esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Influence of Parameter \(\alpha\)  
In the GraphSAGE-CF algorithm, the integration parameter \(\alpha\) has a significant impact on the recommended performance. A larger \(\alpha\) value means that the prediction scores are more heavily weighted by user ratings and rely more on scoring data in GraphSAGE-CF. Conversely, a smaller \(\alpha\) value means that the predictive scores in GraphSAGE-CF are more dependent on the social network. In Epinions and NC, \(\alpha\) is set to 0.9. In LastFM and WI, \(\alpha\) is set to 0.4. in addition, the number of neighbors \(N\) is set to 5 in Epinions; the number of neighbors \(N\) is set to 5. In this section, a sensitivity analysis of the integration parameter \(\alpha\) is performed through a set of experiments. The experimental results are shown in Figures 3-6.  
Figure 4: The Impact of parameter \(\alpha\) on LastFM.  
Figure 3: The Impact of parameter \(\alpha\) on Epinions.  
It can be seen from Figures 3-6 that the RMSE first decreases with increasing \(\alpha\) in four datasets, and then gradually increases after reaching the minimum value. In addition, in Epinions, GraphSAGE-CF performs best when \(\alpha\) is near 0.6; in LastFM, it performs best when \(\alpha\) is around 0.7; in NC, it performs best when \(\alpha\) is 0.8 and in WI it performs best when \(\alpha\) is about 0.9. The results indicate that the prediction scores derived from both the scoring data and the social network data contribute to the final prediction scores and are more dependent on the prediction scores derived from the scoring data.
## 2 Materials and Methods  
### Study Area  
Taihu Lake (30 deg55'40''-31 deg32'58''N, 119 deg52'32''-120 deg36'10''E) is one of the five largest freshwater lakes in China, located in Jiangsu province (Figure 1). It is a typical large, shallow lake, with a maximal depth of less than 3 m and an average depth of 1.9 m, and covers an area of about 2338 km\({}^{2}\). Across Taihu Lake, there are large difference in water parameter values, with Secchi depths ranging from 0 to 2.5 m, pH from 6.81 to 9.58, total nitrogen from 0.37 to 86.30 mg/L and total phosphorus from 0.01 to 6.48 mg/L.  
Figure 1: Location of our study area within Taihu Lake, China, the six sections relating to environmental characteristics and human activities and the distribution of sample collection sites.  
Taihu lake can be divided into six sections according to spatial variability in the relative influence of human activities and environmental factors [15] (Figure 1). Section I consists of Meiliang Bay and Zhushan Bay, where algae blooms occur frequently because of high concentrations of nitrogen and phosphorus. Section II is mainly situated in Gongshan Bay, where a large number of tributaries of the Yangtze River have been flowing into the lake since 2001. Section III consists of Zhenhu Bay, Guangfu Bay, Xukou Bay and Dongshan Bay, which are the main and traditional distribution areas of aquatic vegetation in Taihu. Section IV is the East Bay, which is covered with submerged vegetation, characteristic of good water quality and rich fishery production. Section V is located in the southeast area, which is the traditional distribution area of floating-leaved vegetation. Section VI is located in the central area and the western shores, making up more than 50% of the Lake and belonging to the algae-type zone. [PERSON] _et al._[24] divided the Taihu Lake into an algae-type zone (algal-dominated zone) and a grass-type zone (grass-dominated zone). Sections I and VI belong to the algae-type zone, and the other sections belong to the grass-type zone. According to field surveys, water quality in grass-type zone is significantly higher than in algae-type zone. We conducted the current study in the grass-type zone, in which exists three types of aquatic vegetation (Table 1).
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 2 Moments from 3D Shapes  
In this section, we briefly introduce the concept of moments of a shape, with three examples, the geometric moments (GM), the spherical harmonics moments (SPHM), and the Zernike moments (ZM). We show that ZM are extensions of the SPH, and that they can be computed from the GM, but with potential problems that we describe.  
We start with notations. Any point \(\mathbf{p}\) within a domain \(D\subset\mathbb{R}^{3}\) can be \(0\) either in terms of its vector of Cartesian coordinates \(\mathbf{x}=(x,y,z)\) with respect to an origin, or in terms of its spherical coordinates \((r,\theta,\phi)\) where \(r\) is the distance from \(\mathbf{p}\) to the sphere center, and \(\theta\) and \(\phi\) are the inclination and azimuthal angles, respectively. A shape \(S\) in the domain \(D\) is represented through its density \(f(\mathbf{x})\) at all points in the domain. We assume that \(f\) is square integrable over the domain \(D\), i.e., \(f\in L^{2}(D)\). In the special case that the shape is homogeneous, it is represented with an indicator function \(f(\mathbf{x})\) with value \(1\) if \(\mathbf{x}\) is inside the shape, and \(0\) otherwise.
