esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
When a new acquisition became available, the histogram was computed considering only the spectral features showing a significant correlation with the ground truth. In synthesis, the proposed active sampling procedure can be summarized as follows. 1. At time zero, i.e., when just one image is available: 1. Consider each plot of the field as cluster and make the spatial average for each spectral band; 2. Calculate the histogram considering all the elements of the average spectral response starting from one randomly selected plot. Calculate the entropy \(H_{1}\) of the histogram according to Equation (1); 3. Add another plot to the dataset by appending its (average) spectral response to the vector constituted by the one of the first plot. Calculate the new histogram and its entropy \(H_{2}\); 4. If \(H_{2}>H_{1}\) mark the plot as informative and continue by adding new plots to the dataset without deleting those marked as not informative. The plots marked as informative will be sampled to retrieve model calibration data. 2. Use ground data collected with the above procedure to calculate the linear correlation with each spectral band. 3. When a new acquisition becomes available, repeat the procedure described at Point 1, this time using only the spectral features showing significant correlation with ground data (see point 2) for histograms calculation. A reasonable threshold to select features showing at least moderate correlation can be set to 0.4 [32]. All the available data should be used for samples selection. In other words, the new clusters should be appended to those already available from time zero in order to set-up an incremental sampling framework. This growing vector is the one used for entropy calculation at each iteration. 4. As the number of flights increases, the linear correlation analysis between the different spectral features and ground data continues to modulate the active sampling procedure. In this case, the computation of histograms will consider only the features showing, on average, a linear correlation coefficient higher than 0.4. Figure 3: Schematic representation of the proposed active sampling methodology. Following the diagram depicted in Figure 2, active sampling feeds the PLSR to estimate the value of specific biophysical parameter within the entire area of interest. The proposed model initially exploits a total of 141 variables constituted by the entire hyperspectral data-cube (101 reflectance bands) plus a selection of 40 vegetation indices, as suggested, among the others in [8; 33]. For the ease of the reader, the list of the indices used in this study is reported in Table 1, which has a twofold purpose. The first is to collect all the formulas in one place, as it could be difficult to retrieve each of them from the literature. The second is to suggest a possible set of variables to exploit in similar studies.
# Tire Defect Detection via 3D Laser Scanning Technology
## 3 Experiments and Discussion
### 3D Tire Dataset and Evaluation Metrics  
As aforementioned, we proposed four types of datasets of tire defects: three 2D datasets (i.e., rendered images, depth images, and photoed RGB images) and one point cloud dataset. All datasets consisted of 880 training samples and 220 testing samples except for the RGB image dataset. RGB images were taken by 2D cameras, which hardly reflected the properties of the defects. Meanwhile, the point cloud dataset consisted of patches of the same sizes. Depth images and rendered images were generated using our 3D-to-2D approaches and augmented by our novel data augmentation, and the two corresponding images of these two datasets share the same annotations. Note that tire defect detection was not performed directly on point clouds because of the extremely high computational costs and low accuracy [23; 24]. Instead, point clouds served as intermediate representations in data augmentation.  
We trained all methods under standard settings on training datasets and evaluated them on testing datasets. We followed the evaluation metrics of the COCO dataset [25] and employed standard protocols to evaluate all methods on 2D datasets. Average precision (AP) is a popular metric in measuring the accuracy of object detectors and can be calculated as  
\[AP=|classes|\sum_{c\in classes}\frac{TP_{c}}{FP_{c}+TP_{c}}. \tag{3}\]Here, \(|classes|\) refers to the number of categories. \(TP_{c}\) and \(FP_{c}\) are true positive and false positive bounding boxes, respectively, calculated by setting different thresholds of IoU. Specifically, AP\({}_{50}\) and AP\({}_{75}\) are calculated using the thresholds of 50% and 75%, respectively. \(mAP\) is the mean AP with a threshold from 50% to 95% at a step size of 5%. AP\({}_{S}\), AP\({}_{M}\), and AP\({}_{L}\) are used to evaluate the performance of detectors for objects with small, middle, and large sizes.
# Fatigue Damage Evaluation of Aviation Aluminum Alloy Based on Strain Monitoring
## 3 Experimental Results and Analysis
### Analysis of Simulation and Test Results
This critical value is characterized by a substantial accumulation of plastic slip and strain energy dissipation, as shown at point A in Figure 10. The critical values for cumulative plastic strain and strain energy dissipation are obtained from the fatigue test. The polycrystalline plasticity model predicts the fatigue crack initiation life, which is then compared with the test results, as illustrated in Figure 11.  
