esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 2 System Models for RIS-Assisted Systems  
Table 1 summarizes the latest research on channel estimation in different RIS-assisted system settings. We noticed that most research has been carried out in narrowband for one or more users and channel estimation has been accomplished in cascaded channels. For the remaining categories, more research is needed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System Setup** & **Antenna Setup** & **Main Results** \\ \hline \multirow{8}{*}{MISO} & \(\bullet\) & Usage of ON/OFF training reflection pattern at RIS [29]; \\  & \(\bullet\) & Usage of Full-ON training reflection pattern at RIS [30]; \\  & \(\bullet\) & Estimation based on compressed sensing by exploiting sparsity of mmWave channels [31]; \\  & \(\bullet\) & Deep learning techniques for estimation [32; 33; 34; 35]; \\  & \(\bullet\) & Kalman filter in high-mobility scenarios for estimation [36; 37]. \\ \cline{1-1}  & \(\bullet\) & Compressed sensing by exploiting low-rank/sparse channels for estimation [38; 39; 40; 41; 42]; \\ \cline{1-1}  & \(\bullet\) & Matrix factorization/decomposition techniques for estimation [43; 44; 45]; \\ \cline{1-1}  & \(\bullet\) & Deep learning for THz channels for estimation [35]; \\ \cline{1-1}  & \(\bullet\) & Estimation in high mobility scenarios with fixed-position RIS [46; 47]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: RIS channel estimation for different system setups [28].  
From Table 1 we observed that there are many different methods on how the channel can be estimated in RIS-assisted MISO and MIMO systems. For this reason, we searched and present below additional research on deep learning techniques, high user mobility scenarios, estimation in cascaded channels, use of neural networks and ZF detection techniques. We focused on these scenarios because most of the literature uses and develops them. In addition, we present other methods, such as tensor algorithms, which are used in more recent literature.
## 2 Materials and Methods
Precise geolocation of the two events in the WGS84 datum was inferred based on one DigitalGlobe VHR satellite image acquired on 29/05/2013 and available in Google Earth. Photographic and video evidence published on the web in relation to the two events, as well as literature, web news, reports, and press releases, were used to extract supporting information about the geolocation and spatial extent of the two events. In order to verify if any surface reflectance changes occurred, HR imagery acquired by the Sentinel-2B satellite of the Copernicus multispectral Sentinel-2 constellation before (18/01/2018) and after (28/01/2018) the January 2018 event was also analysed. In particular, among the 13 visible to near and short-wave infrared spectral bands acquired by the MultiSpectral Instrument (MSI) onboard Sentinel-2B, the 10-m spatial resolution B2 (490 nm), B3 (560 nm), and B4 (665 nm) bands in the visible region of the spectrum were used. No Sentinel-2 image could be retrieved for the December 2014 event, because neither of the two Sentinel-2 satellites had been launched yet at that time.
## 5 Fuel Flowrate Control Related to Thermal Management
### Causes of Temperature Rise  
The fuel system should firstly satisfy the functions in all engine operational conditions and then absorb the thermal load of the engine and airborne electric-mechanical system as heat sink [65]. When the fuel temperature is lower than the cooking temperature, the fuel is the best heat sink from TMS operation cost. However, kerosene-type aviation fuel has the tendency to degrade at the temperature higher than its cooking temperature and forms solid deposits [66]. Decreasing the power loss of the fuel metering and supplying will help the fuel to absorb more thermal load in airborne mechatronic systems. The absorbed thermal load by the fuel and the power loss of the fuel itself result in the fuel temperature rise. In addition to the power losses caused by the fuel pump and HX, there are two main power losses in the fuel system, which lead to the temperature rise [67].  
1. The first power loss is the by-pass power loss, which mainly occurs in FMS flowrate control with fixed displacement HP (high pressure pump) pump. Moreover, for the fuel supplying circuit from LP to HP, the speed of the LP is determined by AGB driven by the aeroengine, and the rest flowrate in which LP exceeds HP also causes by-pass power loss because the fuel pump must ensure that the aeroengine can work normally in all flight profiles. This type of the power loss will have an impact on the temperature of the fuel in HP inlet and fuel-recycling port of the fuel tank.
