esa-sceva/satcom-chunk-collection huggingface dataset:
# Galileo: The Added Value for Integrity in
## 5 Experimental Results
### Comparison with Respect to the Classical RAIM Algorithm  
In order to analyze the benefits of the additional checks introduced, the proposed algorithm and the classical RAIM algorithm are compared considering GPS and Galileo measurements. Classical RAIM does not have geometry and separability tests and performs only one exclusion.  
The CDFs of the horizontal errors obtained in the two cases are plotted in Figure 21. As for the previous cases, the CDFs considering all available epochs is shown in the upper box of the figure; in the central box, the performance of the considered configurations is obtained considering only reliable  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Configuration** & **Max H (m/s)** & **Max up (m/s)** & **SD H (m/s)** & **SD up (m/s)** & **Mean H (m/s)** & **Mean up (m/s)** \\ \hline GPS noRAIM & 0.453 & 0.464 & 0.017 & 0.039 & 0.022 & 0.010 \\ GG noRAIM & 1.680 & 0.403 & 0.028 & 0.037 & 0.021 & 0.004 \\ GPS RAIM & 0.163 & 0.318 & 0.014 & 0.037 & 0.021 & 0.010 \\ GG RAM & 0.239 & 0.187 & 0.012 & 0.035 & 0.019 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Velocity error statistics considering only reliable epochs.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Configuration** & **Max H (m/s)** & **Max up (m/s)** & **SD H (m/s)** & **SD up (m/s)** & **Mean H (m/s)** & **Mean up (m/s)** \\ \hline GPS noRAIM & 0.453 & 0.464 & 0.016 & 0.038 & 0.021 & 0.010 \\ GG noRAIM & 1.680 & 0.403 & 0.032 & 0.034 & 0.021 & 0.008 \\ GPS RAIM & 0.149 & 0.318 & 0.013 & 0.036 & 0.021 & 0.010 \\ GG RAIM & 0.092 & 0.183 & 0.011 & 0.030 & 0.018 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Velocity error statistics considering only common reliable epochs.  
Figure 20: CDFs of the vertical velocity errors. Upper box: CDFs computed using all available epochs; central box: CDFs computed using only reliable epochs; lower box: CDFs computed using only reliable common epochs.  
provides better performance in terms of maximum horizontal and vertical errors. However, in this case, the performance of the two configurations is evaluated considering different epochs, because the algorithm provides different RA, which in the case of the classical algorithm is 42.33% (78% was obtained with the approach proposed). The classical RAIM approach is more conservative and does not foresee the possibility of having multiple exclusions. In order to perform a fair comparison between the considered configurations, the performance is evaluated considering only common reliable epochs in Table 12: the proposed algorithm reduces all of the statistical error parameters considered.
## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm
#### 3.3.2 Application of the IPBBIS Method on a Sample with Controlled Concentrations  
A controlled protein concentration was expressed to evaluate the method's effectiveness. Figure 9A shows the gel obtained, where bandwidth (or stain) increases as the protein is concentrated. Subsequently, the image was pre-treated (binarization, dilation, color inversion, and erosion; Figure 9B) for image analysis using IPBBIS. This procedure originated the new image profile graph shown in Figure 9C, where the maximum peak corresponds to the highest concentration of BSA protein (2 mg/mL). The lowest peak is related to the lowest BSA concentration (0.5 mg/mL).
## 1 Introduction
In particular, our contributions are summarized as follows: (a) we analyze the effect of the super-resolution algorithm on two different datasets of thermal sequences (with original resolutions: 80 \(\times\) 60 and 320 \(\times\) 240), (b) we investigate if the processing of super-resolved thermal sequences allows for improving the RR estimation in comparison to RAW data processing, (c) we compare accuracy of the RR estimation from images super-resolved with our DL model to the results of sequences with magnified color changes using the Eulerian Video Magnification algorithm, (d) we analyze the role of thermal image bit-depths on the RR estimation accuracy and on image quality metrics (Peak Signal to Noise Ratio and Structural Similarity Index) for sequences with resolution degradation and enhancement. The rest of the work is organized into following sections: Section 2 provides details about a data collection and pre-processing process, as well as resolution degradation and enhancement methods. In Section 3 we present achieved preliminary results of calculated image quality metrics and errors of RR estimation. These results are further discussed in Section 4. Readers may access the code and trained models used in our study at the link provided in Section 5. Finally, Section 6 concludes our work and specifies ideas for future improvements.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 3 Exploiting Spatial Diversity for Interference Mitigation
### Power Inversion Beamformer  
The Power Inversion beamformer (PI) aims to minimize the power at the output of the antenna array while keeping one of the channels, namely the reference element, undisturbed [16]. This entails computing the weights minimizing the total output power,\(\left|\mathbf{w}^{H}\mathbf{x}[n]\right|^{2}\), but keeping the coefficient associated with the reference element equal to one, which leads to the optimization problem stated in Equation (5).  
