esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis  
### Parameter Settings  
(1) Parameters of SpaceX LEO Constellation System  
Without loss of generality, we use the SpaceX core system constellation for simulation analysis. Its main parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Orbital Parameter Type** & **Value** & **Error Parameter Type** & **Value** \\ \hline Period (min) & About 120 & Number of surfaces & 32 \\ Height (km) & 1150 & Number of satellites per & 50 \\ Radius (km) & 7521 & orbit & 1600 \\ Inclination (\({}^{\circ}\)) & 53 & Total number of satellites & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Core Constellation of SpaceX Parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter Type** & **Accelerometer** & **Gyro** & **Cost Level** \\ \hline Quantization noise (m/s\({}^{2}\)) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & Low \\ \hline Scale factor (ppm) & 100–500 & 100–500 & Low \\ \hline Cross-coupling error (ppm) & 100–500 & 100–500 & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: IMU main parameters.  
For the UKF-related parameters, we assume that the initial attitude error, initial position error, and initial velocity error are all zero, and we assume that the initial velocity of the carrier is 200 m/s. In addition, we assume that the positions of the carrier are \(-3.5958^{\circ}\) E, \(50.425^{\circ}\) N, at an altitude of 10,000 m; the initial pitch, roll, and yaw angles are \(0^{\circ},0^{\circ}\), and \(90^{\circ}\), respectively; the carrier was set to fly during the process, there were two \(45^{\circ}\) turns in opposite directions, and a 500 m altitude climb was completed at the same time.  
Environmental interference parameter setting  
Other environmental parameter settings are shown in Table 3.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Constructing the Geochemical Topology Graph  
As shown in Figure 2, the geochemical dataset can be collected as a group of centroids in an area. At the suggestion of [24], for regional geochemical exploration, only capturing the multivariable (multi-elemental) geochemical anomalies may be insufficient because their spatial distribution also reflects complex geological processes such as mineralization. To achieve this, the undirected graph (\(G\)) [24] which is used to connect closely related centroids to represent the spatial structure from point data is constructed as:  
\[X=\{\overrightarrow{X_{1}},\ \overrightarrow{X_{2}},\ \ldots,\ \overrightarrow{X_{N}}\},\ \overrightarrow{X_{i}}\ \in\ R^{F} \tag{1}\]  
\[A=\{A_{1,\ 1},\ A_{1,2},\ \ldots,A_{N,\ N}\},\ A_{i,\ j}=\begin{cases}1,&d_{i,j} \leq K\\ 0,&d_{i,j}>K\end{cases} \tag{2}\]  
where \(X\) and \(A\) represent the set of nodes/centroids, and edges, respectively; \(N\) represents the number of nodes in the graph; \(F\) denotes the number of features, namely geochemical variables (elements), of each node; \(d_{ij}\) denotes the spatial distance between node \(j\) and \(i\); \(K\) represents the distance threshold when constructing the geochemical topology graph, and generally, the smaller the distance between these two centroids is, the more related they are [24]; 1--connected edge and 0--disconnected.  
In our case, \(N=663\); \(F=11\), which means there are 11 I-series or S-series ore-forming elements; and \(K\) is determined by the global Moran's I analysis according to [24].  
Global Moran's I is a measure of the overall clustering of the spatial data. Figure 3 gives the global Moran's I values versus different distance bands of all the geochemical variables. According to [32], if the Moran's I value decreases rapidly as the distance band increases, there is a strong dependency relationship between the spatial structure and distance; on the contrary, if the Moran's I value decreases slowly as the distance band increases, this indicates a stable spatial structure [32]. Obviously, the inflection point of the curve can act as the optimal threshold \(K\) to balance partial heterogeneity (i.e., anomaly) and the background features. An edge connects two adjacent centroids when the distance between them is less than \(K\), and remains disconnected otherwise.  
Figure 3: Variation in global Moran’s I index with different distance bands (namely \(K\)).  
It can be seen from Figure 3 that there is a clear turning point at about 20 km for most of the ore-forming elements. For insurance purposes, here we use \(K=22\) as the distance threshold to construct the geochemical topology graph for subsequent network training and anomaly detection. Figure 4 is the generated topology graph, and each node of it corresponds to \(F=11\) features or geochemical variables [33]. Obviously, such a topology graph integrates both the spatial structural features and multi-elemental concentrations for a group of centroids in a given area.
