esa-sceva/satcom-chunk-collection huggingface dataset:
# Adversarial Training for Mitigating Insider-Driven XAI-Based Backdoor Attacks
## 3 Proposed Approach
### Adversarial Training  
This section explains how adversarial training validates the robustness of the model. Here, we perform adversarial training using GAN-generated synthetic samples, focusing on reducing the extreme class imbalance that hinders the usage of efficient threat detection methods for insider analysis. Adversarial samples are merged into the original training set to perform adversarial training.  
In the paper [7], we proposed a generative adversarial model named the CWGAN-GP (conditional Wasserstein GAN with gradient penalty) for adversarial training to improve the robustness of insider threat identification, thereby reducing the effects of data imbalance. This GAN model generates realistic synthetic data to help machine and deep learning classifiers generalize more effectively. We evaluated multiple classifiers, including linear, non-linear, and ensemble algorithms, in that work.  
However, in this study, the focus is on using DL models for threat identification and their robustness to an insider-driven backdoor attack using XAI. Figure 3 shows the adversarial training process in the presence of the backdoor and synthetic data for data augmentation and the original training data.  
We study the impact of a backdoor attack on the adversarially trained models used for insider threat detection, assuming that the attacker can somehow be an insider related to the organization.  
Figure 3: Adversarial training in the presence of backdoor and synthetic data, and the original training data.  
We designed multi-class classification DL models in such a way that the goal is to construct a function that, given a new data point, will correctly predict the class to which the new point belongs. It can be denoted as \(f(x,\theta)=y\). An adversarially trained model merges a subset of original training data \(X^{Train}\) and the synthetic data \(X^{Synthetic}\) to perform the model training.  
The distribution of data samples affects the decision boundary of the classification model, and adding new data samples distorts the original decision boundary. The adversarial training proved to result in complex decision surfaces, such that the adversarial samples are also accommodated in the updated model. This is how AT models become resistant to adversarial attacks. Data poisoning occurs during training time, so this type of backdoor attack aims to affect the model. Hence, AT models should be able to resist the data poisoning samples.
## 3 Numerical Modelling and Analysis
### Nonlinear Static Analysis
Lastly, N is the total number of discrete locations where the weight and pushover mode shape are considered. Figure 9: Pushover curve and bilinearized capacity curves. To transform a demand spectrum from the S\({}_{\text{a}}\) and T format into the ADRS format, it is necessary to calculate the S\({}_{\text{d}}\) value for each point on the curve via the specified equation. \[S_{d}=\frac{1}{4\pi^{2}}S_{a}T^{2}, \tag{6}\]  
From the analysis conducted, the capacity curve is translated into spectral acceleration and spectral displacement coordinates. This transformation gives rise to the capacity spectrum, which serves as a tool to assess the intended seismic performance of the structure. By overlaying the demand spectrum onto the capacity spectrum, we accelerate this process. According to the data depicted in the accompanying diagram, the structure demonstrates substantial deformation capability up until a displacement of approximately 0.08 m. This is the point at which the structure's stiffness starts to deteriorate significantly, marking the transition into the ductile behavior phase. Tables 5 and 6 provide the values for the PF\({}_{1}\) and \(\alpha_{1}\) for the structure. Eventually, the structure succumbs to failure when the displacement reaches 0.29 m. At this juncture, the base shear, which is the total horizontal force experienced by the structure, amounts to 10,871.69 kN. This level of force, combined with the deformation experienced, leads to the permanent collapse of the structure. This is depicted in Figure 10. Beyond the standard steps of the pushover analysis, special attention has been given to potential shear failures. As the structure is loaded incrementally, the shear demand in each member is compared with its shear capacity. Shear vulnerabilities, especially in critical zones such as beam-column junctions and wall bases, are monitored closely. Identifying and addressing these vulnerabilities is paramount, as premature shear failures can drastically alter the seismic response of the structure.
## 4 Dual-Band Passive Metasurface Based Beam Steering Techniques
### A Comparative Analysis of Antenna Beam Steering Based on RAs, TAs and NFMS  
Figure 4 illustrates the three different steering techniques discussed earlier in this section, showcasing the difference in steering methods based on their structure and operating principles.  
