esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method and Implementation  
In this section, we describe the method used for malware classification based on API call lists made from dynamic and static analysis, as shown in Figure 1, where the dash arrow line gives the direction to the data as input into Cuckoo S-Box and the thin and thick arrows lines show different directions from a given component to another. The proposed framework mainly consists of three modules: a database of malware and the PE file, modules of static and dynamic analysis, and a module of classification by similarity analysis. The module concerns all datasets used as input to the static and dynamic modules. The static analysis consists of the de-obfuscation of packed malware in order to know the packers names and the contribution level of each of them. For this, a simple python code have been used and one of the codes (to identify malware samples which are related to one another) used in this research is given in Appendix B. In addition, IDA Pro and PeStudio are used to enhance the de-obfuscation steps. These tools are mainly used in the reverse engineering of malware. They help us to identify logic and arithmetic and address manipulation and flow control features from malware as opcodes. A dynamic analysis module consists of a virtual environment set up by Cuckoo Sand-Box to run the executable files of malware without infecting the rest of the system. The API call list database is obtained from the network behavior and other important features. The final module concerns the malware classification process using Machine Learning algorithms. In our research, we have used a similarity analysis based on API sequence, which includes benign and malware files. The comprehensive API reference, which is the Microsoft Developer Network (MSDN), is used to operate a match and classify files between malware and benign.  
Figure 1: Framework for the analysis and classification of malware. API: definition.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Comparison of Fault and Outlier Detection Techniques for IoTs
#### 5.6.3 Machine Learning Techniques  
**Pros:**  
* Can be used when the sensors produce poor, noisy and fragmentary data, as the inherent behaviour of this model generalises the produced data points.
* There is a limited need, sometimes no need, to re-train the model when there is new sensor data added.
**Cons:**  
* The model requires fine-tuning and simulations before being made operational in a real-life setting.
* As this model is often rules-based, if the number of sensor data variables increases, this will also exponentially increase the number of rules.
# GANBA: Generative Adversarial Network for
###### Abstract  
Automatic speaker verification (ASV) is a voice biometric technology whose security might be compromised by _spoofing_ attacks. To increase the robustness against _spoofing_ attacks, presentation attack detection (PAD) or anti-spoofing systems for detecting replay, text-to-speech and voice conversion-based _spoofing_ attacks are being developed. However, it was recently shown that adversarial _spoofing_ attacks may seriously fool anti-spoofing systems. Moreover, the robustness of the whole biometric system (ASV + PAD) against this new type of attack is completely unexplored. In this work, a new generative adversarial network for biometric anti-spoofing (GANBA) is proposed. GANBA has a twofold basis: (1) it jointly employs the anti-spoofing and ASV losses to yield very damaging adversarial _spoofing_ attacks, and (2) it trains the PAD as a discriminator in order to make them more robust against these types of adversarial attacks. The proposed system is able to generate adversarial _spoofing_ attacks which can fool the complete voice biometric system. Then, the resulting PAD discriminators of the proposed GANBA can be used as a defense technique for detecting both original and adversarial _spoofing_ attacks. The physical access (PA) and logical access (LA) scenarios of the ASVspoof 2019 database were employed to carry out the experiments. The experimental results show that the GANBA attacks are quite effective, outperforming other adversarial techniques when applied in white-box and black-box attack setups. In addition, the resulting PAD discriminators are more robust against both original and adversarial _spoofing_ attacks.  
s 2022
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 2 Nonlinear Media and Solitons
### Nervous Nonlinear Medium  
Solitons require a dissipative physical medium characterized by a certain degree of nonlinearity. In keeping with this claim, nervous nonlinear dynamics have been ubiquitously detected in both experimental settings and ecologically valid conditions [23; 24]. Using brain network analysis, data-driven approaches and machine learning [25], various types of nonlinear dynamic behavior have been found in the wave series extracted from EEG data sets [23]. Nonlinear dynamics in the central nervous system consist of phase transitions, branching processes, metastability, limit cycles, self-oscillations, non-stationarity, neuronal avalanches, chaotic behavior and collective phenomena with the emergence of order [26; 27]. Increasing evidence suggests that cortical neuronal networks operate near a critical state where balanced activity patterns support optimal information processing, scale-free correlations and the emergence of adaptive collective behavior [26].  
