^{\circ}\)C to introduce it in the Ansys Workbench model, as indicated in Figure 1. The equations for the linear convection coefficient, _h_(T), are shown in Table 5.  
As can be seen, the more open the lattice structure is--see lattice 4--the more easily the air flows between the reticular struts and the higher the convection coefficient. Moreover, in  
Figure 6: Manufactured lattice structures: (**a**) inside the RenAM-400 PBF-LB machine attached to the printing plate; (**b**) final components removed from the plate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & _h_**(T) (W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\))** \\ \hline
1 & 0.05\(\cdot\)T + 3.5 \\
2 & 0.05\(\cdot\)T + 6.5 \\
3 & 0.10\(\cdot\)T + 6.0 \\
4 & 0.133\(\cdot\)T + 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Temperature-dependent convection coefficient approximated to a linear function.  
case 2, a higher convection coefficient than in case 1 is obtained, which is logical considering that the lattice pattern has been extended in the Y direction.
## 1 Introduction
The result is a 3D map of displacement vectors, in terms of impulse functions, representing the collective displacement experienced by all the speckles within the subset of voxels. Mathematically the DVSP algorithm may be considered as an equivalent operation to the phase-only cross-correlation at the spectrum plane. The phase-only filter (POF) can result in a sharper impulse response function, and its signal is considerably stronger than that of the noise spectrum [6; 40]. However, if the displacement between the subsets is large, a fair amount of noise is imposed in the correlation surface resulting from the nonoverlapping areas. This effect gives rise to a poor signal-to-noise ratio. In this paper, a multi-scale and coarse-fine subset calculation process and a subvoxel shifting techniques are introduced into the DVSP algorithm to improve its accuracy. As a demonstration, we applied the new algorithm to analyzing the internal deformation of a red sandstone cylinder specimen exposed to uniaxial compression and a woven composite beam specimen under three-point bending.
## 3 Background
### Basics about Particle Swarm Intelligence  
The theory of particle swarm intelligence was proposed by [45] to solve some optimization problems in engineering. The theory is inspired by the animal world by imitating the swarm behavior in searching for food [59]. Particle swarm optimization (PSO) algorithms prove effective for handling nonlinear spaces with discontinuities [48; 59; 60].  
Each position of a particle in the swarm is a potential solution to the given problem. The particle's position is modified through a variable vector, called \"velocity vector,\" at each iteration of the algorithm [45]. The mathematical equation, which describes the velocity vector, has no unitary form, a wide diversity of formulations existing in the literature, depending on the specific problem [48; 59; 61]. In the classical form of the PSO algorithm, the position and velocity of a particle are given by [45]:  
\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{1}\]  
\[v_{i}(t+1)=\omega\cdot v_{i}(t)+c_{1}\cdot r_{1}(t)\cdot[y_{i,t}-x_{i}(t)]+c_{2 }\cdot r_{2}(t)\cdot[z_{t}-x_{i}(t)] \tag{2}\]  
where: \(x_{i}(t)\) is the position of particle \(i\) at iteration \(t\), \(x_{i}(t+1)\) is the position of particle \(i\) at iteration \(t+1\), \(v_{i}(t)\) is the velocity of particle \(i\) at iteration \(t\), \(v_{i}(t+1)\) is the velocity of particle \(i\) at iteration \(t+1\), \(\omega\) is the inertia (a coefficient, which serves as the memory of the previous velocities), \(c_{1}\) and \(c_{2}\) are \"acceleration\" constants, \(r_{1}(t)\) and \(r_{2}(t)\) are random real numbers, chosen in the interval [0,1]; \(0\leq r_{1}(t)\), \(r_{2}(t)\leq 1\), \(y_{i,t}\) is the best position visited by particle \(i\) up to that moment, \(y_{i,t}-x_{i}(t)\) is the cognitive component (representing the own experience of particle \(i\)), \(z_{t}\) is the best position up to the moment \(t\) identified by the whole swarm of particles, \(z_{t}-x_{i}(t)\) is the social component (representing the opinion of the whole swarm about the best experience that the swarm met up to the moment \(t\)). According to the literature in the field, \(c_{1}\) and \(c_{2}\) are chosen in the interval [0,4] (e.g., [45; 46; 59; 60; 61; 62; 63; 64; 65; 66]). The evolutionary algorithm is repeated while a termination criterion is achieved or the changes to the level of velocity are close to 0. A utility function \(f(x_{i}(t))\) is associated with the algorithm for assessing the solution's optimality. In the context of Equations (1) and (2), one can conclude that:  
\[y_{i,t+1}=\left\{\begin{array}{ccc}y_{i,t}&\{f(x_{i}(t+1))\ \ \ \lhd\ f(y_{i,t})\}\\ x_{i}(t+1)&\{f(x_{i}(t+1))\ \ \rhd\ f(y_{i,t})\}\end{array}\right. \tag{3}\]  
where \(\lhd\) shows that \(f(x_{i}(t+1))\) is a better solution than \(f(y_{i,t})\), whereas \(\rhd\) shows that \(f(x_{i}(t+1))\) is a better solution than \(f(y_{i,t})\).
