esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Environment
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Run** & **Scenario** & **Request Type** & **Topology** \\ \hline \multirow{8}{*}{Long} & Simple-Small & Simple/Forking & BT Europe \\  & Multiple-Small & Multiple & BT Europe \\  & Simple-Large & Simple/Forking & Interout \\  & Multiple-Large & Multiple & Interout \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\) & \(P_{5}\), \(P_{10}\), \(P_{20}\) & BT Europe \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\) & \(P_{5}\), \(P_{10}\), \(P_{20}\) & BT\({}^{+}\) \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\) & \(P_{5}\), \(P_{10}\), \(P_{20}\) & Interout \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B^{+}}\) & \(P_{5}\), \(P_{10}\), \(P_{20}\) & Int\({}^{+}\) \\ \hline \multirow{4}{*}{Short} & \(\frac{\mathcal{E}_{3}^{A}}{\mathcal{E}_{3}^{B}}\), \(\mathcal{E}_{4}^{A}\) & \(P_{5}\) & Synthetic \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B}\) & \(P_{10}\) & Synthetic \\  & \(S_{B^{+}}^{B^{+}}\), \(S_{B^{+}}^{B}\) & \(P_{20}\) & Synthetic \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Scenarios. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Topology** & **Nodes** & **Links** \\ \hline BT Europe & 24 & 37 \\ Interout & 110 & 148 \\ BT\({}^{+}\) & 24 & 65 \\ Int\({}^{+}\) & 110 & 180 \\ \hline
**Topology** & **Nodes** & \(E_{3}\) & \(E_{4}\) \\ \hline \multirow{4}{*}{Synthetic} & 10 & 14 & 21 \\  & 30 & 50 & 64 \\  & 60 & 98 & 133 \\  & 90 & 156 & 198 \\  & 120 & 227 & 265 \\  & 150 & 265 & 333 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Physical network topologies. technique type of each node is randomly selected from the available virtualization technique types for both physical nodes and virtualized nodes of the service request. Moreover, the service requests are generated based on Poisson process with an average equals to 4 requests each 100 time units. Each service has a few decompositions that are uniformly distributed between 2 to 5, and the number of virtual nodes in each decomposition is uniformly distributed between 2 and 10, while the required resources for each node are uniformly distributed between 1 and 20 capacity units. The virtual links between every pair of nodes are generated with a chance of (0.5) and the bandwidth of each virtual link is uniformly distributed between 1 and 50 bandwidth units, while the virtual links delays are set to 1000 time units. As for the life time of each service request, it is exponentially distributed with an average equals to 1000 time units. The decomposition selection is used in ILP-A and DSBM schemes, while the weighting parameters used for the cost are \(a=0.25,b=0.25\) and \(g=0.50\) as recommended in Reference [11]. The ILP-P is not using any selection function and the DcPSM uses the following weighting parameters \(w_{e}=0.60,\ w_{p}=0.30\) and \(w_{n}=0.10\) in the decomposition selection function. The number of allowed additional hubs constraint for Equation (16) is set to \(h=1\).
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
###### Abstract  
The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes.  
geopositioning; UAS; visual odometry; real-time pipeline +
Footnote †: journal: Journal of Optics and Photonics
# A Clutter Loss Model for Satellite Communication Systems
###### Abstract  
Buildings and vegetation in the proximity of a terrestrial base station induce a significant additional loss, typically referred to as \"clutter loss\", which sums up to free space loss and atmospheric attenuation. Clutter loss is essentially due to the radiowave reflection and diffraction caused by buildings and vegetation, and tends to reduce the interference between terrestrial systems, such as upper 6 GHz (U6G), and satellite systems operating in the same frequency bands. In fact, for example, at low elevation angles, the clutter loss could reach some tens of dB in the U6G band. A novel clutter loss model in urban and suburban environments for frequencies up to 10 GHz is proposed. The model relies on the Monte Carlo simulation approach presented in Report ITU-R P2402-0, but some limitations have been removed to extend its applicability to more complex scenarios and possibly increase its accuracy for U6G systems. An analytical approach is also proposed to model the clutter loss statistics obtained by properly fitting the obtained statistics for the cities of London and Melbourne. Finally, the proposed model is validated by comparing its results to those obtained by a commercial ray tracer.  
