esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation of the Model-Based Algorithm and the Experimental Setup
### Measurement Setup  
Real thermograms of rotor blades of in-service wind turbines are required to validate the algorithm. Therefore, thermographic measurements are performed on a 1.5 MW wind turbine of the type GE 1.5 sl, manufactured by General Electric (Boston, MA, USA), with a hub height of 62 m and rotor diameter of 77 m. The thermograms are taken with an actively cooled infrared camera called imageIR 8300, manufactured by InfraTec GmbH (Dresden, Germany). This thermographic camera has an InSb focal plane array with a format of \((640\,\times\,512)\) pixel\({}^{2}\) where 1 pixel \(\hat{=}\,15\,\upmu\)m, and is sensitive to light of a wavelength of 2-5 \(\upmu\)m. The dynamic range is 14 bit, the integration time is set to 1600 \(\,\upmu\)s and the noise equivalent temperature difference is about 25 mK at 30\(\,\)C. The measurements are taken over multiple days on a wind turbines at a measurement distance of 100 m. Due to the length of the rotor blades and to improve the spatial resolution, a 200 mm telephoto lens is used. Consequently, the rotor blades are captured in segments. For this purpose, the thermographic camera is triggered externally with an optical trigger camera when the rotor blade is positioned horizontally, i.e., parallel to the ground. An example of a typical measurement setup can be seen in Figure 7, where thermograms of the suction side of the rotor blade are acquired.  
With the measurement setup, thermographic measurements of different rotor blade segments are taken, where the turbulence wedge have varying sizes as well as different CNR values, which are used to validate the wedge detection algorithm. Furthermore, the distance between wedges differs between the measurements, which has not been considered in the verification and which demonstrates the applicability of the wedge detection algorithm to real thermograms.  
Figure 7: Photo of experimental setup for field measurements of wind turbines in motion. The thermographic camera, which takes thermograms of the wind turbine in the background, the optical trigger camera, which triggers the thermographic camera, and the laptop, which acquires the images, can be seen.
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
###### Abstract  
In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user's physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizing the maximum network E2E latency. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). In order to see the performance change according to the type of RL agent, we implemented a single-agent environment where the controller is an agent and a multi-agent environment where each node is an agent. Since the agents were unable to identify E2E latencies in the multi-agent environment, the state and reward were formulated using the estimated E2E latencies. To precisely evaluate the RL-based scheduler, we set out benchmark algorithms to compare with which a network-arrival-time-based heuristic algorithm (NAT-HA) and a maximum-estimated-delay-based heuristic algorithm (MED-HA). The RL-based scheduler, first-in-first-out (FIFO), round-robin (RR), NAT-HA, and MED-HA were compared through large-scale simulations on four network topologies. The simulation results in fixed-packet generation scenarios showed that our proposal, the RL-based scheduler, achieved the minimization of maximum E2E latency in all the topologies. In other scenarios with random flow generation, the RL-based scheduler and MED-HA showed the lowest maximum E2E latency for all topologies. Depending on the topology, the maximum E2E latency of NAT-HA was equal to or larger than that of the RL-based scheduler. In terms of fairness, the RL-based scheduler showed a higher level of fairness than that of FIFO and RR. NAT-HA had similar or lower fairness than the RL-based scheduler depending on the topology, and MED-HA had the same level of fairness as the RL-based scheduler.  
min-max criterion; reinforcement learning; double-deep Q-learning; prioritized experience replay; end-to-end latency; fairness Article
## 6 Results and Discussion
### Full Horizon (40-Day) Performance
#### 6.2.2 Warm-Start Initialization Performance  
Across all disturbance handling approaches, warm-starting the solver (CW, PW, IW) consistently yielded substantial improvements in computational efficiency without adverse effects on agronomic or economic outcomes. Average solver times, as shown in Table 7 and Figure 11, were reduced by approximately 30-60% compared to their cold-start counterparts. For instance, NMPC-CW (0.320 s) was considerably faster than NMPC-C (0.457 s), and NMPC-IW (0.308 s) outperformed NMPC-I (0.908 s). This reduction in both the mean and variance of solver times is crucial for ensuring the feasibility and reliability of NMPC in real-time greenhouse operations. The warm-started controllers (CW, PW, IW) exhibited both lower average solver times and a more concentrated distribution of solution times compared to their non-warm-started counterparts, as illustrated in Figure 11.