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Individual Differences in Quiet Eye Strategies  
While general trends were observed across skill levels, notable individual differences emerged:  
* Two elite players demonstrated exceptionally long QE durations (>350 ms) across all shot types.
* One intermediate player showed QE characteristics similar to elite players, particularly in smash shots.
* Novice players exhibited the highest variability in QE duration and onset across trials.  
Figure 7 is a scatter plot of QE duration vs. shot accuracy for all badminton players, color-coded by skill level to differentiate between each group in QE strategies.  
Scatter Plot: QE Duration vs. Shot Accuracy.  
X-axis: QE Duration (milliseconds).  
Y-axis: Shot Accuracy (percentage).  
Color coding: Elite players (Blue), Intermediate players (Green), Novice players (Red).  
Figure 6: SHAP dependency plot for QE duration and racket speed.  
The pink shaded region highlights the high variability in performance among novice players, showing a wide spread in both QE duration (100-250 ms) and shot accuracy (30-60%).  
Key features of the plot are as follows:  
From the distribution, we see elite players (in blue) clustering towards the upper right. These players exhibit longer Quiet Eye periods, typically 250-400 milliseconds, paired with accuracy rates of 70-95%. The green dots, representing intermediate players, scatter across the middle ground. Their QE durations fall between 150 and 300 milliseconds, with accuracy levels spanning 50-80%. In contrast, novice players (shown in red) populate the lower left quadrant. Their pattern is more dispersed, characterized by brief QE durations of 100-250 milliseconds and generally lower accuracy, hovering between 30 and 60%.  
Two blue points stand out on the far right, labelled as elite players with exceptionally long QE durations (>350 ms) and very high accuracy (>90%). One green point is noticeably higher and further right than the others, representing an intermediate player with elite-like characteristics, especially for smash shots. The area in pink highlights the high variability among novice players. A positive trend line runs from the bottom left to the top right, indicating the general correlation between QE duration and shot accuracy across all skill levels. A few points from each group fall outside their main clusters, highlighting individual variations within skill levels.  
This scatter plot illustrates how QE duration relates to shot accuracy and how this relationship varies among individuals and skill groups, demonstrating the correlation between skill level, QE duration, and shot precision.
# Assessing Efficiency in Artificial Neural Networks
## 4 Results  
### Accuracy, alQ, and Neural Efficiency  
Testing 81,004 model combinations was performed to understand trends in alQ, neural efficiency, and accuracy. For the training data (not shown), accuracy increased monotonically with the number of neurons added in each layer, but the test data showed a slight decrease in accuracy as the number of neurons in Hidden Layer 1 (\(N_{1}\)) contained more than 128 neurons (Figure 1a). In contrast, alQ (\(\beta=2\)) values reached a local maximum when \(N_{1}=8\) and \(N_{2}=4\) (Figure 1b). The decrease in alQ values are due to the trend in neural efficiency to decrease as the number of neurons in a layer increases (Figure 1c,d). It was generally observed that neural efficiency was inversely related to the number of neurons, and changing the number of neurons in one layer had an impact on the neural efficiency of other layers. For example, for networks where \(N_{1}=8\), the neural efficiency of Layer 1, \(\eta_{1}\), generally increased as \(N_{2}\) increased, except there was a local minimum at \(N_{2}=16\). Local minima can be observed in other areas where the number of neurons in either layer is held constant, and the changes in efficiency for the same layer are observed (e.g., when \(N_{2}=8\), a local minima occurs when \(N_{1}=32\)). Similar trends were observed in the LeNet-5 models, where changes in the number of neurons in one layer affected the efficiency of other layers.  