As shown in Figure 11, the low-cycle fatigue test of aluminum alloy provides valuable information through both fatigue indicators and the predicted fatigue crack initiation life. It is worth noting that strain energy dissipation offers more accurate predictions in this experimental process compared to cumulative plastic slip. However, the predicted life for cumulative plastic slip exceeds the actual experimental results. Figure 10: Equivalent plastic strain and strain energy dissipation at the fortieth cycle for a strain amplitude of 0.3%: (**a**) equivalent plastic strain; (**b**) strain energy dissipation. Table 3.0 Geometric must dislocation density variation: (**a**) geometric mean must dislocation density variation; (**b**) GND at 3000 cycles. To investigate the relationship between strain and fatigue damage, both experimental and simulated fatigue were analyzed. In Figure 11b, the average strain is plotted against the number of fatigue cycles for the model simulated with the fitted parameters at a strain amplitude of 0.15%. The figure shows that the strain at the notch initially decreases and then increases before reaching 3300 cycles of fatigue. After that point, it exhibits a consistent increasing trend. However, it remains relatively stable around 200 \(\mu\epsilon\) throughout the fatigue cycles. On the other hand, the cumulative plastic strain fatigue indicator factor demonstrates an increasing trend after 3900 cycles, but remains relatively stable during the pre-fatigue period. This suggests that the emergence of fatigue damage is closely related to the change in microstrain. As shown in Figure 11b, the change in strain is approximately 2300 \(\mu\epsilon\), whereas the results from the test presented in Figure 7b indicate only 26 \(\mu\epsilon\). This discrepancy is attributed to the contact issue between the surface of the fatigue specimen and the strain gauges, which reduces the sensitivity of strain monitoring. This can be observed in Figure 8a-c, where the strain essentially remains unchanged during the first 5000 cycles of fatigue, aligning with the simulation.
## 3 Results and Analysis  
### Behavior of Isotropically Consolidated UGRS  
The results of drained and undrained shearing tests with constant \(\sigma_{3}\) on the UGRS consolidated isotropically are shown in Figure 5. As shown in Figure 5a,b, all specimens exhibited characteristics of strain-hardening before the emergence of failure planes passing through soil specimens. It is evident that confining pressure exerts a significant impact on the shearing strength. The peak shearing strength mobilized in the drained test is higher than that in the undrained test under the same confining pressure due to a higher level of mean effective stress.  
The shearing stress paths are displayed in \(p\)'-\(q\) space in Figure 5c,d. For undrained tests, the difference along the \(p\)'-axis between total and effective stress path represents the excess pore water pressure built up during shearing. As presented in Figure 5f, the evolution patterns of excess pore water pressure are similar for different confining pressures: positive excess pore water pressure is developed at first and a subsequent decrease is observed after reaching the peak, indicating the tendency of initial contraction followed by volume dilatancy. When sheared under drained conditions, the UGRS displays nearly monotonic volume contraction except for the specimen with a confining pressure of 100 kPa, as can be seen in Figure 5e. Moreover, it is found that the shear-induced volume contraction is enhanced as confining pressure increases.  
By defining the failure of soil specimen as the arrival of peak deviator stress, failure lines are also demonstrated in \(p\)-\(q\)(or \(p^{\prime}\)-\(q\)) plane. With the slope and the intercept of failure line denoted by \(M\) and \(Q\), the cohesion \(c\) and friction angle \(\varphi\), which are the most important strength parameters used in geotechnical engineering, can be determined using Equations (3) and (4) according to [PERSON] [55]:  
\[\sin\varphi=\frac{3M}{6+M}, \tag{3}\]  
\[c=\frac{Q(3-\sin\varphi)}{6\cos\varphi}. \tag{4}\]  
Figure 5: Results of ICD and ICU tests: (**a**) \(q-\varepsilon_{s}\) curves of ICD tests; (**b**) \(q-\varepsilon_{s}\) curves of ICU tests; (**c**) effective stress paths of ICD tests; (**d**) effective stress paths of ICU tests; (**e**) \(\varepsilon_{v}-\varepsilon_{s}\) curves of ICD tests; (**f**) \(\varepsilon_{v}-\varepsilon_{s}\) curves of ICU tests.  
