esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Creep Life Estimation  
The HPT geometry used for the creep life estimation is described in Table 4. The material constant C described in Equation (28) is taken as 20 and an HPT rotational speed of 15,000 rpm. The stress values and thermal load were used to obtain the LMP from the master curve of the blade material. The nickel alloy material Inconel 625 was assumed, with a density of 8250 kg/m\({}^{3}\). The Inconel 625 has a melting temperature of 1350 \({}^{\circ}\)C and a specific heat capacity of 800 J/kg \({}^{\circ}\)C [52]. Figure 5 shows that the rupture time to creep failure for the LH2 seems longer than the jet fuel, due to the fuel heating value on the expander after the combustion process is completed. There was a slight decrease in the turbine entry temperature, which would have also contributed to a longer life on the turbine blade, as the stress and thermal load values are lower than when the jet fuel was utilized in the turbofan engine as described by [PERSON] et al. [33]. The assessment shows a 15% improvement in extended blade life, compared with jet fuel. The effect of TET on creep life for the HPT blade is aligned with the work of [PERSON] et al. [53] for a given radial temperature distribution factor, which showed a 70% reduction in creep life for a 3% increase in TET. The TET variation between LH2 and kerosene showed a 0.53% reduction in LH2 TET than kerosene for a given thrust. This assessment did not account for coating, which can improve the life of the blade. Coating provides thermal insulation to the turbine blade. The coating thickness gives a linear temperature variation, as described in [PERSON] et al. [54] and [PERSON] et al. [55].  
It is expected for a new engine program that the engine's first overhaul is after approximately 10,000 h [56]. However, this value also depends on whether the engine does a long- or short-haul flight. The value obtained for jet fuel in this assessment is closely aligned with the results of [PERSON] et al. [56]. Aside from the LH2 and jet fuel comparison on creep life, [PERSON] et al. [35] compared the hydrogen fuel and natural gas effect on the HPT blade's creep life for an industrial gas turbine. The result also shows an improved creep life when hydrogen fuel was used rather than natural gas, which aligns with other literature reviews [2] on the longer engine life and lower maintenance cost for LH2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{2}{c}{**Environmental Effect Factor**} & \multicolumn{2}{c}{**Exergy Sustainability Index**} \\ \cline{2-5}  & **LH2** & **Jet Fuel** & **LH2** & **Jet Fuel** \\ \hline Whole engine & 2.90 & 2.95 & 0.345 & 0.339 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Environmental effect factor and sustainability index.
# Asymmetry Propagation in a Pipe Flow Downstream of a 90\({}^{\circ}\) Sharp Sarp Elbow Bend
## 2 Numerical Setup
### Computational Mesh  
Our meshing strategy followed best practice experiences from LES in OpenFOAM [42; 43]. We applied fully hexahedral meshes, which were generated with blockMesh utility in OpenFOAM. The coarsest mesh was used for fast calculation only, allowing us to accelerate the attainment of the developed turbulent flow. The obtained turbulent fields were then mapped to the middle and fine mesh as the initial conditions for the main simulations. Special care was taken for the middle and fine mesh to prepare good-quality computational cells with a maximum mesh non-orthogonality of 46\({}^{\circ}\) (average is around 4\({}^{\circ}\)) and a maximum skewness of 0.88, which all respect the best practice guidelines [42; 43]. The meshing details are given in Table 1, and a cross-sectional view of the pipe is shown in Figure 3. It should be noted that the non-dimensional cell sizes presented in Table 1 are calculated based on flow conditions at a Reynolds number of 5600. For our final simulations, the middle mesh was used for Reynolds numbers of 1400 and 5600, while the fine mesh was employed for the simulation at a Reynolds number of 10,000.  