Therefore, it can be stated that the nonlinear dynamics required by a nervous soliton-like model are widely diffused in the brain.
## Appendix A Direct Time Integration
### Methods for 1 st Order vs. 2 nd Order ODEs
#### Stability, Accuracy and Convergence  
Numerical methods for integrating equations of motion are compared in terms of their _stability_, _accuracy_, _and convergence_. In general, these properties depend upon the ratio of the time step, \(\Delta\tau\), to the shortest natural period of the structural system \(\mathbb{T}\).  
_Stability_ is defined in a slightly different manner, depending on the family of integration methods, but its concept is associated \"_with the propagation of errors of the numerical technique as the calculations progress with a finite interval size, that is, what the effects of errors made on one step will have on succeeding steps_,\" as pointed out by [PERSON] [51]. Explicit numerical methods usually require smaller time steps \(\Delta\tau\) in order to ensure stability, implicit numerical integration methods allow for \(\Delta\tau\) values. In detail, all implicit [PERSON]'s methods in Table 11 are unconditionally stable.  
By _convergence_, one means that the finite difference solution approaches the true solution as the interval size approaches zero \(\Delta\tau\to 0\). It should be clear from the above definitions that convergence does not necessarily imply stability, i.e., the [PERSON]-\(\beta\) method is unconditionally stable but convergent only for \(\Delta\tau/\mathbb{T}<0.318\)[44].  
The roman numerals in parenthesis in Table 11 represent the order of accuracy of the method, i.e., the truncation error between a \"true\" reference solution and that offered by the integration scheme is \(\mathcal{O}(\Delta\tau)^{a}\), where \(a\) is the order of accuracy. Therefore, the smaller the time step, the higher the accuracy for a given algorithm. Provided that convergence and stability limits are met, some methods ensure, for a given \(\Delta\tau\), lower truncation errors than others (i.e., 4 th order Runge-Kutta vs.  
2 nd order Runge-Kutta): higher accuracy, however, also implies greater computational effort, e.g., 4 th order Runge-Kutta requires four (vs. two) function evaluations per time step to achieve a truncation error \(\mathcal{O}(\Delta\tau)^{4}\).  
In [PERSON]'s family of methods, accuracy is also measured in terms of period elongation and numerical damping [44]. Implicit methods such as Wilson-\(\theta\) and Houbolt, which were derived from the classical Newmark's constant average acceleration, are unconditionally stable but introduce 'distortions' in the solution.
## 4 Experiments and Results
### Hydra Trunk Performance  
We evaluated the performance of our customized SISR model using the combined loss (MAE + MS-SSIM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and MS-SSIM evaluation metrics. Various studies [35; 36] found that SSIM provides a better indication of SISR performance than PSNR because it can better represent human visual perception. Also, it has been reported [35; 36] that MS-SSIM is better than SSIM because it is calculated over multiple scales through a process of multiple stages of sub-sampling. We used the RSNA CXR dataset with 70%, 20%, and 10% patient-level splits for training, validation, and testing, respectively. We downscaled all the images (\(256\times 256\times 1\)) by scale factor 2, 3, 4, and 8 using bi-cubic interpolation. We used interpolation following existing methods in the literature [20; 21; 23]. We implemented our SISR model using Keras [37] Python library with TensorFlow backend.  
