esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
FG elastic nano-rods under tension have been investigated by the modified nonlocal strain gradient (NSG) theory. The new formulation contains a nonlocal parameter and a material length scale parameter to incorporate the scaling effects of nonlocal stress and microstructure-dependent strain gradient. In comparison to other strain-driven methodologies, the new proposal has been shown to be well-posed and does not require higher-order boundary conditions. In fact, in addition to the classical static and kinematic boundary conditions, closure of the NSG model has to be carried out by prescribing suitable constitutive boundary conditions. Closed-form nonlocal solutions of FG clamped-free and clamped-clamped nano-rods have been provided, exhibiting stiffening or softening effects depending on the values of nonlocal and gradient parameters. Single-Walled Carbon Nanotubes (SWCNT) of armchair (10, 10) were modeled as NSG nano-rods, showing that the new approach could capture the small-scale behavior of [PERSON]'s modulus as predicted by the MD simulations. The nonlocal parameter was thus tuned to characterize [PERSON]'s modulus vs. the SWCNT length.  
Conceptualization, Methodology, Software, Validation, Investigation, Writing--Review & Editing: R.B., M.C. and F.M.M.S. All the authors contributed equally to this work.  
This research received no external funding.  
Financial supports from the Italian Ministry of Education, University and Research (MIUR) in the framework of the Project PRIN 2015 \"COAN 5.50.16.01\"--code 2015 JW9 NJT--and from the research program ReLUIS 2018 are gratefully acknowledged.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\lambda\setminus\mu\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Non-dimensional maximum axial displacement \(\bar{u}(1/2)\) for the proposed NSG method in terms of the non-dimensional parameters \(\lambda\) and \(\mu\).
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 4 Calculation and Verification of Difficulty Evaluation for Real Hull Plates  
By summarizing the common outer hull plate sizes in the current shipbuilding field, the plate specification of the research object is determined. In this paper, some complex curved plates of 3900 TEU container ship are selected for difficulty evaluation calculation and verification. Ten types of curved plate data are obtained, including total forming time, plate length, plate width, plate thickness, radius of curvature, deflection, heating speed, heating line number, curved plate difference and heating line spacing. The data of nine selected curved plates are summarized in Table 1. Curved plate difference is a qualitative variable, which is converted into a quantitative variable. The curved plate difference is expressed by the difference in the heating line number on both sides of the curved plate.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Plate Name**} & **Plate Length** & **Plate Width** & **Plate Thickness** & **Radius of Curvature m** & **Deflection** & **Heating Line** & **Heating Line Number** & **Heating Line** & **Heating Speed** & **Forming Time s** \\  & **m** & **m** & **m** & **Curvature m** & **m** & **Number** & **Difference** & **Spacing m** & **10\({}^{-3}\) m/s** & **Time s** \\ \hline
1010-CPA2-17P & 8.035 & 1.651 & 0.014 & 7.290 & 0.063 & 5 & 1 & 2.296 & 2.13 & 800 \\ \hline
1021-CPA1-1P & 5.349 & 1.390 & 0.020 & 2.675 & 0.022 & 9 & 1 & 0.973 & 2.03 & 938 \\ \hline
1021-CPA1-3P & 6.654 & 1.943 & 0.020 & 2.996 & 0.026 & 8 & 2 & 1.331 & 2.26 & 1348 \\ \hline
1021-CPA1-5P & 5.343 & 1.623 & 0.022 & 3.438 & 0.055 & 14 & 2 & 0.668 & 2.59 & 1719 \\ \hline
1021-CPA1-7P & 6.629 & 1.905 & 0.022 & 3.678 & 0.083 & 15 & 1 & 0.780 & 2.27 & 2640 \\ \hline
1031-CPA1 SP-3P & 5.072 & 1.930 & 0.022 & 3.899 & 0.015 & 7 & 3 & 1.127 & 2.27 & 1192 \\ \hline
1031-CPA2-1P & 5.098 & 1.418 & 0.020 & 3.353 & 0.017 & 4 & 0 & 1.699 & 2.16 & 464 \\ \hline
1041-CPA1-1P & 9.708 & 2.633 & 0.022 & 7.147 & 0.033 & 7 & 1 & 2.157 & 2.27 & 1232 \\ \hline
1041-CPA1-3P & 9.845 & 2.419 & 0.020 & 26.798 & 0.039 & 4 & 0 & 3.282 & 2.27 & 704 \\ \hline \end{tabular}
\end{table}
Table 1: Hull plate data of 3900 TEU container ship.  
