esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method Validation
#### 2.6.5 Extraction Efficiency and Matrix Effect  
Extraction efficiency is defined as the ratio of extracted analyte and the total amount of analyte in a certain sample. Two matrix-matched QC batches with three different analyte concentrations each (unspiked endogenous AA, matrix spiked with 1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL AA and 5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL AA), containing the same concentration of IS, were used to assess extraction efficiency. In the first batch, AA standards and IS were added to the matrix prior to the extraction procedure, while in the second batch both AA and IS were added post-extraction. Extraction efficiency (%EE) was calculated as follows [19]:\[EE\%=\frac{(AreaAA/Area1S)\ sample\ pre-extraction}{(AreaAA/Area1S)\ sample\ post -extraction}\times 100 \tag{2}\]  
Matrix effect was evaluated by serial dilutions of the QC serum prior analysis and plotting the ratio AA peak area to IS peak area against the calculated concentrations to test for a linear relationship.  
For more reliable results, it is recommended to evaluate the matrix effect by comparing the slope variability of solvent-based, surrogate matrix- and authentic matrix-matched calibration curves [24]. Slopes that vary below 5% were considered to be equivalent [25]. The matrix effect was calculated using the following equation [19]:  
\[ME\ (\%)=\frac{Slope\ of\ matrix\-matched\ curve\ -\ Slope\ of\ solvent\-based\ curve}{Slope\ of\ solvent\-based\ curve}\times 100 \tag{3}\]  
Low slope variability would indicate minimal or absent matrix interferences.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 4 Results and Discussion
### Comparison between Modeled and Measured Uncertainties
Indeed, the need for information on the sediment composition makes the prediction of the contribution of the additive noise complicated as such information might not be always available. For the calculation of the SNR, the transmitted source level also needed to be known. Here, this was assumed to be 20 dB lower than the maximum power available at the transmission ([PERSON], Kongsberg personal communication, June 2018). The transmission loss, the noise level, or the reverberation level inside the receiver band, the transmission and reception directivity patterns, the sensitivity of the transducer with respect to center frequency, and the receiver gain applied by the receiver electronics are other parameters required for the calculation of the SNR. The joint impact of the SNR and baseline decorrelation was then derived following Vincent [43]. Added to Figures 7 and 8 with solid light blue lines with diamond markers and solid light pink lines are the echosounder contribution accounting for the uncertainties induced by the baseline decorrelation and additive noise and the resulting total depth uncertainty, respectively. For a water depth of 10 m, compared to the situation where the contribution of additive noise was discarded, no noticeable change is observed for the inner and middle sector of the beams, i.e., this means that the prediction model still (slightly) underestimates the measured uncertainties. However, as expected from the nature of the additive noise, the predicted depth uncertainty for the outer beams increased. This increase improved the ability of the prediction model in capturing the variation of the bathymetric uncertainty with the beam angle. Regarding the longest pulse length, a slight over-prediction occurs when accounting for the additive noise contribution. As for the deeper surveyed area and the two shorter pulse lengths in Figure 8a,b, although still an underestimation occurs for the inner and middle beam sector, the increase of the bathymetric uncertainties with increasing beam angle is now well predicted. As for the longest pulse length, a very good agreement between the modeled and measured depth uncertainties is obtained for all the beam angles, which was expected as the discrepancy observed between them discarding the contribution of the additive noise was mainly concerned with the most outer beams.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 2 Design Methodology
### Simulation Environment and Measurement Setup  
In this research, the proposed MIMO antenna's simulation and optimized design were conducted utilizing HFSS 2021. As shown in Figure 2a, the simulation was performed in a 7-layer homogeneous human head model and a real human head model in order to be closer to the actual human body, and to analyze the actual working performance of this antenna. The total dimensions of the 7-layer homogeneous human head model are \(100\times 100\times 51\) mm\({}^{3}\), and the electrical characteristics of the individual tissues are dependent on the resonant frequency. Table 1 provides details on the specified thickness, along with the relative permittivity (\(\varepsilon_{r}\)) and conductivity (\(\sigma\)) for each layer of the human head model at 2.45 GHz [23; 24]. The device was implanted at a 3 mm depth within both the 7-layer uniform human head model and the actual human head model.  