\[\mathbf{w}_{\text{PI}}=\underset{\mathbf{w}}{\text{min}}\mathbf{w}^{H}\mathbf{x}[n]\mathbf{x}^{H}[n] \mathbf{w}\text{ subject to }\mathbf{w}^{H}\underbrace{\begin{bmatrix}1\\ 1\\ \vdots\\ 1\end{bmatrix}}_{\mathbf{\delta}}=1 \tag{5}\]  
Note from the expression above that \(\mathbf{x}[n]\mathbf{x}^{H}[n]\) is the instantaneous autocorrelation matrix, denoted by \(\mathbf{R}_{\mathbf{x}}[n]\). After processing \(N\) consecutive samples, one can estimate the averaged autocorrelation matrix \(\mathbf{R}_{\mathbf{x}}\) as \(\mathbf{R}_{\mathbf{x}}=\frac{1}{N}\sum_{n=1}^{N}\mathbf{x}[n]\mathbf{x}^{H}[n]\), and use it to obtain a more robust formulation.  
The problem statement presented in Equation (5) is easily solved by Lagrange multipliers, leading to the optimum solution of the weights to be the one presented in Equation (6).  
\[\mathbf{w}_{\text{PI}}=\frac{\mathbf{R}_{\mathbf{x}}^{-1}\mathbf{\delta}}{\delta^{H}\mathbf{R}_{ \mathbf{x}}^{-1}\mathbf{\delta}} \tag{6}\]  
See that in order to obtain the previous expression, which provides the coefficients of the beamformer, no more information than that received by the antenna array is used, and thus this technique can be rightly called blind.
## 5 Case Study
### Evaluation Criteria  
To compare the prediction performance of the proposed hybrid model EPIJaya-SVR with that of four selected competitors (EIJaya-SVR, IJaya-SVR, Jaya-SVR, and SVR), no single evaluation index can reflect the distributional characteristics of the errors. Therefore, four indices--the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and equal coefficient (EC)--are used as forecasting accuracy measures. The model with lower RMSE, MAE, and MAPE and higher EC (closer to 1) can predict grouting power more effectively.  
In addition to these evaluation indices, the Diebold-Mariano (DM) test [51] is also adopted to verify whether the prediction performance of two models is significantly different. The null hypothesis of the DM test assumes that the prediction accuracy of the tested model is not as good as that of the reference model. To be specific, the prediction error \(e_{te,t}~{}=~{}x_{t}-\hat{x}_{te,t}\) of the tested model is greater than or equal to the prediction error \(e_{re,t}~{}=~{}x_{t}-\hat{x}_{re,t}\) of the reference model. The DM statistic can be expressed as:  
\[S~{}=~{}\frac{\overline{g}}{\left(\hat{V}_{g}/N\right)^{1/2}}~{}\sim N(0,1) \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Set** & **Number** & \begin{tabular}{c} **Maximum** \\ **(MPa-I/min)** \\ \end{tabular} & \begin{tabular}{c} **Minimum** \\ **(MPa-I/min)** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **(MPa-I/min)** \\ \end{tabular} & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{c} **Median** \\ **(MPa-I/min)** \\ \end{tabular} \\ \hline All samples & 600 & 11.025 & 1.044 & 5.890 & 1.126 & 5.880 \\ Training set & 500 & 10.512 & 1.044 & 5.901 & 1.043 & 5.880 \\ Testing set & 100 & 11.025 & 3.360 & 5.838 & 1.480 & 5.913 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical descriptions of the grouting power series.  
