esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 4 Discussion
### Choosing Parameters  
The inputs of the AEAD using ChaCha20 cipher and Poly1305 Authenticator include a 256-bit key, a 96-bit nonce, an arbitrary length plaintext, and an arbitrary length additional authenticated data. For simplicity, here we assume the latter to be empty, though one could consider a situation for our protocol where parts of the payload does not require confidentiality. The output of the AEAD is a ciphertext of the same length as the plaintext and a 128-bit tag. It seems reasonable to choose 128 bits as a block size for the input and output of the AEAD. For further detailed parameters of the AEAD, one can refer to [22].  
Once we have preprocessed the plaintext using the AEAD, we will apply the restricted AONT. We use the linear construction based on the matrix \(M^{-1}\) as defined in Section 3.2.1. To take advantage of the property of the restricted AONT, we would like its input to be greater than 128 bits. Let us consider the case where the inputs are 256-bit blocks. That means the matrix \(M^{-1}\) will have dimension  
Figure 5: How the message is sent using the hybrid construction with authenticated encryption scheme; AEAD represents the application of ChaCha20 and Poly1305.  
Figure 6: A symmetric-key encryption scheme using mesoscopic coherent states with incorporated AEAD.  
256 by 256. The outputs of the restricted AONT are also 256-bit blocks. We collect the first 128 bits as the hidden bits to be sent over the AlphaEta channel. The remaining bits will be sent over the classical channel.  
We apply a binary repetition code of odd length to our collection of hidden bits. Recall the probability of error for transmitting one bit:  
\[P_{err}=1-\sum_{i=0}^{t=1}\binom{r}{i}\delta^{i}(1-\delta)^{r-i} \tag{17}\]  
where \(r\) is the length of the code and \(\delta\) is the channel error probability. Table 1 (computed by means of the computer algebra system Magma [24]) demonstrates the probability of error \(\delta^{\prime}\) for transmitting 128 hidden bits given the length of the binary repetition code and the channel error probability.  
One can see the improvement in the correctness parameter of the symmetric-key encryption scheme using mesoscopic coherent states.
# A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
## 3 Basic Theory
### Implementation of HAPSOLM Algorithm in ANN Training  
In pre-training and training process, the implementation of HAPSOLM algorithms forms two stages, shown in Figure 2. In the first stage, APCO is used to initialize the connection weights and threshold values for acquiring better-conditioned initial network. In the second stage, LM Algorithm is adopted to minimize loss function by updating the connection weight and threshold values until meet the convergence or reach the maximum number of iterations.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**APSO for Pre-Training**} \\ \hline Step 1 & Randomly initialize \(x_{il}^{(0)}\) and \(v_{il}^{(0)}\); set \(k=0\) \\ \hline Step 2 & Calculate the fitness of particle \(i\); evaluate \(p_{il}\) and \(p_{gl}\) \\ \hline Step 3 & If meet the convergence or reach the maximum number of iterations, set \(x_{i}^{\text{best}}\)\(\rightarrow\)\(x_{LM}^{(0)}\) and go forward step 5, otherwise perform step 4 \\ \hline Step 4 & APSO-optimize \(x_{il}^{(k)}\)\(\rightarrow\)\(x_{il}^{(k+1)}\) and \(v_{il}^{(k)}\)\(\rightarrow\)\(v_{il}^{(k+1)}\) for inner iterations, \(k=k+1\) \\ \hline \multicolumn{2}{c}{LM for training} \\ \hline Step 5 & Initialize \(x_{LM}^{(0)}\) and \(\lambda^{(0)}\), set \(k=0\) \\ \hline Step 6 & LM-optimize \(x_{LM}^{(k)}\)\(\rightarrow\)\(x_{LM}^{(k+1)}\) and update \(\lambda^{(k)}\)\(\rightarrow\)\(\lambda^{(k+1)}\) for inner iterations, \(k=k+1\); updates the values of weights and thresholds \\ \hline Step 7 & Check the Prediction error of \(x_{LM}^{(k)}\) and keep record of the best \(x_{LM}^{(k)}\)\(\rightarrow\)\(x_{LM}^{\text{best}}\) \\ \hline Step 8 & If no better \(x_{LM}^{(k)}\) is observed over stop generations, output \(x_{LM}^{best}\), otherwise go back to step 6. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pseudo code of HAPSOLM.  
