esa-sceva/satcom-chunk-collection huggingface dataset:
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 2 Moving Object Detection  
The moving object detection consists of frame-to-frame subtraction (abbreviated as frame subtraction) followed by thresholding, morphological operation, and false alarm removing. In the previous works, only gray-scaled images were considered and the threshold for false alarm rejection were heuristically decided. In the paper, all three-color components are used, and the threshold is based on the actual object size and shape properties. First, the frame subtraction and thresholding in each color layer and the logical OR operation generate a binary image as:  
\[B_{k}(m,n)=\left\{\begin{array}{c}1,if\left[\begin{array}{c} \left|I_{k}^{R}(m,n)-I_{k-1}^{R}(m,n)\right|>\theta_{T}\\ OR\\ \left|I_{k}^{B}(m,n)-I_{k-1}^{B}(m,n)\right|>\theta_{T}\\ OR\\ \left|I_{k}^{G}(m,n)-I_{k-1}^{G}(m,n)\right|>\theta_{T}\\ 0,\text{\emph{otherwise}}\end{array}\right],k=1,2,\ldots,N_{k}, \tag{1}\]  
where \(I_{k}^{R}(m,n)\), \(I_{k}^{G}(m,n)\), and \(I_{k}^{B}(m,n)\) are the red, green, and blue components, respectively, at frame \(k\), \(N_{K}\) is the total number of frames, and \(\theta_{T}\) is a threshold value. The morphological operation, closing, is applied to the binary image. Finally, we test the size, squareness, and rectangularity of the base rectangle to eliminate false alarms [22]. A base rectangle is defined as the smallest rectangle that contains objects. The squareness is defined as the ratio between the minor and major axes of the basic rectangle, and the rectangularity is defined as the ratio between the object size and the basic rectangle size. The maximum values for both squareness and rectangularity are equal to one. Although the object resolution is very low as in Figure 2, it will be shown that the object detection based on the frame subtraction can generate measurements for target tracking.
# Dielectric Slabs-Based Lens for Millimeter-Wave Beamforming
###### Abstract  
This paper proposes a dielectric slabs-based lens for millimeter-wave beamforming systems. The proposed lens is based on the graded steps of the effective refractive index of the semi-spherical lens. It consists of multiple dielectric slabs that match the selected gradient effective refractive index. These slabs have the same thicknesses and different radii. The slab thickness in this lens should not exceed a quarter of the operating wavelength to keep on a similar effective refractive index of the original semi-spherical lens. A horn antenna is used to examine the performance of the designed lens at 28 GHz frequency in terms of the maximum gain, sidelobe level, and 3 dB beamwidth. Sixteen switchable horn antennas are used to demonstrate lens capability for millimeter-wave beamforming. Every single antenna element is selected individually, thus the dielectric lens steers and enhances the corresponding radiation of the selected element in the desired direction.  
millimeter-wave; beamforming; dielectric slabs; dielectric lens; MIMO; graded refractive index; effective medium theory; metamaterials +
Footnote †: journal: 2022, 12, 638. [[https://doi.org/10.3390/app12020638](https://doi.org/10.3390/app12020638)]([https://doi.org/10.3390/app12020638](https://doi.org/10.3390/app12020638))
## 3 Path Planning Algorithm Based on the Offset Curve
### Generation of the Offset Curve on a Triangular Mesh Surface  
The generation of the offset curve is based on a triangular mesh surface. [PERSON] et al. [21] proposed an incremental approach to compute the offset curve. Their method completely eliminates the formation of local self-intersections. However, small and irregular triangles would increase the error of the offset accuracy and greatly reduce the stability of the algorithm. Therefore, we propose a method of curve offsetting based on a discrete point (CODP).  
Figure 4: Normal direction printing mode. 1 a posture when printing Letter Z, 2� a posture when printing Letter J, 3� a posture when printing Letter U.  
