esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Methods in Image Matting: A Survey
## 3 Traditional Methods
### Sampling-Based Methods  
Sampling-based methods [44; 45; 46; 47; 48; 49] sample a series of pixel pairs from known regions of the trimap before predicting unknown pixels; then, they directly estimate the alpha mattes by comparing the similarity between the pixels to be predicted and the sample pixel pairs. Sampling-based methods can be further divided into two categories: (1) Local sampling-based methods, in which the samples are collected near the edge of the known regions in the image. Then, the alpha mattes are estimated according to the sample pixel pairs. This method has the advantage of having a smaller sampling range and faster processing speed. However, as the sampling space is confined to the edge of the known regions, this method may overlook the optimal sample pixel pair for the pixels to be predicted, leading to a lower accuracy in the predicted alpha mattes; and (2) Global sampling-based methods, which collect samples from all known regions and focus on the trade-off among the sample set size, coverage of the optimal pixel pair, and design of the pixel pair evaluation functions. Collecting samples from all known labeled regions in the images can effectively compensate for the shortcomings of a method using local sampling and improve the accuracy of alpha mattes. However, the processing speed decreases as the number of samples increases, and the sampling quality is unstable as the collected samples may also miss the optimal pixel pairs in complex situations.
## 4 Exploring the TES Performances Improvement by a Numerical Parametric Study
### Model Definition
#### 4.1.2 PCM Nodules  
The heat flow \(\phi\dot{i}_{j}(t)\) from the nodule \(i\) depends on the PCM state. The heat transfers inside the nodule are predicted to be purely conductive in the model, and the change of state occurs by concentric layers, starting from the nodule wall. This hypothesis is accurate during the PCM crystallization, but less valid during the melting, as the remaining solid phase will tend to fall and disturb the concentric fusion.  
The heat equation inside the nodule filled with PCM is:  
\[\rho_{PCM}\frac{\partial h_{PCM}}{\partial t}=\lambda_{PCM}\Delta T_{PCM} \tag{5}\]  
with \(\rho_{PCM}\) the PCM density, \(h_{PCM}\) its enthalpy, \(\lambda_{PCM}\) its thermal conductivity and \(T_{PCM}\) its temperature.  
The boundary condition is:  
\[-\lambda_{PCM}S_{int,Nod}\overset{\rightarrow}{\
abla}T_{PCM|R=R_{ext}} \cdot\overset{\rightarrow}{n}=h_{conv}S_{ext,Nod}\left(T_{w,Nod}-T_{j} \right)=\frac{T_{PCM,M}-T_{j}}{R_{th,Nod}}=\phi_{i} \tag{6}\]  
where \(h_{conv}\) is the heat transfer coefficient between the HTF and the nodule wall at the temperature \(T_{w,Nod}\), calculated by an empirical correlation derived from [PERSON] et al. considering the Prandtl and Reynolds numbers, \(Pr\) and \(Re\)[PERSON] et al. (2017).  
\[Nu=\frac{h_{conv}\cdot D_{ext}}{\lambda_{HTF}}=1.5\cdot Pr^{1/3}\cdot Re^{1/2} \tag{7}\]  
with \(D_{ext}\) as the outer nodule's diameter and \(\lambda_{HTF}\) the HTF thermal conductivity.  
\(R_{th,Nod}\) is the thermal resistance between the PCM at a temperature \(T_{PCM,M}\) and the HTF, including the nodule's conductive resistance and the fluid's convective resistance.  
The PCM is considered as a pure body, its enthalpy varying with the temperature as described by Equation (8) [47]. This approach allows for fast calculation, and induces in this case a minimal variation compared to the implementation of the experimental enthalpy variation curve.  
\[h(T)=\left\{\begin{array}{ll}c_{s}\cdot\left(T-T_{ref}\right)+h_{ref}&T<T_{m} \\ c_{s}\cdot\left(T_{m}-T_{ref}\right)+\chi_{l}\cdot L_{m}+h_{ref}&T=T_{m}\\ c_{s}\cdot\left(T_{m}-T_{ref}\right)+L_{m}+c_{l}\cdot\left(T-T_{m}\right)+h_{ ref}&T>T_{m}\end{array}\right. \tag{8}\]  
with \(c_{s}\) and \(c_{l}\) being the specific heat capacity of the solid and liquid PCM phases, \(L_{m}\) its latent heat of melting and \(\chi_{l}\) its liquid mass fraction. During the phase change, the temperature remains constant (\(T=T_{m}\)), while the liquid mass fraction varies. The reference \(h_{ref}=0\) J/kg is set at an arbitrary temperature lower than the melting temperature.  
