esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 2 System Description
### Comprehensive Analysis  
The zero-bias of the subcarrier phase discriminator is smaller than the code phase discriminator according to the simulation in Section 2.3. The symbols 0 and 1 in the subcarrier signal are alternately arranged with no randomness, leading to better symmetry of the correlation curve while assuming  
Figure 2: Schematic diagram of resolution error and zero-bias of the digital discriminator of the BOCs (1, 1) signal.
## 3 Results and Discussion
### Potential Economic and Environmental Impacts  
The hydrothermal CFRP recycling method studied is very interesting, especially for the aeronautics sector. CFRPs are essential for reducing the weight of aircraft, which improves fuel efficiency and reduces emissions. However, their disposal poses problems due to their durability and the fact that they are not biodegradable. [PERSON] et al. (2018) [PERSON] et al. (2018) conducted an economic analysis of CFRP waste management, showing that recovery processes, including hydrothermal techniques, can be economically viable, especially when targeting the recovery of high-quality fibers for aerospace applications. In this regard, [PERSON] et al. (2021) [PERSON] et al. (2021) emphasized how hydrothermal hydrolysis offers a sustainable recycling route, especially from high-volume industries such as aerospace.  
From an economic point of view, this process enables the following:  
* Reducing dependence on expensive virgin fibers, making recycled fibers a more cost-effective alternative, especially for industries such as automotive and sports equipment.
* Encouraging the development of industries related to the production and application of recycled fibers, thus promoting a circular economy.
* Saving on the cost of disposing of waste that is currently sent to landfill, especially as waste legislation is becoming more and more restrictive.
* Providing a new source of revenue for waste-management companies, as they can sell the recovered carbon fibers. In terms of environmental impact, this recycling process allows the following:
* Reducing the consumption of raw materials and natural resources that would be used to produce new fibers.
* Reducing greenhouse gas emissions and meeting sustainability targets, as CF recycling uses less energy than virgin fiber production.
* Reducing waste to landfills by recovering or recycling waste from industries such as aviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **TS (MPa)** & **TS Loss (\%)** & **E (GPa)** & **E Loss (\%)** \\ \hline T800S CF & 5880 & - & 294 & - \\ P11 & 5769 \(\pm\) 650 & 1.88 \(\pm\) 11.1 & 228 \(\pm\) 29 & 22.4 \(\pm\) 10 \\ (400 \({}^{\circ}\)C//242 bar//35 min//5\%IPA) & 5735 \(\pm\) 544 & 2.47 \(\pm\) 9.3 & 280 \(\pm\) 77 & 4.8 \(\pm\) 26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tensile strength (TS) and elastic modulus (E) of the recycled carbon fibers.  
* Promoting a circular economy that contributes to more sustainable industries.  
The main challenges for the implementation of this recycling technology are the high initial investment required for equipment and infrastructure, the optimization of the process to minimize its environmental footprint, the market acceptance of the recycled fibers, and the compliance with the regulations that will be developed to guarantee the quality of the recycled materials.
# Privacy-Preserving and Explainable AI in Industrial Applications
## 2 Privacy Preservation in Industrial AI Applications
### Review of Privacy-Preserving AI in Industrial Applications
However, the manufacturer may not feel comfortable with externalizing photos of nonacceptable parts. In a privacy-preserving setting, the photos would first be homomorphically encrypted or obfuscated, such that the external party cannot reconstruct the original images. The AI model would be trained on the encrypted or obfuscated images, and the trained model would be deployed as an AI service. During inference, the same encryption or obfuscation method would be employed to ensure that the AI model is not fed with out-of-distribution data. A possible technical solution was recently published for a healthcare application [44], which could be similarly applied in the industrial domain for the casting application. Therein, an image obfuscation algorithm was proposed that combines a variational autoencoder with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions. Although a drop in accuracy could be observed when the classifier was trained on obfuscated images, the performance was deemed satisfactory in the context of a privacy-accuracy tradeoff.
