esa-sceva/satcom-chunk-collection huggingface dataset:
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
## 4 Experiments and Discussion
In application, the detection sensitivity can be improved by properly increasing the monitoring frequency band, and small cracks can be detected and crack propagation can be monitored. At the same time, the experimental results are basically consistent with the finite element numerical analysis results, which shows the accuracy and validity of the finite element model. That indicates the established finite element model can effectively help choosing the sensitive frequency band and the installation location of the piezoelectric patch in practical applications. \begin{table}
\begin{tabular}{c c c c} \hline  & **18.9–19.2 kHz** & **62.2–62.6 kHz** & **174.1–174.6 kHz** \\ \hline \(\Delta f_{1}\) (Hz) & \(-8\) & \(-15\) & \(-40\) \\ \(\Delta f_{2}\) (Hz) & \(-12\) & \(-25\) & \(-65\) \\ \hline \end{tabular}
\end{table} 
Table 4: The frequency shifts at different frequencies bands. Figure 11: Experimental results (**a**) piezoelectric admittance spectrum and (**b**) peak frequency tendencies in 174.1 kHz–174.6 kHz with different crack sizes.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 3 Modified 1D Map
### Lyapunov Exponent  
The Lyapunov exponent (LE) is a mathematical tool used to measure the rate of separation of nearby trajectories in a dynamic system. It is an important concept in the study of chaos theory and provides a measure of the sensitivity of the system's behavior to initial conditions.  
The Lyapunov exponent is calculated by taking the average rate of change of the distance between two nearby trajectories over time. It is represented mathematically as given in Equation (5):  
\[\lambda=\lim_{n\to\infty}\frac{1}{n}\sum_{i=0}^{n-1}\ln\frac{|\delta_{1}|}{| \delta_{o}|} \tag{5}\]  
where \(\lambda\) denotes the LE, while \(\delta_{1}\) and \(\delta_{o}\) show the spacing between the two trajectories. The positive Lyapunov exponent implies that the system is sensitive to initial conditions and that small variations in initial conditions will grow exponentially over time, leading to vastly varying outcomes. To calculate the Lyapunov exponent numerically, one can use the method in [30], which involves linearizing the system of equations at a given point and then iterating the linearized equations to calculate the exponential growth rate of the distance between nearby trajectories.  
Figure 4: Bifurcation diagram of the function given by Equation (4) showing full chaotic behavior in the range of \(0\leq c\leq|2|\).  
Figure 5: Examples of the bifurcation diagram of (4) depending on \(r\) when (a) \(c=0.1\), (b) \(c=1.8\).  
Figure 6 shows that the function of Equation (4) has a \(\lambda>0\) in the range of \(-2\leq c\leq 2\). This indicates that the system is chaotic within this range of the control parameter \(c\), as the Lyapunov exponent measures the rate of separation of the nearby trajectories, and a positive exponent implies exponential divergence.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 4 Discussion
### Evaluation of Digital Terrain Models  
Digital terrain models are not globally available in adequate detail (minimum 10 m \(\times\) 10 m) and area coverage to support any possible location for a foreign operation at short notice. Currently, it is not possible to perform accurate and detailed CCM analyses in missions abroad without the use of mobile mapping devices (satellite systems, drones), which use various types of sensors and technologies (photogrammetry, laser scanning, radar data, etc.). The biggest disadvantage of this mobile sensors approach is the time delay (months) in collecting and processing data to cover the entire area of a recently established operation. The solution lies in creating a universal database with complete coverage and sufficient detail. This coverage should include the entire NATO area of interest (AOI), which reflects NATO nations' territory and the NATO area of responsibility (AOR) covering adjacent territories in Europe, the western part of Asia and the northern part of Africa. NATO does not have sufficient resources to create or purchase such data. Nevertheless, it could utilise the capabilities of national geographic services, the Multinational Geospatial Support Group (MN GSG), the NATO Communication and Information Agency (NCIA) or a type of direct support of foreign operations, which was done using a Resolute Support Reach-Back Afghanistan Cell (RS RAC). After some time, a military operation is supplied with detailed data, which corresponds to the production in the territories of NATO nations [48]. Nevertheless, sufficient data that would cover immediate needs in the case of a crisis operation are not available, either in the required extent or quality.  