Figure 1: Trends in accuracy (**a**), alQ (**b**), and neural efficiency for Hidden Layer 1 (**c**) and Hidden Layer 2 (**d**) in the LeNet-300-100 model. Data shown are for test data. All values range from 0 to 1, and values are the mean \(\pm\)95% confidence interval of 11 replicates.  
Additional experiments were performed to identify networks with the highest alQ for each architecture. Analysis of the top three neural networks for accuracy or alQ for both LeNet models are shown in Table 1. For the LeNet-300-100 models, the model with the highest test accuracy (\(N_{1}=128\), \(N_{2}=1024\)) achieved an accuracy of 97.58% \(\pm\) 0.04% with an alQ of 32.7 \(\pm\) 0.01 (values are mean \(\pm\) 95% CI, n = 11). The highest alQ model (\(N_{1}=11\), \(N_{2}=4\)) had an accuracy of 92.91% \(\pm\) 0.19% with an alQ of 86.41 \(\pm\) 0.71. Thus, the network with the highest alQ was 4.76% less accurate but contained 3.60% of the parameters.  
The differences between the networks with the highest accuracy and those with the highest alQ were even more drastic for the LeNet-5 models. The network with the highest accuracy (\(N_{1}=N_{2}=N_{3}=1024\)) had an accuracy of 99.58% \(\pm\) 0.02% and an alQ of 24.28 \(\pm\) 0.004 (values are mean \(\pm\)95% CI, n = 11). However, the network with the highest alQ (\(N_{1}=3\), \(N_{2}=9\), \(N_{3}=4\)) had an accuracy of 96.84% \(\pm\) 0.18% and an alQ of 88.28 \(\pm\) 0.46. The network with the highest alQ had a lower accuracy by 2.32% but contained 0.0032% of the parameters.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 3 Finite Element Modeling of the Micropump
### Boundary Conditions
#### 3.2.1 Opening Condition  
The opening boundary condition was applied to the inlet of the microneedle and pressure was considered 100 kPa, equivalent to human blood pressure.
# Mobile Network Operators' Assessment Based on Drive-Test Campaign in Urban Area for iPerf Scenario
## 3 Correlation Analysis of QoS Metrics
### PCC Results for UDP  
The PCC results for UDP are presented in Figure 5. Similar to TCP (see Figure 4), Figure 5 illustrates one heatmap averaged for all MNOs (a); and four heatmaps for each MNO, i.e., (b) Orange, (c) Play, (d) Plus, and (e) T-Mobile, respectively. Each heatmap shows PCCs for UL and DL illustrated in the upper-right and lower-left edges, respectively.  
The evaluation of PCCs for UDP generally shows that the correlation degrees of parameters are lower than for TCP. Th-Rx is very weakly correlated with Th-Sx and RDR. Meanwhile, the correlation of Th-Rx with Th-Rate is very strong (i.e., linear), and with JT, it is moderate or strong. As mentioned in Section 2.3, for UDP, Th-Sx should be set to a large value that exceeds the throughput achievable. When analyzing measured Th-Sx values, they are usually constant values, much larger than Th-Rx. Therefore, considering Equation (2), we should expect a small absolute value of PCC for Th-Rx and Th-Sx and a very large one for Th-Rx and Th-Rate. On the other hand, the practically constant value of Th-Sx is the reason for a very weak correlation with other analyzed metrics. In the UDP analysis, JT is additionally included. As mentioned, this parameter is moderately (Plus), borderline moderately/strongly (Orange, Play), or strongly (T-Mobile) correlated with Th-Rx and Th-Rate. On the other hand, the correlation of JT with signal metrics is weak. In the case of Th-Rx and Th-Rate, correlation degrees with signal parameters are usually moderate for UL and DL. The nature of the correlation between the signal parameters is similar for UDP and TCP, i.e., from moderate to strongly correlated. This is due to the fact that the signal parameters depend primarily on the propagation conditions and the mutual location of the UE and base station and not on the provided services.
