esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 4 Case Study
### Prediction of Ship's Estimated Time of Arrival
#### 4.1.2 Dataset 2: Marine Weather Data  
The marine weather data used in this study were collected in 2018 and acquired at intervals of 1 day at midnight. The datasets are defined in 0.5\({}^{\circ}\) resolution for each latitudinal and longitudinal direction, as shown in Table 4. The factors from the first to the ninth describe height, direction, and period of the total wave, wind wave, and swell wave, respectively. It is common to use the total wave information because the total wave is the combination of the wind and swell wave [28]. The wind UV and VV are the velocities along the longitudinal and latitudinal directions in forward order, and the pressure Mean Sea Level (MSL) and pressure surface represent the atmospheric pressure measured at MSL and surface of the earth, respectively. The sea surface temperature and salinity are measured at the sea surface. Finally, the current UV and VV are the velocity components of the ocean current in the same sequence as the format of the wind UV and VV factors. The composition of the marine weather data is shown in Table 4 where the marine weather data were defined in a resolution of 0.5\({}^{\circ}\) for latitude and longitude.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameter** & **Unit** \\ \hline
1 & Total wave height & m \\
2 & Total wave direction & deg \\
3 & Total wave period & sec \\
4 & Wind wave height & m \\
5 & Wind wave direction & deg \\
6 & Wind wave period & sec \\
7 & Swell wave height & m \\
8 & Swell wave direction & deg \\
9 & Swell wave period & sec \\
10 & Wind UV & m/s \\
11 & Wind VV & m/s \\
12 & Pressure MSL & hPa \\
13 & Pressure surface & hPa \\
14 & Ambient temperature & \({}^{\circ}\)C \\
15 & Sea surface salinity & psu \\
16 & Sea surface temperature & \({}^{\circ}\)C \\
17 & Current UV & m/s \\
18 & Current VV & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Marine weather data configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameter** & **Unit** & **Remark** \\ \hline
1 & MMSI & - \\
2 & Latitude & DDMM.mmmm \\
3 & Longitude & DDDMM.mmmm \\
4 & SOG & knot \\
5 & ROT & deg/min & Type 1,2,3 \\
6 & COG & deg & (Dynamic information) \\
7 & True heading & deg & \\
8 & Navigational status & - \\
9 & Msg type & - \\
10 & Date time stamp & KST & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamic AIS data configurations.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Services and Platforms Prone to Malware Propagation
#### 3.2.3 Cloud Networks and Services  
Cloud computing (CC) has rapidly become a paradigm offering various services such as storage, computing, messaging, multimedia, artificial intelligence, and security. This technology enables access to data from anywhere and at any time. However, this advantage also poses a significant threat, as it facilitates malware access [37].  
1. Service Models: * SaaS: Cloud-hosted services providing user tools. * PaaS: Development environment for application creation without managing infrastructure. * IaaS: Access to virtualized computing resources over the internet[2].
2. Deployment Types: * Public Cloud: Shared resources at a lower cost. * Private Cloud: Dedicated infrastructure for specific organizations. * Community Cloud: Shared infrastructure for organizations with similar needs [38].
3. Risk Factors: Large data volumes, exploitable web applications.
4. Attack Examples: Phishing, malware in cloud-based services [36; 39].
# A Novel Way to Enhance the Spark Plasma-Assisted Ignition for an Aero-Engine Under Low Pressure
## 2 Experimental Setup and Procedure  
Figure 1a shows an anode and a cathode, surface-mounted with a 2 mm wide gap, that is assembled in a conventional spark igniter (SI). As shown in Figure 1b, a multichannel discharge jet-enhanced spark (MDJS) is mainly composed of a cavity, an orifice, and two pairs of electrode couples, where one electrode couple with a 2 mm wide gap is assembled in the same conventional way, and the other electrode couple with a 2 mm wide gap is placed inside a horizontally arranged and cylindrical cavity that is 4 mm in length and 1 mm in diameter. The volume of the confined cavity is approximately 2.28 mm\({}^{3}\), and a 1 mm orifice with a depth of 1 mm is punched in the middle of the surface-mounted electrode couple. The breakdown of the internal electrode couple (EC1) instantly releases energy to heat up the gas inside the cavity and thus forms a jet stream which pushes the arc between the external electrode couple (EC2) forward. Therefore, a novel method based on a multichannel discharge jet-enhanced spark was discovered.  
