esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 4 Experiments  
### Datasets  
We used the CMU pronunciation ([[http://www.speech.cs.cmu.edu/cgi-bin/cmudict](http://www.speech.cs.cmu.edu/cgi-bin/cmudict)]([http://www.speech.cs.cmu.edu/cgi-bin/cmudict](http://www.speech.cs.cmu.edu/cgi-bin/cmudict))) and NetTalk (we are grateful to [PERSON] for providing the data) datasets, which have frequently been chosen by various researchers [3; 16; 32]. The training and testing splits are the same as those found in [4; 5; 8; 12]; thus, the results are comparable. CMUDict contains a 106,837-word training set and a 12,000-word test set (reference data). 2670 words are used as development set. There are 27 graphemes (uppercase alphabet symbols plus the apostrophe) and 41 phonemes (AA, AE, AH, AO, AW, AY, B, CH, D, DH, EH, ER, EY, F, G, HH, IH, IY, JH, K, L, M, N, NG, OW, OY, P, R, S, SH, T, TH, UH, UW, V, W, Y, Z, ZH, <EP>, </EP>) in this dataset. NetTalk contains 14,851 words for training, 4,951 words for testing and does not have a predefined validation set. There are 26 graphemes (lowercase alphabet symbols) and 52 phonemes (\"!\", \"#, \", '+', '@', 'A', 'C', 'D', 'E', 'G', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'R', 'S', 'T', 'U', 'W', 'X', 'Y', 'Z', \", 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l','m', 'n', 'o', 'p', 'r','s', 't', 'u', 'v', 'w', 'x', 'y', 'z', <EP>), </EP>) in this dataset.  
We use **<EP>** and **</EP>** tokens as beginning-of-graphemes and end-of-graphemes tokens in both datasets. For inference, the decoder uses the past phoneme sequence to predict the next phoneme, and it stops predicting after token **</EP>**.
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 1 Introduction
However, since FWMAVs depend on the flapping mechanism, the actuation system for generating the aerodynamic forces and moments as well as the sensors and control dynamics, mathematical models of FWMAVs solely based on the first principles may not accurately describe the actual dynamics of the MAVs. The review provided in this paper is intended to give insights into system identification approaches as an alternative way in obtaining mathematical models of various FWMAVs based on experimental data. Such approaches differ from the conventional modeling approaches through physics (i.e., modeling through the first principles). With system identification approaches, the richness of experimental or flight data can be exploited to obtain the flight dynamics and aerodynamics characteristics of FWMAVs. The models obtained through such approaches can be used to validate mathematical models (including those obtained through the first principles) and predict the effect of any design changes. System identification uses mathematical modeling techniques, which are based on observed input-output data. This process was first coined by [PERSON] in the late 1950s [53]. It has been used successfully to identify models of various complex systems such as the cardiovascular system [54], aircraft [55; 56], and underwater vehicles [57]. This type of modeling approach is beneficial in developing realistic mathematical models of complex systems based on experimental data for prediction, simulation, system design, and/or control [58]. System identification is usually conducted via (1) the Black box approach in which system identification is carried out by fitting the system's input-output response with some flight data, or (2) the Grey box approach (see [59] for more details), which is carried out by first developing the structure of models mathematically, then the required parameters in the models are identified. In both approaches, the overall models are validated against the experimental data. The typical steps involved in system identification are shown in Figure 2.  
In this paper, the most prominent strategies relevant to system identification of the flight dynamics and aerodynamics of FWMAVs as well as natural fliers were overviewed. This paper is organized as follows. A general description of FWMAVs is presented and some major works done so far are highlighted in the Introduction. In Section 2, the background of the flight of FWMAVs is presented comprehensively and recent developments in this area are highlighted. Section 3 contains a brief discussion on system identification approaches, which then leads to the main review that presents different strategies in system ID adapted for modeling the FWMAVs' dynamics. The article is presents our concluding remarks and some recommendations in Section 5, where the authors present their own proposed strategy for an online system's stability derivative estimation that is potentially advantageous in obtaining the flight dynamics of any existing FWMAVs as future works.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [1991-2005]  
Although OCT devices date from 1991 [1], the first publication on measurements of human retinal structure in vivo was published in 1993 [85]. During these early years, when TD-OCT was used, only axial misalignments between adjacent A-scans (fast axis) were corrected using cross-correlation. The registration was considered as part of the pre-processing, and no information was given about the validation or the computation time.  
