esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error.  
aircraft inspection; defect detection; thermography; visual inspection; defect estimation; photogrammetry; machine-learning; NDT; composites; metallic
# Operational Temperature Effect on Positioning Accuracy of a Single-Axial Moving Carrier
## 4 Conclusions  
This study investigated the effect of the ambient environmental temperature on the positioning accuracy of a periodically-moving carrier. The moving carrier was operated in an environmental chamber in which the operational temperature could be controlled by an air conditioning system. Different operational temperature modes, including a stable temperature, a temperature rise, a temperature decline, summer daytime hours, and winter nighttime hours, in terms of seasonal climate change in Taiwan, were generated within the environmental chamber by the air conditioning system to investigate the operational temperature's effect on positioning accuracy. From the experimental  
Figure 8: The flow fields and temperature contours in the chamber in the cases where the air inlets and outlets were located, respectively, on opposite sides and on the same side at the inlet air velocity of 2.36 m/s.  
measurements of the periodically-moving carrier, it is found that the operational temperature conditions can significantly affect the positioning accuracy of the moving carrier, especially in the case of an operational temperature decline. Under stable operational conditions, the positioning accuracy of a moving carrier can be considerably improved. In comparison to the case of an operational temperature decline, the positioning accuracy improvement reached 29.6%.  
Thus, under a stable operational temperature, the positioning accuracy of a moving carrier can be significantly improved. Moreover, the temperature distributions within the chamber were numerically investigated, showing that with a parallel flow pattern in the chamber, the movement of the carrier can cause less of a temperature variation in the chamber. The positioning accuracy can be further enhanced by a parallel flow pattern design. In the study, the ventilation design can be applied to the designs of semiconductor equipment, and electronic, optical, and measuring components in which effective heat dissipations are necessary.  
In this study, the positioning accuracy analysis for a single-axial carrier was investigated experimentally. The results obtained in this study can become a useful reference for the design of one-dimensional moving machines, such as a surface measuring system, hardness testing device, image measuring machine, and so on. Based on the results, the positioning accuracy in two-axial, three-axial, or even five-axial machines can be evaluated.  
The author gratefully acknowledges the financial support provided to this study by the Industrial Technology Research Institute under grant no. NCUT15 TEP001.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; and [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Pressure Sensors
#### 4.1.1 Capacitive  
Capacitive sensors quantify the magnitude of force by detecting variations in the separation between the upper and lower electrodes within an elastomer medium. This technique offers a superior degree of force sensitivity and exceptional dynamic performance. However, the range of force the sensor can detect is limited, and it is susceptible to humidity and electromagnetic interference (EMI). A capacitive pressure sensor comprises two primary layers: the dielectric layer and the lead-out electrode layer [5]. These sensors offer significant benefits because they can be used with biological materials, opening new opportunities for monitoring human biopotential energy. Capacitive sensors provide accurate and stable measurements without requiring direct skin contact, conductive liquids or gels, or fixation, distinguishing them from other approaches [18]. They are also an excellent option for daily use because they are not sensitive to environmental factors [3].
# Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
## 1 Introduction
### Studies About Affective Touch
#### 1.2.1 Different Actuation Technologies to Imitate Pleasant Stroking  
In the field of Human-Computer Interaction, many studies focus on using different actuation technologies to reproduce CT stroking. The common approach is using vibrotactile stimulation. With varying intensity, frequency, velocity, and direction of motion, researchers generate pleasant sensations, similar to real touch [38; 39; 40]. [PERSON] et al. also found the more continuous the stimulation, the more pleasant it was and the lower the arousal level [41].  