Figure 2 depicts the experimental apparatus. A cylindrical pressure vessel having an inner diameter of 30 cm and a circular observation window was used to measure the discharge characteristic and capture the spark luminosity imaging at low pressure (0.12-0.26 bar). The in-vessel pressure was measured with a pressure transducer (MKS BARATRON 112A). To measure voltage and current during the discharge, a 75-MHz high-voltage probe (Tektronix P6015A) and a 120-MHz current probe (Pearson 6600) were employed. A 1-GHz oscilloscope (Tektronix DPO4014) was utilized to record the two signals, and the output trigger command to the high-speed CCD camera (Phantom-v2512), with which the development of the spark plasma volume, the spark penetration depth and the activated area at sub-atmospheric pressures (0.12-0.26 bar) were captured, was obtained through a pulse generator (DG535). When characterizing spark plasma volume and activated area development, the Phantom-v2512 imaging was performed at a shutter speed of 200,000 fps with a resolution of 256 \(\times\) 256 pixels and an exposure time of 1 \(\upmu\)s. A high-speed camera equipped with a bandpass filter (centered at 430 nm, with a 10 nm FWHM (full width half maximum) was used to record the CH\({}^{*}\) chemiluminescence from the combustor side view.  
As shown in Figure 3, the ignition test was conducted at 0.26 bar in a model swirl combustor, and a quartz window with a height and length of 220 mm and 68 mm, respectively, was mounted on the side wall. In addition, an atomizing nozzle (Danfus 0.85) with a spray angle of 30 degrees was employed.  
Figure 1: Structure of (**a**) a conventional spark igniter (SI) and (**b**) a multichannel discharge jet-enhanced spark (MDJS).  
Figure 2: Schematic diagram of the experimental apparatus.
## 7 Simulation Study
### Multipath vs. Single Routing  
In the single path routing, the multimedia traffic flow between source and destination pairs should be routed on the single path. Thus, in single path routing problem the task is to find a feasible path between source and destination pairs that fulfills the QoS requirements of multimedia traffic flow as well as fulfills the capacity constraint. Single path routing approach may not fully utilize the assigned capacities.  
In multipath routing, the multimedia traffic flow is divided into multiple sub-flows. In multipath routing problem the task is to find the set of feasible paths that fulfills the QoS requirements of each multimedia sub-flow along with their capacity constraints. Multipath routing scheme may provide more effective bandwidths utilization.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Projection of a Node on the Trajectory
#### 3.2.3 Determining the Projection  
We find the maximum element in \(\hat{\mathbf{R}}\) by the element comparison, and determine the corresponding element in \(\hat{\mathbf{X}}\) as the projection of a node on the trajectory. As shown in Figure 4, the predicted RSSI curve has the maximum predicted RSSI value at \(x_{ig}\) after vectors are extended, and \(x_{ig}\) is determined as the projection of the node. \(x_{ig}\) is closer to the actual projection than \(x_{i}\), and the error between the determined projection and the actual projection position is reduced.
## EMD_CS_ST Denoising Method  
### Empirical Mode Decomposition  
The EMD method assumes that any complex signal consists of some simple Intrinsic Mode Functions (IMF) and that each IMF is independent of each other [26].  
The IMF component must meet the following two conditions: (1) the number of extreme values and the number of zero crossings are the same or differ by at most one; (2) the upper and lower envelopes have local symmetry about the time axis. Any signal \(x(t)\) can be decomposed and can be expressed as follows:  
\[x(t)=\sum\limits_{i=1}^{n}imf_{i}(t)+r(t) \tag{1}\]  
After decomposition, \(n\) IMF components are arranged in order of frequency from high to low, and the last one is the residual component \(r(t)\). Because the MS signal has the characteristics of low frequency and high energy, the noise in the MS signal is generally distributed in the higher frequency component, and the MS effective signal dominates the lower frequency component. Therefore, after the decomposition is completed, the components with more noise in the high-frequency region are discarded, and the residual components can be reconstructed to achieve denoising. The EMD method based on local features of signals has the advantages of a multi-resolution wavelet transform. It overcomes the difficulties in choosing a wavelet basis and determining the decomposition scale in the wavelet transform. It is suitable for signal analysis, non-linear and non-stationary. However, there may be some effective MS signals in the higher frequency components. If the high-frequency components are directly removed, signal distortion will inevitably occur [27,28].  
