esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Rare earth ions (RE\({}^{3+}\)) have many optical applications due to their unique spectral properties, in relation to the features of their electronic energy levels, covering UV, visible and IR [1]. This makes them excellent candidates for lighting [2; 3; 4], displays [5], biosensing [6; 7; 8], optical amplification [9], anticounterfeiting [10; 11] and solar cells [12; 13; 14]. Among them, Yb\({}^{3+}\) ions provide near infrared (NIR) emission around 950-1100 nm, which is interesting for many of the previously cited applications. However, the absorption and emission of Yb\({}^{3+}\) ions are due to transitions between only two levels: \({}^{2}\)F\({}_{7/2}\) (ground state) and \({}^{2}\)F\({}_{5/2}\) (excited state). Therefore, Yb\({}^{3+}\) ions are not able to directly absorb UV or visible photons, and other lanthanides are often added as codopants to provide an alternative excitation path by energy transfer, and sometimes to offer the additional possibility of photon multiplication by quantum cutting [15; 16; 17; 18; 19]. Nevertheless, codoping with other RE\({}^{3+}\) ions still do not solve the limited excitation and absorption bandwidths and their small excitation cross sections, which are major limitations for their implementation in thin film downconverting devices and other specific applications.  
In the past, silicon [20; 21; 22; 23] or silver aggregates [24; 25; 26; 27] were proven as broadband efficient sensitizers for Er\({}^{3+}\) ions. Ag sensitization was successfully observed also for other RE\({}^{3+}\) ions (Eu, Tb, Yb, Dy, Sm) [28; 29; 30; 31; 32; 33; 34; 35; 36]. In this paper, we will further investigate the interaction between Ag aggregates and Yb\({}^{3+}\), analyzing the role of the glass-ceramic (GC) matrix and its crystalline structure on the optical properties of the composite material. A GC is a homogeneous dispersion of ceramic nanocrystals in a glass. EXAFS studies suggest that RE\({}^{3+}\) ions should be preferentially located in the nanocrystals [37], resulting in better spectroscopic properties. Additionally, zirconia has lower phonon energy and higher refractive index than silica [38]. By combining Ag enhancement with Yb\({}^{3+}\)-doped silica-zirconia GC, the possibility to obtain a more efficient optical device is discussed in this paper. Compositional, structural and optical measurements were combined for retrieving information on the role and nature of Ag-aggregates controlled by Ag\({}^{+}\)\(\leftrightarrow\)Na\({}^{+}\) ion exchange, followed by annealing at different temperatures, and their interaction with Yb\({}^{3+}\) ions for obtaining efficient sensitization and photoluminescence boosting.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO
### Impedance Sensitivity Analysis
#### 3.1.3 Impedance Sensitivity of PLL Parameters  
Both voltage and current reference set at 1.0 pu increase the \(K_{p}\) and \(K_{i}\) and capture the impedance curve dynamics. The results are shown in Figure 12. From (a) we can know that when \(K_{p}\) increases alone, \(X_{PMSG}\) curve changes lightly while the equivalent \(R_{PMSG}\) curve becomes \"wider\", which means the frequency range of negative resistance is larger. Thus, larger \(K_{p}\) is not conducive to system stability. Whereas, the variation of \(K_{i}\) does not have obvious impact on stability.
## 5 Buoyancy-Driven Square Cavity
### Case \(Ra=10^{6}\), \(\epsilon=0.01\), Constant Viscosity with Adaptivity  
In order to adapt the mesh, the Variational Multiscale (VMS) a posteriori error estimator is applied to this problem [57; 58; 59]. In particular, the version for compressible-incompressible flows is found in [60]. As shown in [61], the adapted meshes attained with this method are very smooth.  