The obtained strength parameters of the isotropically consolidated UGRS are presented in Table 4. The effective cohesion obtained from drained tests is about triple that obtained from undrained tests, as a result of the volume contraction during drained shearing. However, the effective friction angles are almost the same. This result indicates that the cohesion of the UGRS are sensitive to the drainage condition, while the friction angle seems unaffected.
## 3 Results
### Localization
What is more, the accuracy of the indoor localization solution allows for navigation of the user in the confined areas of the vessel.
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 2 Materials and Methods  
### Description of the Study Area  
Kenya is an equatorial country that geographically extends between latitudes 4\({}^{\circ}\)63\({}^{\prime}\) N and 4\({}^{\circ}\)68\({}^{\prime}\) S and longitudes 33\({}^{\circ}\)9\({}^{\prime}\) E and 41\({}^{\circ}\)9\({}^{\prime}\) E (Figure 1). The country has a moderate tropical climate with large regional variations influenced by multiple factors [13]. It has a high undulating topography and heterogeneous landscape (Figures 2 and 3) covering an area of approximately 582,646 km\({}^{2}\), with a human population of 47.5 million [33; 56]. The mountains and plateaus of the inland area (Figure 3b) have a temperate climate, while the north-eastern region that forms part of the eastern-end of the Sahelian zone is mostly hot and dry all year long. Kenya receives an average annual rainfall (bimodal) of about 680 mm (Figure 2d), with the dominant African savannah climate (Figure 2a) having a mean annual precipitation of about 2000 mm. The 'Very Arid' zone occupies about 43.5% of the total area (Figure 2c) followed by the 'Arid' zone which has 21.6% coverage. The major soil types (Figure 2b) are Luvisoil (19.2%) and Yermosols (18.7%). According to the 2016 Climate Change Initiative Land Cover map of Africa (CCI-LULC) [57]) (Figure 3c), the dominant land classes include grassland (37.2%) and shrubland (30.2%).  
Figure 1: Map of the study area: (**a**) position of Kenya within Africa (**b**) the regions of Kenya.  
Figure 2: Characteristics maps of the study area: (**a**) Climate types; (**b**) Soil types; (**c**) Agroclimatic Zones; (**d**) Mean annual rainfall in Kenya.  
Figure 3: Characteristics maps of the study area: (**a**) Elevation; (**b**) Landform types; (**c**) Land use and land cover, 2016; (**d**) Major river basins in Kenya.  
Approximately 21.6% of the country experienced a browning trend as per the 2015 land degradation-neutrality (LDN) national baseline [30]) due to climate change and increasing human population pressure. This has endangered sustainable agriculture that forms the country's mainstay economy. In recent years, rainfall-related risks have plagued the country, including floods, landslides and droughts. Currently, most of the Great Lakes have drastically bulged (Figure 4) flooding large tracts on adjacent farmlands and settlements. This has been attributed to a 50-year period of climatic phenomena [39], as well as the recent enhanced OND East African short rains linked to the Indian Ocean Dipole (IOD) [37]. Rampant deforestation in the country's water towers and adverse farming practices within highland areas could also have altered surface water runoff patterns into these lakes [58; 59; 60].
