esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Weak Interaction Analysis
Expressing these functional dependencies represents a challenging problem. Similar difficulties arise when looking for an equivalent circuit of the rail launcher. Figure 5 shows a rail launcher fed by an ideal voltage source whose waveform is given by \(e(t)=50\Big{(}1-e^{\frac{c_{s}}{4}}\Big{)}V\). Details about the geometry are in Table 2. In this figure, 20 elementary volumes located near the launcher breech are shown. The device was analyzed by EN4 EM and the current density waveforms in the evidenced volumes are reported in Figure 6. The solid curves are associated with the labelled sections in reported in the inset of Figure 5; the greatest current density occurs in the section #5 in the inner part of the rail. The peak values became smaller as the outer boundary of the rail is approached. The reported waveforms put into evidence the uneven current density distribution in the rail section for most of the launch time, the ratio between the maximum and the minimum value is greater than two. This behavior is a consequence of the skin and proximity effects; also, the velocity skin effect has an heavy impact on the current distribution, which is a function of the rate of change of the electrical quantities imposed by the generator and the rate of change of the mechanical quantities i.e., the speed of the armature [36; 37]. Both these quantities change during the launch. Figure 4: Eddy current distribution on the conductive compensating shield. (Reproduced with permission from [29], IEEE, 2017.) The basic equivalent circuit of the railgun, here shown in Figure 7, is usually composed of three elements. A resistor and an inductor, both varying with the distance travelled by the armature, and another resistor, that takes into account the motional induced electromagnetic force, is related to the inductance gradient and its resistance depends on the speed of the armature. In the light of the above discussion, these circuital components should be function of the frequency which influences the effective current density distributions in both the rails and the armature, as well as of the time, because of the speed and of the distance travelled by the armature. Some lumped equivalent circuits of the rail launchers adopt simplified expressions of the form: \(R(z,f)=R_{a}+R_{0}+R^{\prime}z\) and \(L(z,f)=L_{0}+L^{\prime}z\), where \(R^{\prime}=\frac{dR}{dz}\) is the rail resistance gradient, \(L^{\prime}=\frac{dl}{dz}\) is the rail inductance gradient, \(R_{a}\) is the armature resistance and \(R_{0}\) and \(L_{0}\) are the resistance and the inductance due to the connection wires [1]. All these expressions discard the dependence of these parameters on the frequency and are
# Crossover Operator Inspired by the Selection Operator
## 4 Conclusions  
The novelty of the developed crossover operator is that it is an intermediate form between the most popular, purely random solutions and the recently developed fully deterministic implementations of the crossover operator. Despite its simplicity and almost a direct copy of the idea of the selection operator, in the conducted research, it demonstrated superior performance compared with existing crossover operators.  
The simplicity of the new operator provides great opportunities for its quick, often trivial integration with a huge database of existing solutions in the area of evolutionary task sequencing. The proposed crossover operator was modeled on the most basic, proportional variant of the selection operator and has already demonstrated its power. An interesting field for further research would be variants of the crossover operator modeled on other types of selection from the quite extensive set available in the bibliography for evolutionary algorithms. Validation of the new crossover operator has been performed for a symmetric variant of task sequencing. However, it seems that further research should also cover the application of this operator for task scheduling with an asymmetric (dependent on the transition direction) cost function.  
Conceptualization, methodology, and writing--review and editing, P.C. and S.G.; investigation and visualization, P.C. All authors have read and agreed to the published version of the manuscript.  
Publication supported by the Excellence Initiative--Research University programme implemented at the Silesian University of Technology, year 2023, grant number 11/040/SDU/10-22-01.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Figure 22: Influence of the population size on the effectiveness of the crossover operators for the 500-task problem (cost based on Euclidean distance) over 1000 generations.
## 3 Preliminaries
### Hamming Distance  
Since the authorization list for some operations is a Boolean matrix, each row (or each column) can be regarded as a binary vector of the same length. The well-known Hamming distance [34], which is widely used to measure the distance between two different equal-length vectors, can identify clusters of the same (or similar) use-object pairs. Hamming distance states that, given two equal-length Boolean vectors \(x\) and \(y\), the Hamming distance between x and y, denoted as \(Dis(x,y)\), is the number of positions where the vectors take different values for the same column position.  