**[PERSON] et al. [53] (1993)** and **[PERSON] et al. [54] (1996)** obtained an estimate of the retinal motion using a cross-correlation technique in which the axial index was chosen as the location of the peak correlation of adjacent A-scans. Cross-correlation was also used by **[PERSON] et al. [55] (2005)** to improve macular segmentation for glaucoma assessment. The A-scan lines were shifted so that the sum of the products of adjacent pixels could be maximized. Forty-seven subjects (23 normal and 24 with glaucoma) were analyzed in this study.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 3 Improved Spatiotemporal Multi-Channel Network (IQGMCL)
### IQGMCL Framework  
In 2020, [PERSON] proposed a spatiotemporal multi-channel network, the MCLDNN network [18], which can extract features more effectively from the perspective of time and space. The network divides the input IQ signals into three channels and extracts the time sequence features of route I, route Q, and route IQ, respectively. The IQ data input form during model training is [Batchsize, 2, 128, channels], which belongs to four-dimensional data. MCLDNN networks are spliced in the _channels_ dimension when merging channels. Then, the convolution kernel of (2,5) is used for further feature extraction. The extracted feature results are input into the two-layer LSTM to further extract the time sequence feature, and the two-layer DNN is connected for classification.  
This paper proposes an IQGMCL network model, which divides the input IQ signals into three channels (I, Q, IQ). For the IQ path, the relevant features are first extracted with (2,1) convolution kernels with a quantity of 24. Then two layers of (1,3) convolution kernels with numbers 24 and 50 are used to further extract time series features. The I and Q paths are extracted with (1,8) convolution kernels with a quantity of 50, respectively. The number of convolution kernels of these two paths is consistent with the MCLDNN network, both of which are 50. Finally, the three channels are merged in the _axis = 1_ dimension, and the data form is [Batchsize, 3, 128, channels]. Further extraction of features with (3,5) convolution kernels with a quantity of 100 is conducted. Two-layer LSTM is used to extract features more effectively from time and space. Through comparison experiments, replaced LSTM with Bi-LSTM, and a full-connection layer is removed to prevent overfitting. MultiplicativeGaussian noise is introduced to naturally corrode the characteristic parameters, further alleviating the overfitting of the model and improving the robustness and accuracy of the model. The IQGMCL network model is shown in Figure 4.  
Figure 4: IQGMCL structure. (**a**) Intuitive model; (**b**) Plot_model visualization.
## 3 Preliminaries
### Hyperelliptic Curve Cryptosystems (HECC)  
Hyperelliptic curves can be viewed as generalizations of ECC (elliptic curve cryptosystems), introduced by [PERSON] [50]. A hyperelliptic curve [51] is denoted over curves, whose genus is greater than 1, as shown in Figure 1. Similarly, the curves with genus 1 are generally known as elliptic curves. The group order of the field \(\mathrm{F}_{q}\) for genus 1, 160 bit long operands are required, that is, we need at least g.log\({}_{2}(q)\approx 2^{160}\), where g is the genus of curve over \(\mathrm{F}_{q}\) that is a set of finite fields of order q. Likewise, for curves with genus 2, 80 bit long operands, and, for curves with genus 3, 54 bit long operands are needed [52].  
