esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sampling and Laboratory Methods  
On 9 June 2018, a sediment core (NAS-01) was collected from North Aral Sea (60.96052\({}^{\circ}\) E, 43.32531\({}^{\circ}\) N). The core was 30 cm in length and was sampled in situ in the field at 1 cm intervals for a total of 30 samples.  
The specific activity of \({}^{137}\)Cs is typically used to determine the chronological sequence of lake sediment cores. \({}^{137}\)Cs analysis was performed by \(\gamma\)-spectrometry using an ORTEC HPGe GWL germanium detector (EG&G ORTEC, Oak Ridge, TN, USA).  
Samples were ground to 200 mesh, digested with nitric acid (HNO\({}_{3}\)), hydrofluoric acid (HF) and perchloric acid (HClO\({}_{4}\)), and analyzed by ICP-AES (Prodigy, Teledyne Leeman Labs, Hudson NH, USA) to determine the V concentration (limit of detection: LOD = 2 mg/kg). ICP-MS (7700x, Agilent Technologies, Palo Alto, USA) was used to measure PTE concentrations Cr (LOD = 0.1 mg/kg), Co (LOD = 0.01 mg/kg), Ni (LOD = 0.05 mg/kg), Cu (LOD = 0.02 mg/kg), Zn (LOD = 0.2 mg/kg), As (LOD = 0.1 mg/kg), Cd (LOD = 0.01 mg/kg) and Pb (LOD = 0.02 mg/kg). The standard reference material GBW07311 [42] was used to ensure the reliability of the analytical data. The relative error was determined to be less than 5%.  
To measure the magnetic susceptibility (MS) with low-frequency (470 Hz) and high-frequency (4700 Hz) with a Bartington MS2 fitted with a MS2B dual-frequency sensor (Bartington Instruments, UK), a freeze-dried sediment sample was loaded into a special box with a volume of 8 mm\({}^{3}\), and the box containing the sample was weighed and measured. Each sample was measured three times and the mean value was taken.
## 3 Processes to Eliminate B-O LID
### Thermal Processing  
Thermal processing can reduce the extent of B-O related degradation by reducing the concentration of the B-O defect and precursors [72; 73]. For example, high-temperature processes such as phosphorus diffusion, thermal oxidation and annealing can reduce the B-O defect concentration and therefore increase the stable excess carrier lifetime of the silicon [8; 74; 75; 76; 77]. However, if inappropriate parameters are used during thermal processing, it is possible to worsen the situation by increasing the defect concentration [72; 74]. It appears that the rapid cool down from the high temperature plays a critical role in determining the defect concentration [75; 78], possibly through a reduction in [O][79]. This influence is supported by recent studies, which have shown that the fast-firing process used for the metallisation of industrial silicon solar cells can lead to a permanent reduction in the B-O defect concentration [73; 80; 81; 82]. Extended anneals at lower temperatures in the range of 400-700 \({}^{\circ}\)C can also reduce the B-O defect concentration probably through a reduction in the concentration of interstitial oxygen dimers [78; 79]; however, extended annealing at sub-optimal conditions can also lead to an increase in the B-O defect concentration [72].
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 3 Materials and Methods
### Parametric Equalizer Filters
Among in-vehicle entertainment systems, Class-D amplifiers are particularly preferred. These amplifiers are advantageous due to their high energy efficiency and quality sound output. Class-D amplifiers process audio signals in digital format, amplifying them directly,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Center Frequency Values (Hz)** & **Gain Values (dB)** & **Q Factor Values** \\ \hline
55 & 10.4 & 0.9 \\ \hline
71 & 11.2 & 0.6 \\ \hline
125 & \(-\)2.4 & 0.7 \\ \hline
250 & \(-\)2.4 & 0.7 \\ \hline
500 & \(-\)1.2 & 0.7 \\ \hline
1060 & 7.5 & 1.0 \\ \hline
1280 & 2.0 & 0.9 \\ \hline
3220 & \(-\)10.0 & 1.0 \\ \hline
6400 & 10.5 &without converting them back to analog signals. This process allows them to achieve high sound levels with lower energy consumption, compared to traditional analog amplifiers. Additionally, these amplifiers provide a balanced and consistent response across a wide frequency range, making them ideal for music and sound effects [16]. In this study, an amplifier suitable for 4-ohm speakers was selected. Figure 5 details the frequency response of the chosen amplifier at 4 ohms.
