esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Rule-Based Dijkstra Data Transmission Path Scheduling Method
#### 3.4.1 Method Process
Calculate the satellite cluster's status and determine whether the constraints are satisfied for each recorded path by order. If a data transmission path satisfies the constraint, the mission can be transmitted through the path. Update solutions \(x_{ki}\), \(y_{sk,l}\), \(z_{ikm}\), inter-satellite data transmission time, and satellite-ground data downloading time, and return to step 2. If no path satisfies the constraints, the observation solution does not satisfy the constraints and is considered infeasible, and the algorithm ends. If there are no remaining missions, the observation solution is feasible, the data transmission path planning for the corresponding observation solution is completed, and the algorithm ends.
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
###### Abstract  
This paper presents a systematic parameter study on mesh pattern for optically transparent flexible antenna with feedline. In implementing a transparent flexible antenna using a metal mesh, transparency and performance of antenna and feedline are opposite factors. To understand how both elements are affected by the design parameters of the mesh, we analyze the performance of the feedlines and antennas according to the design parameters of diamond and square meshes. Moreover, the effect of the difference in the shape of diamond mesh and square mesh on performance is analyzed. The measured results of the fabricated samples offer the feasibility of implementing transparent feedlines and antennas with similar performance to nontransparent feedlines and antennas.  
diamond-grid; flexible antennas; meshed antennas; metal mesh film; monopole antennas; transparent antennas +
Footnote †: journal: Applied  
1
Footnote 1: Department of Electrical and Computer Engineering, Ajou University, Suwon-si 16499, Korea;  
[EMAIL_ADDRESS]
## 3 Methods  
The method can be divided into the following steps:  
* Training data (output side) preparation of ANN model. The output side was the elevation of the tidal flats. Due to a lack of measured data, a result map from a previous study based on the waterline method was used as the output side of the training data. Thus, a topographic map of the Tiaozini tidal flats was obtained using remote sensing images over half a year (31 December 2013 to 28 May 2014 in Table 1) according to the literature [13].
* Training data (input side) preparation of the ANN model. The input side has seven parameters in three categories (multi-spectral bands, geographical coordinates, the distance from waterlines) which were exacted from the HJ-1 image of 21 March 2014 under the lower tidal level.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Satellite /Sensor** & **Acquisition Time (Beijing)** & \multicolumn{2}{c}{**Measured Tide Height (m), The Chinese National Height Datum 1985 (CNHD 1985)**} \\ \cline{2-4}  & & **Dongdagang** & **Xintaoyugang** \\ \hline HJ-I/CCD & 09:43/21 March 2014 & –3.49 & –3.50 \\ \hline HJ-I/CCD & 10:15/11 February 2012 & –3.19 & –3.12 \\ \hline HJ-I/CCD & 10:02/11 July 2013 & –3.23 & –3.24 \\ \hline \end{tabular}
\end{table}
Table 1: Satellite remotely sensed data and in situ water level data.  
Figure 1: Tiaozini area in the center of the Jiangsu coast.  
Model design. Based on training data, the \"7-15-15-1\" double hidden layers with optimized training structures were confirmed via continuous training and comparisons.
* Model simulation and precision evaluation. The elevation estimations obtained for the testing and training datasets based on the HJ-1 image (21 March 2014) were compared to the initial DEM obtained by the waterline method. After geometric correction and histogram matching, the input parameters were extracted from the two HJ-1 images (11 February 2012 and 11 July 2013), input into the ANN model, and used to output the estimated elevation. The height accuracy of the two DEMs was evaluated based on three transects of the in situ measured data.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design  
This section explains GCS-routing design. Section 3.1 presents an overview of GCS-routing operations, and Section 3.2 presents the route management process in detail. Section 3.3 describes determination of link failure and link cost, and Section 3.4 analyzes transient loops and proves GCS-routing to be fundamentally loop-free. Finally, Section 3.5 analyzes route update cost.