Biomedical implantable antennas designed for use in the human body must be biocompatible [25]. Furthermore, each implantable biomedical device has a designated implantation area and specific architectural requirements [26]. As shown in Figure 2b, an IMD typically includes an antenna, a printed circuit board, sensors, and batteries, all of which are encapsulated in a biocompatible alumina container. Therefore, the biocompatibility of the device is of primary importance. In this work, alumina (\(\varepsilon_{r}=9.8\)) was selected as the biocompatible material for the device housing. The length, width, and thickness of the flatbed device are 25 mm, 11 mm, and 0.2 mm, respectively. It contains a PCB that incorporates surface-mounted components, batteries, sensors, and the new ultra-wideband circularly polarized MIMO antenna. The device enclosures are sealed with epoxy resin to prevent liquid ingress during experimental measurements. The physical antenna and its measurement configuration are shown in Figure 2c. For actual measurements, the entire antenna system was embedded in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissue** & **Thickness (mm)** & \multicolumn{3}{c}{**2450 MHz**} \\ \cline{3-4}  & & \(\varepsilon_{r}\) & \(\sigma\) \\ \hline Skin & 4 & 38 & 1.5 \\ Fat & 2 & 5.3 & 0.1 \\ Muscle & 4 & 52.7 & 1.7 \\ Skull & 5.5 & 18 & 0.81 \\ Dura & 0.5 & 42 & 1.7 \\ CSF & 5 & 66.2 & 3.5 \\ Brain & 30 & 48.9 & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thickness and electromagnetic properties of the 7-layer head model.  
Figure 1: Geometry of the designed dual-unit implantable MIMO antenna: (**a**) Radiating patch. (**b**) Ground plane. (**c**) Side view. (**d**) Decomposed view.  
minced pork to a depth of 3 mm. The S\({}_{11}\) and S\({}_{21}\) parameters for the constructed antenna were assessed within minced pork using a vector network analyzer.
## 3 Methodology
### Hydrological Model  
In this study, the Xin'anjiang (XAJ) model [28] was utilized for hydrological modelling because of its effective applications across the semi-humid and humid zones in China [29; 30; 31; 21]. Within the framework of the lumped XAJ model, the basin is generalized into a soil box, encompassing an upper, lower, and deep soil layer. The computational mechanics of the XAJ consists of four modules: (1) the evaporation module, which utilizes the watershed saturation-excess runoff theory to ascertain evaporation rates. (2) The runoff yielding module, which uses the storage curve to calculate total runoff. This approach is based on the concept that runoff only forms when storage is replenished, indicating that runoff does not occur until soil moisture reaches its full capacity. (3) The runoff sources partitioning module, which uses a free water capacity distribution curve to divide the total runoff into three components: surface runoff, interflow, and groundwater runoff. (4) The runoff concentration module, in which the surface runoff is channeled using the unit hydrograph method, wherein both the interflow and groundwater runoff are directed using the linear reservoir approach. The XAJ model is configured with a total of fifteen parameters: four related to evaporation, two associated with runoff generation, and the remaining nine dedicated to runoff routing. These fifteen free parameters need to be calibrated by using the observed discharge data to effectively capture the hydrological characteristics of a specific basin [32]. An extensive exploration of these parameters can be found in Ren-Jun [28].  
When the XAJ was built for the upper Qingjiang River basin, the summer periods of 2014-2016 and 2017 were used for calibration and validation, respectively. The basin-averaged inputs such as precipitation, temperature, and potential evaporation were used as inputs to yield an output of the basin outlet's simulated discharge. The calibration was conducted using the SCE-UA algorithm [33] to refine the model parameter-sets according to the widely used objective function, the Nash-Sutcliffe coefficient (NSE) [34].
