esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Single-Frequency MOPA Using a Silica Fiber Highly Doped with Yb\({}^{3+}\)
## 3 Results and Discussion
The SNR was 61 dB when the seed power was 1.5 W, and 68 dB for 2.5 W. It was limited by amplified spontaneous emission (ASE), which decreased as the seed power was increased. The PER was measured with a half-wave plate and a polarizing beam splitter. It varied with pump power between \(\sim\)19 dB and \(\sim\)35 dB, with a mean value of \(\sim\)27 dB. To quantify the impact of PD on the amplifier's output power, the latter was recorded continuously over 150 h at a nominally constant pump power, starting from an output power of 3 W. This test was carried out at lower power than the maximum output power (80 W) to avoid residual thermal effects. Performing this test at higher power would have introduced thermal fluctuations in the fiber amplifier output power, which would have partly masked the photodarkening effect that the test aims to characterize, thereby rendering the outcome of the test inconclusive. To validate this decision from the standpoint of photodarkening, we estimated the values of the photodarkening loss at these two powers. Simulations using a validated commercial code showed that, at 3 W, the inversion along the fiber was approximately uniform with an average value of 12.2%. In comparison, at maximum power (80 W), it had a similar distribution with an average only slightly higher (14.1%). Since the equilibrium PD loss is proportional to inversion [30], the loss at the 3-W power used in long-term measurements and at 80 W are very close to each other, and the PD behavior is expected to be similar at both powers. Based on the core loss inferred from PD measurements at 600 nm in fiber I (200 dB for an inversion of 50%, see Figure 1b), the proportionality factor is 4 dB/m per percent of inversion. Therefore, the influence on the photodarkening loss at 600 nm of the 1.9% higher inversion (at 80 W compared to 3 W) is 7.6 dB/m. As mentioned earlier, this loss value is 80-100 times lower at 1064 nm, or only 0.076-0.095 dB/m, and \(\sim\)40 times lower at 976 nm, or 0.19 dB/m, which, as pointed out earlier, is less important than the loss at 1064 nm. The difference in photodarkening losses at 3 W and 80 W is clearly negligible. Furthermore, at increased temperature the PD loss actually decreases [31]. Therefore, this second effect at least partially cancels the first effect,which is very weak in the first place. Based on the foregoing, a low-power test is adequate to characterize the power degradation at any power. The result (Figure 4) indicates that the power degraded minimally, decreasing asymptotically to 2.4 W (20%) over 150 h. Experimental observations suggest that the increase in fiber temperature at higher power reduces the equilibrium loss due to PD [31]. Therefore, at higher power the fractional power drop may be even lower. We expect that at the maximum pump power of 107 W, after degradation, the MOPA would output \(\sim\)80% of 81.5 W, or \(\sim\)65 W. Our numerical prediction of the SBS power threshold shows that this concept can be scaled to output powers up to 100 W for an available pump power of 130 W.
## Results
### Effect of Dealcoholization on Volatile Compounds  
The aroma and taste of wine are influenced by the composition of volatile compounds ([PERSON], 1971; [PERSON], 1971). More than 1000 volatile compounds of various chemical classifications (alcohols, esters, fatty acids, aldehydes, terpenes, ketones, and sulfur compounds) are found in wine, and about 400 volatile compounds are formed during wine fermentation ([PERSON], 1971). The removal of alcohol from wine is generally accompanied by the removal of water and some volatile compounds during dealcoholization ([PERSON] et al., 2019). Table 2 shows the main volatile compounds and their concentrations found in all original and dealcoholized wines. For better visualization, all the identified volatile compounds were classified into five groups: esters, alcohols, acids, terpenics and C13-norisoprenoids, and others (carbonyl compounds, aldehydes, and volatile phenols). A total of 57 different volatile compounds were identified and quantified in all wines, including 23 esters, 16 higher alcohols, 8 organic acids, 8 terpenics and C13-norisoprenoids, and 2 others. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & **RI** & \multicolumn{3}{c}{**White Wine**} & \multicolumn{3}{c}{**Rosé Wine**} & \multicolumn{3}{c}{**Red Wine**} & \multicolumn{3}{c}{**Odor**} & \multicolumn{3}{c}{**Odor**} \\  & **DB-** & **WAX** & **CK** & **RO** & **VD** & **CK** & **RO** & **VD** & **CK** & **RO** & **VD** & **Descriptor * & **(Hg/L)** & **Aroma** \\ \hline Esters & & & & & & & & & & & & & & \\ Ethyl acetate & 897 & 61* & 41* & 6* & 398* & 49* & 3* & 40* & 16* & ND & Fruity & & & \\ Isobutyl acetate & 1019 & ND & ND & ND & 6* & 3* & ND & ND & ND & ND & & Fruity & 160 & 2 \\ Ethyl butanoate & 1041 & 38* & 5* & ND & 19* & 9* & 1* & 31* & 16* & ND & Fioral, fruity & 400 & 1,2 \\ Isoamyl acetate & 1128 & 2155* & 730* & 516* & 5422* & 1777* & ND & 1122* & 453* & 173* & Fruity & 30 & 2 \\ Ethyl benzoate & 1238 & 2152* & 18* & 2* & 1712* & 2* & 696* & 160* & 1 & 1* & Fruity & 14 & 2 \\ Heyyl acetate & 1277 & 30* & 19* & 9* & 9* & 1157* & 196* & 86* & 72* & ND & Furity, herb & 670 & 2,6 \\ Ethyl & 1296 & 9* & 1* & ND & ND & ND & ND & ND & ND & ND & / & / & / \\ Ethyl & 1338 & 1* & ND & ND & 3* & ND & ND & ND & ND & ND & Furity & 220 & 2 \\ Ethyl & 1350 & 7* & 1* & 1* & 1* & 17* & 3* & ND & 8* & 2* & ND & / & / & / \\ Hepyl acetate & 1377 & ND & ND & ND & 6* & ND & ND & ND & ND & ND & Foral & 1500 & 1 \\ Ethyl octanoate & 1439 & 8991* & 92* & 18* & 3856* & 206* & 17* & 250* & 58* & 6* & 6* & \multicolumn{1}{c}{Foral, fruity} & 240 & 1,2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentrations (\(\mu\)g/L) of volatile compounds present in the original wines and the fractions dealcoholized (0.7% \(\
u\)/\(\
u\) ethanol) with both methods.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 4 Proposed HT, DCT, and SVD and Block-Based Watermarking Method  
The present work reports a blockwise image watermarking method to insert two watermarks, a digital image (LOGO) and information about the owner or the host image (MetaData). The proposed method is based on the HT and DCT as a spatial-frequency representation of the cover image with the HVS characteristics to add imperceptibility to the watermark. In addition, an SVD strategy adds robustness against attacks.
## 4 Control Design
### Evolutionary Algorithms  
The parameters \(k_{\text{p}}\), \(k_{\text{d}}\), \(F_{\text{s}}\), \(F_{\text{c}}\), \(\dot{\theta}_{\text{s}}\) and \(\sigma\) are optimized offline by evolutionary algorithm (DE algorithm or GA). DE algorithm and GA are optimization algorithms based on swarm intelligence  
Figure 7: Control architecture.  
theory. They both can intelligently guide the optimization search through the group generated by cooperation and competition among individuals. The GA controls the parental hybridization according to the fitness value, and the probability of selecting the offspring after mutation is relatively large [24; 35]. While the mutation vector in the DE algorithm is generated by the parent difference vector and intersects with the parent individual vector to generate a new individual vector, which will be directly compared with the parent individual [25; 36]. It is obvious that the approximation effect of the DE algorithm is more significant than that of the GA.  
Before describing these two algorithms, we use the following equation as the objective function of the parameter selection.  
\[I=\sum_{n=1}^{Z}\Big{[}w_{1}|e(n)|+w_{2}\theta_{\text{in}}^{2}(n)\Big{]}T_{P}, \tag{25}\]  
where \(I\) is the objective function; \(n\) is the sampling times, and \(n=1\), \(2\), \(3\),..., \(Z\); \(T_{P}\) represents the sampling period; \(w_{1}\) and \(w_{2}\) denote the weights.
