esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Dynamics of Water Droplet Impact on Rotating Hydrophilic Disk
## 3 Results and Discussions  
### Phenomenal Description  
#### 3.1.1 On Stationary Disks  
First, the disk was set at rest. Figure 2 presents the evolution of droplet profiles after impacting the center of the stationary disk at seven different Weber numbers. The moment of contact between the droplet and the disk is defined as \(t=0\) ms, at which the inverted droplet, reflected by the disk, can also be observed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working Fluid** & **Temperature (\({}^{\circ}\)C)** & \begin{tabular}{c} **Density (\(\rho\))** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Viscosity (\(\mu\))** \\ **Pa-s** \\ \end{tabular} &
\begin{tabular}{c} **Surface Tension** \\ **(\(\sigma\)) N/m** \\ \end{tabular} \\ \hline Deionized water & 25 & 997 & 0.9 \(\times 10^{-3}\) & 0.072 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of water droplets ([PERSON] et al., 2018).  
outward, forming a lamella surrounded by an axisymmetric rim, visible at \(t=1.5\) ms. This shape is maintained until the droplet reaches its maximum spreading diameter, \(D_{\text{max}}\), at \(t=3\) ms. The spreading factor reaches its maximum value \(\beta_{\text{max}}=D_{\text{max}}/D_{0}\), referred to as the maximum spreading factor. Following this, the droplet begins to retract due to surface tension, with liquid from the rim moving inward, as shown at \(t=6\) ms. During retraction, the size of the central lamella decreases while the rim's thickness increases. By \(t=10\) ms, the lamella has disappeared, and the rim has merged. The droplet then continues recoiling upwards, eventually reaching a final state where its diameter undergoes small-amplitude oscillations, as later discussed in Section 3.2.1. As _We_ increases, the spreading process slightly accelerates, and the maximum spreading factor rises. Furthermore, the rim's width at maximum spreading becomes thinner with increasing We. The oscillation with small amplitude at the final state diminishes for \(We\geq 33\). At even higher Weber numbers (e.g., \(We\geq 100\) in Figure 2), small fingering instabilities appear along the rim. However, within the Weber number range considered in this study, these disturbances do not eventually result in the formation of secondary droplets.
## 1 Introduction
### Abnormal Propagation Paths in the Power Grid During Tropical Cyclones
#### 4.2.1 Rescheduling Considering Power Flow Redistribution  
According to Equation (20), when the system experiences a fault operating state due to the impact of a tropical cyclone at time period t, rescheduling is required to restore supply-demand balance. The rescheduling process outlined in Equations (21)-(23) can be broken down into the following steps:  
Step 1: Unit regulation and emergency load shedding or generator tripping  
The primary goal of unit ramping and load shedding or generator tripping is to restore supply-demand balance. Since the cost of load shedding or tripping is higher than that of unit regulation, unit ramping has a higher scheduling priority than emergency load shedding or tripping. Assuming the supply-demand imbalance caused by the fault is \(\delta P\), for the regulating unit \(i\in\mathcal{N}_{\text{g}}\), if the power supply is less than the load demand \(\Delta P>0\) and the imbalance does not exceed the total upward regulation capacity \(\sum_{i}\left(P_{\text{gmax},i}-P_{\text{g0},i}\right)\), the adjusted power can be expressed as follows:  
\[\Delta P_{\text{g}i}=\min\left(\Delta P\frac{P_{\text{gmax},i}-P_{\text{g0},i }}{\sum_{i}\left(P_{\text{gmax},i}-P_{\text{g0},i}\right)},P_{\text{gmax},i} -P_{\text{g0},i}\right) \tag{26}\]  
In this equation, \(P_{\text{gmax},i}\) represents the maximum power output of unit \(i\), and \(P_{\text{g0},i}\) represents the rated power of unit \(i\). If, after further adjustment, \(\Delta P>\sum_{i}\left(P_{\text{gmax},i}-P_{\text{g0},i}\right)\), i.e., unit regulation, still fails to restore the supply-demand balance, load shedding must be implemented:  
\[\Delta P_{\text{Id}}=\Delta P-\sum_{i}\left(P_{\text{gmax},i}-P_{\text{g0},i}\right) \tag{27}\]  