Figure 2: HAPSOLM in artificial neural network (ANN) pre-training and training.
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 2 Problem Modeling
### Potential Targets  
Typical potential target of a post-earthquake rapid-assessment task is a building whose size is generally smaller than the UAV's vision field. The sensor of a UAV can completely cover the target when it flies over the building, so the potential target is defined as the point-target. Let \(\mathbf{T=}\{1,\cdots,i,\cdots,N\}\) be the set of \(N\) potential targets with known spatial configuration. Since the Dubins curve is used to fit the UAV path in this paper, the spatial configuration of a potential target can be defined as \((x_{i},y_{i},\theta_{i})\).  
Let \(w_{i}\in\{1,2,\ldots,10\}\) be the weights of potential targets. A larger weight value means that the target is more important, and the higher the weight of the target visited by the UAV, the greater is the profit. Due to the detection error of the sensor, when the UAV accesses the potential target \(i\in\mathbf{T}\), the valid information about the target can be successfully obtained with a certain probability, which is recorded as \(P_{i}\). The probability \(P_{i}\) is related to the following three factors: i) the weight of the potential target \(i\), \(w_{i}\); ii) the error probability of the sensor carried by UAV \(k\), \(p_{k}\); and iii) the number of times that the potential target \(i\) is visited by UAV \(k\), \(y_{i}^{k}\). The formula of \(P_{i}\) is  
\[P_{i}=1-\prod_{k=1}^{K}{(p_{k})^{y_{i}^{k}}} \tag{2}\]
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods  
### Mathematical Model  
#### 2.1.1 Imaging Principle of the Orthogonally Splitting Imaging Pose Sensor  
The orthogonally splitting imaging pose sensor consists of a spherical mirror, a beam splitting prism, cylindrical lens and line array CCDs as shown in Figure 1. The target point \(P_{w}(x_{w},\,y_{w},\,z_{w})\) generate a two-dimensional image \(P_{u}\) (\(x_{u},\,y_{u}\)) through the aperture diaphragm and spherical mirror. Through the beam splitting prism, two same 2d images are generated. Horizontal and vertical cylindrical lenses respectively compress the 2d images into 1d images, namely the X-direction image and Y-direction image. According to optical property of cylindrical lens, the beams parallel to the optical axis generate the image perpendicular to the generatrix direction. In order to realize imaging with a wide range, line array CCD's placement is parallel to the generatrix direction [13]. And horizontal and vertical line array CCDs receive the respective corresponding 1d image. Therefore, a 2d image is transformed to two orthogonal 1d images. The X-direction and Y direction of the line array CCDs respectively capture the image coordinate \(u\) and \(v\). According to the two 1d image coordinates, 2d image coordinate of the target point \(I\) (\(u\),\(v\)) can be restored as shown in Figure 2. Owing to the high resolution of the line array CCD, the 2d image has high resolution to improve the measurement precision of the system.
# The HD-Domain Metalloprotein Superfamily:
### HD-Domain Hydrolases: The Phosphatase Subfamily  
HD-domain phosphatases play essential roles in regulating the cellular pool of (deoxy)ribonucleotides and signaling molecules involved in bacterial stress responses [3; 4; 10; 16; 42], such as (p)ppGpp and Ap\({}_{4}\)A (Figure 2). Phosphatases are further subdivided into mono-, di-, and triphosphatases with distinct structural features that may provide clues for the future classification  
Figure 2: Known substrates of HD-domain proteins. Phosphatases can remove one to three terminal phosphate groups from (deoxy)ribonucleotides or cleave (a)symmetrically polyphosphate containing nucleotides (represented in gray). The position of cleavage has been highlighted in red for substrates with four phosphates. PDEs hydrolyze phosphodieser bonds of cyclic (di)nucleotide substrates via either one-step hydrolysis (cleavage of one side of the diester bond) releasing a linearized product or two-step hydrolysis releasing individual nucleotides. PDEs can also act on RNA and DNA substrates. HD-domain oxygenases cleave a C-X bond (indicated in red).  
