esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Details
The elastic constant calculation of Co-Cr-Fe-Ni HEAs can be implemented in the CASTEP package [49] by the stress-strain method based on [PERSON]'s law. [PERSON]'s law describes the linear relationship between stress component \(\sigma_{i}\) and strain component \(\epsilon_{j}\), and it is defined as [50]:  
\[\sigma_{i}=\sum\limits_{j=1}^{6}C_{ij}\epsilon_{j} \tag{6}\]where \(C_{ij}\) represents the elastic constant and is described by a 6 \(\times\) 6 tensor. The elastic constants in all directions can be calculated by the finite displacement method. According to the Voigt-Reuss-Hill approximation method [51], the polycrystal elastic moduli of the alloys was calculated from the elastic constants, including bulk modulus (\(B\)), shear modulus (\(G\)), [PERSON]'s modulus (\(E\)), [PERSON] ratio (\(B/G\)), and Poisson's ratio (\(\
u\)). In this work, a series of supercell solid-solution alloy models containing 20 and 32 atoms were established through the SQS method, and they were expanded to 1 \(\times\) 1 \(\times\) 5 and 2 \(\times\) 2 \(\times\) 2 on the basis of a single cell. The Co-Cr-Fe-Ni HEAs model and the percentage of each element are shown in Table 1 and Figure 1. The first-principles calculation uses the CASTEP package [49] based on plane-wave pseudopotential. This module is a quantum mechanics program developed by the University of Cambridge and has been widely used in the study of properties of complex multicomponent alloys [34; 52]. We choose spin polarization and used formal spin as initial conditions because the ferromagnetic state is more stable than the paramagnetic state of room temperature at 0 K [29; 53; 54]. The Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) [55] was used to describe the exchange-correlation function. The ultrasoft pseudopotential was used to treat the electron-ion interaction. The energy cutoff for the plane-wave basis set was 400 eV. The self-consistent field tolerance is 1 \(\times\) 10\({}^{-6}\) eV/atom, and the Brillouin zone was integrated using a Monkhorst-Pack grid. When \(x\) is 0.2 and 0.56, 1 and 1.57, and 2, the k-point set in the reciprocal space is 4 \(\times\) 4 \(\times\) 3, 4 \(\times\) 3 \(\times\) 4, and 1 \(\times\) 7 \(\times\) 7, respectively. The convergence tolerance is that the maximum force is less than 0.03 eV/A, the energy is less than 1 \(\times\) 10\({}^{-5}\) eV/atom, the maximum stress is less than 0.05 GPa, and the maximum displacement is less than 0.001 A. Based on the above parameters and alloy models, the structure was first optimized, and then the elastic properties were calculated. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alloy** & **Alloy Model** & \(x\)**Value** & \(x\)**Elements/at.\%** & \begin{tabular}{c} **Other** \\ **Elements/at.\%** \\ \end{tabular}  \\ \hline \multirow{6}{*}{Co\({}_{x}\)CrFeNi} & Co\({}_{2}\)Cr\({}_{10}\)Fe\({}_{10}\)Ni\({}_{10}\) & 0.2 & 6.25 & 31.25 \\  & Co\({}_{5}\)Cr\({}_{6}\)Fe\({}_{8}\)Ni\({}_{9}\) & 0.56 & 15.625 & 28.125 \\  & Co\({}_{8
## 4 Discussion
The present study was performed under natural non-controlled blinking conditions to represent the natural state of the eye as accurately as possible. The IBI is the interval time between two consecutive blinks, and it has been demonstrated that, together with other parameters related to the blink, it is useful for distinguishing between healthy and DED subjects [30; 44; 45]. Considering that the subjects were asked to close their eye for 3 s right before the start of the recording, the first IBI might be more susceptible to bias. Therefore, the last complete IBI was considered the most realistic and natural interval. In the current study, no differences were found between groups for any thermal parameter characterized in the last IBI. Conversely, [PERSON] et al. measured the mean OST during a 5-s IBI and found significant differences between DED and controls, but the assessment was performed under controlled blinking conditions [46]. Another factor that could have affected the results of the current study is the DED diagnosis criteria. It is well known that due to the disease's complexity and unclear etiology, diagnostic criteria for DED are still not fully standardized. DED diagnosis according to DEWS II includes symptomatology assessment (via OSDI questionnaire) as a screening tool, and the scheme also allows consideration of various related manifestations, such as non-obvious disease involving ocular surface signs without related symptoms [7]. According to these criteria, subjects with signs but no symptoms were classified as the control group in the current study. Knowing that OST significantly correlates with some clinical DED signs [47], having subjects with signs in the control group could have equalized the mean temperature with the DED group. In fact, 30 of the 39 subjects of the control group presented