esa-sceva/satcom-chunk-collection huggingface dataset:
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 5 Current Anti-Phishing Methodologies and Techniques
### Traditional Non-Computerized Anti-Phishing Techniques
#### 5.1.1 Legal  
One way to prevent phishing attacks is to have the proper legal recourse against these phishing attacks. However, legislation relating to this was slow to catch on, with the first instance of this being shown in 2005 in the state of California in the USA, with some other states such as Texas following suit. However, the federal government (and most other states) have not passed legislation regarding phishing attacks specifically and instead choose to prosecute offenders under more general computing laws such as fraud.  
The United Kingdom followed suit, enacting more severe sentences for cybercrime, including fraud and identity theft. Under the fraud act established in 2006, computer-aided fraud can result in up to 10 years in prison. This act also includes statutes that prevent the owning of a phishing site with the intent to deceive users and commit fraud [72]. Canada also adopted an anti-spam act in 2010 that seeks to protect Canadians from cybercrime.  
The US company Microsoft has taken a stance against phishing, collaborating with governments outside the US to help prevent phishing attacks and bring justice to those perpetrating them. For example, in 2011, Microsoft signed an agreement with the Australian government to prevent phishing by training law enforcement officials.  
Whilst these laws will act as a deterrent for more casual phishers, more serious threats like advanced persistent threats will not be threatened by these laws. As such it is vital other methods of phishing prevention are developed and implemented.
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
###### Abstract  
Wireless Multimedia Sensor Networks (WMSNs) based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in detecting intrusions in selected monitored areas. The IEEE 802.11-based WMSNs offer high bit rate video transmissions but are challenged by energy inefficiency issues and concerns. To resolve the energy inefficiency challenges, the salient research studies proposed a hybrid architecture. This newly evolved architecture is based on the integration of IEEE 802.11-based mesh WMSNs along with the LoRa network to form an autonomous and high bitrate, energy-efficient video surveillance system. This paper proposes an energy-aware and Quality of Service (QoS) routing mechanism for mesh-connected visual sensor nodes in a hybrid Internet of Things (IoT) network. The routing algorithm allows routing a set of video streams with guaranteed bandwidth and limited delay using as few visual sensor nodes as possible in the network. The remaining idle visual sensor nodes can be turned off completely, and thus it can significantly minimize the overall energy consumption of the network. The proposed algorithm is numerically simulated, and the results show that the proposed approach can help in saving a significant amount of energy consumption while guaranteeing bandwidth and limited delay.  
wireless mesh network; energy efficiency; quality of service (QoS); energy-aware routing; IEEE 802.11; Internet of Things (IoT) network +
Footnote †: journal: Computer Physics Communications  
1
## 2 Materials and Methods
### Geomagnetic and Geophysical Conditions  
The geomagnetic data downloaded from OMNIWeb Plus Data ([[https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)]([https://omniweb.gsfc.nasa.gov](https://omniweb.gsfc.nasa.gov)), last accessed on 11 May 2022) for the 10 June 2021 annular solar eclipse indicates a period of low activity. Except in DoY 163, where it was 4\({}^{-}\) between 3 and 6 UT, the estimated 3-hour planetary index (Kp) was \(\leqslant\)3\({}^{-}\). The disturbance storm time index (Dst) peak was \(>\)\(-\)17 nT, except after DoY 162 where a minimum of \(-\)37 nT was reached at 11 UT. The interplanetary magnetic field (IMF) Bz component in GSM coordinate peak was \(>\)\(-\)7.4 nT after DoY 162 and the solar wind speed (Vsw) was 330-520 km/s during 8-13 June 2021 (see Figure 2).  
Figure 2: Variations of 3-hourly Kp, Dst, IMF-Bz, AE, and Vsw indices that characterize the geomagnetic conditions on 8–13 June 2021. P1–P4 time (light grey bar) and GE time (red dashed line) are also represented.  