A hyper elliptic curve \(C\) of genus greater than 1 over \(F\) is a set of solutions \((x,y)\in F\times F\) to the following equation:  
\[C:\ y^{2}+h(x)y=f(x). \tag{1}\]  
A divisor \(D\) is a finite formal sum of points on hyper elliptic curve and represented as \[D=\sum\
olimits_{P_{j}\in C}m_{i}p_{i}\,\ m_{i}\in\mathbb{Z}. \tag{2}\]  
The two divisors can be added as follows:  
\[\sum\
olimits_{P_{j}\in C}m_{i}p_{i}+\sum\
olimits_{P_{j}\in C}n_{i}p_{i}=\sum \
olimits_{P_{j}\in C}(\ m_{i}+n_{i})p_{i}. \tag{3}\]  
Each element of the Jacobian can be represented in the semi-reduced divisor form [53]:  
\[D=\sum\limits_{i}m_{i}p_{i}-\left(\sum\limits_{i}m_{i}\right),\ \forall mi\ \geq\ 0. \tag{4}\]  
If the divisor is subjected to the additional constraint, that is, \(r\leq g\), such a divisor is defined as a reduced divisor. Additionally, in [50], the author shows that the divisors of the Jacobian can be denoted as a pair of polynomials \(a(x)\) and \(b(x)\) with following degrees: \(b(x)\ \leq\text{deg}\ a(x)\leq\ g\), where the coefficients of \(a(x)\) and \(b(x)\) are elements of \(\mathbb{F}\) and \(a(x)\) divided by \(y^{2}\ +\ h(x)y\ -\ f(x)\).
## 2 Experiment
### Experiment Scheme  
To study the influence of the number of reinforcement layers and the loading frequency on the dynamic characteristics of reinforced saturated gravel soil under high-speed train load, consolidated undrained cyclic loading tests were performed on saturated samples with 150 mm diameter and 300 mm high samples as shown in Figure 4. The diameter of the sample was 15 times that of the maximum diameter of the sand sample to minimize the size effect [53]. The reinforcement layers were installed at equal intervals shown in Figure 5. Half sinusoidal loading was used to simulate train traffic [54] as shown in Figure 6. The stopping criteria of the tests are 10,000 loading cycles or 5% of axial strain, whichever is reached first. The GDS dynamic triaxial test system used in this test is controlled by a stress-servo system, which can effectively control the stress amplitude under long-term cyclic loading. During the test, the pre-test is carried out under the same test conditions. The estimated stiffness coefficient in the GDS dynamic triaxial test system is adjusted to stabilize the loading amplitude near the target amplitude, and the loading amplitude will not be large under long-term loading fluctuation. Through the pre-test, the author also verified that loading 10,000 times during the test can ensure that the loading amplitude is stable near the target amplitude without major fluctuations.  
Figure 4: A test sample.  
Figure 3: GDS dynamic triaxial test system: (**a**) physical diagram of the test system; (**b**) schematic diagram of the test system.  
In the tests, the confining pressure was set as 30 kPa. The value was selected based on the self-weight of soil and rail track. The amplitude of the cyclic loading was determined using the following equation recommended by [PERSON] [55] for train loading:  
\[\sigma_{\text{d}}=0.26\times\text{P}\times(1+\alpha\upsilon) \tag{1}\]  
where \(\sigma_{\text{d}}\) is the dynamic stress amplitude at the top surface of the subgrade; P is the static axle load of the train, in the unit of kN; and \((1+\alpha\upsilon)\) is the impact coefficient. For train speed \(\upsilon=300\)-\(350\) km/h, \(\alpha=0.003\), and for \(\upsilon=200\)-\(250\) km/h, \(\alpha=0.004\). In the tests, the following parameters were adopted: p = 200 kN, \(\upsilon=300\) km/h; and \(\alpha=0.003\). Based on this, an amplitude of 100 kPa was adopted for the cyclic loading in the tests.  
To consider the effect of reinforcement layers and loading frequency on the response of reinforced soils, the tests shown in Table 2 were performed. In total, 7 sets of samples were tested. The samples were named using loading frequency and reinforcement layers, for example, F1-L1 is the sample with 1 layer (L1) of reinforcement tested under 1 Hz loading frequency (F1). All the samples were saturated to a Skempton's B \(\geq\) 0.96, and consolidated under the isotropic pressure of 30 kPa before applying cyclic loading.  