Table 1 shows the results of our customized SISR model and VDSR on the testing set. Because both models are trained on multiple scales (2, 3, 4, and 8) at once, they can be tested with any scale used during training. As can be seen in the table, our SISR model outperformed VDSR in most cases. Recall that VDSR, which contains 20 convolutional layers, is a large model with a relatively large number of parameters as compared to our model (7 layers). These results suggest that our customized SISR can faithfully and efficiently construct HR images. Figure 3 shows examples of SR results with scale factors of 2, 3, 4, and 8 using the Hydra trunk.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scale** & **Model** & **Proposed Loss** & **PSNR** & **SSIM** & **MS-SSIM** \\ \hline \multirow{2}{*}{2} & Proposed & 0.0072 & 34.822 & 0.9366 & 0.9911 \\  & VDSR & 0.0091 & 32.564 & 0.9355 & 0.9801 \\ \hline \multirow{2}{*}{3} & Proposed & 0.0161 & 32.274 & 0.9000 & 0.9861 \\  & VDSR & 0.0157 & 31.972 & 0.8858 & 0.9815 \\ \hline \multirow{2}{*}{4} & Proposed & 0.0201 & 30.764 & 0.8706 & 0.9745 \\  & VDSR & 0.0250 & 30.691 & 0.8652 & 0.9722 \\ \hline \multirow{2}{*}{8} & Proposed & 0.0454 & 27.306 & 0.7839 & 0.9233 \\  & VDSR & 0.0478 & 25.087 & 0.7644 & 0.9088 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed SISR model (_Hydra_ trunk) and VDSR.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 6 Security Analysis
### Comparison on Security Properties  
In this section, the comparison in terms of the major security properties for D2D authentication is presented. The proposed protocol is compared with the stat-of-the-art D2D authentication and key agreement schemes including SeDS [19], LRSA [32], GRAAD [12], and PPAKA [9] in order to prove its superiority on security properties. As shown in Table 2, our protocol yields the desirable security properties.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 5 Applications of Quantum Baths to Realistic Systems
### Structural and Thermodynamic Properties
#### 5.1.2 Diamond  
Diamond has a particularly high Debye temperature \(\Theta_{D}\simeq 2000\) K. [PERSON] and coworkers [PERSON] and [PERSON] (2014) applied the quantum thermostat (QT) to model diamond crystals, where interatomic interactions were described with a semi-empirical potential, and compared their results to existing path integral (PI) simulations. The internal energy \(E_{int}(T)\) and lattice parameter \(a(T)\) as provided by the QT follow the PI trends with temperature down to \(T\sim 0.06\Theta_{D}\). Below this temperature, the quantum thermostat failed to counterbalance the strong phonon-phonon coupling, which results in zero-point energy leakage from high to low frequencies and a too-short phonon lifetime. Therefore, \(E_{int}(T)\) and \(a(T)\) sensitively diverge from the PI behavior at low temperatures.  
Isotope Effects: LiH vs. LiD  
Simulations that rely on classical statistical mechanics cannot account for isotope effects straightforwardly, because statistical averages are independent of the nuclear mass. A marked isotope effect takes place in LiH/LiD crystals; the Debye temperatures, as obtained by fitting the thermal conductivity of LiH and LiD crystals, are 851 K and 638 K, respectively (see [53] and references therein). One of the first QTB simulations in conjunction with the first-principle description of interatomic forces via the density functional theory [53] explained why the lattice parameter of LiH is significantly larger than that of LiD from very low temperatures up to 600 K, as experimentally observed. The volume expansion coefficient is also different in this \(T\) range between the two isotopes. The elastic properties of LiH and LiD with increasing pressure are markedly distinct, which reflects in two different equations of state for the two isotopes as measured by X-ray diffraction experiments in the 0-94 GPa pressure range [54]. Remarkably, the quasi-harmonic approximation fails in reproducing the isotope shift in pressure \(\Delta P\), which is defined as the difference in pressure between LiH and LiD at fixed volume (see Figure 8). The QTB simulations follow the experimental trend in \(\Delta P\), while the quasi-harmonic approximation deviates from it, especially when \(P\) increases. This has been interpreted as a consequence of the importance of anharmonic contributions to the thermal properties of those crystals.  