According to the advantages of EMD decomposition, some scholars denoise the noise-containing components by determining the boundary component \(imf_{k}\) of the noise and the effective signal, and then reconstruct the residual components to obtain the denoised signal. The method of determining the boundary is as follows: Calculate the correlation coefficient _Cofe_ between each IMF component and the original signal according to the following formula, denoted as \(C=\{\textit{cofe}_{1},\textit{cofe}_{2},\ldots,\textit{cofe}_{k},\ldots,\textit{ cofe}_{n}\}\). Find the component that corresponds to the first minimum value in the coefficient \(C\), and its next component is the boundary component between signal and noise, which is recorded as \(imf_{k}\).  
\[C(x,imf_{i})=\frac{\sum\limits_{t=0}^{N-1}\left[x(t)-mean(x)\right][imf_{i}(t )-mean(imf_{i})]}{\sqrt{\sum\limits_{t=0}^{N-1}\left[x(t)-mean(x)\right]^{2 }\sqrt{\sum\limits_{t=0}^{N-1}\left[imf_{i}(t)-mean(imf_{i})\right]^{2}}}} \tag{2}\]  
where \(x(t)\) is the original signal, \(imf_{i}(t)\) is the \(i\)-th IMF component, N is the number of sampling points, and \(mean\) is the mean function.
## 4 Calibration and Application Experiments
### Measurement Repeatability Tests  
The measurement repeatability of the proposed precision level was obtained by measuring 5 times at 9 positions on a class 00 granite table, as shown in Figure 8. The experiment results are shown in Figure 9. The measurement uncertainty expressed by \(\pm 3\sigma\) of the dual-axis precision level in the X and Y directions are \(\pm 1.1\) arc-sec and \(\pm 0.92\) arc-sec, respectively. This result proves that the measurement repeatability of the proposed precision level is satisfactory.
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 2 Experimental and Numerical Models of the Fin and Rudder
### Computational Domain and Boundary Conditions of Acoustic Field  
The flow around the fin and rudder and in the wake is characterized by turbulence and disorder, resulting in significant contributions to flow-induced noise. Therefore, a rectangular domain was selected as the source domain for acoustic analysis [19,20], as shown in Figure 7. The elliptical domain surrounding the rectangular domain serves as the acoustic propagation domain, with its ellipsoidal surface acting as an infinite boundary surface. The monitoring points for flow-induced noise in the simulation were arranged based on the positions of the pressure sensors used in the experiment.  
In the simulation, a circle of far-field measurement points with a radius of 50 m was arranged in the XY, XZ, and YZ planes, taking the center of the fin and rudder as the origin, for the directional evaluation of radiated noise. The pressure fluctuation within the frequency range of 100 to 5000 Hz and the flow-induced noise within the frequency range of 20 to 1000 Hz were calculated at flow velocities of 3 m/s, 5 m/s, and 8 m/s, corresponding to the Reynolds number based on the maximum width of the fin and rudder ranging from 3.6 \(\times\) 10\({}^{6}\) to 9.7 \(\times\) 10\({}^{6}\).