Models with a basic resolution of at least 1 m \(\times\) 1 m (e.g., DTM 5) are the most detailed terrain models of NATO member countries. However, models with a resolution of 5 m \(\times\) 5 m (e.g., DTM 4) are also sufficient in their detail to achieve accurate results of passability analyses. This type of model can be less demanding in terms of data flow for calculations via a network. DTM 3 corresponds in detail with the new worldwide terrain surface model TREx. At this level of detail (10 m \(\times\) 10 m), the model cannot be used for detailed CCM analyses. Nonetheless, data can be used for less fragmented terrain without vegetation cover. This is the case for some foreign operations, such as in Mali. These areas are already mostly covered by the TREx model. DTED 2 is a low-detail elevation model that allows for only general terrain analyses. The optimal terrain model with a balanced ratio of detail and accuracy of the results of passability analyses when considering the complexity of its acquisition is a model with a resolution of 5 m \(\times\) 5 m. The goal of geographical development within NATO should be to ensure comprehensive coverage with enhanced data in the NATO AOI.
# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 7 Benchmark Datasets

Table 6: _Cont._The openly accessible E_ophtha Ex dataset carries 47 exudates, and 35 healthy images. E_ophtha MAs consist of 233 images have no lesions and 148 images with minor HMs and MAs are included in the E_ophtha MAs dataset [178]. The 400 images in STARE dataset were taken with a TOP-CON-TRV camera. It is also obtained by the general public and utilized for the detection of MAs, HMs, and irregular blood vessels [179]. DRIVE dataset gives 40 color images with a resolution of 786 \(\times\) 584. 20 of them are utilized for testing, while another 20 are used for training. These images were taken by 3 CCD camera with 45\({}^{\circ}\) FoV [180]. Kaggle provides 88702 images with a high resolution of 433 \(\times\) 289 to 5184 \(\times\) 3456 pixels. The Kaggle dataset was gathered by EyePACS [181]. The whole count of color fundus images in DIARETDB0 dataset is 130 in which 20 images have no DR but the other 110 images containing DR lesions like HE, SE, MAs, HMs, and neovascularization. Images were taken using an unidentified camera setting on a digital camera with 50\({}^{\circ}\) FoV [182]. DIARETDB1 consists of total 89 images, in which 84 MAs images and 5 healthy images. The resolution of images is 1500 \(\times\) 1152 and was captured by a digital camera at 45\({}^{\circ}\) FoV. Researchers utilized this dataset for identification of damage blood vessels, MAs, HMs, and Ex [10, 183]. The two databases such as DR1 & DR2 were introduced by federal brazil university to assist the researcher for DR detection. DR1 dataset consists of total 234 images and DR2 dataset consists of total 520 images. Researchers identified the DR with the help of DR1 and DR2, which were employed for HE detection [184]. CHASE DB1 is the freely accessible database allowed to segment the retina blood vessels. It has 28 images, each measuring 1280 \(\times\) 960 pixels and having a 45\({}^{\circ}\) FoV [185]. There are 100 publicly accessible retinal images in ROC that were shot at a 45\({}^{\circ}\) FoV. Size variations include 768 \(\times\) 576 to 1389 \(\times\) 1383 pixels. The images were marked up to identify MAs. This datasets contains only ground truth for training [186]. The segmentation of blood vessels was made possible by publicly accessible images in HRF dataset. Total 45 images with measuring 3504 \(\times\) 2336 pixels in size. In which 15 images contain glaucomatous, 15 normal, and 15 are DR images [187]. The publicly available dataset HEI-MED consists of 54 healthy images and 115 irregular images. The images of datasets were acquired by Zeiss VISUCAM PRO camera. This dataset are utilized for the detection of exudates [188]. DRiDB dataset is accumulated by the University of Zagreb to assist the researcher for DR lesions identification. The dataset consists of 50 retinal images [189]. Table 7 shows a comparison of the datasets.