# Numerical Study of Mixed Convection and Heat Transfer in Arc-Shaped Cavity with Inner Heat Sources
## 3 Results and Discussion  
Figure 3 shows the time evolution of Nu on the heat source wall in the range of Re from 10\({}^{5}\) to 10\({}^{7}\). With the development of time, the general trend of Nu increases firstly, then fluctuates slightly and finally becomes quasi-steady. As Re increases from 2.19 \(\times\) 10\({}^{5}\) to 4.38 \(\times\) 10\({}^{6}\), the changes in black and red curves are disordered and irregular in the full development stage. When Re = 7.67 \(\times\) 10\({}^{6}\), the variation of Nu shows a pattern of oscillating fluctuation, which is approximately periodic. When Re is increased to 1.39 \(\times\) 10\({}^{7}\), the Nusselt number does not change with the development of time, showing a steady-state characteristic. It can be preliminarily seen that when the Ra is fixed, with the increase in Re, the flow in the curved cavity of the double heat source presents a transformation from weak turbulence to periodic and then to steady state, and the heat transfer near the heat source also first intensifies and then becomes weak, with little overall fluctuation. However, to determine how the flow field changes with Re and to calculate the range of Re values of flow transition, a large number of numerical calculations are necessary, which will be discussed in detail in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Mesh** & \multirow{2}{*}{**Mesh Quantity**} & **Relative Error of Mean** & **Relative Error of Mean** \\  & & & **Temperature at** & **Temperature at** \\  & & & **P6** & **P6*** \\ \hline
1 & 90 \(\times\) 410 & 40,000 & 2.710\% & 1.110\% \\
2 & 170 \(\times\) 480 & 81,000 & 0.478\% & 0.469\% \\
3 & 210 \(\times\) 495 & 97,000 & — & — \\
4 & 250 \(\times\) 600 & 160,000 & 0.005\% & 0.002\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid independence verification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Time Step**} & **Relative Error of** & **Relative Error of** \\  & & **Mean Temperature at P6** & **Mean Temperature at P6*** \\ \hline
1 & 0.2 & — & — \\
2 & 0.4 & 1.2\% & 2.1\% \\
3 & 0.6 & 28.9\% & 16.5\% \\
4 & 1.0 & 2.5\% & 5.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Step size independence verification.
## 5 Conclusions  
The work presented in this study successfully integrates the DSI-24 EEG headset with a ChatGPT-enabled Furhat robot to monitor cognitive stress in video gaming environments, showcasing a novel approach to understanding and managing player well-being. The study capitalizes on the DSI-24's ability to rapidly capture brainwave activity and utilizes real-time EEG signal processing, which, coupled with the interactive capabilities of the Furhat robot, enhances both the gaming experience and stress monitoring. While acknowledging limitations like the headset's electrode count and the depth of player-robot interaction analysis, the study opens pathways for future research. These include employing advanced EEG systems and machine learning techniques, as well as expanding the scope to various gaming genres and settings. Overall, this work marks a significant contribution to EEG-based cognitive monitoring and highlights the potential of integrating advanced technology and AI in interactive and dynamic environments.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classification Metric** & **Baseline Model** & **LBPH Model** \\ \hline Accuracy & 0.53 & 0.68 \\ Recall & 0.63 & 0.71 \\ F1-score & 0.71 & 0.73 \\ Precision & 0.79 & 0.75 \\ \hline \end{tabular}  
* Obtained with 70% of the dataset for training and 30% for testing.  
\end{table}
Table 1: Classification metrics for Baseline and LBPH Machine Learning Models *.  
**Author Contributions**: Conceptualization, A.S.K.; methodology, A.S.K., A.A.A., A.Z.A., F.J.A., N.M.Y.A., S.N.A. and K.Y.; software, A.S.K. and N.M.Y.A.; validation, A.A.A., A.Z.A., F.J.A., N.M.Y.A., S.N.A., A.S.K., S.S., S.A.K. and K.Y.; formal analysis, A.A.A., A.Z.A., F.J.A., N.M.Y.A., S.N.A., A.S.K., and K.Y.; investigation, S.S., S.A.K. and K.Y.; resources, S.A.K., A.S.K. and K.Y.; writing--original draft preparation, A.S.K.; writing--review and editing, A.S.K.; supervision A.S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research received no external funding.  
**Institutional Review Board Statement**: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of American University of the Middle East (code: SP000-0403-2023 date: 3 April 2023).  
**Informed Consent Statement**: Informed consent was obtained from all subjects involved in the study. No patient-identifying information or images were used or published in this study.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors thank the AUM Robotics Center for their support specifically [PERSON], [PERSON] and [PERSON] for valuable discussions.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 4 Signal Pretreatment
### Binarization  
In this stage, the proposed modified k-means clustering algorithm is utilized to compute a classification voltage threshold \(V_{T}\) and partition the voltage samples into two levels, representing \"1\" (i.e., high level) and \"0\" (i.e., low level), respectively. The specific steps of this process are as follows:  
1. Initialize a similarity value \(s\), to quantify how similar two clusters are to one another;
2. Perform the mk-means algorithm to classify all the voltage data samples into two sets with the cluster centroids of \(C_{V1}\) and \(C_{V2}\);  
Figure 6: Flowchart of optical Morse signal processing.  
Figure 7: Frequency domain of optical Morse signal.  
