esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation of the Pre-Structured Material Behavior
### Transverse Isotropic Law Identification
#### 4.1.1 The In-Plane Shear Modulus \(\mathbf{G_{LT}}\)  
By establishing the relationship between the global reference and the local reference defined in Figure 6, where the plane stress assumption is presumed, [PERSON]'s modulus in the loading direction (global reference 1) can be expressed as a function of the transverse isotropic elastic constants and the printing angle. The same relation (see Equation (1)) [34] is used to study the stress analysis of fiber-reinforced composite materials.  
\[\mathbf{E_{1}}=\frac{\mathbf{E_{L}}}{\mathbf{\cos^{4}\theta}+\left(\frac{\mathbf{E_{L}}}{\mathbf{ G_{LT}}}-\mathbf{2\
u_{LT}}\right)\mathbf{\sin^{2}\theta}\mathbf{\cos^{2}\theta}+\frac{\mathbf{E_{L}}}{ \mathbf{E_{T}}}\mathbf{\sin^{4}\theta}} \tag{1}\]  
where:  
* \(\mathbf{\theta}\): Printing angle
* \(\mathbf{E_{L}}\): [PERSON]'s modulus in the longitudinal direction
* \(\mathbf{E_{T}}\): [PERSON]'s modulus in the transverse direction
* \(\mathbf{\
u_{LT}}\): The in-plane Poisson's ratio
* \(\mathbf{G_{LT}}\): The in-plane shear modulus
* \(\mathbf{E_{1}}\): [PERSON]'s modulus in the loading direction (global reference)  
By using the \(45^{\circ}\) specimen, the following equation is deducted:  
\[\mathbf{G_{LT}}=\left(\frac{\mathbf{4}}{\mathbf{E_{45^{\circ}}}}-\frac{\mathbf{1}}{\mathbf{E_{L}} }-\frac{\mathbf{1}}{\mathbf{E_{T}}}+\frac{2\mathbf{\
u_{LT}}}{\mathbf{E_{L}}}\right)^{-\mathbf{1}} \tag{2}\]  
Figure 6: Local and global references definition for the \(45^{\circ}\) specimen.  
where: \(E_{45^{\circ}}\) is the experimental Young's modulus from the 45\({}^{\circ}\) specimen. A value of 545 MPa of the in-plane shear modulus was calculated. A higher value was given by [PERSON] et al. [31] equal to 641 MPa, which was already explained by the difference of the extrusion temperature.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion
### Winter Wheat Mapping
#### 4.2.1 SAR Image Classification and Accuracy Assessment
For the two classifiers, although the F1 measure using VV + VH + C + T was higher than that of VV + VH + C, the overall accuracy using VV + VH + C was better than that of VV + VH + C + T. This indicates that the combination of coherence, texture, and backscatter intensity images can improve the classification accuracy. For forest, urban areas, and water fields, the F1 measure using VV + VH + C + T were better than 90%, with an overall accuracy and kappa coefficient equal to more than 94% and 0.9, respectively. For rapeseed fields, VV + VH + C + T had the best performance of all combinations, while it could not distinguish rapeseed areas well (with an F1 measure of no more than 85%). Compared with the previous research results (26), rapeseed classification accuracy is low because the rapeseed planting structure is complex and the planting area is small.
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 3 Expression for Displacement-Dependent Active Earth Pressure  
### Soil Strain Distribution  
Based on the deformation compatibility between a supporting structure and the surrounding soil, the soil strain behind a wall will change with structural deformation during an excavation. In practice, measuring soil strain with conventional methods is  
Figure 10: Comparison between the measured and predicted radial stress–strain relationships.  
difficult; therefore, the method for determining the soil strain is a bridge to establish the relationship between deformation and earth pressure. [PERSON] [50] divided the soil behind retaining walls into the plastic zone, the elastic zone, and the undisturbed zone and took the logarithmic spiral as a separation line [51; 52]. simplified the boundary into a straight line, as shown in Figure 11.  