## 3 Verification  
### Grid Independence Verification  
The accuracy of numerical simulations is intricately linked to the grid resolution. However, escalating the grid count significantly amplifies computational resource consumption. Hence, determining an optimal grid scale becomes crucial to balance calculation precision and resource efficiency. This study focuses on analyzing the falling trajectory and posture of debris, verifying grid independence by altering the grid size on the falling debris surface. In Figure 5a, three grid models depicting different grid sizes on the falling debris surface are presented. All models incorporate ten boundary layers on the debris surface, with total grid counts of 44 million (coarse grid), 55 million (medium grid), and 67 million (fine grid). The IDDES turbulence model is uniformly applied across all models, considering a train speed of 300 km/h, and simulating the passage of a single train through a tunnel.  
The grid independence is evaluated using displacement and aerodynamic force coefficient in the \(X\) direction of the falling debris as comparison indices. Figure 5b,c display the  
Figure 5: Grid independence verification: (**a**) grid of different thicknesses; time history curve of (**b**) \(x^{\prime}\) and (**c**) \(C_{Fi}x^{\prime}\).  
time history curves for each comparison index obtained through numerical simulations with three distinct resolution grid models. Corresponding peaks are identified in the figures. Although the trends in displacement and aerodynamic force coefficient time-history curves remain consistent across all three conditions, the results derived from the medium and fine grid models exhibit greater similarity. The maximum values of \(x^{\prime}\) are \(-0.026\) and \(-0.027\), respectively, with only a 3.8% difference. Similarly, the minimum values of \(C_{Fx}\) are \(-0.035\) and \(-0.037\), respectively, displaying a 5.7% difference. These findings underscore the rationale of employing a medium grid strategy in this research, striking a balance between computational efficiency and simulation accuracy.
# Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input
## 1 Introduction
The color body part segmentations with 12 semantic parts are obtained by the segmentation CNN network RefineNet [13]. By utilizing body part segmentation, the gesture recognition network can focus on only the target person in the presence of multiple objects in the scene. The 3D human skeleton joints information of the target person is extracted by the 3D human pose estimation network called Pose3D [14]. Pose3D is a deep learning network that obtains the sequence of 3D poses based on the temporal information across the sequence of 2D joint locations in order to prevent temporally incoherent estimation. The gesture classes are predicted by a combination of these three submodels that are effectively fused by an integrated stacking module as the late fusion layers. The contributions of the paper are:  
* The reason for the distraction of skeleton joints extraction has been addressed, which hinders from the proper functioning of gesture recognition methods. In other words, the existence of noise or extra persons in the background can cause such distractions. * The solution to the problem of multiple actors in the scene, which caused the distraction of skeleton joints extraction, has been presented with target person extraction. The target person is segmented, and used for eliminating distraction in skeleton joints extraction. This idea of using target person extraction has never been addressed before in the literature as far as we know. The remainder of this paper is organized as follows: related works are given in Section 2. In Section 3, the proposed algorithm for gesture recognition is presented. The experiments on four public datasets to evaluate the effectiveness of our approach are given in Section 4. Finally, a conclusion is given in Section 5.
# Accurate RANS Simulation of Wind Turbine Stall by Turbulence Coefficient Calibration
## 5 Verification of the Calibration to the MEXICO Rotor  
### Introduction to the MEXICO Rotor  
In order to study how robust the present approach of calibration is for different airfoils and rotors, the calibration is applied to RANS simulation of the turbine used in the MEXICO (Model Experiments in Controlled Conditions) project [21]. The MEXICO turbine consists of a three-bladed rotor with a diameter of 4.5 m. Three different airfoils of DU91-W2-250, RISO-A1-21, and NACA 64-418 were used in the blade design. The DU91-W2-250 airfoil was applied from 20% to 45.6% span, the RISO-A1-21 airfoil from 54.4% to 65.6% span, and the NACA 64-418 airfoil outboard to 74.4% span. The turbine was tested in the 9.5 \(\times\) 9.5 m\({}^{2}\) open section of the largest European wind tunnel, the Large-Scale Low Speed Facility (LLF) of the German Dutch Wind Tunnels (DNW), in December 2006. The tests were performed at both non-yawed and yawed flows at different wind speeds and resulted in a database of combined blade pressure distributions, loads, and flow field measurements. As a follow up of the first MEXICO campaign, the New MEXICO measurement on the same turbine was carried out in the same wind tunnel between 20 th June and 4 th July 2014. Several open questions from the first campaign have been resolved, and good agreement has been found between the first and the new measurements [42].  
