esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Inflow Turbulence on the Flow Characteristics around a Circular Cylinder
## 2 Setup of the Wind Tunnel Test  
To study the effect of turbulence on the surface flow of the circular cylinder, a cylindrical model made of plexiglass was designed with a diameter of 0.3 m and height of 0.5 m. A total of 180 measuring points were arranged in the middle section of the model, with the angle between adjacent measuring points being 2\({}^{\circ}\). The test model in the wind tunnel test is shown in Figure 1. [PERSON] et al. [26] found that as the size of the end plate increases, the absolute values of the drag coefficient and the leeward pressure coefficient gradually increase and finally tend to a constant value. Meanwhile, they suggested that the diameter of the end plate should be larger than 8.5 times the model depth. [PERSON] et al. [27] showed that the end effect of the flow field around the model was suppressed when the end plate is four times the diameter of the test model's diameter. Therefore, in order to suppress the end effect of the flow field of the test model, two square end plates with a side length of 1.5 m were used above and below the model for creating two-dimensional flow conditions between the two end plates. The tests were conducted in the ZJU-1 wind tunnel at Zhejiang University, China. The testing section is 4.0 m wide and 3.0 m high and the testing wind speed regimes range from 0.0 to 50.0 m/s. The maximum blocking ratio of this wind tunnel test is 2.4%.  
Figure 1: Layout of testing model. (**a**) Testing model in the wind tunnel; (**b**) the drawing of the testing model.  
The reference wind speed in the wind tunnel is measured and monitored using a pitot tube and a micromanometer. The wind pressures of the 180 testing points were measured by Scanivalve electronic pressure scanning valve system DSM3400 with ZOC33 modules. The measuring range of the system is \(\pm 2500\) Pa, the precision of the system is 0.1% (full-scale) and the highest sampling frequency is 625 Hz. The wind pressures of all the testing points were measured simultaneously and the sampling frequency is 333 Hz.  
By adjusting the spires and grids in the wind tunnel, three turbulent flows, which are nominally 4%, 8% and 12%, were generated. The turbulent intensity \(I_{u}\) and average wind speed \(U\) of the four flow fields simulated by the wind tunnel are shown in Figure 2, in which \(H\) is defined as the vertical height above the wind tunnel ground. It was found that the smooth flow and the three turbulent flow fields are well simulated in the wind tunnel.  
The test wind speed of the smooth flow in the wind tunnel is 3-36 m/s and the corresponding Reynolds number is \(6.2\times 10^{4}\)-\(7.5\times 10^{5}\). Due to the blockage of the spires and grids in the wind tunnel test, the maximum testing speed decreases in high turbulent flow. The working conditions of this test are shown in Table 1.  
Figure 2: Four types of flow fields simulated in wind tunnel. (**a**) Turbulence intensity; (**b**) wind speed.
###### Abstract  
The classification of shale is commonly based on lithofacies structure, mineral content, organic carbon content, physical and chemical parameters, and element contents. A geochemical classification method based on the compositional data of shale is proposed which divides shale, sedimentary rocks, sediments, and soils into six types, named siliceous, felsic, silicate, calcsilicate, dolomitic, and calcareous components, and numbered from type 1 to type 6, respectively. Sedimentary rocks in China, including mudstone (shale), sandstone, carbonate rocks, and siliceous rocks, are selected to test the method, and the results show that the method can accurately classify not only shale but also other sedimentary rocks. Shale samples from a drill well in southern Sichuan area are classified based on the proposed method as an illustration in a line graph. Furthermore, the stream sediments and soils from geochemical survey projects in the southern Sichuan area are also classified based on their compositional data. Based on the classification results, a geochemical map is drawn and compared with the strata lithology. These results indicate that the new classification method is suitable for shale, sedimentary rocks, sediments, and soils, and illustrates clear geochemical properties based on their classified types.  
shale classification; geochemical component; sedimentary rock; sediment; soil 2025  
3 March 20259 9 April 20  
classification on geochemical genes has been proposed, but this classification is mainly for silicate samples [19] and is limited in regard to the siliceous and calcareous components.  