The core idea of CODP is to create a series of discrete points on the original curve, and the offset curve is generated by the offset calculation of the discrete points. This method could effectively avoid the occurrence of local self-intersection and global self-intersection. Even on complex triangular mesh, the method still has good stability.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 6 Result
### Performance of System
#### 6.2.2 Packet Processing Time  
Secondly, we use Scapy to construct a variety of packet formats, including TCP, UDP, HTTP, etc. During the testing, the maximum number of packets sent from Scapy per batch in batch mode was 32. In three modes, we send packets of different byte sizes to calculate the average running time of the packet. The result is shown in Figure 7. In the three modes, the byte size of the packet has little effect on the average running time. In baseline mode, the average running time of the packet is approximately 3.88 \(\upmu\)s. However, the average run time of S-VNFs is approximately 5.57 \(\upmu\)s, and its run time is increased by 43%. The average run time of optimized S-VNFs (w/opt.) is approximately 4.63 \(\upmu\)s, which is a 16% improvement over pre-optimization, due to the use of shared memory and batch processing which reduce enclave conversion. Compared with baseline, the optimized runtime increased by approximately 19%.
## 3 Results and Discussion
### Characterization of the Inputs and Output Products of the Phosphate Laundries
#### 3.1.2 Mineralogy  
The X-ray diffraction mineralogical study was performed on the solid samples (RP, MP, CW, and FW) from the two laundries. In all samples, the results have confirmed the presence of the following minerals: carbonate-fluorapatite CFA (Ca\({}_{5}\)(PO\({}_{4}\),CO\({}_{3}\))\({}_{3}\)F), clinoptiolite ((Na\({}_{2}\),Ca,K\({}_{2}\)) (Al\({}_{2}\)SiO\({}_{2}\))6-7H\({}_{2}\)O), calcite (CaCO\({}_{3}\)), dolomite (Ca,Mg(CO\({}_{3}\))\({}_{2}\)), ankerite (Ca(Fe,Mg,Mn)(CO\({}_{3}\))\({}_{2}\)), gypsum (CaSO\({}_{4}\).2H\({}_{2}\)O), and quartz (SiO\({}_{2}\)), with percentages varying according to the samples being employed (Figure 2). The similarity in the mineralogical composition between the input and the output products indicates that the processes of ore treatment in the washing units do not alter the mineralogy. Other silicates, such as smectite ((Si\({}_{3+x}\)Al\({}_{1-x}\))O\({}_{10}\)(Al\({}_{2-y}\)Mg\({}_{y}\))(OH)\({}_{2}\)M+1\({}_{1-(x-y)}\)(H2O)\({}_{n}\)) and palygorskite ((Mg,Al)\({}_{2}\)Si\({}_{4}\)O\({}_{10}\)(OH)-4(H\({}_{2}\)O)), are present in the fine wastes from the two laundries. However, regarding clinoptiolite, it was revealed that this mineral is more abundant in the fine waste of the Redeyef laundry (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fraction**} & \multicolumn{6}{c}{**Sample**} \\ \cline{2-7}  & **RP–ZL** & **MP–ZL** & **FW–ZL** & **RP–R** & **MP–R** & **FW–R** \\ \hline Medium and coarse sand (250 \(\upmu\)m\(\times\) X \(<\) 2 mm) & 24.7 & 30.62 & 6.45 & 23.64 & 21.76 & 1.07 \\ Fine to very fine sand (63 \(\upmu\)m\(\times\) X \(<\) 250 \(\upmu\)m) & 65 & 69.42 & 34.5 & 66.54 & 78.23 & 15.94 \\ Medium to coarse silt (16 \(\upmu\)m\(\times\) X \(<\) 63 \(\upmu\)m) & 7.2 & 0 & 24.8 & 7.20 & 0 & 35.37 \\ Fine to very fine silt (2 \(\upmu\)m\(<\) X \(<\) 16 \(\upmu\)m) & 2.83 & 0 & 32.94 & 2.51 & 0 & 45.87 \\ Clay (X \(<\) 2 \(\upmu\)m) & 0 & 0 & 1.13 & 0 & 0 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle-size distribution (% of dry sample). RP: raw phosphate; MP: marketable phosphate, and FW: fine waste from M’Dhilla-Zone L (ZL) and Redeyef (R) laundries.