2. The second power loss is the throttling loss of metering valve and the pressure compensated valve in the fuel system, and load sensing pump-based FMS motor variable speed pump-based FMS can cancel the pressure loss of the pressure difference compensator. The rise in temperature caused by throttling takes up a large proportion in the total temperature.
## 2 System Modeling and Converter Control  
### Wind Turbines and Modeling PMSG  
The operational performance of a wind turbine can be modelled from the mathematical relationship between wind speed and mechanical power as follows:  
\[P_{m}=0.5p\pi R^{2}S_{w}^{3}C_{p}(\lambda,\beta) \tag{1}\]  
\[\lambda=\frac{\omega_{m}R}{S_{w}} \tag{2}\]  
Here, the power coefficient \(C_{p}\) represents the rotor aerodynamics as a function of both the pitch angle of the rotor blades and tip speed ratio (TSR).  
The PMSG modeling facilitates the generation of electricity from mechanical energy and a dynamic model is derived using a two-phase synchronous reference frame, in which the \(q\)-axis is 90\({}^{\circ}\) ahead of the \(d\)-axis in accordance with the direction of rotation. In three-phase systems, such as PMSGs, each phase quantity has a time-varying characteristic. Using [PERSON]'s transformation, which is the projection of the phase quantities onto a rotating two axes reference frame, the AC quantities can be converted to DC quantities with time independence. The \(abc\) to \(dq0\) transformation is represented in the matrix form:  
\[\left[\begin{array}{c}u_{d}\\ u_{q}\\ u_{0}\end{array}\right]=\sqrt{\frac{2}{3}}\left[\begin{array}{ccc}\cos( \theta_{r})&\cos(\theta_{r}-\frac{2\pi}{3})&\cos(\theta_{r}+\frac{2\pi}{3})\\ -\sin(\theta_{r})&-\sin(\theta_{r}-\frac{2\pi}{3})&-\sin(\theta_{r}+\frac{2\pi} {3})\\ \frac{\sqrt{2}}{2}&\frac{\sqrt{2}}{2}&\frac{\sqrt{2}}{2}\end{array}\right] \left[\begin{array}{c}u_{a}\\ u_{b}\\ u_{c}\end{array}\right] \tag{3}\]  
The inverse Park's transformation is:  
\[\left[\begin{array}{c}u_{a}\\ u_{b}\\ u_{c}\end{array}\right]=\sqrt{\frac{2}{3}}\left[\begin{array}{ccc}\cos( \theta_{r})&-\sin(\theta_{r})&\frac{\sqrt{2}}{3}\\ \cos(\theta_{r}-\frac{2\pi}{3})&-\sin(\theta_{r}-\frac{2\pi}{3})&\frac{\sqrt{2 }}{2}\\ \cos(\theta_{r}+\frac{2\pi}{3})&-\sin(\theta_{r}+\frac{2\pi}{3})&\frac{\sqrt{2 }}{2}\end{array}\right]\left[\begin{array}{c}u_{d}\\ u_{q}\\ u_{0}\end{array}\right] \tag{4}\]  
In Equations (3) and (4), \(u_{abc}\) and \(u_{dq0}\) express the stator voltages, stator currents or flux linkages of the AC machines, respectively. Applying the equality \(u_{0}=0\) for balanced conditions, the voltage function of PMSG in the \(d\)-\(q\) axis reference frame [4] is given by  
\[V_{ds}=-R_{s}I_{ds}+-L_{d}\frac{dI_{ds}}{dt}+\omega_{e}L_{q}I_{qs} \tag{5}\]  
\[V_{qs}=-R_{s}I_{qs}-L_{q}\frac{dI_{qs}}{dt}-\omega_{e}L_{d}I_{ds}+\omega_{e} \Psi_{f} \tag{6}\]  
To complete the PMSG mathematical model, its mechanical equation is required [26]; the electromagnetic torque equation is described by  
\[T_{e}=1.5p_{n}\Big{[}(L_{d}-L_{q})I_{ds}I_{qs}+I_{qs}\Psi_{f}\Big{]} \tag{7}\]
## 4 Experiment Setup and Results
### Measuring the First Charging Time  
The first charging time is also an important metric to be known, since one should have an estimation of how long the process should take from the very beginning, if the capacitor has to be fully discharged before the recharging process can take place. Analog discovery hardware and a waveform software oscilloscope have been used to measure the first charging time. The line that signifies the capacitor's voltage keeps increasing until it reaches the threshold, which in turn produces the first pulse at Vout and at this point, the threshold value was found to be 1.26 V. The first charging time of the 50 mF has been taken at six different distances and the results are shown in Figure 10(a), which demonstrates that the charging time increases as the distance increases.