The first cell height at the pipe walls \(\Delta y^{+}=\Delta y\cdot u_{\tau}/\
u\) was kept well below 1 for most of the cells in order to resolve the viscous sublayer of the momentum boundary layer. In the bulk region, we applied cubed cells with an aspect ratio close to \(1\), which are best suited for isotropic vortices in the turbulent flow. Grading with a constant stretching factor was applied to the adjacent cells in the intermediate region between the bulk region and the walls. The stretching factor was carefully chosen in order to guarantee the accuracy of the computations [42].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh** & **Coarse** & **Middle** & **Fine** \\ \hline Number of cells [in millions] & 0.305 & 5.95 & 13.7 \\ Number of near-wall prism layers & 31 & 31 & 37 \\ Stretching ratio (SR) & 1.0 & 1.1 & 1.1 \\ Max. aspect ratio AR & 52.6 & 108 & 127 \\ Non-orthogonality (mean/max) [\({}^{\circ}\)] & 5.6/35 & 4.1/45 & 3.9/46 \\ Maximum skewness & 1.22 & 0.86 & 0.88 \\ First cell height at walls [mm] & 0.3–0.6 & 0.04–0.56 & 0.02–0.56 \\ Streamwise cell size [mm] & 11.6 & 1.2 & 0.6 \\ First cell height at walls [wall units] & 2.7–5.4 & 0.36–4.8 & 0.21–4.8 \\ Streamwise cell size [wall units] & 100 & 10 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Meshing parameters for the three applied meshes. Cell sizes in wall units are calculated for the flow conditions at \(Re=5600\).  
Figure 3: Two details of the computational meshes: (**a**) Insight into the elbow section for the middle mesh. (**b**) Pipe cross-section for the fine mesh.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 4 Experiments and Results
### Posture-Based Gesture Recognition
#### 4.2.2 Model Implementation and Results  
As aforementioned in Section 3.3.3, we utilize a grid search [37] to explore the optimal designs considering the model size and performance. Specifically, the search space for the width, which determines the \(W\) size of each hidden layer, is set to four values by linearly scaling from 16: 16, 32, 48, and 64. \(N\) is verified by increasing it by one from 1 until the minimum depth that yields the optimal performance is found. The value of dropout for training is explored from 0 to 0.5 in increments of 0.1. We split the acquired dataset into training and validation sets at a ratio of 3:1. The batch size is configured to 64 and the models are trained for 1000 epochs using the Adam optimizer [38] with a learning rate of \(1\times 10^{-3}\). As a result, we design the MLP with three hidden layers containing 16, 32, and 16 nodes, respectively, resulting in a total of 1930 trainable parameters. These configurations are chosen to balance the model accuracy and size. Additionally, to enable deployment on resource-constrained environments such as edge devices, we convert the model weights to TensorFlow Lite [39].  
Figure 8 shows the confusion matrix of the posture-based hand gesture recognition. As shown in the results, the average classification accuracy for 10 types of hand gestures is over 98% on the test set, with the exception of the gestures of 'down' and 'down+left', whose values are 95.4% and 96.9%, respectively. We observed that extending the pinky finger in slightly occluded views resulted in noisy estimated joint values, leading to inaccuracies in the data acquisition and inference results. This issue can be mitigated by employing a robust hand pose estimator. Overall, the gestures were classified with high accuracy, making them well suited for the control of the drone.  
To evaluate the real-time feasibility of the proposed model, we measured the average recognition time per gesture using the trained model on a set of 100 samples. The inference was performed on an Intel i5-1135G7 processor, and the average time per gesture was  
Figure 8: The confusion matrix of the posture-based hand gesture recognition model.  
found to be 74 milliseconds (ms), demonstrating that the model is capable of recognizing hand gestures at a rate suitable for real-time applications.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Mathematical Model Building
#### 2.3.2 Wave Model  
The wind-generated wave model adopts the third-generation WWM (WWM-III) model, the full name of which is the Wind Wave Model. WWM-III solves the 2D wave spectrum equation of a plane [22]. The wave spectrum equation is:  
\[\frac{\partial}{\partial t}N+\
abla_{X}(\dot{X}N)+\frac{\partial}{\partial \sigma}(\dot{\theta}N)+\frac{\partial}{\partial\theta}(\dot{\sigma}N)=S_{tot} \tag{4}\]  
where the wave motion index \(N\) is defined as:  
\[N_{(t,\textbf{X},\sigma,\theta)}=\frac{E_{(t,\textbf{X},\sigma,\theta)}}{\sigma} \tag{5}\]  
where \(E\) represents the variance density of sea level elevation; \(\sigma\) is the relative fluctuation frequency; and \(\theta\) is the direction of fluctuation.  