The Auroral Electrojet index (AE) is a good proxy of the geomagnetic activity level at mid/high latitudes [29; 45]. Following [PERSON] et al. [46], we selected two distinct datasets corresponding, respectively, to geomagnetically quiet (AE \(<\) 50 nT) and active (AE \(>\) 300 nT) periods. Figure 2 also illustrates that the AE index was over 500 nT between 5 and 15 UT on DoY 162, and between 1 and 5 UT on DoY 163; so these time periods showed some activity in the auroral regions. Since these days are used by comparison with the day of the eclipse, these periods of time were treated with care to avoid interfering with the eclipse analysis.  
Therefore, the geomagnetic conditions were generally quiet, except on DoY 162 where a weak geomagnetic storm took place between 8 and 16 UT. DoY 162 did not cause problems in the ionospheric TEC background to our results for the eclipse day. However, the geomagnetic activity the day after the eclipse had significant effects on GNSS positioning errors comparable to the positioning errors caused by the annular solar eclipse. These effects will be presented in more detail in the coming section.  
Earthquake Occurrence  
We also reviewed the occurrence of earthquakes (EQs) around the world, with a moment magnitude greater than 5 Mw and a depth of over 70 km on 8-13 June 2021. This review is important because EQs are sources of TEC disturbances and thus positioning errors. In the period analyzed, 15 moderate EQs of less than 5.7 Mw occurred ([[https://earthquake.usgs.gov](https://earthquake.usgs.gov)]([https://earthquake.usgs.gov](https://earthquake.usgs.gov)), accessed on 17 January 2022). However, none of them produced noticeable effects on TEC or on the position estimation on the GNSS receivers during the analyzed period of days.
## 3 Results
### Performance Analysis
#### 3.3.2 ROC Curves of the Models Trained Using the Mode Matrix Every 3 Years  
A ROC curve plot was generated to analyze how well the models differentiate between class 1 (mortality) and class 0 (survival). Figure 3 shows the ROC curve of the random forest model that receives the mode matrix every three years as input. The value of 0.8 means that this model has an 80% probability of distinguishing between positive and negative classes. Considering that the objective was to create a mortality predictor as accurately as possible using CHS, it is clear that the random forest model performs a good prediction with this result. Figures 4 and 5 show the ROC curves of SVM and logistic regression, respectively. With SVM, there is a ROC of 0.76, lower than the RF result but still a good result. Finally, the ROC of logistic regression is 0.74, leading to the conclusion that the logistic regression model has the worst performance.
## 1 Introduction
### Simulation Tools to Improve PEPT Measurements  
The Monte Carlo software GATE (Geant4 Application for Tomographic Emission) [31; 32; 33] has been applied to PEPT measurements to explore different aspects of a PEPT measurement and the corresponding uncertainty [34; 35; 24]. GATE is built upon the Geant4 (GEometry ANd Tracking 4) toolkit developed at CERN [36]. GATE simulations are improving the treatment of PEPT data, specifically for the quantification of uncertainty in a PEPT measurement [26], dynamic optimization of tracking parameters where the flow behaviour of the system is predictable [37], validation with other simulation techniques such as Discrete Element Method (DEM) models [28], and comparison with the performance of different PEPT location algorithms relative to the scanners commonly used for PEPT [24].  
One of the main advantages of GATE simulations for PEPT is that the true path of the tracer particle is known, since this is created as an input to the simulation. This can be used to determine the optimum reconstruction of time-series location data for PEPT measurements as well as to test the limits of PEPT as a technique and to determine if the PET scanner is able to track paths characteristic of a specific application, e.g., turbulent fluctuations in the flow of multiphase fluids. PEPT experiments are generally expensive to perform and time-consuming, making the use of GATE simulations valuable to optimise the parameters that decrease uncertainty in the measurement arising from the experimental protocol and data treatment.