clutter loss; diffraction; reflection; satellite communications 20231218613390/electronics12010186 Article  
A Clutter Loss Model for Satellite Communication Systems
# A Deep Attention Model for Action Recognition from Skeleton Data
## 4 Experimental Results
### Results on NTU RGB + D Dataset
#### 4.1.3 Verification of the Learned Attention  
Figure 5 shows the attention on the joints of hands and feet over time for different classes. The feet are represented by joints 15, 16, 19, and 20 (left ankle, left foot, right ankle, and right foot), and hands include joints 7, 8, 22, 23, 11, 12, 24, and 25 (left wrist, left hand, tip of the left hand, left thumb, right wrist, right hand, tip of the right hand, and right thumb). The average percentages of the joints being actively focused (where the joint attention weight is larger than 0.04 which represents the case that all 25 joints of a skeleton are equally weighted if the second person is not attended at all) are shown. Figure 5 shows the attention on the joints of feet over time for action \"kicking something\" and action \"hand waving\". It can be seen that for \"kicking something\", with the progress of the actions in time, more joints of feet are being focused, until it reaches the midpoint of the action, and toward the end of the action, it starts to shift back to the original state. On the other hand, for \"hand waving\", the attention on the joints of feet decreases over time. This agrees with the intuition that \"kicking something\" focuses on the joints of feet while \"hand waving\" does not. Figure 5 shows the attention on the joints of hands over time for action \"playing with phone/tablet\" and action \"kicking something\". Similar behavior as Figure 5 can be observed where attention on the joints of the hands improves for \"playing with phone/tablet\".
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 3 Method
### Finding Matched Feature Points from Satellite and Base Images  
Automated procedure to find matched feature points is essential for processing huge amount of satellite images. In this subsection, we propose an automated and robust procedure that correctly matches the feature pairs. The procedure determines the satellite attitude of each image by feature detection in both satellite and base-map images, and then provides the map-projected satellite images using the determined attitude. It first performs a rough matching between two images, which includes incorrect feature pairs incidentally selected. The incorrect pairs are then rejected by an exact matching (attitude determination) by an algorithm from the RANSAC family.
## 3 Results  
### Parameter Optimization  
Different combinations of optimization parameters (L2R, SR, and SP) were explored for both layers, whereas the best parameter values that minimized the mean squared error (MSE) were chosen for corresponding layers. Figure 4 shows the MSE for both layers (maximum epochs of 500) along with different combinations of the three parameters. The chosen parameter values were the same for the two layers (L2R = 0.0001, SR = 0.01, and SP = 0.5).
# A Novel Secondary Side Series LCD Forward Converter with High Efficiency and Magnetic Reset
## 1 Introduction
### Analysis of the Switching Characteristics of the Switch
According to the initial conditions \(u_{C2}(0)=V_{C2,\max}\), \(u^{\prime}_{C2}(0)=-\frac{I_{L1,\min}}{C_{2}}\), by solving Equation (28), the voltage expression of \(C_{2}\) can be obtained using the following. \[ \begin{split}& u_{C2}(t)=-I_{L1,\min}\cdot\sqrt{\frac{L_{2}+L_{1}+L_{ W2}}{C_{2}}}\sin\left(\frac{t}{\sqrt{(L_{2}+L_{1}+L_{W2})C_{2}}}\right)+\\ &(V_{C2,\max}-V_{o})\cdot\cos\left(\frac{t}{\sqrt{(L_{2}+L_{1}+ L_{W2})C_{2}}}\right)+V_{o}\end{split}  \tag{29}\]  
From Equation (7), the time \(t_{3}\) for the capacitor \(C_{2}\) the deliver energy to \(L_{1}\), \(L_{2}\), and \(W_{2}\) during the switch-off period can be calculated using the following equation. \[t_{3}=(1-D)T-t_{m}=(1-D)T-\frac{\pi\sqrt{L_{W2}C_{2}}}{2} \tag{30}\]  
By substituting Equations (9) and (30) into Equation (29), the voltage across the capacitor \(C_{2}\) at the switch-on time can be obtained using the following. \[ \begin{split} V_{C2-2}&=-I_{L1,\min}\cdot\sin \left(\frac{\lambda}{\sqrt{L_{1}+L_{2}+L_{W2})C_{2}}}\right)\\ &+\left(\frac{V_{i}dT}{\sqrt{2}L_{m}C_{2}}-V_{o}\right)\cdot\cos \left(\frac{\lambda}{\sqrt{L_{1}+L_{2}+L_{W2})C_{2}}}\right)+V_{o}\end{split}  \tag{31}\]  