## 2 The Attitude Algorithm  
The proposed redundant attitude system aims to be used in the estimation of the attitude angles of the small satellites, monitoring in this way the attitude of the satellite towards an Earth frame. As general architecture, the attitude system can be organized as shown in Figure 2 and includes the following: (1) the redundant detection unit; (2) three data fusion blocks, which implement software with the same data fusion method; and (3) the attitude algorithm block which implements software the numerical processing procedures for the fused gyro data obtained from the three detection channels.  
Figure 1: Detection unit with three gyros clusters in redundant linear configurations.
## 3 Key Technologies Achieved by the System
### Duplicated Data Filtering and Missing Data On-line Filling
#### 3.3.2 Fitting-Interpolation Combinational Algorithm Estimation Based on Temporal Correlation  
Essentially, the estimation algorithm based on temporal correlation is an interpolation algorithm. The latest missing data are estimated based on the current acquisition values and historical acquisition values. To fully utilize historical data and current acquired data, missing data are estimated using an interpolation-fitting combinational algorithm. There are many interpolation methods, mainly including the linear interpolation method, the spline interpolation method, the cubic polynomial interpolation method, and the Lagrange's interpolation method. These interpolation methods were used to estimate 100 groups of data records based on the true acquisition data of the system, and methods with the minimum SSE were chosen. The test results showed that when temperature, CO\({}_{2}\) concentration and NH\({}_{3}\) concentration were estimated using the linear interpolation algorithm and humidity was estimated using the cubic polynomial interpolation algorithm, estimation errors were the smallest. Similarly, when temperature was fitted with a quadratic polynomial and humidity, CO\({}_{2}\) concentration and NH\({}_{3}\) concentration were fitted with linear functions, estimation errors were the smallest. Finally, weights of estimated values from interpolation algorithms and estimated values from fitting algorithms were determined based on the principle of minimum SSE, and their weighted averages were the estimated values based on spatial correlation.
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
## 4 Results and Discussions
### Electro-Optic Measurements
The values obtained from the LBS are \(A\)s \(\sim\)19 dB and \(A\)p \(\sim\)27 dB, and from the FLC are \(A\)1 \(\sim\)21 dB and \(A\)0 \(\sim\)19 dB. The main source of cross-talk in FLC is the imperfect alignment of LC molecules. In one of the previous work, [PERSON] et al. developed a free-space switch based on the FLC material [38]. Cross-talk in the developed \(1\times 14\) single-stage switch varies from \(-\)21 dB to \(-\)45 dB in between the addressed and non-addressed fibers with the switching time only in the order of sub-millisecond. In another work, [PERSON] et al. demonstrated a fast \(2\times 2\) free-space photonic switch exhibiting cross-talk of \(-\)33 dB [39]. This switch was based on FLC exhibiting switching times in the order of microseconds. In this study, the obtained crosstalk of approximately \(-\)19 dB can be modified to obtain better results by introducing cross-talk cleaning stages to the final switching stage.
## 2 Basic Principles
### Principle of F-P Etalon Frequency Selection  
The F-P etalon is an optical device that selectively suppresses incident light of different wavelengths using multi-beam interference effects. Its key structure consists of a resonator formed by two parallel reflective surfaces with the same reflectivity. Between the two reflective surfaces, there is an F-P etalon medium with a certain thickness. Typically, the same uniform medium is used for a given etalon, and different materials have different refractive indices. Common choices include air-gap etalons and fused silica solid etalons, among others [21].  