In RAs, the reflection magnitude of the metasurface is always one (0 dB) due to the reflections of the entire incident wave from a metal ground plane; thus, only the reflection phase of the elements can be controlled. RAs also suffer from feed blockage issues [74]. Furthermore, most RAs have a bandwidth limitation of around 10% or less [74]. The feed  
Figure 4: Dual-band metasurface based beam steering techniques.  
must also be maintained at a specific distance from the reflecting surface to achieve better aperture illumination and higher gain. One advantage of RAs is that they can combine the positive features of both reflectors and phased arrays without needing additional phase shifters or power dividers [75].  
The issue of feed blockage is resolved in TAs, as the metasurfaces are used in transmission mode, allowing planar beam steering antenna system designs for single-band, broadband, and dual-band applications with high gain while maintaining the pattern shape during beam steering. One disadvantage associated with TAs involving the feed-tuning technique is pattern degradation due to phase alteration during feed displacement. Another disadvantage is the requirement of placing the metasurface at a focal distance from the feed to maintain the F/D ratio, which notably hinders size reduction in TAs.  
An advantage of NFMS systems over TAs is a significantly reduced overall system height since the metasurfaces are placed in the near-field region of the antenna. That said, dual-band high-gain NFMS systems are not as extensively explored in the literature compared to TAs. One future research direction could be to investigate dual-band and broadband NFMS systems for high-gain applications suitable for SOTM applications.  
In the three steering mechanisms discussed in this section, the fundamental component of the system facilitating beam steering is a free-standing metasurface placed at various locations with respect to the feed, distinguishing the steering mechanisms from one another. In a RA, such a surface is used in reflection mode with a reflection coefficient close to 1 (0 dB), while in TA or NFMS systems, highly transmitting metasurfaces are used with a transmission coefficient close to 1 (0 dB). This review article further discusses various passive highly transmissive dual-band metasurfaces used in the literature to design the full-duplex beam steering antenna systems, i.e., TAs-based beam steering antennas and NFMS systems. These dual-band metasurfaces have been referred to as Phase Shifting Surface (PSS) [76; 77; 78], Phase-Gradient Metasurface (PGM) [24; 79], time delay metasurface [23], discrete lens, planar lens, and flat lens [22; 32] in the literature. In this article, these metasurfaces are consistently referred to as PGMs.
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 5 Guarding against Load Altering Attacks
### Transactive Energy to Guard against Load Altering Attacks  
The flexibility of the flexible loads in a power grid can be utilized to thwart a load altering attack on the power distribution system. Toward this end, the power consumption of the flexible loads should be reduced when the frequency of sinusoidal voltages drops below the rated frequency of 60 Hz. Similarly, the power consumption of the flexible loads should increase when the frequency of sinusoidal voltages surges above the rated frequency of 60 Hz. The adjustments in power consumption of the flexible loads should be made in such a way that, the responses to the frequency disturbances stay in-proportion to the frequency disturbances. Otherwise, the adjustment in power consumption of the flexible loads may transform an over-supply of energy to an under-supply of energy, and vice versa. Therefore, when reacting to the frequency disturbances there is a need for coordination between flexible loads and the power system operator.  
To ensure such coordination, the power system operator should be able to notify the flexible loads about the required adjustment in power consumption of the distribution system. The flexible loads also should be able to indicate their willingness to adjust their power consumption. Once these information are collected, an adequate number of flexible loads among all the available flexible loads should be selected to perform the adjustments in their power consumption. Finally, the above process should be repeated on a moment-to-moment basis, so as to allow update of stakeholders' responses to moment-to-moment changes in voltages' frequencies. Fortunately, all the above prerequisites for ensuring the coordination between stakeholders are accessible in a transactive energy framework.  