# Time Series Feature Selection Method Based on Mutual Information
## 3 Experiments
### Calculation of the Mutual Information of Sensor Signals  
The first engine data in the CMAPSS dataset is taken as an example to analyze the mutual information values of different sensor signals with the target variable. It should be noted that among the 21 sensor signals in Table 1, some sensor signals are constant values, which do not vary with engine operation, such as s1, s5, s6, s10, s16, s18, and s19. After removing these data, a total of 14 sensor signals are obtained in the C-MAPSS dataset. Table 2 shows the mutual information values of the nine different sensor signals with the target variable, and Figure 6 shows the histogram of the mutual information values of sensor signals with the target variable.  
It can be seen from Table 1 and Figure 6 that the five sensor signals with high mutual information with the target variable are s7, s9, s11, s12, and s13, corresponding to the total pressure at the high-pressure booster outlet, the physical speed of the core engine, the static pressure at the high-pressure booster outlet, the ratio of fuel flow and P30, and the corrected speed of the fan.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### Data Preprocessing and Training of Signal Detection
#### 3.1.1 Data Preprocessing  
In this paper, the input data of the IM-LSTMNet model are the output signal of the MIMO-OFDM system. Given that the signal is modulated using QPSK and IFrFT, the final output is complex. At present, the input data of the neural network are mostly real numbers, not complex numbers. Therefore, in the whole research work, direct processing of complex numbers is avoided and complex data are converted to real data. The input data follows the equation  
\[\begin{bmatrix}\Re(Y)\\ \Im(Y)\end{bmatrix}=\begin{bmatrix}\Re(H)&-\Im(H)\\ \Im(H)&\Re(H)\end{bmatrix}\begin{bmatrix}\Re(X)\\ \Im(X)\end{bmatrix}+\begin{bmatrix}\Re(N)\\ \Im(N)\end{bmatrix} \tag{4}\]  
where \(Re(^{j})\) and \(Im(^{i})\) represent, respectively, the real and imaginary parts. After merging, the data dimension has changed, the received signal is \(Y\in\Re^{2N_{l}\times\tau}\), the transmitted signal is \(X\in\Re^{2N_{l}\times\tau}\), the channel matrix is \(H\in\Re^{2N_{l}\times 2N_{l}}\), and the Gaussian white noise is \(N\in\Re^{2N_{r}\times\tau}\)[22].
## 3 Experimental Results and Discussion
### Hyperparameter Tuning  
For the classification tasks, we set the following hyperparameters for SVM as cost (C) = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]; kernel = [\"rbf\", \"linear\", \"poly\", \"sigmoid\"]; and gamma: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1]. We also set the hyperparameters for RF as max_depth = [2, 3, 5, 10], n_estimators = [50, 100, 200, 300, 400], min_samples_split = [2, 3, 10], min_samples_leaf = [1, 3, 10], bootstrap = [True, False], and criterion = [\"gini\", \"entropy\"]. We implemented grid search algorithms to tune these hyperparameters. We choose the hyperparameters that will provide the highest classification accuracy.
## 4 Early Warning of Rockburst Based on Microseismic Indices
### Commonly Used Microseismic Indices
#### 4.1.1 Apparent Stress  
The apparent stress is defined as the radiated energy of inelastic deformation per unit volume [56]:  
\[\sigma_{\text{A}}=\frac{\mu E}{M} \tag{3}\]  
where \(\sigma_{\text{A}}\) is the apparent stress; \(\mu\) is the shear modulus of the rock mass; \(E\) is the total energy contained in the seismic waveform radiated by the source, which is proportional to the energy released by the rock fracture; \(M\) is the seismic moment, which is equal to the work done to produce the inelastic deformation in the process of source tension or dislocation. The larger the seismic moment, the more serious the damage in the focal region. The apparent stress represents the stress level at the source. The greater the apparent stress of the microseismic event, the greater the energy released in the process of producing the same inelastic deformation. The apparent stress is a noncumulative parameter; however, the evolution trend in the apparent stress of microseisms over a time period can be counted. If the trend tends to increase, it indicates that stress accumulation occurs in this area, indicating an increase in the rock mass instability and increased risk of rockburst disasters. [PERSON] et al. [57] studied the microseismic evolution characteristics of an underground powerhouse during excavation and found that the apparent stress of the surrounding rock increased significantly before the appearance deformation. [PERSON] et al. [58] established an EMS method for rockburst prediction using a combination of seismic energy, seismic moment, and apparent stress.