## 4 Conclusions  
In this study, the influences of FSW welding parameters (welding traverse speed and tool rotation speed) on the weld strength of AA1050 to AZ91 were investigated. To visualize the formation of eutectic and liquefaction structures at the interface of AA1050 and AZ91, SEM and EDS analyses were applied. The main results obtained by friction stir lap welding of AA1050/AZ91 are listed as follows:  
* The tool rotation speed plays a major role in the joint quality and intermetallic compounds;
* At low rotation speed of the tool, a thin intermetallic layer forms at the interface, and the joint strength is the highest among all the parameters. The fracture propagates through the AZ91 layer;
* At high rotation speed, liquidation takes place in the weld nugget which is composed of a eutectic compound. This liquation causes the slippage of the tool and lowers the heat generation during welding. Moreover, the diffusion rate of elements decreases due to the existence of the eutectic layer. Hence, a thin layer of intermetallic compound forms at the interface. The fracture propagates through the eutectic compound;
* At a higher welding traverse speed with a lower rotational speed, insufficient frictional local heat input and lower exposure time lead to inadequate metallurgical bonding at the AA1050-AZ91 interface. Some defects are observed in this sample due to insufficient material flow;
* The results suggested that rotation rate affects the formation of eutectic structures at the interface;
* The formation of eutectic compounds is influenced by the rotation speed, which is not related to the advanced speed at higher rotation speeds;
* When aluminum penetrates magnesium, it enhances the hardness of the magnesium, whereas magnesium infiltration in aluminum decreases the hardness of the aluminum;
* The formation of eutectic compositions plays a significant role in ensuring consistent hardness levels.  
Conceptualization, R.B.; Investigation, K.T.; Writing--original draft, K.T.; Writing--review & editing, R.B. and G.E.B.; Supervision, L.F.M.d.S. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully thank Arak University for supporting this project. The authors also acknowledge funding under the reference \"UIDP/50022/2020--LAETA--Laboratorio Associado de Energia, Transportes e Aeronautica\".  
The authors declare no conflict of interest.
## 3 Transfer Learning Applied to Fault Diagnosis Methods
### Fault Diagnosis Model
#### 3.3.2 Attention Mechanism  
The application of the attention mechanism in fault diagnosis helps focus on extracting important information while ignoring irrelevant details, thereby reducing the impact of low-correlation data on the network and improving fault detection and diagnosis performance [23]. The basic principle of the attention mechanism is to assign varying attention weights to different features during data processing, enabling the model to focus on the most critical features.  
The efficient channel attention (ECA) mechanism is an attention mechanism designed to enhance the performance of transfer learning models [24]. The working principle of the efficient channel attention mechanism is shown in Figure 12.  
By using the efficient attention mechanism, the network's feature extraction ability surpasses that of general deep learning networks, and the model's generalization capability is also enhanced. Its main features include the absence of dimensionality reduction operations, local cross-channel interaction, and adaptive kernel size. H, W, and C represent the height, width, and channel number of the input signal x, respectively; k is the adaptive size of the convolution kernel; GAP refers to global average pooling; X is the output feature map; and the activation function Sigmoid is denoted as \(\sigma(\cdot)\).  
Figure 12: Working principle of the efficient channel attention mechanism.  
ECA automatically selects the size of the one-dimensional convolution kernel based on the number of channels. It operates directly on the original channels, avoiding information loss, and captures the relationships between local channels through one-dimensional convolution.  
\[\mathrm{k}=|\frac{\log_{2}\mathrm{c}+\mathrm{b}}{\mathrm{a}}|_{\mathrm{odd}} \tag{14}\]  
In this equation, \(\mathrm{a}\) and \(\mathrm{b}\) are trade-off parameters, and \"odd\" denotes the nearest odd integer to the result.  
