esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary
### Existing VPLS Models  
Generally, five configurations are recommended for VPLS implementation:  
* Full mesh [40]: In this model, as shown in Figure 3, we apply a PW from each Provider Edge (PE) router to those of other PEs. To prevent Loop in this model, the Split horizon in layer 2 is run. A disadvantage of this model is that it will cause PW redundancy as the complexity of the network grows. Therefore, we see less implementation in practice.  
Figure 2: Exhibiting the router’s sides to take frames when establishing tunnels [4; 12].  
* Hub and Spoke [40]: In this model, as shown in Figure 4, instead of having PW between all PE routers, PW is inserted between all PE routers with a central P router. The advantages of this model are the simplicity and lower number of PW required. Also, since there is no Redundant path, it makes a loop-free connection. The drawback of this model is the single point of failure because if the central router fails, all VPLS communications will fail. Therefore, this model is not implemented in the real world.
* Partial Mesh [40]: This model, as shown in Figure 5, was created to add Redundancy to the Hub and Spoke model. Due to the structure of this model, we must disable the Split horizon, but due to having the Redundant path, we must activate Spanning Tree Protocol (STP). Since providers do not want to run STP on the network core, this model is also less common in practice.
* H-VPLS with MPLS Access Network [40]: In this model, as shown in Figure 6, the network is divided into two parts, Top Tier with Full mesh, and Bottom Tier with Hub and Spoke. This architecture will reduce the number of PWs and will be a hierarchical model. The disadvantage of this model is the single point of failure. Since each of the U-PE routers is connected to the Top Tier network with a link, if this connection is corrupted or disconnected, the entire Bottom Tier connection of the Top Tier network will be disconnected.
* H-VPLS with Q-in-Q Access Network: In the H-VPLS model proposed by the authors of reference [40], the Provider network is partitioned into ethernet-based islands, as depicted in Figure 7. These islands are interconnected using MPLS. Specifically, in the Bottom Tier of this architecture, PW Q-in-Q is utilized as the type of PW. The advantages of this topology, including easy availability through ethernet and hierarchical backing via Q-in-Q access, scalable customer VLANs (4K \(\times\) 4K), and supporting 4K customers per access domain, have encouraged us to implement this model.  
Figure 4: VPLS Hub and Spoke configuration in MPLS environment.  
Figure 3: VPLS full mesh configuration in MPLS environment.
## 4 Empirical Study
### Evaluation Measures  
To assess the ensemble models' prediction performances, we chose a set of evaluation metrics. One of the selected measures is a threshold-dependent measure (F-measure), and the other two are threshold-independent measures (AUC and Brier). All of the selected measure were employed by previous studies to assess the performance of defect prediction. The used evaluation measures are:  
* F-measurement (F-measure): is the harmonic mean of both precision and recall. F-measurement is calculated as: \[\text{F-measure}=2\times\frac{precision\times recall}{precision+recall}\times 100\] (1) \[Precision=\frac{\text{TP}}{\text{FP}+\text{TP}}\] (2) \[Recall=\frac{\text{TP}}{\text{FN}+\text{TP}}\] (3) where true positives (TP) is the number of defective instances correctly classified as defective, false positives (FP) is the number of defective instances incorrectly classified as non-defective and false negatives (FN) is the number of non-defective instances incorrectly classified as defective.
* Area under the curve (AUC): The percentage of the area that is underneath the receiver operator characteristic (ROC) curve. True positive rates are plotted against the false positive rates. The AUC metric calculates the area under the curve, and a large area indicates high performance. AUC can have a value between 0 and 1:1 indicates the best performance and zero is considered the worst performance.
* Brier [51]: The mean squared difference between the predicted probability and the actual outcome. It can be calculated using the following formula: \[\text{Brier}=\sum_{i=1}^{n}(p_{i}-y_{i})^{2}\] (4) where \(p_{i}\) is the predicted probability and \(y_{i}\) is the actual outcome. Brier has a range of values from 0 to 1, with 0 denoting the highest performance, 1 denoting the worst and 0.5 denoting a random estimate.