Calculate the distance between two centroids by \(d=abs\) (\(C_{V2}-C_{V1}\)). If \(d<s\), it means that two clusters are similar. At this time, all sampling values are divided into high or low level. Then, set \(V_{T}=0\); otherwise, set \(V_{T}=(C_{V2}+C_{V1})/2\);
4. Iterate over all data samples; a value above the obtained threshold in step (3) indicates \"1;\" meanwhile, a value below indicates \"0.\"
## 3 Basic Idea of Adomian Decomposition Method (ADM)  
The Adomian Decomposition Method (ADM) is used to show the unknown function \(u(y,t)\) in the form of an infinite series.  
\[u(y,t)=\sum_{n=0}^{\infty}u_{n}(y,t), \tag{40}\]  
The components \(u_{0}(y,t),u_{1}(y,t),u_{2}(y,t),.....\) of the infinite series can easily be obtained through simple integrals. The summary of the ADM is shown by considering a partial differential equation in an operator form as:  
\[L_{t}u(y,t)+L_{y}u(y,t)+Ru(y,t)+Nu(y,t)=f(y,t), \tag{41}\]  
\[L_{y}u(y,t)=f(y,t)-L_{t}u(y,t)-Ru(y,t)-Nu(y,t), \tag{42}\]  
where \(L_{y}=\frac{\partial^{2}}{\partial y^{2}}\) and \(L_{t}=\frac{\partial}{\partial t}\) are invertible and linear operators, \(Ru(y,t)\) is a remaining linear term, \(f(y,t)\) is a source term, \(Nu(y,t)\) is non-linear part of the partial differential equation and easily expandable in the Adomian polynomials \(A_{n}(y,t)\). After applying the inverse operator \(L_{y}^{-1}\) to both sides of Equation (49), we get:  
\[L_{y}^{-1}L_{y}u(y,t)=L_{y}^{-1}f(y,t)-L_{y}^{-1}L_{t}u(y,t)-L_{y}^{-1}Ru(y,t)-L_{y }^{-1}Nu(y,t), \tag{43}\]  
\[u(y,t)=g(y,t)-L_{y}^{-1}L_{t}u(y,t)-L_{y}^{-1}Ru(y,t)-L_{y}^{-1}Nu(y,t), \tag{44}\]  
here, the function \(g(y,t)\) represents the terms arising from \(L_{y}^{-1}f(y,t)\) after using the given conditions. \(L_{y}^{-1}=\iint\left(.\right)dydy\) is used as the inverse operator for the second order partial differential equation. Similarly, it is used for the higher order partial differential equation \(L_{y}^{-1}\) and \(L_{y}\\[\sum_{n=0}^{\infty}u_{n}(y,t)=g(y,t)-L_{y}^{-1}R\sum_{n=0}^{\infty}u_{n}(y,t)-L_{ y}^{-1}N\sum_{n=0}^{\infty}u_{n}(y,t), \tag{46}\]  
The non-linear term expands in Adomian polynomials as:  
\[N\sum_{n=0}^{\infty}u_{n}(y,t)=\sum_{n=0}^{\infty}A_{n}, \tag{47}\]  
where the components \(u_{0}(y,t),u_{1}(y,t),u_{2}(y,t),.....\) are periodically derived as:  
\[u_{0}(y,t)+u_{1}(y,t)+u_{2}(y,t)+.....=g(y,t)-L_{y}^{-1}R(u_{0}(y,t)+u_{1}(y, t)+u_{2}(y,t)+...)-L_{y}^{-1}(A_{0}+A_{1}+...), \tag{48}\]  
To determine the series components \(u_{1}(y,t)=-L_{y}^{-1}R[u_{0}(y,t)]-L_{y}^{-1}[A_{0}]\), it is important to note that ADM suggests that the function \(g(y,t)\) actually describes the zeroth component \(u_{0}(y,t)\).  
The formal recursive relation is defined as:  
\[u_{0}(y,t)=g(y,t), \tag{49}\]  
\[u_{1}(y,t)=-L_{y}^{-1}R[u_{0}(y,t)]-L_{y}^{-1}[A_{0}], \tag{50}\]  
\[u_{2}(y,t)=-L_{y}^{-1}R[u_{1}(y,t)]-L_{y}^{-1}[A_{1}], \tag{51}\]  
\[u_{3}(y,t)=-L_{y}^{-1}R[u_{2}(y,t)]-L_{y}^{-1}[A_{2}], \tag{52}\]  
and so on.