## Appendix A Construction of the Phase Diagram  
In order to give a more detailed explanation about how the phase diagram was constructed, please consider Figure 14. It can be noticed that the four phases are defined from the variation of traffic flow. When flow is at its maximum level, for all combinations of \(\alpha\) and \(\beta\), it can be said the system is in the maximum current (MC) phase. The limits of this phase are depicted in Figure 14, where the area of MC can be seen when \(\beta\geq 0.5\) (see the red circle that shows the point where the flow reaches its maximum ) and \(\alpha_{p}\leq 0.3\).  
Contrasting to MC, gridlock is characterized by the fact the flow becomes zero. In the studied system, the gridlock appears when \(\alpha_{p}\) exceeds 0.8. In Figure 14, the lines representing the flow variation for \(\alpha_{p}\geq 0.8\) are overlapped from the yellow-circled point.  
The jamming phase is defined when the flow is still stable and the microscopic characterization of the vehicles shows the stop-and-go event over the whole system (see Figure 5b). Jamming worsens if \(\alpha_{p}\) increases, as shown in Figure 11 between the two blue-circled points.  
Finally, the congestion phase depends strongly on \(\alpha_{p}\) and \(\beta\), which means as \(\alpha_{p}\) increases the traffic flow worsens until becomes null. In this way, it can be deduced that in the present study this phase is only the preparation phase, where the system transitions to the gridlock (see the inclined line that separates the other phases and that decreases if \(\beta\) decreases). In other words, if the boundary of the three phases is correctly determined, the fourth phase can be defined directly from the line that separates the others phases.
## 3 Control Approach
### Formation Tracking Controller Based on ESO-MPC
#### 3.3.3 Cost Function Design of the MPC  
When aiming at achieving formation control, the cost function of the \(i\)-th small spherical robot at the sampling time \(k\) is designed as the following based on the formation problem formulation described in Section 2.1:  
\[\mathbf{I}_{total}^{i}(k)=\mathbf{I}_{form}^{i}(k)+\mathbf{I}_{in}^{i}(k). \tag{34}\]  
The above cost function consists of two parts. \(\mathbf{I}_{form}^{i}(k)\) is designed as being responsible for the tracking trajectory and keeping the relative distance with the other robots at the same time. \(\mathbf{I}_{in}^{i}(k)\) is designed for preventing the collisions among the small spherical robots. \(\mathbf{I}_{form}^{i}(k)\) is defined as follows:  
\[\begin{split}\mathbf{I}_{form}^{i}(k)&=\sum_{s=1}^{N_{ p}}\left\|\mathbf{\eta}^{i}(k+s|k)-\mathbf{\eta}_{ref}^{i}(k+s|k)\right\|_{Q_{i}}^{2}\\ &+\sum_{s=1}^{N_{p}}\left\|\mathbf{p}^{i}(k+s|k)-\mathbf{p}^{j}(k+s|k)-( \mathbf{p}_{ref}^{i}(k+s|k)-\mathbf{p}_{ref}^{j}(k+s|k))\right\|_{Q_{ij}}^{2}\\ &+\sum_{j=1}^{N_{c}}\left\|\Delta\mathbf{\tau}(k+s|k)\right\|_{R_{i}} ^{2}+\rho\varepsilon^{2}\end{split} \tag{35}\]  
where \(N_{p}\) is the predictive horizon, \(N_{c}\) is the controlling horizon, \(\rho\) is the weighting factor, and \(\varepsilon\) is the relaxation factor. \(Q_{i},R_{i},Q_{ij}\) are the weighting matrices. The first term of Equation (35) is to ensure that the state vector \(\mathbf{\eta}\) of the robot approaches the desired state vector. The second term guarantees the distance between the \(i\)-th robot and the \(j\)-th robot. The third term aims at minimizing the increment of control input. The purpose of the last term is to prevent an optimization problem from being unsolved.  