Figure 9 shows the first two adapted meshes obtained with the naive error estimator, driven by the Polner error-stabilization matrix, the \(L_{1}\) norm over the new mesh and an error tolerance \(1.0\times 10^{-3}\), taking the global minimum element size among all dependent variables. The mesh size was pruned within the interval \([1\times 10^{-4},60]\). Indeed, the mesh-size evolution in the adapted mesh is very smooth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference Solution** & **Nu** & **Nu\({}_{\text{min}}\)** & **Nu\({}_{\text{max}}\)** \\ \hline vahl Davis [46] & 8.817 & 0.989 & 17.925 \\ Le Quere [56] & 8.8252 & 0.97946 & 17.5360 \\ Vierendeels [52] & 8.8257 & & \\ Masud [35] & 8.81490 & & \\ \hline
**Present Method** & **tau** & **Nu** & **Nu\({}_{\text{min}}\)** & **Nu\({}_{\text{max}}\)** \\ \hline Full NS & Classic & 8.0833 & 0.803 & 13.932 \\ Full NS & HH & 8.8524 & 0.984 & 17.704 \\ Full NS & Polner & 8.8526 & 0.984 & 17.703 \\ Boussinesq SUPG & HH & 8.8540 & 0.982 & 17.689 \\ Boussinesq SUPG & Polner & 8.8542 & 0.982 & 17.688 \\ Boussinesq SGS & HH & 8.8632 & 0.985 & 17.704 \\ Boussinesq SGS & Polner & 8.8634 & 0.985 & 17.703 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nusselt numbers along hot vertical wall. \(\text{Ra}=10^{6}\), \(\epsilon=0.01\), constant \(\mu\). Present methods on a \(140\times 140\) uniform mesh with various taus.  
Figure 9: Buoyancy-driven square cavity. Initial mesh and adapted meshes for \(\epsilon=0.01\).  
Figure 10 shows the better accuracy obtained with the adapted meshes for a much smaller number of degrees of freedom. Indeed, the dimensionless Nusselt numbers obtained with the coarsest adapted mesh basically match the results obtained with the finest mesh. Thus, VMS adaptivity succeeds at improving accuracy with respect to a solution computed in a uniform mesh.
## 4 Results and Discussion
### Compressive Strength  
In this research, 98 cubic (100 mm) concrete samples are made, and the compressive strength of the specimens is measured. Since several samples were constructed with the same percentage of materials, the mean compressive strength of the specimens is used in Figure 8, which includes three diagrams for three water-to-binder ratios: (a) 0.44, (b) 0.49, and (c) 0.62.  
For specimens with a water-to-binder ratio of 0.44 and a curing period of 56 days, the mean value of the compressive strength reaches 6.20 MPa for samples with 0.5% nano-MgO, showing a 7.4% increase in comparison to the control samples. With the same percentage of materials, the mean compressive strength reaches 6.30 MPa for samples with 1% nano-MgO, which is 9.12% more than the control samples. As observed in Figure 8a,b, in all cases, there is a positive effect of nano-MgO on compressive strength. By increasing the water-to-binder ratio, the effect of nano-MgO becomes clearer. The role of nano-MgO on the compressive strength of the samples is different depending on the water-to-binder ratio. For instance, when the water-to-binder ratio is 0.44, the maximum compressive strength is belonging to samples with 1% nano-MgO. It is noteworthy that the same amount of nano-MgO in samples with a higher water-to-binder ratio leads to a smaller compressive strength. It seems that there are two reasons for this behavior. The first one is that nano-MgO has an expansive feature which means the production of hydration needs more space; therefore, excessive pressure is created in the texture of concrete by the completion of the hydration process. The other reason is related to the high specific surface of nano-MgO. Regarding this matter, during the dispersion process, nano-MgO can be swelled, which increases the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model’s Properties** & **GEP 1** & **GEP 2** & **GEP 3** & **GEP 4** & **GEP 5** & **GEP6** & **GEP 7** & **GEP 8** \\ \hline Number of & & & & & & & & \\ chromosomes & 30 & 30 & 30 & 35 & 35 & 35 & 35 \\ \hline Number of head size & 8 & 8 & 8 & 8 & 10 & 10 & 10 & 10 \\ \hline Number of genes & 3 & 3 & 3 & 3 & 3 & 3 & 3 \\ \hline Linking function & Addition & Addition & Multiplication & Multiplication & Addition & Addition & Multiplication & Multiplication \\ \hline Constant per gene & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Properties of different Gene Expression Programming (GEP) models.  
viscosity of suspension, and as a result, when the amount of nanomaterial is growing, the uniform dispersion of nano-MgO becomes difficult. By non-uniform dispersion of nano-MgO, the agglomeration will be inevitable, and compressive strength can be decreased. Figure 8 shows the standard deviation (STD) for compressive strength data, as well. In statistics, the standard deviation is a factor that means how the dataset is dispersed and shows that every piece of data tends to be close to the mean value.  