## 3 Performance Evaluation of 2D Hyperchaotic Map
### Complexity Analysis  
PE is based on time-series data, where the system's permutation entropy is determined by partitioning the time series into distinct modes and calculating the frequency of each mode. Higher permutation entropy values reflect increased randomness and disorder in the system [30]. PE is faster and more effective in estimating complexity; thus it was chosen to analyze the complexity of the proposed 2D hyperchaotic map.  
When the system parameter b is set to 6 or a is set to 15, the results are presented in Figure 6. The results demonstrate that the value of the new 2D hyperchaotic sequence surpasses 0.997, and its randomness is signifying exceptional. To further validate the sequence's randomness, this paper compares the proposed 2D hyperchaotic map and the four existing chaotic systems mentioned earlier as shown in Figure 6. The comparison highlights the higher and more stable PE complexity of the proposed 2D hyperchaotic map, indicating superior randomness.
## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Infrared Observations Only
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Authors**} & \multirow{3}{*}{**Year**} & \multicolumn{3}{c}{**Satellite**} & \multirow{3}{*}{**Main features**} & \multirow{3}{*}{**Uncertainty or Accuracy**} \\ \cline{3-3}  & & **Sensor** & **Channel** & **Frequency** & **of the Method (When** \\  & & & **[GHz]** & & **Declared/Applicable)** \\ \hline [PERSON] et al. [82] & 2020 & MODIS, & 0.4–0.7 & Ice-over-water & \\  & & VIIRS & & cloud properties & - \\ [PERSON] et al. [83] & 2021 & MODIS, & - & Convective & - \\  & & MISR & & clouds & - \\ [PERSON] et al. [84] & 2023 & MODIS, & & CTP, CTH in & \\ [PERSON] et al. [84] & 2023 & MISR & - & multilayer & - \\  & & & clouds & \\ [PERSON] et al. [85] & 2019 & MODIS, & & COT, R\({}_{\text{eff}}\), CTT, & \\  & & MISR & - & IWP & - \\ [PERSON] et al. [86] & 2017 & VIIRS, CrIS & - & - & - \\  & & & Cloud mask, & \\ [PERSON] et al. [43] & 2022 & VIIRS, CrIS & - & - & thermodynamic \\  & & & phase, CTH & \\  & & & Cloud layer & \\ [PERSON] et al. & 2021 & IASI, & - & altitude, cloud & \(\text{RMSE}_{\text{CBH}}>30\%\) \\ [87] & 2021 & IASI-NG & & thickness of ice & \\ [PERSON] et al. & 2018 & MODIS & - & CTH with neural & - \\  & & & network & - \\ [PERSON] et al. & 2017 & SEVIRI & - & properties (CTH, \\  & & & IOT, IWP) & \\  & & & Cloud mask and & \\ [PERSON] et al. [95] & 2020 & VIIRS & - & thermodynamic & - \\  & & & phase with & \\  & & & random forest & \\ [PERSON] et al. [96] & 2020 & SEVIRI & - & \(\text{LIWP with}\) & \(\text{BIAS}_{\%}=9\%\) \\ [PERSON] et al. [97] & 2020 & AHI, & - & CTH with & 1.54 km \(<\) MAE\({}_{\text{CTH}}<\) \\  & & CALIPSO & - & machine learning & 2.72 km \\  & & & Cloud & \\ [PERSON] et al. [98] & 2022 & AHI & - & macrophical & \\  & & & parameters with & \(\text{RMSE}_{\text{CTH}}=18.67\%\) \\  & & & & XGBoost & \\  & & & Cloud mask, & \\  & & & COT, R\({}_{\text{eff}}\), CH, & \\ [PERSON] et al. [99] & 2022 & MODIS & - & CTH with & - \\  & & & convolutional & \\  & & & neural network & \\  & & & Cloud & \\  & & & microphysical & \\ [PERSON] et al. [100] & 2023 & AHI & - & properties & - \\  & & & combining RT & - \\  & & & model and & \\  & & & machine learning & \\ [PERSON] et al. & & & Cloud products & \\
[101] & 2023 & AVHRR & - & evaluation & - \\  & & & & PATMOS-x & \\ [PERSON] et al. [102] & 2023 & MODIS & - & improved & - \\  & & & version & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method
### Pre-Processing  
Pre-processing is essential to accurate change detection results. We perform ortho-rectification on input satellite images using Ground Control Points (GCPs) and a Digital Elevation Model (DEM) to remove pixel location errors and relief displacement due to surface elevation. For this paper, we used Komsat-3A images with a ground sampling distance (GSD) of 50 cm. For ortho-rectification, we used GCPs extracted from the National Integrated Control Point Database of the National Geographic Information Institute (NGII) of the Government of Korea, with an expected accuracy of several centimeters, and the National 5 m Grid DEM generated and maintained by NGII [45]. It is expected that rectified image grids are aligned with each other to accuracies within two pixels. After rectification, sub-images were extracted from previous and recent images to carry out the change detection experiments.  