In addition, studies have used SMA or voice coil to generate mechanical movement on the skin, thereby producing a feeling of pressure or friction and providing pleasant and natural sensations [42; 43; 44]. [PERSON] et al. studied the VPS (Vibration, Pressure, and Shear) display and found that participants rated the shear mode the highest pleasantness and continuity [45]. [PERSON] et al. designed a wearable device consisting of a fabric sleeve and thermoplastic pneumatic actuators arranged in a linear array and found pressure changes during the inflation process did not affect continuity or pleasure [46]. [PERSON] et al. developed a wearable device, using synthetic fur which simulated a gentle stroking sensation on the user's forearm. Results showed that participants who received affective touch experienced lower state anxiety and the same physiological stress response level compared to the control group participants. They also found that affective touch facilitated stress regulation by rendering pleasantness, providing emotional support, and shifting attention [47].
## 3 Application Results
### Results for the Integration of OPELM, CHAID and ARCH Models
It was followed by Dizaj-ARCH-CHAID (NSE = 0.83), Tapik-ARCH-CHAID (NSE = 0.82) and Tapik-ARCH-OPELM (NSE = 0.82). Similar to the RMSE metric, MAE metric had the highest value in Dizi-ARCH-OPELM (MAE = 1.268 m\({}^{3}\)/s). It is followed by Dizaj-ARCH-CHAID (MAE = 2.039 m\({}^{3}\)/s), Tapik-ARCH-OPELM (MAE = 3.215 m\({}^{3}\)/s), and Tapik-ARCH-CHAID (MAE = 3.167 m\({}^{3}\)/s). In the case of the ratio of RMSE to the standard deviation, Tapik-ARCH-OPELM had the highest value (RSD = 0.422). It was followed by Tapik-ARCH-CHAID (RSD = 0.418), Dizi-ARCH-CHAID (RSD = 0.348), and Dizaj-ARCH-OPELM (RSD = 0.319). Figure 6: The histograms of observed vs. predicted monthly streamflow using hybrid ARCH-OPELM/CHAID models for calibration and validation stages, and scatter plots of hybrid proposed models in validation stage with least square regression line and coefficient of determination for (**a**) Dizaj and (**b**) Tapik stations. In the validation stage, Dizaj-ARCH-CHAID had the highest R-value of 0.96. It is followed by Tapik-ARCH-CHAID (R = 0.94), Dizaj-ARCH-OPELM (R = 0.91), and Tapik-ARCH-OPELM (R = 0.89). Dizaj-ARCH-CHAID had the lowest error (RMSE = 1.289 m\({}^{3}\)/s). It was followed by Dizaj-ARCH-OPELM (RMSE = 1.496 m\({}^{3}\)/s), Tapik-ARCH-CHAID (RMSE = 2.662 m\({}^{3}\)/s) and Tapik-ARCH-OPELM (RMSE = 3.671 m\({}^{3}\)/s). By considering NSE, Dizaj-ARCH-CHAID had the highest value (NSE = 0.92). It was followed by Tapik-ARCH-CHAID (NSE = 0.86), Tapik-ARCH-OPELM (NSE = 0.78), and Dizaj-ARCH-OPELM (NSE = 0.38). By considering MAE, ARCH-CHAID had the lowest value for both Dizaj and Tapik, respectively, 0.719 (m\({}^{3}\)/s) and 1.467 (m\({}^{3}\)/s) compared to other standalone and hybrid models. In the case of the ratio of RMSE to the standard deviation for Tapik station, ARCH-CHAID with RSD = 0.419 was better validated than the ARCH-CHAID (RSD = 0.487).
## 3 Simulation Results
### Joint Design System Based on Deep Learning
#### 3.8.2 DNN Trade-Off Analysis
This phenomenon can be explained by the characteristics of the concurrent transmission of the communication and radar signals in SSF D-JCAS. This simultaneous operation results in probable interference during the sensing process. Meanwhile, the interference is reduced by the separation of the communication and radar functions in the traditional DCAS, resulting in improved sensing accuracy. Furthermore, with a higher communication SNR, better detection performance is achieved from the MSE curves under DL application for both SSF D-JCAS and DCAS. Specifically, upon examining the turning points in MSE curves, we recognize that the DNN approach for the SSF D-JCAS exhibits a faster leftward shift compared to the DNN approach for the traditional DCAS. This observation emphasizes the effectiveness of the DL approach for the SSF D-JCAS system when evaluating the radar sensing performance of the joint design system.
