esa-sceva/satcom-chunk-collection huggingface dataset:
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
###### Abstract  
Through-focus scanning optical microscopy (TSOM) is one of the recommended measurement methods in semiconductor manufacturing industry in recent years because of its rapid and nondestructive properties. As a computational imaging method, TSOM takes full advantage of the information from defocused images rather than only concentrating on focused images. In order to improve the accuracy of TSOM in nanoscale dimensional measurement, this paper proposes a two-input deep-learning TSOM method based on Convolutional Neural Network (CNN). The TSOM image and the focused image are taken as the two inputs of the network. The TSOM image is processed by three columns convolutional channels and the focused image is processed by a single convolution channel for feature extraction. Then, the features extracted from the two kinds of images are merged and mapped to the measuring parameters for output. Our method makes effective use of the image information collected by TSOM system, for which the measurement process is fast and convenient with high accuracy. The MSE of the method can reach 5.18 nm\({}^{2}\) in the measurement of gold lines with a linewidth range of 247-1010 nm and the measuring accuracy is much higher than other deep-learning TSOM methods.  
deep learning; dimensional measurement; computational imaging; through-focus scanning optical microscopy +
Footnote †: journal: Applied sciences
## 3 Monitoring Data Collection and Preparation
### 3D Dilatometer Data  
Two 3D dilatometers named HIE2 and HIE3 are placed on the SAL's detachment plane. They are about 2.5 km apart (Figure 1) and the measurements are taken every 24 h at 00:00 UTC. The records (Figure 2) are analyzed in a Cartesian coordinate system with \(X\)-axis showing compression/extension perpendicular to the detachment plane; \(Y\)-axis showing strike-slip movement along the detachment plane and \(Z\)-axis showing vertical movement perpendicular to the detachment plane. As the records are taken at 00:00 UTC the records were correlated with climatic and geophysical variables from the previous day. Automated monitoring started on 16/10/2013 and covers a period of 2010 d till 17/04/2019. Due to technical difficulties, several instrument failures resulting in missing records happened. In the case of HIE2 device, there are 494 missing measurements (24.6%), in the case of HIE3 there are 358 missing records (17.8%).  
Figure 2: Data used in the analysis: (**a**) HIE2 dilatometer: \(X\)-axis—black line, \(Y\)-axis—red line, \(Z\)-axis—blue line; (**b**) HIE3 dilatometer: \(X\)-axis—black line, \(Y\)-axis—red line, \(Z\)-axis—blue line; (**c**) daily average air temperatures; (**d**) daily precipitation; (**e**) registered earthquakes; (**f**) calculated PGA.
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 2 Clustering on Sparse Grids  
In this section, we describe the sparse grid clustering algorithm on a high level. We describe its components in Sections 3 and 4 in more detail.  
Sparse grid clustering assumes a \(d\)-dimensional dataset \(T\) with \(m\) data points that was normalized to the unit hypercube \([0,1]^{d}\):  
\[T:=\{\mathbf{x}_{i}\in[0,1]^{d}\}_{i=1}^{m}. \tag{1}\]  
We further assume that the dataset has been randomized.  
The sparse grid clustering algorithm is a four-step algorithm. Except for the last one, these steps are shown in Figure 1 using as an example a 2-D dataset with three clusters. The sparse grid clustering algorithm first calculates a density estimation of the dataset using the sparse grid density estimation algorithm (Figure 0(b)). Sparse grids use a set of grid points at which basis functions are centered; these are shown as black dots. As the next step, a \(k\)-nearest-neighbor graph of the dataset is computed (Figure 0(c)). In the third step, the density estimation is used to prune the graph. The algorithm prunes nodes in low-density regions and edges that intersect low-density regions (Figure 0(d)). In the fourth and final step the weakly connected components of the pruned graph are retrieved. The connected components of the graph are returned as the detected clusters.  
Figure 1: The application of the sparse grid clustering algorithm to a 2-D dataset with three slightly overlapping clusters. After calculating the sparse grid density estimation and the \(k\)-nearest-neighbor graph, the graph is pruned using the density estimation. This splits the graph into three connected components.  