The images used in this paper include a panchromatic band and pan-sharpened multispectral blue, green, red, and near-infrared bands. For feature extraction and matching, the panchromatic band was used. For pixel-based change analysis, the pan-sharpened multispectral bands were used.  
Figure 1: Workflow of the proposed feature-based change detection method.
# Large-Scale Modeling of Absorbing Aerosols and Their Semi-Direct Effects
## 2 Methods
### Model Setup
For this direct radiative forcing atmospheric heating rate changes in a model grid cell due to absorbing aerosol were calculated as:  
\[\Delta T(z)/\Delta t=(c_{p}\rho)^{-1}\left(\Delta F(z_{top})-\Delta F(z_{bottom })\right)/(z_{top}-z_{bottom}) \tag{3}\]  
where \(c_{p}\) and \(\rho\) are heat capacity and density of air in the grid cell, respectively, and \(z_{top}\) and \(z_{bottom}\) denote the height of the top and bottom of the model grid cell. The semi-direct effect \(SDE_{aerosol}\) is then calculated as residual of the direct radiative effect and the radiative forcing as:  
\[SDE_{aerosol}=DRE_{aerosol}-IDR_{aerosol} \tag{4}\]  
In addition to the radiative effects, differences in column averaged cloud cover CLC were analyzed as this factor is mostly responsible for the semi-direct aerosol effects.
## 3 Methods
### The Architecture of the Fusion Network
#### 3.1.1 Generator Architecture  
In the feature extraction phase, two encoders respectively process infrared and visible light images. Both encoders contain four multi-scale convolution blocks (MSCB). Each MSCB has two convolution layers with a 3 \(\times\) 3 kernel, and strides set to 1 and 2. The filter banks for different scales are all set to 16 \(\times\) l. When source images are input into these blocks, four-scale features of each source image are obtained, represented as \(\Phi_{ir}^{l}\) and \(\Phi_{vis}^{l}\), l = 1, 2, 3 and 4. In the feature reconstruction phase, a cross-scale attentional decoder (CSAD) is built, as shown in Figure 2. Features of different scales and modalities are fed into the decoder. Use the proposed cross-modal differential features attention module (CDAM) to reconstruct features from deep to shallow scales. The upsampled output of each CDAM, combined with the next-layer features, becomes the input for the next CDAM.
# Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen
## 6 Methods: Volumetric Deformation
### Corotational Coarsening  
The corotational coarsening model takes as input two tetrahedral meshes, one fine and one coarse, that discretize the same deformable volume, where the coarse nodes are a subset of the nodes of the fine mesh. Given a heterogeneous linear corotational FEM model [52] defined on the fine mesh, corotational coarsening computes homogenized stiffness matrices and nonlinear shape functions on the coarse mesh, such that the behavior of the fine mesh is closely approximated. Coarsening is designed for each coarse element independently, and the result is assembled to produce the coarse model on the full coarse mesh. The method pays special attention to the accurate approximation of Dirichlet and Neumann boundary conditions applied on the fine mesh. However, for clarity, and w.l.o.g., in the remainder we ignore the presence of fine nodes with non-zero Neumann boundary conditions (i.e., non-zero external forces), and we consider only fine nodes with Dirichlet boundary conditions (i.e., constrained positions).  