According to the classification of mineral content, the main types of shale are siliceous, calcareous, and argillaceous [20; 21; 22; 23], with the main components comprising SiO\({}_{2}\), CaO, MgO, and Al\({}_{2}\)O\({}_{3}\). If a geochemical classification of shale is proposed, contents of these major oxides should be considered. Furthermore, if the common sediments and soils are composed of these major oxides, the geochemical classification of shale could also be applicable to sediments and soils. With respect to stream sediments and soils, a large number of samples covering the Chinese mainland of more than 7 million square kilometers have been collected and analyzed as part of the regional geochemistry-national reconnaissance (RGNR) project [24] and national multi-purpose regional geochemical survey (NMPRGS project) [25].  
In this study, firstly, a geochemical classification method is proposed to classify shale, sediments, and soils. Then, sedimentary rocks in China are selected to test and validate the new method. Finally, the new method is applied on drill well shale samples, stream sediments, and soils in the southern Sichuan area, and its implications for lithology are discussed.
# Analysis of Damage and Permeability Evolution of Sandstone under Compression Deformation
## 3 Numerical Implementation of Damage-Permeability Model  
The seepage equation considering the coupling effect of damage and permeability is a partial differential equation, which is extremely difficult to solve theoretically. The validation and application of the model require the help of numerical methods. The coupled model is usually solved by the iterative method, but the mutual invocation of various parameters greatly increases the difficulty of solving. The solution steps in this paper were as follows:  
1. First, a constitutive model that can well describe the damage evolution and deformation characteristics of rock was proposed and implemented numerically.
2. Second, the deformation and damage parameters in the stress field were implemented in the seepage field to induce the fluid flow.
3. Third, the effective stress and pore pressure in the seepage field were implemented in the stress field for influencing the rock deformation.
4. Finally, the last iteration was repeated until the end.  
Step 1 has been completed in our previous works; that is, an elastoplastic damage constitutive model that can describe the characteristics of rock deformation and damage has been proposed [30]. More importantly, this model has been implemented by finite element programming code and verified by a series of triaxial compression tests. The next core problem was to realize seepage analysis with a fluid-solid coupling. The non-linear partial differential equation with multivariate interaction could be solved with the help of numerical methods. The COMSOL Multiphysics software version 5.6 provides a user-defined material model interface where any constitutive model can be programmed and applied. The elastoplastic damage constitutive model in Step 1 could be compiled in the C-program and linked to an external material model of this finite element software, which can be implemented at any time when it is used. The implementation of Step 2 and Step 3 required a specific application situation, such as fluid seepage in strata, leakage analysis in formations surrounding salt cavern gas storage, and seepage analysis in rocks under triaxial compression.
## 5 Data Imbalance
### Limitations of Data Imbalance Handling Techniques
#### 5.4.3 Lack of Adaptability and Scalability  
Traditional data balancing methods, such as oversampling and undersampling, may perform well in fixed environments and with specific attack patterns, but they often lack the adaptability needed to cope with continuously evolving network threats and diverse attack modes [115; 116]. As network environments become more complex and attack methods evolve, the scalability of these techniques becomes increasingly insufficient [117]. For instance, static sampling strategies may fail to dynamically adjust to new emerging attack patterns, leading to suboptimal model performance in real-world scenarios [118].
## 6 Future Studies  
Future studies should be conducted by applying the same procedure on patient groups, and also considering several age groups, such as elderly people, children, and adults, to assess CLS's broader applicability. These studies would assess the effect of CLS on the balance condition with clearer understanding.  
Furthermore, it is necessary to consider the daily life routine in future work to compare those who used CLS occasionally to those who use CLS long-term, looking into the lower limb muscular strength, muscular electrical activities, and balance analysis. Moreover, assessing the effects of CLS over longer durations (e.g., 1 h or more) would provide a clearer understanding of its long-term impact.  
In addition, future studies might be necessary to widen the understanding of how CLS can affect the muscular strength in the front and transverse planes, and of how it can clearly impact the static and dynamic balance. Therefore, evaluating muscles responsible for abduction, adduction, and rotation would offer a more comprehensive understanding of CLS's impact on lower limb function.  
Regarding the balance tests, incorporating additional static balance tests, such as the Romberg test or balance platform assessments, would strengthen the study's conclusions. In addition, including a control group engaging in different sitting postures (e.g., chair sitting or kneeling) would contextualise CLS's effects and provide a benchmark for its benefits and drawbacks.
