esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical and Experiment Results of Metamaterials
### Straight Beam with Conical Metamaterials  
For the second example, the conical metamaterials are simulated in Figure 7a. The frequency responses in Figure 7b are matched with the dispersion relationship in Figure 8. Similar to the cylindrical unit cell, the changes of the effective mass and stiffness make the shifts of the band gap. It is interesting that the radius of the cone has a large effect on the band gap. By increasing 0.01 m, the frequency range of band gap is shifted at about 2000 Hz. The height of the cone, the distance between the cone, and the number of the unit cells have little effect.  
The frequency response is shown in Figure 9. With 0.03 m for the radius of cone, the band gap can occur at 17,300 Hz. With 0.04 m, the band gap about 11,300 Hz to 21,000 Hz is observed. By increasing the radius, the band gap becomes lower in Figure 9a. The height of the cone has influence too; the band gap at 15,000 Hz with 0.03 m, 13,000 Hz with 0.04 m, and 11,400 Hz with 0.05 m. The effect of the distance is similar to that of the cylindrical metamaterials. By increasing the number of the unit cells, the depth of the band gap becomes lower. In conclusion, the simulations show that the frequency ranges of the band gap are different, but the geometric parameters have the similar effects.
# GANBA: Generative Adversarial Network for
## 5 Results
### Defenses against Adversarial Spoofing Attacks
The usage of the resulting PAD system of the proposed GANBA framework is the best solution for defending the target biometric system. It is even able to significantly improve the results of the baseline biometric system which is not exposed to any adversarial _spoofing_ attacks by 1.96% and 2.13% of absolute EERjoint in the PA and LA scenarios, respectively. This means that the trained PAD discriminator with the proposed GANBA framework is not only helpful for detecting adversarial _spoofing_ attacks but also for detecting the original _spoofing_ attacks of the ASVspoof 2019 test dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Adv. Attacks / Defenses**} & \multicolumn{4}{c}{**Logical Access Attacks**} & \multicolumn{4}{c}{**Physical Access Attacks**} \\ \hline
**Adv. Attack** & **Adv. Defense** & **ERF\_PAD (\%)** & **ERF\_AV (\%)** & **ERF\_joint (\%)** & **min+DCF** & **ERF\_PAD (\%)** & **ERF\_AV (\%)** & **ERF\_joint (\%)** & **min+DCF** \\ \hline No Processing & No Processing & 5.85 & 30.58 \({}^{*}\) & 19.87 & 0.1237 & 4.62 & 18.43 \({}^{*}\) & 13.12 & 0.1221 \\ \hline PCD & Adv. Train FGSM & 8.42 & 30.78 \({}^{*}\) & 24.68 & 0.1532 & 7.50 & 19.87 \({}^{*}\) & 16.42 & 0.1434 \\ GANBA & Adv. Train FGSM & 20.34 & 29.18 \({}^{*}\) & 40.12 & 0.3304 & 16.23 & 17.56 \({}^{*}\) & 35.43 & 0.3012 \\ FGSM & Adv. Train PGD & 7.34 & 31.03 \({}^{*}\) & 23.75 & 0.1476 & 6.73 & 19.02 \({}^{*}\) & 15.78 & 0.1389 \\ PGD & Adv. Train PGD & 8.07 & 30.89 \({}^{*}\) & 23.12 & 0.1502 & 7.82 & 18.07 \({}^{*}\) & 16.19 & 0.1476 \\ GANBA & Adv. Train PGD & 17.65 & 29.87 \({}^{*}\) & 38.39 & 0.3009 & 15.03 & 17.10 \({}^{*}\) & 34.22 & 0.2893 \\ FGSM & Adv. Train GANBA & 5.14 & 30.51 \({}^{*}\) & 18.95 & 0.1102 & 3.96 & 18.56 \({}^{*}\) & 12.16 & 0.1084 \\ PGD & Adv. Train GANBA & 5.33 & 30.44 \({}^{*}\) & 19.22 & 0.1204 & 4.22 & 18.31 \({}^{*}\) & 12.34 & 0.1121 \\ GANBA & Adv. Train GANBA & 5.45 & 30.66 \({}^{*}\) & 19.66 & 0.1222 & 4.37 & 18.72 \({}^{*}\) & 12.48 & 0.1145 \\ \hline FGSM & PAD from GANBA & 4.12 & 30.33 \({}^{*}\) & 17.74 & 0.1011 & 3.21 & 18.22 \({}^{*}\) & 11.16 & 0.0976 \\ PGD & PAD from GANBA & 4.46 & 30.74 \({}^{*}\) & 18.15 & 0.1056 & 3.53 & 18.31 \({}^{*}\) & 11.24 & 0.0996 \\ GANBA & PAD from GANBA & 4.75 & 30.62 \({}^{*}\) & 18.53 & 0.1079 & 3.68 & 18.56 \({}^{*}\) & 11.36 & 0.1015 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the TDNN + PLDA/LCNN biometric system evaluated on the ASVspoof 2019 logical access (L/A) and physical access (P/A) test datasets in terms of \(\text{ERF}_{\text{PAD}}(\%)\), \(\text{ERF}_{\text{AS}}(\%)\), \(\text{ERF}_{\text{joint}}(\%)\) and min-DCF. The FGSM and PGD attacks employs their best attack configuration (\(e=1.0\)). (\(\%\)) The \(\text{ERF}_{\text{AS}}\)v metric is evaluated considering both _bm/dist_ and _sproking_ utterances.
## 5 Evaluation Protocol and Results
### Evaluation Protocol  
#### 5.1.1 Evaluation Method  
Ten-fold cross-validation was used to evaluate the proposed method, where the dataset is divided into ten folds. Each time, nine folds (90% of the data) are used for training, and one-fold is used for testing. This process is repeated for each fold. Therefore, all folds are used for training and testing. The training set was further divided to 10% for validation and 90% for training the model.
## 2 Time-Frequency Representations for Vibration Signals
### Spectrogram for the Vibration Signal  
The spectrogram is a time-frequency representation of a 1-D signal. Such bi-dimensional representation is calculated by capturing the time-shift invariant description of the signal delimited by a window of finite time length. Details concerning their calculation are reported in [40]. Denoting the vibration signal as \(x\), the spectrogram is calculated by sliding a window filter \(W\) over the signal considering a stride value:  
\[\hat{x}(l,k)=\sum_{n=0}^{N_{w}-1}x_{l}(n)W(n)e^{-j2\pi nk/N_{w}} \tag{1}\]where \(l\) and \(k\) are in the range \(0\leq l<L\); \(0\leq k<K\), and \(N_{w}\), is the dimension of the processing Hanning window \(W(.)\) and \(x_{l}(n)\) is a fragment of the signal \(x\) covered by the window. The Hanning window [41] is calculated as:  
\[W(n)=0.5-0.5 cos\{\frac{2\pi n}{N-1}\},\ \ \ \ \ \ 0\leq n\leq N-1 \tag{2}\]  
The energy spectrum is given by:  
\[X_{l,k}=|\hat{x}(l,k)|^{2},\ \ \ \ \ \ \ \ 0\leq l<L,\ \ \ \ 0\leq k<K \tag{3}\]  
where \(L\) is taken as \(N_{w}/2\) as only half of the spectrum is considered. \(K\) represents the number of temporal frames, and its value depends on the window length, signal length, and stride value. A limitation of this representation is related to stability concerning the variability of the signal due to time-warping deformations that can have an important impact on classification results [42].
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 4 Experimental Results and Analysis
### Experimental Results of Synthesizing Uniform Haze Images
Table 1 shows the quantitative evaluation of different defogging methods on the SOTS dataset. It can be seen that the quantitative evaluation metrics of the recovered results  
Figure 4: The experimental results of synthesizing homogeneous hazy images. (**a**) Hazy image; (**b**) method in [5]; (**c**) method in [6]; (**d**) method in [11]; (**e**) method in [12]; (**f**) method in [15]; (**g**) proposed method; (**h**) standard haze-free image. using [4; 5] were low, while the quantitative evaluation metrics of the recovered results using these deep learning methods in [9; 10] were improved to some extent. The algorithm in this paper was 6.36 and 0.08 higher than that in [9] in the PSNR and SSIM, respectively, and only below that in [15] in both the PSNR and SSIM. Therefore, from the combination of the subjective comparison of the above algorithms and objective indicators, it can be concluded that the algorithm in this paper can better remove the thin fog. Moreover, the integrity of image information is retained, which is closer to the real fog-free image.