## 5 Simulation Results and Water Tank Experiments
### Simulation Results
#### 5.1.3 Path-Following Control Simulation  
As shown in Figure 13, in the path-following mode, six waypoints P\({}_{i}\)\((i=1,2,\cdots,6)\) are set, with the expected path being hexagonal and the path being P\({}_{1}\)-P\({}_{2}\)-P\({}_{3}\)-P\({}_{4}\)-P\({}_{5}\)-P\({}_{6}\). The coordinates of each point are P\({}_{1}\) (402, \(-\)775), P\({}_{2}\) (488, \(-\)725), P\({}_{3}\) (575, \(-\)775), P\({}_{4}\) (575, \(-\)875), P\({}_{5}\) (488, \(-\)925), and P\({}_{6}\) (402, \(-\)875).  
Figure 12: The simulation result of heading control. (**a**) Surge velocity and thrust variation curves. (**b**) Yaw angle and azimuth angle variation curves.  
Set the initial position of the WAMV near the dock, with an initial yaw angle of 0\({}^{\circ}\) and a surge velocity of 0. The path-following simulation test is carried out in four scenarios as shown in Table 4.  
Figure 13(a) shows the trajectory of the WAMV, where the dashed line represents the desired path and the solid line represents the actual trajectory. Figure 13(b) shows the variation curve of cross-track error. Figure 13(c) shows the variation curves of surge velocity and thrust. Figure 13(d) shows the variation curves of yaw angle and azimuth angle. In each figure, the red, blue, green, and yellow lines correspond to the four scenarios mentioned above.  
In all four scenarios, the USV can smoothly and quickly track the desired path, with cross-track errors of less than 0.8 m, 0.9 m, 2.7 m, and 1.3 m, respectively; the yaw angle changes are stable without overshoot, and the azimuth angle changes steadily with a small amplitude; the overshoot of the surge velocity in scenarios (1), (2), and (3) is less than 0.9%, 1.1%, and 1.4%, respectively. The adjustment time is less than 3.8 s and there is no steady-state error. In scenario (4), the surge velocity fluctuation is less than 0.1 m/s. The results show that under the same interference conditions, the control effect of surge velocity joint control on path-following (0.9 m) is significantly better than that of surge velocity open-loop control (1.3 m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **(1)** & **(2)** & **(3)** & **(4)** \\ \hline Wind speed & 0 & 3 m/s & 6 m/s & 3 m/s \\ Wind direction & - & 90\({}^{\circ}\) & 90\({}^{\circ}\) & 90\({}^{\circ}\) \\ Wave gain & 0 & 0.5 & 1 & 0.5 \\ Wave period & - & 5 s & 5 s & 5 s \\ Average wave direction & - & 90\({}^{\circ}\) & 90\({}^{\circ}\) & 90\({}^{\circ}\) \\ Surge velocity & 2 m/s & 2 m/s & 2 m/s &
\begin{tabular}{c} open-loop with \\ \(T_{\rm c}\) = 200 N \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Four different scenarios.  
Figure 13: Waypoints and the desired path.
# Investigation of Regenerative Braking Performance of Brushless Direct Current Machine Drive System
## 2 Principle of Regenerative Braking in BLDC Machine  
Regenerative braking is an essential feature of electric vehicles. Thanks to recovering energy via the generator operation of electric propulsion motor and boosting operation of the motor driver, it is possible to regain some part of kinetic energy as electrical energy for battery charging. Besides, it creates a proper braking effect particularly at higher speeds and increases overall efficiency. At a given speed, kinetic energy due to mass and rotating parts of a vehicle can be directed to battery charging while a braking action is applied. Energy, which causes the deceleration of a vehicle is partly recovered to charging the battery pack. Of course, the efficiency of generated electrical energy is dependent on combined operations of the electrical machine and power electronic circuit. If the battery is fully charged, regenerated energy can be consumed in a braking resistor, i.e., dynamic braking. In this study, the kinetic energy storage is implemented by using a flywheel coupled to the BLDC machine, to simulate a light electric vehicle powertrain. So, the braking effect can be easily calculated from the known value of kinetic energy change. However, there are retardation effects in the system other than regenerative braking, namely bearing friction and ferromagnetic (iron) loss due to interaction between stator magnetic core and rotor permanent magnets. The analysis can be complex due to the decomposition of those retarding components. To overcome this problem, a slotless BLDC motor topology which has negligible iron loss, is chosen. This way, the kinetic energy at the start of deceleration, the retarding effect of bearings, and the applied regenerative brakingcan be easily calculated. The regenerative braking action of a BLDC machine, which is driving a flywheel can be summarized as:  