# An Aerial Mixed-Reality Environment for First-Person-View Drone Flying
## Mixed Reality for Drones
### Aerial Mixed Reality Using a Drone with a Stereo Camera  
The proposed system uses a stereo camera (StereoLabs' ZED mini, San Francisco, USA [43]) with a baseline distance of 64 mm, which is the binocular distance of an average adult. As shown in Figure 2, the ZED stereo camera mounted on the drone is used for passive 3D depth estimation and provides depths of 0.7-20 m with an accuracy of 0.4-1 mm. For outdoor applications, a ZED camera is significantly better than a Microsoft Kinect, which is an active 3D depth estimator using an infrared laser. In this study, a ZED camera was mounted at the front of a DJI F550 hexacopter (Shenzhen, China) to observe the flight path of the drone from the FPV. The drone has a maximum load of approximately 600 g and is appropriate for aerial photography. In the current system, the ZED camera was connected to a mobile PC through a USB 3.0 interface. Therefore, the drone can lift a 10 m cable and capture videos with excellent air stability because it sends video streams using a special USB 3.0 cable, known as a repeater cable. In an environment where wireless transmission is possible and the weather is fine and not windy, the camera can be connected to a smaller drone. In this drone system, a stereo camera simulates the human binocular vision of actual scenes. It provides FPV flight scenes through the Oculus Rift CV1 headset connected to the user's VR backpack weighing 4.6 kg, comprising an Intel Core i7-7820 HK, NVIDIA GeForce GTX 1070, and 16 GB of RAM.  
Figure 1: Software architecture of the proposed system.  
To avoid drone crashes, inexperienced drone users should fly a drone in a location with fewer obstacles, such as a playground, as shown in Figure 3a. In our system, users can select one of the virtual scenes, including a forest, grassland, and city, and can fly around in the viewpoint of the drone in the desired location. In fact, users can fly the drone by mixing all or part of the virtual location they desire with the real world without being present in that location. The real scenes obtained from a stereo camera mounted on the drone are synthesized with virtual scenes in the user's backpack PC. Finally, the user can feel as if he/she is flying a drone in the desired location while looking at the synthesized scenes via an HMD in FPV mode. Figure 3b-d shows the pilot's view while flying in a virtual path passing through a forest. The head-up display, including the altitude and three-axis information required for drone flight, is displayed on the left of the screen. Figure 4a,b shows the pilot's view while flying between buildings in a virtual city. Hence, the user can experience a sense of flying in an engaging immersive manner by mixing the desired virtual environment with the real world.
## 3 Results and Discussion
### Negative Axicon Generated by SLM  
Negative axicons can generate annular beams, which can be applied in laser drilling to improve the efficiency [19; 20]. Since demonstrating the SLM can also produce the annular beams with the CGHs, experiments were carried out with the same experimental setup in Figure 5 by adding a lens (focal length \(f=110\) mm). From Figure 4b, it can be seen that the beams from the SLM have a divergence angle, and its distribution area will exceed the receiving size of the CCD. Therefore, the lens was used to converge the beams and guarantee that the CCD will capture them, as shown in Figure 9. The hologram used to simulate the negative axicon, as shown in Figure 4a, was loaded into the SLM, and the period was set as \(d=15\) pixels, the distance from the SLM to the lens \(l_{1}=230\) mm, and the height of the grooves \(h=0.0012\) mm.  
Figure 10 presents the images of annular beams generated by the hologram at the planes \(z=75\), \(80\), \(85\), and \(90\) mm behind the lens, and the corresponding radii of the inner and outer are shown in Table 2. From Table 2, it can be found that the width of annular beams decreased with the increase of the \(z\) place positions, and the experimental results were consistent with the theory. The images prove that the hologram can work as a negative axicon.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**z Plane**} & \multicolumn{3}{c}{**Theory (mm)**} & \multicolumn{3}{c}{**Generated by the SLM (mm)**} \\ \cline{2-7}  & \(r_{1}\) & \(r_{0}\) & \(r_{0}-r_{1}\) & \(r_{1}\) & \(r_{0}\) & \(r_{0}-r_{1}\) \\ \hline
75 & 0.474 & 1.275 & 0.801 & 0.464 & 1.271 & 0.806 \\
80 & 0.445 & 1.132 & 0.687 & 0.439 & 1.122 & 0.684 \\
85 & 0.352 & 0.988 & 0.637 & 0.355 & 0.980 & 0.626 \\
90 & 0.387 & 0.845 & 0.458 & 0.381 & 0.839 & 0.464 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radii of the annular beam of theory and generated by the SLM.  