## 2 Analysis of Two-Step Method  
Let the scalar function \(f:\mathbb{R}^{d}\rightarrow\mathbb{R}\) from \(\mathcal{F}_{l,L}^{1,1}\) be considered. We try to find its minimizer \(x^{*}\). So the unconstrained minimization problem is stated:  
\[f(x)\rightarrow\min_{x\in\mathbb{R}^{d}}. \tag{1}\]  
The gradient descent method (GD) for numerical solution of (1) is written as  
\[x^{k+1}=x^{k}-h\
abla f(x^{k}), \tag{2}\]  
where \(h>0\) is a step. If we additionally propose that \(f(x)\in\mathcal{F}_{l,L}^{2,1}\), the optimal step and convergence rate for (2) are presented as in [7]  
\[h_{opt}=\frac{2}{l+L},\quad\rho_{opt}=\frac{\kappa-1}{\kappa+1},\]  
where \(\kappa=L/l\) is the condition number and \(L,l\) can be associated with the minimum and maximum eigenvalues of a Hessian of \(f(x)\).  
[PERSON]'s heavy ball method is presented as in [7; 8]  
\[x^{k+1}=x^{k}-h\
abla f(x^{k})+\beta(x^{k}-x^{k-1}), \tag{3}\]  
where \(\beta\in[0,1)\) is the momentum. The optimal values in the case of strongly convex quadratic function are written as in [7]  
\[h_{opt}=\frac{4}{(\sqrt{L}+\sqrt{l})^{2}},\quad\beta_{opt}=\left(\frac{\sqrt{ \kappa}-1}{\sqrt{\kappa}+1}\right)^{2},\quad\rho_{opt}=\frac{\sqrt{\kappa}-1} {\sqrt{\kappa}+1}.\]  
[PERSON] et al. [28] proposed the following method with an additional momentum parameter:  
\[x^{k+1}=x^{k}-h\
abla f(y^{k})+\beta_{1}(x^{k}-x^{k-1}),\quad y^{k}=x^{k}+ \beta_{2}(x^{k}-x^{k-1}). \tag{4}\]  
As can be seen, for the case of \(\beta_{2}=0\), method (4) leads to (3). In [29], the global convergence of this method for \(f(x)\in\mathcal{F}_{l,L}^{1,1}\) with the convergence rate \(\rho\in\left[1-\frac{1}{\sqrt{\kappa}},1-\frac{1}{\kappa}\right]\) is demonstrated for the following specific choice of parameters:  
\[h=\frac{\kappa(1-\rho)^{2}(1+\rho)}{L},\quad\beta_{1}=\frac{\kappa\rho^{3}}{ \kappa-1},\quad\beta_{2}=\frac{\rho^{3}}{(\kappa-1)(1-\rho)^{3}(1+\rho)}.\]  
In the theoretical part of the presented paper, we try to analyze the influence of parameter \(\beta_{2}\) on the convergence of method (4) for the case of a quadratic function, written as  
\[f(x)=\frac{1}{2}(x,Ax)-(b,x), \tag{5}\]  
where \(b\in\mathbb{R}^{d}\), \(A\) is a positive definite and symmetric matrix with eigenvalues \(0<l=\lambda_{1}\leq\lambda_{2}\leq\ldots\leq\lambda_{d}=L\). The gradient of this function is computed as \(\
abla f(x)=Ax-b\), and \(x^{*}\) is treated as the solution of the linear system \(Ax=b\). The obtained results can be considered as the results of the _local_ convergence of method (4), applied to \(f(x)\in\mathcal{F}_{I,L}^{2,1}\), because in the neighborhood of \(x^{*}\ f(x)\) from this class can be presented as (5) with \(A=\
abla^{2}f(x^{*})\). This approach for obtaining local convergence conditions and optimal parameters values is widely used in literature [7; 18].  