at least one clinical sign. The mean OST of those 30 subjects was 34.9 \(\pm\) 0.7 \({}^{\circ}\)C, whereas the OST of the other 9 with no signs was 35.4 \(\pm\) 0.2 \({}^{\circ}\)C, showing a 0.5 \({}^{\circ}\)C increment. Considering the coefficient of repeatability of OST in 0.31 \({}^{\circ}\)C [26], this increment reveals the importance of the DED diagnosis system. Therefore, it seems that the presence of clinical signs cools the temperature. This is supported by a recent study showing positive correlations between OST and TMH and [PERSON]'s test values, which are often decreased in DED [47]. However, this hypothesis needs to be further studied. The study presents some limitations. First, only one ROI was established for the entire ocular surface, limiting the information obtained by area (for example, nasal and temporal conjunctiva). On the other hand, it was established in this way to avoid errors delimiting the ROI in the different areas, since in the thermal image it is not possible to observe the anatomical limits of the different structures. Secondly, as mentioned previously, our control group consisted of subjects without symptoms but with clinical signs, which could influence the results. For this reason, this approach should be addressed in future studies.
## 5 Results and Discussion
### Discussion
#### 5.2.1 Comparison with Previous Studies  
Compared to the method described in [25], we incorporated transformers into our process and included transformer-based summarization for each topic. It allowed us to provide detailed information about each opportunity. We achieved better results in extracting opportunities from the review data using transformers. When employing the RAKE algorithm to extract key phrases from our dataset, we obtained 102,850 n-grams with a maximum length of two. However, when using transformers for crucial phrase generation, we obtained 60,122 critical phrases with a maximum length of three. These transformer-generated key phrases required less cleansing than the RAKE key phrases, as they exhibited higher quality. If we had used RAKE with a maximum length of three, it would have resulted in excessive key phrases, requiring more cleansing effort.  
Applying the LDA algorithm to the extracted RAKE keywords, as proposed in [25], using bag-of-words encoding resulted in similar topics. Words like \"sound\", \"music\", \"play\", \"voice\", and \"room\" were the most common words across almost all topics. Consequently, the topics were too similar to be effectively used in the subsequent steps. Since the generated keywords by KeyBert are not necessarily an exact match with the review text, using LDA with bag-of-words encoding on these keywords is not feasible. To address this issue and the problem of highly similar topics identified earlier, we adopted a topic-generation approach using DBSCAN clustering. However, as the DBSCAN model cannot determine the confidence score of a topic for a document, we employed zero-shot classification with transformers on the sentences to calculate the confidence score. This calculation is crucial for assessing importance and satisfaction and identifying opportunities.  
The Python library _BERTopic_ offers topic modeling based on a hierarchical DBSCAN (HDBSCAN) of embedded documents. It also has a variant that uses KeyBert combined with c-TF-IDF, and another variant utilizes zero-shot classification. However, it does not combine both methods. In [63], one of the weaknesses of _BERTtopic_ is that it allocates each document to only one topic.
## 3 Enhanced SoC-Balancing Strategy with Saturation
### Theoretical Comparisons of Balancing Performance
Figure 9: Ideal curves in series applications: (**a**) currents under different \(i_{\text{satur}}\), (**b**) currents under load change with fixed \(i_{\text{satur}}\), (**c**) currents under load change with variable \(i_{\text{satur}}\), (**d**) SoC under different \(i_{\text{satur}}\), (**e**) SoC under load change with fixed \(i_{\text{satur}}\), and (**f**) SoC under load change with variable \(i_{\text{satur}}\). effectiveness of the proposed strategy in such a scenario. Figure 11 exhibits the ideal curves when the proposed strategy is applied in a scenario wherein batteries degrade. Here, \(i_{\text{satu}}\) is 33 A in parallel applications and 24 A in series applications. Moreover, the three battery capacities, \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\), are 45 Ah, 38.4 Ah, and 41.7 Ah, respectively. The results show that this balancing method can still mitigate SoC deviations when battery capacities are inconsistent. Note that \(i_{1}\), \(i_{2}\), and \(i_{3}\) are 16.2 A, 13.8 A, and 15 A, respectively, after SoC balance is achieved. In fact, they are proportional to the corresponding capacities of their own ESUs. In this case, the equal declining rate of SoC is shared by all ESUs and thus SoC balance can be maintained.