where \(\lambda=(1-d)T-\frac{\pi\sqrt{L_{W2}C_{2}}}{2}\). According to Figure 6, the voltage across the secondary winding \(W_{2}\) can be calculated using the following equation. \[V_{W2}=(V_{C2-2}-V_{o})\frac{L_{W2}}{L_{2}+L_{1}+L_{W2}} \tag{32}\]  
When the switch is turned on, the voltage across the primary side is \(nV_{W2}\), coupled from \(V_{W2}\) through the transformer, which is opposite to the polarity of the input voltage. Therefore, the voltage stress of the switch S can be obtained using Equations (31) and (32). \[ \begin{split}& V_{ds}=V_{i}-nV_{W2}\\ &=V_{i}+I_{L1,\min}L_{m}\sqrt{\frac{1}{2C_{2}(n^{2}L_{1}+n^{2}L_ {2}+L_{m})}}\cdot\sin\left((\frac{n\lambda}{\sqrt{n^{2}L_{1}+n^{2}L_{2}+L_{m}) C_{2}}}\right)\\ &-\left(\frac{nV_{i}dT}{n^{2}L_{1}+n^{2}L_{2}+L_{m}}\cdot\sqrt{ \frac{L_{m}}{C_{2}}}-\frac{nV_{o}L_{m}}{n^{2}L_{1}+n^{2}L_{2}+L_{m}}\right) \cdot\cos\left(\frac{n\lambda}{\sqrt{n^{2}L_{1}+n^{2}L_{2}+L_{m})C_{2}}}\right) \end{split}  \tag{33}\]  
From the above analysis, we can see that the proposed converter can achieve a low-voltage turn-on of the switch, and the additional LCD can transmit both the excitation energy and the forward energy.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 3 Results  
### Variable Structure PD Controller  
In the field of satellite attitude control, the PD control algorithm is a mature and widely used method since its simple structure and satellite attitude system governed by the PD controller could be treated as a second order system. The main drawback of a PD controller could be concluded as the following two aspects: system convergence rate is relatively low compared with current control methods, such as finite time control and fixed time control; the system anti-perturbation capability is also relatively low, and the system is sensitive to disturbance torques. In order to deal with the first issue, a common method involves enlarging the control gain parameters, which could aggravate the second issue. In addition, a common method to deal with the second issue is to add an integral term to the PD controller, i.e., design a PID control algorithm, which could slow down the system convergence rate and aggravate the first issue. Based on the discussion above, it could be found that these issues are deeply coupled and are hard to solve based on standard methods. In this section the variable structure control method is implemented, and a variable structure PD controller will be designed to improve the system convergence rate when the system state is away from its equilibrium point.  
The proposed variable structure PD controller could be written as follows:  
\[\mathbf{u}=\begin{cases}-k_{d}\mathbf{\omega}-k_{p}\mathbf{q}_{v}-\overline{d}sign(\mathbf{ \omega})+\mathbf{\tau}&\|\mathbf{q}_{v}\|\geq\alpha\\ -k_{d}\mathbf{\omega}-k_{p}\mathbf{q}_{v}-\overline{d}sign(\mathbf{\omega})&\|\mathbf{q}_{v}\| <\alpha\end{cases} \tag{8}\]  
where \(\alpha\) is a positive scalar whose function is to detect the system state as it approaches its equilibrium point, \(\overline{d}\) is the norm upper bound of disturbance torque, and \(k_{d}\) and \(k_{p}\) are also positive control parameters, defined as follows:  
During the first stage of controller (8), the derivative of \(\mathbf{\eta}\) can be obtained by  
\[\begin{array}{ll}\dot{V}_{2}&=\mathbf{\eta}^{T}\dot{\mathbf{\eta}}\\ &=\mathbf{\eta}^{T}\big{(}\dot{\mathbf{\omega}}+Jk_{1}\dot{\mathbf{e}}\big{)}\\ &=\mathbf{\eta}^{T}\big{(}\mathbf{u}+d-\mathbf{\omega}^{\times}\mathbf{I}\mathbf{\omega}+\frac{1}{ 2}k_{1}\mathbf{I}\mathbf{e}^{\times}\big{(}I_{3}-\cot\frac{\sigma}{2}\mathbf{e}^{\times} \big{)}\mathbf{\omega}\big{)}\\ &\approx-k_{d}\mathbf{\eta}^{T}\mathbf{\eta}\leq 0\end{array} \tag{15}\]  
Thus, auxiliary state \(\mathbf{\eta}\) is also asymptotic stable, i.e., \(\mathbf{\eta}\to 0\), and that means that during this stage \(\mathbf{\omega}=-k_{1}\mathbf{e}\). Hence, the system could have a constant convergence rate during this stage, and by implementing this variable structure controller, the system convergence rate could be improved. The properties in Remark 2 have been proven.