For instance, given two row vectors, \(x\) = \"100110\" and \(y\) = \"110011\", \(Dis(x,\,y)=3\). Clearly, the distance between any two rows in \(A_{op}\) increases with an increasing number of column positions that take different values.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods  
### Pol-SAR Features Extraction  
Polarimetric radar measures the complex scattering matrix of a medium with quad polarizations. The scattering matrix in the linear orthogonal polarization basis can be expressed as [19]:  
\[S=\begin{bmatrix}s_{\text{hh}}&s_{\text{hv}}\\ s_{\text{vh}}&s_{\text{vv}}\end{bmatrix}, \tag{1}\]  
where \(s_{\text{hv}}\) is the scattering element of horizontally polarized transmitting and vertically polarized receiving waves, and the other three elements are defined similarly. In the case of reciprocal backscattering, \(s_{\text{vh}}=s_{\text{hv}}\). The corresponding vectors \(k_{\text{T}}\) and \(k_{\text{C}}\) can be expressed by the following forms [19]:  
\[k_{\text{T}}=\frac{1}{\sqrt{2}}\left[\begin{array}{cc}s_{\text{hh}}+s_{\text {vv}}&s_{\text{hh}}-s_{\text{vv}}&2s_{\text{hv}}\end{array}\right]^{\text{T}} \tag{2}\]  
\[k_{\text{C}}=\left[\begin{array}{cc}s_{\text{hh}}&\sqrt{2}s_{\text{hv}}&s_{ \text{vv}}\end{array}\right]^{\text{T}}, \tag{3}\]  
where \(\left[\begin{array}{c}\\ \end{array}\right]^{\text{T}}\) is an ordinary transpose operation.  
For the distributed targets, the \(3\times 3\) coherency matrix \(T\) and covariance matrix \(C\) can be computed by [19]:  
\[T=\tfrac{1}{n}\sum\limits_{i=1}^{n}k_{\text{T}i}\cdot k_{\text{T}i}^{H}= \left[\begin{array}{ccc}T_{11}&T_{12}&T_{13}\\ T_{12}^{*}&T_{22}&T_{23}\\ T_{13}^{*}&T_{23}^{*}&T_{33}\end{array}\right] \tag{4}\]  
\[C=\frac{1}{n}\sum\limits_{i=1}^{n}k_{\text{C}i}\cdot k_{\text{C}i}^{H}= \left[\begin{array}{ccc}C_{11}&C_{12}&C_{13}\\ C_{12}^{*}&C_{22}&C_{23}\\ C_{13}^{*}&C_{23}^{*}&C_{33}\end{array}\right], \tag{5}\]  
where \(*\) stands for conjugate, and H denotes conjugate transpose.  
Both the coherency matrix and covariance matrix contain the scattering characteristics of a distributed target. Pol-SAR data can provide characteristics of the land cover, such as permittivity, surface roughness, material, and so on. To clearly show the difference between different lithologies, some new polarimetric features should be defined and extracted based on the cross polarized ratio, co-polarized correlation coefficient, and the proportion of different scattering mechanisms, etc.
## 4 Method
### Attitude Controller  
In order to achieve high-precision attitude stabilization in the presence of external wind and model uncertainties, a backstepping controller is built here combined with FASTESO for UAV attitude system. The main objective of this part is to guarantee that the state of attitude \(\mathbf{q}\) and angular rate \(\mathbf{w}\) converge to the reference values \(\mathbf{q}_{d}\) and \(\mathbf{w}_{d}\) in real time. As this part is identical to our previous work [30] except the UAV model, which would not impact the controller design. Therefore we are not going to describe it in detail.  
Figure 4: Fuzzy input membership functions.  
Figure 5: Fuzzy output membership functions.  
