esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Signal Processing Based on Laplace Techniques
\[f(t)=\frac{1}{2\pi i}\int_{\beta-\mathrm{i}\infty}^{\beta+\mathrm{i}\infty} \mathrm{e}^{s\mathrm{t}}F^{*}(s)\mathrm{d}s=\sum_{j=0}^{N}\mathrm{Res}\big{(} \mathrm{e}^{s\mathrm{t}}F^{*}(s)\big{)}\big{|}_{s=s_{j}} \tag{2}\]  
The residues for poles of order \(m>1\) and \(m=1\) are calculated using the general formula (Equation (3)), which stems from the definition of the Laurent series. \[\left.\mathrm{Res}(F^{*}(s))\right|_{s=s_{j}}=\lim_{s\to s_{j}}\frac{1}{(m-1)!} \frac{\mathrm{d}^{m-1}}{\mathrm{d}s^{m-1}}\big{[}F^{*}(s)(s-s_{j})^{m}\big{]} \tag{3}\]  
In the case of \(m=1\), and when the function is expressed as a fraction with \(X^{*}(s)\) as the numerator and \(Y^{*}(s)\) as the denominator, the residues for simple poles are calculated using Equation (4). The denominator is differentiated, and the entire term is evaluated at the simple poles \(s=s_{k}\):  
\[\left.\mathrm{Res}(F^{*}(s))\right|_{s=s_{k}}=\left.\mathrm{Res}\!\left(\frac{ X^{*}(s)}{Y^{*}(s)}\right)\right|_{s=s_{k}}=\frac{X^{*}(s_{k})}{\frac{\partial Y ^{*}(s)}{\partial s}|_{s=s_{k}}} \tag{4}\]For practical applications of these formulae, see Xie [30]. It is also crucial to remember that the product of two functions in the Laplace domain is equivalent to the convolution integral in the time domain [28]:  
\[F^{*}(s)G^{*}(s)=f(t)*g(t)=\int_{0}^{t}f(t-\tau)g(\tau)\mathrm{d}\tau \tag{5}\]  
Furthermore, the inverse Laplace transform (denoted as \(\mathcal{L}^{-1}\)) of a function multiplied by \(s\) is given by:  
\[\mathcal{L}^{-1}\{sF^{*}(s)\}=\frac{\partial f(t)}{\partial t} \tag{6}\]
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 5 IberSpeech-RTVE 2022 Results and Discussion
### Search on Speech Challenge  
The results of the search on speech challenge are presented in Table 20 for the RTVE2022 DB test dataset, which was the only dataset that received submissions. They show that the Multistream CNN+rescoring system performs better than the Multistream CNN system. A paired \(t\)-test shows that this better performance is significant (\(p<0.02\)). This indicates that the rescoring approach presented in the Multistream CNN+rescoring system helps for term detection. The small performance gap between MTWV and ATWV results clearly indicates that the detection threshold is well-calibrated. When comparing the best evaluation result (ATW = 0.6694) with the best system submitted to the 2020 SoS challenge (ATWV = 0.2123), the performance is much better. This is due to the more robust ASR system constructed for this year's evaluation.  
The DET curves of the submitted systems are presented in Figure 1. They show that the Multistream CNN+rescoring system does generally perform the same as the Multistream CNN system for high miss ratios and does outperform the Multistream CNN system for low miss ratios.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**System** & **Rejected** & **Accepted** & **Correct** & **Wrong** & **Score** \\ \hline Base & 11.97 & 2410.33 & 2218.70 & 191.63 & 2027.07 \\ \hline Base-opt (0.11) & 10.80 & 2411.50 & 2219.50 & 192.00 & 2027.50 \\ \hline BUT-con & 0.00 & 2458.89 & 2282.26 & 176.63 & 2105.63 \\ \hline BUT-con-opt (1.00) & 109.21 & 2349.68 & 2252.09 & 97.59 & 2154.50 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Performance (time, in seconds) of the baseline and BUT contrastive systems on the TaSAC-BP BP development set. Optimal performance is shown too (with the optimal threshold in parentheses).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**System ID** & **MTWV** & **ATWV** & **p(FA)** & **p(Miss)** \\ \hline Multistream CNN & 0.6496 & 0.6483 & 0.00000 & 0.346 \\ \hline Multistream CNN + rescoring & 0.6696 & 0.6694 & 0.00001 & 0.325 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Search on speech challenge results for the RTVE2022 DB test dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**System** & **Rejected** & **Accepted** & **Correct** & **Wrong** & **Score** \\ \hline Base & 4.63 & 1794.82 & 1628.96 & 165.86 & 1463.10 \\ \hline Base-opt (0.25) & 5.85 & 1793.60 & 1628.51 & 165.09 & 1463.42 \\ \hline BUT-pri & 0.00 & 1820.98 & 1688.50 & 132.48 & 1556.02 \\ \hline BUT-pri-opt (1.00) & 72.13 & 1748.85 & 1668.58 & 80.27 & 1588.31 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Performance (time, in seconds) of the baseline and BUT primary systems on the TaSAC-BP test set. Optimal performance is shown too (with the optimal threshold in parentheses).