Figure 8: Lattice parameter a of cubic LiH and LiD vs. temperature, with respect to the LiD lattice parameter at \(T=0\) K. **Left**—experimental; **right**—QTB simulations.
# Two-Color Operation of a Soft X-ray FEL with Alternation of Undulator Tunes
## 2 Two-Color Lasing with the Alternation of Undulator Tunes  
Let us consider gap-tunable segmented undulators that are normally used at X-ray FEL facilities. For generation of two colors, all odd segments of an undulator are tuned to wavelength \(\lambda_{1}\), and even segments to \(\lambda_{2}\) (see Figure 1). With respect to the amplification of the electromagnetic wave with the wavelength \(\lambda_{1}\), the FEL process is disrupted as soon as the electron beam leaves a \(\lambda_{1}\) segment and enters a \"wrong\" segment tuned to \(\lambda_{2}\). However, energy modulations in the electron bunch, accumulated due to the interaction with the electromagnetic field in the \(\lambda_{1}\) segment, continue to be converted into density modulations (bunching) in the \(\lambda_{2}\) segment due to its longitudinal dispersion. Thus, in the next \(\lambda_{1}\) segment, the beam with an enhanced bunching quickly radiates a stronger field than the one coming from the previous \(\lambda_{1}\) segment (which in addition is diffracted), and the FEL process continues with higher amplitudes. In some sense, the mechanism is similar to that of the multi-stage (or distributed) optical klystron [18; 19; 20; 21]. In this qualitative description, we do not consider effects of longitudinal velocity spread due to the energy spread and emittance of the electron beam. According to our estimates, these effects were practically negligible in our experiments.  
The longitudinal dispersion of an undulator segment is characterized by a transfer matrix element \(R_{56}=2N_{w}\lambda\), where \(N_{w}\) is the number of undulator periods per segment, and \(\lambda\) is the resonance wavelength. If \(\lambda_{1}<\lambda_{2}\), the FEL gain is, obviously, weaker for \(\lambda_{1}\) in the corresponding \(\lambda_{1}\) segments. However, the \(R_{56}\) in \(\lambda_{2}\) segments is stronger and gives a larger addition to the final gain at \(\lambda_{1}\) wavelength. Thus, the total gain in the linear regime can be comparable even if \(\lambda_{1}\) is significantly shorter than \(\lambda_{2}\). In principle, the number of segments does not have to be the same for \(\lambda_{1}\) and \(\lambda_{2}\); this depends on desired intensity ratio. If the undulator line is sufficiently long, nonlinear effects start to play a role in the last segments. In this case, the amplification processes at both wavelengths are not independent anymore, and they start to compete in terms of modification of the longitudinal phase space of the electron beam. As a result, the radiation power is somewhat smaller compared to standard single-color lasing in saturation regime. However, it can still be sufficient for many experiments.  
Finally, we should note that the considered scheme was the only possibility to establish two-color operation of FLASH because the undulator is too short to have a sufficient gain in the split-undulator configuration.
## 4 Results
### Estimation of Ecosystem Respiration Using the Remote Sensing Ecosystem Respiration Model  
The remote sensing ecosystem respiration model involves four parameters that need to be fitted: \(\alpha\), \(PC_{max}\), \(R_{ref}\) and \(E_{0}\). In the Huoditang forest area, MODIS and NEP data from June to September of 2003-2004 were used to fit these four parameters in the ecosystem respiration model. The NEP and MODIS data from June to September of 2005 were then used for accuracy validation. Table 4 presents the fitted respiration model parameters for the Huoditang forest area.  