Figure 8: Cont.
## 5 Simulation Results
### Navigation and Victim Localization  
This is a complete case where the swarm navigates through the area full of obstacles and some places with potential victims. The experiment uses 23 agents and 6 victims distributed in 5 victim places, given there are 2 victims in the same place as shown by Figure 10 this case depict how the swarm covered the area navigating through obstacles and localizing potential victims at the same time. The simulation shows how the proposed model generates sub-swarms with more drones in places where there are more victims or where the probability of finding victims is greater. This case is represented in the virtual environment simulation, where there is a place with two victims and the model assigns a sub-swarm with 6 drones. This victims place has more assigned drones that the other places. VRep Video simulation of Navigation and Victim Localization in Ref. [42].
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 4 Experimental Validation
### Analysis of Prediction Results of Normal Data of Gearbox Operation
#### 4.2.4 Comparative Analysis of the Iteration Effect of Different Models  
To evaluate the performance and fitting effect of the IALO-GRU algorithm during parameter optimization, the fitness function's MSE values, shown in Figure 11 show that the modified IALO-GRU model stabilized after 21 iterations. Relatively, the PSO-GRU model also stabilized after 15 iterations, but its loss value was slightly higher than the IALO optimization algorithm compared to the LSTM prediction method. The GRU network shows a faster convergence rate and smaller loss values under the same optimization algorithm. Thus, the improved IALO-GRU model exhibits faster convergence under the present experimental conditions, thus verifying the advantages of the proposed IALO-GRU algorithm in the solution process.  
Figure 8: Different model evaluation.
## 2 System Description and Analysis
### ACV Model  
In this section, based on two reference frames, the earth coordinate system frame \(O_{E}X_{E}Y_{E}Z_{E}\) and the body coordinate system frame \(o_{B}x_{B}y_{B}z_{B}\), are shown in Figure 1. \(X_{E}\) points north, \(Y_{E}\) points east, \(Z_{E}\) points into the earth. The origin \(o_{B}\) is assumed to be located in the vessel center of mass. The \(x_{B}\) points from aft to fore, \(y_{B}\) points to right starboard. \(z_{B}\) points to the bottom of the ship.  
The four-degree of freedom kinetic equations of an air cushion vehicle is:  
\[\begin{split} m(\dot{u}-vr)&=X\\ m(\dot{v}+ur)&=Y\\ I_{x}\dot{p}&=K\\ I_{z}\dot{r}&=N\end{split} \tag{5}\]  
where m is the Mass of ACV, \(I_{x}\) and \(I_{z}\) are the moment inertia of \(x\)-axis and \(z\)-axis around the body coordinate system, \(u,v,p,r\) are the surge velocity, the sway velocity, the roll angular velocity and the yaw rate of the ship, respectively. Then vector form of (4) is written as:  
\[\mathbf{M_{H}\dot{v}+C(v)v=\tau} \tag{6}\]  
where \(\mathbf{M_{H}}\) denotes ACV's hull inertia matrix. \(\mathbf{v}=[u,v,p,r]^{T}\) is the velocity vector in the body-fixed frame. \(\mathbf{C(v)}\) denotes the skew-symmetric Coriolis. \(\mathbf{\tau}=[X,Y,K,N]^{T}\) denotes the forces and moments. They are given as:  
\[\mathbf{M_{H}}=\left[\begin{array}{cccc}m&0&0&0\\ 0&m&0&0\\ 0&0&I_{x}&0\\ 0&0&0&I_{z}\end{array}\right]\mathbf{C(v)}=\left[\begin{array}{cccc}0&0&0&-mv\\ 0&0&0&mu\\ 0&0&0&0\\ 0&0&0&0\end{array}\right]\]  
Figure 1: Scheme diagram of the trajectory tracking system of Air Cushion Vehicle (ACV).  