The horizontal displacement of the supporting structure can be expressed by the following integration:  
\[\delta(z)=\int_{0}^{L(z)}\varepsilon(x)dx \tag{8}\]  
The width of the disturbed zone, letting \(L(z_{\text{i}})=l\), is determined by  
\[l=(H_{\text{i}}+D_{\text{n}}-z_{\text{i}})\tan(45^{\circ}-\frac{\varphi}{2}) \tag{9}\]  
\[D_{\text{n}}=2D_{\text{i}}e^{\frac{\pi(45^{\circ}-\varphi/2)\tan\varphi}{180}} \cos(45^{\circ}-\frac{\varphi}{2}) \tag{10}\]  
where \(H_{\text{i}}\) is the excavation depth and \(D_{\text{i}}\) is the depth from the pit bottom to the zero displacement point of the supporting structure, i.e., \(D_{\text{i}}=(1{\sim}1.2)\)\(H_{\text{i}}\)[52].  
In this study, according to the continuity and isotropy hypotheses of the soil, the soil strain distribution in the disturbed area was assumed to be reduced linearly, as follows: \(\varepsilon(x)=a+bx\). Introducing the boundary conditions \(\varepsilon(0)=\varepsilon_{\text{r}}\) and \(\varepsilon(l)=0\), the soil strain on the interaction surface was obtained: \(\varepsilon_{\text{r}}=\varepsilon\left(0\right)=2\delta\left(z\right)/l\). Substituting \(\varepsilon_{\text{r}}\) into Equation (7), the displacement-dependent active earth pressure can be written as follows:  
\[e_{\delta}=e_{0}-\frac{2(e_{0}-e_{\text{a}})E_{\text{i}}\delta(z)}{(e_{0}-e_{ \text{a}})I+2R_{\text{s}}E_{\text{i}}\delta(z)} \tag{11}\]  
The calculation from Equation (11) shows that earth pressure decreases monotonically with an increase in displacement, i.e., \(\text{d}\varepsilon_{\delta}/\text{d}\delta<0\), which is in accordance with the variation in earth pressure with displacement in the active condition.
## 3 System Model
### Computing Model
#### 3.5.2 LEO Satellite Computing Latency
 \tag{26}\]In summary, the LEO satellite computing latency for the MLLM task of IoTD \(k\) is calculated as  
\[T_{k,n}^{sat}(t)=T_{k,n,n^{\prime}}^{up}(t)+T_{k,n^{\prime}}^{exc}(t)+T_{k,n^{ \prime},n}^{dl}(t). \tag{27}\]  
The energy consumption for LEO satellite computing associated with the MLLM task of IoTD \(k\) is expressed as  
\[E_{k,n}^{sat}(t)=E_{k,n,n^{\prime}}^{up}(t)+E_{k,n^{\prime}}^{exc}(t)+E_{k,n^ {\prime},n}^{dl}(t). \tag{28}\]  
The total latency for the MLLM service request of IoTD \(k\) is  
\[T_{k}(t)=\sum_{m=1}^{M}x_{k,m}(t)T_{k,m}^{ULV}(t)+\sum_{n=1}^{N}x_{k,n}(t)T_{k,n}^{sat}(t). \tag{29}\]  
The total energy consumption for the MLLM task of IoTD \(k\) across all server nodes is given by  
\[E_{k}(t)=\sum_{m=1}^{M}x_{k,m}(t)E_{k,m}^{ULV}(t)+\sum_{n=1}^{N}x_{k,n}(t)E_{k,n}^{sat}(t). \tag{30}\]
# Vector Optical Bullets in Dielectric Media: Polarization Structures and Group-Velocity Effects
## 1 Introduction
The extension of the concept of nondiffracting X-waves from the scalar domain to the vector domain was introduced [52] and extended to vortical properties with an arbitrary frequency spectrum [53]. Vector light sheets of nondiffracting light were also recently studied, [54; 55]. Moreover, vector parabolical, elliptical optical bullets were considered [56; 57]. These advances are especially interesting, given the applications of pulsed beams polarized radially and azimuthally for material processing [58; 59], together with the applications of nondiffracting beams [60; 61]. We have already investigated highly nonparaxial vector Bessel-X pulses [62], introducing analytical expressions and revealing novel properties introduced by inhomogeneous polarization. In this work, we move on to the investigation of another type of optical bullets--vector focus wave modes, which are generalizations of the X-waves and Bessel-X pulses inside a dielectric dispersive medium. For this purpose, we selected one of the glass materials and analyzed