esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Environmental Conditions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **Long-Term** \\ \hline \(\mathbf{T_{a}}\) (\({}^{\circ}\)C) & 6.8 & 5.8 & 5.7 & 6.0 & 7.0 & 7.6 & 5.7 \(\pm\) 0.8 \\ \(\mathbf{T_{a,g.s}}\) (\({}^{\circ}\)C) & 13.5 & 14.3 & 12.3 & 14.8 & 13.4 & 13.8 & 13.6 \(\pm\) 0.8 \\ \multicolumn{8}{l}{**Start of the g.s.**} & 09.04 & 07.04 & 28.04 & 13.04 & 16.04 & 22.04 & 12.04 \\ \multicolumn{8}{l}{**End of the g.s.**} & 06.10 & 11.10 & 19.10 & 24.10 & 05.10 & 17.10 & 11.10 \\ \multicolumn{8}{l}{\(\mathbf{Pr}\) (mm)} & 671 & 864 & 956 & 560 & 848 & 992 & \(778\pm 123\) \\ \multicolumn{8}{l}{\(\mathbf{Pr}_{8,\text{mm}}\)} & 300 & 479 & 562 & 343 & 492 & 640 & \(445\pm 114\) \\ \multicolumn{8}{l}{\(\mathbf{Rg}\) (\(\mathbf{M}\)\(\cdot\)m\({}^{-2}\))} & 3592 \({}^{\ast}\) & 3474 & 3325 & 3733 & 3571 & 3381 & NA \\ \multicolumn{8}{l}{\(\mathbf{T_{s}}\) (\({}^{\circ}\)C)} & 9.4 \({}^{\ast\ast}\) & 6.9 & 6.2 & 6.8 & 6.7 & 6.9 & NA \\ \multicolumn{8}{l}{**SWC (m\({}^{3}\cdot\)m\({}^{-3}\))**} & 0.40 \({}^{\ast\ast}\) & 0.45 & 0.51 & 0.47 & 0.48 & 0.53 & NA \\ \hline \hline \end{tabular}  
* Annual Rg for 2015 was obtained at paludified spruce forest (Ru-Fyo FLUXNET ID) located 1.5 km from the study site [51]. **T\({}_{s}\) and SWC was calculated for 25 May–31 December 2015.  
\end{table} 
Table 1: Meteorological conditions in the period 2016–2020: mean annual air temperature (T\({}_{a}\)); mean air temperature calculated for the long-term growing season (LTGS, 12 April–11 October) (T\({}_{a,g.s}\)) and the growing season (g.s.) start and the end dates at meteorological station “Toropets”; annual precipitation (Pr) and precipitation sums calculated for the LTGS period (\(\text{Pr}_{g.s}\)) at meteorological station “Lesnoy Zapovednik” (MS) as well as the long-term (1991–2020) mean values of T\({}_{a}\), T\({}_{a,g.s}\), Pr, and \(\text{Pr}_{g.s}\) with standard deviations (\(\pm\)SD); annual sums of global radiation (Rg); mean annual soil temperature (T\({}_{s}\)) at 5 cm depth; and the mean annual soil water content (SWC) in the memorose spruce forest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**2015/2016** & **2016/2017** & **2017/2018** & **2018/2019** & **2019/2020** & **Long-Term** \\ \hline \(-2.2\) & \(-3.0\) & \(-3.5\) & \(-2.4\) & \(1.3\) & \(-3.5\pm 1.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean winter air temperature (1 November-31 March) in the 2015–2020 period as well as the mean long-term value with standard deviation (\(\pm\)SD) of air temperature at meteorological station “Toropets” (\({}^{\circ}\)C) [51].
## 2 H\({}_{2}\)O Collisional Broadening Measurements
### Setup for H\({}_{2}\)O Self-Broadening and Temperature Dependence at 1.37 \(\mu m\)  
Figure 1 depicts the experimental setup employed in the H\({}_{2}\)O self-broadening measurements. The setup in Figure 1 consists of a dTDLAS spectrometer equipped with a DFB laser (manufacturer: NTT Electronics) emitting at about 1.37 \(\mu\)m, a water bath with a 14 L volume (whose temperature can be varied from 22-80 \({}^{\circ}\)C) and a stainless-steel gas cell with an optical path length of 3.5 mm. The water bath was used to ensure a high temperature stability of the gas cell, and by varying the water bath temperature, the gas temperature in the gas cell was also varied. The temperature stability of the water bath, at each temperature step, was found to be better than 90 mK (both horizontally and vertically).  