The equation is resolved by the finite element method (FEM). The model is initialized at \(t=0\) with an equal initial temperature of the PCM and the HTF. Then, at each time step, the fluid temperature and the PCM enthalpy are calculated. The mesh number \(N\) inside the tank is limited to 8 as one mesh cannot be smaller than a nodule, and the mesh number M inside a nodule is set at 24 as a compromise between accuracy and speed.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.3 Fast Fourier Transform (FFT)  
The Cooley-Tukey algorithm is considered a breakthrough in implementing fast Fourier transform (FFT), which reduces the DFT complexity. The DFT of a signal in the time domain \(x(n)\) is introduced as follows:  
\[x(k)=\sum_{k=0}^{n-1}x(n)W_{N}^{kn} \tag{25}\]  
The \(W_{N}^{kn}=e^{-\frac{i2\pi nk}{N}}\) is known as the twiddle, which is a complex value. In order to compute the \(N\) point of the DFT, it is required to perform \(O\big{(}N^{2}\big{)}\) operations; however, by using the FFT, the required operations are down to \(O\big{(}N\text{log}_{2}\big{(}N^{2}\big{)}\big{)}\).  
The implemented algorithm is 16-point FFT using Radix \(2^{2}\) single-pass delay feedback (SDF) algorithm, which is the best fit for the limited NB-IoT power budget. The SDF is implemented with parallelization \(P=1\) to reduce the power consumption and the area of the design. It also helps in enhancing the block latency and performance [25]. Radix \(2^{2}\) has been developed to inherit the simple control structure of radio 2, and it has the advantage of saving hardware, i.e., reducing the number of used multipliers in contrast with radio 4 [26; 27].  
The output of the CFO block passes first through a synchronous first in first out (FIFO), then enters the FFT block. As depicted in Figure 9, the block mainly consists of memory and a butterfly, where the butterfly's output is stored in the 3D memory. Therefore, it is stored in the same row, which helps in power optimization as there is no need to use an address generated circuit. Moreover, as the pipeline is deployed, there is no need to have a large memory like the memory-based algorithm. That improves the design performance and reduces the leakage power and the switching activity. At last, the 16-point FFT requires the data to pass through four stages to complete their operation, but only three stages are implemented, as the fourth stage is shared with the first stage to reduce the consumed resources and the power consumption. In addition, the bit reversal operation for the FFT output symbols is performed while storing in the storage element before entering to the resource de-mapper.
## 3 Proposed Method
### FDCNB-MINING Algorithm  
To solve the problem of varying network bandwidths, we propose the FDCNB-MINING algorithm, namely, fast distributed mining in changing network bandwidth. The algorithm is as shown in Section 3.3 and is conducted as follows:  
1. Before the kernel node is activated, the monitor node must begin operation to constantly monitor the network bandwidth environment. (Line 1)
2. When the kernel node needs to select one of the computing nodes to transmit compressed files, the monitor node provides the best computing node to the kernel node. (Line 5)
3. If the monitor node finds the kernel node of using low bandwidth during transfer, it informs the kernel node to discontinue transmission and change into a better computing node to transfer information; otherwise, transmission is continued. FDCNB-MINING sorts computing nodes in descending order according to the performance index of bandwidth that reflects their real-time network bandwidth. A packet with size is sent to each computing node periodically from the kernel node, and the computing node acknowledges the kernel node after receiving the packet. In this way, the response time between kernel node and every computing node can be obtained. The performance index of bandwidth \(B_{p}\) is defined as \(\hat{s}\)f, where \(s\) is the size  
Figure 1: System structure of the fast distributed mining in changing network bandwidth (FDCNB-MINING) algorithm.  
of transferred packet and \(t\) is the elapsed time the packet sent from the kernel node to the computing node. Paused transmitted data are saved in the computing node until the monitor node finds a recovery bandwidth. The kernel node then waits to be informed by the monitor node to mine and continue to transfer the unfinished part. (Line 7-10)
* This process is repeated until the kernel node does not need the computing node to help in the mining operation.