of unknown HD-domain phosphatases. Interestingly, all have a strictly conserved arginine residue prior to the first histidine of the metal binding motif (Figure 3), which is pivotal for activity [16; 18; 22]. The exact chemical role of this arginine in catalysis has not yet been delineated. Its importance in hydrolysis most likely stems from the ability of this residue to ensure proper substrate positioning and/or to form intermolecular interactions with the substrate phosphate groups. Most biochemically characterized HD-domain mono- and diphosphatases are dimers in solution [3; 5; 16], which appears to be related to enzymatic function and may represent a regulatory mechanism for tuning activity. However, it is currently unknown if both sites are catalytically active or if one site allosterically activates the other. In contrast, triphosphatases are allosterically regulated by nucleotide binding to secondary sites, and most are active as tetramers [4; 20] or hexamers [38].  
In most cases, phosphate hydrolysis is supported by non-redox metal ions, with the most active cofactors being Co or Mn for monophosphatases, Mn for diphosphatases, and Mg or Mn for triphosphatases (Table 1). With a few notable exceptions (YqeK, SAMHD1, and OxsA), these metals are bound in a mononuclear configuration by a conserved \"H \(\ldots\) HD \(\ldots\) D\" motif (Figure 3).
## 3 Results
### Neutron/X-ray Diffraction (ND/XRD)  
The neutron powder diffraction profiles of NaAlO\({}_{4}\) and NaAlO\({}_{4}\)-0.02 TiCl\({}_{3}\) before the desorption and after the re-absorption of deuterium were measured at NOVA, as indicated by the circles in Figure 2a. In the plotted profile, the contaminations of the background intensities from the sample cell and the spectrometer were subtracted, and the neutron attenuation factors of the sample and sample cell were calibrated. Figure 2b shows the synchrotron X-ray powder diffraction profiles. Table 1 represents the Rietveld refinement results. The deuteride sample without TiCl\({}_{3}\) includes a small amount of Na\({}_{3}\)AlD\({}_{6}\) in the neutron and X-ray diffraction profiles; nevertheless, the purity is high enough (97%) compared to a previous report [6]. The TiCl\({}_{3}\)-doped samples before the desorption were composed of NaAlO\({}_{4}\), NaCl, and Al in both the diffraction profiles. The Rietveld refinement results implied that Ti was substituted at approximately 2% of the Al sites in NaAlO\({}_{4}\). The unit cell volume change is not significant from the substitution despite the difference in the effective ionic radii with four coordination (Al\({}^{3+}\): 0.39 A and Ti\({}^{4+}\): 0.42 A) [55]. On the other hand, the phases of NaAlO\({}_{4}\), Na\({}_{3}\)AlD\({}_{6}\), NaCl, and Al were identified in the neutron diffraction profile for the sample after the re-absorption; however, Na\({}_{3}\)AlD\({}_{6}\) was very small in the X-ray profile because of its low symmetry and small amount. The amount of NaAlO\({}_{4}\) decreased from 4% (neutron) and 10% (X-ray) by one-time desorption and re-absorption, whereas the \(p\)-\(c\) isotherms showed 13%. Although the results are the same in terms of the reduction tendency, it is difficult to make a quantitative evaluation. In the sample after the re-absorption of deuterium, there was hardly any Ti at the Al site in NaAlO\({}_{4}\). The phase ratio of Al after the re-absorption was 2.1 times as much as that before the desorption in the X-ray diffraction profile, and approximately 4% of Na\({}_{3}\)AlO\({}_{6}\) appeared in the neutron diffraction. Compared with before the desorption and after the re-absorption in the X-ray diffraction profiles, a new peak appeared at the low scattering angle (long lattice spacing, \(d\)) side of the 111 Bragg peak (\(d\) = 2.3 A) of Al after the reaction, but there is no change in the 200 peak (\(d\) = 2.0 A), as shown in Figure S1. [PERSON] et al. insisted that an Al-rich Al-Ti intermetallic phase of fcc with small substitutions of Ti (on the low scattering angle side of the 111 and 200 peaks of Al) is formed depending on the additives of 10% TiCl\({}_{3}\) and 2% Ti(OBu)\({}_{4}\) after prolonged cycles of desorption at 433 K and absorption at 393 K, respectively [35]. There is a possibility that cycling at different temperatures could result in different compositions of Al-Ti (different Bragg peak positions and relative intensities in the diffraction profile).