the dispersion relation of the spatiotemporal spectra [21]. The general expression of the spatiotemporal spectra describes the cone angles of forward-propagating Bessel beams that result in the pulsed signal propagating in the Bessel zone with controllable velocity \(V\), which can be superluminar [63; 64], subluminar [65], or even negative (i.e., propagate backward) [25]. These conditions may require high cone angles of superposed Bessel beams, so a valid vector description is required. We introduce this vector description to the optical bullets and study the electromagnetic structure of resulting pulsed fields in a glass, relating the duration to the transverse size of the beam, central frequency, and propagation velocity of the optical bullet. Our aim is to investigate the generation and behavior of vector optical bullets within a dielectric medium. For the experimental application of nondiffracting and nondispersive light pulses, we need a proper mathematical description of highly focused vector pulses. Using our mathematical model, we can investigate the properties of the beam in a dielectric media with group velocities that can be superluminal, subluminal, or even negative. The results allow us to understand the relations between the transverse size of the beam, the intensities of each component (paying special attention to the z component of the electric field) depending on the central frequency, polarizations, different group velocity \(V\) and integration constant \(\gamma\) values.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 4 Decision Algorithm for the Broadcast Time of Star of Cliques
### Broadcasting from the Universal Vertex \(u\)
The maximum possible value of \(t\) is \(k+\lceil\log l_{1}\rceil\), and since \(k,l_{1}\leq n\), where \(n=l_{1}+l_{2}+\ldots+l_{k}\) represents the total number of vertices in the graph, it follows that \(t,k=O(n)\). Consequently, the overall time complexity of the algorithm is \(O(n^{3})\).
## 2 System Model and Problem Formulation
### Request Model
#### 2.4.1 Content Request  
When \(\pi_{n}(t)\) is a content request, i.e., \(q_{n}(t)=0\). If vehicle \(n\) can download the requested content \(k\) from a specific UAV \(u\), we consider that the request \(\pi_{n}(t)\) is satisfied. Meanwhile, vehicle \(n\) needs to be within the coverage of UAV \(u\), i.e., \(x_{u,k}=1\) and \(I_{n,u}(t)=1\). Otherwise, \(\pi_{n}(t)\) is not satisfied.
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 2 Background of the Work
### Sort Codewords by Projected Values  
A sorted codebook is an essential component for a codeword index reordering scheme [4]. There is a VQ codebook \(CB=\{cv_{0},\ cv_{1},\ \cdots,cv_{m-1}\}\), where \(m\) is the number of the codewords in the codebook. A data hider will find an n-dimensional point \(D=\{r_{1},\ r_{2},\ \cdots,r_{n}\}\), where \(r_{1},\ r_{2},\ \cdots,r_{n}\) are randomly generated using a random seed. A line \(\stackrel{{\rightarrow}}{{OD}}\) connecting \(D\) and the origin \(O=\{0,\ 0,\ \cdots,\ 0\}\) is a line that codewords in a codebook can project and obtain their projected values. These projected values are denoted as \(\lambda_{1},\ \lambda_{2},\ \ldots,\ \lambda_{n}\) using Equation (1) and are to be used to sort the codewords in the codebook.  
\[\lambda_{k}=cw_{k}\stackrel{{\rightarrow}}{{OD}},\ \ 0\ \leq k\ \leq m-1. \tag{1}\]  
The codebook \(CB\) is sorted and resulted to a sorted codebook \(CB^{\prime}=\{cw^{\prime}_{0},\ cw^{\prime}_{1},\ \cdots,\ cw^{\prime}_{m-1}\}\).  
An example of a codebook consisting of eight two-dimensional codewords is demonstrated in Figure 2 to provide a better understanding of the projected values and the sorting result according to their projected values.