Similar to [PERSON] et al. [1], we introduce a _block notation_\([\cdot]\) to indicate replication of a \(3\times 3\) matrix into a block-diagonal matrix, or replication of a \(3\times 1\) vector into a long vector. The size of \([\cdot]\) can be inferred in each case from the terms it multiplies.  
Let us denote with \(\mathbf{x}_{c}\) and \(\mathbf{x}_{d}\) vectors that concatenate the positions of all coarse nodes and all fine Dirichlet nodes in a coarse element, respectively. We denote with an overline the rest-state positions for the corresponding nodes. Let us also denote as \(\mathbf{R}_{c}\) the best-fit rotation matrix of a coarse element. Then, using the block notation, the homogenized linear corotational problem on a coarse element can be expressed as:  
\[\left[\mathbf{R}_{c}\right]\mathbf{K}_{h}\left(\left[\mathbf{R}_ {c}\right]^{T}\mathbf{x}_{c}-\overline{\mathbf{x}_{c}}\right)=\mathbf{f}_{c}+ \mathbf{f}_{d}, \tag{13}\] \[\text{with }\mathbf{f}_{d}=-\left[\mathbf{R}_{c}\right]\mathbf{K}_{ hd}\left(\left[\mathbf{R}_{c}\right]^{T}\mathbf{x}_{d}-\overline{\mathbf{x}_{d}} \right). \tag{14}\]  
The matrices \(\mathbf{K}_{h}\) and \(\mathbf{K}_{hd}\) denote the homogenized stiffness matrices of the coarse element, and we refer the reader to the work of [PERSON] et al. [1] for details about their computation. The vectors \(\mathbf{f}_{c}\) and \(\mathbf{f}_{d}\) denote, respectively, external forces applied on the coarse nodes and coarse forces produced by the fine Dirichlet nodes.  
The strength of the corotational coarsening method is that it imposes a small computational overhead w.r.t. a pure coarse FEM model, but it achieves accuracy comparable to a fine FEM model under certain types of boundary conditions. Next, we extend the method to handle accurately and efficiently rigid transformations of fine nodes, as produced by our handle manipulation method.
## 2 Various Types of Flexible Sensors (Temperature, Pressure, Humidity and Chemical)
### Humidity Sensors  
Flexible humidity sensors with a graphene oxide (GO) sensing layer deposited on piezoelectric ZnO thin film on polyimide substrate have been fabricated [8]. A total of 4 \(\upmu\)L of the GO of various concentrations were drop cast on the surface of the devices and left to evaporate to obtain GO thin films. These flexible sensors were bent 8000 times and remained operable, demonstrating their excellent flexibility and reliability for industrial applications. Another humidity sensor based on fabric and graphene oxide was developed in 2020 for respiration monitoring to support healthcare delivery in response to the COVID-19 pandemic [15]. The researchers claim that the fabric material ranks above polymer materials when fabricating flexible sensors due to the poor hygroscopicity and breathability of polymers, which limits their comfortability and sensitivity. Before this, in 2016, another research team developed a humidity sensor used in respiratory disease monitoring composed of biodegradable paper infused with carbon nanotubes [26]. The sensor is easily attached under the person's nose and can measure respiratory related signals (airflow rate, humidity and temperature) which can be correlated to respiratory disease (see Figure 3). The sensor measures the real time humidity, temperature and airflow rate of the patient by correlating changes in resistance due to heat coming from the person's breath. The sensor is reported to be eco-friendly and cheap, paper and carbon being the raw materials.