## 2 Research Method
### Numerical Analysis Techniques and Conditions
#### 2.2.2 Boundary and Initial Conditions  
The SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm [11] was applied to calculate the velocity and pressure values in the flow field, and the hybrid scheme [11] was used to calculate the convection term of the standard turbulent model. Moreover, the drag was calculated based on the drag coefficient using Equation (4):  
\[C_{d}=\frac{2\,F_{d}}{\rho u^{2}A} \tag{4}\]  
\[\rho=1.204\ \mathrm{kg/m^{3}}(Air20\,^{\circ}\mathrm{C},1 atm)\]  
where \(u\): flow speed, \(A\): projection area for flow direction.  
Boundaries and initial conditions were set as follows:  
In case of \(k\)-\(\epsilon\) model, boundary conditions for \(k\)-\(\epsilon\) are required. The boundary condition for \(k\) is determined from the equation below.  
\[k=\frac{3}{2}\big{(}u_{avg}I\big{)}^{2} \tag{5}\]  
where \(I\Big{(}=\frac{u}{u_{avg}}\cong 0.16\ Re_{D_{H}}-\frac{1}{8}\Big{)}\) is the turbulence intensity.  
\[\epsilon=C_{H}\frac{\frac{3}{2}}{l} \tag{6}\]  
where \(C_{H}\) is the experimental constant (\(\approx\)0.09) [12] and \(l\) is the turbulence length measure.  
* Inlet: speed boundary condition ((\(V\_SUV=100\ \mathrm{km/h}\,V\_Truck=90\ \mathrm{km/h}\), \(V\_PbV=50\ \mathrm{km/h}\)).
* Outlet: pressure boundary conditions under the condition that the flow field is fully developed (\(=1 atm\)).
* Surface of model vehicle: no-slip boundary condition.
* Low wall: speed boundary conditions equal to vehicle speed.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Validation
#### 2.5.3 Comparison with ESA WorldCover  
The WorldCover project marks a groundbreaking initiative, offering finely detailed global land cover information at a remarkable 10 m resolution, with an estimated OA hovering around 75% ([PERSON] et al., 2019). This dataset, derived from Sentinel-1 and Sentinel-2 satellite observations, is available for the years 2020 and 2021.  
In our research, we assess the congruence between our product for the year 2020 and the one computed by ESA for the same year. ESA's product comprises eleven distinct land cover categories, which we have grouped to simplify the comparison process. We categorised Tree Cover, Shrubland, Mangroves, Moss and Lichen, and Herbaceous Wetland classes as Dense Vegetation. Sparse Vegetation encompasses the Grassland and Cropland classes, while Built-up Areas represent the Built-up class. Water Bodies include the Snow and Ice and Permanent Water Bodies classes, and the Bare/sparse Vegetation class falls under the category of Other. Additional information on the classes and the product can be found in the User Manual edited by [PERSON] et al. (2019).
# Why Do Tethered-Bilayer Lipid Membranes Suit for Functional Membrane Protein Reincorporation?
## 4 Different Types of Tethered-Bilayer Lipid Membranes
### Vesicle Fusion Method to Form tBLMs: A \"Top-Down\" Approach
#### 4.5.1 Using PEG as Anchoring Spacer
The main advantage of this approach is to promote the direct incorporation of membrane proteins into the planar bilayer without using detergents or reconstitution, and to preserve its natural environment. But on the contrary, the protein of interest must be overexpressed in cell membranes to be sure to not bias the functional studies since in situ, the analyzed compound is not pure. Indeed, if the membrane protein activity is analyzed inside the cell membrane, the result is highly challenging since many artefacts can modify the accuracy of the measurement: cell membranes can also contain homologous proteins with identical activity, or agonists and antagonists. That being said, the platform presented here retains the complexity of the plasma membrane and preserves the natural orientation and mobility of MPs. It corresponds so far to the best representative mimic of native cell membranes, which may find many applications in biological assays aimed at understanding cell membrane phenomena.