Figure 4: Original grouting power, grouting pressure, injection rate time series.  
where \(\overline{g}\) is the mean value of the loss function \(g_{t}\left(=e_{te,t}^{2}-e_{te,t}^{2}\right)\); \(\hat{V}_{g}=\gamma_{0}+2\sum_{t=1}^{\infty}\gamma_{t}(\gamma_{t}=\text{cov}(g_{t +1},g_{t})\), where \(\gamma_{0}\) is the variance of \(g_{t}\); and \(\hat{x}_{te,t}\) and \(\hat{x}_{te,t}\) denote the predicted values of \(x_{t}\) calculated by using the tested model \(te\) and reference model \(re\), respectively.  
The DM statistic obeys a standard normal distribution and \(\alpha\) is set to be the significance level. \(Z_{\alpha/2}\) is the critical value of the test. If \(|\text{DM}|\leq|Z_{\alpha/2}|\), there is not enough evidence to suggest that the two models exist significant difference, and, if \(|\text{DM}|>|Z_{\alpha/2}|\), the null hypothesis can be rejected.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 2 Related Work and Our Contribution
### Our Contributions  
In this paper, we present the details of our proposed GCMR scheme which is a SSM sparse mode multicast scheme. The objective is two-fold. On the one hand, we provide a performance analysis of GCMR and compare it with the PIM-SSM, BIER, and Steiner Tree (ST) schemes (see Section 5 for more details). For this performance study, we use an ad-hoc c-based simulator and four large CAIDA maps of the internet topology.  
On the other hand, we present the development and implementation of GCMR in a prototype developed using the libraries of the Quagga open source routing suite ([[http://www.nongnu.org/quagga/](http://www.nongnu.org/quagga/)]([http://www.nongnu.org/quagga/](http://www.nongnu.org/quagga/)) accessed on 15 September 2021). We then use this prototype to experimentally validate GCMR and compare its performance against PIM-SSM in the context of inter-domain multicast routing. These experimental tests are performed in a network comprising 207 Autonomous Systems (ASes) and executed using the iLab.t Virtual Wall ([[https://doc.ilabt.imec.be/ilabt/virtualwall/index.html](https://doc.ilabt.imec.be/ilabt/virtualwall/index.html)]([https://doc.ilabt.imec.be/ilabt/virtualwall/index.html](https://doc.ilabt.imec.be/ilabt/virtualwall/index.html)) accessed on 15 September 2021) platform, which is a large-scale experimental testbed available at the IMEC research centre in Ghent, Belgium.
## 5 Evaluation of Reliability Cost
### Background on Reliability Theory  
The reliability theory provides a model of a multi-component system that can be used to find the probability that the system is functional under a given set of constraints. The theory models the system as a set of components connected in series, parallel or hybrid (series-parallel) configurations. The reliability of the complete system is then computed as a function of the reliability of the individual components. In this section, we introduce basic concepts and definitions used in reliability theory literature [30].
# Position Sensing with a Compact 0.1 THz Amplitude Modulated Source
## 3 Results
### Distance Measurement
This results in a \(\pm\)2.5 mm resolution of the distance measurement at the scanning step of 1 mm. The difference from the reference position can be seen in the signal peak amplitude. This is due the fact that the focus is not perfect because of the change of the travelled distance of the THz signal. All the positions of the sensor PCB, lenses and THz source were set by an operator, and human error mainly contributed to the difference in amplitude. The measurement with Styrofoam cover is an easy display of the THz measurement's method ability to replace laser position measurement, which does not penetrate into Styrofoam or other visible light opaque materials. This is especially important in environments where thick fog/gas could be present and a laser light has difficulties with the distance measurement, which leads to an error in industrial processes. Furthermore, the usage of other materials instead of a metal plate for distance measurement in this research is redundant. If, for example, water, mirror or any other reflective material were to be used instead of a metal plate, the position measurement would be the same. The only difference would be in the amplitude of the signal, which is defined by the reflective index of the material.
## 2 BDS-3 SAIM Monitoring Method
### Enhanced Ephemeris Monitoring Methods
#### 2.2.2 Autonomous Orbit Determination Ephemeris  
The Beidou-3 satellite is equipped with an autonomous orbit determination function unit, which has the ability to operate independently and generate ephemeris independently, and run in parallel with the navigation signal generation unit. Among them, the autonomous orbit determination function unit mainly relies on the inter-satellite link to generate ephemeris information, and the navigation signal generation unit mainly relies on the ground annotation to generate ephemeris information. Therefore, the ephemeris information generated by the two units is relatively independent and can be used as a comparison to monitor the correctness of the ephemeris to be broadcast.  