## 2 Numerical Model Development
### Equation of Motion  
Each hydrodynamic coefficient and wave excitation force/moment can be calculated by solving the boundary value problem. The radiation velocity potential was used to obtain the added mass or added moment of inertia (Equation (13)), as well as the radiation damping coefficient (Equation (14)).  
\[A_{ij}=Refr\iint\limits_{S_{B}}F_{i}n_{j}\;dSg \tag{13}\]  
\[B_{ij}=Im^{\prime}\;\;rw\iint\limits_{S_{B}}F_{i}n_{j}\;dSg \tag{14}\]  
where \(A_{ij}\) and \(B_{ij}\) denote the added mass or added moment of inertia and radiation damping coefficients, respectively. Furthermore, the wave excitation forces were evaluated based on the Bernoulli equation with the incident wave potential and the diffracted wave potential as in Equation (15).  
\[F_{ex_{j}}=\iint\limits_{S_{B}}\bigg{(}\;\;r\frac{\fint\!(F_{I}+F_{D})}{\fint \!t}\;\bigg{)}n_{j}\;dS=irw\;\;\iint\limits_{S_{B}}(F_{I}+F_{D})n_{j}\;dS \tag{15}\]  
The equation of motion, including the external force and the PTO force, was set according to Equation (16).  
\[\big{(}m+A_{ij}\big{)}\ddot{z_{j}}+B_{ij}\ddot{z_{j}}+C_{ij}z_{j}=F_{ex_{j}}+ F_{pto} \tag{16}\]  
where \(C_{ij}\) represents the restoring coefficients, which can be easily obtained by a theoretical solution. \(F_{pto}\) is the additional applied force from the power take-off (PTO) system. The effect of the PTO system is usually presented as a linear damping system (Equation (17)). Equation (18) represents the mean power generated by the floating body velocity according to the incident wave frequency. In general, the linear generator or the hydraulic system was selected as the PTO system for the movable body-type WEC system [1; 27; 28; 29]. On the other hand, the PTO force was not easy to consider in the frequency domain model because of its strong nonlinearity. In this calculation, an equivalent linear model was applied. In addition, the radiated wave elevation from the oscillating body can be evaluated as Equation (19).  
\[F_{pto}=b_{pto}\dot{z} \tag{17}\]\[\overline{P_{g}}=\frac{1}{2}{b_{pto}}^{2} \tag{18}\]  
\[h_{\text{R}_{\text{j}}}=\quad\frac{1}{g}\left(\frac{\text{i}\text{f}F_{j}}{\text {i}\text{f}t}\right)_{2=0} \tag{19}\]  
where \(b_{pto}\) is the additional damping coefficient from the PTO system.
## 3 Structures
### Thin Films
Electrodes are positioned perpendicular to the poling direction in both d31 and d33 modes. The shear mode, which is used by several energy harvesters, requires charge constant d15 mode. The most common piezoelectric material, PZT, has a piezoelectric coefficient that is about two times that of d31. If both the modes d31 and d33 have similar design parameters, the output voltage for the d33 mode is predicted to be greater than the d31 mode energy harvester. Moreover, the distance between electrode fingers influences the voltage produced by the d31 and d33 mode devices. As the PZT layer is often very thin, so the distance of electrodes in the d31 mode is less than in the d33 mode. In terms of power output produced by the product of both voltage and current, [PERSON] et al. discovered that the d31 mode energy harvester outperformed the d33 mode [90]. The compliance matrix, S\({}^{\text{E}}\), is the inverse of C\({}^{\text{E}}\), the stiffness matrix and is measured under the constant electric field. There are 10 independent matrix elements for the frequently used poly-crystalline ceramic materials, i.e., PZT. Ideally, under low electrical field and stress, and for the material having low losses, these ten constants include all the information necessary to determine how the material will behave when an electrical field, stress or strain is applied to it. If stiff piezo ceramics are attached directly to the vibration host, they are unable to absorb much mechanical energy. The solution to this is to install the piezo-electric element on the flexible beam structure. The most frequently utilized structure is the cantilever. Around the high-stress end, the sub-structure is usually covered with one (unimorph) or two (bimorph) piezo-electric elements and frequently attached with tip-mass to reduce the resonant frequency and enhance the vibration-induced stress.