Similarly, for each grid within the Qinba Mountain forest area, the remote sensing ecosystem respiration model parameters were independently fitted using the NEP and MODIS data from June to September of 2003-2004. This resulted in the remote sensing ecosystem respiration model for each grid in the Qinba Mountain forest area. The NEP and MODIS data from June to September of 2005 were then used for accuracy validation. By subtracting the NEP data from the GPP dataset for June to September 2005, the ecosystem respiration values estimated from the literature were obtained. A scatter plot comparing these literature-estimated ecosystem respiration values with the estimated values was created (as shown in Figure 6), showing a good fit with an R\({}^{2}\) of 0.7682.  
Additionally, a 1% perturbation was applied to the fitting parameters of the ecosystem respiration model for the Huoditang forest area (Table 5). The results showed that the parameter \(PC_{max}\) exhibited high sensitivity, while \(\alpha\) and \(R_{ref}\) displayed moderately high sensitivity, and \(E_{0}\) demonstrated low sensitivity. This discrepancy arises because \(PC_{max}\), the most sensitive parameter, is incorporated into Equation (14) in a multiplicative form, leading to a compounding effect. Small perturbations in such parameters can accumulate, resulting in significant changes in the model outputs. In contrast, the relatively lower sensitivity of the other parameters suggests that the overall model sensitivity remains within an acceptable range, ensuring a certain level of reliability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\alpha\) & \(PC_{max}\)/(mgCO\({}_{2}\) m\({}^{-2}\).s\({}^{-1}\)) & \(R_{ref}\)/(gC\(\cdot\)m\({}^{-2}\)) & \(E_{0}\)/K \\ \hline \(-\)60.67 & 4.01 & 160.94 & 207.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fitted parameters of the ecosystem respiration model for the Huoditang forest area.  
Figure 5: Scatter plot of GPP data product vs. estimated GPP. Note: The black dots represent the scatter plot of the GPP data product versus the estimated GPP, and the blue line represents the linear regression line between the two.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 Chi-Square Test  
The chi-square test analyzes categorical data by using the chi-square distribution to verify the significance of the observed and expected frequencies. It is primarily used to verify the goodness of fit, homogeneity, and independence of the data [8]. The chi-square test is used when comparing the distributions of individual groups; and the independence test determines whether a dependency exists between the two characteristics of the data. In this study, the chi-square test was compared in 16 cases with one dependent variable and 15 independent variables, and a variable with a p-value of 0.05 or less was used to select the main variable.
## 3 Signal Morphology  
The morphology of EEG signals is shaped by the brain activity at the time of waveform capture, which depends on the active section of the brain. The anatomy of the human brain comprises three primary lobes, as illustrated in Figure 6. The cerebrum, being the largest and most significant part, is split into two hemispheres, each containing four lobes: the frontal, parietal, temporal, and occipital lobes. The frontal lobe manages planning and complex voluntary movements, personality, judgment, decision-making, and cognitive functions, like language, abstract thinking, and problem-solving. The parietal lobe processes somatosensory information, such as touch and coordinate movements [86], while the temporal lobe handles auditory information and is integral for memory and speech. The occipital lobe, the smallest of the lobes, is tasked with vision, including color and spatial perception. The brain stem oversees basic life functions like breathing, blood pressure, and swallowing, and the cerebellum controls balance and fine movement coordination, including activities like walking and speaking [87]. EEG waveforms exhibit distinct shapes that are indicative of various types of brain activities. The EEG signal is represented as a time series of voltage values corresponding to electrical activity detected by electrodes, measured in microvols (\(\mu\)V), and is typically sampled at regular intervals ranging from 128 Hz to 1024 Hz. Common EEG signals have a frequency range of up to 45 Hz, and EEG frequencies are usually standardized as delta, theta, alpha, beta, and gamma waves [72]. However, in certain cases, the EEG frequency range can extend beyond 200 Hz. These frequencies offer insights into the functioning of different brain areas and are instrumental in diagnosing and treating various neurological and psychiatric conditions. They are also employed to monitor brain activity during various states like sitting, sleeping, and meditating [73].  
Figure 6: Brain anatomy with the primary lobes highlighted.