Figure 10: The experimental results for the simulating negative axicon with the SLM, (**a**) \(z=75\) mm, (**b**) \(z=80\) mm, (**c**) \(z=85\) mm, (**d**) \(z=90\) mm.  
Figure 9: The principle of the annular beams focused by a lens.
## 4 Network Model
### Assumptions  
We consider an IP-over-WDM network, i.e., a two-layer network where the layers share a common set of nodes \(V\). In the WDM layer, Optical Cross-Connects (OXCs) at the nodes are interconnected by fiber links that are installed on an undirected physical supply network \(G=(V,E)\). Each fiber carries \(B\) WDM channels. In the IP layer, IP routers equipped with colored LCs are interconnected by logical links that are established on an undirected logical supply network \(H=(V,L)\). Each logical link is realized by one or multiple parallel lightpaths having the same source and target nodes; a lightpath is defined as a WDM channel originating and terminating in the colored LCs and traversing two or more OXCs [30]. It may span multiple fiber links along a path through \(G\) (routing over the physical topology), is assigned a unique wavelength (we assume that the wavelength assignment is done in a post processing step in order to be able to solve the formulated MILP problems), and has capacity \(C\) bps in both directions. The terminating LCs have also capacity \(C\) bps for both transmitting and receiving. Signal regeneration is realized by back-to-back transponders (or colored LCs), what effectively means that a long lightpath is realized by two shorter lightpaths transiting an IP router. No dedicated regenerators are used.  
Eventually, traffic (as defined in Section 3) arriving at the IP layer has to be routed over the set of logical links from its source node to its target node over the shortest logical path (routing over the logical topology). The network has to provide sufficient lightpath capacity on the involved logical links.  
Figure 2: Example for the spatial variation of traffic demands—total network traffic (red) and single demand curves (black) for granularity \(\Delta t=1\,\mathrm{h}\) and different values of the displacement parameter \(k\) in a three-node network.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 2 Methodology
### Spectral Decomposition  
Spectral analysis is widely used for time series analysis, enabling the identification of frequency components present in the original signal [16; 55]. Spectral decomposition involves breaking down the original signal into constituent components for further analysis. According to spectral decomposition principles, a time series \(X\) of length \(T\) starting at the time \(t\) can be decomposed into a linear combination of sines and cosines with different frequencies \(f\).  
\[X_{t:t+T}=\sum_{f}A(f)cos(2\pi f(t:t+T))+B(f)sin(2\pi f(t:t+T)) \tag{1}\]  
Here, \(A(f)\) and \(B(f)\) are the amplitudes of the sine and cosine components at frequency \(f\). Traditionally, while performing forecasting using spectral decomposition, the extracted spectral components are extensively repeated to the desired horizon length, and then, merged. We adapt the concept of spectral decomposition to clarify the frequency components upon which the input signal is dependent. We use DEFT [24] to obtain the spectral representation of the signal, which is used to learn relevant features for LSTF. Since, the output of DEFT is imaginary, its real and imaginary portions are treated as separate channels.
## 2 Materials and Methods  
### Experiments Setup  
For this research, fresh apples of the Golden Delicious variety were used. Apples were purchased from an orchard in Ardabil Province, Iran, and we tried to select all the samples from the same size and level of ripeness, maturity (pH = 3.5), and without disease. Apples were kept in a refrigerator at a temperature of 4 \({}^{\circ}\)C until preparing for the experiments. An oven with a temperature of 103 \({}^{\circ}\)C was used to determine the initial moisture content of the sample [25], and the value of the initial moisture content of the apple was obtained 86% (w.b.). For drying, the apple samples were obtained via horizontal cutting with a thickness of 5 mm by a homemade slicer. For experiments, a combined hot air-infrared dryer (designed and manufactured at Mohaghegh Ardabili University) was used [34]. The schematic of the combined hot air-infrared dryer used in this research is provided in Figure 1. This device was able to provide hot air and infrared radiation in different ranges with the help of the central controller system. The characteristics of the air at the inlet and inside the chamber were continuously displayed on a digital monitor. The details of the instrument used in this research are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrument** & **Specification** & **Uncertainty** \\ \hline Centrifugal fan & Range 0–252 m\({}^{3}\)/h, 51 W-0.23 A & - \\ Infrared lamp & 250 W, 220–230 V, 125 \(\times\) 185 mm & - \\ Wattmeter & Range 0–9999 w & \(\pm\)1 W \\ Thermometer & Range \(-\)10–50 \({}^{\circ}\)C & \(\pm\)1 \({}^{\circ}\)C \\ Temperature sensor & Range 0–900 \({}^{\circ}\)C & \(\pm\)1 \({}^{\circ}\)C \\ Moisture sensor & Range 0–100\% RH & \(\pm\)2\% \\ Digital balance & Range 0–600 g & \(\pm\)0.01 g \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the tools used for the research.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 4 Detailed Algorithm Description
### Algorithm Parameters and Performance Analysis
At the same time, the LEO satellite network has relatively dense business at low latitudes, and relatively few at high latitudes [19], so the strategy of dissemination of failure information to three hops is more important at low latitudes. In summary, satellites at high latitudes can adopt the strategy of dissemination of failure information to two hops, and satellites at low latitudes can adopt the strategy of dissemination of failure information to three hops.