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 2 Materials and Methods
### Validation  
In fin literature, there are limited experimental data on single fins available, and, currently, there are no experimental studies on multi-fin configurations. As this study is based on a multi-fin configuration, it is important to compare the initial baseline results with other 3-fin simulations. Figure 6 compares multi-fin \(C_{L}\) results from two [PERSON] et al. studies and [PERSON] et al. with extended 3-fin baseline results [2; 3; 4].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mesh** & **Number of Elements** & **Time (h)** & **Lift Force (N)** & \(C_{L}\) \\ \hline Coarse (M5) & 1,164,030 & 0.19 & 501.1528 & 0.6200 \\ M4 & 2,500,329 & 0.60 & 496.0812 & 0.6137 \\ Medium (M3) & 6,284,662 & 2.55 & 489.3098 & 0.6054 \\ M2 & 11,720,165 & 4.96 & 485.0050 & 0.6000 \\ Fine (M1) & 18,296,932 & 7.93 & 484.2851 & 0.5991 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid sensitivity results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh** & **Error \(|E_{n}|\)** & **GCI** \\ \hline M3 & \(6.40\times 10^{-3}\) & \(7.99\times 10^{-3}\) \\ M2 & \(1.07\times 10^{-3}\) & \(1.34\times 10^{-3}\) \\ M1 & \(1.79\times 10^{-4}\) & \(2.24\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error and uncertainty estimations mesh resolutions.  
The strongest comparison here is the [PERSON] et al. results as the methodology in that study is very similar to this study. Up to 25\({}^{\circ}\), the validation results closely match with 3-fin results, with the validation underreporting lift by 7.8% at 10\({}^{\circ}\). At angles higher than 25\({}^{\circ}\), [PERSON] et al. found significant fluctuations in lift coefficient using URANS simulations, and this is the main contributor to the differences seen at the highest angles. The [PERSON] et al. results, also displayed in Figure 6, have a different trend. The stall point is not only much higher in these results but is also at a later angle. The major differences in this study may be attributed to the use of the k-\(\epsilon\) turbulent model to solve the flow or the different fin geometries. Despite the higher magnitudes seen in the [PERSON] et al. results, there is still  
Figure 5: Grid convergence study.  
Figure 6: Multi-fin configuration CFD literature validation.  
strong confidence in the methodology used in this study. The extremely close relationship to the [PERSON] et al. results shows the simulations performed in this study produce results in line with similar literature both in trend and magnitude.
## 7 Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Benchmark** & \(\mathbf{U_{1}}\) & \(\mathbf{U_{3}}\) & \(\mathbf{U_{4}}\) & \(\mathbf{U_{5}}\) \\ \hline ex1 & 141.43 & 105.78 & 158.28 & 212.93 \\ \hline kirkman & 125.78 & 107.81 & 155.11 & 174.73 \\ \hline planet & 122.01 & 105.41 & 124.31 & 187.95 \\ \hline planet1 & 122.01 & 105.41 & 124.31 & 187.95 \\ \hline pma & 115.41 & 114.49 & 127.65 & 186.22 \\ \hline s1 & 124.49 & 117.80 & 132.85 & 178.84 \\ \hline s1488 & 127.80 & 112.79 & 131.77 & 186.37 \\ \hline s1494 & 122.79 & 124.92 & 135.73 & 181.62 \\ \hline s208 & 144.92 & 128.04 & 144.05 & 209.36 \\ \hline s420 & 148.04 & 112.66 & 152.65 & 192.14 \\ \hline s510 & 122.66 & 111.42 & 138.75 & 192.87 \\ \hline s820 & 121.42 & 88.65 & 133.36 & 163.18 \\ \hline s832 & 98.65 & 115.57 & 100.53 & 184.69 \\ \hline sand & 135.57 & 104.68 & 146.78 & 178.65 \\ \hline styr & 114.68 & 116.47 & 115.69 & 181.22 \\ \hline Total & 1887.66 & 1671.90 & 2021.82 & 2798.72 \\ \hline Percentage & 67.4\% & 59.7\% & 72.2\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of experiments (the operating frequency, MHz). As follows from Table 9, the \(U_{5}\)-based FSMs have the highest operating frequency as compared to other investigated FSMs. We think that this is due to the smaller number of logic levels and inter-level connections compared to other investigated FSMs. But we cannot prove this statement because Vivado does not show these details about implemented circuits. There is the following gain in operating frequency: (1) 32.6% regarding \(U_{1}\); (2) 44.3% regarding \(U_{3}\); (3) 27.8% regarding \(U_{4}\). The lowest frequency takes place for \(U_{3}\) --based FSMs. It is connected with rather big amount of inputs. Because \(L+R>>SL\), the circuit of LUTerP is multi-level. For discussed benchmarks, the number of logic levels in \(U_{3}\)-based FSMs is higher than it is for FSMs produced by other investigated methods. As follows from Table 10, the \(U_{5}\)-based FSMs consume less energy than their counterparts based on other FSM models. There is the following economy: (1) 39.8% regarding \(U_{1}\); (2) 51% regarding \(U_{3}\); (3) 11.4% regarding \(U_{4}\). It is connected with the fact that \(U_{5}\)-based FSM circuits have fewer LUTs and, therefore, interconnections compared to other investigated FSMs. Interconnections are known to be responsible for up to 70% of energy losses in FPGA-based circuits [26]. The results shown in Table 10 include the total power value in Watts. It should be noted that the total power consists of individual powers such as: static power, I/O, signals, LUT as Logic, F7/F8 Muxes, BUFG, registers and others. Furthermore, the frequency has a very strong impact to the power consumption. Therefore, our approach produces better results for FSMs whose circuits cannot be implemented as a single EMB. Of course, this conclusion is true only for the benchmarks [11] and the device XC7 VX690 TFFG1761-2. It is almost impossible to make similar conclusion for the general case.
# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 2 Materials and Methods
### Dataset
This design also ensures that each pattern consists of three segments, making them structurally comparable to the triangular movement (TRA). Each participant performed 5 distinct target movements 10 times, resulting in a total of 1000 target movement samples. Non-target movements are defined as actions that do not correspond to the predefined target movements. These movements closely resemble target movements, which increases the likelihood of misclassification. Non-target movements were derived from the five target movements by intentionally omitting specific distinguishing features, such as pauses or directional changes. Each non-target movement corresponds to a specific target movement. The primary objective of designing non-target movements was to assess whether the model could accurately classify unseen non-target movements by understanding the general characteristics of target and non-target movements. Unlike target movements, which include momentary pauses at corners and sharp directional changes, non-target movements were executed in a continuous and smooth, curved manner (Figure 1b). For instance, in the FRU, FRD, FLU, FLD, and TRA movements, the non-target versions were designed to move fluidly without interruptions, with the triangular pattern replaced by a circular motion. Non-target movement data were collected from 11 out of the 20 participants, with each participant performing 15 non-target movements, yielding a total of 165 non-target samples. The data were collected using sensors embedded in a smartwatch, which measured accelerometer and gyroscope values in three-dimensional space. Each movement was recorded for a duration of three seconds at a sampling rate of 50 Hz. The collected data were transmitted from the smartwatch to a smartphone and subsequently transferred to a PC via a wired connection or Wi-Fi. The sampling rate was limited to 50 Hz due to transmission constraints between the smartwatch and the smartphone used in this experiment, which occasionally caused data loss at higher frequencies. Figure 1: Gesture patterns for targets and non-targets. (**a**) Target motion movements, where dashed arrows indicate forward movements. (**b**) Non-target motion movements with curved trajectories. Red dots indicate the starting points of each movement. The experimental setup consisted of an LG Watch W7 smartwatch (LG Electronics, Seoul, Republic of Korea), a Nexus 5X smartphone (LG Electronics, Seoul, Republic of Korea), and a PC. Data collection was conducted using Android Studio Arctic Fox (Figure 2).
## 5 Validation  
### Validation of Grid Independence  
For the CFD simulations, we checked grid independence by calculating skewness of mesh elements and the velocity of the fluid flow. Figure 6 shows the normalized liquid velocity with different grid size at 500 RPM and an inlet velocity of 0.1 m/s. \(u_{l}\) is the velocity of the liquid phase, \(u_{l}\), \(t_{tip}\) is the velocity of the liquid phase at the tip of the impeller, and \(H\) is the height of the tank, respectively. When the number of mesh elements increased from 353,020 to 488,239, the change in the average velocity of the fluid flow at \(x\) = 0.03 m was less than 3%. The results with medium and dense meshes were similar, hence the grid independence was achieved with the number of mesh elements of 353,020 as shown in Table 1.  
Figure 5: (**a**) Axial distribution of the turbulent dissipation rate, (**b**) number density of each size of bubble, and (**c**) axial distribution and contour of the gas hold-up at 300 and 900 RPM. (**d**) Pearson correlation coefficient between the volumetric mass transfer coefficient and dissolved carbon monoxide concentration with different RPMs.  
Figure 6: Axial distribution of the velocity of the fluid flow at \(x\) = 0.03 m depending on the number of mesh elements with 500 RPM and inlet velocity of 0.1 m/s.