## 2 Theory  
### Notations  
Vectors and matrices are noted in bold. The paper considers univariate response models. Assume that \(\mathbf{y}\) is a vector containing \(n\) training observations of one given response variable and \(\mathbf{x}\) a vector of \(p\) independent predictors for one given observation. The training observation \(i\) is noted (\(\mathbf{x}_{i}\), \(y_{i}\)) where \(\mathbf{x}_{i}\)' (\(1\times p\)) is the row \(i\) of matrix \(\mathbf{X}\) (\(n\times p\)). A new observation to predict is noted (\(\mathbf{x}_{\text{new}}\), \(y_{\text{new}}\)).
## 3 PDM and Parameter Identification
### Flexible Dynamic Model of Robot  
The flexibility of a robot is mainly embodied in its joints and links. As depicted in Figure 3, the elastic joint can be represented by a linear torsional spring characterized by a stiffness coefficient K and a reduction gear with a specified gear ratio R. Given that the stiffness of the link significantly exceeds that of the joint, it is reasonable to regard the link as a rigid body for the purposes of modeling. Therefore, the flexible dynamic model of an n-DoF robot can be described as follows:  
Figure 2: Overall structure of the proposed HDPP.  
\[M(q_{e})\ddot{q}_{e}+C(q_{e},\dot{q}_{e})+f_{e}(\dot{q}_{e})+G(q_{e})=\tau_{e} \tag{2}\]  
\[\tau_{e}=K(R^{-1}q_{m}-q_{e}) \tag{3}\]  
\[J_{m}\ddot{q}_{m}+f_{m}(\dot{q}_{m})+R^{-1}\tau_{e}=\tau_{m} \tag{4}\]  
where \(q_{e}\), \(\dot{q}_{e}\), and \(\ddot{q}_{e}\) correspond to the vectors representing the joint position, velocity, and acceleration for n-DoF vectors, \(\tau_{e}\) is the joint torque vector, \(M(q_{e})\) is the inertia matrix, \(C(q_{e},\dot{q}_{e})\) is the centrifugal and Coriolis forces, \(f_{e}(\dot{q}_{e})\) is the reducer friction torque, \(G(q_{e})\) is the gravity, \(q_{m}\), \(\dot{q}_{m}\), and \(\ddot{q}_{m}\) denote the respective position, velocity, and acceleration of the motor, \(J_{m}\) is the rotor inertia, \(f_{m}(\dot{q}_{m})\) is the motor friction torque, and \(\tau_{m}\) is the vector of the motor driving torque.  
Joint friction has significant impact on the dynamics of IRs. The Coulomb-viscous friction model is utilized for the servo motors due to their straightforward design and high manufacturing accuracy. The frictional characteristics for the i-th servo motor are described as follows:  
\[f_{mi}(\dot{q}_{mi})=\tau_{smi}\text{sgn}(\dot{q}_{mi})+\sigma_{vmi}\dot{q}_{mi} \tag{5}\]  
where \(\tau_{smi}\) and \(\sigma_{vmi}\) are the unknown Coulomb and viscous friction coefficients.  
For joint reducer, meshing transmission process results in huge nonlinear friction characteristics, especially at low speed. Hence, it is feasible to formulate a Coulomb-viscous-Stribeck friction model for the i-th reducer, which can be delineated as follows:  
\[f_{ei}(\dot{q}_{ei})=[\tau_{ci}+(\tau_{si}-\tau_{ci})e^{-\frac{(\dot{q}_{ei}) }{\tau_{si}}}]^{1.5}\text{sgn}(\dot{q}_{ei})+\sigma_{ei}\dot{q}_{ei} \tag{6}\]  
where \(\tau_{ci}\) and \(\tau_{si}\) are the Coulomb friction torque and maximum static friction torque, respectively, \(\dot{q}_{sci}\) is the Stribeck speed, and \(\sigma_{ei}\) is the viscous friction coefficient.