# Influence of Chinstrap Stiffness on Cerebrospinal Fluid Dynamics and Brain Stress in Helmet Impacts
## 4 Discussion
### Comparison with Prior Helmet Studies  
The present study's findings on chinstrap stiffness and its nonlinear effects on brain stress distribution extend and refine the understanding established in prior helmet research across both motorcycle and football contexts. [PERSON] (2017) demonstrated that motorcycle helmets provide substantial protection against skull fracture and intracranial hemorrhage, but their efficacy varies by injury type, with less protection against concussions, attributed in part to limitations in managing rotational acceleration and force transmission through the helmet structure [18]. Our results parallel these observations by showing that non-stretchable chinstraps, while maximizing helmet stability, transmit undampened forces directly to the skull base, potentially amplifying stress in vulnerable neurovascular regions, much like the limitations observed in rigid motorcycle helmet designs. Conversely, highly flexible straps in our study dissipate energy but risk helmet instability, echoing the trade-offs in helmet retention and energy management highlighted in the motorcycle helmet literature.  
In the context of football helmet research, [PERSON] et al. (2019) and subsequent FE modeling studies ([PERSON] et al., 2020) have emphasized the importance of validating helmet subcomponents (shell, padding, facemask, and straps) and quantifying their contributions to energy absorption and head kinematics [25; 46]. These works established that the strap system can share a significant portion of impact energy, especially in facemask-involved impacts, but did not systematically explore how varying strap stiffness modulates intracranial stress or instability. Our study advances this line of inquiry by employing a high-fidelity computational model that integrates neuroanatomical detail and FSI, directly quantifying how chinstrap stiffness governs not only energy dissipation but also the emergence of hazardous instability regimes at intermediate stiffness. Unlike previous football helmet FE studies, which primarily assessed linear and angular kinematics or component-level strain energy, our work reveals a critical nonlinear relationship between strap material properties and neuroprotection, identifying an \"instability zone\" where partial coupling paradoxically heightens injury risk. This mechanistic insight bridges the gap between epidemiological findings in motorcycle helmet studies and component-level analyses in football helmet research, providing actionable design criteria for optimizing both energy dissipation and helmet stability. Thus, our results contribute a new dimension to helmet safety science by demonstrating that optimal chinstrap design must avoid not only extremes of flexibility and rigidity but also intermediate regimes that introduce dynamic instabilities, which is an aspect not previously addressed in the helmet literature.
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 3 Machine Learning Model  
### Data Preprocessing  
Data preprocessing begins with data cleaning, which involves checking and converting inconsistent or invalid values in the raw data to ensure consistency and quality. Data are first converted into the appropriate type, with unconvertible parts marked as missing (NaN). This step identifies and addresses potential data format issues. Subsequently, features and labels are separated to ensure a clear and logical data structure for subsequent analysis and model training. For missing value handling, a tailored approach is adopted. Numerical features are filled using the mean value to maintain the overall data distribution, and categorical features are filled using the mode to preserve their representativeness. Outliers were detected using the interquartile range (IQR) method, with values below Q\(1-1.5\times\)IQR or above Q3 + 1.5 \(\times\) IQR considered potential outliers. Instead of modifying or estimating outliers via methods like winsorization or imputation, we replaced them with a predefined value (9999) and removed rows containing this value or NaNs. This conservative approach aimed to preserve the integrity of non-outlier data and avoid potential biases from estimation. However, it might cause data loss, especially with many outliers. We acknowledge this trade-off and suggest future studies explore other outlier-handling strategies for a balance between data retention and quality. The resulting dataset has 13,304 entries. Eighty percent of the data (about 10,643 samples from the first four wells) forms the training set, and twenty percent (around 2661 samples from the fifth well) serves as the test set. This dataset is sufficient for training machine learning models and ensures data quality and reliability. By eliminating the impact of missing and abnormal values, the trained model achieves higher accuracy, stability, and generalization ability for more precise practical predictions.  