Wave propagation from deep water to shallow water is accompanied by complex nonlinear processes, such as bed bottom friction, wave breaking and tumbling, and wave energy input imposed by the boundary, which are considered in the source terms of Equation (4). WWM-III is transformed by [PERSON] into a triangular unstructured grid model to achieve seamless coupling with SCHISM.
## 4 Experiments
### Exploratory Data Analysis  
In the exploratory data analysis (EDA) phase, we thoroughly investigated the distributions and interrelationships of key target variables, as depicted by Figure 2. Data were meticulously prepared and visualized using ggplot2 for data visualization, along with dplyr and tidyr for data manipulation, ensuring that actionable insights could be extracted. The analysis revealed that appendicitis was the predominant diagnosis, with 59.5% of patients confirmed to have the condition. Moreover, 61.8% of these cases were managed conservatively, reflecting a significant preference for non-surgical interventions. Severity analysis highlighted that 84.8% of appendicitis cases were classified as uncomplicated, underscoring the effectiveness of conservative management in these scenarios.  
Patients diagnosed with appendicitis exhibited significantly higher clinical scores (median AS = 7, PAS = 6) compared with those without the condition (median AS = 5, PAS = 4), demonstrating the effectiveness of these diagnostic tools. Additionally, infection indicators such as WBC count (White Blood Cell, a standard measure in blood tests to check for infections or inflammation) and CRP levels were elevated in appendicitis cases (median WBC count = 14.3 \(\times\) 10\({}^{9}\)/L, CRP = 16.0 mg/L), indicating a strong inflammatory response. Further analysis of management strategies revealed that primary surgical management was associated with the highest clinical scores (median AS = 8, PAS = 7) and inflammatory indicators. Patients undergoing surgery also exhibited a higher prevalence of symptoms, such  
Figure 2: Distribution of diagnosis, management, and severity categories.  
as lower right abdominal pain and nausea, compared with those managed conservatively. This suggests that more severe cases were appropriately triaged to surgical intervention, ensuring targeted and effective care. Finally, severity analysis showed that complicated appendicitis cases had higher median values for clinical scores (median AS = 8, PAS = 6), further validating the diagnostic scoring systems employed.  
In summary, the EDA provided critical insights into the dataset, reinforcing the diagnostic utility of clinical scoring systems and highlighting key factors that influence management strategies, which will inform subsequent phases of this study.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 3 Methodology
### SCN Inference Details  
The aim of this work is to utilize SCN for the detection of the carrier signal results directly with the fusion of PSD and BW regressions in the inference phase.  
First, we apply a \(1\times 7\) max pooling layer on the PSD regression to avoid the local maximum at inference time. Then, we pick the top 300 centers from the PSD regression, which are the scores of the predicted subcarriers. As a result, we can achieve the predicted subcarriers with the corresponding BW prediction by applying NMS.  
In the NMS process, the intersection-over-unit (\(loU\)) on carriers is defined by the following formula:  
\[IoU_{carrier}=\frac{BW_{overlap}}{BW_{union}} \tag{5}\]  
Figure 4: The PSD target example of the input power spectrum.  
where the \(BW_{overlap}\) and \(BW_{union}\) are the length of overlap and length of union between the two predicted subcarriers, as shown in Figure 5. The predicted subcarrier scores remain as \(>0.7\) and \(IoU>0.9\).
## 2 Study Area and Methods
### Monitoring Scheme
#### 2.2.2 Monitoring Methodology  
In order to measure the pressure from the tunnel's surrounding rock on the steel reinforcement, earth-pressure cells are positioned between the steel reinforcement and the steel arches, directly touching the steel reinforcement. These cables are fastened along the support lines of the steel arches during earth-pressure cells installation to ensure the monitoring cables remain intact during secondary lining pouring. Figure 3 displays a schematic representation of this arrangement.  
Figure 3: Diagram of installing earth-pressure box between arch and surrounding rock.  
Figure 2: Layout diagram of three types of sensors at different monitoring points.  
Five monitoring points are strategically placed along the tunnel monitoring section to observe anchor bolt axial forces. These points are located at the crown, left and right spring lines, and left and right haunches of the arch. Each point utilizes a vibrating steel anchor bolt dynamometer approximately 3 m in length. This load cell consists of three vibrating steel strain gauges connected in sequence, as illustrated in Figure 4. Installation involves creating anchor rod drill holes and using cement grouting to affix the load cells securely.  