SKNet is a lightweight network mechanism introduced by [PERSON] [25]. To make it suitable for the data dimensions in rolling bearing fault diagnosis, the improved network structure is shown in Figure 13. In this study, the improved SKNet module is positioned after the fourth residual layer [22].  
\[U_{3}=U_{1}+U_{2} \tag{15}\]  
\[U_{4}=\frac{1}{N}\sum_{i=1}^{N}U_{3i} \tag{16}\]  
Here, \(U_{4}\) represents the output result, \(U_{3i}\) denotes the elements in the dimension, \(\mathrm{N}\) represents the total number of elements in the dimension, and the operation used is adaptive average pooling.  
\[U_{5}=\delta(\beta(conv(U_{4}))) \tag{17}\]  
Here, \(\delta\) represents the ReLU activation function, and \(\beta\) denotes the batch normalization function.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 3 Example Analysis
### Experimental Analysis and Verification
#### 3.2.3 Performance Analysis and Comparison of FL-XGB-Encrypted Traffic Identification Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **F1-Score** & **Recall** \\ \hline SVM & 0.6824 & 0.5169 & 0.3671 & 0.5333 \\ GDBT & 0.739 & 0.6278 & 0.644 & 0.6949 \\ NB & 0.6911 & 0.5228 & 0.5441 & 0.6001 \\ LR & 0.5966 & 0.4398 & 0.4498 & 0.4667 \\ KNN & 0.7023 & 0.6775 & 0.6962 & 0.6618 \\ Adaboost & 0.7079 & 0.6911 & 0.5884 & 0.6321 \\ LDA & 0.6824 & 0.6778 & 0.69 & 0.6333 \\ C4.5 & 0.762 & 0.7827 & 0.7969 & 0.8333 \\ FL-XGB & 0.996 & 0.9959 & 0.9963 & 0.9967 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Identification results of different algorithms on the ISCXTor2016 dataset. identifying Tor-encrypted traffic data compared to the other eight algorithms. In summary, the FL-XGB-encrypted traffic identification model is also applicable to the encrypted traffic identification of the dataset ISCXTor2016. The FL-XGB-encrypted traffic identification model has high universality for the identification of encrypted traffic and can effectively solve the problem of low accuracy of VPN-encrypted traffic identification.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 2 Materials and Methods
### Background and Definitions  
Causal ordering was developed to remove inconsistencies in message delivery, which is produced by an unpredictable delay in the communication channels. Causal order is based on the \"happened before\" relation defined by [PERSON] [30]. This relation is denoted by \(\rightarrow\) as follows.  
**Definition 1**.: _The relation \(\rightarrow\) on the set of events of a system is the smallest relation satisfying the three following conditions:_  
If \(a\) and \(b\) are events in the same process, and \(a\) comes before \(b\), then \(a\to b\).  
If \(a\) is the sending of a message by one process and \(b\) is the receipt of the same message by another process, then \(a\to b\)  
If \(a\to b\) and \(b\to c\) then \(a\to c\).  
Two distinct events \(a\) and \(b\) are said to be concurrent \(a\parallel b\) if \(a\
rightarrow b\) and \(b\
rightarrow a\). This relation can be extended to messages in the following form: message \(m\rightarrow\) message \(m^{\prime}\) if and only if \(send(m)\rightarrow send(m^{\prime})\) where \(send\) is the message sending event.