Figure 9: Forming difficulty evaluation process of ship complex curved plate.
## 2 Polygonal Approximation Algorithm of Curvilinear Obstacle
### Douglas-Peucker Algorithm
To overcome this problem, a modified DP algorithm is proposed in this paper. ```
Input:\(pl\leftarrow\) Point list of the given curve \(\varepsilon\leftarrow\) Threshold value for maximum dissimilarity tolerance Output:\(r\leftarrow\) Result of the polygonal approximation of the given curve represented as a point list Initialization:\(line_{\text{base}}\leftarrow\) line segment connected from \(pl[0]\) to \(pl[\text{end}]\) // end: number of points in \(pl\)\(dist_{\text{max}}\leftarrow-1\)\(i_{\text{max}}\leftarrow-1\) Begin recursiveDP Procedure
1 for each point \(p\) in \(pl[1\ldots\text{end}-1]\)do
2\(dist\leftarrow\) perpendicular distance between \(line_{\text{base}}\) and \(p\)ifdist > dist\({}_{\text{max}}\)\(dist_{\text{max}}\leftarrow\)dist\(i_{\text{max}}\leftarrow\) index of \(p\)
3 end if
4 end for
5
6 end for
7
8 if\(dist_{\text{max}}>=\varepsilon\)then
9\(r_{1}\leftarrow\) recursiveDP(\(p[0\ldots\)\(i_{\text{max}}]\), \(\varepsilon\)) // \(pl\): point list of a segment of the given curve
10\(r_{2}\leftarrow\) recursiveDP(\(p[l_{\text{max}}\ldots\text{end}]\), \(\varepsilon\))
11\(r\leftarrow\) point list[\(r_{1}[0]\ldots\)\(r_{1}[\text{end}_{1}-1]\)\(r_{2}[0]\ldots\)\(r_{2}[\text{end}_{2}]\)] // end\({}_{i}\): # of points in \(r_{i}\)
12 else
13 insert \(pl[0]\) to \(r\)
14 insert \(pl[\text{end}]\) to \(r\)
15 end ifEnd recursiveDP Procedure
```  
**Algorithm 2**Pseudo Code of the Recursive DP. Figure 2: Douglas–Peucker (DP) process between \(P_{1}\) and \(P_{2}\): (**a**) If \(dist_{\text{max}}\geq\varepsilon\), then (**b**) \(l_{2}\) between \(P_{1}\) and \(dist_{\text{max}}\) point \(P_{3}\) is used for the next base line; (**c**) If \(dist_{\text{max}}<\varepsilon\), then (**d**) \(l_{3}\) between \(P_{3}\) and \(P_{2}\) is used for the next base line.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
###### Abstract  
Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a \(f_{1}\) score of 75.89%, and average accuracy of 73.35% ), the proposed methodology achieves a competitive overall accuracy of 81.20%, a \(f_{1}\) score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.  
convolution neural network (CNN); crop classification; post-processing; satellite images +
Footnote †: journal: Medical Imaging  
1 Faculty of Engineering, Department of Electronics, Corporacion Universitaria Autonoma de Narino, Pasto 520001, Colombia; [EMAIL_ADDRESS]  
2 Department of Mechatronics, Universidad Internacional del Ecuador, Av. Simon Bolivar, Quito 170411, Ecuador 2  
Department of Electrical, Electronic and Computing Engineering, Universidad Nacional de Colombia, Sede Manizales, Manizales 170001, Colombia 4  
Department of Electronic Engineering, Universidad de Narino, Pasto 520001, Colombia 4  
Smart Data Analysis Systems Group (SDAS Research Group), Ben Guerir 47963, Morocco 5  
Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco 5  
Correspondence: [EMAIL_ADDRESS] (M.Y.M.-R.); [EMAIL_ADDRESS] (L.G.-G.); [EMAIL_ADDRESS] (B.G.-M.); [EMAIL_ADDRESS] (J.R.-F.)