\[\frac{1}{2}J_{T}\omega_{1}^{2}=\frac{\int_{0}^{T_{d}}v_{d}(t)i_{d}(t)dt}{\eta_{R }}+W_{fr}(T_{d}) \tag{1}\]  
where \(J_{T}\), \(\omega_{1}\), \(\eta_{R}\), \(v_{d}\), \(i_{d}\), \(T_{d}\), \(W_{fr}\) are the total inertia of rotating system, angular speed at the starting of deceleration, regeneration efficiency, instantaneous DC-link voltage, i.e., battery terminal voltage, instantaneous DC-link current, i.e., battery charging current, deceleration time to zero, and frictional braking energy of bearings, respectively. Frictional braking energy can be determined by calculating the difference between the free deceleration time and the deceleration time with regenerative braking. It defined as:  
\[W_{fr}(T_{d})=\int_{0}^{\theta_{d}}\tau_{fr}(\theta)d\theta \tag{2}\]  
where \(\tau_{fr}\) and \(\theta_{d}\) are the frictional torque and angular distance taken during deceleration, respectively. The main source of that viscous friction is split roller bearings of the flywheel and there is a contribution from the bearings of motor. Thanks to slotless brushless DC motor topology, there are no losses due to commutator-brush and stator core-permanent magnet interactions.
## 3 Results and Discussion
### Effect of the Bio-Augmentation on the Composting Process
#### 3.2.1 Evolution of Bacterial Growth
This was confirmed in a previous study using infrared spectroscopy analysis, thus showing that the wastes used in this study are rich in carbohydrates and polysaccharide compounds, which is considered as a source of carbon and energy for the bacterial growth as mentioned above [21]. Through this investigation, a decrease attributed to these compounds (carbohydrates and polysaccharide, which are rich in carbon) depending on the composting time was recorded, which could be assigned to the degradation and/or the assimilation of these compounds by the microorganisms of the compost, especially as TOC in negative test remains stable during the composting process (Table 2). Indeed, this decrease of TOC could explain the increase of the temperature and confirm our previous hypothesis. Similarly, for TKN from the beginning, a significant increase was recorded (\(p<0.05\)) (Table 3), which could be attributed to the organic matter decaying (proteins and peptides) to ammoniaal compounds, which are assimilated forms by bacterial isolates and their consortium [23; 24]. Together with a decrease of TOC and the increase of TKN, a significant decrease in the C/N ratio was noticed, but it still remains above the maturity standard (15-20), with values of 20.00, 19.87, and 19.30 for 4%, 6%, and 8%, respectively, in the case of pure strains, and below the maturity standard in the case of bacterial consortium 18.69, 15.70, and 13.36 for 4%, 6%, and 8%, respectively (Table 4). Nevertheless, whether for TOC, TKN, or even C/N ratios, the content remained stable over time within the negative Test (T). This could confirm that the organic matter degradation was carried out by the three strains used. Additionally, the decrease in C/N ratios was higher using the consortium compared to that using pure culture. Using these strains, whether on pure culture or their consortium, the results obtained are significantly higher and swiftly compared to the results obtained during classic composting without selected inoculums [6]. In this respect, [PERSON]'s multiple comparison test, revealed that the use of the inoculum affected positively the quality of the final compost produced. Equally, by increasing the inoculums concentration (from 4%, 6%, to 8%), the TOC content and C/N ratios were decreased; at the same time, the temperature and TKN were increased. For the consortium of the three strains (S1, S2, and S3), there is a significant effect (\(p\)-value \(<0.05\)) between the consortium concentration and the degradation of organic matter.