When referring to the collision avoidance problem for the spherical robots, a concept of priority was proposed. Each robot in the formation is marked a label that representspriority levels. The label updates with the state changes of the robot. If a collision occurs, the robot at the lower level needs to adjust the state. The priority from high to low is set as per the following:  
1. Robots with urgent tasks such as target hunting.
2. Robots that have minor tracking errors.
3. Robots that could obtain the information of the other robots.  
The above rules can be implemented according to specific circumstances. The \(\mathbf{J}_{in}^{i}(k)\) is written as follows:  
\[\begin{split}&\mathbf{J}_{in}^{i}(k)=\begin{cases}0,&if\quad d_{ij}(k)>2R\\ 0,&if\quad d_{ij}(k)\leq 2R,lev_{i}(k)>lev_{j}(k)\\ &\sum_{s=1}^{N_{p}}-S_{in}(d_{ij}(k+s|k)-2R),if\quad d_{ij}(k) \leq 2R,lev_{i}(k)\leq lev_{j}(k)\end{cases},\end{split} \tag{36}\]  
where  
\[\begin{split}& d_{ij}(k)=\sqrt{(x_{i}(k)-x_{j}(k))^{2}+(y_{i}(k)- y_{j}(k))^{2}+(z_{i}(k)-z_{j}(k))^{2}}\\ & d_{ij}(k+s|k)=((x_{i}(k+s|k)-x_{j}(k+s|k))^{2}+(y_{i}(k+s|k)-y_{ j}(k+s|k))^{2},\\ &\qquad\qquad\qquad+(z_{i}(k+s|k)-z_{j}(k+s|k))^{2})^{0.5}\end{split} \tag{37}\]  
where \(S_{in}\) represents the weight as a positive constant value. \(lev_{i}(k),lev_{j}(k)\) represent the levels of the \(i\)-th robot and \(j\)-th robot.
## 5 Conclusions  
1. The coefficient of variation of daily sap yield is very high, especially in trees with higher DBH values. Considerable variation in the total amount of collected sap was observed between trees growing in the same location.
2. Analysis of the relationship between seasonal birch sap yield and the biometric features of trees (DBH, total height, crown base, crown length, relative crown length, and slenderness) for any of the above features did not show statistically significant correlations--both for all trees together and divided into groups of forest site types.
3. This paper proposes a new method for determining the phases of birch sap leakage based on the intensity of leakage in relation to the average value for the entire season. This variable turned out to be a feature that significantly differentiates the amount of sap obtained, and it is most useful in modeling this feature. The smallest amount of sap was obtained in the initial phase, a slightly higher amount was obtained in the final phase, and the largest amount of sap was obtained in the main phase. Statistically significant differences were found in the average amount of collected sap depending on the collecting phase.
4. Regarding the forest site type and the interaction between the collection phase and the forest site, no statistically significant relationship was found with the average amount of collected sap.
5. The mixed-effects linear model indicates features related to the amount of sap potentially collected, which allows for selecting the forest site, tree, and time when sap collection will be most efficient. The presented model can be used to analyze the profitability of birch sap collection.
6. Taking into account the obtained results, it can be assumed that in the case of birch sap harvesting on an industrial scale, the selection of an appropriate forest site type is of little practical importance, but the use of high sap yield in the main leakage phase is important.  
**Author Contributions:** Conceptualization, S.K. and P.S.; methodology, P.S., S.K. and M.B.; formal analysis, R.T.; investigation, S.K. and D.Z.; data curation, R.T.; writing--original draft preparation, S.K. and P.S.; writing--review and editing, T.M., S.K. and P.S.; visualization, R.T.; supervision, P.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Numerical Results and Discussion
### ADP Weighted MV-MOS Performance  
Figure 7 shows that ADP weighted MV-MOS performance indicators by different caching schemes. The ROCS scheme achieves a poor performance in ADP weighted MV-MOS, almost the same as the CES algorithm. This is because the video's bitrate is directly proportional to the file size. A high bitrate means a large storage cost. Hence, when the service time is fixed, the ROCS can only complete a small proportion of tasks, which will cause vehicle users to only enjoy a short time of the high-definition video within the service area. It will immediately interrupt when it leaves the caching service area. Therefore, based on the average MV-MOS model cannot be directly applied to the vehicular networks. Figure 7 also shows that the ADP weighted MV-MOS performance of the P3 DVQC can converges to the optimal BFS, which is better than other benchmark algorithms.  
Figure 8 shows the cumulative reward of the five algorithms in Figure 7. The BFS is a straight line in Figure 7, so the cumulative gain is meaningless. To increase the caching hit rate, FOCS only caches the lowest bitrate videos so that its solution space is much smaller than the P3 DVQC, so the cumulative return obtained in the early stage is more significant. However, with the P3 DVQC converging to the optimal strategy, it achieves a greater cumulative return than the FOCS. Other benchmark algorithms can be seen from the analysis of Figure 7, and their performance is lower than the P3 DVQC.  
Figure 6: Average MV-MOS by different kinds of caching schemes.  