Method (4), when applied to (5), leads to the following difference system:  
\[x^{k+1}=(E-hA)x^{k}+(\beta_{1}E-\beta_{2}hA)(x^{k}-x^{k-1})-hb, \tag{6}\]  
where \(E\) is the unity matrix.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 2 Related Work
They performed a simulation study to evaluate the proposed expected transmission count metric-based strategy in terms of energy consumption, packet delivery rate, throughput, and rank change in the network. However, mobility was not addressed. Additionally, a hardware security chip is required along with the nodes. In [18], the authors have addressed transmission attacks in vehicular ad hoc IoT networks using a trust-based technique. However, the routing attacks were not considered and it was limited to vehicular ad hoc networks. Similarly, in [19], the authors have addressed security issues in vehicular ad hoc IoT networks using a trust-based protocol for jamming attacks and identifying malicious nodes in such IoT-enabled networks. However, RPL attacks were not considered in the proposed approach. Numerous review studies and surveys have been conducted for exploring different techniques to address the security attacks in IoT and RPL-based IoT. For instance, in [20], the authors have performed a systematic literature review of ML and DL strategies for attack detection in RPL-based IoT. Similarly, in [21], the authors have conducted a detailed survey for the evaluation of RPL attacks. They have also assessed various detection and mitigation methods using RPL control messages. Table 1 presents a summary of the related works in addition to the dataset(s) used, methodology, attacks considered, and limitations or research gaps. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Dataset** & **Methodology** & **Evaluation** & **Limitation/Gap** \\ \hline \multirow{3}{*}{[13]} & \multirow{3}{*}{Self-generated} & An ML-based model for the detection of version number & Accuracy, precision, recall, F1 score, log loss & SN-inherited attacks were not considered, and mobility was not considered \\  & & & \\  & & & \\ \hline \multirow{6}{*}{[14]} & \multirow{3}{*}{Self-generated} & A DL model using a gated & \multirow{3}{*}{Accuracy, mean squared error, mean absolute error, root mean square error} & Protocol-specific attacks were not considered, mobility was not considered, DL-based methods require high computation and memory, scalability issues \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Related Works, Limitations, and Research Gaps.
## 3 Evaluation Approach
### Vertical Handover Decisions
#### 3.3.1 SAW  
The overall score of each candidate network is determined by the weighted sum of all attribute values. The score \(S_{SAW}\) of each candidate network \(C_{i}\) is obtained by summing the contributions of each \(n_{ij}\) normalized metric multiplied by the assigned importance weight \(w_{j}\). The selected network is:  
\[S_{SAW}=\max_{i}\sum_{j=1}^{M}w_{j}\cdot n_{ij} \tag{2}\]  
Such that, \(w_{j}\) is the weight vector taken from Table 3. \(n_{ij}\) is the value of normalized attribute \(j\) of network \(i\). \(N\) and \(M\) are respectively the number of candidates and the number of network attributes considered.
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
## 3 Prototype
### Software Components
#### 3.3.1 Internet of Things Device  
Regarding the IoT device (Raspberry Pi), all software utilized in its operation was developed using the Python language. This is because it already offers a plethora of user-friendly libraries for incorporating object detection algorithms, such as OpenCV [31] and CVLib [32]. The entirety of this device's operation is depicted in the flowchart in Figure 20. The sequence of actions is as follows: The YOLOv4-tiny model is loaded, followed by the GPS and camera modules, which are fundamental to the system's operation. The device then enters a waiting loop, waiting for the model to detect a crosswalk and its wear and tear. As soon as this process takes place, the current coordinates are read via the /dev/tyAMA0 serial port connected to the GPS module. Once the coordinates have been read, they are converted to UTF-8 encoding using the pymnea2 library [33]. The data are formatted in JSON, as expected by the API (detailed in the next section), and finally sent. It is important to note that an internet connection is essential for sending the data relating to crosswalk wear signs. In this sense, if the device loses connectivity during use and data needs to be sent, it will be stored locally.  