This description suggests an important parameter of the algorithm. To decide whether to prune a node or edge from the \(k\)-nearest-neighbor graph, the sparse grid clustering method requires a density threshold value \(t\). Nodes are pruned from the \(k\)-nearest-neighbor graph if the density at their location is below \(t\). Edges are removed if they intersect regions with density values below \(t\).
# A Reinforcement Learning Model of Multiple UAVs for Transporting Emergency Relief Supplies
## 3 Model Description  
In this section, we describe a model that assumes the transportation of supplies during a disaster. The model is composed of an environment and agents that move in the environment.  
Notations are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline Environment & \\ \(N_{S}\) & Number of shelters \\ \(j,k\) & Indices for shelters \\ \(\textbf{S}=\{0,1,2,\dots,N_{S}\}\) & Set of shelters including the depot (\(j=0\)) \\ \(N_{I}\) & Number of item types \\ \(i\) & Index for item types \\ \(\textbf{I}=\{1,2,\dots,N_{I}\}\) & Set of item types \\ \(d_{ijt}\) & Demand of the item \(i\) of the shelter \(j\) at time instant \(t\) \\ \(D_{ji}=\{d_{11},d_{21},\dots,d_{N_{I}i}\}\) & Set of demands of the shelter \(j\) at instant \(t\) \\ \(Y_{i}=\{D_{11},D_{21},\dots,D_{N_{I}}\}\) & Set of the remaining demand of all shelters at instant \(t\) \\ \(h_{jk}\) & Distance between shelter \(j\) and \(k\) \\ \(p_{ij}\) & Penalty cost of item \(i\) \\ \(b_{ij}\) & Time limit of the item \(i\) of the shelter \(j\) \\ \hline Agent & \\ \(N_{ll}\) & Number of UAVs \\ \(l\) & Index for UAVs \\ \(\textbf{U}=\{1,2,\dots,N_{ll}\}\) & Set of UAVs \\ \(w_{ijklmn}\) & Amount of item \(i\) transport from shelter \(j\) to \(k\) as \(n\)th location in \\  & the trip \(m\) by UAV \(l\) \\ \(N_{l}\) & Number of trips of UAV \(l\) \\ \(m\) & Index for trip \\ \(M_{lmt}\) & Number of location that UAV \(l\) traveled in trip \(m\) (include depot) \\ \(n\) & Index for number of location \\ \(C\) & Maximum payload of UAV \\ \(E\) & Maximum amount of energy \\ \(a\) & Acceleration \\ \(V_{max}\) & Maximum speed \\ \(t_{take}\) & Take-off time \\ \(t_{land}\) & Landing time \\ \(t_{serve}\) & Servicing time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations description.
# Aerodynamic Analysis and Noise-Reducing Design of an Outside Rear View Mirror
## 2 Design and Analysis  
### 3D Modeling and Geometry of OSRVM  
In order to improve the aerodynamic flow around the side mirror, we calculate the aerodynamic noise radiation through a direct solution of commercial CFD code FLUENT. In this study, we consider three types of OSRVM models to compare shape-dependent aerodynamic effects. The first model is the reference mirror, which is obtained by three-dimensional (3D) scanning the OSRVM of the R company (Ussac, France)'s C-segment CUV, as shown in Figure 1a. Figure 1b shows the side mirror geometry extracted from the scanned data of an A-pillar and the side mirror. In addition, the OSRVM was reversed by using a commercial software Geomagic Design X, which is based on the gathering scan data, and the layout shape of the reference OSRVM is shown in Figure 1c.  
The second OSRVM model is designed by adding a duct outside the mirror housing, as shown in Figure 1d, which is normally used in commercial vehicles for improving the aerodynamic flow. The duct is located between the OSRVM and the gutter. The last model is designed by inserting two inner ducts (line holes) on the inner and outer edges of the mirror housing, as shown in Figure 1e. Airflow passes from the leading edge to the trailing edge of the mirror housing through the line holes. Since the aerodynamic flow distributes by the inner ducts, airflow rate become stable and reduce the vorticity around edges, gutter, and the mirror surface. Two modified OSRVM models are designed to maintain the continuity of the existing mirror shape without the size reduction of the original mirror.  