## 4 Experimental Analysis
### Analysis of the Spaceborne Measurement Noise
According to the variance-covariance propagation law, if the noise levels for L1 and L2 are assumed to be the same, we can calculate that the L1 and L2 measurement noises are 0.0069 m, 0.0029 m, and 0.0016 m for the Swarm-A, GRACE-A, and Sentinel-3A spaceborne data, respectively. Therefore, the average value of 0.0049 m was used as the calibration noise for our subsequent observation simulation.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### System-Level Techniques
#### 2.5.3 Dynamic Voltage and Frequency Scaling (DVFS)  
While the frequency (F) is proportional to the voltage (V), the dynamic power is proportional to the square of the voltage. Thus, lowering both (F, V) induces a cubical drop in power [8]. This DVFS hardware strategy hinders the overall performance and brings overheads penalties due to the changes of the (F, V). Software solutions come into play to counter this limitation, including task migration, injection of idle cycles and scheduling that balances the load between the processors to enhance overall energy consumption [33].  
The EA-DVFS algorithm of [34] delays task execution if sufficient energy is not available. The system runs at high speed if not. However, the energy value is counted on a per-task basis. Until the system's remaining running time at maximum speed exceeds the task's relative deadline, the system assumes sufficient energy. If the system has an energy of around 1% it will operate at a maximum speed, which will deplete the energy more quickly. When the voltages are selected and the tasks are scheduled, the EA-DVFS algorithm considers only one task in the queue of ready jobs instead of considering them all. As a result, slack time or periods of inactivity are not fully exploited to save energy. It should be noted that our contribution covers all the tasks ready to elect the one that will be executed and is based on several couples of voltage/frequency and not only the maximum and minimum value.  
In [35], DVFS is implemented in MSP430, but the disadvantages of this strategy are the synchronization mechanisms that should be considered with caution.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### Asia
Unless indicated otherwise, times are given in local solar time and trends in mm year\({}^{-1}\). **India**. [PERSON] et al. [107] analyzed GNSS IWV from five stations in India during the period 2004-2012. They found that high altitude sites had weaker annual variability than low altitude ones. However, seasonal variability was similar in the five sites. Seasonal IWV was strongly linked to the monsoon, with maximum in summer and minimum in winter. The diurnal cycle was marked in high altitude sites, while weak in low altitude ones. [PERSON] et al. [111] presented a new GNSS network in the Southwest Indian Ocean in the frame of the Indian Ocean GNSS Application for Meteorology Project, which is being deployed since November 2017. They analyzed the period from 2018 to 2020 and found that minimum seasonal values decrease away from the equator. Diurnal cycle was found to differ at both sides of Madagascar. This study was extended by [PERSON] et al. [112], where the seasonal time-scale was studied. The annual amplitude of IWV was reported to vary in the interval 10-15  mm near the equator and in the interval 20-30  mm in the subropics. The strongest daily IWV amplitudes were found in the mountainous islands. The reanalysis ERA5 was found to reproduce the main features of IWV time series. **Thailand**. [PERSON] and [PERSON] [113] studied GNSS IWV series of 11 CORS stations in Thailand to analyze seasonal features and trends. The annual variation amplitude was reported to be in the range 6-19  mm.
## 3 Chaos Oscillator Synchronization Noise Immunity
### Study Methodology  
The study methodology follows the block diagram presented in Figure 4. In this figure, \(X\), \(Y\), and \(Z\) represent the state variables in the drive chaos oscillator, while \(X^{\prime}\), \(Y^{\prime}\), and \(Z^{\prime}\) denote the corresponding state variables in the response chaos oscillator.  
In this setup, \(Z\) is selected as the synchronization signal, replacing \(Z^{\prime}\) in the response oscillator. The \(X\) and \(Y\) signals generated by the drive oscillator will be employed in the final data transmission system for chaos shift keying (CSK). Once the drive oscillator generates the signals, they are transmitted through an additive white Gaussian noise (AWGN) channel, where the noise level is controlled between \(-20\) dB and \(30\) dB in terms of the signal-to-noise ratio (SNR).  
At the receiver side, the signals pass through a low-pass filter (LPF), which is specifically tuned to preserve the main spectral components of the chaotic signal. The LPF bandwidth is determined by comparing the original chaotic signal (before noise addition and filtering) with the filtered version (without noise). The bandwidth is selected when the mean square error (MSE) reaches \(-25\) dB, ensuring the signal maintains its chaotic properties. After filtering, the synchronization signal substitutes the variable in the drive-response chaotic system to ensure the oscillators are in the same state. Once synchronization is achieved, the Pearson correlation coefficient is computed for the corresponding state variables: \(X\) and \(X^{\prime}\), \(Y\) and \(Y^{\prime}\), and \(X^{\prime}\) and \(Y^{\prime}\). This analysis provides insights into the effect of noise on chaotic synchronization in the transmission channel.  