Figure 7: ADP weighted MV-MOS by different kinds of caching schemes.
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 3 Network Model
### The Construction of Parking Cluster  
A parking cluster, which includes some parked vehicle member nodes and a cluster head node, is formed by parked vehicles on both sides of a road. The parking cluster contains sufficient computational capacity. We let the maximum distance between the two endpoint members of a cluster be L meters. In order to reduce the rogue node detection latency, the length of the cluster cannot be too long. Hence, when the road is longer than L meters, the road will be divided into multiple clusters. In each cluster, the parked vehicle that is closest to the center of the cluster is set as the cluster head, and other parked vehicles become cluster members. Each member vehicle periodically sends a notification message to the cluster head to report its ID number, its current location and its remaining power to the head node. The cluster head manages all cluster members and maintains the cluster' structure. Since the cluster head may drive away, the vehicle adjacent to the cluster head is set as the secondary node, with the aim of improving the reliability of the cluster.
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 2 Destruction Range of Single Blasting Hole
According to the detonation theory of a condensed explosive, the detonation velocity of the explosive would increase with the cartridge diameter in the range from a critical diameter and a limited diameter [37; 38], as shown in Figure 3. Then, the detonation velocity tester was adopted to determine the detonation velocity of \(\Phi\) 27 mm, \(\Phi\) 35 mm, and \(\Phi\) 42 mm cartridges, the testing results of which are presented in Table 1. In addition, the rock pieces taken from the later cutting blasting site were processed and polished into regular specimens to test the main physical and mechanical parameters of the rock, and the testing results are shown in Table 2. After that, the known charge parameters, explosive performance parameters, and rock mechanical parameters were submitted into Equation (1) to compute the radii of the fracture zone under the above three charges' diameters, and the computed results are presented in Table 3.  
Figure 2: Diagram of cylindrical charge blasting. From Table 3, according to the order of charge diameters from small to large, the radii of the fracture zones under the three charge diameters were equal to 288 mm, 396 mm, and 525 mm, respectively. It was observed that the radius of the fracture zones increased visibly with the charge diameter. Accordingly, when the charge diameter of the cutting holes was increased, the blasting fractures between the cutting holes could be connected easily and well with each other, which would enhance the destruction degree of the rock far away from the cutting holes in the cutting cavity. Thus, the hard rock in the cutting cavity could form the small rock fragments, which could be easily expelled out.
## 3 System Model
### Simulations Results  
#### 4.3.1 Average Temperature Rise versus Simulation Time  
Figure 5 shows the behavior of a network in terms of the average temperature for five protocols, i.e., Multipath Ring Routing Protocol (MRRP), the proposed protocol ATEAR, Enhanced ATEAR protocol, thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). At the beginning of the simulation, each node within the network has the same temperature, i.e., 37 \({}^{\circ}\)C. The graph in Figure 5 shows that the simulation started from 1000 s and ended at 6000 s. The result demonstrates the thermal-aware feature of the ATEAR and Enhanced ATEAR protocol. The average temperature of the network with ATEAR reached 37.02 \({}^{\circ}\)C at 1000 s and 37.749 \({}^{\circ}\)C at 6000 s. Contrary to this,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Description** \\ \hline Simulation Area & 3 cm \(\times\) 2 cm \\ Implanted nodes count & 9 \\ Sink Node & 1 static \\ Node Initial energy & 0.3 j \\ Initial temperature of a node & 37 \({}^{\circ}\)C \\ Size of a Packet & 296 bits \\ Threshold temperature value & 40 \({}^{\circ}\)C \\ Threshold energy value & 0.1 j \\ Application type & Event-Driven \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation setup.  
with MRRP, TARA, and SHR initially the average temperature rose to 37.8 \({}^{\circ}\)C, 38.8 \({}^{\circ}\)C, and 39.8 \({}^{\circ}\)C at 1000 and in the end at 6000 s it was 62.8 \({}^{\circ}\)C, 80.8 \({}^{\circ}\)C, and 101.8 \({}^{\circ}\)C (not feasible for implanted nodes). ATEAR significantly performed better in terms of the overheating of the sensor nodes. Furthermore, the average temperature of Enhanced ATEAR was also investigated, which rose to 37.25 \({}^{\circ}\)C at 1000 s and 37.70 \({}^{\circ}\)C till the end of simulation at 6000 s. Thus, Enhanced ATEAR performed slightly better than ATEAR and much better than MRRP. Consequently, the temperature peaked in the above graph showing the unawareness of MRRP, TARA, and SHR towards the overheating of the embedded sensors while ATEAR and Enhanced ATEAR sensibly avoided it. The low peak of the temperature of ATEAR and E-ATEAR is due to the selection of the forwarder node with the minimum temperature-rise.