Figure 5: Schematic diagram of geogrid layout. (**a**) 1 layer reinforced sample; (**b**) 2 layer reinforced sample; (**c**) 3 layer reinforced sample.  
Figure 6: Loading waveform.
## 2 Materials and Methods
### Tittatable Acidity and Growth  
The tittatable acidity was measured as the amount of acetic acid (%) titrated with 0.1 N sodium hydroxide using 1% phenolphthalein as an indicator [19]. Growth was measured at OD660 using a UV spectrophotometer. The optimal alcohol concentration and initial pH of the isolates were evaluated by calculating the fermentation efficiency, which represents the pure acetic acid production amount as a percentage of the amount, according to the initial fermentation acidity [20]. The calculation formula is as follows:  
\[\text{Fermentation efficiency}\left(\%\right)=\frac{\text{Final tittatable acidity }\left(\%,\text{ w}/\text{v}\right)-\text{initial tittatable acidity }\left(\%,\text{ w}/\text{v}\right)}{\text{Initial concentration of EtOH }\left(\%,\text{ v}/\text{v}\right)\times 1.304}\times 100\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region (Province)** & **Origin** & **Sample Name** & **Species** & **Strain** \\ \hline \multirow{2}{*}{Gyeongsangnam-do} & Brown rice & GN\_BWR & _Acetobacter pasteurianus_ & GV–5 \\  & Black rice & GN\_BR & _Acetobacter ascendens_ & GV–8 (A) [1] \\ \hline \multirow{2}{*}{Gyeongsangbuk-do} & Five grains [2] & GB\_FG & _Acetobacter ascendens_ & GV–12 (B) \\  & Brown rice & GB\_UR & _Acetobacter pasteurianus_ & GV–22 (D) \\ \hline \multirow{2}{*}{Chungcheongnam-do} & Brown rice & CN\_UR & _Acetobacter pasteurianus_ & GV–16 \\  & & & _Acetobacter pasteurianus_ & GV–17 (C) \\ \hline \hline \end{tabular}  
* [1] The strains selected as multiple starter candidate are indicated using letters (A–D). [2] Mixture of brown rice, barley, sorghum, millet, and glutinous millet.  
\end{table}
Table 1: Acetic acid bacteria (AAB) isolated from traditional grain vinegar in various regions in Korea.
## 2 Results
### Protective Effect against Hydrogen Peroxide Induced Oxidative Stress  
Concentration-effect relationship for the cytotoxic action of H\({}_{2}\)O\({}_{2}\) towards NIH3T3 and MDA-MB-231 cells were obtained by testing concentration values of the hydrogen peroxide ranging from 0.082 to 1.60 mM. Cytotoxicity was determined as decreasing of cell viability after H\({}_{2}\)O\({}_{2}\) pre-treatment followed and not by contact with 7.20 \(\times\) 10\({}^{-3}\) mg/mL  
Figure 3: Percentage of viable NIH3T3 and MDA-MB-231 after 24 h of contact with increasing concentration of _R_oex dry extracts as determined by the Neutral Red Uptake. Date are mean \(\pm\) SD of six replicates. * Values are statistically different versus negative control (complete medium), _p_\(<\) 0.05. # Values are statistically differently versus percentage of viable MDA-MB-231, _p_\(<\) 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Concentration** & **\% Scavenged** & **Total Phenolic** & **Total Hydroxycinnamic** & **Total Flavonoid** & **Total Triterpenoids** \\