If the power supply exceeds the load demand \(\delta P<0\), and the imbalance is not less than the total down-regulation capacity \(\Delta P\geq\sum_{i}\left(P_{\text{gmin},i}-P_{\text{g0},i}\right)\), the adjusted power can be expressed as follows:  
\[\Delta P_{\text{g}i}=\max\left(\Delta P\frac{P_{\text{gmin},i}-P_{\text{g0}, i}}{\sum_{i}\left(P_{\text{gmin},i}-P_{\text{g0},i}\right)},P_{\text{gmin},i} -P_{\text{g0},i}\right) \tag{28}\]  
In the equation, \(P_{\text{gmin},i}\) represents the minimum power output of unit \(i\). Furthermore, if \(\Delta P<\sum_{i}\left(P_{\text{gmin},i}-P_{\text{g0},i}\right)\), i.e., unit regulation, still fails to restore the supply-demand balance, generator tripping should be implemented:  
\[\Delta P_{\text{gt}}=\Delta P-\sum_{i}\left(P_{\text{gmin},i}-P_{\text{g0},i}\right) \tag{29}\]  
Step 2: Power flow analysis  
After restoring the supply-demand balance through unit adjustment or generator/load shedding, the branch transmission power is determined using power flow analysis.  
Step 3: Disconnection of overloaded branches  
Based on the transmission power calculated in Step 2, the status of overloaded branches is determined using Equations (22)-(23).  
Repeat steps 1 through 3 until the system reaches a new state of equilibrium.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 5 Simulation Results
### Algorithm Equilibrium Energy Analysis  
The variation in the number of surviving nodes with the increasing number of rounds in DBR, LEACH, UCUBG and the present algorithm is shown in Figure 4. DBR is a planar structure algorithm and the remaining three are all clustering algorithms. As shown in Figure 5, the DBR algorithm has the earliest node death. A large number of nodes still survived until 2000 rounds. This indicates that the DBR algorithm, a planar structure, has a problem--the poor balancing of network energy consumption, which causes a large difference in node energy consumption and the premature death of some nodes. Among the three clustering algorithms with relatively good balanced energy consumption, this algorithm has the latest round number with the first node death. The death time of the last node is the closest to that of the initial one, which indicates that this algorithm has better balanced energy consumption than the other two clustering algorithms.
# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 2 Proposed Method
### Event Camera Imaging
Designed for Autostereoscopic 3D PC displays and vehicular AR 3D HUD systems, this distance was selected to guarantee the best pupil-tracking results in these settings. In our evaluations, participants with eyeglasses posed no issues for our methodology; however, sunglasses that occlude the eyes were excluded. Our system, developed for both Autostereoscopic 3D displays and AR 3D HUDs and utilizing the high dynamic range of event cameras, is expected to function efficiently outdoors. Yet, direct outdoor evaluations were not part of this study. This setup ensures that we can efficiently monitor and analyze the eye movements from a certain distance without the need for physical contact with the user. The remote pupil-tracking approach offers non-intrusive and user-friendly eye-tracking solutions for various real-world scenarios. With the DAVIS 346 event camera's capabilities and the remote pupil tracking setup, we are well-equipped to explore the potential of event camera imaging for advanced pupil-tracking tasks. These features enable us to effectively capture and analyze eye movements, especially in challenging conditions, thereby paving the way for more accurate and efficient eye-tracking solutions.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 4 River Flow Velocity Inversion Result and Analysis
### Influence of Reflected Signal Strength  
The quality and strength of the signal can be measured by the signal-to-noise ratio (SNR) of the reflected signal. In this paper, I and Q of the reflected signal are used to calculate the SNR. Figure 16 shows the Lissajous figures of four satellites (GPS PRN 4, PRN 9, BDS GEO 2, and IGSO 10) to display the I and Q branches of the reflected signal demodulated by the software receiver.  
\begin{table}
\begin{tabular}{c c c} \hline
**Satellite System** & **PRN** & **R** \\ \hline GPS L1 & PRN 4 & 0.652 \\ GPS L1 & PRN 9 & 0.876 \\ GPS L1 & ALL & 0.727 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation analysis between precision and elevation change rate.  