## 1 Introduction
Additionally, the combined effects of design features and operational parameters on sediment dynamics need to be quantified for effective environmental impact mitigation. To address these knowledge gaps, this study employs advanced computational fluid dynamics (CFD) simulations coupled with multivariate regression analysis. Through systematic variation in geometric and operational parameters, the investigation aims to elucidate the complex interactions governing sediment resuspension beneath non-powered floating objects. Therefore, the primary objectives of this study are as follows:  
1. Investigate the hydrodynamic effects induced by non-powered floating objects with varying geometries (blunt and raked bows) and dimensions when towed over flat seabels. Quantify the resultant sediment resuspension and morphological changes on the seabed under different operational parameters, including towing speeds and clearance heights. * Develop a predictive multivariate regression model that captures the relationships between object geometry, operational parameters, and sediment resuspension, thereby providing a tool for designing environmentally sustainable floating structures. To achieve these objectives, 96 CFD simulation scenarios were conducted using FLOW-3D Hydro V1.0 software, systematically varying key parameters to capture a comprehensive range of conditions. The simulations were complemented by a multivariate polynomial regression analysis to identify the most significant predictors of sediment resuspension and to develop a robust predictive model.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 5 Harmonic Estimation Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Ref.** & **Advantage** & **Disadvantage** & **Applications** \\ \hline \multirow{4}{*}{Prony Method} & \multirow{4}{*}{[64]} & Accurate in estimating & \multirow{4}{*}{Requires high computational resources, not efficient for real-time use} & Harmonic estimation in industrial converters \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{4}{*}{Min-Norm Method} & \multirow{4}{*}{[64]} & Provides better accuracy & Limited by its dependence & Harmonic detection in systems with precise frequency requirements \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Adaptive Kalman Filter \\ (AKF) \\ \end{tabular} } & \multirow{4}{*}{[65]} & Adaptive, handles & \multirow{4}{*}{Complex, high computational cost, can estimate limited harmonics} & Dynamic systems with fluctuating grid voltage and harmonic distortion \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Unscented Kalman \\ Filter (UKF) \\ \end{tabular} } & \multirow{4}{*}{[66]} & High accuracy in tracking & \multirow{4}{*}{Limited harmonic estimation} & \multirow{4}{*}{Unbalanced three-phase power systems with dynamic harmonic content} \\  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Harmonic Estimation Methods. Figure 20: Schematic description of waveform variation defined model. Recent research has also focused on developing adaptive and robust harmonic estimation algorithms that can handle non-stationary and non-linear signals. In addition, other techniques, like time-frequency-based approaches and machine-learning-based approaches like neural networks, have also been used in recent research to improve the robustness and accuracy of harmonic estimation. In conclusion, harmonic estimation is a crucial task in power system fields, and recent research has focused on developing efficient and robust algorithms that can accurately estimate the harmonic content of a signal in the presence of noise and other distortions; it has also focused on developing an adaptive and robust model to improve the robustness and accuracy of harmonic estimation. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Ref.
# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 3 Deceiving the Ticketing System
### Multisensor-Based Transport Mode Identification  
In this section, we present a unique method designed to detect attacks to the satellite signal that induce errors in the transport mode identification. Existing solutions use the position and the velocity provided by GNSSs to identify the transport mode [5]. The position and velocity can also be used as input for artificial intelligence [10] or machine learning algorithms [11]. Other existing solutions combine GNSSs with information about the traffic network, such as surrounding railway or bus stops, to identify the transport mode [12]. Lastly, there are multisensor solutions that do not use GNSSs, such as [13], which uses accelerometer and magnetometer data processed with a convolutional neural network.  
Figure 8: Modified journey in Paris. The orange line represents walking, and the green line represents the subway.  
Figure 7: Modified journey in Munich. The orange line represents walking, the blue line represents the tram and the lilac line represents the bicycle.  
The presented method makes use of sensors embedded in the smartphone, particularly the accelerometer, the magnetometer and the barometer at 50 Hz, and of the out-of-the-box GNSS position and the GNSS velocity at 1 Hz.  
Figure 9 shows the flow diagram of the multisensor-based transport mode identification.  
On the one hand, the identification of transport modes is carried out with a convolutional neural network using the signals of the accelerometer, the magnetometer and the barometer. On the other hand, in parallel, the identification of transport modes is carried out based on map matching. To this end, the GNSS position and GNSS velocity are correlated with the available map of the bus, tram and subway stops/stations. The predefined trajectories of the bus, tram and subway are included in the map.  