The study is conducted for both the Colpitts and Vilnius chaos oscillators. Since each chaotic oscillator has three state variable signals, all of them are tested for synchronization capability. Table 1 summarizes the different configurations tested. For the Colpitts chaos oscillator (Figure 1), synchronization is tested using \(v_{\text{C1}}\), \(v_{\text{C2}}\), and \(v_{RL}\), while for the Vilnius chaos oscillator (Figure 2), synchronization is evaluated using \(v_{\text{C1}}\), \(v_{\text{C2}}\), and  
Figure 4: Chaos oscillator synchronization noise immunity study block scheme.  
\(v_{R1}\). The remaining signals are analyzed to assess synchronization quality across different configurations.  
By systematically testing noise immunity across multiple configurations, this methodology provides knowledge on the robustness of chaotic synchronization.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach
### Moving in Formation Using Virtual Linkage  
In order to illustrate the bi-directional flow and hierarchical architecture presented in Figures 5 and 6, the detailed virtual linkage approach is illustrated to enable a group of agents to move in formation.  
Table 1 shows the moving-in-formation algorithm using a virtual linkage. In step 1, the virtual linkage is aligned with the agents. The virtual linkage then moves to track the predefined trajectory in step 2. In step 3, the agents update their destinations which enable them to move like a mechanical linkage. In step 4, individual agent is controlled to move to the desired position using a local controller. The individual steps will be explained in more details in the following section.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Effect of the Number of Moving Objects  
In this simulation, we varied the number of moving objects from 10,000 to 100,000 to study how the number of moving objects affects the performance of the proposed method.  
Figure 8: Effect of the query distance on the communication cost and the server computation cost.  
**(a)** Total number of messages vs. the query distance; **(b)** The amount of CPU-time vs. the query distance.  
Figure 9: Effect of the number of moving objects on the communication cost and the server computation cost. **(a)** Total number of messages vs. the number of moving objects; **(b)** The amount of CPU-time vs. the number of moving objects.  
As shown in Figure 9, as the number of moving objects is increased, the overhead of all methods is increased in terms both of the communication cost and the server computation cost. The communication cost of CM is proportional to the number of moving objects because they periodically send location-updates to the server. In contrast, the communication cost of DM(naive SSP-tree) and DM(SSP-tree) is slightly increased due to the benefits of utilizing the computational capabilities of moving objects. Similarly, the server computation cost of DM(naive SSP-tree) and DM(SSP-tree) is less sensitive to the number of moving objects than CM. We can observe from Figure 9 that DM(SSP-tree) performs best in terms both of the communication cost and the server computation cost.
# Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming
## 2 Materials and Methods
### Software and Preprocessing  
Data processing and classifier analysis were performed in Python using Scikit-Learn (version 1.0.1) [53] and SciPy (version 1.7.1) [54]. The preprocessing pipeline was implemented using the MNE-Python toolbox (version 0.24.0) [55]. The dataset was converted to BIDS-EEG format [56] and managed and loaded with MNE-BIDS (version 0.9) [57]. The Riemannian classifier from Section 2.6.3 was implemented using pyRiemann (version 0.2.7). Statistical tests were performed in R (version 4.1.2).  
The EEG recorded at 2048 Hz was re-referenced off-line to the average of the mastoids. The reference electrodes were dropped from the analysis. Data were subsequently filtered between 0.5 Hz and 16 Hz using forward-backward filtering with a fourth-order Butterworth IIR filter. The EEG signal was corrected for ocular artifacts using independent component analysis (ICA) by rejecting components that correlated with the bipolar EOG channels vEOG and hEOG, according to adaptive Z-score thresholding. Finally, epochs were cut from 0.1 s to 0.6 s after stimulus onset. No baseline correction was performed since this affects the temporal covariance of the data, violating the [PERSON] structure assumption [38].