**of a.e. (mg/mL)** & **H\({}_{2}\)O\({}_{2}\)\(\pm\) SD** & **Content (mg/mL)** & **Acids Content (mg/mL)** & **Content (mg/mL)** & **(mg/mL)** \\ \hline
0.024 & \(15\pm 3\) & \(2.3\times 10^{-3}\) & \(1.1\times 10^{-3}\) & \(1.6\times 10^{-4}\) & \(1.7\times 10^{-3}\) \\ \hline
0.12 & \(28\pm 4\) & \(1.2\times 10^{-2}\) & \(5.6\times 10^{-3}\) & \(7.8\times 10^{-4}\) & \(8.6\times 10^{-3}\) \\ \hline
0.24 & \(41\pm 4\) & \(2.3\times 10^{-2}\) & \(1.1\times 10^{-2}\) & \(1.6\times 10^{-3}\) & \(1.7\times 10^{-2}\) \\ \hline
0.48 & \(53\pm 6\) & \(4.6\times 10^{-2}\) & \(2.2\times 10^{-2}\) & \(3.1\times 10^{-3}\) & \(3.4\times 10^{-2}\) \\ \hline
0.72 & \(66\pm 3\) & \(6.9\times 10^{-2}\) & \(3.3\times 10^{-2}\) & \(4.7\times 10^{-3}\) & \(5.2\times 10^{-2}\) \\ \hline
0.96 & \(73\pm 5\) & \(9.2\times 10^{-2}\) & \(4.4\times 10^{-2}\) & \(6.2\times 10^{-3}\) & \(6.9\times 10^{-2}\) \\ \hline \end{tabular}
\end{table}
Table 3: Total phenolic content and percentage of scavenged hydrogen peroxide as a function of increasing concentrations of _R_oex.  
R_Oex, the concentration value at which the extract showed the lowest degree of cytotoxicity for both cell lines.  
As shown in Figure 4a, _R_Oex was able to increase significantly NIH3T3 cell viability after pre-treatment with H2O2 concentrations ranging from 0.33 and 1.3 mM but not at highest values, i.e., 1.5 and 1.6 mM.  
The addition of _R_Oex increased the percentage of viable MDA-MB-231 after pre-treatment with hydrogen peroxide in a range of concentrations between 0.82 and 1.6 mM (Figure 4b). Human adenocarcinoma breast cells demonstrated to be more resistant to H2O2 pre-treatment in comparison to NIH3T3 fibroblasts and the rosmarinic extract improved cell viability neutralizing the toxic effect of H2O2 also at the highest concentrations, demonstrating to be able to protect cells and to reduce cell death.  
Figure 4: Influence of hydrogen peroxide pre-treatment on viability of **(a)** NIH3T3 and **(b)** MDA-MB-231. \({}^{\star}\) Values are statistically different versus H2O2 treated cells, \(p\) < 0.05.
## Theoretical Modeling
### Nonlinear Trim of the Flexible Aircraft  
The nonlinear trim of the flexible aircraft is performed for zero resultant force and moment of the whole aircraft. A cost function is defined as:  
\[f=[\mathbf{F}_{R}^{T},\mathbf{M}_{R}^{T}]^{T} \tag{46}\]  
where \(\mathbf{F}_{R}\) and \(\mathbf{M}_{R}\) are the resultant force and moment of the whole aircraft, which can be expressed as:  
\[\mathbf{F}_{R}=\sum_{i=1}^{N}\mathbf{F}_{ai}+\sum_{i=1}^{N}\mathbf{F}_{ti}+ \sum_{i=1}^{N}\mathbf{F}_{gi} \tag{47}\]  
\[\mathbf{M}_{R}=\sum_{i=1}^{N}\mathbf{M}_{ai}+\sum_{i=1}^{N}\mathbf{d}_{i}\times (\mathbf{F}_{ai}+\mathbf{F}_{ti}+\mathbf{F}_{gi}) \tag{48}\]  
where \(\mathbf{F}_{ai}\), \(\mathbf{M}_{ai}\), \(\mathbf{F}_{ti}\) and \(\mathbf{F}_{gi}\) are, respectively, the aerodynamic force and moment, thrust and gravity acting on the \(i\)th node of the aircraft, and \(\mathbf{d}_{i}\) is the distance vector from the moment reference point (e.g., center of gravity or the constraint point of the aircraft) to the \(i\)th node.  