Under normal circumstances, the output of both subsystems of the parallel detection is congruent. However, under attack, the output of both parallel systems differs. In the case of non-coincident outputs for a long period of time, the map-matching subsystem, which is only based on GNSSs, is switched off since it is considered under attack.
# Techniques for Robust Imputation in Incomplete Two-Way Tables
## 2 Material and Methods
### Alternatives to Obtain Robust Imputation  
The SVD88 algorithm depends directly on the standard SVD, so the presence of outliers can affect the performance and decrease the quality of the imputations. To avoid this behavior, we have three possible strategies: (i) Substitute in the SVD88 iterations the classic robust standardization, in which the column standardization is done by subtracting the median \(\mathrm{M_{j}}\) and dividing the result by the median absolute deviation-MAD\({}_{j}\). Having thus standardized the matrix, use rSVD84 until stability in the imputations is obtained and then return the matrix to the original scale; (ii) Find a robust low-rank approximation to the incomplete matrix using rSVD84 and then take the values obtained in that approximation as imputations for those matrix positions that were not originally obtained. (iii) Perform the imputation in two stages, first obtaining a robust low-rank approximation of the incomplete matrix and then applying the SVD88 method to refine the imputations.  
Some additional implementation details for these options are as follows. In option (i) we tried to obtain a robust version of SVD88 but using the robust standardization and rSVD84 together did not perform well and in preliminary tests it never achieved convergence. However, if we eliminated the singular values of Equation (1) of imputation then convergence was achieved. This robust version of the SVD88 is called M5 RobSVD. Option (ii) or rSVD84 has always shown in preliminary tests to be the fastest but for both this and M5 RobSVD an appropriate number of components to be used in the imputation must be specified. To do this, the robust SVD was calculated on the incomplete matrix and \(k\) was chosen such that the sum of the first \(k\) ordered singular squared values divided by the sum of all singular squared values was greater than 0.75 [11]. Finally, option (iii) depends on the sequential application of two procedures, rSVD84 and SVD88. This sequence of procedures in the preliminary tests has always reached convergence. In view of the original authors of the procedures, we call the imputation methods GOKimputation and M5 GOKimputation when the singular values were disregarded.  
Pseudocode for M5 RobSVD  
1. \(\mathbf{Y}\) <- incomplete matrix
2. \(\mathbf{Y^{*}}\) <- completed matrix (columns means and divide by MAD\({}_{j}\)).
3. \(\hat{y}_{ij}\) <- Imputation update (Equation (1)), eliminate \(\vec{d}_{h}\) and \(\vec{d}_{h},\forall(i,j)\) with missing values.
4. Return to step ii. until stability is achieved in the imputations,
## 3 Results and Discussion  
### Choice of Materials and Sensor Layout  
As shown in previous work [26], 3D PhCs can be prepared in a very efficient way via controlled arrangement of periodic dielectric, highly charged, monodisperse polystyrene spheres. Styrene can easily be polymerized and crosslinked, and its size and low polydispersity can be well adjusted by surfactants. Sulfonate groups are introduced by using styrenesulfonate as a secondary monomer. The fraction of reagents and the reaction conditions are the result of an optimization of the concentrations of cross-linker and initiator and of polymerization time. The concentration, surface charge and size of the particles are exerting several effects. Surface charges and particle diameters that are too small will lead to particle-particle distances where diffraction occurs in the UV rather than in the visible. If, on the other side, the surface charge and spheres are too large, the opposite effect will take place, and the diffracted light will come to lie in the near IR, so that films will appear colorless to the eye. Generally speaking, the particle number densities must be in the proper range for light diffraction in the visible to occur.  
Polyacrylamide (PAM) is a good choice in terms of matrix (host) material for the polystyrene particles. It is well permeated by ions, and all of the components are water-soluble and transparent, so the structural colors of the photonic crystal dispersions can be recorded without the optical influence of the hydrogel. In order to render the film sensitive to ionic strength, the amide groups of PAM are (partially) hydrolyzed to form (anionic) carboxylate groups. We have shown in an earlier study [26] that the reflected wavelength of such generated sensor films is stable between 4 and 60 \({}^{\circ}\)C.  
The second film is composed of a plasticized PVC membrane and doped with the cyclodepsipeptide, valinomycin, the most common and best ionophore for K\({}^{+}\), in the same fashion as reported for K\({}^{+}\)-selective electrodes [10, 31]. Valinomycin shows a \(\sim\)10\({}^{5}\)-fold selectivity for K\({}^{+}\) over Na\({}^{+}\) and is very stable. The combination of a valinomycin-modified plasticized PVC membrane with a PhC system sensitive to ionic strength resulted in a new sensor for the determination of potassium ions. A schematic of the sensor layout is shown in Figure 1.