In this paper, the CFD analysis is performed for the modified mirror models, based on the variation of the drag coefficient or the rate of change of the air contact area. Any vehicles moving through a fluid experience drag force in the direction of flow due to pressure and shear forces acting on the surface of the object. The drag coefficient \(C_{d}\) can be expressed by  
\[C_{d}=\frac{F_{d}}{\frac{1}{2}\rho v^{2}A} \tag{1}\]Here, \(F_{d}\) is the drag force, \(\rho\) is the fluid density, \(A\) is the characteristic frontal area of the mirror, and \(\
u\) is the flow velocity. The drag coefficient is a function of several parameters, such as the mirror shape and size. In general, it is possible to predict changes in sound pressure levels through the drag force and pressure variations that are applied to the OSRVM's housing and the surface of the mirror. The gradient of the flow rate, such as velocity and vorticity around the OSRVM, causes the pressure gradient. Then the variations in the pressure gradient affect the aerodynamic performance, allowing for the increase or decrease in aerodynamic radiation noise to be predicted. In addition, for the design considering the aerodynamic performance of the OSRVM, it is necessary to determine the pressure gradient formed around the OSRVM through the CFD solution.
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Segmentation Results Based on Object Cropping  
In this section, we focus on segmentation results by specifically examining the cropping of detected objects and comparing them with their corresponding ground-truth images  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Medical Images**} & \multirow{2}{*}{**HE**} & \multicolumn{6}{c}{**Evaluation Metrics**} \\ \cline{3-8}  & & **Accuracy** & **Precision** & **Recall** & **Specificity** & **Dice** & ** Jaccard** \\ \hline \hline \multicolumn{8}{c}{**PSO Segmentation Approach**} \\ \hline Lung CT & No & 0.91893 & 0.90036 & 0.97545 & 0.88041 & 0.9364 & 0.82984 \\ Lung CT & Yes & 0.9563 & 0.97529 & 0.96811 & 0.93322 & 0.967 & 0.9361 \\ Chest X-ray & No & 0.9065 & 0.9350 & 0.93162 & 0.87751 & 0.90202 & 0.82153 \\ Chest X-ray & Yes & 0.90363 & 0.86891 & 0.93714 & 0.87369 & 0.90173 & 0.82105 \\ \hline \multicolumn{8}{c}{**Otsu’s Approach**} \\ \hline Lung CT & No & 0.91323 & 0.9422 & 0.928 & 0.88283 & 0.93504 & 0.87801 \\ Lung CT & Yes & 0.91893 & 0.90036 & 0.97545 & 0.88041 & 0.9364 & 0.82984 \\ Chest X-ray & No & 0.91796 & 0.88379 & 0.95155 & 0.88786 & 0.91642 & 0.84573 \\ Chest X-ray & Yes & 0.90796 & 0.92473 & 0.89751 & 0.91947 & 0.91091 & 0.8364 \\ \hline \multicolumn{8}{c}{**Watershed Approach**} \\ \hline Lung CT & No & 0.87057 & 0.88893 & 0.91348 & 0.79259 & 0.90104 & 0.8199 \\ Lung CT & Yes & 0.9035 & 0.94483 & 0.91266 & 0.88346 & 0.92847 & 0.86649 \\ Chest X-ray & No & 0.89371 & 0.89279 & 0.89778 & 0.88954 & 0.89528 & 0.81041 \\ Chest X-ray & Yes & 0.88755 & 0.92746 & 0.86203 & 0.91842 & 0.89355 & 0.80758 \\ \hline \multicolumn{8}{c}{**K-means Approach**} \\ \hline Lung CT & No & 0.82512 & 0.8654 & 0.87008 & 0.73812 & 0.86773 & 0.76636 \\ Lung CT & Yes & 0.9132 & 0.94216 & 0.92799 & 0.88276 & 0.93502 & 0.87798 \\ Chest X-ray & No & 0.91754 & 0.88928 & 0.94544 & 0.84587 & 0.9165 & 0.89192 \\ Chest X-ray & Yes & 0.90794 & 0.92473 & 0.89749 & 0.83638 & 0.9109 & 0.91947 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation metrics for the PSO segmentation approach compared to other methods (Otsu, Watershed, and K-means) on medical images, both with and without histogram equalization (HE) preprocessing.  