The cost function \(f\) is minimized over the solution space using the body angle of attack \(\alpha\), elevator deflection angle \(\delta\) and thrust \(T\). Newton-Raphson iteration is employed to find the optimal trim parameter \(S\), i.e.,  
\[S_{k+1}=S_{k}-\left[\frac{\partial f}{\partial S}\right]_{k}^{-1}f_{k} \tag{49}\]  
where \(S=[\alpha,\delta,T]^{T}\) and \(\left[\frac{\partial f}{\partial S}\right]\) is the Jacobian matrix that relates the resultant force and moment of the whole aircraft with the trim parameters, which is computed numerically trough finite differences as:  
\[\mathbf{J}_{ij}\equiv\left[\frac{\partial f_{i}}{\partial S_{j}}\right]\simeq \frac{f_{i}(S+\Delta S_{j})-f_{i}(S)}{\Delta S_{j}} \tag{50}\]  
It is worth pointing out that, theoretically, Equation (49) is at risk of falling into a local minimum, but this situation is never seen to happen in practical applications.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 4 Simulation Verification
### Segmented Mirror Aberration Correction
It can be seen from Figure 7 that Scheme 1 can correct the magnitude of each order of aberration of the segmented mirrors below \(10^{-4}\)nm. Figure 7a shows that Scheme 1 has an obvious correction effect on the aberration of the segmented mirrors. The implementation of Scheme 2 only corrects the aberrations of the segment. So Scheme 2 in this section only has a certain correction effect on certain aberrations. In summary, by comparing the two segmented mirror correction methods through the finite element method, we can obtain the aberration correction mode proposed in this paper with the segmented mirror aberration as the correction target, which has a simple correction method, a wider target aberration range and higher accuracy.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 5 Conclusions  
The article presents the design of a compact \(4\times 2\) array antenna with dimensions of \(2.02\lambda_{1}\times 1.82\lambda_{1}\) for 5G millimeter-wave applications. The proposed antenna achieved a quad-beam radiation pattern through the innovative use of non-uniform element spacing. A detailed progression was provided, beginning from the analysis of a single-element antenna and scaling up to the final \(4\times 2\) array configuration. The non-uniform inter-element spacing of greater than \(0.5\lambda_{1}\) was identified as the primary factor enabling the formation of the quad-beam pattern. This unique configuration resulted in two beams in the xz-plane and two beams in the yz-plane, improving the coverage and beam steering capabilities in the azimuthal and elevation planes, which are essential for modern 5G communication networks.  
The innovation in this work lies in the antenna's ability to achieve complex multi-beam radiation using a simple planar structure and a parallel feed technique. The design maintains structural simplicity and reduces the fabrication challenges often associated with multilayer or waveguide-based antenna systems. By optimizing the element spacing and tuning the feed network, the antenna effectively mitigates mutual coupling and radiation nulls, ensuring a stable performance with a peak gain of 10.6 dBi and a bandwidth spanning 28.5-31.1 GHz. Furthermore, the design is scalable, with gain improvements demonstrated through aperture replication.  
These advancements make the proposed antenna highly suitable for 5G wireless applications where multi-beam coverage, high gain, and compact size are critical performance factors. Compared to existing designs, this antenna offers a balanced trade-off between performance and complexity, achieving better or comparable directivity, bandwidth, and polarization characteristics without the need for intricate feed structures or multiple layers.  
Conceptualization, P.S.B.G. and T.A.; methodology, P.S.B.G.; software, P.S.B.G., S.P. and B.S.V.; validation, P.S.B.G., S.P.G., J.A. and P.M.P.; formal analysis, P.S.B.G., P.R.M. and T.A.; writing--original draft preparation, P.S.B.G.; writing--review and editing, T.A., P.R.M. and B.S.V.; visualization, S.P. and P.M.P.; supervision, T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 21: Figure-of-merit plot comparing the proposed antenna Ant4 performance with other planar arrays.  
**Data Availability Statement:** The data are available in the manuscript in the form of results.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Ignion. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.