## 3 Results  
### 2D-EPE Diffractive Waveguide Architecture  
Figure 10a shows the basic architecture of 2D-EPE, where a volume holographic grating (VHG) is used for coupling. The angle between the central field ray of the propagated image in the waveguide and the interface normal is 60\({}^{\circ}\). The output coupling grating is a 2D grating, where the incident light enters the reflective 2D grating structure with a polar angle of 60\({}^{\circ}\) and an azimuth angle of 90\({}^{\circ}\). The (0,0) diffraction order continues to propagate by total internal reflection (TIR) after interacting with the 2D grating structure, while the (0,1) diffraction order reflects with a polar angle of 60\({}^{\circ}\) and an azimuth angle of 30\({}^{\circ}\), and the (\(-\)1,0) diffraction order reflects with a polar angle of 60\({}^{\circ}\) and an azimuth angle of 150\({}^{\circ}\). The (\(-\)1,1) diffraction order directly exits vertically as the center field egress ray. The (0,0) order ray interacts with the 2D grating structure after TIR with the waveguide upper surface, repeating the aforementioned process. The (0,1) and (\(-\)1,0) order diffracted rays repeat the same process in their respective local co-ordinates. The parallel component of the k-vector of the replicated egress rays is shown in Figure 10b.  
Figure 8: Algorithm simulation; (**a**) Holo-RCWA Fourier harmonic number convergence; (**b**) Holo-RCWA layer number convergence; (**c**) Diffract-MOD layer number convergence; (**d**) TM mode incident Holo-RCWA layer number convergence.  
Figure 9: The x-z profile of holographic grating prepared by photoresist.  
The design of the diffractive waveguide coupled grating period is as follows: when the tangential component of the k-vector of the (\(-\)1,1) order diffracted ray is equal to zero, central field vertical emission can be achieved. The grating vector of the 2D grating is given by:  
\[\left\{\begin{array}{c}\overset{\rightarrow}{k}_{i,\parallel}-\overset{ \rightarrow}{K}_{\alpha}+\overset{\rightarrow}{K}_{\beta}=0\\ \overset{\rightarrow}{K}_{\alpha}=\frac{2\pi}{a}\mathbf{e}_{x}+\frac{2\pi}{a \sqrt{3}}\mathbf{e}_{y}\\ \overset{\rightarrow}{K}_{\beta}=\frac{2\pi}{a}\mathbf{e}_{x}-\frac{2\pi}{a\sqrt {3}}\mathbf{e}_{y}\end{array}.\right. \tag{13}\]  
Therefore, the required lattice constant of the 2D holographic grating is \(a=4\lambda_{1}/3n\), where \(n\) is the refractive index of the waveguide medium and \(\lambda_{1}\) is the wavelength of the display's light source in vacuum. The grating vectors \(\overset{\rightarrow}{K}_{\alpha}\) and \(\overset{\rightarrow}{K}_{\beta}\) of 2D grating are the primitive translation vectors of the 2D periodic lattice shown in Figure 3a. Based on the center field EPE, the initial architecture of the diffractive waveguide using a 2D grating as the exit pupil coupling device has been designed.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 4 Experimentation and Discussion
### Discussion  
The _virtual museum_ service in the experiment is simulated under a testbed environment, in which we performed the experimentations with several real VLC transmitters and receivers. The VLC transmitters used in the experiment are 18 W, white-colored (5700 K), 120 degrees of view angle, 1440 lm flux LED lights. The VLC modulation uses PWM with a 1.6 ms period and 4 m communication range. The PD-based VLC receiver has a 115,200 baud rate through UART.  
To extend the _virtual museum_ service into real-world environments, we need to consider large numbers of VLC transmitters and receivers, since there are many exhibition rooms and a lot of users in the museum. In this case, the performance may depend on how to effectively configure the platform server and many aggregation agents with VLC transmitters and receivers in the network. In addition, the security issue should be considered, because the VLC frames have no encryption scheme and contain channel information of VR service network. In the deployment of VLC transmitters in real networks, we also need to consider that VLC frames are generated with the same modulation scheme and wavelength, and thus this may cause interference.