(GT). We recognize the pivotal role played by preprocessing techniques to enhance the visual quality of segmented images, and we aim to elucidate the profound influence of preprocessing on image clarity and precision. Furthermore, we conducted a comprehensive comparative assessment: evaluating our particle swarm optimization (PSO) approach against alternative segmentation methods, including Otsu, Watershed, and K-means. This multifaceted analysis offers valuable insights into the quality of medical image segmentation achieved by our proposed approach.
## 2 Detection Model and Existing Problems
### Existing Problems  
Based on the detection model, there exist several problems which promote the development of the proposed methods:  
* First, the satellites and receiver are moving fast, which will produce a large Doppler frequency. With different dual-station angle of targets, the Doppler frequencies among the targets shows big differences, which weakened target detection peaks to varying degrees. A Doppler compensation method should be given to restore and unify the target peaks.
* Second, since the BDS signal was not devised for GNSS-R detection, its range resolution is not sufficient to detect ship targets well. Further RCS distribution information of targets needs to be detected for target recognition.
###### Abstract  
The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA\(-\)GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm's global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.  
surface coal mine; container reversal; vehicle scheduling; GA\(-\)GWO hybrid algorithm +
Footnote †: journal: Applied Sciences
## 1 Introduction
[25] and further investigates the use of Sentinel-2 MSI in modeling GPP. The methodology of [PERSON] et al., is extended to evaluate not only several EVs but also multiple VIs, therefore, assessing the sensitivity of specific bands to the climate conditions and vegetation characteristics of the studied area. Eight Sentinel-2 derived VIs are integrated with in situ measurements of different EVs; their relationship with ground base GPP is investigated to select a model with the highest accuracy. A robust empirical approach employed by [PERSON] et al. that follows previous research [45; 46] is adopted. Additionally, a methodology is implemented to upscale the model across larger areas. This upscaling methodology relies on an unsupervised classification algorithm used to identify regions with similar reflectance properties to those within the EC area. Those regions are assumed to have vegetation with similar biophysical properties and photosynthesis activity. The study methodology is demonstrated in the case study of a marshland ecosystem located in Donana National Park, Spain. High-resolution maps of GPP at the local scale are provided to support conservation and restoration practices, policy and decision-making. The integration of multiple remotely sensed indices and additional environmental variables (EVs) allows our workflow to be flexible, facilitating its uptake to different ecosystems.
## 4 Test Results and Discussion
### The Effect of Soil-Caisson Kinematic Interaction
#### 4.3.1 Effect on the Horizontal Acceleration Motion
Figure 8: The impact of the kinematic interaction on the horizontal acceleration of the caisson foundation under different loading cases: (**a**) Time histories of the horizontal acceleration for caisson foundation and free-field ground motion; (**b**) Relationship between the amplitude of the horizontal acceleration of the caisson and that of the free field; (**c**) Horizontal transfer functions under the Chichi wave; (**d**) Horizontal transfer functions under the Kobe wave. The observations mentioned above are due to the fact that the dominant frequency and peak acceleration of the seismic wave affect the extent of plasticity of the soil around the caisson foundation, which corresponds to the different shear modulus, affecting the contact stiffness between the caisson foundation and the surrounding soil, resulting in frequency-dependent radiation damping and energy dissipation as the waves emanating from the vibrating caisson foundation propagate into the soil medium.
## 3 Results
### Dielectric Properties  
Samples for dielectric tests were prepared in a similar way to samples for a corrosion resistance test. A temperature range from 25 \({}^{\circ}\)C to 100 \({}^{\circ}\)C was selected due to the fact that electrical devices for indoor applications most often operate in such environmental conditions. The values of the dielectric constant depending on the frequency of the applied voltage and the measurement temperature are presented in Figure 3.  