After each crosswalk detection, the system will ascertain the availability of local data, which will be transmitted in conjunction with the newly identified crosswalks.
# Imaging Diagnostics of Inside of a Building Wall Using Millimeter-Wave Reflectometer
## 5 Conclusions  
In conclusion, a reflectometric diagnostic system was developed to measure the inside of a building wall, that is, to inspect tile materials attached to the building wall. The measurement principle utilized the phase interference effect of the reflected wave due to the multiple reflections between the two layers (Fabry-Perot interferometry effect). The results show the imaging inside the surface related to the non-adhesive condition between the tile and concrete wall, and the quantitative evaluation of the adhesion ratio area within an accuracy of 4.1% of the actual one. In addition, it is posited that the evaluation of an adhesive state with a hollow concrete base (extruded cement panel) and tiles with organic glue using conventional hammering inspection methods is difficult, including a hammering robot. However, there are no difficulties with the microwave/millimeter-wave reflectometric method, which indicates the superiority of the present method.  
The development of a system such as the one proposed here is becoming important, since many constructions including buildings are aging. Since the present system has several advantages, it is expected to be applied to actual measurements in the field.
## 2 Proposed Preventive Control Strategy  
This work develops an underfrequency preventive control strategy for an isolated micro-grid or islanded power system with a high penetration of renewable generation, in which four different methods to provide frequency support is proposed and available to be implemented. The developed control strategy includes five main modules--the frequency nadir forecasting, GR, BESS, DLC and EDRP modules. Firstly, the frequency nadir forecasting module is used to predict the frequency nadir under an \(N\)-1 contingency event [1,2]. Generally, the \(N\)-1 contingency test has been utilized in many isolated island power systems to examine the system frequency stability. If the predicted frequency nadir is below the predetermined frequency threshold, then one of the four methods can be selected to provide frequency support to solve potential frequency stability problems.  
Following the proposed underfrequency preventive control strategy, next the optimal scheme for diesel generator re-dispatch, instantaneous active power output from BESS, contract-based load-shedding in DLC and contract-based load-reduction in EDRP are determined. The developed control strategy ensures the system is operating with sufficient frequency stability and minimal operating cost.  
The proposed underfrequency preventive control strategy is composed of several main steps--including the prediction of frequency nadir and four preventive control methods, that is, GR, BESS, DLC and EDRP for frequency support. Figure 1 shows the overall structure of the flowchart of the proposed control strategy.  
Figure 1: The flowchart of the proposed preventive control strategy including generator rescheduling (GR), battery energy storage system (BESS), direct load control (DLC) and emergency demand response program (EDRP) methods.
## 4 Results and Discussions  
Table 3 presents the RMSE, MAPE, and MAE results for the four deep-learning models (RNN-1-30, RNN-4-30, LSTM-1-30, and GRU-1-30), and the four machine-learning models (SVR-0.1-linear, Random Forest-3-50, Random Forest-6-50, and Random Forest-3-100) identified by the grid search method as well as by the manual and ARIMA benchmarks.  
Based on the RMSE metric, the deep-learning models outperformed the machine-learning models and the manual and ARIMA benchmarks. This behavior can be explained by the ability of deep-learning models have to achieve insights outside of the domain of training data. The GRU model presented the best performance of all models tested as well as reduced the complexity inherent in the other deep-learning models analysed; the simple RNN models presented the worst performance. In contrast, based on MAPE and MAE, the ARIMA model outperformed the deep-learning models, the machine-learning models, and the legacy manual approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **RMSE** & **MAPE (\%)** & **MAE** \\ \hline ARIMA & 0.0471 & 3.52 & 0.0249 \\ RNN-1-30 & 0.0316 & 4.42 & 0.0316 \\ RNN-4-30 & 0.0320 & 4.40 & 0.0320 \\ LSTM-1-30 & 0.0310 & 4.37 & 0.0310 \\ GRU-1-30 & 0.0305 & 4.33 & 0.0305 \\ SVR-0.1-linear & 0.0556 & 5.09 & 0.0400 \\ Random Forest-3-50 & 0.0561 & 4.94 & 0.0377 \\ Random Forest-6-50 & 0.0573 & 4.82 & 0.0356 \\ Random Forest-6-100 & 0.0580 & 4.83 & 0.0355 \\ Case site technique & 0.4119 & 51.61 & 0.4039 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE, MAPE, and MAE results for the selected analysed model.  