## 7 Simulation and Evaluation
### Network Throughput  
In the process of block consensus, the broadcast of a large number of transactions will increase the network throughput. This section compares the throughput without regional division and after regional division, as shown in Figure 10. When the number of users processed by a single satellite is 4 per second and no region is divided, the network throughput is 5.139 MB/s in the process of block consensus and 1.462 MB/s after regional division. From a range of 4 to 32 users, with an increase in the number of users, the growth rate of network throughput in divided regions and non-divided regions is similar, and the network throughput decreases by 71% after the region is divided.  
Figure 9: Comparison of block generation time.
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 5 Discussion  
Due to the modulation of two kinds of signals at the same frequency point, the subpeaks of the two signals have an impact on the TT&C and data transmission performance of each other. This section is conducting simulation research to study the deterioration of the performance.
## 2 Materials and Methods
### Verification of ABM-Predicted Cellular Dynamics  
VF-ABM were verified for Day 7, Week 2, and Week 4 with the flow cytometry data. The VF-ABM reproduced the dynamics of all cell populations from the empirical data between Day 1 to Week 4 (Figure 4). For neutrophils, the peak was delayed by one day. Macrophage counts reached a maximum concentration on Day 2, in contrast to Day 1 in the data. For fibroblasts, the model reproduced the peak at the correct time point on Day 2 but did not resemble the oscillation pattern as observed in the empirical data.  
Figure 4: Empirical and ABM-simulated trajectories of cell populations in injured vocal folds. **(A)** Number of Neutrophils. **(B)** Number of Macrophages. **(C)** Number of Fibroblasts.  
In addition to the overall patterns, statistical tests of 95% confidence intervals were used to quantitatively evaluate the VF-ABM cell outputs. As the VF-ABM was run 100 times, a 95% confidence interval was obtained by computing the 2.5% and 97.5% quantiles from the ABM simulation output samples for each time point and cell type. The goal here was to evaluate for each time point (i.e., Day 7, Week 2 and Week 4), how many empirical data points (i.e., counts of neutrophils, macrophages and fibroblasts) fell within the two-sided 95% confidence interval of simulated outputs (Table 6). For Day 7, all empirical data were within the 95% confidence intervals (i.e., 100%, 3/3). For Week 2 and Week 4, all empirical data points fell outside the 95% intervals except for neutrophils at Week 4. However, when examining the data closely, the VF-ABM outputs were reasonably close to the empirical data. For instance, the empirical cell counts for neutrophils and macrophages were 180 and 186 at Week 2 respectively; whereas the VF-ABM outputs were from 188.5 to 222.5 and from 103.5 to 155.5 respectively.
## 3 Results
### Pore Size Analysis  
Figure 11 shows the pore size distribution of the concrete samples with different contents of AMF, aluminum sulfate, and PPF. It can be seen that the incorporation of AMF, aluminum sulfate, and PPF significantly reduced the pore size and the overall porosity of the concrete. In Figure 11a, the pore size distribution of the concrete with 0% and 10% AMF is compared. The AMF exhibited a filling effect, and the porosity of the concrete was remarkably decreased in the concrete with 10% AMF. The pores in the concrete with 10% AMF were mainly distributed in the range of 10-100 nm, compared with the case that the pores in the concrete with no AMF were mainly distributed in the range of 10-2000 nm.  
Figure 10: Effect of PPF on permeability of concrete.  
Figure 9: Effect of PPF on compressive strength of concrete.  
Under the condition that the optimum content of AMF (10%) was added, the effect of aluminum sulfate and PPF on the pore size distribution are shown in Figure 11b,c. In Figure 11b, when the aluminum sulfate content was 1%, the pore size distribution was more uniform than that of the concrete with no aluminum sulfate. The amount of small pores in the range of 20-1000 nm increased, and the amount of large pores \(>\)1000 nm was significantly reduced. It is known that the large pores should be controlled to a minimum level in order to achieve a low permeability property. In Figure 11c, the PPF clearly reduced the total porosity of the concrete, and the pores of the concrete were focused between 10-30 nm. This optimized the pore distribution in concrete and the concrete microstructure was more compact, reflected by the improvement of the resistance of permeability.  