According to the authors of [30], the control signal vector \(\mathbf{u_{r}}\) is designed as  
\[\mathbf{u_{r}}=-\mathbf{M_{22}}(\mathbf{S}(\mathbf{\omega_{e}})\mathbf{C_{d}^{b}}\mathbf{ \omega_{d}}-\mathbf{C_{d}^{b}}\mathbf{\dot{\omega}_{d}})+\mathbf{M_{21}}\mathbf{\ddot{p}^{I}}+ \mathbf{M_{23}}\mathbf{\ddot{\eta}}+\mathbf{C_{21}}\mathbf{\dot{p}^{I}}+\mathbf{C_{22}}\mathbf{\dot{\Psi}} \tag{37}\] \[\qquad\qquad+\mathbf{C_{23}}\mathbf{\dot{\eta}}+\mathbf{G_{2}}-\mathbf{M_{22}}\mathbf{ K_{b1}}\mathbf{\dot{q}_{ev}}-\mathbf{q_{ev}}-\mathbf{K_{b2}}\mathbf{\ddot{\omega}_{e}}-\mathbf{ \dot{d}_{r}}\]  
where \(\mathbf{\dot{d}_{r}}\) is the estimated result of disturbance from FASTESO. Additionally, like what we did at FASTESO, TD will also be adopted here to process several variables in Equation (37) to reduce noise. Moreover, \(K_{b1}\), \(K_{b2}\) are controller gains to be designed, and \(\mathbf{\omega_{e}}\), \(\mathbf{\ddot{\omega}_{e}}\), and \(\mathbf{q_{ev}}\) are defined in Equation (38), details of which can be found in [30].  
\[\left\{\begin{array}{l}\mathbf{\omega_{e}}=\mathbf{\omega}-\mathbf{C_{d}^{b}}\mathbf{\omega _{d}}\\ \mathbf{\ddot{\omega}_{e}}=\mathbf{\omega_{e}}+\mathbf{K_{b1}}\mathbf{q_{ev}}\\ \mathbf{q_{e}}=\left[\begin{array}{cc}q_{0}&\mathbf{q_{ev}}\\ \mathbf{q_{e}}=\mathbf{q_{d}^{*}}\otimes\mathbf{q}\end{array}\right]^{T}\end{array}\right. \tag{38}\]
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 3 Results
### Depth-Dose Profile  
The depth-dose profile represents the dose deposited along the beam direction (in our case, the Z-axis). Herein, as previously mentioned, we tried to reproduce a SOBP with the combination of four different beam energies. The values of the depth-dose profile quality parameters are presented in Table 4 and the contribution of each energy together with their final combination are shown in Figure 5.  
In this case, all the beam quality parameters were within the tolerances recommended of the international dosimetry code of practice [33], therefore this depth-dose distribution would be acceptable for clinically relevant radiobiology irradiation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Tolerance** & **Result** \\ \hline M\({}_{95}\) & \(\geq\)1 mm & 1.96 mm \\ Distal fall-off & \(<\)1.5 mm & 0.94 mm \\ Flatness & \(<\)5\% & 3.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Beam quality parameter tolerances and obtained results of the final depth-dose profile. The calculation of quality parameters is defined in detail in [31]. Here, the distal fall-off was defined as the 80–20% Penumbra.  
Figure 5: Depth-dose profile obtained at the irradiation point with the contribution of the four selected energies: 18 (yellow line), 20 (magenta line), 22 (black line), and 25 (blue line) MeV; while the red line corresponds to the combination of all them.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Tolerance** & **X Profile** & **Y Profile** \\ \hline FWHM &
\begin{tabular}{c} As close as possible to the \\ beam diameter \\ \end{tabular} & 3.57 cm & 3.56 cm \\ Left penumbra & \(\leq\)1.5 mm & 0.85 mm & 0.91 mm \\ Right penumbra & \(\leq\)1.5 mm & 0.82 mm & 0.96 mm \\ Ratio 90\%/50\% & \(>\)0.9 & 0.96 & 0.95 \\ Flatness & \(\leq\)3\% & 4.1\% & 4.7\% \\ Symmetry & 97–103\% & 101.2\% & 100.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Beam quality parameter tolerances and obtained results of the final lateral dose profiles. The calculation of quality parameters is defined in detail in [34; 35].
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 5 Experimental Results on the TEXBAT Dataset  
This section presents an evaluation of various spoofing detection methods using a publicly accessible dataset provided by the University of Texas at Austin. The dataset includes eight authentic spoofing instances. We have conducted a comprehensive performance verification of different detection methods. Given the constraints of article length, we selected Cases 1, 2, and 3 as representative examples for comparison and evaluated the ROC curve performance of each method.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 3 Link Quality Measurement and the Metric for Evaluating
### Burstiness Distribution Metric  
In this section, we present a scheme to calculate the number of transmissions by using the BDL with a named burstiness distribution (Bdist) metric.