The dielectric constant shows a decreasing trend for all the investigated temperatures, and it can observed that the decrease is rapid at lower frequency values and less significant and constant at higher frequency values. The observed phenomenon, namely, the decrease in the dielectric constant value with the increase in the applied voltage frequency, is a typical dielectric behavior commonly found in the literature [37; 38].  
Figure 3: Variation in the real part (e\({}^{\prime}\)) of the dielectric permittivity as a function of frequency at various temperatures for a sol-based hybrid binder layer.  
Figure 2: Images of samples coated partially with a silane-based hybrid binder before and after aging in a climatic chamber. The lower half was coated with the binder, while the upper half was left untreated.  
The investigated ceramic bonding layer can be considered as a heterogeneous material that can undergo interfacial polarization according to the Maxwell-Wagner theory, which indicates that, in the low-frequency region, the movement of charge carriers trapped in the interfacial region is caused by the inhomogeneous dielectric structure [39]. On the other hand, at high frequencies, the leading mechanism responsible for the dielectric constant behavior is the hopping mechanism under the influence of the AC current. The hopping frequency between ions cannot follow the frequency of the applied electric field; therefore, the dielectric constant values decrease at a higher frequency [40].  
The observed temperature dependence on the dielectric constant can be explained by the influence of several factors, such as the following:  
* the effect of volumetric thermal expansion;
* the effect of temperature on polarizability [41].  
From the application point of view in electrification devices, the most important are the values of the dielectric constant at 50 Hz and 60 Hz. Figure 3 shows that it varies from 6.7 to 6.9 depending on the temperature, so there is no significant influence of temperature. This behavior can be explained by the fact that the measured coefficient of thermal expansion for the sol layers is very small [23]. Therefore, the measured values of the dielectric constant in the selected temperature range make the investigated bonding material suitable for application as a dielectric separation layer between wound amorphous steel ribbons with the potential to be used in electrical devices.
## 1 Introduction
Further, considering the effect of melt flow on temperature contributes to the accurate determination of the melting boundary, [PERSON] et al. developed a simulation model of laser joining considering the melting and flow processes and found that thermal convection in the molten polymer increased the melting width, and increasing the laser power increased the rate of thermal convection [28]. The joining temperature is not only related to the melting and solidification at the interface, but also determines the defect formation during the laser direct joining. [PERSON] et al. [29] measured the interfacial temperature between a CFRTP composite and titanium alloy using a K-type thermocouple, and [PERSON] et al. [30] measured the surface temperature of an aluminum alloy during laser direct joining using an infrared thermographic camera. They both found insufficient melting of the CFRTP composite at low temperatures and bubbles from thermal decomposition at high temperatures. Based on these studies, it was proven that laser direct joining can be improved by the interfacial temperature control, which ranges between the melting and thermal decomposition temperatures of the polymer matrix [31]. Meanwhile, it is also important to increase the interfacial bonding area of the CFRTP composite to the metal. However, in optimized laser direct joining processes based on temperature control, the bonding area is related to the melting width and can be increased by increasing the temperature, but is accompanied by the risk of thermal decomposition of the polymer [32]. Considering that thermal convection in the molten polymer contributes to the increase in the melting width, to simultaneously promote interfacial bonding and reduce joining defects, it is also necessary to investigate the effect of temperature distribution on the polymer matrix melting, flow, and bonding with the metal. To reveal the effect of temperature distribution on interfacial bonding between CFRTP composites and metals, this paper designs an observation device for the interfacial bonding process during laser direct joining, and the carbon fiber reinforced polyether-ether-ether-ketone thermoplastic composite (CF/PEEK) and 6061 aluminum alloy used in aviation are taken as the experimental materials, realizing the observation of the polymer melting, flowing, and bonding with metal during laser direct joining. For the interface with and without polymer layer filling, simulation models of the joining temperature field of considering the anisotropic heat transfer and temperature-varying material properties are established, respectively. The relationship between the joining temperature and the melting and flow of the thermoplastic polymer is analyzed, and the influence of the filled polymer on the interfacial bonding process is revealed, which can provide a foundation for promoting interfacial bonding and reducing the defects of laser direct joining.