For spectroscopic measurements, pure water vapor gas samples (from distilled water) were prepared (via evaporation from a preparation chamber as shown in Figure 1). The gas pressure stability 1\(\sigma\), expressing the reproducibility, was typically better than 0.1 hPa. The measurements were performed in static conditions. The wavelength of the laser was continuously swept at 139.8 Hz across the probed H\({}_{2}\)O transition. As shown in Figure 1, the light from the laser was coupled into a single mode fiber, and subsequently passed through the absorbing medium (pure water vapor sample in the gas cell) and detected after a 3.5 mm absorption path with an InGaAs photodiode (Hamamatsu, Type: G8370-01, diameter = 1 mm). The light was emitted from the fiber as a cone with a full opening angle of \(<\)8 degrees. The light was deliberately not collimated to avoid the fringes typically generated by lenses and to achieve a sufficient signal to noise ratio and stability (1 \(\times\) 10\({}^{-4}\) cm\({}^{-1}\)) of the measured spectra. This was also possible and advisable due to the high output power (\(\sim\)32 mW) of the diode laser. The detector window was inclined against the light path in order to minimize feedback back into the fiber. Parasitic absorption in the laser head (observed to be in the ppm range [14]) was assumed to be insignificant for the measurements here, as the laser and fiber connectors, placed in a homemade enclosure, were purged with N\({}_{2}\) at a constant flow rate.  
The signal of the photodetector was pre-amplified with a low noise amplifier (Femto DLPCA 200), digitized by a data acquisition card (DAC, NI PCI-6289, 625 KS/s, 18 bit) and read in a personal computer (PC). An MKS pressure (resolution: 0.1 hPa) sensor and a PT100 (connected to the gas cell and in contact with the gas molecules: resolution of PT100 was 0.001 K) were employed to record the H\({}_{2}\)O vapor pressure and the gas temperature, respectively.
## 3 Generalization of CTP  
### The Collection Tree Protocol (CTP)  
CTP is a tree-based collection protocol intended to provide best effort routing. In the CTP some number of nodes advertise themselves as tree roots of the network. Then other nodes in the network form a set of routing trees to these roots. A particularity of CTP is that the network is address-free (_i.e._, a node does not send a packet to a particular root). Instead, nodes implicitly choose a root by choosing a next hop, best routes to the sink node are determined by using a gradient metric. CTP uses expected transmissions (ETX) as its routing gradient. A root has an ETX of 0. The ETX of a node is the ETX of its parent node plus the ETX of its link to its parent node. Given a choice of valid routes, CTP chooses the one with the lowest ETX value. A perfect link has an ETX of 1, however, certain implementations such as the CTP implementation for TinyOS uses the value of 10.  
Routing loops are a problem that can emerge in a CTP network. Routing loops generally occur when a node choose a new route that has a significantly higher ETX than its old one, perhaps in response to losing connectivity with a candidate parent. If the new route includes a node which was a descendant, then a loop occurs.  
CTP addresses loops through two mechanisms. First, every CTP packet contains a node's current gradient value. If CTP receives a data frame with a gradient value lower than its own, then this indicatesthat there is an inconsistency in the tree. CTP tries to resolve the inconsistency by broadcasting a beacon frame, with the hope that the node which sent the data frame will hear it and adjust its routes accordingly. If a collection of nodes is separated from the rest of the network, then they will form a loop whose ETX increases forever. CTP's second mechanism is to not consider routes with an ETX higher than a reasonable constant. The value of this constant is implementation dependent.