In the transactive energy framework, an electricity market runs in the power distribution system and enables real time and peer-to-peer energy exchanges between the power consumers and the power system operator [27]. By deploying the transactive energy framework, the power system operator can purchase energy from power consumers when the frequency of sinusoidal voltages drops below the rated frequency. Similarly, the power system operator can sell energy to the power consumers when the frequency of sinusoidal voltages surges above the rated frequency. The transactive energy framework is precisely explained in [27; 46]. The next section provides a brief summary of the discussion in [27; 46] as is pertinent to this paper.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### 5 GCAR Use Case Technical Requirements
#### 4.3.5 Remote Driving for Automated Parking  
Automated parking is a distinctive subcase of the remote driving class, involving the operations that are necessary for remotely driving a vehicle on the \"last mile\" towards a parking place, and completing the parking operations itself. The most critical automotive requirement is localization, as the remote driving for parking can take place in covered places and, therefore, cannot only rely on GPS. Parking maneuvers could require a high precision localization and identification of obstacles, in the order of 50 cm to be able to avoid collisions. Moreover, the network requirements are particularly strict since the driving is conducted by a remote server based on real-time sensor information collected by the vehicle: notably, a high-data-rate, low-latency video, and sensor data flow are required in the uplink, requiring a data rate between 14 and 29 Mbps, estimated based on similar methods as for the see-through use case. An overall latency of 300 ms is needed for an urban environment, 200 ms for the server processing time, and 100 ms for the round trip. For the uplink, the need is lower due to the continuous data aggregation and trajectory prediction. For the downlink, as a reference of the vehicle commands, the repetition rate of the steering wheel sensor is 10 ms, and then 5 ms latency will be expected. Simultaneously, an ultra-high reliability flow of 99.999% is required to coordinatethe information received from the remote server with the control commands sent to the actuators. An availability of 99.999%, equivalent to a downtime around five minutes per year, must be maintained since the use case involves the last mile remote driving.
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 5 Discussion
### Advantages of Smart Helmet-Based PWS  
Personal PWSs using a smart helmet have the following notable advantages at the mining site. First, this system can solve the problems that occurred in existing PWSs. The driver had to repeatedly check the smartphone to receive the proximity warning alerts. It caused a decrease in the operators' concentration on driving. Smart glasses caused discomfort when workers wore regular glasses or when they slipped. On the other hand, smart helmets can provide visual proximity alerts to both the operator and the pedestrian without work interruption, enabling quick identification of dangerous situations and quick evacuation. Workers who are wearing regular glasses, industrial goggles, and soundproof headsets can also wear smart helmets without discomfort.  
Second, smart helmet-based PWS is relatively easy to use for workers. Operators and workers who use existing PWSs need to operate a touchpad controller to execute PWS application. However, subjects who participated in the NASA-TLX test tended to  
Figure 13: Results of the overall workload score assessment. (**a**) Overall workload score according to the type of experiment on the driver side; (**b**) overall workload score according to the type of experiment on the pedestrian side.  
feel difficulty in such an operation. On the other hand, the smart helmet-based PWS is a relatively convenient and easy to execute system because only a power supply is required.  
Finally, the proposed personal PWS can be implemented and utilized in the mining site at a relatively low cost. Since this system utilizes Arduino, an open-source hardware, relatively low cost is required to constitute system components (i.e., microcontroller board and sensors). Therefore, it is possible to distribute multiple sets of smart helmets and Bluetooth beacons to the worksite, regardless of the size of the mine.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 8 Results and Discussion
Figure 13: \(PV\) curve obtained from simulation for Module AS-M3607-S (G1 CELLS) at various temperatures ranging from \(-30\)\({}^{\circ}\)C to \(70\)\({}^{\circ}\)C at a fixed irradiance of 1000 W/m\({}^{2}\) using the Hybrid BESSA technique. Figure 15 shows a comparison of the experimental curve with the Hybrid BESSA simulated power-voltage characteristics and the BES simulated power-voltage characteristics. The simulation results obtained by applying the Hybrid BESSA and BES techniques were discussed in the earlier section. It is evident that the PV module simulated parameters extracted using the Hybrid BESSA technique yielded a better result and better fit with a lower degree of error when compared against the hardware results and those to obtained using the BES technique. The Hybrid BESSA simulated power-voltage characteristics more closely resembled the hardware results. This was also evident from the simulation result analyses. As depicted in Tables 10 and 11, the fitness function value derived using the Hybrid BESSA technique was less than that of the BES technique. In the same way, the percentage of error obtained for the open circuit voltage, output power, and short circuit current was less when derived using the Hybrid BESSA in comparison with the BES technique. Thus, it has been proven that extracted parameter values derived using the Hybrid BESSA technique have better modeling capability when compared to those of the BES technique, proving its superiority both theoretically and experimentally. This is due to the hybridization of the BES and SA techniques. This process embodies the hybridization of a global search with local searches.