## 4 Results
### Performance Evaluation
#### 4.4.2 Comparison of AISAC versus Well-Known Learning Algorithms  
Table 6 shows the performance of the AISAC and other well-known learning algorithms over the 10 cancer-related datasets. The best results are highlighted in bold.  
Again, we used the Wilcoxon test to compare the algorithms, and results are presented in Table 7.  
These values showed significant differences for two algorithms (nearest neighbor and naive Bayes), while there were no significant differences for the remaining five algorithms.  
It is important to emphasize the breast cancer detection ability of the new model, which was significantly better than both nearest neighbor and naive Bayes, models considered among the top 10 best algorithms in data mining. In addition, it obtained a comparable performance to the remaining ones. Although no significant differences were found, it obtained more wins than losses, and it was the best for 5 out of 10 datasets.  
In 2019 [52] a preliminary work was carried out using the first version of an algorithm based on the immune system. Good results were obtained, however in this new study, analysis with a larger number of datasets and a comparison with more classification algorithms was considered. In addition, we refined the adjustment of the immune response, as well as the selection of antibodies.  
Having a low computational cost while being competitive and highly configurable, this algorithm stands out as a more than adequate and novel pattern classifier.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **SVM** & **MLP** & **NN** & **RIPPER** & **C4.5** & **Naïve Bayes** & **Random Forest** & **AISAC** \\ \hline BCDR & 80.111 & 79.006 & 72.928 & 75.414 & 74.862 & 72.652 & **81.491** & 64.360 \\ BCWO & 96.996 & 95.422 & 95.279 & 95.565 & 95.136 & 95.994 & 96.852 & **97.420** \\ BCSEER & **100.00** & **100.00** & 98.363 & **100.00** & **100.00** & 97.153 & **100.00** & **100.00** \\ BCWD & **97.715** & 96.661 & 95.958 & 95.606 & 93.146 & 92.970 & 96.836 & 93.670 \\ BCWP & 76.263 & 76.263 & 70.707 & 77.778 & 72.727 & 66.667 & **80.303** & 79.290 \\ LCDS & 56.250 & 53.125 & 53.125 & 50.000 & 46.875 & 59.375 & 43.75 & **68.750** \\ MMDS & 79.501 & 81.166 & 75.234 & **82.934** & 82.310 & 77.836 & 79.708 & 78.980 \\ BCDS & 69.930 & 66.783 & 68.182 & 70.280 & **74.126** & 72.727 & 71.328 & 73.776 \\ HSDS & 72.876 & 72.222 & 66.013 & 73.856 & 70.261 & 74.837 & 67.647 & **76.471** \\ TSDS & 84.468 & 81.489 & 75.957 & 84.681 & 84.468 & 74.468 & 84.893 & **85.319** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average performances obtained by the well-known learning algorithms. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**AISAC versus** & **Wins** & **Losses** & **Ties** & \(p\) & **Decision** \\ \hline AISAC1 & 7 & 1 & 2 & 0.035 & Reject H0 \\ CLONALG & 10 & 0 & 0 & 0.005 & Reject H0 \\ Immunos1 & 10 & 0 & 0 & 0.005 & Reject H0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wilcoxon test for of the immune-based algorithms.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 3 Problem Formulation
### Problem Formulation for MWSC  
To improve the spectrum efficiency of the ISTN, the resource allocation problem can be formulated as  