# Spectral Envelope Transformation in Singing Voice for Advanced Pitch Shifting
## 3 Analysis of Parameters and Their Variation along Pitch
### Formant Analysis
#### 3.2.1. Formant 1  
It is known that formants are dependent to varying degrees on the vowel sung, as well as on the sex and age of talkers or singers [25]. With the goal of finding a global tendency of the specific parameters along pitch, all of these variables are mixed in the analysis.  
Figure 3 shows the obtained results regarding the central frequency of the first formant (\(F1\)) in all of the analyzed samples. \(F1\) is the most strongly affected parameter in our model as pitch varies with an upward trend with increasing pitch (\(f0\)).  
Taking this tendency into account, a linear approximation has been carried out by using robust regression [26]. Note that the chosen approach is more accurate than ordinary least squares regression in this particular case, achieving a more reliable slope by rejecting some outliers from the data samples. This algorithm uses an iteratively re-weighted least squares scheme with the following bi-square weighting function:  
\[\omega(r)=\begin{cases}\left[1-\left(\frac{r}{k}\right)^{2}\right]^{2}&|r|\leq k \\ 0&|r|>k\end{cases} \tag{8}\]  
where \(r\) is the residual or the error to minimize, which is defined as the difference between the actual value of the dependent variable (\(f0\) in this case) and the value predicted by the model. \(k\) is the tuning constant:  
\[k=6.94588\cdot MAD \tag{9}\]  
with \(MAD\) the median absolute deviation of the residuals from their median.  
As already mentioned, in contrast to the approximately linear relationship between \(F1\) and pitch, a meaningful relationship between the bandwidth (\(B1\)) of the first formant and pitch was not found. Conversely, by changing \(B1\), some unintended variations of intensity were attained, which is ultimately coherent with the non-linear energy damping model of vocal resonators proposed in [27], in which the Q of each resonator depends on the input signal. Because of this, we decided not to modify the bandwidth \(B1\) in order to keep the intensity of the result of the transformation as close as possible to the original intensity.  
As is well known, different vowels with the same \(f0\) involve different parameters of \(F1\). Because of this and before trying to find a global tendency, some analyses of separated vowels and singers were carried out. It was found that \(F1\) had the tendency to increase with \(f0\) for all of the vowels and the singers, which encouraged us to find a commonly valid relationship.  
If the pitch shifting is performed without changing the first formant, the vowel seems to loose thickness and becomes breathy, especially in higher notes, according to our observations. Thus, shifting \(F1\) appears to be necessary in order to keep the original thickness of the sound.  