Due to the interference effect of light, the F-P etalon has different transmittances for light of different frequencies. Let \(T\) represent the transmittance of the F-P etalon for a light beam, and for incident light with a wavelength of \(\lambda\), it follows [20; 22] the equation  
\[T(\lambda)=\frac{1}{1+\frac{4R}{(1-R)^{2}}\sin^{2}(\frac{2\pi mdcos\theta 1}{ \lambda})} \tag{2}\]  
where \(R\) represents the reflectivity of the two reflective surfaces of the F-P etalon, \(d\) is the thickness of the F-P etalon, and \(\theta 1\) is the angle of refraction of the incident light, which is commonly approximated as cos\(\theta 1\) equals \(1\). It can be observed that the frequency-selective  
Figure 1: Basic model of the ECDL.  
performance of the F-P etalon is primarily determined by these two parameters, \(R\) and \(d\). Analysis of this periodic transmittance function reveals that when the wavelength of the incident light beam and the thickness and refractive index of the F-P etalon satisfy the relationship \((2\pi nd)/\lambda=k\pi(k=0,1,2,\dots)\), the transmittance reaches its maximum value \(T_{\max}=1\). Accordingly, within one frequency cycle, the F-P etalon has the maximum transmittance for light at the central frequency. At this point, when the reflectance \(R\) is constant, the F-P etalon finesse \(F\), i.e., \(4R/(1-R)^{2}\), is fixed. As the wavelength moves away from the central frequency, \(sin^{2}(2\pi ndcos\theta/\lambda)\) increases. This ultimately results in an increase in the denominator of Equation (2), leading to a decrease in the transmittance of the light wave. The net effect is that the further the wavelength is from the central frequency, the stronger the inhibitory effect of the F-P etalon on it. Therefore, selecting the F-P etalon parameters appropriately in the external cavity of a laser can serve the purpose of longitudinal mode selection.
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 2 Theoretical Modeling of Linear Alternators
The code integrates the linear theory of thermoacoustics, firstly formulated by [PERSON] [11] and subsequently refined by [PERSON] [12], which has demonstrated accurate precision for pressure amplitudes of up to 10% of the mean pressure. The code performs 1D numerical integration of the momentum, continuity, and energy equations through each segment of the acoustic network. These equations, considered in their linear approximation (low-amplitude) version, are:  
\[\frac{dp}{dx}=-\frac{j\omega\rho_{0}}{A(1-f_{v})}U, \tag{17}\]  
as far as the momentum equation is concerned,  
\[\frac{dU}{dx}=-j\frac{A\omega}{\rho_{0}c^{2}}\bigg{[}1+\frac{\left(\gamma-1 \right)f_{k}}{\left(1+\varepsilon_{s}\right)}\bigg{]}p+\beta\frac{dT_{0}}{dx} \bigg{[}\frac{\left(f_{\kappa}-f_{v}\right)}{\left(1-f_{v}\right)\left(1- \Pr\right)\left(1+\varepsilon_{s}\right)}\bigg{]}U, \tag{18}\]as far as the continuity equation is concerned, and  
\[\frac{dT_{0}}{dx}=\frac{H-\frac{1}{2}\text{Re}\bigg{\{}p\tilde{U}\bigg{[}1-\frac{T _{0}\beta(f_{\kappa}-\widetilde{f}_{\
u})}{(1+\varepsilon_{s})(1+\text{Pr})(1- \widetilde{f}_{\
u})}\bigg{]}\bigg{\}}}{\frac{\rho_{0}c\rho_{U}|U|^{2}}{2A \omega(1-\text{Pr})|1-f_{\
u}|^{2}}\text{Im}\bigg{\{}\widetilde{f}_{\
u}+ \frac{(f_{\kappa}-\widetilde{f}_{\
u})(1+\varepsilon_{s}f_{\
u}/f_{\kappa})}{( 1+\varepsilon_{s})(1+\text{Pr})}\bigg{\}}-(KA+K_{s}A_{s})}, \tag{19}\]  
with  
\[\frac{1}{\eta_{ae}}=1+\frac{R_{e}}{R_{L}}+\frac{1}{\left(Bl\right)^{2}}\frac{R_{ m}}{R_{L}}{\left(R_{e}+R_{L}\right)^{2}}. \tag{11}\]0  
as far as the energy equation is concerned. In the above equations, \(\rho_{0}\) is the mean density, \(c\) is the sound velocity, \(\gamma\) is the ratio of isobaric to isochoric specific heats, \(T_{0}\) is the mean temperature, \(\beta\) is the thermal expansion coefficient, Pr is the Prandtl number, \(c_{P}\) is the gas isobaric specific heat, \(K\) and \(K_{s}\) are the thermal conductivity of the gas and solid, respectively, \(H\) is the enthalpy flux along the direction of acoustic vibration \(x,f_{\kappa}\) and \(f_{\