## 2 Materials and Methods
### Data  
#### 2.2.1 MODIS  
The Moderate Resolution Imaging Spectroradiometer (MODIS) is a sensor on board the Terra and Aqua (EOS AM-1) satellites of the National Aeronautics and Space Administration (NASA) [66]. In this study, we used only the data acquired by Terra to keep the workflow straightforward and simple. Furthermore, countless other studies have shown that the combination of Terra and Aqua does not completely remove gaps, which are instead entirely handled in the first step of the proposed algorithm. The Terra satellite has been collecting daily data since 24 February 2000. It follows a sun-synchronous, near-polar, circular orbit at an altitude of 705 km, with a swath width of 2330 km, collecting data in 36 bands at various spatial resolutions (250, 500, or 1000 m) depending on the different spectral bands, ranging from 0.4 \(\upmu\)m to 14.4 \(\upmu\)m in wavelength. In addition to MODIS images at different processing levels, there are several MODIS products readily available. In this study, we used the Level 3 dataset MOD10A1 [62], distributed in sinusoidal grid tiles at 500 m of resolution by the National Snow and Ice Data Center (NSIDC). We collected the scenes of tile h18 v04 from 30 July 2015 to 31 December 2021 (available at [[https://nsidc.org/data/MOD10A1/versions/61](https://nsidc.org/data/MOD10A1/versions/61)]([https://nsidc.org/data/MOD10A1/versions/61](https://nsidc.org/data/MOD10A1/versions/61)), accessed on 24 March 2022). Each scene includes two data quality files, two auxiliary data files, three geolocation files, three metadata files, the FSC (NDSI_Snow_Cover), the raw NDSI, and the albedo (Snow_Albedo_Daily_Tile) data.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 3 Proposed Methodology
### Segmentation Network
#### 3.1.1 MBConv Block  
The MBConv structure, as shown in Figure 3, consists primarily of a 1 \(\times\) 1 regular convolution (for dimensionality expansion, including BN and Swish activation), a k \(\times\) k depthwise convolution (including BN and Swish activation, where the specific value of k can be 3 \(\times\) 3 or 5 \(\times\) 5, as seen in the EfficientNet-B0 architecture), an SE (squeeze-and-excitation) module, and a 1 \(\times\) 1 regular convolution (for dimensionality reduction, including BN), followed by a dropout layer.  
The first layer of dimension expansion employs a 1 \(\times\) 1 convolutional layer, with the number of convolutional kernels being n times the dimension of the input feature matrix (the 1 and 6 in MBConv1 and MBConv6 represent the multiplicative factor n). The depthwise convolution is a special case of standard convolution. The standard convolution operation takes an input feature map \(X\) with the dimensions \(DX\times DX\times M\), applies a convolution kernel \(Y\) with the dimensions \(DK\times DK\times M\times N\), and produces an output feature map \(Y\) with the dimensions \(DY\times DY\times N\). Here, \(DX\) signifies the spatial width and height of the square input feature map, M represents the number of input channels (input depth), \(DY\) denotes the spatial width and height of the square output feature map, \(N\) stands for the number of output channels (output depth), and \(DK\) represents the assumed spatial dimension of the square convolution kernel. The standard convolution formula is depicted in Equation (1):  
\[F_{k,l,n}=\sum_{i,j,m}K_{i,j,m,n}\cdot X_{k+i-1,l+j-1,m} \tag{1}\]  
Figure 3: Illustration of the MBConv block.  
The formula for depthwise convolution is shown in Equation (2), where \(\overset{\sim}{K}\) is a depthwise convolution kernel with the dimensions \(DK\times DK\times M\). In this convolution, each filter in \(\overset{\sim}{K}\) with the size of \(DK\times DK\times M\) is applied to the corresponding channel in the input feature map \(F\) with \(m\) channels. This generates the output feature map \(\overset{\sim}{G}\) with \(m\) channels. The formula is expressed as follows:  
\[\overset{\sim}{F}_{kJ,n}=\sum_{i,j,m}\overset{\sim}{K}_{i,j,m}\cdot X_{k+i-1,J +j-1,m} \tag{2}\]  
SE (squeeze-and-excitation) [30] is a lightweight attention mechanism. It enables the model to align feature information, and a connection operation is performed between the output and input to prevent the issues of gradient explosion and vanishing that may arise with an increase in the number of network layers.