## 4 Simulation Experiments and Results Analysis  
In Section 4, the proposed method is utilized to evaluate its SFDR improvement, both on simulation signals and signals from a real RF front-end. To illustrate the advantages of the best delay-based nonlinear mitigation, the spectrum after nonlinear compensation at the best delay is compared with the one without delay, and the curves of optimized power residuals \(E(\omega,\tau)\) in Equation (24) at each delay in the searching range are also provided. Then, constellation diagrams are obtained to verify the demodulation improvement and SFDR enhancement. Finally, by the energy detection (ED) method [37], sensing performance promotion is offered.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 1: The PQ8 Satellite
#### 4.1.3 Constellation Phase Times
This variation yields an average angular acceleration of 0.0102\({}^{\circ}\) day\({}^{-2}\) and a corresponding phase time of approximately 256.5 days. Figure 11: (**a**) Early orbit separation (note the scaled theta axis). (**b**) PQ8 satellite phasing polar plots. Figure 10: Phase times for PQ1\({}_{7}\) for various \(\Delta v\) magnitudes. The output \(\Delta v\) values indicate the change in velocity required in the in-track direction for the desired orbital phase times to be achieved. In practice, the pointing accuracy of the mechanism also needs to be considered. Considering the case of a 0.35 m s\({}^{-1}\) disengagement and the same pointing accuracy of 5\({}^{\circ}\), this would result in in-track and cross-track velocity components of 0.3486 m s\({}^{-1}\) and 0.0306 m s\({}^{-1}\), respectively. Simulating these conditions with this 5\({}^{\circ}\) uncertainty yields a maximum phase time of approximately 256.53 days, an increase of approximately 45 min. The small increase in phase time is attributed to the slight decrease in initial angular velocities. Similarly, the cross-track component is too small to produce any meaningful change in the orbital dynamics. As presented in Appendix A, the case studies were performed under the assumption that 90% of the incident atmospheric particles re-emitted from the satellite surface were diffuse, with the remainder being quasi-specular. To assess the sensitivity of this assumption, the simulations were extended to explore cases with \(f\) values of 0.85 and 1, where a value of 1 corresponds to a completely diffuse reflection. The ballistic coefficient of the PQ1 satellite at these\(f\) values becomes 28.2 and 28.8, respectively, accounting for a 5\({}^{\circ}\) uncertainty introduced by the attitude control subsystem. For a relative velocity of 0.35 m s\({}^{-1}\), these ballistic coefficients result in phase times of 254 and 256.8 days, respectively. Table 5 collates the phase times discussed in this section, including those of the ideal case, the attitude control uncertainty on differential drag, the attitude control uncertainty on the disengagement direction, and the uncertainty on the estimation of the ballistic coefficient. The pointing and modeling tolerances introduce an uncertainty of approximately \(+2/-1\) days over the ideal case phase time. While this uncertainty is relatively minor, larger discrepancies in the phase time are expected due to the more significant uncertainties inherent in the atmospheric model. Variations in the atmospheric density, composition, and temperature are not adequately captured and introduce inaccuracies that remain the dominant sources of error in differential drag control simulations.
## 4 Experiment
### Complex Curve Trajectories  
In practical applications, the tracking trajectory of a robot is rarely a simple straight line or curve. These trajectories usually include curves and lines with different radii. Evaluating the performance of the proposed controller in this paper via using complex curve trajectories can better simulate actual application scenarios. Thus, the effectiveness of the DMPC is evaluated using complex curve trajectories.  
We conducted trajectory tracking experiments with MPC and DMPC using the curve generated from Figure 4. In Figure 9, the advantages of the DMPC performance over MPC are well illustrated. By zooming in on the tracking results, it can be observed that DMPC is closer to the reference trajectory in the large curvature trajectory segment.  
Figure 8: Heading error of straight-line trajectory using DMPC and MPC.  
Figure 7: Tracking the linear trajectory in real-time.  
To verify the relationship Equation (27) between trajectory curvature and prediction horizon, as well as their equivalent Equation (28), and to solve for parameters \(b_{0}\) and \(b_{1}\), MPC is applied to simulations of trajectory tracking with different curvatures. The prediction horizon is adjusted until the lateral error in trajectory tracking is less than 0.01 m, resulting in the optimal prediction horizon value for the corresponding curvature. The optimal prediction horizon values for different path curvatures are shown in Table 1.  