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 3 Proposed Encryption Scheme
### Key Generator
#### 3.3.2 Unclonable Behavior in EB devices
Finally, it is necessary to acquire or sense the magnetic induction to generate an analog electrical signal. As variable components are always considered in the fields, it is easy to generate that electrical (voltage) signal through an induced electromotive force in a second copper coil (22). \[\varepsilon=\oint_{\text{C}}E\cdot dl=\int_{S}NB\cdot\hbar dA=\int_{S}NB_{n}dA =-\frac{d\phi_{m}}{dt} \tag{22}\]  
With the previous expressions and discussions, it is demonstrated that there is a direct relationship between the magnetization of the material and its behavior as a transmission line for radio frequency signals. Then, as (nowadays) magnetization in EB devices is uncontrollable and unclonable (no work has reported a method to control the natural frequency \(\omega_{M}\)) [45], the response of these devices as a transmission line to the applied excitation (challenge) will also be unclonable. REQ#3 is then met. Then, to create a cipher and decipher pair, an EB material must be generated, divided into two identical samples; and using these samples, two EB devices are constructed. The same process must be repeated to build all required EB devices for both the cipher and the decipher.
# Opal-Like Photonic Structuring of Perovskite Solar Cells Using a Genetic Algorithm Approach
## 2 Design of the Perovskite Solar Cell
The idea behind the variation of this parameter is to allow the spiro-OMeTAD layer to act as a Fabry-Perot cavity, maximizing the reflection of non-absorbed light by the Au counter-electrode and using constructive interferences to enhance light absorption inside the hybrid photo-active layer. The thickness of the Au layer is fixed to 100 nm, which is sufficient to almost perfectly block and reflect any remaining light (transmittance \(T=0\)) [43]. This particular device design is considered here so as to focus on the purely photonic optical properties of the system and their potential impact on the efficiency of absorption in PSCs (using the  
Figure 1: Schematic of a perovskite solar cell (PSC) using a hybrid photo-active layer made of structured and homogeneous methylammonium lead triiodide (_MAPbl3_) perovskite subunits. The structured part is made of perovskite spheres with radius \(R\), arranged according to \(ABC\) stacking. The thicknesses of the fluorine-doped tin oxide (FTO) layer (\(t_{TiO}\)), the homogeneous perovskite layer (\(t_{uni}\)), the opal-like structured layer (\(t_{struc}\)), and the 2,2’,7,7’-Tetrakis[N,N-di(4-methoxyphenyl)amino]-9,9’-spirobifluorene (spiro-OMeTAD) layer (\(t_{spiro}\)), as well as the radius \(R\) of the spheres, are the parameters for the genetic algorithm (GA) optimization. The thicknesses of the \(TiO_{2}\) blocking layer and of the \(Au\) layer are fixed to 40 and 100 nm, respectively, while the refractive index of the incident (emergence) medium is set to 1.5 (1). iQE, as defined below). Effects related to the electronic properties are, therefore, not tackled in the present work as this would require a different approach in terms of calculation methodology and material design. The thermodynamic (un)stability of the perovskite material and the consecutively assembled PSCs, known to be potentially detrimental in reality, are not discussed here, considering that the cell constituents are protected and isolated from external degradation factors such as moisture, heat, and intense solar irradiation. The different parameters for the GA are summarized in Table 1 below, as well as their range of variation.
# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
## 2 Methods
### Minnesota Leisure-Time Physical Activity Questionnaire  
The Minnesota Leisure-Time Physical Activity Questionnaire (MLTPAQ) is an energy expenditure assessment tool that focuses on gathering information on various forms of leisure-time physical activities. The questionnaire was presented in 1978 by [PERSON] et al. [33]. It is a recognized subjective method [34; 35; 36] used in studies into the leisure-time physical activities of adults in various regions of the world [37; 38; 39], including Poland [40; 41; 42; 43]. The MLTPAQ included leisure-time physical activity in kcal per week, and the final result  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Variable** & **Description** & \(\bar{\pi}\) & \(sd\) \\ \hline \(y\) & VO\({}_{2}\)max (mL-kg\({}^{-1}\)-min\({}^{-1}\)) & 44.9 & 7.0 \\ \(x_{1}\) & Age (years) & 20.3 & 1.2 \\ \(x_{2}\) & Body weight—BW (kg) & 58.9 & 9.8 \\ \(x_{3}\) & Body height—BH (cm) & 165.0 & 5.7 \\ \(x_{4}\) & Waist circumference—WC (cm) & 75.1 & 8.1 \\ \(x_{5}\) & Hip circumference—HC (cm) & 95.7 & 7.5 \\ \(x_{6}\) & Body fat—FAT (\%) & 23.2 & 7.0 \\ \(x_{7}\) & Fat-free mass—FFM (\%) & 76.7 & 7.0 \\ \(x_{8}\) & Total body water—TBW (\%) & 56.1 & 5.2 \\ \(x_{9}\) & Body mass index—BMI (kg/m\({}^{2}\)) & 21.6 & 3.2 \\ \(x_{10}\) & Waist-to-height ratio—WHtR & 0.46 & 0.05 \\ \(x_{11}\) & Waist–hip ratio—WHtR & 0.78 & 0.05 \\ \hline \(x_{12}\) & Light intensity—MLTPAQ\({}_{L}\) & 488 & 497 \\ \(x_{13}\) & Moderate intensity—MLTPAQ\({}_{M}\) & 386 & 493 \\ \(x_{14}\) & High intensity—MLTPAQ\({}_{H}\) & 932 & 1798 \\ \(x_{15}\) & Total activity—MLTPAQ\({}_{T}\) & 1806 & 2071 \\ \hline \hline \end{tabular} \(\bar{\pi}\)—mean value; \(sd\)—standard deviation; MLTPAQ—Minnesota Leisure-Time Physical Activity Questionnaire.  