## 3 Method
### Reduced-Dimension MUSIC Algorithm Based on Ephemeris Information
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(M\) & 8 \\ \(N\) & 8 \\ Signal Frequency & 35 GHz \\ Element Spacing & \(\lambda/2\) \\ Noise Type & Gaussian Noise \\ SNR & 20 dB \\ Signal Elevation & \(5^{\circ},15^{\circ},25^{\circ},35^{\circ},45^{\circ},55^{\circ}\) \\ Signal Azimuth & \(5^{\circ},65^{\circ},125^{\circ},185^{\circ},245^{\circ},305^{\circ}\) \\ Number of Snapshots & 50 \\ Angular Resolution & \(1^{\circ}/0.1^{\circ}/0.01^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Parameters for simulated reduced-dimension MUSIC angle search. \begin{table}
\begin{tabular}{c c c c} \hline
**Azimuth** & **Angle Search Step** & **Average Error (*)** & **Standard Deviation (*)** \\ \hline \multirow{2}{*}{\(5^{\circ}\)} & \(1^{\circ}\) & 0.02 & \textless{}0.001 \\  & \(0.1^{\circ}\) & 0.01 & \textless{}0.001 \\  & \(0.01^{\circ}\) & 0.01 & \textless{}0.001 \\ \hline \multirow{3}{*}{\(65^{\circ}\)} & \(1^{\circ}\) & 0.20 & \textless{}0.001 \\  & \(0.1^{\circ}\) & 0.04 & \textless{}0.001 \\  & \(0.01^{\circ}\) & 0.02 & \textless{}0.001 \\ \hline \multirow{3}{*}{\(125^{\circ}\)} & \(1^{\circ}\) & 0.38 & 0.012 \\  & \(0.1^{\circ}\) & 0.05 & \textless{}0.001 \\  & \(0.01^{\circ}\) & 0.02 & \textless{}0.001 \\ \hline \multirow{3}{*}{\(185^{\circ}\)} & \(1^{\circ}\) & 0.47 & 0.052 \\  & \(0.1^{\circ}\) & 0.05 & \textless{}0.001 \\  & \(0.01^{\circ}\) & 0.05 & \textless{}0.001 \\  & \(0.01^{\circ}\) & 0.02 & \textless{}0.001 \\ \hline \multirow{3}{*}{\(245^{\circ}\)} & \(1^{\circ}\) & 0.26 & \textless{}0.001 \\  & \(0.1^{\circ}\) & 0.03 & \textless{}0.001 \\ \cline{1-1}  & \(0.01^{\circ}\) & 0.01 & \textless{}0.001 \\ \hline \multirow{3}{*}{\(305^{\circ}\)} & \(1^{\circ}\) & 0.08 & \textless{}0.001 \\  & \(0.1^{\circ}\) & 0.01 & \textless{}0.001 \\ \hline \end{tabular}
\end{table} 
Table 7: Search results when the angle search step is \(0.01^{\circ},0.1^{\circ},1^{\circ}\). \begin{table}
\begin{tabular}{c c c c} \hline
**Complexity** & \multicolumn{3}{c}{**Number of Operations**} \\ \cline{2-4}  & **Angle Search Step \(1^{\circ}\)** & **Angle Search Step \(0.1^{\circ}\)** & **Angle Search Step \(0.01^{\circ}\)** \\ \hline Number of Operations for & \(2.06\times 10^{5}\) & \(2.06\times 10^{5}\) & \(2.06\times 10^{5}\) \\ Spectrum Model Establishment & \(1.76\times 10^{10}\) & \(1.76\times 10^{12}\) & \(1.76\times 10^{14}\) \\ Number of Operations for Search & \(1.76\times 10^{10}\) & \(1.76\times 10^{12}\) & \(1.76\times 10^{14}\) \\ Total Number of Operations & \(1.76\times 10^{10}\) & \(1.76\times 10^{12}\) & \(1.76\times 10^{14}\) \\ Computation Time & \(0.0449\) s & \(0.3790\) s & \(4.0327\) s \\ \hline \end{tabular}
\end{table} 
Table 8: Complexity of the reduced-dimension MUSIC algorithm under different angle search steps. accuracy. Conversely, systems handling a greater number of NLOS signals and requiring higher real-time performance may opt for a larger search step as a trade-off between precision and computational demand. Figure 5: Spectrum plot of the reduced-dimension MUSIC algorithm with different angle search step.