## 2 Algorithm
### LAMBDA Method  
The initial coordinates of the centimeter level precision can be used to update the float solution. The LAMBDA method is used to search the ambiguities and the fixed solution is obtained through the ratio test. The LAMBDA method consists of two parts, including integer transform and integer least square estimation. The transform matrix \(Z\) is employed in integer transform and the float solution \(a\) is transformed into a new parameter vector \(z\). \(z\) is easier to search than \(a\). When the optimal integer estimation of \(z\) is obtained, it can be converted to an integer ambiguity solution through inverse transformation. The process is shown in Figure 2.  
Some coordinates corresponding to wrong peaks can be excluded in the ratio test. If the wrong peaks still exist, some constraints can be used to solve this problem. For example, a linear constraint can be added to the rover station. The coordinate of the real peak will change along a straight line and then the wrong peaks will be excluded. The coordinate corresponding to the real peak here can reach the initial coordinate accuracy requirement of the LAMBDA method in the indoor pseudolite system.
## 3 Linear and Non-Linear Predictive Analytics Comparison Framework: L&NL-PAC
### Predictive Methods
#### 3.2.2 Penalized Regression Methods  
The class of penalized regression methods is characterized by the fact that a penalty term is employed for the magnitude of the regression coefficients, constraining their magnitude to be small. The penalty serves as a model regularization term and helps to mitigate the effects of collinearity and overfitting, and to improve model robustness. In this class, three methods were considered: ridge regression (RR), least absolute shrinkage and selector operator (LASSO), and the elastic net (EN). EN is a more general method and contains RR and LASSO as particular cases. Equation (2) presents the objective function used to obtain the EN model:  
\[\hat{\mathbf{b}}_{\mathbf{EN}}^{\mathbf{\ \hat{\alpha}}}=\underset{b=[b_{0},b_{p}]^{ \mathrm{T}}}{\text{argmin}}\left\{\sum_{i=1}^{n}(y(i)-\hat{y}(i))^{2}+\gamma \left(\alpha\sum_{j=1}^{p}|b_{j}|+\frac{1-\alpha}{2}\sum_{j=1}^{p}b_{j}^{2} \right)\right\}, \tag{2}\]  
where \(\alpha\) (\(\alpha\in[0,1]\)) is a hyperparameter that weights the relative contributions of the different types of penalization to the magnitude of the coefficients (the L\({}_{1}\)-norm and the L\({}_{2}\)-norm penalization), and \(\gamma\) controls the bias\(-\)variance trade-off, by weighting the contribution of the classical least-squares term with the penalization term for the regression coefficients size.  
Figure 3: Linear and non-linear regression methods considered in the comparison study. Kernel methods include the polynomial (poly) kernel and the radial basis function (rbf) kernel.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 5 Experiment, Results, and Analysis
### Results, Discussions, and Analysis
That is because the higher the different SNR is, the less the noise interferes with the TFTs is, and the easier it is for the effective features of the TFTs to be extracted. The recognition accuracies of 2 FSK-4 FSK, 2 FSK-BPSK, 2 FSK-EQFM, 2 FSK-FRANK, 2 FSK-LFM, 2 FSK-NS, 2 FSK-SFM, LFM-4 FSK, LFM-BPSK, LFM-EQFM, LFM-FRANK, and LFM-SFM are up to \(76.25\%\), \(63.75\%\), \(82.625\%\), \(75.625\%\), \(70.625\%\), \(72\%\), \(67.5\%\), \(71.5\%\), \(60.625\%\), \(78.25\%\), \(73.75\%\), and \(70.625\%\), at different SNRs of \(-12\) dB, respectively. When the SNR of 2 FSK is \(-12\) dB and of 4 FSK it is \(4\) dB, the recognition accuracy of 2 FSK-4 FSK is up to \(88.56\%\). For 2 FSK at \(4\) dB and 4 FSK at \(-6\) dB, the recognition accuracy of 2 FSK-4 FSK is \(1\). When SNR of 2 FSK is \(-4\) dB and 4 FSK is \(-2\) dB, the recognition accuracy of 2 FSK-4 FSK is up to \(1\). For 2 FSK at \(-6\) dB and 4 FSK at \(-8\) dB, the recognition accuracy of 2 FSK-4 FSK is \(93.125\%\). When SNR of 2 FSK is \(-12\) dB and BPSK is \(4\) dB, the recognition accuracy of 2 FSK-BPSK is up to \(81.875\%\). For 2 FSK at \(4\) dB and BPSK at \(-6\) dB, the recognition accuracy of 2 FSK-BPSK is \(98.75\%\). When SNR of 2 FSK is \(-4\) dB and BPSK is \(-2\) dB, the recognition accuracy of 2 FSK-BPSK is up to \(96.125\%\). For 2 FSK at \(-6\) dB and BPSK at \(-8\) dB, the recognition accuracy of 2 FSK-BPSK is \(85.25\%\). When SNR of 2 FSK is \(-12\) dB and EQFM is \(4\) dB, the recognition accuracy of 2 FSK-EQFM is up to \(92.625\%\). For 2 FSK at \(4\) dB and EQFM at \(-6\) dB, the recognition accuracy of 2 FSK-EQFM is \(1\). When SNR of 2 FSK is \(-4\) dB and EQFM is \(-2\) dB, the recognition accuracy of 2 FSK-EQFM is up to \(1\). For 2 FSK at \(-6\) dB and EQFM at \(-8\) dB, the recognition accuracy of 2 FSK-EQFM is \(96.5\%\). Note that the recognition accuracies of randomly overlapping dual-component signals are all more than \(90\%\) at \(-2\) dB, which demonstrates better recognition performance. The reason is the  