\end{table}
Table 1: The basic statistics of variables used to calculate predictive models (N = 154).  
of energy expenditure (total activity) was presented in terms of exercise intensity of high (\(\geq\)6 MET), moderate (4.5-5.5 MET) and light (\(\leq\)4 MET). The examination of student leisure-time physical activity using the MLTPAQ questionnaire was conducted in direct contact with the respondents (face to face).
## 4 Simulation and Physical Experiments
### Comparison between NR Method and SAPSO  
In this section, we compare the success rate and the time consuming between the NR method and the SA-PSO algorithm. Five groups of experiments are designed. Each group solves 1000 randomly generated L points. The target accuracy is set as \(10^{-8}\). The solving results are illustrated in Table 8.  
The NR method fails in some points when the initial value is set as [0 0 0 0 0 0], which may be not proper for these points. When the iteration of NR method is active, it no longer converges. However, because of the randomness introduced to the optimization progress, the SAPSO can work well in these points. Moreover, the SA-PSO method is more efficient than NR method with the operation time less than 40 milliseconds, almost 10% of the NR method.
## 4 Results and Discussions
### DMB Comparisons to the FINN and GFAS
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **7 November 2019–13** & **3 December 2019–7** & **16 December 2019–21** & **30 December 2019–4** & **7 November 2019–4** \\  & **November 2019** & **December 2019** & **December 2019** & **January 2020** & **January 2020** \\ \hline This study & 24.82 & 23.27 & 18.13 & 37.97 & 146.83 \\ \hline GFAS & 16.4 & 12.49 & 12.75 & 44.04 & 129.07 \\ \hline FINN & 28.02 & 26.19 & 24.94 & 55.08 & 204.72 \\ \hline \end{tabular}
\end{table} 
Table 3: The DMB (Tg) produced by four relatively centralized combustion periods. Finally, based on the emission factors and methods of each land-use type described in Section 3.2, we converted DMB into the estimated value of each pollutant emitted by fire (Figure 7). A total of 229064 Gg CO\({}_{2}\), 14640 Gg CO, 1807 Gg PM\({}_{2.5}\), 78 Gg BC was emitted during the study period. It can be seen from Figure 7 that forest combustion emissions are the main source of pollution (specifically, 80.5% of CO\({}_{2}\), 85% of CO, 90% of PM\({}_{2.5}\) and 85.4% of BC). Figure 7 also shows that November 2019 was the month with the highest fire emissions during the study period, and the total 115966 Gg CO\({}_{2}\) and 7437 Gg CO, 920 Gg PM\({}_{2.5}\) and 40 Gg BC emissions in January 2020 were halved compared with December 2019. The reason for this is that the fire in January 2020 gradually extinguished after 15 January, and the fire was almost always burning in December 2019, so the amounts of pollutants emitted was high. Figure 6: Spatial distribution of DMB (Tg, 2 km grid cells) of different datasets in November 2019, December 2019 and 1 January 2020 to 15 January 2020. (**a–c**), DMB estimated in this study. (**d–f**), GFAS DMB. (**g–i**),FINN DMB.
# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 2 Materials and Method  
### Dataset Description  
In this study, the HuSHeM dataset, a publicly available dataset for evaluating the sperm head classification algorithms, is used to investigate the proposed method. The samples were acquired from 25-38 years old patients at Isfahan Fertility and Infertility Center (IFIC) and then processed by the Diff-Quik method. After a series of biochemical treatments, sperm images were obtained under the microscope with a camera. More details about the HuSHeM dataset are available in [25]. According to the WHO criteria, the sperm samples in the HuSHeM dataset are classified and labeled by three specialists into four categories: normal, tapered, pyriform and amorphous (see Figure 2a). Totally 216 sperm cell images (54 normal, 53 tapered, 57 pyriform, and 52 amorphous) constitute the HuSHeM dataset and each image is in RGB format with the size of 131 \(\times\) 131 pixels.
## 3 Fuzzy-Logic Autonomous Navigation System  
### Sensors of Navigation System  
Figure 5 describes the sensor system of the mobile robot used for evaluating the performance of the developed navigation system. The mobile robot was mounted with eight distance sensors and the fuzzy positioning system investigated in [24]. Figure 5a shows the function of the distance sensors mounted at the front, right, and left of the robot. Among the distance values, three minimum values from each side, expressed as fd = MIN {d1, d2}, rd = MIN {d3, d4, d5}, ld = MIN {d6, d7, d8}, were inputs to the fuzzy navigation system. Figure 5b shows the function of the fuzzy positioning system that uses the x and y coordinates of the robot's current position to calculate the angle (0d) between the current moving direction and the direction to the target. \(\theta_{d}\) is also used as an input to the fuzzy navigation system.  
To reach the target, the mobile robot requires navigation skills, such as heading to the target, avoiding obstacles, and following the edges of obstacles. The effectiveness of these skills has been verified in previous studies [10; 11; 12; 13; 14; 15].
## 3 Data Analysis
### Development of the Calibration Model  
In this study, a PLS-DA model, which is a predictive and descriptive modeling technique, was built to discriminate between normal almond kernels and those adulterated with apricot kernels. PLS-DA has been successfully demonstrated in modeling high-dimensional datasets for diverse purposes, e.g., product authentication in food analysis, disease classification in medical diagnosis, and evidence analysis in forensic science [24]. This supervised classification analysis technique initially classifies the calibration and validation sets as known classes based on the measured feature [16].  
PLS-DA has been used for evaluations of various food products' quality and has been proven to be a powerful technique for this purpose [18; 25; 26]. There are many articles giving detailed descriptions of the basic theory of PLS-DA [24], which is well-expressed by [27], which explains that PLS-DA is a robust method for classification models, commonly used for model classification. PLS-DA is a modified form of partial least square regression (PLS-R). It is expressed as  
\[\mathbf{Y}=\mathbf{X}\times\mathbf{b}+\mathbf{E}, \tag{2}\]  
Figure 3: The raw and pre-processed spectral data of almond and apricot kernels. (**a**) The pure spectral data and (**b**) the standard normal variate (SNV) pre-processed spectra in the spectral region between 950 and 1622 nm.  
where X is an n x p matrix that holds the spectral values of each class, b is the regression coefficient, and E is the error term. In the matrix, n indicates the number of samples and p represents the number of spectral variables. To develop a PLS-DA model, the total pre-processed full pixel-based spectral data from almond and apricot kernels were arranged in the independent (X) variable matrix, while the dependent variable matrix (Y) was grouped and consisted of given artificial values of (0) for almond kernels and (1) for apricot kernels. To classify each group of kernels into its own category, a threshold value of 0.5 was set between both groups to distinguish them from each other.  
To build a linear relationship between the predictors and response variables, both X and Y values were changed by latent variables (LVs).  
\[\mathbf{X}=TP^{T}\ +Ex \tag{3}\]  
\[\mathbf{Y}=UQ^{T}\ +Ey \tag{4}\]  
According to the model, X and Y are the independent and independent variables, respectively; T and U indicate the score matrices; \(P^{T}\) and \(Q^{T}\) are the \(\mathbf{X}\) and \(\mathbf{Y}\) variable loading in matrices; and \(E_{x}\) and \(E_{y}\) represent the error of the matrices. The X-axis indicates the spectral data extracted from almond kernels, and the Y-axis represents the spectral data extracted from apricot kernel samples.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Random Forest Model  
The Random Forest (RF) algorithm was used in this study. RF is an ensemble learning method for classification, regression, and other tasks [56]. This study implemented RF using the MATLAB built-in function TreeBagger with fixed hyperparameters based on the error curve, set tree = 30, leaf = 5.