u}\) are spatially averaged thermal and viscous functions depending on the geometry of the gas pores, \(A\) and \(A_{s}\) are the area of the transverse section open to gas flow and obstructed by the solid, respectively, \(\varepsilon_{s}\) is the ratio of specific heats per unit area of gas and solid, and \(q\) is the heat exchanged with external thermal reservoirs along the direction normal to \(x\) per unit length in the heat exchangers. The solutions found for adjoining segments are then matched by imposing the continuity of pressure and volume flow rate at their junction. For further details the reader is referred to [12].
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 2 Unit Cell Modeling and Design  
The proposed equivalent circuit model of the unit cell can set up a direct link among the reflection phases, the bandwidth, and the physical layout of the MSRRA.
## 6 Numerical and Simulation Results
### Equilibrium Analysis  
As shown in Figure 6, the simulation results (Sim in the figure) match well with the equilibrium analysis (EA in the figure) and analytical results (Ana in the figure). For S-ALOHA protocol, the maximum difference is found in the average request number, which is 5.54%. For TDMA protocol, though the simulation results are shifting to the right, they have the same tendencies with the equilibrium analysis and analytical results.  
As shown in Figures 7 and 8, when the available RBs are insufficient, the ACB scheme would ensure the system converges to a saturated equilibrium state. Then according to Equations (44) and (48), for both the S-ALOHA and TDMA protocols, the average success probability is increasing linearly with the number of available RBs and the resource utilization ratio remains constant. The transmission limit has no impact on the success probability and resource utilization ratio under such a condition. However, as shown in Figure 8, the average request number for \(Q=10\) is much higher than that for \(Q=5\).  
Figure 5: Performance results of pure S-ALOHA protocol and pure TDMA protocol with different packet size (Q = 10, W\({}_{\text{BO}}\) = 0): (**a**) Average success probability; (**b**) Resource utilization ratio; (**c**) Average request number.  
As shown in Figure 7, when the number of available RBs is around the saturation threshold, the average success probability is still increasing but the growth is decreasing. The success probability for \(Q=10\) is a little higher than that for \(Q=5\). And the request number for \(Q=10\) is still higher than that for \(Q=5\). This demonstrates that the device can increase its success probability by trying more times.  
When the number of RBs is large enough, almost all the devices can succeed in their transmission attempts. However, the resource utilization ratio is decreasing due to the underutilization of RBs. On the other hand, as shown in Figures 7-9, the performance results for \(Q=10\) and \(Q=5\) are almost the same.  
As a conclusion, the system performance can be improved dramatically by increasing the available resources when it is in the saturation state. However, the performance improvement would be slight when the system is in the non-saturation state. Increasing the transmission limit can impose a positive impact on the success probability at a cost of increasing in the average request number, which means more energy consumption and higher delay.  
Figure 6: Comparison of simulation results with equilibrium analysis and iterative analytical results (\(\lambda=50\), Q = 10, \(W_{\text{BO}}=0\)): (**a**) Average success probability; (**b**) Average request number.  
Figure 7: Comparison of average success probability between equilibrium analysis and iterative analytical results (\(W_{\text{BO}}=0\)): (**a**) S-ALOHA protocol; (**b**) TDMA protocol.