Figure 6: Variation in the recognition performance of eight types of signals with SNR. (**a**) 2 FSK, EQFM, 4 FSK, BPSK; (**b**) LFM, SFM, NS, FRANK. powerful feature extraction and classification ability. Therefore, this work offers important experimental guidance in further enhancing recognition performance under different SNRs and promoting the actual applications. Figure 7: Recognition accuracy of dual-component radar signals as a function of different SNRs. The floating point of operations (FLOPs), parameters, and time are adopted to investigate computational complexity. Table 3 lists the computational complexity of CNN-ST and CNN-DQLN. As can be seen from Table 3, the FLOPs, parameters, and the time of CNN-ST are all lower than CNN-DQLN. Therefore, the proposed CNN-ST demonstrates higher computational accuracy and lower computational complexity over others, and possesses enough computational novelty.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 4 Simulation Results and Discussion  
In this section, the performance of the proposed scheme is investigated against several non-ideal channel conditions such as frequency offset, phase offset, timing offset, varying symbol number as well as different Doppler shifts. An analytical comparison is also provided with previously proposed different learning-based modulation classifiers.
# Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
###### Abstract  
Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.  
Keywords:Pairwise Markov Chain; probability hypothesis density; particle filter; multi-target tracking system +
Footnote †: journal:
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 1 Introduction
### Contribution  
Our approach includes evaluating and discussing several modified Chebyshev public key cryptographic algorithms and understanding their performance and security compared to our proposed algorithm. Specifically, in our proposed improved chaotic multiple public key cryptographic algorithm based on Chebyshev polynomials (CMPKC-\(k_{i}\)), we aim to design a multiple chaotic public key algorithm using Chebyshev polynomials. The algorithm introduces the concept of selective coefficient \(k_{i}\) based on the properties of Chebyshev polynomials, allowing the original system's special functions \(f_{1}(x)\) and \(f_{2}(x)\) that need to be negotiated to be freely chosen as Chebyshev polynomials \(f_{k_{x}}(x)\) and \(f_{k_{g}}(x)\), where the selection of \(k_{a}\) and \(k_{g}\) is randomly determined by \(k_{i}\) and can also be extended to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes the four shortcomings of the Bose algorithm by iteratively iterating \(f_{k_{a}}(x)\) and \(f_{k_{g}}(x)\) based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and experimental results show that the algorithm has strong robustness against common attacks, achieving a higher attack time to break private keys compared to other variants, proving the algorithm's security against such attacks. Compared to other similar variants of Chebyshev polynomial public key algorithms, our proposal achieves better performance in the encryption and decryption stages. Furthermore, we combine this multiple chaotic  
Figure 1: Diagram of the development history of chaotic public key cryptography.  
system key exchange protocol with the Advanced Encryption Standard (AES) algorithm and provide a demonstrative example.
# Searching Open-Source Vulnerability Function Based on Software Modularization
## 4 Results and Analysis
### Effectiveness of Component Detection  
We evaluate the effectiveness on Dataset I to compare BMVul with B2 SFinder and Louvain detection. Table 5 reports the performance of BMVul in terms of efficacy in detecting open-source components.  