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 2 Problem Formulation and Solution  
### Problem Formulation  
Figure 2 shows the hierarchical domain-based SDN-enabled space-air-ground integration network model adopted in this paper; the controller plane mainly consists of a multi-domain controller and central controller deployed near the gateway station. The data plane mainly consists of LEO satellites (Walker constellation Iridium constellation et al.),MEO satellites, GEO satellites and ground network nodes. Each group of domain controllers is responsible for the communication tasks of its own domain. Only when there is a cross-domain communication task will it send a request to the central controller, and the hierarchical and domain-divided network design pattern reduces the time of synchronous information consumption between controllers and increases the flexibility of controller expansion. The next problems to be solved are how to divide the network nodes into domains, how to deploy the controller, and how to make the satellite switch migration strategy due to the satellite network dynamics.
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods
### Models and Set-Up
#### 2.1.4 The Lagrangian Model  
OpenDrift [46] is a freely available open-source off-line Lagrangian particle trajectory model that contains several modules for the advection of, e.g., oil spills [47], larvae and passive tracers [48]. For this study, the passive tracer module is used, which advects tracers only due to currents and winds. The sea and wind drift input is described in the next section. To investigate the influence of the wind, three different windage (referred to as leeway) coefficients when the bulk effect of waves and wind on the object is considered [32] (\(L_{w}\)) are used (0.1%, 0.5% and 1.0%). The real wind drag of the drifters is not known, so these values are used as estimates. The windage coefficients are multiplied by the wind velocity and added to the current velocity. The advection scheme is a 2 nd-order Runge-Kutta scheme, and no additional diffusion is added because we only want to investigate the wave effects without any additional random \"disturbances\" [49]. Each particle thus follows a trajectory influenced by the sea surface currents and the equations read:  
\[\Delta x(t) =\bigg{(}u\bigg{(}t+\frac{\Delta t}{2}\bigg{)}+L_{w}u_{wind}\bigg{(}t+ \frac{\Delta t}{2}\bigg{)}\bigg{)}\Delta t \tag{1}\] \[\Delta y(t) =\bigg{(}v\bigg{(}t+\frac{\Delta t}{2}\bigg{)}+L_{w}v_{wind} \bigg{(}t+\frac{\Delta t}{2}\bigg{)}\bigg{)}\Delta t \tag{2}\]  
here \(\Delta x(t)\), \(\Delta y(t)\) are the particle displacements and \(u(t+\Delta t/2)\), \(v(t+\Delta t/2)\) are the horizontal velocity components at the particle's position at \(t+\Delta t/2\). If a particle reaches land, it is not further advected and considered beached. These particles are not further taken into account. If a particle leaves the domain through an open boundary, e.g., from the North Sea into the Atlantic, this particle is treated the same way. The model time step \(\Delta t\) and the particle seeding strategies are dependent on the specific experiment (provided in Section 3).