# Nanoindentation of Historic and Artists' Paints
## 2 Nanoindentation Studies of Artists' Paints over the Last Two Decades
Some fundamental problems related with how artists' paint samples are prepared for characterization of their mechanical properties were addressed in a series of articles prepared at the Getty Conservation Institute. The effect of infiltration of resin when embedding the paint sample [16], and the effect of indenting a finite-volume sample close to its edge [17], were analyzed experimentally. In both studies, dynamic indentation with a Berkovich indenter [18] was used to continuously monitor the complex stiffness as a function of penetration depth. The performed analyses resulted in the development of testing protocols dedicated to cross-sectional samples of paint that are small in volume, embedded in a material of dissimilar properties, and have a rough surface. The most recent work from this group proposes an original method of using a flat punch indenter to characterize soft and viscoelastic paints [19]. Dynamic indentation was also used by other researchers. In the previously mentioned work of [PERSON] et al. [13], a testing protocol using a spherical probe, originally designed for biomechanical research on soft tissues, was adapted to analyze samples from a 17 th-century Rijksmuseum painting by [PERSON]. It was demonstrated that the developed measuring system can produce a two-dimensional map of modulus--similarly to that using atomic force microscopy--over a relatively large area of the sample. More recently, they used the same purpose-built system to analyze the impact of repeated relative humidity variations on the viscoelastic behavior of laboratory-prepared paint stratigraphies [20]. The use of a spherical indenter was also instrumental in the work of [PERSON] at al. [21]. They studied the role of substrate in the formation of craquelure patterns in paintings using a multilayered model system. A crack pattern observed in paintings was mimicked by drying dispersions of colloidal particles in a volatile solvent on a nonporous substrate. The nanoindentation results of the mimicked system showed that it is possible to deduce the elastic properties of the sublayer by measuring the length-scale of the stress relaxation dictated by the crack pattern. Although the measured system was substantially different from the original painting, the proposed method shows that the application of nanoindentation in heritage studies can provide unique information beyond quantifying hardness and modulus in very small scales. Indentation studies of artists' paints carried out over the past two decades address a wide range of research questions. They also show that to take full advantage of the technique it is necessary to adapt it to the specific sizes and properties of historic and artists' paint samples.
## 2 Modeling and Experimental Validation
### Numerical Method
#### 2.1.2 Governing Equations  
The main governing equations include the conservation equations for mass, momentum, energy, and component mass [24].  
1. Continuity equation \[\frac{\partial\rho}{\partial t}+div(\rho\overset{\rightarrow}{v})=0.\] (1)
2. Momentum conservation equation \[\frac{\partial(\rho u)}{\partial t}+div\Big{(}\rho u\overset{ \rightarrow}{v}\Big{)}=div(\mu gradu)-\frac{\partial p}{\partial x}+S_{u},\] (2) \[\frac{\partial(\rho v)}{\partial t}+div\Big{(}\rho v\overset{ \rightarrow}{v}\Big{)}=div(\mu gradv)-\frac{\partial p}{\partial y}+S_{v},\] (3) \[\frac{\partial(\rho w)}{\partial t}+div\Big{(}\rho w\overset{ \rightarrow}{v}\Big{)}=div(\mu gradw)-\frac{\partial p}{\partial z}+S_{w}.\] (4)
3. Energy conservation equation \[\frac{\partial(\rho T)}{\partial t}+div\Big{(}\rho\overset{\rightarrow}{v}T \Big{)}=div(\frac{\lambda}{c_{p}}gradT)+\frac{S_{T}}{c_{p}}.\] (5)
4. Species mass-conservation equation of components \[\frac{\partial(\rho C_{s})}{\partial t}+div\Big{(}\rho C_{s}\overset{ \rightarrow}{v}\Big{)}=div[D_{s}grad(\rho C_{s})]+S_{s}.\] (6)  