To enhance model training and generalization, data normalization is essential. This study uses min-max normalization to map different feature curves to the [0, 1] interval. This eliminates the impact of varying feature scales on prediction accuracy. The formula is as follows:  
\[\mathrm{x}_{i}^{\prime}=\frac{\mathrm{x}_{i}-\mathrm{x}_{\mathrm{min}}}{ \mathrm{x}_{\mathrm{max}}-\mathrm{x}_{\mathrm{min}}} \tag{8}\]  
In the formula, \(\mathrm{x}_{\mathrm{min}}\) represents the minimum value of the sample data, and \(\mathrm{x}_{\mathrm{max}}\) represents the maximum value of the sample data.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 5 Visual Simulations and Analysis (Constellation Scenarios)
### Earth-Based Constellation Simulation  
Several design considerations have been made to make sure that the desired objectives are attained and synchronize with the proposed methodology of this paper. The finalized small satellite constellation of a hybrid/non-uniform constellation has been selected after various trials along with the given operating environment and Solar Radiation Pres  
Figure 5: Visual demonstration of the Earth side of the Cislunar space (small satellite constellation) [16]. (**a**) Flower orientation (VLEO-inertial frame); (**b**) Flower orientation (VLEO—Body frame); (**c**) Hybrid orientation (LEO—Walker and Flower); (**d**) Hybrid orientation (LEO—Body frame); (**e**) Hybrid orientation (MEO orbits inclusion); (**f**) Comprehensive orientation (GEO inclusion—body frame).  
sure (SRP) included during the propagation. The propagation cycles were planned to be continuous and in systematic orientation to observe the required parameters closely.
## 3 Results and Discussion
### Grain Composition Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Response Variable** & **Source** & **Df** & **Sum Sq** & **Mean Sq** & **F value** & **Pr (\textgreater{}F)** \\ \hline \multirow{6}{*}{Protein} & Variety & 4 & 0.05 & 0.01 & 15.10 & 1.78 \(\times\) 10\({}^{-7}\) *** \\  & Location & 1 & 0.0 & 1 \(\times\) 10\({}^{-5}\) & 0.01 & 0.91 \\  & Year & 4 & 0.05 & 0.12 & 153.55 & \(\textless{}\)2 \(\times\) 10\({}^{-16}\) *** \\  & Variety: Location & 3 & 0.02 & 0.01 & 6.76 & 0.00091 *** \\  & Variety: Year & 3 & 0.01 & 0.01 & 3.91 & 0.01 * \\  & Location: Year & 1 & 0.01 & 0.01 & 21.81 & 3.70 \(\times\) 10\({}^{-5}\) *** \\  & Variety: Location: Year & 3 & 0.01 & 0.00 & 2.31 & 0.09 \\  & Residuals & 38 & 0.03 & 0.00 & & \\ \hline \multirow{6}{*}{Beta Glucan} & Variety & 4 & 0.56 & 0.14 & 5.7 & 0.001076 ** \\  & Location & 1 & 2.85 & 2.85 & 114.74 & 4.88 \(\times\) 10\({}^{-13}\) *** \\  & Year & 4 & 0.76 & 0.19 & 7.72 & 0.000118 *** \\  & Variety: Location & 3 & 0.33 & 0.11 & 4.47 & 0.008769 ** \\  & Variety: Year & 3 & 0.77 & 0.25 & 10.36 & 4.04 \(\times\) 10\({}^{-5}\) *** \\  & Location: Year & 1 & 0.32 & 0.32 & 12.96 & 0.000905 *** \\  & Variety: Location: Year & 3 & 0.36 & 0.12 & 4.95 & 0.005355 ** \\  & Residuals & 38 & 0.94 & 0.02 & & \\ \hline \multirow{6}{*}{Starch} & Variety & 4 & 1288.9 & 