This is because DMPC dynamically increases the prediction horizon, improving the trajectory tracking accuracy. According to Table 1, let the prediction horizon \(N_{pmn}=5\), \(N_{pmn}=15\). Figure 10 shows the prediction horizon adjustment status for complex trajectory tracking. For complex trajectory tracking within 1000 index of trajectory points, the curvature varies between 0 and 4 m\({}^{-1}\), and the prediction horizon changes proportionally in the range of 5 to 15. As the curvature increases, the prediction horizon becomes larger to obtain better tracking performance. Figures 11 and 12 discuss the comparison of a lateral error and a heading angle error of DMPC and MPC. It can be observed that the lateral error fluctuation of DMPC is within 2 mm, whereas that of MPC is within 4 mm. DMPC significantly reduces the lateral error during the tracking process, resulting in more minor fluctuations. The maximum yaw angle error of MPC is 0.3101\({}^{\circ}\), while the maximum yaw angle error of DMPC is 0.2212\({}^{\circ}\). Table 2 summarizes the quantitative comparison of  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{\kappa/m^{-1}}\) & \(\mathbf{N_{p}^{*}}\) \\ \hline
0.0 & 5 \\
0.5 & 6 \\
1.0 & 8 \\
1.5 & 9 \\
2.0 & 11 \\
3.0 & 13 \\
3.5 & 14 \\
4.0 & 15 \\
4.5 & 16 \\
5.0 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data of prediction horizon and curvature in different single curvature trajectories.  
Figure 9: Tracking the complex curve trajectory in real-time.
## 3 Results
### Determination and Comparison of Stability Coefficient  
Table 3 is the recommended stability coefficient calculated in this chapter. Figure 9 is the comparison between the recommended stability coefficient curve calculated in this chapter and the curve given in the 'thin gauge' (GB50018-2002) and the class c curve of the European steel structure design code EN1993 [10] (the European steel structure design code classifies cold-formed steel tubes as class c).  
Figure 9: Comparison of proposed stability factor with code GB 50018 and European EN 1993.  
It can be seen from Figure 9 that the proposed stability coefficient curve calculated in this chapter is relatively different from the stability coefficient curve in the \"thin gauge\" (GB50018-2002) of steel pipe support in China and is closer to the stability coefficient curve given in the European standard EN1993. The closest range of slenderness ratio section is 50-250, which is also a commonly used section of steel pipe support in engineering. In this section, the stability coefficient curve of \"thin gauge\" (GB50018-2002) is significantly higher than that of Europe and the proposed curve given in this paper. Comparing the stability coefficient, it is found that within a certain range, with the increase of slenderness ratio, the stability coefficient varies greatly, and the influence of defects is also greater. The maximum value can reach 94%. This paper lists the relative difference between the recommended stability coefficient and the 'thin gauge' (GB50018-2002), as shown in Table 4.
## 3 Results and Discussion
### Performance of Electrical Conductivity and EMI SE  
Figure 8 shows that the conductivity of pure PP is 10\({}^{-13}\) S/m (_i.e._, the orders of magnitude), which shows the classical insulating property of polymer. The combination of 5 wt% of GNs results in an increase of several orders of magnitude in the PP/GNs conductive composites, reaching 10\({}^{-6}\) S/m, which indicates that the conductive composites have the percolation phenomenon; namely, a conductive network is formed between the conductive fillers. According to the percolation theory, the percolation threshold of PP/GNs conductive composites is 2.8 vol%. An increasing content of GNs results in a more complete conductive network and a greater conductivity. When combined with 20 wt% of GNs, the conductivity of PP/GNs conductive composites reaches 0.36 S/m. Figure 9 shows that PP/CF conductive composites containing 3 wt% of CF have a conductivity of 10\({}^{-6}\) S/m. Moreover, the conductivity constantly retains the same regardless of the increasing content of CF. Such results are likely due to the initial properties of CF.  
The EMI SE of polymer conductive composites is correlated with factors such as the electrical conductivity and magnetic conductivity of composites, the thickness and geometry of shield, frequency, the distance between the shield and the field source, and the dispersion of the conductive fillers in  
Figure 8: Conductivity of PP/ GNs composites as related to various blending ratios.  
Figure 9: Conductivity of PP/ CF composites as related to various blending ratios.  
composites. Figures 10 and 11 show the EMI SE of the polymer conductive composites containing different contents of GNs or CF, respectively. The EMI SE of the polymer conductive composites increases as a result of an increasing content of GNs or CF. Due to an increase in fillers, the conductivity of the polymer conductive composites increases, which in turn augments their EMI SE [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. At some specified frequencies, the EMI SE appears to be really high, and the extremum value of both PP/GNs and PP/CF conductive composites occur at a similar frequency, which is surmised to be determined by the initial properties of fillers belonging to the carbon category [18; 43].  