According to the user's algorithm of the downlink ephemeris (refer to BeiDou Navigation Satellite System Signal In Space Interface Control Document Open Service Signal B1C (Version 1.0) [20] for the specific algorithm), we can calculate the position of the BDS-3 MEO satellite in the X, Y, and Z directions in the BeiDou coordinate system (BDCS), and compare the ephemeris message to be broadcast and the autonomous orbit determination message at the same time. The difference between the obtained positions is compared with the threshold to confirm the correctness of the ephemeris message to be broadcast in the next hour. If it is greater than the judgment threshold, an alarm will be issued.  
The autonomous orbit determination message can be used to realize the monitoring of the ephemeris parameters when the satellite is in the on-orbit autonomous operation state, but the accuracy will be lower than that of using the uplink posted message, and with an extension of the running time of the autonomous operation state, the accuracy will be further reduced, which is expected to be in the order of 10 m.
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
## 6 Performance Evaluation
As expected, with the increase in the embedding capacity (payload), the PSNR (in dB) value decreases. As shown in the figure, the scheme proposed by [10] offers small PSNR (in dB) compared to other reversible watermarking schemes. In the other schemes, like difference-expansion (DE) proposed by [9; 11; 12; 13] the tradeoff between capacity and image quality is possible and relatively high PSNR can be achieved. However, as shown in the figure, the proposed scheme offers higher embedding capacity with lower distortion than the other schemes [18; 19; 20]. This is due to the joint use of lifting and companding technique. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Colluders** & **3** & **5** & **7** & **10** \\ \hline \hline \multicolumn{5}{c}{**No. of Identified Colluders**} \\ \hline \hline \multicolumn{5}{c}{Fading based (SNR 85)} & 3 & 5 & 6 & 7 \\ Non-fading based & 3 & 5 & 7 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Collider identification performance. Figure 11: Results for collusion attack (Frame 1 of Lung DICOM image):(**a**) Averaged image without fading, (**b**–**f**): Results for different channel SNR, (**b**): SNR = 50 dB, (**c**): SNR = 60 dB, (**d**): SNR = 75 dB, (**e**): SNR = 85 dB, (**f**): SNR = 100 dB. (P, M) above each image represents the PSNR (in dB), and MSSIM values of the image. Figure 12: Comparative performance against collusion attack. Figure 13: Capacity vs. distortion comparison. The execution time required to run the whole procedure as a measure of the computational load is also computed. The scheme takes on an average of 1.3543 s. We have also tested the same procedure for traditional DWT. It is seen that the average execution time is 2.1086 s. This result clearly shows that the proposed scheme is much faster than convention DWT based scheme.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 2 Related Work  
### Introduction to Friendly Jamming  
[PERSON] defined the concept of security capacity in a study on eavesdropping channels and proved that security can be achieved according to the information theory perspective when the quality is poorer in the eavesdropper's channel than in the legitimate receiver [8]. Security channel capacity (secrecy capacity) is defined as the difference in the channel capacity between a legitimate sender and a legitimate receiver and the channel capacity between a legitimate sender and an eavesdropper. If this value is negative, the security channel capacity is zero, which means that no information can be safely sent. Here, signals and artificial interference can be generated and sent to improve the channel quality of the receiver and deteriorate the channel quality of the eavesdropper. Various studies have been conducted for this, including jamming, beamforming jamming, relay jamming, and friendly jamming techniques for improvements in wireless security. It has recently been proven that if a beam is formed using multiple antennas for jamming signals that can reduce the eavesdropping performance of malicious eavesdroppers, the communication security and reliability between legitimate communicators can be significantly improved. However, beamforming technology that uses multiple antennas or a massive antenna is complex and requires a large power consumption, making it difficult to use in IoT or mobile devices [9; 10].