P1: \[\max_{\{x_{i,t}\},\{y_{j,t}\}} \sum_{i=1}^{N_{slot}}\min(C_{i}^{sat},R_{i}^{sat})+\gamma\sum_{j= 1}^{N_{slot}}C_{j}^{ter}\] (11) s.t. \[x_{i,t}\in\{0,1\},\forall i\in\mathcal{N}_{sat},t\in\Gamma\] (12) \[y_{j,t}\in\{0,1\},\forall j\in\mathcal{N}_{ter},t\in\Gamma\] (13) \[\sum_{i=1}^{N_{sat}}x_{i,t}\leq N_{b},\forall t\in\Gamma\] (14) \[C_{j}^{ter}\geq R_{j,min}^{ter},\forall j\in\mathcal{N}_{ter}\] (15) \[d_{ij}^{st}\geq d_{pro}^{st}\text{, if }x_{i,t}=y_{j,t}=1, \forall i\in\mathcal{N}_{sat},j\in\mathcal{N}_{ter},t\in\Gamma,\] (16)where \(R_{i}^{sat}\) denotes the requested capacity of the \(i\)-th satellite terminal in a BH time window, \(R_{j,min}^{ter}\) denotes the minimum required capacity of the \(j\)-th terrestrial system in a BH time window, and \(\gamma\) is used to control the weight of the capacity of the satellite and terrestrial systems. To avoid wasting the limited energy of the satellite, the actual transmission capacity provided to the \(i\)-th satellite terminal should not exceed its requested capacity, so the actual capacity depends on the minimum value of \(C_{i}^{sat}\) and \(R_{i}^{sat}\). In practical systems, if \(C_{i}^{sat}\) is larger than \(R_{i}^{sat}\), the satellite can either reduce the transmit power or shorten the transmission time to make the actual transmission capacity equal to \(R_{i}^{sat}\). Constraints (12) and (13) define the optimization variables \(x_{i,t}\) and \(y_{j,t}\), respectively, \(\forall i\in\mathcal{N}_{sat}\), \(j\in\mathcal{N}_{ter},t\in\Gamma\), which indicate the resource allocation results in each time slot. Constraint (14) means that at most \(N_{b}\) satellite terminals can be served in each time slot. Constraint (15) states that the transmission capacity of the \(j\)-th terrestrial system should not be lower than its minimum required capacity. Constraint (16) represents the protection distance constraint between the satellite and terrestrial systems.
_Applied_  
_sciences_  
Effect of Calcination Temperature on Photocatalytic Activity of Synthesized TiO\({}_{2}\) Nanoparticles via Wet Ball Milling Sol-Gel Method  
**[PERSON] 1\({}^{\text{(\textregistered)}}\)**, **[PERSON] 2, [PERSON] 2, [PERSON] 1 and [PERSON] 2,*  
Footnote 1: footnotemark:  
Received: 8 January 2020; Accepted: 29 January 2020; Published: 3 February 2020  
**Abstract:** In this work, TiO\({}_{2}\) nanoparticles were successfully synthesized with narrow size distribution via a wet ball milling sol-gel method. The effect of calcination temperature on photocatalytic activity was observed from particle size, crystallite size, and phase transition of TiO\({}_{2}\) nanoparticles. Increasing calcination temperature increased particle size, crystallite size, and the crystallinity of synthesized TiO\({}_{2}\). Phase transition depended on variation in calcination temperatures. A two-phase mixture of anatase and brookite was obtained with lower calcination temperature whereas a three-phase mixture appeared when calcination temperature was 500-600 \({}^{\circ}\)C. With higher temperature, the rutile phase kept increasing until it was the only phase observed at 800 \({}^{\circ}\)C. Anatase strongly affected the photocatalytic activity from 300 \({}^{\circ}\)C to 600 \({}^{\circ}\)C while the particle size of TiO\({}_{2}\) was found to have a dominant effect on the photocatalytic activity between 600 \({}^{\circ}\)C and 700 \({}^{\circ}\)C. A mixture of three phases of TiO\({}_{2}\)-600 exhibited the highest methylene blue degradation with the rate constant of \(9.46\times 10^{-2}\) h\({}^{-1}\) under ultraviolet (UV) irradiation.  
**Keywords:** TiO\({}_{2}\) nanoparticle; calcination temperature; phase transition; particle size; photocatalytic activity
## 4 Generation Scheme
### Direct Generation  
The ACE-FHBOC signal can be directly generated according to (11) or its decomposed form, thus, there are two direct methods to generate ACE-FHBOC.