## Appendix A Derivations of (14) and (15)  
Based on (8), with binomial expansion and mathematical simplification, \([c(z_{2})]^{M-K}\) in (11) can be expressed as  
\[\begin{split}[c(z_{2})]^{M-K}&=\left(\frac{p_{k}}{ \Gamma(m_{k})}\right)^{M-K}\left[\Gamma(m_{k})-\Gamma\left(m_{k},\frac{z_{2}}{ \eta_{k}}\right)\right]^{M-K}\\ &=\sum_{l=0}^{M-K}\binom{M-K}{l}\frac{(-1)^{l}p_{k}^{M-K}}{ \left(\Gamma(m_{k})\right)^{l}}\left(\Gamma\left(m_{k},\frac{z_{2}}{\eta_{k}} \right)\right)^{l}.\end{split} \tag{11}\]  
In (11), the \(k\)th power of \(\Gamma(n,x)\) for a non-negative integer \(k\) can be rewritten as [55]  
\[\left[\Gamma(n,x)\right]^{k}=\left[(n-1)!\right]^{k}\exp(-kx)\left[\sum_{m=0}^ {n-1}\frac{1}{m!}x^{m}\right]^{k}. \tag{12}\]  
Note that, based on [56] (Equation (9)), \(\left[\sum_{m=0}^{n-1}\frac{1}{m!}x^{m}\right]^{k}\) in (12) can be reformulated from the multiple-product form to the summation form as follows:  
\[\begin{split}\left[\sum_{m=0}^{n-1}\frac{1}{m!}x^{m}\right]^{k}& =\sum_{\begin{subarray}{c}i_{0}i_{1},\cdots,i_{n-1}\geq 0\\ i_{0}+i_{1}+\cdots+i_{n-1}=k\end{subarray}}\frac{k!}{i_{0}!i_{1}!\cdots i_{n- 1}!}\prod_{m=0}^{n-1}\left(\frac{1}{m!}x^{m}\right)^{i_{m}}\\ &=\sum_{\begin{subarray}{c}i_{0}i_{1},\cdots,i_{n-1}\geq 0\\ i_{0}+i_{1}+\cdots+i_{n-1}=k\end{subarray}}\frac{k!}{i_{0}!i_{1}!\cdots i_{n- 1}!}\prod_{m=0}^{n-1}\left(\frac{1}{m!}\right)^{i_{m}}x^{\sum_{m=0}^{n-1}m\,i_ {m}}\\ &\overset{\text{or}}{=}\sum_{m_{1}=0}^{n-1}\sum_{m_{2}=0}^{n-1} \cdots\sum_{m_{k}=0}^{n-1}\frac{x^{m_{1}+m_{2}+\cdots+m_{k}}}{\prod_{i=1}^{k} m_{i}!}\\ &=\sum_{m_{1}=0}^{n-1}\sum_{m_{2}=0}^{n-1}\cdots\sum_{m_{k}=0}^{n- 1}\frac{x^{\sum_{k}^{k}m_{i}}}{\prod_{i=1}^{k}m_{i}!},\end{split} \tag{13}\]  
which is appropriate for the integral operation. Hence, we obtain the following form:  
\[\begin{split}\left[\Gamma\left(m_{k},\frac{z_{2}}{\eta_{k}} \right)\right]^{l}&=\left[(m_{k}-1)!\right]^{l}\exp\left(-l\frac{ z_{2}}{\eta_{k}}\right)\\ &\times\sum_{\begin{subarray}{c}i_{0}i_{1},\cdots,i_{m_{k}-1}\geq 0 \\ i_{0}+i_{1}+\cdots+i_{n_{k}-1}=l\end{subarray}}\frac{l!}{i_{0}!i_{1}!\cdots i_{ m_{k}-1}!}\prod_{j=0}^{m_{k}-1}\left(\frac{1}{j!}\right)^{i_{j}}\left(\frac{z_{2}}{ \eta_{k}}\right)^{j\cdot i_{j}}\\ &\overset{\text{or}}{=}[(m_{k}-1)!]^{l}\exp\left(-l\frac{z_{2}}{ \eta_{k}}\right)\\ &\times\sum_{i_{1}=0}^{m_{k}-1}\sum_{i_{2}=0}^{m_{k}-1}\cdots\sum _{i_{1}=0}^{m_{k}-1}\frac{1}{\prod_{j=1}^{l}i_{j}!}\left(\frac{z_{2}}{\eta_{k}} \right)^{\sum_{j=1}^{l}i_{j}},\end{split} \tag{14}\]  
which yields (14). By using the same property as (13), we can also obtain \(\left[e(z_{2},-s_{1})\right]^{K-1}\) in the summation form, as shown in (15).