Figure 3: \(F1\) evolution with pitch (\(f0\)), harmonic component.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 2 Conventional Equalization Algorithm
### Cma  
The CMA mainly uses the steepest gradient descent method to find the minimum value point of the cost function. In [10], when the value of the cost function is minimal, equalization weight coefficients are stable near the optimal solution.  
\[J^{2}(k)=\frac{1}{4}E\bigg{[}\Big{(}|y(k)|^{2}-R_{2}\Big{)}^{2}\bigg{]}, \tag{1}\]  
where \(y(k)\) is the output of the equalizer, and the correction parameter \(R_{2}\) can be defined as:  
\[R_{2}=E\Big{[}|a(k)|^{4}\Big{)}\Big{]}/E\Big{[}|a(k)|^{2}\Big{]}, \tag{2}\]  
where \(a(k)\) is the input sequence; the correction parameter \(R_{2}\) is different for different modulations.  
The error signal of the CMA can be expressed as:  
\[e(k)=y(k)\Big{(}|y(k)|^{2}-R_{2}\Big{)}, \tag{3}\]  
The formula for updating the filter coefficients can be expressed as:  
\[\mathbf{w}(k+1)=\mathbf{w}(k)-\mu e^{*}(k)\mathbf{x}(k), \tag{4}\]  
where \(\mu\) is the step factor, \(\mathbf{x}(k)\) is the equalizer input sequence, \(\mathbf{w}(k)\) is the vector of the filter coefficients, and \(e^{*}(k)\) is expressed as the complex conjugate of \(e(k)\). The larger the parameter \(\mu\), the larger the residual error and the faster the convergence speed.  
The CMA is insensitive to the carrier phase shift, which has a slow convergence speed and large steady-state error. The correction parameters need to be adjusted to match different modulations when mode switching is performed in the VM system.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Training and Predicting  
In our study, we wrote the Python (version 3.9) code to implement the calculation. Using the constructed sample data pairs, we randomly split 70% of the whole dataset as the training dataset and 30% as the validation dataset. The validation dataset is independent of the training dataset and is only used to guide parameter adjustment and avoid overfitting. At the same time, we separately calculated the 2000 synthetic dispersion curves as the test dataset. The corresponding S-wave velocity models are known and can be used to compare the results before and after inversion. In training, we used a personal computer with an Intel (R) i7-10700 TCPU core, 64 GB memory, and no GPU acceleration. The learning rate was set to 0.01, the maximum number of epochs was set to 200 to ensure the convergence of the training process, and other parameters were set to the system's default value.  
To avoid overfitting, we also calculated the loss function of each epoch using the accuracy expression defined in Equation (6) to calculate the accuracy of the validation dataset and use this data to evaluate the performance of the DNN.  
Figure 3 shows the training loss and validation loss curves after 200 epochs, and Figure 4 shows the corresponding accuracy curve of the validation dataset. Figures 3 and 4 show that the loss function of each iteration is decreasing, and it tends to be stable after 110 epochs. The training loss and the validation loss keep changing in synchronization, and the accuracy of  
Figure 4: Accuracy of validation dataset.  
Figure 5: The inversion results of the randomly selected dispersion curves from the 2000 test samples. Among them, subfigures (**a**,**e**–**g**) exhibit shear wave velocities linearly increasing with depth in the subsurface layers, (**d**,**h**,**j**) denote a sharp decrease in shear wave velocity at a certain depth in the subsurface, and (**b**,**c**,**i**) stand for a sharp increase in shear wave velocity at a certain depth in the subsurface.  
Figure 6: Distribution of the relative errors for the test dataset. (**a**) Relative errors for all data points, and (**b**) relative errors for all samples.