The flow of air and smoke has the obvious characteristic of high Reynolds number turbulence. The standard \(k-\varepsilon\) two-equation turbulence model was introduced, and the finite volume method was used to solve the governing equations [25]. The turbulent viscosity \(\eta_{t}\) can be derived from the \(k-\varepsilon\) calculation model as [26]  
\[\eta_{t}=C_{\mu}\rho\bigg{(}\frac{k^{2}}{\varepsilon}\bigg{)}. \tag{7}\]  
The \(k\) equation is expressed as  
\[\rho\frac{\partial k}{\partial t}+\rho u_{j}\frac{\partial k}{\partial x_{j}}= \frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\eta+\frac{\eta_{t}}{\sigma_{k }}\bigg{)}\frac{\partial k}{\partial x_{j}}\bigg{]}+\eta_{t}\frac{\partial u_ {i}}{\partial x_{j}}\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\bigg{)}-\rho\varepsilon. \tag{8}\]  
The \(\varepsilon\) equation is expressed as  
\[\rho\frac{\partial\varepsilon}{\partial t}+\rho u_{k}\frac{\partial \varepsilon}{\partial x_{k}}=\frac{\partial}{\partial x_{k}}\bigg{[}\bigg{(} \eta+\frac{\eta_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{k}}\bigg{]}+\frac{C_{1\varepsilon}}{k}\eta_{t}\frac{\partial u_ {i}}{\partial x_{j}}\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\bigg{)}-C_{2\varepsilon}\rho\frac{\varepsilon ^{2}}{k}, \tag{9}\]  
where \(C_{1\varepsilon}=1.44\), \(C_{2\varepsilon}=1.92\), \(C_{\mu}=0.09\), \(\sigma_{k}=1.00\), and \(\sigma_{\varepsilon}=1.30\)[27].
# A Novel Test Rig for the Basic Nonlinear Characterization of Bolted Joints
## 6 Influence of the Head/Nut Contact Area  
Figure 22 shows the contact interfaces between the bolt head/nut and beams. For both contact interfaces, the outer diameter is determined by the bolt size, while the inner diameter of the contact area is limited by the hole's perimeter. To study the influence of the contacts bolt head/nut-beam, the hole diameter was increased from 18 to 19 mm, resulting in a 22% decrease of the contact area (Figure 22b).  
The new systems were tested as for the previous experiments, and the FRFs, as well as the damping characteristics, were identified under the same loading condition (i.e., bolt tension equal to 5 kN and excitation force of 1 N) on the two-beams test rig.  
The experimental results given in Figure 23 show a different amplitude at resonance as the extension of the contact area changes. In particular, the structure with smaller interface exhibits a peak response that is significantly larger than that obtained with a larger contact area (i.e., 332.5 m/s2/N vs. 208.9 m/s2/N). The reason for that can be found in a larger contact pressure caused by a smaller contact interface under the same preload. A larger excitation force is needed to overcome the friction force if the contact pressure is higher, which enhances the tangential stiffness and increases the resonance frequency. The test results confirm the great importance the contact interfaces between the bolt head/nut and the beams have on the dynamic response of the structure.  
The effects of different excitation forces and different preload on the dynamic response of the bolted structures are shown in Figure 24. The influence of either the excitation force or bolt tension on the measured FRFs is mutually consistent for the two-beam structures. As the excitation force increases, the FRFs amplitude, and the resonance frequency reduce  
Figure 23: Results for different hole size, (**a**) FRFs curves, (**b**) modal damping ratio.  
Figure 22: Contact interfaces between the bolt head/nut and beams, (**a**) larger interfaces (hole diameter 18 mm), (**b**) smaller interfaces (hole diameter 19 mm).  
in both cases (Figure 24a), while the same quantities increase with an increasing bolt tension (Figure 24b). If the results are compared with those of a larger head/nut contact interface, it is possible to see that an increase of the excitation force from 0.1 to 2 N causes a decrease in the amplitude of the 66.8% (i.e., 430.1 m/s\({}^{2}\)/N vs. 142.6 m/s\({}^{2}\)/N) with a large head/nut contact interface, and of the 56.4% (i.e., 542.6 m/s\({}^{2}\)/N vs. 236.7 m/s\({}^{2}\)/N) for the smaller head/nut contact interfaces, respectively.