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 6 Conclusions and Future Work  
The C-DCOP framework extends the DCOP framework to solve applications with continuous variables. Addressing the limitations of the utility function and computational overheads for the existing C-DCOP algorithms, we designed a fully decentralized algorithm (PLSA) and a global search anytime algorithm (PGSA). In PLSA, each agent only exchanges value assignments with neighbors to avoid privacy leakage. In PGSA, agents pass utility through pseudo-tree to achieve global search and guarantee anytime property. In summary, there is less computational and memory overhead in PLSA, and it can obtain high-quality solutions faster. PGSA guarantees the anytime property and achieves higher quality solutions, but it requires more computational and memory overhead for utility passing. Extensive evaluations on three benchmark problems demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms in terms of the runtime and solution quality. However, there are two limitations: (i) PLSA adopts a stochastic strategy to escape from the local optimum, which may lead to instability in the solution; and (ii) PGSA converges slowly in the early period since it performs the global search. Therefore, the future research avenues can be summarized as follows: (i) we plan to combine some other heuristic methods to improve the quality of and maintain stability in PLSA's solution; and (ii) we will try to modify the search strategy of PGSA to improve its convergence speed.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
## 2 Materials and Methods
### Analysis  
For all comparative results in this research, the manual measurements are taken as a baseline reference to eliminate other influences and ambiguity between methods. Comparing two methods directly with each other would lead to the issue of not knowing what method is the accurate one and which has the offset, since in practice, both will have their own errors. Hence, using the manually measured measurements as reference abstracts the results from the application. Thus, the results can also be compared to other methods and variability in clothing preference is excluded. Therefore, sometimes, extra measurement examples are provided besides only the chest and hip circumference as an additional insight on the accuracy of other measurement predictions. To compare the results, a set of three statistics are selected.  
First, the root mean square error (RMSE) is calculated to determine the unsigned difference between the manual measurements and the given prediction model. The RMSE is used to indicate how far off the predictions are in comprehensible units (mm for most measures).  
Second, the relative error of all errors is calculated in relation to the base measurement, represented as percentages. With this method, the results are signed, and thus, they provide an indication whether the result is, on average, over- or undersized compared to the original measurements.  
Third, the intraclass correlation coefficient (ICC) is determined to give an indication of the predictability of the regression model between the input parameters and the resulting predictions. Overall, a score above 0.75 is considered excellent, and is in this research highlighted in all charts. Values between 0.6 and 0.75 are considered good, but for this research, they are deemed insufficient to use. For use in a real-life commercial application, perhaps even a bar of 0.9 should be reached to fulfil requirements. However, that depends greatly on the circumstances and the allowed margins.  
The combination of these three measures gives the best overall insight on the accuracy and precision of the predictions. For example, for the given sports garments in this research, where sizes in the size chart increase in steps of 6 cm, an RMSE of below this 6 cm would be perfectly acceptable. Relative errors could point to consistent deviation in predictions that could, thus, be improved upon by adding extra data samples or offsetting the prediction results. Finally, the ICC is the most used descriptive statistic method to determine the quality of the prediction.