Figure 15: The relationship between satellite rate and error (**a**). The relationship between satellite elevation change rate and error elevation change rate and error (**b**).  
The carrier-to-noise ratio (CNR) of a reflected signal can be defined as follows [31]:  
\[CNR=10 lg(\frac{(I_{i}^{2}+Q_{i}^{2})}{(\sqrt{I_{i}^{2}+Q_{i}^{2}}-\frac{1}{N}\sum \limits_{i=1}^{N}\sqrt{I_{i}^{2}+Q_{i}^{2}})}) \tag{14}\]  
where \(N\) is the signal tracking time (2 min). The unit of SNR is decibel (dB). After completing the signal carrier synchronization and baseband demodulation processing, SNR and CNR have the following relationship:  
\[SNR=G_{B}+CNR \tag{15}\]  
where \(G_{B}\) is baseband signal gain, including coherent cumulative gain value \(G_{C}\), incoherent cumulative gain value \(G_{I}\), and square loss \(G_{I}\):  
\[G_{B}=G_{C}+G_{I}-G_{L} \tag{16}\]  
SNR is obtained by demodulating the \(I\) and \(Q\) branch of the raw IF data by the software receiver [36], including the instantaneous SNR and the average SNR in Figure 17. The instantaneous SNR fluctuates over time, so the average SNR is selected as the standard for evaluating signal strength. The SNR of GPS PRN 4, GPS PRN 9, BDS GEO 2, and BDSI GSO 10 in this period are 49.76 dB, 47.51 dB, 43.79 dB, and 46.64 dB, respectively. MEO signal strength of GPS is slightly stronger than the signal strength of GEO and IGSO of BDS. The four satellites analyzed in this experiment all have good signal quality. The influence of signal quality on the inversion accuracy cannot be judged by the slight difference between the average SNR of the four satellites. Combining the analysis of the results in Table 2,  
Figure 16: The Lissajous figures of four satellites’ reflected signals. The Lissajous examples include GPS PRN 4 SNR 10.00–10.02 a.m. (LT), GPS PRN 9 SNR 11:00\(-\)11:02 a.m. (LT), BDS GEO 2 SNR 10:00–10:02 a.m. (LT), and BDS IGSO 10 SNR 11:00\(-\)11:02 a.m. (LT).  
there is no apparent correlation between signal strength and the inversion accuracy of shore-based river flow velocity.
[25] treated the ITO surface with oxygen plasma using 4, 6, and 8 min to compare the properties of films. They found that the plasma treatment improved the surface morphology and changed the thickness, electrical properties, and roughness of the ITO surface from 2.34 nm to 0.899 nm, which led to the applications with organic light-emitting diodes (OLEDs) that exhibited a decrease in resistance and an improvement of light intensity. Similarly, [PERSON], [PERSON] et al. [26] improved the ITO surface with oxygen plasma, where they reported that plasma discharge could increase the work function of ITO without causing damage to the surface. They also confirmed that plasma treatment could improve the adhesion between molecules and ITO surface and benefit the fabrication of high-performance devices. As mentioned, plasma technology has been used to modify the structure of materials to improve efficiency and a wide range of applications. The properties of the obtained films will vary depending on the production conditions. In particular, the method of generating plasma and the treatment process are considered the core that can determine the desired properties of the final products. As mentioned, most previous studies have confirmed the successful application of plasma treatment to improve the surface of ITO materials. However, there needs to be a clear report of plasma treatment to improve the performance of materials applied to DSSC devices in depth. Changing the morphology of the photoanode materials and increasing the surface energy, adhesion, and performance of ITO can help improve the adhesion of TiO\({}_{2}\) nanoparticles to ITO. This can lead to better dye-loading materials and more efficient charge transport in DSSC devices, increasing the current density and leading to high-efficiency devices and cost-effective manufacturing processes. For this reason of interest, it is essential to study and report on the progress of ITO material development in the solar cell industry. Thus, the improvement of the ITO materials with a technique conducive to depositing a good-quality film is necessary. A bias applied to the surface can attract positive ions in the plasma to attack the grown film [27; 28; 29]. This bombardment causes the molecules and clusters to condense on the surface to gain energy, and the molecules detach from the film with weak bonds [30]. This type of bombardment is beneficial to the crystallinity of the film, and the effect of suitable ions on the deposited film improves the film's properties [31; 32; 33]. In this study, the ITO films were deposited on glass substrates by E-beam evaporation with glancing angle deposition. Then, the ITO was deposited on glass treated with oxygen plasma via a bias-magnetron RF sputtering process. The effects of oxygen gas content on the film surfaces and electrical, optical, and the \(\eta\) of DSSC devices were evaluated.