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 2 Proposed Facial Expression Recognition System Using the Mixture of CNN and SVM Classifier
### CNN-Based Facial Motion Flow Classification  
The dataset is divided into two parts, one for training and the other for testing, which are collected in advance, and the motion flows and geometric displacement are estimated from the dense optical flow and landmark tracking. In the training dataset, validation data are further separated and are not used in the classifier training.  
The dense facial motion flow was color-coded, and the color-coded dense motion flows were used as the input to the deep CNN. The flow intensity and its direction were calculated at each pixel for a given optical flow frame. The flow intensity was normalized  
Figure 3: Examples of dense motion flow of different facial expressions. Optical flow is color-coded by its direction and intensity. (**a**) Anger, (**b**) Contempt, (**c**) Disgust, (**d**) Fear, (**e**) Happiness, (**f**) Sadness, (**g**) Surprise.  
by dividing each pixel flow intensity using maximum intensity. After the normalization, the direction of each flow was color-coded by the chronicity values of the pre-defined color wheel, and the normalized intensity of the flow was coded by the saturation value [60]. Thus, the red, blue, and green (RGB) color-coded values of the motion flow provided a continuous representation of the motion flow direction and its intensity at every pixel in each frame.  
We have used the basic CNN architecture proposed by [PERSON] et al. [64] for ImageNet classification. The dense optical flow file was rearranged into \(256\times 256\times 3\) RGB color as an input to the network. Several different architectures were applied to evaluate the facial expression performance. The selected basic architecture is shown in Figure 3(a). Three convolution layers, two pooling layers, and two fully connected layers are used for the facial expression recognition. There is no pooling layer between the second and the third convolution layers. For the activation function, rectified linear units (ReLU) are used as in [64]. The first convolution layer filters the \(225\times 225\times 3\) input images with a kernel of size \(5\times 5\times 3\) with a stride of 2 pixels. Overlapping max pooling with windows of size \(3\times 3\) is used in each pooling layer. The output layer takes 140 neurons from the last fully connected layer into seven facial expression class labels using the softmax classification. The architecture was further modified to improve the performance of facial expression recognition. The number of convolution layers, the number of pooling layers and their location, and the number of fully connected layers were modified. Based on the validation dataset, we selected another architecture for the facial expression recognition experiment, as shown in Figure 3(b).
# GIS-Analysis for Active Tectonics Assessment of Wadi Al-Arish, Egypt
## 4 Results and Discussion
### Relative Tectonic Activity Levels Discussion
The distribution of the different tectonic activity levels is illustrated in Figure 6. Over the study region, level 4 (lowest tectonic activity level) covers 9701.79 Km\({}^{2}\) (47.52%) as estimated by RTAL; 31.91% (6515.26 km\({}^{2}\)) has moderate relative tectonic level as estimated by RTAL (class 3); high relative tectonic level (class 2) comprises (20.42 %) 4169.66 km\({}^{2}\); while the smallest areas of the study region were recorded for class 1 (very high relative tectonic level), at 29.45 km\({}^{2}\) (0.14%). Therefore, half of the study area is covered by class 4 (low relative tectonic activity) and another half is divided mostly between class 3 and 2. Within different tectonic regions with greater active tectonic levels, the index values provided different estimations and classification ranges [6,7]. Accordingly, RTAL could also provide different values and boundaries between levels of relative tectonic activity. Our approach is based on regions that define different relative tectonic activity levels (Figure 9). The morpho-tectonic indices may reflect anomalies in the drainage systems or catchments/sub-catchments areal calculations [6,7,54]. These anomalies could be caused by the local tectonic signals resulted from uplifting or subsidence. Different anomalies were recorded in different regions. In [6,7] anomalies along mountain fronts and in drainage system in southern Spain and the Hindu Kush in northwestern Pakistan and NE Afghanistan, are discussed, respectively. The authors of [9] discuss anomalies along normal, active faults in the central eastern Desert of Egypt. In the present study, RTAL reflects a number of anomalies associated with structural control of folding and shear zones (Figure 9). Magarah, Libni, minsherah, Arif El-Naga, and El-Mineidra El-Kebira are folds associated with the lowest level of tectonic activity signals, and were recorded as RTAL anomalies.