## 2 Vehicle Model
### Tire-Pavement Model  
The mathematical tire model established by [PERSON] [23] can accurately represent the longitudinal adhesion relationship between the tire and the road surface during vehicle braking. The expression for this model is as follows:  
\[\mu(\lambda)=c_{1}[1-exp(-c_{2}\lambda)]-c_{3}\lambda \tag{5}\]  
where \(c_{1},c_{2},c_{3}\) are the fitting parameters for each typical road surface, as well as the optimal slip rate and adhesion coefficient for six different road surfaces [24], as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(\lambda_{ref}\) & \(\mu_{max}\) \\ \hline Dry asphalt & 1.2801 & 23.99 & 0.52 & 0.17 & 1.17 \\ Dry cement & 1.1973 & 25.168 & 0.5373 & 0.16 & 1.09 \\ Wet asphalt & 0.857 & 33.822 & 0.347 & 0.13 & 0.8013 \\ Cobblestone & 0.4004 & 33.708 & 0.1204 & 0.14 & 0.34 \\ Snow & 0.1946 & 94.129 & 0.0646 & 0.06 & 0.1907 \\ Ice & 0.05 & 306.39 & 0.001 & 0.03 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fitted parameter values for typical road surfaces.  
Figure 1: Braking force diagram.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 2 Motivation and Background
### Background on 5G AKA and Cryptographic Primitives  
In this paper, we use acronyms standardized by 3 GPP for 5G telecommunication networks. Table 2 provides a list of the acronyms used throughout this paper.
## 2 SISO Impedance Measurement and Oscillation Analysis of FSC-VSPSU
### Oscillation Analysis Method Based on the Measured SISO Impedances  
In the analysis of grid-connected FSC-VSPSUs, stability is contingent upon an adequate phase margin at the point of intersection between the magnitude curves of the grid impedance and the FSC-VSPSU impedance, as determined by measured impedances. Oth  
Figure 4: Structure diagram of the converter controller.  
Figure 5: SISO impedance measurement procedures for FSC-VSPSU.  
erwise, the system may experience harmonic amplitude amplification and even oscillation or instability.  
The equivalent impedance model of the grid-connected FSC-VSPSU is shown in Figure 6. \(\overset{\rightarrow}{U}_{\text{g}}(\mathrm{j}\omega)\) denote the equivalent grid power supply voltage; \(\overset{\rightarrow}{U}_{\text{p}}(\mathrm{j}\omega)\) denote the FSC-VSPSU port voltage; \(\overset{\rightarrow}{Z}_{\text{net}}(\mathrm{j}\omega)\) represents the grid network impedance; and \(\overset{\rightarrow}{Z}_{\text{pump}}(\mathrm{j}\omega)\) represents the FSC-VSPSU impedance. Thus, \(\overset{\rightarrow}{U}_{\text{p}}(\mathrm{j}\omega)\) can be expressed in (4).  
\[\begin{split}&\overset{\rightarrow}{U}_{\text{p}}(\mathrm{j} \omega)=\overset{\rightarrow}{U}_{\text{g}}(\mathrm{j}\omega)\times\frac{ \overset{\rightarrow}{Z}_{
###### Abstract  
This study proposed a dynamic characteristic analytical method (ANM) of a composite box girder bridge with corrugated steel web (CBGCSW) by completely considering the impact of shear lag effect and accordion effect of corrugated steel webs. Based on energy principles and variational principles, a vibration differential equation and the natural boundary conditions of a CBGCSW were developed. The analytical calculation formula for solving the vibration differential equation was then obtained. The results calculated using the ANM agreed well with previous experimental results, which validated the correctness of ANM. To demonstrate the superiority of the ANM, the vibration frequencies of several abstract CBGCSWs with varying ratios of span-width, obtained using the elementary beam theory (EBT) and the finite element method (FEM), were compared with those obtained by ANM. The efficacy of the ANM was verified and some meaningful conclusions were drawn which are helpful to relevant engineering design, such as the observation that a higher natural vibration frequency and smaller span-width ratio significantly magnified the shear lag effect of CBGCSW. The first five-order natural vibration frequencies of the CBGCSW were significantly lower than those of the composite box girder bridge with general steel web (CBGGSW), which indicates that the impact of the accordion effect is significant.  
corrugated steel webs; shear lag effect; accordion effect; span-width ratios; analytical method +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
### Multimodal Decision-Level Fusion Approach
#### 3.3.1 Multi-Probability Labeling Strategy  
The traditional classification task performs the final classification through the fully connected layer. As shown in Figure 4, the classification label confidence prediction of the input fingermark image is performed by the fully connected layer based on the features obtained from the network.  