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 6 Simulation Implementation
### Network Realizations  
In the context of our WSN example, we assumed a dedicated network tailored for a specific purpose. Consequently, the entirety of network traffic comprised exclusively time-critical data packets and packets containing routing information. Most simulations were performed with mesh networks of 60 nodes placed randomly with a uniform distribution on a square area of 30,000 m\({}^{2}\), as shown in Figure 2. The sink was placed in the southwest corner, and 60 networks were generated this way and used for all reported results.  
Figure 2: Typical random topology used for simulations, where 60 nodes were placed randomly on a square area of 30,000 m\({}^{2}\). The sink was placed in the southwest corner.  
Line topologies are sometimes found in real WSN deployments [39], such as those involved in monitoring gas pipelines [40]. These simpler topologies enable a focused examination of the behaviors exhibited by the protocols under study. Two experiment sets were carried out in a line topology, with 60 nodes placed at a distance of 5 m from each other and covering a total distance of 295 m. In this case, simulations were repeated 60 times with different seeds.  
All simulation parameters and their default values are shown in Table 1.
## 6 Finite Element Analysis
Figure 11 compares the lateral load-displacement relationships of N00-150 and S15-150. The analysis and test results are denoted as the dashed and solid lines, respectively. Overall, the results of the FEM analysis, such as the elastic stiffness, slip at the bolted joint of the branch member, and post-slip ultimate strength, agreed well with the test results. For S15-150 with the chord face stiffener, the energy dissipation due to frictional slip at the bolted joint of the branch member was observed in the analysis, similar to in the test. However, the detailed behavior was different in the analysis and test, as follows. First, the slip strength at the bolted joint of the branch member was greater in the analysis than in the test. Second, the bolted joints underwent scratches on the faying surface due to slip and threaded marks or dents on the inner face of the hole (see Figure 6b, Figure 7b, and Figure 8b). Such effects were not taken into account for the analysis. Thus, for accurate analysis, fine modeling on the bolted joint is required. Figure 12 shows the Mises stress distributions (\(S\)) and deformations at the peak points denoted in Figure 11. In the region where Mises stresses \(S\) are equal to or greater than the yield strength  
Figure 11: Comparison of load-displacement relationships by finite element modeling (FEM) analysis and test. Figure 10: Analysis model, support constraints, and load conditions of S15-150. \(F_{y}\) (= 405 MPa for the RHS chord member and 429 MPa for the longitudinal plate and chord face stiffener), tensile or compressive yielding takes place and plastic strains occurs. For the unstiffened connection N00-150 (see Figure 12a), plastic regions where \(S\geq F_{y}\) were both in the longitudinal plate and connecting chord face; particularly in the longitudinal plate, the plastic regions formed mainly around the bolt holes. This shows that, in the analysis, the behavior of N00-150 was governed by plastification of the connecting chord face and by slip and subsequent hole bearing at the bolted joint. For the stiffened connection S15-150 (see Figure 12b), on the other hand, the connecting chord face remained elastic as the chord face stiffener reduced stress concentration. Instead, plastic regions where \(S\geq F_{y}\) were mainly in the longitudinal plate. This shows that the behavior of S15-150 was governed by slip and subsequent hole bearing at the bolted joint.