## 3 Results
### Mean Field Material Model Parameter Calibration  
In Digimat, reverse engineering was carried out to create a mathematical model of each material (Table 1). Refining the fiber aspect ratio and matrix parameters in the mean field homogenization material model allowed us to adjust the elasticity model of the composite to the results of ISO 527 sample tensile testing. The fiber aspect ratio and the following matrix parameters were fitted: Young's modulus, yield stress, hardening modulus, hardening exponent, and hardening linear modulus.  
The obtained models most accurately describe the conducted strength experiment and reflect the properties of the composite materials. Glass fibers' Young modulus is in the range of 60 GPa to 90 GPa [60]. In [61], the elastic modulus range of the most popular E-glass fibers was estimated in the range of 72-76 GPa. In our work, we use Young's modulus = 72 GPa for glass fibers, and Poisson's coefficient of glass fibers is 0.22, which corresponds to the values for short-reinforced polyamide fibers in the Digimat MX system. To determine the matrix Poisson's ratio in a small strain region, it is necessary to use a biaxial extensometer. The measurements of the polyamide-6 matrix Poisson's ratio in a small strain region were found experimentally in [44] to be 0.3721 and satisfactorily agrees with that presented by [PERSON] [62]. The Poisson's ratio value of 0.3721 was used  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Material**} \\ \cline{2-5}  & **PA6 GF 30** & **PA6 GF 50** & **PA6 GF 15 THSM** & **PA6 GF 30 THSM** \\ \hline Young’s modulus, MPa & 3929 & 4074 & 2985 & 2633 \\ Yield stress, MPa & 19.5 & 36.9 & 21.6 & 28.0 \\ Hardening modulus, MPa & 40.4 & 22.4 & 31.0 & 21.4 \\ Hardening exponent & 447.8 & 257.9 & 274.7 & 359 \\ Hardening linear modulus, MPa & 203.3 & 217.1 & 154.9 & 209.35 \\ Fibers’ AR & 14.723 & 13.533 & 13.478 & 13.059 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration of matrix parameters and fibers’ aspect ratio.  
Figure 11: Panoramas of sample slides from the electron microscope.  
for the matrix of all material models. Figure 12 shows the reverse engineering results of the homogenization mean field models. To correspond with the mass and volume fiber fractions, a fiber density of 2550 kg/m\({}^{3}\) and a matrix density of 1148 kg/m\({}^{3}\) were used.
## 5 Simulation Results and Analysis
### ADMM Convergence Analysis  
Plots of the norms of the primal and dual residuals, \(d_{pr1}\), \(d_{pr2}\), \(d_{pr3}\), \(d_{dr2}\), \(d_{dr2}\), and the stopping criteria limits \(\epsilon_{1}^{pri},\epsilon_{2}^{pri},\epsilon_{2}^{pri},\epsilon^{dual}\) against the iteration numbers are shown in Figures 2 and 3. The plots show that the stopping conditions are met within 20 iterations.  
Figure 2: Norms of primary residuals per iteration.  
Figure 4 shows a plot of the objective function values. The objective function is the first line of Equation (18). As is obvious in the figure, the objective function settles within 10 iterations. This is in accordance with the settling of the primary and dual residuals, Figures 2 and 3.  
Figure 4: Convergence of the objective function Equation (18).  
Figure 3: Norms of dual residuals per iteration.
# Multifractal Characterization and Modeling of Blood Pressure Signals
## 2 Data and Methodology
### Data Pre-Processing
#### 2.2.2 Filtering out Environmental Factor Outliers  
It is essential to filter physiological signals to place them in a suitable frequency band before further analysis [34]. In order to eliminate high frequency noise due to environmental factors, we processed the signal \(X(t)\) with a stable linear-phase low-pass finite impulse response (FIR) filter [48] with the following characteristics. Let \(f_{N}=\frac{f}{f_{s}/2}\in[0,1]\) be the normalized frequency, where \(f_{s}\) is the sampling frequency (in sample/sec) and \(f_{s}/2\) is the Nyquist frequency; then, the filter design follows:  
* Equirpple filter with pass-band frequency: \(f_{N}^{\text{pb}}=0.10\);
* Stop-band frequency: \(f_{N}^{\text{sb}}=0.15\);
* Stop-band attenuation: 60 dB;
* Amplitude of the ripple in the pass-band: 1 dB.  