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 1 Introduction
This paper proposed the inverse of the current distribution matrix \(W^{-1}\) used in the equivalent control closed-loop model with the current of each coil not being neglected. On the basis of the equivalent control model, the repetitive control is designed and applied to the system of magnetic bearings with redundant structures. The numerical results demonstrate that the proposed control scheme in this paper can not only suppress the harmonic current of each coil but also improve the robustness of the whole system. The paper is organized as follows: the model of mass unbalance and sensor runout of is described in Section 2. The characteristics and control strategy of magnetic bearings with redundant structures are described, and the equivalent control closed-loop model is established in Section 3. The repetitive control (RC) is designed for magnetic bearings with redundant structures, and the simulation results are discussed in Section 4. Section 5 gives the conclusion.
## 2 Power System Model
### Adjustable-Speed Pumping Generator (ASG)
#### 2.4.2 Rotor Excitation System of the ASG  
Figure 6 depicts the basic structure of the rotor excitation system of the ASG. Excitation power was supplied from the stator circuit, then converted to DC power by the stator side converter, and, finally, three-phase excitation current of slip frequency was provided to the rotor windings through the inverter. As a result, the rotor speed of the ASG could deviate from the synchronous speed by the slip frequency. When the ASG was operating in generating mode, the rotor speed was, in general, controlled by the hydro turbine efficiency to be maximum speed. The d-axis component of the stator current on the d-q axis frame defined on the stator voltage reference was proportional to the generator's active output power, and the q-axis component was proportional to the reactive output power. Consequently, by controlling the magnitude and phase angle of the excitation current supplied to the rotor windings, the stator-side active power and reactive power of the generator could be controlled independently.  
Figure 5: Power coefficient (\(Cp\)–\(\lambda\)) curves for several values of pitch angle.  
In addition, the power factor of the converter input and the DC-link voltage could be controlled by changing the magnitude and phase angle of the converter AC-side voltage.  
Figure 7 shows a more detailed structure of the ASG secondary excitation circuit, which is based on Proportional and Integral (PI) controllers. The control system of the inverter is expressed on the d-q axis frame on the stator voltage reference, while the control system of the converter is expressed on the d'-q' axis frame on the voltage reference at the high-voltage side of the converter transformer. Voltage references of the reference frames were obtained through the Phase Lock Loop (PLLs).
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 3 Simultaneous Signal Processing Across Domains
### Tensor-Based Receiver Designs
#### 3.2.2 Application of Tensor Train Decomposition and Tensor Networks
Hence, polar codes have found applications for channel coding in 5G wireless systems [35]. A family of codes, known as branching MERA codes, developed using tensor networks and tensor contraction which generalizes polar codes, has been presented in [36]. Essentially, the encoding sub-system is represented as an even order tensor. At the receiver, the sequential cancellation decoding operation is modeled as a contraction that is represented by a TN. Further, [36] shows that such a generalization of polar codes using TN outperforms traditional polar codes in its error-correcting ability. Another generalization is presented in [37] which develops the decoding algorithm using TN for polar and convolutional polar codes. The use of tensor formalism for the development of error-correcting encoding and decoding algorithms emerges as a novel and interesting research trend that integrates tensors and channel coding for communication systems.
## 3 Results and Discussion
### Modulus of Elasticity and Poisson's Ratio  
The calculated dynamic Young's modulus (E) and Poisson's ratio (v) before and after exposure cycles are presented in Figures 10 and 11. The elastic properties of all samples exhibited a decrease following exposure to all the treatment conditions. These results are consistent with the porosity and permeability results; with the increase in porosity and permeability, the Modulus of Elasticity decreases [42]. The overall reduction in Young's modulus in samples 1Y1, 1Y2, 18M1, and 18M2 after three exposure cycles is about 3%, 5%, 3%, and 4%, respectively. Samples LW1 and LW2 experienced the most significant decrease in their Modulus of Elasticity after hydrogen exposure. This can be because  
Figure 9: Permeability of cement samples after hydrogen exposure.  
the samples with a higher water-to-cement ratio have less strength, which, in turn, can decrease the Modulus of Elasticity of the samples. A similar study concluded by [PERSON] et al. [26] reported a slight increase in their samples of 6% in their study, indicating no major impact on wellbore integrity or the cement seal. The decrease in Young's modulus of the cement indicates that the samples have become less brittle, making them less susceptible to cracking during cyclic loading.  
Figure 11: Changes in Poisson’s ratio of the cement samples after hydrogen exposure.  
Figure 10: Changes in Young’s modulus of the cement sample after exposure.  