## 2 System and Channel Models
### Channel Model  
We assume all FSO links experience Malaga-distributed atmospheric turbulence with the effect of pointing errors. The composite PDF of the Malaga fading channel with pointing errors and atmospheric attenuation effects is given as [2,36]  
\[f_{h}(h)=\frac{\xi^{2}\mathcal{A}}{2h}\sum_{k=1}^{\beta}b_{k}G_{1,3}^{3,0}\Bigg{(} \frac{\alpha\beta h}{h_{l}(g\beta+\Omega^{\prime})A_{0}}\bigg{|}\xi^{2}+1 \bigg{)}, \tag{5}\]  
where  
\[\mathcal{A} =\frac{2\alpha^{\frac{\alpha}{2}}}{g^{1+\frac{\alpha}{2}}\Gamma[ \alpha]}\bigg{(}\frac{g\beta}{g\beta+\Omega^{\prime}}\bigg{)}^{\beta+\frac{ \alpha}{2}},\] \[b_{k} =\binom{\beta-1}{k-1}\frac{\alpha^{\frac{k}{2}}\Big{(}g\beta+ \Omega^{\prime}\Big{)}^{1-\frac{1}{2}}\Big{(}\Omega^{\prime}\Big{)}^{k-1}}{ \beta^{\frac{k}{2}}g^{k-1}(k-1)!}\bigg{(}\frac{\alpha\beta}{g\beta+\Omega^{ \prime}}\bigg{)}^{-\frac{\alpha+k}{2}}. \tag{6}\]  
In (5) and (6), \(\alpha\) and \(\beta\) are atmospheric fading parameters which correspond to the large-scale and small-scale atmospheric fluctuations, respectively. \(g=2b_{0}(1-p)\) and \(\Omega^{\prime}=\Omega+p2b_{0}+2\sqrt{2b_{0}\Omega p}\cos(\Phi_{A}-\Phi_{B})\). Here, \(\Omega\) is the average power of the optical signal for the line-of-sight (LOS) component, \(2b_{0}\) is the average power of total scatter components, and \(p\) is the amount of the scattering power coupled to the LOS component. Further, \(\Phi_{A}\) and \(\Phi_{B}\) are deterministic angles for the LOS component and the coupled-to-LOS scatter component, respectively. \(h_{l}\) is the atmospheric attenuation due to the weather conditions which can be calculated, by using the Beer-Lambert law, as \(h_{l}=\exp(-\omega L)\), where \(\omega\) is the atmospheric attenuation coefficient and \(L\) is the FSO link distance. In addition, \(\xi=\frac{w_{z\alpha}}{2\sigma_{z}}\) defines the ratio between the equivalent beam radius at the receiver and the pointing error displacement standard deviation at the receiver. Also, \(\sigma_{z}^{2}\) is the jitter variance at the receiver and \(A_{0}=(\text{erf}(v))^{2}\) is the fraction of the collected power with zero pointing errors. \(v=\frac{\sqrt{\pi}a}{\sqrt{2}w_{z}}\) and \(w_{z\alpha}^{2}=w_{z}^{2}\frac{\sqrt{\pi\text{erf}(v)}}{2\exp(-v^{2})}\). And \(w_{z}\) is the beam waist at distance \(z\) and \(w_{z\alpha}\) is the equivalent beam waist.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 5 Performance Evaluation
### Effect of Iterations  
Figures 3-5 show the average sum data rate and the average number of links obtained with different iterations for the mobile node. In each iteration, the number of mobile and relay nodes is set to five. Figure 3 shows the average sum data rate with respect to the number of iterations. It can be observed that each algorithm converges in more than 100 iterations. Furthermore, it is observed that the proposed model yields the highest sum data rate among the comparison models. This is because the mobile nodes are connected to the relay nodes with the highest data rate to maximize the data rate at each iteration. Figure 4 shows the average number of links for the mobile node with each iteration. Although the number of mobile nodes is set to five in each iteration, the average number of links is less than five. When the mobile node tries to connect to the relay node, it should satisfy the distance threshold being less than \(\delta_{max}\). If the distance between the nodes cannot satisfy the distance threshold, they are not connected. Furthermore, the proposed model connects to as many relay nodes as possible to increase the data rate. Therefore, the proposed model shows the highest number of links. In the distance scheme, the average number of links is lowest because this model only connects to the relay node that is near the mobile node. Figure 5 shows the seconds required for convergence. For 100 iterations, the times of the proposed, random, and distance schemes are 12.43, 12.57, and 20.34 s, respectively. Our results show that the proposed model converges within a few seconds for up to 100 iterations or equal. For 10,000 iterations, the times required for the proposed, random, and distance are 241, 242.98, and 407.70 s, respectively. These results demonstrate that the proposed model can be applied to real environments without high overhead for calculating optimization problems on the edge layer (i.e., server).  
\begin{table}
\begin{tabular}{l c} \hline
**Notation** & **Description** \\ \hline Deployment area & 50 m \(\times\) 50 m \\ Number of mobile nodes \(i\) & [2, 5] \\ Number of relay nodes & 5 \\ Max D2D link range \(\delta_{max}\) & 30 m \\ Transmission power \((T)\) & 23 dBm \\ Bandwidth \((B)\) & 180 kHz \\ D2D path loss exponent \((\alpha)\) & 4 \\ Noise power spectrum density & \(-174\) dBm/Hz \\ D2D path loss factor for a distance in meters & 0.0173 \\ \hline \end{tabular}
\end{table}
Table 2: List of operation parameters.