P represents the precision of identifying components in binary modules, and \(P_{1m}\) represents the precision of identifying components in a unique binary module. As can be seen from the table, the precision of B2 SFinder is only 47%, and Louvain detection is 72.7%. However, BMVul reaches 75%, which is significantly improved by 3.16% and 59.57% than the above two methods, respectively. There are more false positives in B2 SFinder, which causes a high false positive rate. The detection based on software modularization can decrease the number of false positive cases to reduce the false positive rate. It can be found that BMVul increases by about 39.43% compared with Louvain detection by analyzing the changes of \(P_{1m}\) values, because BMVul divides software modules based  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** \\ \hline openssl & encryption and decryption \\ sqlite & database \\ libshdfile & sound/video processing \\ libjpeg-turbo, libpng & image processing \\ freetype, libtiff & font processing \\ unrar, zlib & compression \\ expat & parser \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset II used for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **P1m** & **F1-Score** \\ \hline BMVul & 75.00\% & 77.80\% & 56.50\% \\ Louvain detection & 72.70\% & 69.60\% & 55.80\% \\ B2 SFinder & 47.00\% & - & 52.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of BMVul with other methods in terms of effectiveness.  
on DBM, considering the directed property of function call graph, which is different from the Louvain algorithm. The Louvain algorithm can only divide a function into a module without considering directed property and the division of overlapping modules, thus reducing the module-based component detection precision. Therefore, given the situation that some functions may belong to multiple modules at the same time, we apply overlapping detection for function clustering to improve the \(P_{1m}\) and \(P\). The F1 score of BMVul achieves 56.5%, which outperforms B2 SFinder by 8.45%, so it is better than the current file-level component detection.
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
## 2 MIMO-Beamforming Techniques for PHY Security
### Tunable Beamforming Circuits  
Tunable beamforming circuits can be developed for adaptive tuning of radio links from one secure sub-band \(f_{k}\) to another, with operations centered at the 60 GHz frequency. Two important challenges to be discussed in such work are the optimization of the beamforming weights for various frequencies \(f_{k}\), and the technique for developing communication circuits with capabilities for adaptive tuning of radio links over a few sub-bands centered around the 60 GHz frequency. The setup for such a system is illustrated in Figure 2.  
Let \(w=\left[w_{0}\ w_{1}\cdot\cdot\cdot w_{N-1}\right]=\left[\ a_{0}e^{i\phi_{0}}\ a_{1}e^{i\phi_{1}}\cdot\cdot\cdot\ a_{N-1}e^{i\phi_{N-1}}\right]\) denote the complex beamforming weights, representing the amplifier gains and phase shifter settings for each element of the array in Figure 2 as it forms directed beams at an angle \(\theta\). Then, the issue of optimization rests in solving the weights, \(w\), maximizing link security and privacy, as well as link reliability at each of the sub-bands in the 60 GHz frequency. This is required at both the TX and RX side of the radio link, resulting in a multiple-input multiple-output (MIMO) beamforming system. Tuning of the beamforming coefficients, \(w\), is realized by controlling the steering vector \(A=\left[\ a_{0}\ a_{1}\cdot\cdot\cdot a_{N-1}\right]\), and the phase-shift vector \(\Phi=\left[\phi_{1}\ \phi_{2}\cdot\cdot\cdot\phi_{N-1}\right]\). To ensure the security and privacy of IoT links, the array parameters \(A\) and \(\Phi\) can be chosen to realize directed pencil beams to the intended receivers. The resulting array gain will dictate the power radiated by the beamforming array and the distance, \(d_{1}\), within which a received signal level above the sensitivity (\(R_{\times}\)) of the communication receiver determines the intended useful communication range, while the distance, \(d_{2}\), in which the received signal level is below \(0.01R_{\times}\) can be used to determine the maximum intercept range beyond which eavesdropping becomes practically impossible since most practical communication receivers cannot detect signal levels hundreds of times below their sensitivity. These parameters can be used to design secure IoT links, where communications between the TX and RX are kept secure and private. The secrecy capacity (SC) for such a wireless system is calculated as:  
\[\text{SC}=\left\{\begin{array}{c}\text{C}_{\text{TX-RX}}-\text{C}_{\text{TX- Eve}},\text{ if }\text{C}_{\text{TX-RX}}>\text{C}_{\text{TX-Eve}}\\ 0\text{ otherwise}\end{array}\right. \tag{1}\]  
where \(\text{C}_{\text{TX-RX}}\) denotes the information capacity of the TX-RX link, \(\text{C}_{\text{TX-Eve}}\) denotes the information capacity of the TX-Evedropper link. It can be shown that SC is maximized, practically, at distances beyond the intercept range, \(d_{2}\).  
Figure 2: Diagram of an N-Element tunable beamforming circuit to be developed.