# Interpretability Study of Gradient Information in Individual Travel Prediction
## 2 Related Work
### Post Hoc Interpretability Framework
These perturbed samples are passed through the black-box model \(f\), yielding corresponding predictions \(f(z_{i})\), forming a new dataset \(\{(z_{i},f(z_{i}))\}\). Based on the perturbed dataset, LIME trains an interpretable linear model by minimizing the weighted loss function as follows:  
\[\underset{g\in G}{\text{argmin}}\sum_{i}\pi_{x}(z_{i})[f(z_{i})-g(z_{i})]^{2}+ \Omega(g) \tag{8}\]  
where \(\pi_{x}(z_{i})\) is sample weight function that responds to the similarity between the perturbed sample \(z_{i}\) and the original sample \(x\), \(\Omega(g)\) is regularization, and \(G\) is a set of simple models. LIME was originally designed to explain a single prediction, but through sensitivity analysis of perturbations, it can identify the most influential features for a particular prediction, indirectly revealing the key factors on which the decision depends [31; 32; 33]. However, the quality of LIME's explanations depends on the reasonable choice of perturbation range and the proper definition of sample weights, and it cannot reveal the temporal dependencies in travel prediction tasks when revealing relationships between different features. Compared to the methods mentioned above, the gradient-based saliency method introduced in this paper not only explains the static feature distribution on which the model's overall decision depends, but also identifies the key historical input points for specific prediction samples. Unlike TimeSHAP, which requires exhaustive evaluation of the marginal contributions of all feature coalitions, this method only requires backward propagation to obtain the feature importance scores for the sequence, with computational complexity linearly related to the number of model parameters. To quantify this computational advantage, we selected 1000 samples and compared the time consumption for explaining each sample. The experimental results show that the average explanation time for TimeSHAP is 38.18 s, while the proposed method only takes 3.15 s, reducing the time cost by approximately 91.7%, improving computational efficiency by an order of magnitude, and providing the possibility for real-time prediction explanations in the future. Table 1 shows a comparison between the proposed method and the current mainstream interpretability solutions in the field of travel prediction, emphasizing its progress in computational efficiency and spatiotemporal dependency analysis.
# Broadband DOA Estimation by Exploiting DFT Extrapolation
## 2 DFT Extrapolation  
### Theoretical Background  
DFT extrapolation is an iterative approach suitable for spectral estimation [11]. It is performed using an algorithm capable of reconstructing an extended series of information from sampled data. In this approach, Fourier transform estimation is performed via linear inversion using original data and the minimization of a cost function [12].  