## 4 Results and Analysis  
As described in Section 3, our experiment involved a setting in which a car generated a CAM and transmitted it to an RSU over a 5G connection. Distances between the OBU and RSU were varied between 30 m, 50 m, 70 m, and 80 m, reflecting common scenarios encountered in urban and highway environments, where shorter communication distances are common due to the presence of intersections, traffic signals, and closely spaced infrastructure elements. Communication distances may be longer in highway scenarios, reflecting the relatively open and linear nature of highways. This experiment allowed us to explore the impact of communication range on the performance of the edge node in terms of energy consumption (see Figure 5 and Table 2) and CPU load (see Figure 6 and Table 3), which is crucial for the seamless operation of vehicular communication networks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **30 m** & \multicolumn{3}{c}{**50 m**} & \multicolumn{3}{c}{**70 m**} & \multicolumn{3}{c}{**80 m**} \\ \hline
**Time(s)** & **Exp** & **Sim** & **Exp** & **Sim** & **Exp** & **Sim** & **Exp** & **Sim** \\ \hline
0 & 57 & 67.9168 & 57 & 67.9168 & 57 & 66.6398 & 57 & 66.6398 \\
1 & 59 & 68.452 & 60 & 68.452 & 63 & 68.452 & 64 & 68.452 \\
2  
Figure 5: _Cont._  
Figure 5: Experimental and simulation energy consumption results.  
Figure 6: CPU load.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time(s)** & **30 m** & **50 m** & **70 m** & **80 m** \\ \hline
0 & 7.14\% & 10.89\% & 12.66\% & 13.11\% \\
0.5 & 11.41\% & 11.79\% & 17.19\% & 13.07\% \\
1 & 12.30\% & 11.86\% & 16.78\% & 13.50\% \\
1.5 & 12.19\% & 10.66\% & 14.91\% & 17.95\% \\
2 & 13.07\% & 15.69\% & 15.57\% & 14.67\% \\
2.5 & 12.93\% & 15.33\% & 16.03\% & 12.24\% \\
3 & 12.97\% & 16.58\% & 17.27\% & 12.42\% \\
3.5 & 12.93\% & 17.75\% & 17.22\% & 13.11\% \\
4 & 12.94\% & 17.67\% & 17.49\% & 13.67\% \\
4.5 & 13.02\% & 17.42\% & 17.87\% & 19.50\% \\
5 & 13.09\% & 16.13\% & 16.50\% & 15.72\% \\
5.5 & 12.97\% & 15.54\% & 15.55\% & 20.02\% \\
6 & 13.06\% & 15.49\% & 18.53\% & 16.49\% \\
6.5 & 13.07\% & 16.55\% & 18.43\% & 13.45\% \\
7 & 10.89\% & 10.43\% & 13.41\% & 13.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: CPU load of the edge node.  
A parallel simulation was conducted to validate and augment our findings in tandem with our real-world experiment. The simulation aimed to replicate the conditions of the experiment in order to provide a comprehensive view of the system's behavior in a controlled virtual environment.
## 3 Inverter Modeling and Control
### Analytical Modeling of Inverter  
An analytical or mathematical model is useful for understanding the dynamics of a system. The model is developed with the details of the inverter and its filter parameters. Analytical modeling is inspiring to perform due to its complexity.  
This model can be alterable according to the need and classification of the inverter. It can also be used for an inverter's stability analysis to check the system's stability and the closed-loop control design [26]. An analytical-based model is constructed for the inverter, as depicted in Figure 3. The first-order differential Equations (11)-(22) have been derived and validated with the MATLAB/Simscape model in an abc frame of reference for the three-phase inverter is presented in Figure 3[1; 23]. This converter model can be applicable for multi-purpose applications such as grid-connected inverters, bidirectional EV chargers, etc.  