SFNet replaces the first fully connected layer with a convolutional layer for dimensionality reduction prior to final classification, as illustrated in Figure 4. This technique significantly reduces the number of parameters and computations required without sacrificing network performance.  
On this basis, the multi-probability labeling strategy is proposed in combination with the patch-label strategy in [21], as shown in Figure 4. The patch-label strategy, described in [21], involves preprocessing the image and inputting it into the Inception network. The network performs dimensionality reduction using a fully connected layer and outputs the classification probability through the convolutional layer with confidence. SFNet employs an improved labeling strategy over the patch-label approach described in [21]. The fully connected layer is replaced with a convolutional layer that achieves the same effect. The SFNet subject network takes an image of size 300 \(\times\) 300 pixels as the input to generate 10 \(\times\) 10 \(\times\) 512 feature maps, and the convolutional layer outputs probability distribution maps of size 10 \(\times\) 10 \(\times\) 2. The final prediction output of the network is obtained by taking the weighted average of the weights corresponding to the 10 \(\times\) 10 probability table generated after the SoftMax operation, which is trained using the ground truth label. The probability table represents a local block at the corresponding position of the input image and is utilized to train the local coding capability of the feature extraction network.
## 5 Discussion
### Application Examples of a SICS-SM Controller  
The application example 1 consists of a mounting system for model cars with five assembly/ disassembly stations (Figure 15).  
In standard mode, the system is operated with five classic PLC controllers (one PLC per station) from Siemens and Phoenix Contact. The average production time for a model car is 37 s.  
For the SICS test, all five stations were connected to the Internet via device gateways/web connectors for OPC DA [16]. This means that all 135 process datums of the stations are available in the IP network. The SICS-SM control system for the station was designed in the web browser via the IoT Platform FlexIOT and by use of a special administration tool. A separate SICS controller instance is used for each of the five stations.  
With the SICS-SM controller, the production time for a model car increases by approximately 10%. However, this allows the removal of five PLC controllers as hardware and their associated maintenance.  
Figure 14: Settlement and analysis in the clearing system for an example with five SISC-SMM controller instances.  
Figure 15: Mounting system for model cars with five assembly/disassembly stations.
## 3 Fault Diagnosis Method Based on LSTM and Multi-Sensor Data Fusion
### Dilated Convolution Module  
Dilated convolution is proposed to solve some problems in image segmentation. For the task of image segmentation, the image fed into the convolutional network is filtered by convolutional layer before pooling layer. Image size can be reduced while the receptive field can be increased. Finally, the image size can be restored by up-sampling. However, in the process of downsampling or upsampling of the image, internal features and spatial hierarchical information are usually lost. Dilated convolution is proposed to alleviate the above problem. The dilated convolution module is shown in Figure 3, and can be described as:  
\[r=\sum_{i}d_{i}(s_{i}-1) \tag{4}\]  
where, \(r\) is the receptive field, \(d_{i}\) represents dilation rate and \(s_{i}\) denotes filter size.  
The advantage is that the receptive field of the dilated convolution module is increased while losing hardly any detail information. The original 3 \(\times\) 3 convolution kernel has a 5 \(\times\) 5 (dilated rate = 2) or larger receptive field with the same number of parameters and computation cost of a normal convolutional module. For LSTM network, dilated convolution can fuse feature information of different scales and establish the dependence between features of different time series, which improves the recognition ability of the LSTM network.
## 5 Predictive Simulation and Analysis
### Support Vector Machine (SVM)
#### 5.3.3 Performance Analysis  
There were 72 samples in this study--70% of the training set and 30% of the test set.  
In order to verify the advantages of the proposed PSO-SVM method, its performance was compared with that of other commonly used intelligent computing-based methods, such as M5\({}^{\prime}\), ANN, and SVM. The predicted fatigue lives are represented in Figure 6 in the form of scatter plots. Table 4 summarizes performance indices of different methods to predict the fatigue life. The comparison presented in Table 4 leads to the conclusion that the PSO-SVM outperformed other methods in terms of accuracy of prediction of the output.  