Figure 11: Pore size distribution results of concrete with different contents of: (**a**) AMF; (**b**) aluminum sulfate; (**c**) PPF.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 4 EGR Protocol
### Data Redundancy Avoidance and EGR Routing Algorithm
In Algorithm 2, a set of nodes that will participate in routing are initialized. Also, we want to put a certain limit to the number of nodes in a cluster. For that, \(Q\) is initialized as the number of CMs that can remain in a cluster. Nodes are included into a cluster until they reach \(Q\). As stated earlier, the operational architecture of EGR is similar to that of LEACH, including the rounding mechanism. \(I_{n}\) and \(I_{f}\) have been initialized as the number of rounds and the final round, respectively. The routing process is repeatedly executed through the setup and steady-state phases till the final round. In the setup phase, clusters are formed (see Section 4.1 for more details), and CHs are elected using EG (see Section 4.2 for more details). The steady-state phase is longer in time duration than the setup phase. Sink nodes broadcast to CHs in the steady-state phase, and CHs decide the sink node based on RSSI value. At this point, Algorithm 1 is used to get rid of redundant data. Then, CMs send data to their CHs, and CHs deliver data to the sink node. ```
1. Initialize \(S\) to be the set of nodes (\(S=\{S_{1},S_{2},S_{3},\ldots,S_{n}\}\))
2. Highest number of nodes to be in cluster \(\to Q\)
3. Number of rounds \(\to I_{n}\)
4. Final round \(\to I_{f}\)
5. Threshold area \(\to T\)
6. \(T=\frac{1}{2}\) (Area of the isosceles triangle created by FoV)
7. loop until every single node is put at least into one cluster
8. Empty cluster creation by associating the un-clustered node
9. \(S_{n}\) \(\rightarrow\) First member of the cluster
10. Overlapped area computation between \(S_{n}\) and \(S_{n}\pm m\)
11. while (number of nodes in the cluster \(<Q\))
12. if (Area calculated \(>T\))
13. \(S_{n}\pm m\) is added to the cluster (\(m=\) any integer)
14. else
15. Consider another node for cluster formation
16. end while
17. end loop
18. Decide the CHs using EG
19. Advertise as CHs to the CMs within the cluster
20. Steady-state phase:
21. Sink nodes broadcast to the CHs. 22. CHs decide which sink to transmit based on the RSSI value. 23. Avoidance of data redundancy using Algorithm 1. 24. CMs send data to the CHs. 25. CHs deliver data to the nearest sink node. 26. Repeat the whole process until final round. ```  
**Algorithm 2** Routing Algorithm  
As mentioned earlier, Algorithm 1 is used in the main routing algorithm of Algorithm 2. Therefore, we analyze Algorithm 2 rigorously. The time complexity of the proposed routing algorithm can be calculated as:  
\[\begin{array}{c}O(1)\ +\ O(n)\ +\ O(2 mn)\ +\ O(Hn)\ +\ O(H)\ +\ O(p(p-q))+\ O(M)
# Improving Landslide Recognition on UAV Data through Transfer Learning
## 3 Methodology
### UAV Data Acquisition and Processing  
#### 3.1.1 Route Planning  
The route planning is designed using the ground station software matched with the UAV system. The study area covers more than 30 square kilometers with 8 routes designed. The designed heading overlap is 75%, and the lateral overlap is 65%. The flight height is 460 m, and the ground resolution is better than 15 cm. The routes are straight in the southwest-northeast direction, and the flight test is conducted before the flight to ensure the normal operation of the UAV system [42]. After completing the flight in the study area, the image data are exported to the ground station, quickly checked and analyzed to determine whether there are aerial photography loopholes, and ensure that the acquired data is valid.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.3 Stratigraphic Thickness  
The thickness of the geological layers has been reported to influence ground subsidence due to groundwater utilization and consolidation settlement [17]. Therefore, the stratigraphic thickness was incorporated as input data for the analysis. The stratigraphic thickness of the target area was obtained from the drilling data and other resources. Stratigraphic thickness was used to compute the permeability coefficients across the entire target area using the Kriging method on point data. The stratigraphic thickness of the target area was distributed in the range of 0.04-33.31 m, and the median value of the grid was selected as a representative value to extract the input data.