The input voltages and currents are calculated using the above expressions.  
\[V_{ia}=\left(\frac{2}{3}d_{a}-\frac{1}{3}d_{b}-\frac{1}{3}d_{c}\right)V_{dc} \tag{20}\]  
Figure 4: Three-phase Simrod inverter board.  
\[V_{ib}=\left(\frac{2}{3}d_{b}-\frac{1}{3}d_{a}-\frac{1}{3}d_{c}\right)V_{dc} \tag{21}\]  
\[V_{ic}=\left(\frac{2}{3}d_{c}-\frac{1}{3}d_{a}-\frac{1}{3}d_{b}\right)V_{dc} \tag{22}\]  
\(V_{ia},\ V_{ib},\ V_{ic}\) (V) are three-phase input converter voltages, \(i_{a},\ i_{b},\ i_{c}\) (A) are AC currents and \(d_{a},d_{b},\ d_{c}\) are duty cycles, \(R_{load}\) (\(\Omega\)) is the load resistance, \(V_{ca},\ V_{cb},\ V_{cc}\) (V) are the voltages across the filter capacitors, and \(V_{dc}\) (V) is DC voltage.
## 2 Materials and Methods
These equations describe processes of momentum, energy transfer, and mass by using a strong discretization of the geometry to be analyzed (see Figure 3a). In sloshing problems, a further complication involves tracing the free surface and defining the boundary of the different phase fluids. The Volume of Fluid (VOF) model tracks the free surface's speed, position, and shape between the different phases of the fluids [30]. The finite volume method solves the Navier-Stokes equations by storing the values of all fluid properties, such as velocity, pressure, density, and volume fraction, at the center of each control volume. The VOF model then extracts the volume fraction data in each control volume to determine the shape and position of the free surface. For example, in the case of a multiphase system consisting of air and water, if the water volume fraction is 1 with respect to the control volume, it means that the control volume is completely filled with water. The solution of the volume fraction conservation equation defined by [PERSON] and [PERSON] is used to trace the free surface throughout the volume [31]:  
\[\frac{\partial F}{\partial t}+u\frac{\partial F}{\partial x}+v\frac{\partial F }{\partial y}+w\frac{\partial F}{\partial z}=0 \tag{10}\]  
The \(F\) function represents the volume fraction corresponding to each control volume. Such function is also used to describe the default domains. As an example, near the container mouth, where only air is present, the air volume fraction is 1, while the water volume fraction is 0. More than the information given by \(F\), there is still the need to solve the problem, because it does not define the tilting of the free surface. To do this, the VOF uses the gradient of the volume fraction at each control volume across the free surface to determine the slope of the free surface, as shown in Figure 3b. The materials' properties and the parameters used in the CFD analysis are reported in Tables 1 and 2, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Water**} & \multicolumn{2}{c}{**Air**} \\ \hline Properties & Values & Properties & Values \\ \hline Density (\(\rho\)) & 997 [\(\frac{\text{kg}}{\text{m}}\)] & Density (\(\rho\)) & 1.185 [\(\frac{\text{kg}}{\text{m}}\)] \\ \hline Ref. temperature (t) & 25 [C] & Ref. temperature (t) & 25 [C] \\ \hline Ref. pressure (p) & 1 [atm] & Ref. pressure (p) & 1 [atm] \\ \hline Dynamic viscosity (\(\mu\)) & 8.899 \(\times\) 10\({}^{-4}\) [\(\frac{\text{kg}}{\text{ms}}\)] & Dynamic viscosity (\(\mu\)) & 1.831 \(\times\) 10\({}^{-5}\)[\(\frac{\text{kg}}{\text{ms}}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CFD materials’ properties. Figure 3: Glass vial mesh in Ansys Fluent (**a**) and water volume fraction during oscillations in a simulation (**b**).