322.2 & 8.77 & 4.06 \(\times\) 10\({}^{-5}\) *** \\  & Location & 1 & 2213.0 & 2213.0 & 60.23 & 2.36 \(\times\) 10\({}^{-9}\) *** \\  & Year & 4 & 727.9 & 182.0 & 4.95 & 0.00259 ** \\  & Variety: Location & 3 & 372.2 & 124.1 & 3.38 & 0.02806 * \\  & Variety: Year & 3 & 161.1 & 53.7 & 1.46 & 0.24 \\  & Location: Year & 1 & 0.4 & 0.4 & 0.01 & 0.91 \\  & Variety: Location: Year & 3 & 296.3 & 98.8 & 2.69 & 0.06 \\  & Residuals & 38 & 1396.2 & 36.7 & & \\ \hline \multirow{6}{*}{Arabinoxylans} & Variety & 4 & 363.0 & 90.74 & 17.48 & 3.24 \(\times\) 10\({}^{-8}\) *** \\  & Location & 1 & 31.3 & 31.26 & 6.02 & 0.01883 * \\  & Year & 4 & 123.2 & 30.81 & 5.93 & 0.000821 *** \\ \cline{1-1}  & Variety: Location & 3 & 128.9 & 42.97 & 8.28 & 0.000231 *** \\ \cline{1-1}  & Variety: Year & 3 & 51.9 & 17.31 & 3.335 & 0.029365 * \\ \cline{1-1}  & Location: Year & 1 & 199.9 & 199.9 & 38.509 & 2.97 \(\times\) 10\({}^{-7}\) *** \\ \cline{1-1}  & Variety: Location: Year & 3 & 215.5 & 71.83 & 13.839 & 2.99 \(\times\) 10\({}^{-6}\) *** \\ \cline{1-1}  & Residuals & 38 & 197.3 & 5.19 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ANOVA results for the effects of year, variety, and site on the composition of wheat grain. While the above findings suggest an influence of environmental factors as an integral part of terroric, additional factors such as soil nutrient composition might also play a role, highlighting the complex interplay of environmental influence.
## 4 Design of Multidimensional Membership Functions
### Assignment of a Function  
For the center of gravity of the regions, a membership function is assigned, which could be  
\[\mu_{i}(x)=\frac{1}{1+d_{i}(x)} \tag{26}\]  
where  
\[d_{i}(x)=(x-r_{i})^{T}\cdot P_{i}\cdot(x-r_{i}) \tag{27}\]  
which satisfies  
\[\mu_{i}(r_{i})=1 \tag{28}\]  
Equation (26) represents a rational function--specifically a modified reciprocity function. It is characterized by being a decreasing function of the term \(d_{i}(x)\) (see Figure 11).  
Figure 10: Location of the centers of gravity of the rules using the second design criterion.  
Figure 11: Function \(u_{i}(x)\).  
These are some characteristics of the function in Equation (26): * Value at the central point * When \(d_{i}(x)=0\), the function takes its maximum value: \[u_{i}=\frac{1}{1+0}=1\] * This implies that at the central point (where \(d_{i}(x)=0\)), the membership function is equal to 1. * Values between 0 and 1 * As \(d_{i}(x)\) increases, the value \(\mu_{i}\) decreases but always remains between 0 and 1. * The function decreases monotonically and never reaches negative values, which is desirable for a membership function in fuzzy logic. * Asymptotic behavior: * When \(d_{i}(x)\) approaches infinity, \(\mu_{i}\) tends to zero: \[\lim_{d_{i}(x)\rightarrow\infty}\frac{1}{1+d_{i}(x)}=0\] * This ensures that points far from the central point have very low membership. * Differentiable: * The function \(\mu_{i}\) is differentiable with respect to \(d_{i}(x)\), which allows its use in optimization methods and parameter tuning.