According to the idea of separate modelling, the forces and moments are specifically expressed as:  
\[\begin{split} X&=X_{a}+X_{h}+X_{m}+X_{p}\\ Y&=Y_{a}+Y_{h}+Y_{m}\\ K&=K_{a}+K_{h}+K_{m}+K_{res}\\ N&=N_{a}+N_{h}+N_{m}+N_{r}\end{split} \tag{7}\]  
where \((\cdot)_{h},(\cdot)_{a},(\cdot)_{m}\) are hydrodynamics, aerodynamics, and air momentum, respectively. \(X_{p},N_{r}\) are the force and moment generated by air propeller and air rudder. \(K_{grav}\) is roll recovery moment. Then, we conduct specific analysis.
## 4 Results of the Bridge Dynamic Responses
### Dynamic Behavior at the Expansion joints
#### 4.3.1 View-S2b and Results  
View S2b observed the expansion joint from the longitudinal direction right underneath the bridge. The radar was set in the dynamic mode with a maximum distance of 100 m. The sampling rate was 200.19 Hz, with a 2.0-m resolution in the range. The data collection started at 18:33 on 13 July 2018  
Figure 13: Vertical temporal displacement maps of the 192-m span for the analyzed six cases. X axis (70 m\(\sim\)170 m) shows the distance to pier #9; dashed lines indicate the middle of the 192-m span.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Accelerometer**} & \multicolumn{2}{c}{**RADAR**} \\ \hline
**Transverse/Hz** & **Vertical/Hz** & **LOS Displacement/Hz** \\ \hline \multirow{3}{*}{0.561} & & 0.267 \\  & & 0.597/0.599 \\  & 0.689 & 0.686 \\  & & 0.820 \\ \multirow{3}{*}{0.959} & & 0.978 \\  & 1.295 & 1.264/1.290 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency values obtained from radar data, compared to accelerometer results.  
and lasted more than 20 min. Figure 14 shows the view S2b geometry and a range profile of the radar backscattering. A very high SNR (>65 dB) was measured due to the corner reflector effect of the steel beams and a relative short range. Different loading cases were observed during the radar acquisition. We selected the four loading cases described in Table 4 as representative ones.  
Figure 15 shows the vertical displacement time histories of the radar bin 21, 23, 26, 27 and 28. The displacement histories include two parts: the first part is mainly caused by the loading of passing train and the second one is its aftershock. The shape of the displacement history is related to the direction of passing train, which has the same characteristics as those at view S2a. The duration of the first part depends on the length of the train and its speed: it takes approximately three seconds for a train with 8 carriages and six seconds for a train with 16 carriages. This agrees with a carriage length of 25 m. The duration of the second part is more than 15 s for both 8- and 16-carriage trains. The maximum vertical displacement of the second part is less than 1 mm for all the cases, while the 16-carriage train induced larger vertical displacement (4.2 mm) in the first part one respect to the 8-carriage train.  
In each case, the differences of the vertical displacement amplitudes of the measured bins in the second part are very small, while those in the first part have a clear relation with the position of the bins: the farer the distance to the pier 1#0, the larger the displacements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **Track No.** & **Direction** & **Carriages** & **Speed (km/h)** \\ \hline
**1** & \�
# Performance Evaluation of Active Noise Control for a Real Device Casing
## 4 Conclusions  
The idea of noise reduction by active control of device casing walls has been successfully developed and employed for a real washing machine. The only modification to the washing machine was adding actuators and placing a reference microphone inside. The proposed control system was able to reduce the emitted noise globally at monitoring microphones by about 5.3 dB on average, and by 8.3 dB on average at error microphones. Such noise reduction is comparable to the difference in  
Figure 9: Power spectral densities of control signals for spinning at 1200 rpm.  
sound pressure level between spinning at the louder 1200 rpm and the quieter 1000 rpm. The average noise reductions for individual frequencies, harmonics of drum rotational frequency, were even higher.  
Such noise reduction is clearly perceptible and can make a substantial difference for people exposed to the noise for a prolonged period of time. The authors believe that it can be attractive both for customers who seek quieter devices and for producers to increase their competitiveness.  