However, the Poisson's ratio changes observed in the samples were insignificant. The Poisson's ratio values before and after hydrogen exposure were in the range of 0.2-0.24, which is similar to the values reported in the literature, confirming cement as a low-brittle material [26].
## 3 Results and Discussion
### Fatigue Test Results of Rim Section Specimens
The correlation factor of the finite life part of the S-N curve is R\({}^{2}\) = 0.73, thus indicating a certain scatter of the data. For that reason, the same analysis of the fatigue data was then repeated, considering the specimens from Rim 1 and Rim 2 separately. The comparison is shown in Figure 16. In general, the fatigue life results are better for Rim 1 than for Rim 2. The recalculated fatigue strength at runout is \(\sigma_{af,RIM1}\) = 166 MPa for Rim 1 and \(\sigma_{af,RIM2}\) = 151 MPa for Rim 2. It must be noted that the number of specimens used from Rim 1 was limited to seven, therefore these data allow just for preliminary and exploratory research, according to ASTM E739-23 [25]. However, this trend was quite clear during the development of the tests and may indicate that the rolling process parameters changed between the manufacturing of Rim 1 and Rim 2. Just a few seconds difference in the rolling  
Figure 14: Fracture surface of the rim section specimen. Figure 15: S-N curve for the bending fatigue test of rim section specimens for rim size TW27\(\times\)38\({}^{\prime\prime}\). Aggregated data for Rim 1 and Rim 2.  
time of the components could locally induce a change in the characteristics of the material. That aspect needs to be confirmed by further investigation as a future development of this study.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 5 An Algorithmic Metatheorem for Constant-Depth Circuits and Its Applications
### The Unary Subset Sum Problem  
Asking our student to solve an NP-complete problem is as unfair as it was in the introduction, so let us make the problem (much) easier once more: suppose I write all numbers in _unary_ (so instead of 13, I have to write ) and to write \(10^{100}\) in unary, I would need a blackboard larger than the universe. How difficult is the resulting unary-subset-sum problem? It is easy to see that the problem lies in NL (so it is already much easier than the normal version) and [PERSON] [24] made an educated guess in 1985 that it is \"a problem in NL which is probably not complete\". However, research got stuck at this point and an improvement on the NL bound was only obtained in 2010 when algorithmic metatheorems became available that apply to logarithmic space and the below:  
**Theorem 12**: unary-subset-sum\(\in L\)_._  
**Proof Ideas.** As in the previous section, where we studied visibly pushdown automata, the unary subset sum problem seems to bear no relation to the setting of algorithmic metatheorems (\"Where are the graphs?\"), but just as in that section, our algorithms will _produce a graph \(G\) internally_ and _apply an algorithmic metatheorem internally_. The idea is very simple: upon input \((a_{1},\ldots,a_{n},s)\), for each \(a_{i}\), we _have a star in \(G\) with exactly \(a_{i}-1\) leafs_ (and, hence, \(a_{i}\) vertices). For instance, the input (\(\textsc{  
As pointed out earlier, algorithmic metatheorems in general, and the algorithmic metatheorem used above in particular, hide huge constants in the \(O\)-notation and naive implementations are more or less useless. However, once we know that there is _some_ logspace or linear-time algorithm, we may try to have a closer look at \"what happens\" inside the proof and try to extract a simpler, more direct algorithm. This approach works very well for the unary subset sum problem and a reverse engineering of the algorithmic steps leads us to the following algorithm: given distinct numbers \((a_{1},\ldots,a_{n})\), pick a relatively large _base_ number \(b\), for instance \(b=2^{n}\) will suffice, and compute the product  
\[(b^{a_{1}}+1)(b^{a_{2}}+1)\ldots(b^{a_{n}}+1)=p.\]  
We can represent the result as a base-\(b\) number: \(p=\sum_{i=0}^{\infty}p_{i}b^{i}\) with \(0\leq p_{i}<b\). _Then, the number \(p_{s}\) will be exactly the number of subsets of \(\{a_{1},\ldots,a_{n}\}\) that sum up to \(s\)_. In particular, \((a_{1},\ldots,a_{n},s)\) will be an instance of subset-sum if, and only if, \(p_{s}>0\).  
All told, the unary subset sum problem can be solved by just doing multiplications and extracting certain bits from the result, but we got to this simple algorithm through an analysis of the much more complicated algorithm arising from the algorithmic metatheorem.