## 2 Literature Review
### Review of Existing Robust Reversible Data Hiding Schemes  
In 2003, [PERSON] et al. [23] introduced a semi-fragile lossless data-hiding technique which provides a certain level of protection against minor altercations. However, the technique has a fatal problem of salt-and-pepper noise which was addressed by [PERSON] et al. [24; 25]. In [PERSON] et al.'s technique, the arithmetic average difference of a block is computed first and the secret message bits are embedded by shifting the difference. More specifically, the difference is shifted in the event that the bit is '1'; otherwise it remains unchanged. Thus, a higher degree of robustness along with a good quality stego-image is provided. In 2010, [PERSON] et al. [26] extended the work of [24; 25] for doubling the embedding capacity by utilizing the different scenarios of the arithmetic difference. [PERSON] et al. [27] discussed a robust image watermarking scheme based on histogram modification. The scheme computes the histogram of the cover image and embeds the information/watermark in the selected peak bins. The work [27] claimed that the scheme can resist against geometric distortions as well as common image processing operations. However, the scheme has limited embedding capacity. Afterwards, multiple robust reversible data hiding (RRDH) schemes, such as [28; 29; 30; 31; 32; 33; 34], were introduced to increase the embedding capacity while maintaining the robustness and providing a decent-quality stego-image. Among all the aforementioned RRDH schemes, a noteworthy introduction was written by [PERSON] et al. [29], discussing a novel spatial-domain-based RRDH scheme in 2017. [PERSON] et al.'s scheme makes use of significant bits for embedding the secret data by using the difference expansion method. Since the correlation among the most significant bits is significantly higher than the least significant bits, the proposed scheme can increase the embedding capacity. Additionally, the usage of a higher significant bit plane (HSB) of an image for embedding secret information increases the robustness when unintentional minor attacks such as JPEG compression make modifications in the lower bit plane. Therefore, the contents of the higher significant bit plane are intact. The work of [29] was extended by [PERSON] and [PERSON] [30] to enhance the embedding capacity, along with image quality, while providing the same degree of robustness. [PERSON] and [PERSON] used a two-layer embedding (TLE) strategy to embed the secret data into the HSB plane as in [29]. The scheme embeds the secret data into two layers by using a PEE strategy with the help of two sets of novel predictors. In addition, the proposed scheme carries out the expansion and shifting of histogram bins on the short tail side so that the distortion can be limited. Thus, the scheme increases the embedding capacity significantly, along with higher PSNR, while providing the same level of robustness as [29]. The brief review of the working process of the TLE scheme [30] is presented in the next section.
# O2O: An Underwater VLC Approach in Baltic and North Sea
## 1 Introduction
In addition to that, the derived PDF, CDF, and closed-form expressions for the end-to-end outage performance and BER under IM/DD technique as on-off-keying (OOK) modulation scheme are investigated;
* The system model is designed as a horizontal VLC link for signal transmission within moderate to strong turbulence channel conditions during the whole year. Additionally, the main contribution of this research is the performance of transceivers at different depths below the water surface. We calculate the large scale \(\alpha\) and small scale \(\beta\)  
Figure 1: The North-Baltic ocean meeting scenario near to Skegen in Denmark [13], (All copyright issues are persevered by Valnet Inc. Adapted with permission from Editor in chief © 2022 worldatlas. com, accessed on 15 December 2021). parameters separately according to the available temperature, salinity, and depth parameters in both of the oceans;
* Optical signal absorption and scattering are the main cause of attenuation in harsh water channel conditions and suspended particles. Due to this, the absorption and scattering effects are included in this work. The typical values of the extinction coefficient are used to obtain the system performances. Therefore, our proposed system model is applicable to deploy in different water types. The remainder of the paper is organized as follows. The proposed UVLC system model is widely discussed in Section 2. The channel conditions have a significant impact in this study to observe BER performance and modeled individually in Section 3. Furthermore, the outage performance of the system under strong channel is formulated in Section 4. The numerical results and simulation work on the basis of experimental data are concluded for the accuracy of the investigated system in Section 5. Finally, Section 6 concludes the whole presented work.