# Application of Semi-Supervised Learning Model to Coal Sample Classification
## 3 Methods
### Generative Adversarial Networks  
Generative Adversarial Network (GAN) is an unsupervised generative model, proposed by [PERSON] et al. (2014) in 2014, whose basic structure includes a Generator and a Discriminator. The Generator generates pseudo-data as realistic as possible based on the distribution of sample data, while the Discriminator is used to discriminate whether the input data are real data or pseudo-data generated by the Generator. Through the game between the generator and the discriminator, GAN can reach a Nash equilibrium, so that the generated data can fit the data distribution of the real samples. The network structure of GAN is shown in Figure 2.  
Typically, the generator \(G\) and discriminator \(D\) can be represented by a convolutional neural network or other functions. Generator \(G\) receives random noise as input for generating pseudo-data \(G(z)\), while discriminator \(D\) discriminates between the input real data \(x\) and the pseudo-data \(G(z)\) generated by the generator, and outputs the probability that it belongs to the real sample. The generator \(G\) and the discriminator \(D\) are trained by playing against each other through a loss function. The optimization process involves a very large very small game with an objective function of Equation (1):  
Figure 1: Diagram of the LIBS system.  
Figure 2: Network structure of GAN.  
\[min_{G}max_{D}V(D,G)=E_{x\sim P_{data}(x)}[logD(x)]+E_{z\sim P_{z}(z)}[\log(1-D(G(z) ))] \tag{1}\]  
where \(x\) represents the real data, \(P_{data}\) is the data distribution of \(x\), \(z\) represents the random noise obeying the standard normal distribution, \(P_{z}\) only represents the data distribution of \(z\), \(G(z)\) represents the pseudo-data generated by the generator, and \(D(x)\) represents the probability of the discriminator to judge that the input samples are from the real samples.  
For the discriminator \(D\), it is expected that the higher the accuracy of the discrimination, i.e., the closer \(D(x)\) is to 1, the closer \(D(G(z))\) is to 0, when the loss function \(V\) (\(D\), \(G\)) achieves a great value. For the generator \(G\), it expects the generated data distribution to be closer to the real data distribution, i.e., it wants \(D(G(z))\) to be closer to 1. In this case, the loss function \(V\) (\(D\), \(G\)) obtains a very small value.  
When the loss function \(V\) (\(D\), \(G\)) obtains a very, very small value, the generative adversarial network reaches a Nash equilibrium, in which the generated data can fit the real data distribution. This game process promotes the discriminator to improve the accuracy, and at the same time prompts the generator to generate more realistic data, realizing the effective training of generative adversarial network.
## 3 Methodology
### Multi-Head Cross-Attention  
Syntactic structure and semantic information collectively influence the prediction of sentiment polarity. When the syntactic structure changes, the semantic meaning also changes to some extent. Considering both syntax and semantics simultaneously helps in better understanding the sentence. Therefore, we analyze the interrelationships between different features to improve the model performance together and utilize multi-head cross-attention to fuse them. Multi-head cross-attention allows the model to simultaneously learn interactions between features in several different representation subspaces. We use syntactic feature vectors as query \(Q\) and semantic feature vectors as key \(K\) and value \(V\). These vectors are partitioned into multiple heads \(head_{i}\), each of which learns different weight distributions from the outputs of the SA and the SE, and performs the attention computation independently. All the outputs of the heads are merged to obtain a final feature representation that fuses the information from both modules. The attention output is computed as follows:  
\[Q =H_{sa}W_{Q}, \tag{7}\] \[K =H_{se}W_{K},\] (8) \[V =H_{se}W_{V},\] (9) \[Attention =softmax\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V\, \tag{10}\]  
where \(W_{V}\) is the learned weight matrix, \(H_{sa}\) is the result of SA output and \(H_{se}\) is the result of SE output, which, respectively, correspond to the feature representation of SA and SE. The multi-head cross-attention mechanism is executed independently on multiple heads to obtain multiple attention outputs. The outputs from all heads are stitched together and transformed through a linear layer to obtain the final fused feature vector.  
\[MultiHead=Concat(head_{1}\cdots head_{n}\ )W_{O}, \tag{11}\]  
Each head \(head_{i}\) is computed from the above-mentioned attention, and \(W_{O}\) is a trainable weight matrix. Ultimately, the output is applied to the subsequent sentiment polarity classification, which allows the two modules to effectively integrate the syntactic structure and semantic information to jointly enhance behavior for sentiment classification.