## 6 Conclusions  
This work is the first study reviewing the instrument requirements based on the end-users needs. The gaps of the future European EO spaceborne infrastructure were identified to select the potential technological areas for complementing and refining the Copernicus infrastructure. The exploration of the requirements and future European EO technology led to detecting and identifying the measurements with gaps and the emerging need to monitor the polar regions. Based on these results, future instruments, services and technology areas to upgrade were identified. Some of the most important characteristics, from which all the Copernicus services can benefit, are latency time and lower revisit time. Specifically, reductions of both revisit time from 24 h to 3 h and product delivery in the marine services from 24-48-h to 1 h would support, e.g., marine services, enabling polar navigation, enhanced marine real-time weather forecast, oil and gas exploration and oil spill remediation.  
**Author Contributions:** [PERSON] and [PERSON] identified the measurement with gaps. [PERSON], [PERSON], [PERSON] and [PERSON] defined the user requirements. [PERSON] and [PERSON] identified the potential technologies to cover the measurement gaps. [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] revised it.  
**Funding:** This project was funded by the EU H2020 ONION project, under Grant Agreement 687490. It has also received support from the projects AGORA(ESP2015-70014-C2-1-R) of the Spanish Ministry of Economy and Competitiveness and the ICREAAcademia Award from the Catalan Government.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Reversible Hydrogen Storage Using Nanocomposites
## 2 Nanocomposite Materials for Hydrogen Storage
### MgH\({}_{2}\)

Table 2: Hydrogen desorption reaction enthalpy (\(\Delta\)H\({}_{\textbf{dec}}\)) of commercial bulk MgH\({}_{2}\), nanocrystalline MgH\({}_{2}\) prepared by ball milling for 9–12 h, and 2 mol% nano-Ni doped nanoMgH\({}_{2}\). The scanning electron microscopy (SEM) images of bulk and micro/nanocrystalline MgH\({}_{2}\) given in Figure 5 aligns with reduced crystallite size inferred from XRD profiles. As for the sharp XRD peaks of bulk MgH\({}_{2}\) (Figure 4) corresponds to the crystallite size of at least 200 nm as obtained from the SEM microstructure (Figure 5, left). Similarly, the reduction in the XRD peak intensity of bulk MgH\({}_{2}\) phase and the corresponding FWHM broadening correlates the smaller sizes of the MgH\({}_{2}\) grains at least an order of magnitude lesser, for example 27 nm as derived from the Figure 5, right image. Figure 3: Differential scanning calorimetric (DSC) profiles of commercial MgH\({}_{2}\), 10.5 h ball milled (BM) micro/nanocrystalline MgH\({}_{2}\), and micro/nanocrystalline MgH\({}_{2}\) doped with nano-Ni catalysts at different mole concentrations and ball milled for additional 15 min. Figure 2: Thermogravimetric hydrogen release profiles of bulk MgH\({}_{2}\), ball milled (BM) micro/nanocrystalline MgH\({}_{2}\) and nano-Ni catalyst doped micro/nanocrystalline MgH\({}_{2}\). Therefore, the reduction in thermal decomposition (up to 70-100 \({}^{\circ}\)C) of molecular hydrogen to atomic hydrogen as demonstrated in Figures 2 and 3 were very well supported by the X-ray and SEM analysis of both bulk and nanocrystalline MgH\({}_{2}\). Figure 4: X-ray diffraction spectra of bulk commercial MgH\({}_{2}\), micro/nanocrystalline MgH\({}_{2}\) by mechanical pulverization of MgH\({}_{2}\) for at least 15 h and mechanically pulverized MgH\({}_{2}\) subsequently doped with metal nanoparticle such as nano-Ni with 0.1 mol%. Figure 5: SEM of (left) commercial bulk MgH\({}_{2}\) and (right) micro/nanocrystalline MgH\({}_{2}\) prepared via mechanical pulverization for at least 10 h.