## 2 Methods
### Signal Processing of the FBG Sensor  
The Fiber Bragg Grating (FBG) is fundamentally characterized by a periodic variation in the refractive index along the fiber axis [34]. In addition to the common benefits associated with optical fiber sensors, FBGs possess an intrinsic self-referencing and multiplexing ability. This structure operates as a reject-band filter, reflecting back the spectral component, \(\Delta\)B [nm], that meets the Bragg condition (provided by Equation (1)) while allowing the transmission of the remaining components. The Bragg wavelength can be calculated as [35; 36; 37]  
\[\Delta\lambda=KeX\times\varepsilon \tag{1}\]  
The wavelength shift, \(\Delta\)\(\Delta\)B [nm], of an FBG sensor due to a physical disturbance is determined by the grating pitch, \(\Lambda\), and the effective refractive index of the fiber core, refl. The expression for \(\Delta\)\(\Delta\)B [nm] is given by Equation (2) [38; 39; 40]:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(1-\rho_{e})\Delta\varepsilon+(\alpha+ \xi)\Delta T \tag{2}\]  
Equation (2) describes the wavelength shift, \(\Delta\)\(\Delta\)B [nm], of a Fiber Bragg Grating (FBG) sensor due to a physical disturbance. The components involved in the equation are pe, which represents the effective photoelastic constant; \(\Delta\)\(\varepsilon\), which represents the axial strain; \(\alpha\), which represents the thermal expansion; \(\xi\), which represents the thermal optic coefficient; and \(\Delta\)T, which represents the temperature shifts. The first term in the equation expresses the strain effect on an optical fiber, which causes a change in the grating spacing and the refractive index due to strain-optic effects. The second term is related to temperature sensing, as changes in temperature cause the FBG to dilate or contract, modifying the grating pitch.  
As seen in Equation (2), FBGs are susceptible to temperature fluctuations. The leg movements' frequency measured in this gait analysis method is faster than the temperature variations (both room and body temperatures). Therefore, the temperature variations could be seen as a DC signal being introduced in the AC signal from the readings obtained. Moreover, with the utilization of a high-pass filter, which is presented in Section III B, the temperature effect is negligible.
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
## 3 Simulated Results
### Equivalent Circuit Simulations  
According to Equations (2) and (3), the two equivalent circuits shown in Figure 5, are used to realize a quadrature phase shift between the x and y components of the incident electric field, providing a 90\({}^{\circ}\) phase difference between the transmission coefficients of the two components and minimizing the insertion loss. Both circuits are matched to the equivalent impedance (Z0 = 377 \(\Omega\)). The capacitances and inductances values shown in Figures 2-4 are used in the equivalent circuit model of the multilayer polarizer shown in Figure 5. These values were then slightly modified in a final optimization. The following values were obtained: Cx1 = 6.3 fF, Lx1 = 6.5 nH, Cx2 = 2.3 fF, Lx2 = 4.8 nH, Cy1 = 2.3 fF, Ly1 = 3.8 nH, Cy2 = 3.3 nH, Ly2 = 4.0 nH, Cy = 72 fF, Ly = 6.3 nH, _hfcam1_ = 3.5 mm, _hfcam2_ = 1.5 mm, _hsub_ = 0.13 mm. To have circular polarization, both Conditions (2) and (3) have to be fulfilled by the two transmission coefficients of the equivalent circuits. Modules for both polarizations are shown in Figure 6a. Condition (2), \(|T_{x}|=|T_{y}|\), is fulfilled in the two bands of interest. Figure 6b shows the transmission phase difference. Condition (3) is fulfilled in the TX band with the '+' sign generating an LHCP polarization. In contrast, RHCP is generated around 30 GHz where the condition is fulfilled with the '-' sign.  
Figure 5: Equivalent circuit model of the multilayer polarizer for: (a) x-polarized input wave; (b) y-polarized input wave.
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 4 Performance Evaluation  
### Simulation Settings  
All the algorithms were executed within MATLAB R2021a using an Intel(R) Core (TM) i7-8665U (2.2 GHz) CPU with 16 GB RAM installed on Microsoft Windows 10. The parameters of the wind-driven genetic algorithm are given in Table 1.