Figures 8 and 9
## 5 Simulation Analysis
### Simulation Results of S21 Transmission Coefficient with Block  
The graph depicts the S21 transmission coefficient for an antenna system with a block (Figure 14) spanning a frequency range of 1 GHz to 15 GHz. The S21 coefficient quantifies the extent of energy transfer between two ports in the system, with lower S21 values signifying more signal loss and higher S21 values indicating more effective energy transfer. The graph's X-axis illustrates GHz frequency, while the Y-axis denotes the coupling coefficient in decibels (dB), spanning from \(-10\) dB to \(-80\) dB. The green dotted line represents the measured S21 values acquired from empirical testing, whereas the red dashed line illustrates the simulated S21 values gained from computational modelling. Both graphs exhibit similar tendencies across the frequency spectrum, while discrepancies in amplitude and particular frequency spots are noted. Between 2 GHz and 7 GHz, the S21 coupling coefficients range from \(-40\) dB to \(-60\) dB, indicating substantial energy transmission. At around 6 GHz, a significant peak is seen when the measured and simulated values align at roughly \(-40\) dB, signifying optimum coupling efficiency within this range. Above 7 GHz, the results oscillate between \(-40\) dB and \(-70\) dB, indicating heightened unpredictability in energy transmission. The most significant disparities between the observed and simulated values are between 12 GHz and 14 GHz when the simulation indicates reduced coupling relative to the measured data. The discrepancies may arise from external sources, such as interference or system defects, which are not considered in the simulation model. The graph offers significant insights into the energy transfer efficiency of the antenna system with the block, emphasizing locations where design alterations might improve coupling performance throughout the frequency spectrum. The comparison of measured and simulated outcomes indicates possible areas for model refinement to enhance the accuracy of real-world performance predictions.  
Figure 13: S11 Reflection coefficient for block.
# A Two-Grid Interline Dynamic Voltage Restorer Based on Two Three-Phase Input Matrix Converters
###### Abstract
The control is carried out in the reference frame \(dq\) to obtain a unit power factor in both feeders, and also in their corresponding loads. In this article, we propose a new IDVR topology based on the TTI-MC that can be used in multi-feeder systems or in a CPP. The relevance of the proposed TI-IDVR is based on the following points: (1) the proposed TI-IDVR has two three-phase inputs; in this way, it integrates the power taken from the two feeders, unlike other IDVRs; (2) the proposed TI-IDVR is able to mitigate different types of PQ problems (that is, it has a multi-functional capacity in the mitigation of disturbances); (3) the TI-IDVR has the ability to behave as a reconfiguration and compensation device in multi-feeder distribution systems and in CPPs; (4) the proposed TI-IDVR consumes power from the feeders depending on the type of disturbance that arises. For example, if a sag arises, the TTI-MC draws power from the two feeders to build the voltage on the dc-link. If the disturbance is a swell, the TTI-MC only draws power from the failed feeder. In contrast, the IDVRs proposed in the literature draw power from the healthy feeder to fulfill the needs of the energy storage device in the dc-link regardless of whether the disturbance is a sag or a swell. In this sense, the proposed TI-IDVR makes a more efficient use of the power provided by the feeders; (5) it does not use external energy storage systems; (6) it has no elements for energy storage in the dc-link, therefore, it is not necessary to control its voltage. Energy storage systems, such as capacitors, have played an important role in power electronic converters, specifically in AC-DC-AC converters. However, capacitors have been shown to be the components that most frequently fail in power electronic systems [31; 32; 33]. To overcome this drawback, a TI-IDVR based on the TTI-MC without a capacitor in the dc-link is proposed as a good alternative; (7) the compensation range against deep and long-term voltage sags is extended. The performance of the proposed TI-IDVR was verified by simulating different types of disturbances. The simulations were carried out in the MATLAB/Simulink environment. The arrangement of this work is as follows: the operation principle of the proposed TI-IDVR is discussed in Section 2. Section 3 presents the mathematical analysis that describes the TI-IDVR operation in the presence of voltage sags and swells. The modulation schemes of the CSIs and the TTI-MC are addressed in Sections 4 and 5, respectively. The control objectives of the proposed TI-IDVR and the control algorithms of the CSIs and the TTI-MC are analyzed in Section 6. The results of the simulation of the TI-IDVR are discussed in Section 7. Finally, the conclusions of this work are presented in Section 8.