The following is an explanation of the aforementioned algorithm. Assume that a length-\(N\) time series \(x_{0},\ x_{1},x_{2}\ \ldots,\ x_{N-1}\) is sampled. Using zero padding, \(N\) sampled series is converted into \(M\) spectral samples:  
\[\mathbf{x}=\mathbf{F}\mathbf{X}, \tag{1}\]  
where \(\mathbf{x}\in\mathbb{R}^{N}\) and \(\mathbf{X}\in\mathbb{C}^{M}\) denote the sampled information and the unknown DFT, respectively, and \(N\times M\) matrix \(\mathbf{F}\) has elements \(F_{n,k}=(1/M)e^{i2\pi nk/N}\). To make the solution unique, the \(l_{2}\) norm cost function is augmented with a selected regularizer \(\Phi(\mathbf{X})\), and is minimized. The augmented cost function \(\mathrm{J}(\mathbf{X})\) is expressed as follows:  
\[\mathrm{J}(\mathbf{X})=\Phi(\mathbf{X})+\|\mathbf{x}-\mathbf{F}\mathbf{X}\|_ {2}^{2}, \tag{2}\]  
where \(\|.\|_{2}^{2}\) stands for \(l_{2}\) norm. We set the regularizer as a Cauchy distribution, which requires iteration [13; 14; 15]. The Cauchy regularizer can be expressed as follows:  
\[\Phi(\mathbf{X})=\sum\
olimits_{k=0}^{M-1}\ln\left(1+\frac{X_{k}X_{k}^{*}}{2 \sigma_{X}^{2}}\right), \tag{3}\]  
where \(\sigma_{X}\) is a parameter of the prior distribution of \(p(X|\sigma_{X})\). Let us assume that the data are contaminated by noise having a distribution of \(N\big{(}0,\sigma_{n}^{2}\big{)}\). Equating the derivatives of the augmented cost function to zero yields  
\[\mathbf{X}=\Big{(}\lambda\mathbf{Q}^{-1}+\mathbf{F}^{\mathrm{H}}\mathbf{F} \Big{)}^{-1}\mathbf{F}^{\mathrm{H}}\mathbf{x}=\mathbf{Q}\mathbf{F}^{\mathrm{H }}\Big{(}\lambda\mathbf{I}_{\mathrm{N}}+\mathbf{F}^{\mathrm{H}}\mathbf{Q} \mathbf{F}\Big{)}^{-1}\mathbf{x}=\mathbf{Q}\mathbf{F}^{\mathrm{H}}\mathbf{b}, \tag{4}\]  
where \(\lambda=\sigma_{n}^{2}/\sigma_{X}^{2}\), \(\mathbf{I}_{\mathrm{N}}\) is an \(N\times N\) identity matrix, \(\mathbf{Q}\) is an \(M\times M\) diagonal matrix with \(Q_{kk}=1+X_{k}X_{k}^{*}/2\sigma_{X}^{2}\), and \(\mathrm{H}\) is the Hermitian transpose. The algorithm starts with the DFT of \(\mathbf{x}\), \(\mathbf{X}^{(0)}\), which generates matrix \(\mathbf{Q}^{(0)}\). We can obtain the updated DFT by computing Equations (5) and (6), as follows:  
\[\mathbf{b}^{(\mu-1)}=\Big{(}\lambda\mathbf{I}_{N}+\mathbf{F}^{\mathrm{H}} \mathbf{Q}^{(\mu-1)}\mathbf{F}\Big{)}^{-1}\mathbf{x} \tag{5}\]  
\[\mathbf{X}^{(\mu)}=\mathbf{Q}^{(\mu-1)}\mathbf{F}^{\mathrm{H}}\mathbf{b}^{(\mu -1)}, \tag{6}\]  
where \(\mu\) is the number of iterations. The iteration process is stopped when the (27) of [11] is satisfied.
# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
###### Abstract
Moreover, the issue is also complicated by the fact that sound information is usually available at different time scales that cannot be captured by the STFT. Motivated by these considerations, in this paper, we propose a new approach for the automatic monitoring of construction sites based on CNNs and scalograms. The scalogram was defined as the squared magnitude of the Continuous Wavelet Transform (CWT) [23]. By overcoming the intrinsic time-frequency trade-off, the scalogram is expected to offer an advanced and robust tool to improve the overall accuracy and performance of ACSM systems. In addition, the wavelet transform allows to it work at different time scales, which is a useful characteristic for the processing of audio data. Hence, the main idea of the paper is to use the scalogram instead of the spectrogram as the input to a CNN-based deep learning model. Although the methodology is not new, the proposed idea has been extensively tested on real data acquired in construction sites and, compared to most popular state-of-the-art methodologies, shows clear and significant improvements. The rest of this paper is organized as follows. Section 2 shows the related work. Section 3 introduces the CWT, while Section 4 describes the proposed approach. Then, Section 5 explains the adopted experimental setup. Section 6 describes some implementation aspects, while Section 7 shows the obtained numerical results and confirms the effectiveness of the proposed idea. Finally, Section 8 concludes the work and outlines some hints for future research.