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 2 Methods
### Contour Integration  
After the boundary of the target region is obtained by an omnidirectional wave algorithm, the process results in four directions that need to be integrated. In contour integration, it is necessary to consider: (a) that the boundary points and non-boundary points are classified uniformly; (b) eliminating other imaging interference, such as hard exudation [23]. Before contour integration, the segmentation results from four directions are binarized, and the boundary points and non-boundary points are marked, respectively. Then, the initial direction is fixed, and the extraction results obtained by operators in other directions are superimposed. As long as a pixel is identified as a boundary in a certain direction, it will become a point on the final omnidirectional boundary line. This method can make up for the shortcomings of the unidirectional wave algorithm or other algorithms which do not have the ability of omnidirectional target segmentation and can obtain a more complete target contour.  
Since the fundus may have hard exudation, it also has the contour of a closed curve. To avoid the interference of hard exudation, the algorithm in this paper filters the segmentation  
Figure 5: Segmentation results of the operator in different directions, (**a–d**) are the results of setting the direction \(\theta\) to \(0\), \(\pi/2\), \(\pi\), \(3\,\pi/2\), (**e**) is the fusion of the segmentation results in four directions.  
results of hard exudation by judging the gray mean value of the connected domain. Since the CME capsule cavity is filled with liquid components such as plasma and vitreous humor, the imaging presents a low-reflective signal, and the internal gray value of the CME is low. Meanwhile, the hard exudation presents high-reflective particles, and its internal gray value is high. The gray-scale screening method uses the average gray value of the whole image as the threshold. For all connected areas, only those whose average gray value is lower than the threshold are retained, and those whose average gray value higher than the threshold are filtered, that is, the interference of hard exudation symptoms is eliminated.  
In addition, since the cyst appears between the layers of the retina, the thickness of the cyst is within a certain range. According to the distribution of cysts in the data set, we choose 1.5 times the average thickness of the retina as the upper limit of the cyst size and 0.5 times the average thickness of the retina as the lower limit. By filtering the position of the fusion result, the contour information can be more accurate.
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
## 2 Measurement Setup and Procedure  
A continuous-wave (CW) radio signal was generated by an Anritsu MG369 xC Series Synthesized Signal Generator. Figure 1 shows the photograph of measurement equipment. A highly directional horn antenna vertically polarized mounted on an antenna mast was used to transmit the radio frequency signal at 28 and 38 GHz bands. The vertically polarized omni-directional receiving antenna is connected to an MS2720T spectrum analyzer operated at zero span. The Tx consisted of a single synthesized sweeper and the Rx consisted of a single spectrum analyzer. The measurement process was carried out in the parking lot next to the Scholar Inn hotel inside the Universiti Teknologi Malaysia (UTM) on the Kuala Lumpur campus as shown in Figure 2a,b. The Tx was located in a stationary position, while the Rx was moved in different locations of parking lot environment. The measurement setup was chosen to emulate the base station in wall for transmitter and as a mobile user in the receiver with height in the range of 1.3 to 1.8 m. In this measurement, the heights of the transmitting and receiving antennas above the ground level was set to 2 m and 1.7 m, respectively. The measurement setup is listed in Table 1. The single parking lot (SPL) and dual parking lot (DPL) floor plan is shown in Figure 2b. The single parking lot contained only one line of parked cars while the dual parking lot contained two parallel lines of parked cars separated by road, as shown in Figure 2b. These two scenarios were used to investigate the millimeter wave propagation in a car parking lot. The definition of SPL and DPL is addressed in Table 2. The starting point resultant distances between the transmitting and receiving antennas were 14 m and 30 m for the single and dual parking lots, respectively. During the measurement process, it was important to ensure that all objects or bodies remained at a constant stationary state to ensure that the measurement was taken as accurately as possible. The measurement was conducted when the parking lot was fully occupied by different cars, with each parking space roughly about 1.5 m width.  
Figure 1: Measurement equipment.