The washing machine also generates random noise which is harder to reduce for the active control system. Due to the spatially-distributed nature of generation of the random wide-band noise component, more reference sensors could be beneficial to the system, helping to satisfy the causality constraint. Nevertheless, the periodic component of the noise (e.g., due to reciprocal motion in the device) often dominates the noise spectrum for numerous devices in real life. Tonal noise components are also often more disturbing for people, compared to a wide band of random noise. Hence, the authors believe that many applications could largely benefit from the proposed solution, for which the proof of concept has been presented in this paper. The authors hope to encourage other researchers to investigate the concept of active casings for alternative applications and that the details provided concerning the employed active control system will be helpful to them.  
Conceptualization, K.M., S.W. and M.P.; methodology, K.M., S.W. and M.P.; software, K.M.; validation, K.M., S.W. and M.P.; formal analysis, K.M., S.W. and M.P.; investigation, K.M., S.W. and M.P.; resources, K.M., S.W. and M.P.; data curation, K.M. and S.W.; writing--original draft preparation, K.M. and S.W.; writing--review and editing, K.M., S.W. and M.P.; visualization, K.M. and S.W.; supervision, M.P.; funding acquisition, M.P.; project administration, M.P. All authors have read and agreed to the published version of the manuscript.  
The research reported in this paper has been supported by the National Science Centre, Poland, decision number DEC-2017/25/B/ST7/02236, and by State Budget for Science, Poland, in 2019.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Substrate**} & \multirow{2}{*}{**Volume (mm\({}^{3}\))**} & \multirow{2}{*}{**Bandwidth**} & \multirow{2}{*}{**Gain (dBi)**} \\  & & & **Dielectric Constant and Loss** & & & \\ \hline
[47] & Screen-printed Ag ink and PDMS & - & \(60\times 60\times 2\) & 105 & 6.987 \\
[48] & Inkjet-printed Ag layer and 65/35 & 1.229 and 0.001 & \(37.4\times 28.1\times 1.6\) & 24.5 & 5.09 \\  & polyester cotton woven fabric & & & & \\
[49] & GO film and cellulose filter paper & 3.06, - & \(119.4\times 70\times 0.46\) & 100 & 12 \\
[11] & Copper sheet and jean fabric & 1.7 and 0.025 & \(40\times 20\times 0.3\) & 300 & 4.48 \\
[50] & Copper sheet and jean fabric & 1.7 and 0.085 & \(100\times 100\times 6\) & 240 & 2.42 \\
[51] & Nickel-/copper-plated textile and thick felt textile material & 1.2 and 0.044 & \(150\times 150\times 4\) & 120 & 3.5 \\  & Screen-printed Ag layer and nonwoven/knit fabric spray coated with BT dielectric ink & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the antennas proposed in previous works.
###### Abstract  
Paint loss is one of the main forms of deterioration in historical wall paintings, generally restored by the application of chromatic reintegration. In the specific case of outdoor exposed paintings, it is essential to find a binder that will withstand diverse weather conditions. Since chromatic reintegrations have to be compatible with the original painting, fresco paint mock-ups were manufactured and compared to chromatic reintegrations made with an aqueous colloidal dispersion of silica nanoparticles as binder. The physical compatibility was studied by colour spectrophotometry and measurements of static contact angle, gloss, and roughness values, together with a peeling test, stereomicroscopy, and polarised light microscopy. They were also characterised from a mineralogical, chemical, and molecular point of view using X-ray diffraction, X-ray fluorescence and Fourier-transform infrared spectroscopy. The microtexture was studied by scanning electron microscopy with energy-dispersive X-ray spectroscopy. Chromatic reintegrations showed similar roughness and lower gloss values than frescoes, and the nano-silica binder ensured the natural breathability of the wall. Overall, the chemical nature of pigments was highly influential. The reintegrations with silicate-based pigments were more homogenous, with hardly any fissures, while those carried out with sulphide- or oxide-based pigments were severely cracked. The use of verdigris is discouraged due to the lack of affinity between the binder and the pigment.  
p _Applied sciences Article_  
**Physico-Chemical Compatibility of an Aqueous Colloidal Dispersion of Silica Nano-Particles as Binder for Chromatic Reintegration in Wall Paintings**  
**[PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Physico-Chemical Compatibility of an Aqueous Colloidal Dispersion of Silica Nano-Particles as Binder for Chromatic Reintegration in Wall Paintings**  
**Keywords:** painting restoration; inorganic pigment; organic pigment; inpainting; fresco