## 2 Methodology
### Mathematical Formulation
\(\sigma\) is a Stefan-Boltzmann constant, and 5.6703 \(\times 10^{-8}\ \left[\text{W/m${}^{2}$\cdot$K$}^{4}\right]\) is a known value. \[Q_{12}=\frac{A_{1}F_{12}\varepsilon_{1}\varepsilon_{2}}{1-F_{12}^{2}\frac{A_{ 1}}{A_{2}}(1-\varepsilon_{1})(1-\varepsilon_{2})}\sigma\left(T_{1}^{4}-T_{2}^ {4}\right) \tag{5}\]  
Among the turbulence models for flow analysis, the Realizable k-\(\varepsilon\) turbulence model [23] with excellent flow predictability of complex shapes was used to calculate the process in which heat emitted from the spar-cap surface diffuses through the air. In the Realizable k-\(\varepsilon\) turbulence model, the flow characteristics are determined by Equations (6)-(8). The lower the value of y +, the more stable this turbulence model can operate as compared with other turbulence models. The modeling grid used in this simulation has a very low y + of 10. The total number of grids used in the simulation was 2.6 million, and a tetra grid with a minimum unit length of about 1.6 mm was used. The tetra grid is one of the grids whose high quality in thermal fluid analysis has been verified. However, considering that the minimum thickness of the spar-cap is 5 mm, it is difficult to satisfy the rate of convergence because the grid having the smallest size is large. As a solution to this, several prism gridswere introduced, prism grids are half the minimum unit length of the tetra grid, to improve the convergence. Figure 1:0  
Figure 1:1  
Figure 1:2  
All models used in the heat transfer simulation were transiently analyzed because it was necessary to confirm the change in temperature distribution over time. Depending on the properties of the material or the environment, blade and spar-cap curing generally progresses slowly over a long period of time. The time at which the epoxy begins to cure during this process may vary depending on the location of the structure. Transient analysis is required to confirm the curing time and position due to this temperature unevenness. Transient analysis for heat transfer simulation was performed in 0.1 s per cycle to minimize the initial error of the analysis. From 1000 cycles when the values converged and entered the stabilization stage, analysis was performed for 100 s per cycle, and then the analysis phase time per cycle increased linearly. At 6 h of analysis when the simulation was completed, 450 s per cycle was calculated.
## 2 Materials and Methods
\({}^{13}\)C-NMR (100.61 MHz, CDCl\({}_{3}\)) ppm, & 164.71, 147.37, 143.96, 140.53, 134.80, 133.75, 133.43, 132.62, 132.27, 130.74, 128.70, 128.07, 127.41, 127.11, 127.08, 122.99, 121.74, 116.95, 52.69. MALDI-TOF mass [M + H]\({}^{+}\) calc. 893.250, exp. 893.251. **Synthesis of 3,4-Bis-4-[(4-fluoro)-phenyl]-phenyl-2,5-diphenyl-1,1\({}^{\prime}\)-bis-malonytil-fulvene (5 nP). ** A mixture of 5n, 0.256 g, 0.0434 mmol), phenothiazize **4P** (173 mg, 0.868 mmol), catalyst palladium diacetate (7.8 mg, 0.0347 mmol), tri-tert-butylphosphonium tetrafluoroborate (20.15 mg, 0.070 mmol), and sodium tert-butoxide (125 mg, 1.302 mmol) in toluene (30 mL) was stirred at 110 \({}^{\circ}\)C for 72 h. After the reaction cooled to room temperature, 50 mL dichloromethane was added into the reaction solution, which was then washed with 100 mL of brine followed by 100 mL of distilled water. The organic layer was collected, dried over Na\({}_{2}\)SO\({}_{4}\), and evaporated under reduced pressure. Column chromatography on silica gel and ethyl acetate/hexane 2:8 as mobile phase afforded pure product (99.1 mg, 32%). \({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\)) ppm, & 7.45-7.35 (m, 10H), 7.11 (d, \(J\) = 7.2, 4H), 6.95-6.85 (m, 8H), 6.87 (m, 8H), 6.44 (d, \(J\) = 7.2, 4H). \({}^{13}\)C-NMR (100.61 MHz, CDCl\({}_{3}\)) ppm, & 168.09, 150.21, 142.97, 142.87, 131.99, 131.83, 131.58, 131.16, 129.89, 129.30, 128.88, 127.65, 126.99, 125.63, 124.14, 123.93, 119.87, 111.48. MALDI-TOF mass [M + H]\({}^{+}\) calc. 827.230, exp. 827.231.