## 3 Results and Discussion
### Topic 3: Innovative Models of Healthcare Delivery
#### 3.4.2 Public-Private Partnerships
Further challenges to implementing PPPs relate to government regulations [59], inadequate resources [59; 62], poor communication between public/private sectors [59; 62], negotiating shared risk [59], inadequate monitoring and evaluation [59], poor management [62], human resource concerns (distrust/power disparity between partners) [62], and IT systems (poor documentation and systems used by the different partners being incompatible) [62]. The administrative burdens associated with PPPs appear to be substantial. For example, [PERSON] et al. [63] described a model of physiotherapy care whereby a private physiotherapy organisation serviced hospital patients and aged care residents and the health service provided treatment rooms for them to see patients. The model was deemed successful, as it improved access for patients and satisfied the various stakeholders. However, while it was branded a successful project, they reported a significant administrative burden in contract management/accounting, and a lengthy negotiation was required to finalise the contract. They also reported that managing a private service within a public-healthcare setting created challenges via the uncertainty in the responsibilities for managing the service and contracts. As such, while there is no definitive reason that PPPs should or should not be implemented in rural/remote healthcare services, serious consideration should be given to the question of whether potential advantages outweigh the administrative burden and time taken to finalise contracts. In addition to the feasibility/practical considerations of models such as PPPs, the evidence also highlights that companies should critically assess how these healthcare models may impact the local communities in the long term. Fluctuations in healthcare access and changes to the local economies where employees (oil, gas, and mining) are coming to work heavily affect more remote communities [64]. While IOGP guidelines [26] recommend that healthcare models in the oil and gas industry be able to be scaled up or down as required, data suggest that future changes to service provision in the local community (i.e., scaling down) likely will have significant impacts. Therefore, any partnerships for the delivery of healthcare should be those which will not negatively impact the local community at the end of the contract period.
## 2 Methods
### Radiometric Properties Computation  
The MSTM is used to calculate the radiometric properties of the particle aggregates in this study. The MSTM is one of the most efficient and accurate models used to numerically calculate the random-orientation absorption properties of spherical particle aggregates with non-overlapping surfaces. It employs the addition theorem of vector spherical wave functions to account for mutual  
Figure 3: Effective refractive indices computed with the Maxwell–Garnett (MG) method (solid lines: the real part of refractive index \(n\); dashed lines: the imaginary part of refractive index k. Black line: \(m_{2}=3.102+0.0925\)i; red line: \(m_{2}=1.46+2.75\)i; blue line: \(m_{2}=0.15+3.15\)i).  
interactions among the multiple-sphere system, and its T-matrix for deriving particle absorption properties can be achieved from the individual spheres [30]. The robust and popular MSTM code has already been widely utilized for many numerical investigations of the absorption properties of fractal aggregates [3; 28; 48]. The MSTM assumes that the T-matrices of all particles in the aggregate are the superposition of individual contributions from each particle. It can be expressed as:  
\[\mathbf{E}_{sca}(\mathbf{r})=\sum_{j=1}^{N}E_{sca,j}(r) \tag{2}\]  
where \(\mathbf{E}_{sca,j}\) is the scattered flied for sphere \(j\). The \(\mathbf{r}\) is the vector connected to the observation point and base reference system. The scattering particles interact with each other and are related to the scattering field. Thus, the excitation field excited on each particle can be described as the superposition of the external incident field and the associated field scattered by other particles:  
\[\mathbf{E}_{\text{inc},j}(\mathbf{r})=\mathbf{E}_{\text{inc},0}(\mathbf{r}) \sum_{l=1,j\
eq j}^{N}E_{sca,l}(r),j=1,\dots,N \tag{3}\]  
where \(\mathbf{E}_{\text{inc},j}\) is the incident flied for sphere \(j\), and \(\mathbf{E}_{\text{inc},0}\) is the external incident flied. With the translation of particle \(i\) to particle \(j\), the T-matrix for the particle aggregates is calculated. The aggregate T-matrix can be used to compute the amplitude matrix for a fixed orientation of aggregates and can be input into analytical procedures for averaging scattering characteristics over orientations [30; 49].  