This specific design for the FIR filter yields a computationally light filter whose order is 87. Since the application allows off-line pre-processing, we employ the above filter to run a zero-phase filtering, that is, processing the input signal in both forward and reverse directions. Specifically, after filtering the signal in the forward direction, we reverse the filtered signal and run it back through the filter, yielding zero-phase distortion. In other words, the forward and reverse filtering direction zeroes the measured group delay \(\tau_{g}=43.5\) samples. We note that the linear-phase compensation comes with the effect of a filter transfer function which equals the squared magnitude of the original filter transfer function and a filter order that is double of the order of the original filter. Finally, the  
Figure 1: Results of the Hampel filtering procedure for outlier removals (**a**). Original time series (top panel) and processed time series (bottom panel) (**b**). In the last panel the presence of a high non-stationary behavior, such as slow varying cycles, can be observed that needs to be addressed before operating the MFDFA. The bottom panel in (**b**) also shows a set of singularities for the processed signal, such as cusps and stairs.  
stop-band frequency \(f_{N}^{\text{sb}}=0.15\) allows the elimination of all frequency components above \(f=0.05\) Hz, corresponding to a timescale \(t=1/f\) of 5 s.
## 3 Numerical Simulations and Discussion
### Accuracy of SDA and TODA
#### 3.2.2 Discussion  
In the first kind of numerical simulation, the simulated unbalance responses calculated by the CRDAM were fed into SDA and TODA to estimate the rotor unbalance. For SDA, the identified rotor unbalance was equal to the set value of rotor unbalance. For TODA, the identified rotor unbalance was equal to the set value of rotor unbalance except for the simulation of g4.4. The error for the identified rotor unbalance for #3 and #4 discs in g4.4 is big at the low rotating frequency (1 Hz, 2 Hz) but very small at other rotating frequencies.  
In the second kind of numerical simulation, the simulated unbalance responses, which were calculated by the CRDAM and contaminated the set measured error (5%, 5\({}^{\circ}\)), were fed into SDA and TODA. For SDA, the identified error was equal to the set error. For TODA, the identified rotor unbalance was almost equal to the set error except for the simulation of g4.4. The error for the identified rotor unbalance for #3 and #4 discs in g4.4 was bigger than the set errors at the low rotating frequency (1 Hz, 2 Hz) but almost equal to the set error at other frequencies.  
The unbalance responses in both the \(x\) and \(y\) directions were needed when using TODA. At a low frequency, the value of the unbalance response is very small. The errors in computer calculations, such as rounding errors, have a considerable influence on small values. Accordingly, there were errors with regard to the unbalance responses in both the \(x\) and \(y\) directions at low frequencies. If the errors in the \(x\) and \(y\) directions are quite different, a big identification error may occur at a low frequency for TODA because the errors in the \(x\) direction are divided by the errors in the \(y\) direction, while, for SDA, the unbalance response in only one direction is needed. Hence, the identification error can be equal to the set measured error.  
Therefore, the error for the identified rotor unbalance response will be equal to the set error if the errors of all the measured unbalance responses are the same. It is indicated that the repeatability precision of each measuring channel of the unbalance response measurement system plays a critical role when it comes to using the proposed algorithms. In engineering, the vibration caused by rotor unbalance can be very small for low-speed  
Figure 13: The maximum identification error for each rotor unbalance in g4.4 based on TODA using #20 point as one of the required measuring points (the other measuring points are at the location of the two bearings and the four discs): (**a**) relative error for the rotor unbalance amplitude; (**b**) absolute error for the rotor unbalance angle.  