# Thermoeconomic Analysis of Concentrated Solar Power Plants Based on Supercritical Power Cycles
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Number of Converging Absorber Panels**} \\ \cline{2-10}  & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **14** & **16** \\ \hline Solar subsystem _TCC_ (Mio.\$) & 108.345 & 102.403 & 99.439 & 95.643 & 93.090 & 91.590 & 91.031 & 91.721 & 97.130 \\ Power cycle _TCC_ (Mio.\$) & 84.641 & 84.641 & 84.641 & 84.641 & 84.641 & 84.641 & 84.641 & 84.641 & 84.641 \\ Annual electricity production (GWh\({}_{a}\)) & 109.5 & 109.5 & 109.5 & 109.5 & 109.5 & 109.5 & 109.5 & 109.5 \\ Payback period _(PB)_ (years) & 11.916 & 11.549 & 11.366 & 11.132 & 10.974 & 10.882 & 10.847 & 10.890 & 11.224 \\ Levelised Cost of Energy (LCOE) (\$/$/kWh\({}_{a}\)) & 0.178 & 0.150 & 0.148 & 0.145 & 0.143 & 0.142 & 0.141 & 0.142 & 0.146 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of the total capital cost and levelised cost of energy of each solar thermal power plant, based on a partial-cooling sCO\({}_{2}\) cycle. In the specific case under study, the optimal energy and exergy efficiency is achieved with around 10-12 panels, while the configuration with the minimum LCOE corresponds to 14 panels. This result depends on the reference values and other parameters assumed for the economic study. In any case, the obtained LCOE values are of the same order as the currently estimated values [1], and it is expected that these values will further decrease as economies of scale are developed for these STPPs. Future lines of work include a more detailed study of the annual analysis, considering transients, startups, and shutdowns. This will involve examining the behavior of the plant throughout the year and optimising its performance under different operating conditions. Additionally, the study aims to explore the integration of thermal energy storage systems within the STPP. This will investigate the feasibility and the benefits of incorporating storage technologies in order to enhance the dispatchability and flexibility of the solar thermal power plant. Furthermore, a more detailed optimisation study of specific components is planned, such as the solar receiver and the primary heat exchanger.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 5 Discussion  
In this paper, the JRC-CWG and the DR-JRC-CWG design methods were proposed, which have low sidelobe levels and good communications performances. The designed JRC-CWG and the DR-JRC-CWG can be used for automotive JRC systems. However, the proposed methods have to make a trade-off between the radar and communications perfor  
Figure 10: PSLRs of the designed JRC-CWG-16-PSK and DR-JRC-CWG-16-PSK versus communications rates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**JRC Waveform** & **Communications Rate (Mbps)** & **PSLR (dB)** \\ \hline DR-JRC-CWG-16-PSK & 9.6 & \(-30.72\) \\ JRC-CWG-16-PSK & 9.6 & \(-33.01\) \\ OPP-16-PSK & 10.24 & \(-17.33\) \\ LFM-BPSK & 2.56 & \(-19.27\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum communications rates and the corresponding PSLRs of the JRC waveforms.  
mance. In our future work, we will try to avoid this compromise. Moreover, polarization is an important characteristic that can be considered in waveform design to improve the performance of JRC waveforms [38]. The multiple-input multiple-output (MIMO) technique is not only used in radar but also in communications, and can also be employed in JRC-CWG design in our future work.
## 2 IEEE 1588 (PTP)
### Time-Synchronization Mechanism of PTP
#### 2.2.1 End-To-End Mechanism  
Figure 1a presents the synchronization mechanism of the E2E method. In the E2E method, four types of synchronization messages are exchanged between the master and slave, through which the delay and offset are calculated and time is synchronized. First, the master transmits a synchronization message to the slave at time t\({}_{1}\), and the slave receives it at time t\({}_{2}\). Next, the master node stores t\({}_{1}\) in a follow-up message and transmits it to the slave node. The slave then transmits a delay-request message at time t\({}_{3}\), and the master receives it at time t\({}_{4}\). Finally, the master stores t\({}_{4}\) in the delay-response message and transmits it to the slave. Through this process, the slave can acquire four timestamps (t\({}_{1}\), t\({}_{2}\), t\({}_{3}\), and t\({}_{4}\)) to calculate the offset and propagation delay. Equations (1) and (2) are used to calculate the delay and offset between the master and slave nodes. In this process, the network must provide a link with a symmetrical structure, in which the transmission rates of the master and slave in both directions are identical.  
\[delay=\frac{[(t_{4}-t_{1})-(t_{3}-t_{2})]}{2} \tag{1}\]\[offset=t_{2}-t_{1}-delay=\frac{\left[(t_{2}-t_{1})-(t_{4}-t_{3})\right]}{2} \tag{2}\]  
The four timestamps used in Equation (1) are the timestamp values generated by the master and slave nodes; the timestamp can be classified as a software- or hardware-based timestamp depending on the measurement method. The accuracy of the timestamp is highest when generated close to the physical layer, and, depending on whether the device supports PTP, the hardware-based timestamp is measured in the media access control (MAC) or physical (PHY) layer.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 3 Initial Structural Parameter Calculation and Design Optimization Process
### System Design Optimization Process  
After the basic parameters of the optical system (pupil diameter, field of view, spectral range) are set up in Zemax software, the initial structure data of the optical system obtained in Section 3.1 are input into the lens data to complete the simulation of the initial structure of the optical system by ZEMAX. The image quality of the initial structure of the optical system is poor and needs to be optimized. The optimization process is as follows.  