Four monitoring points are positioned along the tunnel monitoring section to assess the steel arches' stress. These points are at the crown, the right side at a 60\({}^{\circ}\) angle, the right spring line, and the right haunch of the arch. Every monitoring point is outfitted with a vibrating steel strain gauge for the steel reinforcement of the arches. The installation diagram of the steel strain gauge is provided in Figure 5.
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
###### Abstract  
Blockchain technology is well known due to the advent of Bitcoin. With the development of recent years, blockchain technology has been widely used in medicine, digital currency, energy, etc. The practical Byzantine fault-tolerant (PBFT) algorithm is a consensus algorithm widely used in consortium blockchains. Aiming to address the problems of the PBFT algorithm, low consensus efficiency due to high communication complexity, and malicious behavior of the primary node leading to consensus failure, an improved PBFT algorithm based on a comprehensive evaluation model (TB-PBFT) is proposed. First, nodes are divided into several groups based on the multi-formation control strategy of an unmanned aerial vehicle (UAV) cluster, which significantly reduces the communication complexity. Second, a comprehensive evaluation model combining the entropy method, TOPSIS method, and Borda count is proposed, which uses the behavior of nodes as an evaluation index, and the comprehensive score of nodes is obtained according to the preferences of other nodes. Finally, the highest ranking node is selected as the primary node through the comprehensive evaluation model to ensure the security and stability of the blockchain network. We analyze TB-PBFT algorithms and compare them with other Byzantine fault tolerance algorithms. Theoretical analysis and simulation results show that the TB-PBFT algorithm can improve node scalability and fault tolerance and reduce communication complexity and view switching probability. We also prove that the comprehensive evaluation model can improve the consensus success rate of the algorithm, and the feasibility and effectiveness of the improved consensus algorithm are verified. Hence, it can be applied to the consortium blockchain system effectively and efficiently.  
PBFT; scalability; communication complexity; voting mechanism; comprehensive evaluation model +
Footnote †: journal: Applied Sciences  
1117. [[https://doi.org/10.3390/appl3021117](https://doi.org/10.3390/appl3021117)]([https://doi.org/10.3390/appl3021117](https://doi.org/10.3390/appl3021117))
## 4 Experiments
### Comparison Experiments  
We chose four methods for comparison. The first one was the classic semantic segmentation method UNet [34]; the second one was the point cloud super-resolution model T-UNet [22] with the ability to process point cloud temporal features; the third one was the LKA-T-UNet [23], which introduces the capability of long-distance spatial feature modeling based on T-UNet; the fourth one was the SMT-T-UNet [24], which introduces multi-scale spatial attention based on T-UNet. The experimental results on the two datasets we proposed are shown in Table 3. As can be seen, the method we proposed achieved the best PSNR and the lowest SSIM loss value on both simulated scene and real scene datasets. The results indicate the proposed temporal feature attention aggregation module and spatial feature enhancement module in this paper are designed to fully exploit the temporal and spatial information between consecutive point clouds, leading to improved point cloud super-resolution performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Methods** & **PSNR** & **SSIM Loss** \\ \hline \multirow{6}{*}{CARLA32-128} & UNet & 25.11 & 30.99 \(\times 10^{-5}\) \\  & T-UNet & 26.97 & 11.89 \(\times 10^{-5}\) \\  & LKA-T-UNet & 27.01 & 9.61 \(\times 10^{-5}\) \\  & SMT-T-UNet & 27.24 & 12.40 \(\times 10^{-5}\) \\  & TSE-UNet (ours) & **27.52** & **8.38 \(\times\) 10\({}^{-5}\)** \\ \hline \multirow{6}{*}{Ruby32-128} & UNet & 23.96 & 24.26 \(\times 10^{-5}\) \\  & T-UNet & 24.78 & 14.68 \(\times 10^{-5}\) \\ \cline{1-1}  & LKA-T-UNet & 24.75 & 14.49 \(\times 10^{-5}\) \\ \cline{1-1}  & SMT-T-UNet & 24.80 & 14.45 \(\times 10^{-5}\) \\ \cline{1-1}  & TSE-UNet (ours) & **24.82** & **14.32 \(\times\) 10\({}^{-5}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The PSNR and SSIM loss of different models on the CARLA32-128 and Ruby32-128 datasets. Bold indicates the best result.
# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 2 Related Works
[29] projected 18 machine learning models and 3 feature selection techniques (correlation-based FS, chi-square, and fuzzy rough set) to find the best prediction combination for heart disease diagnosis using the Cleveland dataset of 303 instances and 13 variables. [PERSON] et al. [30] proposed a solution to identify the presence/absence of heart disease by replacing missing values with the mean values during pre-processing. They trained three machine learning algorithms, namely, NB, SVM (linear and radial basis function), and KNN, by splitting the Cleveland dataset of 303 instances and 13 attributes into 50:50, 70:30, 75:25, and 80:20 training and testing ratios. [PERSON] et al. [31] implemented a novel hybrid model on the Cleveland heart dataset of 303 instances and 14 features with a 70:30 ratio for training and testing by applying DT, RF, and hybrid (DT + RF) algorithms. [PERSON] et al. [32] designed a stacked architecture to predict heart disease using RF, gradient boosting machine, and extreme gradient boosting with particle swarm optimization (PSO) feature selection using various heart disease datasets, including the Cleveland with 303 instances and 13 attributes. From the experimental works, it is understood that data pre-processing and feature selection can substantially enhance the classification accuracy of machine learning algorithms. During pre-processing, most researchers [18; 19; 21; 22; 26; 29; 30; 31; 32] replaced the missing values, either by using the mean value or the majority mark of that attribute, to make sure the dataset was comprehensive. In some works [20; 24; 25; 27], the missing valued instances were removed. Feature selection is a challenging task due to the large exploration space. It grows exponentially according to the number of features available in the dataset. To solve this issue, an effective comprehensive search technique is required during feature selection. Furthermore, some studies have employed ensemble models, which combine multiple basic learning algorithms to obtain a better prediction accuracy. However, the performance of these techniques can further be improved regarding accurately predicting disease.
# Passive and Active Solar Systems in Eco-Architecture and Eco-Urban Planning
## 3 Research: Passive and Active Solar Systems in Eco-Architecture and Eco-Cities
### Heliostats and Their Application
#### 3.3.1 Heliostats: Technological Aspects  
Basically, heliostats consist of the following parts: base and foundations, drives for changing the elevation angle and azimuth, and mirrors with their supporting structure. The foundations and base of the heliostat should be solid enough to withstand the effects of changing weather and to ensure the functioning of the heliostat throughout its life cycle (20-30 years) [34; 37]. The most important atmospheric factor influencing the high construction costs is wind pressure on the large plane that the mirror occupies. Different types of anchoring are used depending on the size of the heliostats. A concrete foundation is used for large heliostats (over 50 m\({}^{2}\) aperture), while piles driven into the ground can be used for heliostats in the 5-50 m\({}^{2}\) aperture range. For small heliostats (\(\leq\)5 m\({}^{2}\)), anchors are used [35]. The task of the supporting structure and drives is to maintain a stable and precise positioning of the mirror, such that the reflected light falls on the receiver, often located several kilometers away. Depending on the power source, electromagnetic motors and hydraulic actuators are used, as well as various solutions such as worm, toothed, chain and wave gears, a capstan, planetary gears, linear actuators and others. On mirrors with surfaces in the range of 1-10 m\({}^{2}\) standard glass, thin-film glass with a thickness of \(<\)1 mm is used. In the future, glass will probably be replaced with polymers with appropriate parameters [35]. In Poland, at the Faculty of Energy and Fuels, a part of the AGH University of Science and Technology in Krakow, work is currently underway on the construction of a spherical heliostat, to protect the mirror against the adverse effects of external conditions while limiting wind pressure. According to the authors, the spherical shape of the heliostat will help to favorably distribute and protect its components from wind pressure, facilitate cleaning, and minimize dirt by easy drainage of rainwater from the protective dome. This will simplify the mechanical structure of the elements by replacing the actuators, contributing to better use and environmental protection. This technology is protected by patent no. PL.220774 [21].