In this article, the MSTM is used to calculate the radiometric properties of particle aggregates. A FORTRAN-90 Multiple Sphere T-matrix (MSTM) code developed by [PERSON] [50] is utilized. The latest version of the MSTM code was released in 2013 (available at [51]). The computations were taken on a Dell workstation with 2 Intel Xeon Gold 5118 CPUs (2.30 GHz), 2 \(\times\) 128 GB memory. Each computation took about 4 h to finish.
## 4 Results
### Cost-Effectiveness and Trade-Offs  
The Pareto front is a method used to identify optimal trade-offs between competing objectives: in this case, accuracy and cost. A model is considered Pareto-optimal if no other model performs better in both accuracy and cost simultaneously. This means that improving one aspect (e.g., accuracy) would come at the expense of the other (e.g., cost). Models on the Pareto front represent the best possible trade-offs in terms of the available options.  
Based on the Pareto front analysis in Figure 3, the optimal model selection depends on the desired balance between accuracy and cost. For scenarios prioritizing minimal cost while maintaining reasonable performance, **GPT-4o-mini** emerges as a compelling choice, offering the lowest cost per query on the Pareto front. However, if accuracy is the primary criterion, **GPT-4o** provides the highest accuracy among the analyzed models, although at a moderately higher cost. **Gemini-1.5-pro** strikes a balance between the two extremes, offering a favorable trade-off between cost and performance. **Qwen-2.5-72b**, the only open-source option on the Pareto front, provides a competitive middle ground, with slightly higher cost but improved accuracy compared to GPT-4o-mini. Thus, the choice of model depends on the specific requirements of the use case, such as budget constraints, open-source preference, or the criticality of high accuracy in SQL query generation.  
In the medical domain, where accuracy can directly impact patient care, the higher cost of GPT-4o or Gemini-1.5-pro may be justified for critical applications. However, for large-scale noncritical deployments, such as preprocessing queries for exploratory  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & \begin{tabular}{l} **Price/1M Input Tokens** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{l} **Price/1M Output Tokens** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{l} **Price per Query (s)** \\ \end{tabular} \\ \hline Claude-3.5 & 3.00 & 15.00 & 0.00701 \\ Gemini-1.5-pro & 1.25 & 5.00 & 0.00241 \\ GPT-4o & 2.50 & 10.00 & 0.00484 \\ GPT-4o-mini & 0.15 & 0.60 & 0.00029 \\ Llama3.3-70b & 0.90 & 0.90 & 0.00163 \\ Mixtral-8x22b & 0.90 & 0.90 & 0.00221 \\ Owen-2.5-72b & 0.90 & 0.90 & 0.00184 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cost analysis of models for natural language to SQL transformation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Accuracy (\%)** & **Average Tokens In** & **Average Tokens Out** \\ \hline Claude-3.5 & 43.4 \(\pm\) 0.1 & 1986.052 & 70.085 \\ Gemini-1.5-pro & 60.8 \(\pm\) 0.4 & 1716.036 & 53.653 \\ GPT-4o & 64.1 \(\pm\) 0.6 & 1710.875 & 55.880 \\ GPT-4o-mini & 53.6 \(\pm\) 0.3 & 1710.875 & 55.155 \\ Llama3.3-70b  
analysis, the cost-efficiency of GPT-4o-mini or the open-source nature of Qwen-2.5-72b could be advantageous.