Firstly, the radius of curvature of the optical system is selected as the optimization variable to optimize the optical system because the radius of curvature is easier to control, there are no excessive requirements, and the optimization results are more obvious. Then, other parameters such as mirror spacing, glass thickness, and aspheric coefficient are selected as the optimization var.  
After completing the previous step, the image quality of the initial structure of the optical system is improved. Next, the optimization function editor is opened, and the optimization operands are entered for optimization. The optimization operands set are focal length (EFFL), local ray Y coordinate (REAY), arc vector field curve (FCGS), meridian field curve (FCGT), and coma (COMA). At this point, the initial structure data and optimization parameters are basically inputted completely. Both the radius of curvature and the aspheric coefficient are set as variables for optimization, and the image quality of the optical system disposal structure is further improved.  
Finally, the primary mirror is designed off-axis based on the co-axial optical system. The calculation in the previous section selects a suitable off-axis amount, and here, the off-axis amount of the primary mirror is taken as \(-\)300 mm. In the software, a line is inserted into the column above the primary and secondary mirrors, respectively, and the surface type is selected to be \"Coordinate Break,\" which establishes a breakpoint to facilitate the off-axis design. For one of the primary mirror breakpoint lines behind the \"Decenter Y\" column, enter \(-\)300; select the two mirror breakpoint lines. \"Tilt About X\" is set as a variable for optimization. At the same time, the primary mirror off-axis amount and primary and secondary mirror tilt are set as a variable; the optical system will be optimized until it meets the requirements of the system design index.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 5 Selected HPC Approaches in InSAR Fundamental Functional Stages
### SAR Data Focusing
Although a homogenous comparison of the different approaches is not practicable, most of the mentioned parallel solutions are adequately supported by detailed performanceanalysis. In conclusion, these investigations demonstrate how the SAR focusing on a single image nowadays might be concretely realizable in a timely fashion, with an execution time indicatively ranging from a minute to a few seconds, depending on the considered kind of parallel architecture and implementation strategy.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 2 Categorization of Bridge Boost Converter Topologies
### Conventional Hybrid Interleaved PFC Topology Based on a Boost Converter  
An interleaved hybrid BBC topology with three levels was proposed in [31]; please see Figure 4. This converter may feature an efficiency around 97% when using a PI controller. The authors reported that \(THD_{i}\) normative is reached when an EMI filter is used. Basically, currents flow between the branches of the converter, allowing not only the reduction of the ripple current and but also the losses when compared with the interleaved converter of two levels; nonetheless, the addition of a third branch increases its cost. In general terms, this converter is suggested for medium voltage applications because of its low ripple and \(THD_{i}\) compared to the conventional BBC converter. In addition, this three-level topology is also recommended in low-voltage applications--this together with independent soft switching circuits considering an independent design control for each switch. The interleaved hybrid BBC topology complies with approximately the same function as the interleaved topology (with three states) presented in Section 2.2; then, the interleaved topology is recommended instead of the hybrid one considering that the hybrid is limited by three states.
## 2 Laboratory Setup  
The OAC-DES proposed in this paper was tested in a carefully designed laboratory setup in order to demonstrate possibility of using the (1+1)-DES as an optimization algorithm for online adaptive control. The laboratory setup was comprised of three elements:  
* a non-linear, single-degree-of-freedom robotic mechanism,
* a controller,
* a computer system with power electronics.  
The central device was a non-linear mechanism with a DC-motor windings electrical time constant of approximately 1 ms and a mechanical time constant varying from 5 ms to 25 ms. The latter varies depending on the mode of the non-linear mechanism made by spring and sliding gear-box (see Figure 1). The non-linear mechanism is controlled by a controller with current and velocity feedback control loops. The computer system is used to create an online run-time environment, and the power electronics to amplify and transform the signals. In the following subsections, these elements are described in detail.