## 4 Suggested Frequency Control Support Technique in the Wind-Battery System
### Frequency Control Support Technique  
The summation of the output power of the WPG (\(P_{MPPT}+\Delta P\)) and the battery (\(P_{ref\_B}\)) equals the total output power (\(P_{total}\)) as follows:  
\[P_{total}=P_{MPPT}+\Delta P+P_{ref\_B} \tag{13}\]  
Figure 9 displays the suggested frequency control support technique in the WPG and battery system that applies the adaptable gain of the frequency deviation loop. The variable gain for \(K(\omega_{r},\Delta f)\) is dependent on the speed of the rotor (\(\omega_{r}\)) and the change in frequency (\(\Delta f\)). A 3D space plot of the variable gain for \(K(\omega_{r},\Delta f)\) is seen in Figure 10, and Figure 11 depicts the resultant output \(\Delta P(\omega_{r},\Delta f)\) of the frequency deviation loop. For the suggested technique, \(\Delta P\) and \(K(\omega_{r},\Delta f)\) are illustrated as follows:  
\[\Delta P=-K(\omega_{r},\Delta f)(f_{sys}-f_{nom}) \tag{14}\]  
For this investigation, \(K(\omega_{r},\Delta f)\) is determined as follows:  
\[K(\omega_{r},\Delta f)=200\frac{\omega_{r}^{2}-\omega_{\min}^{2}}{\omega_{\max }^{2}-\omega_{\min}^{2}}(-\Delta f+1) \tag{15}\]  
In Equation (15), \(K(\omega_{r},\Delta f)\) is composed of \(\omega_{r}\) and \(\Delta f\). The \(\omega_{r}\) adds to ensuring the stability of WPG's operation and the \(\Delta f\) adds to improving the minimum point of the frequency dependent on the change in frequency. A larger \(K(\omega_{r},\Delta f)\) is determined by the large speed of the rotor and frequency change. Additionally, to prevent \(P_{ref}\) from reaching the output power limit of the DFIG, this investigation considers the gain limit (\(K_{\text{limit}}(\omega_{r},\Delta f)\)), which also varies with \(\Delta f\) and \(\omega_{r}\) as follows:  
\[K_{\text{limit}}(\omega_{r},\Delta f)=\frac{\min(P_{\text{limit}},\omega_{r} T_{\text{limit}})-P_{MPPT}}{\Delta f} \tag{16}\]  
Finally, the gain is set as the minimum value between the gains in Equations (15) and (16) during the frequency decrease.  
Figure 8: Proposed frequency control support scheme in the wind–battery system.  
Figure 11: \(\Delta P\) represented in the 3D space.  
Figure 9: Suggested frequency control support technique.
## 2 Materials and Methods
### Real-Time Kinematic (RTK)  
Following the late 1990s, RTK technology utilizing GNSS/GPS networks has emerged as the favored method globally. It is capable of achieving precision to within centimeters in a matter of seconds over distances of several kilometers. RTK surveying involves a base station transmitting raw GNSS data to rovers, which quickly computes a baseline between them, ideally within a few seconds (Figure 2). RTK accuracy mainly depends on the baseline length or the distance between the reference station and the rover. This is affected by orbital errors and atmospheric conditions. Radio waves or satellite communications are used to transmit the correction data [51].  
Consequently, the majority of prevalent errors can be mitigated through the application of differential techniques. Double differencing (DD) of carrier phase measurements is the basis of the RTK technique [52]. DD is utilized to eliminate errors from orbit, clock, and atmospheric delays while restoring the integer nature of phase ambiguities in carrier-phase observations.  
While RTK GNSS positioning offers significant advantages, it is essential to acknowledge its practical limitations in real-world applications. RTK positioning is applicable only when a reference station is situated within a limited range, typically between 50 and 100 km. Furthermore, it necessitates substantial data flow channels to facilitate the transmission of correction signals. This situation results in extensive station maintenance and expensive costs.