## 2 Methods and Materials
### Fabrication of OFET Devices and Measurement of the Transport Properties  
All film fabrication processes except substrate cleaning were performed in a glove box. Heavily doped n\({}^{+}\)-Si (100) wafers with 200 nm-thick thermally grown SiO\({}_{2}\) (\(C_{\text{i}}\) = 17.3 nF cm\({}^{-2}\)) were used for the substrate. The Si/SiO\({}_{2}\) substrates were ultrasonicated with acetone and isopropanol for 10 min and then were subjected to a UV/ozone treatment at room temperature for 20 min. A solution of octadecyltriethoxysilane in trichloroethylene (3 mM) was spin-coated onto the cleaned substrate at a rate of 3000 rpm for 15 s, followed by hydrolysis in a closed container in the presence of ammonia hydroxide solution for 24 h. After the hydrolysis, the substrates were rinsed with water and boiling 2-propanol. The polymer layer was spin-coated from a hot (\(\sim\)100 \({}^{\circ}\)C) 2 g/L CB solution at 1000 rpm for 10 s, and then at 2500 rpm for 35 s and subsequently annealed at 200 \({}^{\circ}\)C for 30 min, respectively. On top of the polymer thin films (80 nm), Au drain and source electrodes (thickness 80 nm) were deposited in a vacuum evaporation system through a shadow mask, where the source-drain channel length (\(L\)) and width (\(W\)) were 40 \(\upmu\)m and 1.5 mm, respectively.  
Current-voltage characteristics of the OFET devices were measured at room temperature in air with a Keithly 4200-SCS (Keithly Instruments Inc., Cleveland, Ohio, United States) semiconductor characterization system at \(V_{\text{D}}\) = \(-60\) V and/or 60 V, where \(V_{\text{D}}\) is drain voltage. Field-effect mobilities were calculated in the saturation regime (\(V_{\text{D}}\) = 160 V ) using the following equation:  
\[I_{\text{D}}=(\text{WC}_{\text{i}}/2\text{L})\mu(V_{\text{G}}-V_{\text{T}})^{2}\]  
where \(C_{\text{i}}\) is the capacitance of the dielectric layer, \(I_{\text{D}}\) is the source-drain current, \(V_{\text{G}}\) is the gate voltage, and \(V_{\text{T}}\) is the threshold voltage, respectively. Current on/off ratios (\(I_{\text{on}}/I_{\text{off}}\)) were determined from the minimum current around \(V_{\text{G}}\) = 0 \(-\) 20 V (\(I_{\text{off}}\)), and the current at \(V_{\text{G}}\) = 160 V! (\(I_{\text{on}}\)). The mobility data were collected from more than seven different devices.
## Materials and Methods
### Reactive Oxygen Species (ROS) Detection  
Intercellular ROS generation was detected by DCF-DA (2'.7'-Dichlorofluorescein diacetate) dye in HepG2 cells exposed to the GNPs. (DCF-DA is a fluorogenic dye that measures the activities of hydroxyl, peroxyl, and other reactive oxygen species (ROS) within cells.) The DCF-DA assay was based on the diffusion of DCF-DA in the cells. It was then deactivated by cellular esterases to a non-fluorescent compound, which was then oxidized by the action of intracellular ROS into a highly fluorescent DCF (2'.7'-dichlorofluorescein). This DCF was detected by a fluorescence spectrometer at 485 nm and 535 nm excitation and emission, respectively [58].  
In brief, the HepG2 cells were seeded in a 48-well plate in a density of 2 \(\times\) 104 cells/well and incubated overnight in a CO\({}_{2}\) incubator. Then, cells were exposed to 25, 50, and 100 mg/mL of GNPs for 24 h. After exposure, the cells were incubated with 20 mM of DCF-DA dye for 1 h in the dark. Then intensity of DCF fluorescence was detected at 485 and 538 excitation and emission, respectively, using a fluorescence spectrophotometer (Fluoroskan Ascent, Thermo-Scientific, Finland, UK).