## 2 Study Area and Data Source
### Data and Processing  
#### 2.2.1 MOD17A3 NPP Product and Uncertainty  
This study applies MOD17A3 annual NPP product with a spatial resolution of 1 km of Guangzhou as the study input data from 2001 to 2006. These data are freely available to the public from the Numerical Terra dynamic Simulation Group (NTSG) [43]. These products are saved as formatted HDFEOS files in a two-dimensional array with 1,200 columns and 1,200 rows in a Sinusoidal projection [44]. A specific MODIS tool of ENVI software, MODIS Reprojection Tool (MRT), was used to transform the tiles into the Albers Conical Equal Area projection. Some pixels of the images with NPP values out of range of the triple standard deviation and the ineffective values were omitted.  
The three sources of MOD17 inputs for each pixel included MODIS land cover products, FPAR and LAI products, and daily meteorological data, which are derived from MOD12Q1, MOD15A2, and Data Assimilation Office (DAO) data sets, respectively. MOD12Q1 accuracies are within the range of 70%-80% due to errors between similar classes. The MOD15A2 and DAO dataset in Collection 5 MOD17 also have an improved correlation (-81%) compared with the observational data. Temporal filling and non-linear spatial interpolation approaches have considerably improved the quality of these datasets [45]. All three upstream inputs as well as the algorithm itself can introduce uncertainties in MOD17 data. In the following reports, validation based on field measurements has demonstrated uncertainties associated with the MOD17 products [46]. First, a comparison of MODIS annual GPP values with field data collected from 37 Ameriflux and Fluxnet sites distributed throughout North America, mid-latitude zones, polar regions and a wide variety of ecosystem types, including bracketing forests, shrulbands and grasslands, resulted in an overall high consistency (\(R^{2}\) = 0.6993; relative error = 19%) [47]. Moreover, the validation of NPP data using Ecosystem Model-Data Intercomparison (EMDI) data compiled from more than 1,000 ground points resulted in an overall high consistency (\(R^{2}\) = 0.77). Significantly, using scaled and aggregated BigFoot Project data measured at nine different ecosystem flux towers throughout the world, [PERSON] _et al._[48] found that the annual GPP and NPP  
Figure 1: Location of the study area.  
showed a close agreement between the MODIS and the Bigfoot products. In addition, a local rice map used for rice yield estimation in southern China generated using MOD17 GPP/NPP products was shown to have a low relative error of approximately 10% based on validation with actual yields [29].
# Small-Size Square Ring 1-Bit Reconfigurable Transmitarray Unit Cell for C-Band Applications
###### Abstract  
The paper presents a reconfigurable linear polarized transmitarray unit cell design with 1-bit phase quantization for C-band applications. The unit-cell structure consists of two square ring patches of reduced size, which are connected using a coupling element. Incorporating p-i-n diodes inside the coupling element allows controlling the current flow direction in the antennas and providing a 180\({}^{\circ}\) phase difference. An analysis of the unit-cell insertion loss contributed by small-size antennas and coupling between them is discussed. The p-i-n diode parasitic parameters are taken into consideration of the unit cell performance simulation. It was shown that the insertion loss caused by the p-i-n diodes parasitic parameters can be reduced. The original biasing circuitry providing p-i-n diodes control voltage is proposed. Simulation results of the reconfigurable unit cell are validated by measurements in a waveguide. As a result of measurements, the insertion loss is \(-2.3\) dB at 5.9 GHz, the reflection coefficient module is less than \(-20\) dB, the phase difference error does not exceed \(\pm 1^{\circ}\) in the passband, while 3-dB bandwidth corresponds to 180 MHz (3.4%).  
Antenna array; transmitarray; unit-cell; reconfigurability Article
## 7 Applications and Future Research Directions of RIS
### RIS-Aided Millimeter (mmWave) MIMO Systems  
RIS enabled new possibilities in mmWave communication by supporting MIMO transmission with improved throughput by increasing the spectral efficiency in mmWave communication. Authors in [105] proposed a mmWave system with low-precision analog-to-digital converters (ADCs) and numerous RIS arrays containing many reflectors with a discrete phase shift. These arrays produce a synthetic channel with higher spatial variety and power gain, allowing MIMO transmission using linear spatial processing.