## 5 Dynamic Loading Mitigation of Offshore Platform Using TMD
### Dynamic Responses and Discussion
#### 5.2.3 Experiment on Frequency Detuning Sensitivity of TMD
Based on the data in Table 3, we plotted a curve of the frequency ratio (\(\lambda=f_{new}/f_{0}\)) versus the normalized root-mean-square (RMS) acceleration. As shown in Figure 20, the frequency without disturbance achieves an almost minimum structural response. Figure 20 also intuitively demonstrates that the overall performance decreases asymmetrically. Figure 19: The time-domain simulation results of deck displacement in the Y-axis direction under misaligned typhoon and typhoon-wave loads. (**a**) The comparison between 90% \(M_{TMD}\) and 100% \(M_{TMD}\); (**b**) The comparison between 110% \(M_{TMD}\) and 100% \(M_{TMD}\); (**c**) The comparison between 90% \(K_{TMD}\) and 100% \(K_{TMD}\); (**d**) The comparison between 110% \(K_{TMD}\) and 100% \(K_{TMD}\). Figure 20: TMD Frequency Optimization Curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Disturbance**} & \multirow{2}{*}{\(\Delta f_{off}\)} & \multicolumn{3}{c}{\(\Delta d_{off}\)} & \multicolumn{3}{c}{\(\Delta a_{off}\)} \\ \cline{3-6}  & & **Mean Value** & **Maximum** & **Mean Value** & **Maximum** \\ \hline
90\% \(M_{TMD}\) & \(5.4\%\) & \(-4.38\%\) & \(-3.21\%\) & \(-5.40\%\) & \(-4.41\%\) \\
110\% \(M_{TMD}\) & \(-4.71\%\) & \(-6.95\%\) & \(-3.22\%\) & \(-6.65\%\) & \(-4.76\%\) \\
90\% \(K_{TMD}\) & \(-5.17\%\) & \(-8.60\%\) & \(-5.38\%\) & \(-9.38\%\) & \(-6.40\%\) \\
110\% \(K_{TMD}\) & \(4.82\%\) & \(-7.30\%\) & \(-8.06\%\) & \(-8.23\%\) & \(-5.31\%\) \\ \hline \end{tabular}
\end{table} 
Table 3: Analysis of TMD frequency detuning sensitivity.
# Retractable Wireless Charging Windings for Inspection Robots
###### Abstract  
Limited battery life compromises the usability of inspection and operation robots in hazardous environments such as nuclear sites under decommissioning. Both manually replacing the batteries and installing charging bays may be infeasible. Inductive wireless power transfer is a possible solution to deliver power through barriers such as reinforced concrete walls without physical contact. However, when requiring decent power (e.g., 100 W) to be transmitted over longer distances, the exaggerated dimensions of transmitting and receiving coils restrain the integrations with mobile robots. In this paper, a novel retractable design of the coil used in an inductive wireless power charging system is proposed, proving the minor deformation of the winding shape does not affect the transmission efficiency. A prototype with 5\(\times\) size compression is implemented and tested. It successfully transmits 116.5 W over a distance of 1 m with 68.72% energy efficiency. The principle can be applied to a wide range of mobile platforms with a limited payload area where remote power is needed.  
magnetic resonant coupling inductive; wireless power transfer; mid-sized mobile robots; mechatronics 2025
## 4 Sensitivity Analysis  
### Effect of Natural Gas Compositions  
The analysis of the effect of natural gas composition on exergy efficiency is important because the target LNG plant in this study is movable, and because the uncertain effect of natural gas composition on exergy has already been reported [60]. As illustrated in Figure 10, the process most sensitive to natural gas compositions is SMR, which varies in the range of 36.85-37.46% (0.61%); propane pre-cooled self-refrigeration is the least sensitive and it varies in the range of 13.65-14.07% (0.42%). Overall, the effect of natural gas composition changes on exergy efficiency do not seem to be significant.  
Figure 11 shows the effects of natural gas compositions on the product cost of each process. As shown in Figure 11, the product cost of all three processes tends to decrease as the composition becomes richer. However, the differences are not significant (about 3%) for all three processes.  
Figure 10: Effect of natural gas compositions on each process.  
Figure 9: TAC comparison of the base case with/without the makeup system.  
Figure 12 shows the effects of different natural gas composition on the variable cost. Even if leakage is considered, the raw material cost of all three processes is not affected by the composition of the natural gas. However, the energy cost of all three processes seems to be highly, and it tends to decrease from lean to rich. The largest differences in the variables cost can be seen for SMR (15.5%), followed by propane pre-cooled self-refrigeration (15%) and dual nitrogen expansion (9.18%).
# Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
## 4 Discussion
### Supporting the Optimum Satellite Sensor Selection for Flood Observation
For example, the SAR-2000 sensor (microwave), manufactured by Thales Alenia Space, headquartered in Italy and France, is highly recommended because it ranks extremely high in all tasks.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 1 Introduction
In this work, many kinds of defective _Cerasus humilis_ fruits (Nongda No. 6) were taken as the research object. The image information of rust spots, crack, insect damage, and intact samples of _Cerasus humilis_ fruit was collected using a hyperspectral imaging system combined with chemometrics and image processing methods. A rapid and accurate identification of _Cerasus humilis_ fruit defect type samples in terms of both spectra and images was attained, providing a theoretical basis for the development of _Cerasus humilis_ fruit grading and sorting equipment.