## 2 Materials and Methods
### Leave-One-Group-Out Cross-Validation  
To prevent information leakage between observations with spatial dependencies, we employed a leave-one-group-out cross-validation (LOGOCV) approach, where the groups were defined based on spatial clusters identified through the semivariogram of the standardized mortality ratios (SMRs).  
The semivariogram, a geostatistical tool, was used to quantify spatial autocorrelation and determine the spatial range at which SMR values became uncorrelated. This range informed the creation of spatially contiguous clusters using hierarchical clustering applied to the Euclidean distances between provinces. During LOGOCV, each cluster was sequentially held out as the validation set, while the model was trained on the remaining clusters.  
This method ensured that spatially correlated data were not split between training and validation phases, thereby maintaining the integrity of the evaluation process and providing a robust assessment of the model's predictive performance in the presence of spatial dependencies.  
From the semivariogram of the SMRs, we defined three clusters by applying a threshold to the dendrogram created from the hierarchical clustering of geographic distances between Italian provinces.
# Mapping Noise from Motorised Transport in the Context of Infrastructure Management
## 2 Materials and Methods
### Methods and Data Used to Perform Acoustic Calculations  
The measurements and development of the noise map were conducted according to the requirements of the Minister of Climate and Environment Regulation of 1 July 2021 on the detailed scope of data included in strategic noise maps, as well as the manner of their presentation and the form of their transmission [65]. For the calculations, CadnaA software was used (developer: DataKustik GmbH (Gilching, Germany), Version 2021) [66], which incorporates the CNOSSOS-EU methodology [67], as mandated by the provisions of Directive 2002/49/EC of the European Parliament [68].  
The reference methodology for measuring road noise levels emitted into the environment is established in the Decree of the Minister of the Environment dated 16 June 2011. This decree specifies the requirements for conducting measurements of the levels of substances or energy in the environment by the manager of roads, railway lines, tramway lines, airports, and ports [69]. The method of determining the long-term L\({}_{\text{den}}\) indicator is detailed in the Regulation of the Minister of the Environment dated 10 November 2010 on determining the value of the L\({}_{\text{den}}\) noise indicator [70]. According to this regulation, the L\({}_{\text{den}}\) noise indicator is calculated using the following formula:  
\[\text{L}_{\text{den}}=10\cdot\log\left[\ \frac{12}{24}\cdot 10^{0.1\text{L}_{ \text{D}}}+\frac{4}{24}\cdot 10^{0.1\text{(L}_{W}+5)}+\frac{8}{24}\cdot 10^{0.1 \text{(L}_{\text{N}}+10)}\right] \tag{1}\]  
where  
L\({}_{\text{D}}\)--long-term average A sound level expressed in dB, determined during all daytime periods of the year, including the time of day (understood as the interval from 06:00 to 18:00 h);  
L\({}_{\text{W}}\)--long-term average sound level A expressed in dB, determined over all the evening periods of a year, including the daytime (defined as the interval from 18:00 to 22:00);  
L\({}_{\text{N}}\)--long-term average sound level A expressed in dB, determined during all the night periods of the year (understood as the time interval from 22:00 to 06:00).  
Description of the methodology used to calculate the number of dwellings in residential buildings and the population attributed to residential buildings.  
A key aspect of strategic noise mapping is analysing potential exposure to noise among inhabitants and its associated harmful effects. According to point 2.8 of Annex II of Directive 2002/49/EC, the analysis involves calculating noise emissions at the facades of residential buildings [68]. Subsequently, based on estimating the number of dwellings in a given area, we can approximate the number of inhabitants exposed to noise. For this purpose, we utilised data published by the Central Statistical Office [71] for the municipality where the measurements were carried out.
## 7 Discussion
### Energy Yield Analysis  
Plasma exhibited the lowest energy yield (20.3 g/kWh), while ozonation was nearly twice as energy efficient (39.9 g/kWh).  
The combination of chlorination and ozonation was the most energy-efficient option. On the other hand, combining plasma and chlorination was the least energy-efficient option.  
However, these conclusions were drawn based on data from pilot-scale plasma generators. Companies such as Flowrox OyTM now trading as Roxia OyTM are in the process of producing industrial-size plasma reactors with far greater energy yields: around 88 g/kWh, or more than double that of ozone.