An international research collaborative project (Mexnext) within the framework of IEA Task 29 was created in June 2008. It was carried out in three phases. The first phase from September 2008 until December 2011 included an assessment of the measurement uncertainties and a validation of different categories of aerodynamic models. In the second phase from January 2012 until the end of  
Figure 18: The simulation results of limiting streamlines on the suction side of the blade, for different values of \(\beta_{\infty}^{*}\).  
2014, unexplored aerodynamic measurements on wind turbines (both in the wind tunnel as well as in the field) were analyzed from a wide variety of sources. In the third phase from January 2015 until December 2017, several rounds of comparisons were made between the New MEXICO measurements and the results from various codes. Some comparisons between CFD simulations and measurements are reported in [43; 44; 45].
# Mine Fire Behavior under Different Ventilation Conditions: Real-Scale Tests and CFD Modeling
## 2 Materials and Methods
### Model Formulation  
The large eddy simulation (LES) model was used, which allows the resolution of turbulent models in a computational calculation time shorter than other options by filtering the Navier-Stokes equations. It is a system widely used in the resolution of combustion models [17; 21; 23].  
The initial rate of heat release for the FDS was based on the equations expressed by [PERSON] [25], where parameters \(\Delta H\), \(x\), \(m_{a}\), and \(k\) are characteristic of the type of fuel used.  
\[Q=m\cdot\Delta H\cdot x\cdot S, \tag{1}\]  
\[m=m_{a}\left(1-e^{-kD}\right), \tag{2}\]  
where \(Q\) is the fire power (kW), \(m\) is the mass of fuel burning (g/s\(\cdot\)m\({}^{2}\)), \(H\) is the lower calorific value of the fuel (kJ/g), \(x\) is the combustion efficiency (%), \(S\) is the combustion free surface (m\({}^{2}\)), \(ma\) is the asymptotic mass for large fires (kJ/g), \(k\) is the effective absorption coefficient (m\({}^{-1}\)), and \(D\) is the equivalent diameter of the exposed surface (m).  
As the equivalent diameter was greater than 1 m, flames were considered fully turbulent and the burn rate was independent of the diameter [26]. Table 3 shows the simulation details, while the meshing is detailed in Figure 4. The calculation volume was 3159 m\({}^{3}\), with a cell size of 0.6 \(\times\) 0.6 \(\times\) 0.6 m, obtaining a total of 14,625 cells, which is similar to other previous studies [18]. A preliminary analysis of partial areas of the mesh with smaller grid cells was done, achieving similar results; therefore, the grid was considered as adequate for the purpose of the study.  
The control points of the parameters in the three orthogonal axes were marked on this mesh, with symmetry conditions, taking into account the sensors applied in the experimental tests for the contour conditions. The vertical exhaust rise, which communicates with the ramp, was located in the last meters of the drift (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Experimental Data**} & \multicolumn{2}{c}{**FDS Data**} & \multirow{2}{*}{**Fire Load (MW)**} \\  & **Airflow (m\({}^{3}\)/s)** & **Velocity (m/s)** & **Airflow (m\({}^{3}\)/s)** & **Velocity (m/s)** & \\ \hline
1 (Door 0\(\%\)) & 1.93 & 0.21 & 1.90 & 0.21 & 4.73 \\
2 (Door 17\(\%\)) & 5.10 & 0.57 & 5.30 & 0.59 & 4.73 \\
3 (Door 100\(\%\)) & 10.45 & 1.16 & 12.50 & 1.39 & 4.73 \\
4 (Door 100\(\%\)) & 11.05 & 1.23 & 12.50 & 1.39 & 9.46 \\
5 (Door 100\(\%\)) & 13.38 & 1.49 & 12.50 & 1.39 & 14.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values used in the different scenarios. FDS—fire dynamics simulator.