Table 4 presents the average inference times for the four deep-learning models (RNN-1-30, RNN-4-30, LSTM-1-30, and GRU-1-30), the four machine-learning models (SVR-0.1-linear, Random Forest-3-50, Random Forest-6-50, and Random Forest-3-100), as well as the manual and ARIMA benchmarks.  
With average inference times of 0.8 and 0.0085, respectively, the deep-learning and machine-learning models performed best as a whole; standard deviations were insignificant. Random Forest-3-50 is the model with the shortest average inference time of those compared, while the ARIMA model is the worst performing when compared to the machine-learning and deep-learning models. Although achieving good RMSE, MAPE, and MAE results, the ARIMA inference time is much longer than the deep-learning models, a significant limitation  
Figure 10: Daily load forecasting using (**a**) the SVR-0.1-linear model, (**b**) the Random Forest-3-50 model, (**c**) the Random Forest-6-50 model, and (**d**) the Random Forest-6-100 model.  
Figure 9: Daily load forecasting using (**a**) the RNN-1-30 model, (**b**) the RNN-4-30 model, (**c**) the LSTM-1-30 model, and (**d**) the GRU-1-30 model.
###### Abstract  
The primary objective of this study was to identify and characterize pathogen defense proteins in the _Nicotiana tabacum_ L. proteome, focusing on their structural, functional, and evolutionary properties, as well as their interactions with pathogen-derived molecules. Specifically, we aimed to comprehensively analyze the proteome to pinpoint potential uncharacterized defense-related protein that has emerging roles in immune responses and antioxidant activity across plants and animals. Through integrated computational approaches, we determined evolutionary relationships, and structural modeling of the selected protein was performed using different modeling software, followed by validation through multiple metrics, including stereochemical checks (Ramachandran plot), Mol-Probity analysis, and \(Z\)-scores. We further investigated the functional binding regions or interaction sites. We performed molecular docking to investigate the molecular interactions between selected proteins and pathogen-associated molecular patterns (PAMPs), specifically \(\beta\)-glucan and peptidoglycan (PGN), to elucidate their defensive mechanisms. Last, normal mode analysis (NMA), molecular dynamics simulation (MDS), and post-simulation analyses were employed to evaluate the stability and mobility of the protein-ligand complexes. Uncharacterized vitellogenin-like protein (VLP: ID A0A1S4 CXB2) with the potential defense domain chosen because of its predicted immune-related features, stress response patterns, and unknown pathogen role at new immunity functions. Phylogenetic analysis revealed significant sequence homology with VLPs from other members of the Solanaceae family. Structural modeling showed a high-quality model, with docking studies indicating a stronger affinity for PGN (\(-\)10.16 kcal/mol) and \(\beta\)-glucan (\(-\)7.19 kcal/mol), highlighting its potential involvement in pathogen defense. NMA, MDS, and post-simulation analyses revealed that PGN exhibits more substantial binding stability and more extensive interactions with VLP than \(\beta\)-glucan. Our findings confirmed that VLPs in _N. tabacum_ may function as pattern recognition receptors (PRRs), capable of recognizing and responding to pathogens by activating immune signaling pathways. Future experimental validation of these interactions could further elucidate the role of VLPs in plant defense and their potential application in biotechnological approaches for sustainable agriculture.  
v tiledogenin-like protein; pathogen defense; _Nicotiana tabacum_ L.; bioinformatics; molecular docking; simulation +
Footnote †: journal: Applied Sciences