## 3 Methods
### Gesture-Based Robotic Gripper Teleoperation  
Based on the multiple hand gesture information acquired from the Leap Motion Controller, the attributes of hand type, palm position, finger type, finger length, and tip position are adopted in the robotic gripper teleoperation. Instead of the binary distribution of 0 or 1 from the finger extended attribute, a relative ratio index (RRI, \(r_{i}\)) is proposed as the measure for grasping modulation of the gripper, which is defined as:  
\[r_{i}=\frac{l_{ic}}{l_{in}} \tag{3}\]  
where \(l_{in}\) is the nominal length of the \(i\)th finger, which is acquired from the Leap Motion Controller, and \(l_{ic}\) is the calculated length of the \(i\)th finger, which is calculated as:  
\[l_{ic}=\sqrt{(x_{it}-x_{p})^{2}+(y_{it}-y_{p})^{2}+(z_{it}-z_{p})^{2}} \tag{4}\]  
where \((x_{p},y_{p},z_{p})\) is the palm position of the hand and \((x_{it},y_{it},z_{it})\) is the tip position of the \(i\)th finger (Figure 6).  
The defined RRI can be further transformed to the binary values of 0 or 1 for discrete control as the finger extended attribute. Alternatively, it can be normalized to the values between [0, 1] to act as the grasping modulation index. Compared with the grab strength of the whole hand, each finger has its own RRI, which enables each finger to modulate the grasping state with individual values, not just only one value for all five fingers.  
During the gripper teleoperation, each human hand is in charge of one robotic gripper. The type of human hand (left or right) is automatically identified by the Leap Motion Controller. The two robotic grippers are independent of each other, which can be controlled simultaneously by two human hands. Initially, the gripper is set to the open state. When the gripper teleoperation subsystem runs, it moves to the target state according to the control command. Then, it maintains at the last state until the new command comes.  
Figure 6: The hand parameters in robotic gripper teleoperation.  
Moreover, the position control and torque control strategies are explored in the robotic gripper teleoperation. They are further used to incorporate with the gesture-based control mode. Compared with the position control strategy, the torque control mode is more flexible and robust. It can automatically regulate the grasping tightness with regard to the object's shape, size, and hardness. An initial torque value is predefined and then it can be adjusted with the change of the RRI value. Thence, the human operator can easily control the grasping tightness with hand gestures.  
In the experiments, for the sake of simplicity, the RRI value was transformed into the binary values of 0 or 1 in both the position control and torque control modes. With this assumption, the RRI value acts as an index to control the state (open or closed) of the corresponding finger of the gripper. The binary values of 0 and 1 of the RRI are mapped to the open and closed states, respectively.
## 2 Methodology
### Fault Detection and Isolation (FDI)
#### 2.3.3 Data-Driven Approach  
The data-driven approach for fault detection and isolation proposed in this paper was two-fold. The first part was an ensemble of classification algorithms [26] for detecting faulty condition scenarios. A range of classification models, including logistic regression [27], decision tree [28], random forest [29], Gaussian naive Bayes [30], K-nearest neighbours [31], support vector machine [32], gradient boosting [33], and AdaBoost [34], were instantiated. These classifiers were fitted to training data and evaluated based on their performance for each of the key components (see Table 1) of the system on test degradation data in terms of prediction accuracy. The default parameters for each classifier in Keras were utilised to train on the training dataset and evaluated. Based on the process dynamics for each key component, it was anticipated that different classification algorithms would perform differently under different levels of training data quality and degradation scenarios. A simple modal framework was employed to develop a weighted ensemble classification FDI model, where the highest three occurring classification algorithms were used. The second data-driven approach was an FDI classifier based on a neural network architecture that used a recurrent neural network (RNN) model comprising a single RNN layer with 150 neurons followed by a dense layer and a sigmoid activation function (see Figure 7 below). The model was compiled with binary cross-entropy loss and the Nadam optimiser. Early stopping was then employed to prevent overfitting during the training of the model. The performance of the RNN model was also evaluated using the test degradation data recorded in Table 4.