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 5 Characterization of the ECC Link Slabs' Working State
### Characteristics of Structural Strain Fields  
The strain fields of ECC link slabs can be simulated with the expanded data. The mid cross section of Type II ECC link slab stands as a perfect example. The structural working state leaps can be revealed by its strain field characteristics around the critical points. Figure 13 exhibits the strain fields at different loading steps around the yielding point (M1) and the initial failure point (M2) respectively, where different colors represent different levels of strains and the isopleth curve links the strains at the same level.  
It can be seen from Figure 13a that the whole cross section is basically governed by tensile strain at the 8 th loading step, though a small proportion of it stays in compression state. At the 9 th loading step, dramatic growth of tensile strain is observed, and the yielding point is reached in most regions. However, these strains decrease at the 10 th loading step, as a result of the bridging effect of PVA fibers. Thereafter, tensile forces are shared by more regions hence the tensile strains are evenly distributed within this cross section up to the 19 th loading step, as shown in Figure 13b. It can be inferred that the link slab is mainly in tension state after the yielding point. Besides, tensile forces are gradually transferred to the CFT grid as it can be observed from Figure 13b that tensile strains of the regions where CFT grid exists have increased and the shape of isopleth curves has changed. At the 20 th step, this cross section suddenly leaps into a state totally different from the previous ones, where nearly half area of this cross section is under compression. ECC materials in this zone cannot withstand tensile forces because of high extent of cracking and extensive fiber fracture. Actually, the tensile force bearing capacity is largely provided by the CFT grid. The initial failure occurs at this loading step and the strains keep increasing until the destruction of Type II ECC link slab.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation
### Simulation Scenario Construction for the Network Games
#### 4.1.1 Performance Evaluation of TCP Scenario  
The following is the analysis result of the TCP performance in the scenario based on the traffic statistics: traffic speed (bits/s): the message transmission/receiving rate was the same as the traffic speed (300 bytes/s) which has been set initially and the entire performance analysis result is shown in Figure 8 which shows that all the messages have been received normally without any packet losses. This, and the absence of any background noises, shows that all the messages have been received  
Figure 7: Scenario concept for network games.  
correctly. As before, the end-to-end delays are calculated as the time required for a packet to reach the receiver node from the sender node.
## 3 The Applied Algorithms
### The Walrus Optimization Algorithm  
As mentioned earlier, the update process of the WOA for new solutions will be executed through three phases [34], and the mathematical expression of each behavior will be described one by one as follows:  
**The feeding phase:** The feeding phase of the walrus group is controlled by the strongest individual and the strongest one has a large contribution in finding a new position as shown in the following equation:  
\[W_{n}^{new,\ fp}=W_{n}+rnd(SW-\tau W_{n}),\ with\ n=1,\ \ldots,\ N_{ps} \tag{13}\]  
**The migration phase:** In this phase, the whole population will move to other positions for better living conditions. The mathematical model of this phase is given below:  
\[W_{n}^{new,\ mp}=\left\{\begin{array}{c}W_{n}+rnd(W_{SE}-\tau W_{n}),Fit_{ W_{SE}}<Fit_{W_{n}}\\ W_{n}+rnd(W_{n}-W_{SE}),else\end{array}\right. \tag{14}\]  
**The escaping and fighting phase:** This phase is about the change in position while the walrus must escape or fight against their enemies in nature. The formulation of this phase is described by the following equation:  
\[W_{n}^{new,eff}=W_{n}+[LB_{n}^{*}+(UB_{n}^{*}-rndLB_{n}^{*})] \tag{15}\]  
with  
\[LB_{n}^{*}=\frac{LB_{n}}{CI};\text{ and }UB_{n}^{*}=\frac{UB_{n}}{CI} \tag{16}\]
# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 3 Decoding Results