## 5 Results and Discussion
### Vorticity Contours  
Lastly, to complement all previous results, Figure 17 shows a comparison between the the contours of vorticity obtained in the Ti3 and Ti12 cases and using the highest porosity disk, \(C_{T}=0.62\) and \(C_{T}=0.71\), respectively. The images are taken at the \(x-y\) plane, at \(z=0\) and correspond to the vorticity field computed at the last time step of the LES runs. Make note that black bars are used to represent the disk position but do not portray the complete longitudinal region where the forces modelling the AD act, distributed using Equation (7). This figure permits visualizing the dominant effect of the turbulence structures from the inflow of the Ti12 case, which, unlike the Ti3 inflow, prevail along the  
Figure 16: Longitudinal evolution of spectra at centreline using the Ti12 inflow. The results for the free decaying turbulence (without AD) are shown in the top row (**a**), results with disk \(C_{T}=0.45\) are shown in the middle row (**b**) and results with disk \(C_{T}=0.71\) are shown in the bottom row (**c**). Spectra computed from measurements are included only for the position \(x=4D\). The straight dotted line marks the \(-5/3\) slope that characterizes the inertial range.  
wake. With the Ti3 inflow, there is a clear shear region arising from the edges of the disk with distinct turbulence structures. Conversely, with the use of the Ti12 inflow, the shear region is substantially less noticeable in the higher ambient turbulence. Indeed, the vorticity contours from the inflow appear to dominate in the vicinity of the AD. This is consistent with the comparison of \(k\) in Figures 5 and 6, where its increase within the shear layer is less prominent with the increase of ambient TL. In Figure 17, it can also be seen that, for the Ti12 inflow, structures that appear to arise from within the wake become more apparent than those outside from approximately \(x\simeq 6D\).
## 4 Numerical Simulation of Slope Stability under Different Vegetation
### Vegetated Slope Stability Analysis  
#### 4.3.1 FS  
To study the impact of growth period on slope stability, the results of FS were recorded, as shown in Table 4 and Figure 5. The FS of the bare slope was 1.48, while the FS values of the C.d root-reinforced slope in the different growth periods were 1.51, 1.57, 1.59 and 1.60, respectively. Compared with the bare slopes, the FS increased by 1.7% to 8%. The FS values of the M.m root-reinforced slope in the different growth periods were 1.53, 1.60, 1.64 and 1.66, respectively. The FS increased by 3.3% to 11.9%. The FS values of the G.s mixture root-reinforced slope in the different growth periods were 1.55, 1.62, 1.67 and 1.72, respectively, which increased by 4.5% to 15.7%. All vegetated slopes were more stable than the bare slope. This is due to the fact that as the growth period increased, the roots increased the ability of the vegetation to reinforce the slope. Comparing the three vegetated slopes, the G.s mixture had the best reinforcement effect. Therefore, the reinforcement effect of the G.s mixture plants could be used in slope engineering to improve slope stability.
## 5 Error Analyses of the New Method  
When using multi-probe methods to measure and reconstruct profiles, existing motorized stages often have better positioning accuracies, and the positioning error in the scanning direction is often negligible. The main errors that affect results include the straightness error and yaw error of the measurement reference, the data processing error, the zero-adjustment error, and the measurement error of sensors. As shown in Equations (5) and (6), the differential of measurement data can eliminate the straightness error of the reference. Under most circumstances, the yaw error is relatively small and its impact on the measurement and reconstruction results is often on a smaller order of magnitude and can be negligible. In some cases, if the yaw error is large enough to impact the measurement result and needs to be removed, an autocollimator can be used to collect measurement data of yaw error during the scanning. According to Section 2, the four-probe method is not affected by the data processing error. Therefore, for the new method introduced in this article, we analyze the effects of the zero-adjustment error and the measurement error in the following.
# An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool
## 2 Background
### An Overview of Common Run-Time Errors
#### 2.3.4 Data Races  
A data race is a critical race condition that occurs when several tasks attempt to simultaneously access and modify the same memory location [28]. This can cause the data to become corrupt or inconsistent, leading to errors or incorrect results.  
An example of a data race is when OpenMP uses at least two threads to simultaneously alter one variable located in a parallel area sans synchronisation, leading to unanticipated outcomes. Another example is when several threads that are not synchronised or possess operational atomicity write to one memory location in a CUDA system.