# Multi-LEO Satellite Stereo Winds
## Appendix A
### Solution by Simple Least Squares  
We can solve for \(X\) in a least squares sense by minimizing  
\[\chi^{2}=\sum_{n=1}^{N}\overset{\rightarrow}{\epsilon}_{n}{}^{T}W_{n} \overset{\rightarrow}{\epsilon}_{n}. \tag{13}\]  
A weighting matrix \(W_{n}\) has been introduced. It can be the identity matrix for an unweighted solution. This solution to this least-squares problem is the solution to the linear system:  
\[\left(\sum_{n=1}^{N}B_{n}(X_{0})^{T}W_{n}B_{n}(X_{0})\right)(X-X_{0})=\sum_{n=1 }^{N}B_{n}(X_{0})^{T}W_{n}\overset{\rightarrow}{b}_{n}(X_{0}). \tag{14}\]  
To solve for \(X\) as a nonlinear problem, we start with a guess \(X_{0}\), solve for \(\Delta X=X-X_{0}\), update the guess, and iterate. The solution is considered to have converged once \(|\Delta X|\) is less than a tolerance. If the measurement errors for the disparities are random and uncorrelated and each is weighted by a \(W_{n}\) that is the inverse of the covariance matrix of the measurement uncertainty, then the uncertainty of the estimate for \(X\) is described by a covariance matrix \(P\), which is given by the coefficient matrix in Equation (14):  
\[P=\left(\sum_{n=1}^{N}B_{n}(X_{0})^{T}W_{n}B_{n}(X_{0})\right)^{-1}. \tag{15}\]  
The square roots of the diagonal elements of \(P\) can be interpreted as error bars for each of the solved parameters in accordance with the statistical description of the disparity measurement errors.
###### Abstract  
With strong technology improvement, especially GNSS, coordinate transformation has become an essential tool in everyday practice. For this reason, it is beneficial to have a good understanding of the mathematical process of coordinate transformation, as well as the method for calculating transformation parameters. The purpose of this article is to provide a detailed explanation of the coordinate transformation procedure and the calculation of transformation parameters for seven transformation models: 3-parameter, 5-parameter standard and abridged, 7-parameter, 8-parameter, 9-parameter, and 12-parameter. For each transformation model, a basic transformation equation, a procedure for calculating transformation parameters, inverse expression for reversible transformation, as well as characteristics of each model are provided. As an addition, for each model, a numerical example of calculating transformation parameters for the territory of the Republic of Croatia is provided. Additionally, for the example of the 7-parameter transformation, importance of reversible transformation, rotation convention, as well as the form of the rotation matrix used during transformation are demonstrated.  
3-parameter; 5-parameter; 7-parameter; 8-parameter; 9-parameter; 12-parameter; Croatia; transformation; transformation parameters +
Footnote †: journal: Applied sciences
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 8 Conclusions  
This paper provides a comprehensive overview of approaches for the determination of isocontours and isosurfaces from given data sets. Based on the review of existing algorithms for isocontouring, four dominant methods can be identified: marching cubes algorithms, tessellation-based algorithms, surface nets algorithms and ray tracing algorithms. The algorithms have each been developed from specific problem areas; thus, the corresponding solution strategies for isocontouring have also resulted from these contexts. Nevertheless, all algorithms must solve the same task from a mathematical point of view. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. The preferred areas of application of the algorithms also imply that they have different requirements for the necessary input data. Marching cubes and surface nets algorithms, for example, can only process structured data sets. In contrast, tessellation-based methods and ray tracing algorithms are also able to process unstructured data sets. This is particularly advantageous when using measurement data, as these can be subject to errors due to measurement inaccuracies. Another key finding of the literature review is that there is no standardized scheme for the evaluation of the quality of the algorithms developed. Depending on the use case, different categories are examined, most of which involve a visual inspection of the results. Therefore, a possible scheme for the statistical analysis of the developed algorithms is proposed in this paper.  
Conceptualization, K.J.B., J.P.D. and J.O.; methodology, K.J.B., J.P.D. and J.O.; validation, K.J.B., J.P.D. and J.O.; formal analysis, K.J.B., J.P.D. and J.O.; investigation, K.J.B., J.P.D. and J.O.; resources, K.J.B., J.P.D. and J.O.; data curation, K.J.B., J.P.D. and J.O.; writing--original draft preparation, K.J.B., J.P.D. and J.O.; writing--review and editing, K.J.B., J.P.D. and J.O.; visualization, J.O. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any external funding.  
Not applicable.  
The authors acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology and would like to thank the reviewers for their detailed reading and helpful remarks to improve the quality of the work.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.