Figure 6: Fatigue life prediction by (**a**) M5\({}^{\prime}\), (**b**) ANN, (**c**) SVM, and (**d**) PSO-SVM.
## 6 Conclusions  
WSNs deployed over large areas face significant challenges due to the limited energy resources of the SNs. Data transmission over long distances between SNs and the BS leads to rapid energy depletion. In this study, we proposed an algorithm for dynamically optimizing the BS location using a virtual grid approach. The algorithm relocates the BS to an optimal position after a number of rounds, accounting for the number of active SNs in the network.  
Additionally, we investigated the integration of a RN to improve scalability and energy efficiency in large networks. The proposed algorithms were applied to the conventional SEP protocol with two and three energy levels, and their performance was evaluated in terms of network lifetime and residual energy. Simulation results demonstrated that dynamically adjusting the BS location significantly enhances network performance in large-scale deployments. The inclusion of an RN further increases energy efficiency by reducing transmission burdens on SNs farthest from the BS.  
Finally, the study examined the impact of path loss between SNs and the BS, showing that energy consumption increases as the path loss exponent value increases. The findings highlight the importance of optimized BS placement and RN utilization in mitigating energy loss and extending the operational lifetime of WSNs.  
Conceptualization, T.D. and T.G.; methodology, T.D. and T.G.; software, S.S.A.A.; validation, S.S.A.A.; formal analysis, S.S.A.A.; investigation, S.S.A.A., T.D. and T.G.; resources, S.S.A.A., T.D. and T.G.; data curation, S.S.A.A., T.D. and T.G.; writing--original draft preparation, S.S.A.A.; writing--review and editing, S.S.A.A., T.D. and T.G.; visualization, S.S.A.A.; supervision, T.D. and T.G.; project administration, T.D. and T.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 System Model and Problem Formulation  
In this section, the model of wireless power and information transfer is firstly introduced. Then, based on the classical Petri net, a hybrid colored cyber Petri net system is proposed, and the dynamic behavior of the system is described from the mathematical and graphical perspectives. Finally, the multi-UAV aided wireless power and information transfer is formulated as a multi-objective optimization problem. The schematic diagram of multi-UAV aided wireless power and information transfer is illustrated in Figure 1.
# Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent
## 2 Background and Related Work
### Reinforcement Learning  
RL is a machine learning paradigm [18] in which an agent/machine trains itself by interacting with an environment, as shown in Figure 2. The RL setup comprises the following: the environment, agent, state, action, and reward. In RL, an agent observes the environment at time t, capturing a state (\(S_{t}\)). It then chooses one of the available actions (\(A_{t}\)),which is applied to the environment. This leads to a change in the environment, resulting in a new state (\(S_{t+1}\)). At the same time, the environment provides an evaluation of the goodness of the action in the form of a reward (\(R_{t}\)). Actions that help an agent to progress towards a specific goal are rewarded positively; otherwise, the reward can be zero or negative. RL does not require an existing dataset to train a model, but it requires a training environment that is consistent with the target domain. For example, in order to use RL for ACOs, an agent needs an environment that mimics a network of computer systems, which the agent uses to train by performing actions and observing corresponding rewards (i.e., a sequence of rewards (\(R_{t}\), \(R_{t+1}\)). Defining RL states and actions requires domain knowledge, and the level of accuracy in defining states and actions impacts the quality of the trained agent. RL seems to be a promising choice for ACOs, as no prior dataset is required; hence, red or blue agents can learn an optimal sequence of actions by interacting with the training environment. Deep reinforcement learning (DRL) represents an advancement over simple RL by incorporating neural networks into the RL framework. DRL offers several advantages over traditional RL methods [19]:  
* DRL can effectively manage high-dimensional input spaces due to its capacity to acquire hierarchical data representations using deep neural networks.
* DRL models, when trained on extensive datasets, can exhibit strong abilities to generalize to novel environments or tasks, potentially minimizing the necessity for retraining on similar tasks.
* DRL has the capacity to acquire valuable representations of states and actions, facilitating more efficient learning and decision-making processes.