## Appendix B  
The objective is to make the projection of a vector \(v_{i}\) on a plane P(\(k,\,l\)) to form an angle \(\alpha\) with the positive axis \(k\). Moreover, the vector \(v_{i}\) forms, along with other vectors \(v_{i\,-\,}v_{i\,-\,}v_{i\,+\,}...v_{M}\), an orthonormal base. The condition on \(v_{i}\) is obtained rotating it along with another vector \(v_{j}\) on the plane defined by them by an angle \(\varphi\). Thus the target is to determine \(\varphi\) as a function of \(a\).  
Let's create the matrix \(A\) formed by the vectors \(v\) written in the rows.  
\[A=\begin{bmatrix}v_{1}\\ v_{2}\\ \vdots\\ v_{M}\end{bmatrix}. \tag{17}\]  
For the orthonormality of the vectors, we have \(A\cdot A^{T}=I\), where \(I\) is the identity matrix.  
Consider the matrix \(B\)  
\[B=\begin{bmatrix}1&\cdots&0&&\cdots&0&\cdots&0\\ 0&\ddots&&&&&&\vdots\\ &&\cos\varphi&0\cdots&\cdots 0&-\sin\varphi&&0\\ \vdots&&&1_{\ddots}&&&&\vdots\\ &&&\ddots&1&&&0\\ 0&\cdots&\sin\varphi&0\cdots&\cdots 0&\cos\varphi&&\\ &&&&&&&1_{\ddots}&\ddots\\ 0&&&0&&&&\ddots\\ &&i&&&&j&&\ddots\\ \end{bmatrix} \tag{18}\]  
which rotates the vectors \(v_{i}\) and \(v_{j}\) by an angle \(\varphi\) on their plane. The new vectors after the rotation are computed as:  
\[C=BA=\begin{bmatrix}v_{1}\\ \vdots\\ v_{i}\cos\varphi&-v_{j}\sin\varphi\\ \vdots\\ v_{i}\sin\varphi&+v_{j}\cos\varphi\\ \vdots\\ v_{M}\end{bmatrix}. \tag{19}\]  
It is easy to see that \(C\cdot C^{T}=I\), i.e., the new vectors are still orthonormal.  
Let's call \(v_{ki}\) and \(v_{il}\) the two components of vector \(v_{i}\) on the two axes \(k\) and \(l\) before the rotation. The angle formed by the projection of \(v_{i}\) with the positive axis \(k\) is:  
\[\theta=\arctan(v_{i},v_{ki}) \tag{20}\]  
where \(\arctan\) is the arctangent function over four quadrants.  
After the rotation the two components of the vector \(v_{i}\) are:  
* _l-th_ component: \(v_{li}\cos\varphi-v_{lj}\sin\varphi\)
* _k-th_ component: \(v_{ki}\cos\varphi-v_{lj}\sin\varphi\)So:  
\[\tan\alpha=\frac{\sin\alpha}{\cos\alpha}=\frac{v_{{}_{\mu}}\cos\varphi-v_{{}_{\mu} }\sin\varphi}{v_{{}_{\mu}}\cos\varphi-v_{{}_{\mu}}\sin\varphi} \tag{21}\]  
and some algebra leads to:  
\[\tan\varphi=\frac{\sin\varphi}{\cos\varphi}=\frac{v_{{}_{\mu}}\sin\alpha-v_{{} _{\mu}}\cos\alpha}{v_{{}_{\mu}}\sin\alpha-v_{{}_{\mu}}\cos\alpha}. \tag{22}\]
## 3 Approach and Analysis: Architecture and Methodology
### Learning Algorithm for Sound Classification
#### 3.4.1 Training  
During the training process, the dog sound was used as input, sound features were extracted, and labels of the sound were loaded. Feature extraction and behavior prediction were achieved using the K-NN algorithm. The training dataset was used in training the machine for prediction with the use of the learning algorithm. The training was then carried out by presenting different scenarios of dog sounds. Changes in the patterns of barking information of the dogs were validated for improved performance predictions. Predictions were made based on the dog sound features, and the machine learning process was trained. The training process is described in Figure 5.  
For the performance verification of the model formed, a test input was given to predict the dog sounds. During the training process, it was observed that increasing the training iterations affected the overall prediction and classification accuracy. However, the validation accuracy remained unchanged, even after the 100 th epoch, hence it was concluded that the network was overtraining.