rotors and the measuring system cannot accurately detect tiny vibrations. Therefore, the proposed method, especially TODA, cannot be applied to low-speed rotors.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 4 Enrichment of Low-Grade Pyrophyllite Ore
### Microwave Roasting and Magnetic Separation  
Chemical separation methods are often applied alongside physical separation methods to obtain a higher-purity product [109]. Recently, the efficiency of using microwave heating and magnetic separation in removing impurities (e.g., Fe and Ti) from pyrophyllite was investigated and considered a promising and environmentally friendly method for enriching low-grade pyrophyllite ore [36]. Through previous studies, the use of microwave roasting and magnetic separation removed up to 96% of pyrophyllite impurities based on operating variables such as irradiation time and magnetic field intensity [36]. An increase in impurity removal efficiency was caused by phase changes of the impurities, which became magnetized during microwave roasting. Additionally, the impurity-removal efficiency can be improved by adjusting the operating conditions related to roasting and magnetic separation. Another study confirmed that sequential microwave roasting and magnetic separation could remove Fe and Ti with high efficiency. Kaolinite, quartz, and dickite were the main gangue minerals, and impurities in pyrophyllite occurred in the form of oxide and sulfide minerals. The study results indicated that Fe and Ti were removed from pyrophyllite with 86% and 68% efficiency, respectively, under 30 min of microwave irradiation and a magnetic field intensity of 2000 Gauss. Moreover, the study found that extending the microwave irradiation time and increasing the magnetic field intensity could improve impurity-removal efficiency, especially for paramagnetic Ti impurities [19]. This method can effectively upgrade low-grade pyrophyllite ore and clay minerals, which can then be exploited after removing impurities. However, to use this technique in industry,more studies are needed to better optimize the mineral-phase changes to achieve effective separation and energy consumption.
# Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
## 4 Analysis of Experimental Results
### DoM Assessment for the Proposed Method  
In this section, to demonstrate the superior similarity of the proposed method, an MOS test was performed by comparing the sounds of the proposed method with actual dolphin sounds. The MOS BS1284 standard provided by the International Telecommunication Union was used to evaluate the similarity between the mimicked whistles generated by the proposed method and the actual dolphin whistles. The MOS BS1284 is a standard used to evaluate the degree of distortion in speech communication and is adequate to evaluate the degree of similarity in the mimicked whistles [29]. The MOS score was assigned on a 5-point scale, and the number of participants needed to be greater than 20. The MOS test grading criteria are provided in Table 4.  
Figure 11: Ocean experiments received signals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demodulation Scheme** & **BER (w.o. Turbo)** & **BER (w. Turbo)** \\ \hline Conv. method (Ref. [13]) & 0.13 & \(6.3\times 10^{-2}\) \\ Proposed method & \(6.6\times 10^{-2}\) & \(1.3\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ocean experiment BER results.  
Figure 10: Ocean experiments environments (**a**) Location, (**b**) Configurations, (**c**) SSP.  
In the MOS test evaluation, whistles generated by the proposed technique and real dolphin sounds were played to the participants in a random order for approximately 10 s. The whistles generated by the proposed technique were modulated by Table 1. Every mimicked whistle and real whistle were played three times per test, and 40 tests were conducted. The actual dolphin sounds used in the experiments were obtained from the \"Watkins Mammal Database\" [28]. Figure 12 shows a spectrogram of some of the audio files used in the MOS experiments. The experimental setup included the use of a Terratec A/D converter and AKG K52 headphones.  
A total of 32 participants participated in the experiment. Participants in the MOS test listened to two audio files and rated how similar they were to dolphin sounds using the MOS scale. The results of the experiment are summarized in Table 5.  
The experimental results in Table 4 showed that only a 1.3% difference was measured between the real dolphin sounds and the sounds generated by the proposed method.
## Materials and Methods
### Bird Modeling
#### 2.3.2 Smoothed Particle Hydrodynamics (SPH) Technique  
The Lagrangian methods track the material history by the identification of particles as a material point, and the movement of the particles is modelled by the deformation of that material. One such method to model the motion of a continuum is a smoothed particle hydrodynamics (SPH) method. The SPH model is capable of large displacements, strong discontinuities. and complex interface geometries [20]. The SPH method is based on the idea of using mesh-less particles with individual material properties, and moving as per the governing equations [21].  
The SPH is implemented using a normalized corrected Kernel interpolation [22]. In order to define the SPH particles, ANSYS Autodyn is used. This allows for the user to fill the predefined bird geometry with SPH. This also allows the user to define the particle density. However, increasing the particle density may increase the simulation time.  
The bird strike event is simulated by using the Eulerian formulation and SPH technique in order to compare the results of both techniques.