The high accuracy and balanced precision and recall on unseen data are particularly encouraging, as they indicate that the model can reliably classify EEG signals into periods of high and low gamma activity in novel situations. Overall, these results indicate that our CNN model can effectively classify EEG data into periods of high and low gamma activity with high accuracy. The consistent performance across all three datasets suggests that the model has learned meaningful features from the EEG signals and can apply this knowledge to new, unseen data. This consistency is a strong indicator of the model's robustness and its potential for practical applications in EEG analysis. The slight variations in performance across the datasets also provide valuable insights into the model's learning dynamics and areas for potential improvement in future iterations. The balance between precision and recall in the test set indicates that the model is equally capable of identifying true positives and avoiding false positives. This balance is particularly important in EEG analysis, where both the correct identification of cognitive states and the avoidance of false alarms are crucial. In practical terms, this means that when the model identifies a period of high gamma activity, it is likely to be correct, and it is also likely to catch most instances of high gamma activity. This balanced performance is essential for applications in both research and clinical settings, where missed detections and false alarms can both have significant consequences. These results provide a strong foundation for the use of deep learning techniques in EEG analysis, particularly in the context of cognitive state classification based on gamma band activity. The high accuracy and balanced precision and recall suggest that this approach could be valuable in both research and clinical applications where the detection of specific brain activity patterns is important. For instance, this model could potentially be used in brain-computer interfaces, cognitive workload monitoring, or in clinical settings for the early detection of certain neurological conditions associated with changes in gamma band activity. The robust performance across different datasets also suggests that the model could be adaptable to various EEG recording setups and subject populations, although further testing would be needed to confirm this.
## 3 J-C Constitutive Model and Damage Failure Model
### J-C Damage Model  
According to the definition of the J-C damage model [7], considering the effects of stress triaxiality, strain rate, and temperature, the model parameters have clear meanings and can be determined through experiments. Therefore, it is widely used in many studies related to material failure and failure. The J-C model first defines damage as  
\[D=\sum\frac{\Delta\varepsilon_{p}}{\varepsilon_{f}} \tag{9}\]  
In the equation, \(D\) is the damage parameter factor, with a range of 0 to 1. At the beginning, \(D=0\). When \(D=1\), the material begins to fail. \(\Delta\varepsilon_{p}\) is the plastic strain increment within a time step, and \(\varepsilon_{f}\) is the failure strain under the combined action of the current time-step stress state, strain rate, and temperature. The equivalent plastic strain expression at the beginning of the damage is as follows [7]  
\[\varepsilon_{f}=[D_{1}+D_{2}\exp(D_{3}\sigma^{*})]\Big{(}1+D_{4}\ln\hat{ \epsilon}^{*}\Big{)}(1+D_{5}T^{*}) \tag{10}\]Here \(D_{1}\)-\(D_{5}\) is the failure parameter of the material, while \(\sigma^{\star}=p_{t}/\sigma_{\mathit{eff}}=-\sigma_{\mathrm{kk}}/\sigma_{ \mathit{eff}}=-R_{\sigma},p_{t}\) is the pressure, \(\sigma_{\mathit{eff}}\) is the equivalent stress, \(R_{\sigma}\) is the stress triaxiality, \(\dot{\epsilon}^{\ast}=\dot{\epsilon}/\dot{\epsilon}_{0}\) is the dimensionless plastic strain rate, \(\dot{\epsilon}_{0}\) is the reference plastic strain rate, and \(T^{\star}\) is the same as Equation (1).  
For the damage model, Equation (9), the first term reflects the influence of stress triaxiality, which is expressed in exponential form and controlled by \(D_{2}\) and \(D_{3}\). The equivalent plastic strain at the beginning of most material damage decreases with the increase of stress triaxiality, so a positive value, \(D_{3}\), is often taken. Compared with the commonly used equivalent strain failure rule, this model can reflect the effect of tensile and compressive stresses on material failure more effectively. The failure effect becomes smaller during tension, and the opposite is true: during compression, the failure strain is relatively large.  
The second item reflects the influence of strain rate. Under the same stress state, the failure strain is linearly related to the logarithm of strain rate. By adjusting parameter \(D_{4}\), a practical relationship between strain rate and failure strain can be obtained.  
The third item reflects the influence of temperature, and, at the same strain rate, the failure strain is linearly related to dimensionless temperature.