esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Zone-Based Secure Multicast Techniques
### Zone Construction and Nod Election  
The IP multicast protocol in MANET faces challenges, including the need for all nodes to maintain state information for multicast and the resulting high overhead. While the overlay multicast protocol adapts flexibly to changes in network topology, it can lead to unnecessary transmissions within the multicast group and degrade network performance due to periodic control messages.  
The ZSM approach applies a region-based structure to enhance the reliability of data transmission while minimizing the impact of node mobility on multicast participation. The size and number of regions are determined by the transmission range of the nodes and their mobility. The nodes participating in the network consist of general nodes, member nodes, group management nodes, and authentication nodes. General nodes are nodes that constitute MANET without participating in multicast. Member nodes are nodes participating in multicast, and group management nodes are nodes that manage state information for member nodes participating in multicast within each zone. The group management node is elected based on node connectivity, as nodes with higher connectivity are more advantageous for setting up and managing data transmission paths. Each node broadcasts the number of member nodes in its neighborhood, and the node with the highest number of member nodes is elected as the group management node. The elected group management node then broadcasts the number of member nodes within its zone. The group management node with the highest number of member nodes is designated as the authentication node. The authentication nodes serve to issue group keys to the group management nodes. The group keys issued are used for encryption when transmitting multicast data. It is not greatly affected by the movement of the nodes since the information of the multicast member nodes is maintained by the group management node of each zone. So, it can reduce the control message and transmit the data to the optimal route. In addition, the reliability of the transmitted data can be improved through encryption using the group key. Figure 1 shows three types of nodes within a region in ZSM.
## 3 Methodology
### Back-Propagation Neural Networks  
The second method used in our work is based on artificial neural networks (ANN). Their principles mirror the organization and mechanism of the human brain (10 billion neurons) based on learning and memory [31; 32]. ANN are composed of a specified number of single processing units called neurons (See Figure 4).  
The classical neuron model is described by (10).  
\[y=\sigma\left(\sum_{i=1}^{n}w_{i}u_{i}+b\right) \tag{10}\]  
where \(u_{i}\)\((i=1,\,2,\dots,\,\mathrm{n})\) denotes the neuron inputs, \(b\) is the bias (threshold), \(w_{i}\) denotes the synaptic weight coefficients, \(\sigma\) is the nonlinear activation function such that sigmoid and hyperbolic tangent functions.  
The multilayer neural network is a network in which the neurons are grouped into layers. Such a network has an input layer, one or more hidden layers, and an output layer (Figure 5). The fundamental neural data processing is carried out in hidden and output layers.  
Figure 4: Neuron model.  
Figure 5: Artificial neural networks (ANN) with one hidden layer.  
The neurons constituting each layer are linked to all the neurons of the following layer. For each connection, a weighting coefficient is assigned which is determined according to the tasks that should be solved by this neural network.  
The output layer generates the network response vector \(Y\). Non-linear neural computing performed by the network shown in (Figure 5) can be expressed by (11).  
\[Y=\sigma_{2}\{W_{2}\sigma_{1}(W_{1}U)\} \tag{11}\]  
where:  
* \(\sigma_{1}\) and \(\sigma_{1}\) are vector-valued activation functions which define neural signal transformation through the 1 st and output layers;
* \(W_{1}\) and \(W_{2}\) are the matrices of weight coefficients which determine the intensity of connections between neurons in the neighboring layers;
* \(U\), \(Y\) are the input and output vectors, respectively.  
One of the fundamental advantages of neural networks is that they have the ability of learning and adapting. The training of a neural network is the determination of weight coefficient values between the neighboring processing units.  
The Back-propagation (BP) is a popular feedforward multi-layered network learning algorithms. It optimizes the value of synaptic weights between neurons by minimizing the sum-squared error using the gradient descent method. The modification of the weights is performed according to the Formula (12).  
\[W(k+1)=W(k)-\eta\ \Delta J(W(k)) \tag{12}\]  
where:  
* \(W(k)\) denotes the weight vector at the discrete time \(k\),
* \(\eta\) is the learning rate,
* \(J(W(k)\) is sum-squared error: \[J(W(k))=\big{|}\big{|}d^{k}-y^{k}\big{|}\big{|}^{2}\] (13)  
\(y^{k}\) is the output vector and \(d^{k}\) is the desired output vector.  
The standard BP has proven its performance in practical applications. But the disadvantage of these methods requires fixing the architecture of the network before starting the learning phase. The diagnosis using the Back-Propagation Neural Network method will be seen in Section 4.2.
## 4 Results and Discussion
### Relationship between the Subjective Evaluation Value and Physiological Indices
#### 4.2.1 Relationship between the Drowsiness Level and Physiological Indices  
As a result of the correlation analysis, the correlation coefficients between values of the drowsiness level vote and physiological indices were calculated using data from 57 sets in which the value of the drowsiness level vote varied, but the value of the comfort sensation vote remained at 0 (comfortable). There was a significant correlation between the drowsiness level and 102 physiological indices (data \(N=57\), \(r>0.339\), \(p<0.01\)). The indices in relation to the ECGs were not included in these parameters; however, the indices in relation to the EEGs were included. Thus, this result showed that the relationship between the reaction of the central nervous system and changes in arousal levels is more significant than that between the reaction of the autonomic nervous system and changes in arousal levels.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Measurement and Analysis on a Land-to-Ship Offshore Wireless Channel in 2.4 GHz  
Many of the existing wireless channel modes designed for over-the-sea communication use VHF radios or satellite communication systems. These channel models do not support a very high data rate because of the lack of bandwidth and the high cost incurred due to expensive satellite services.  
To address this issue, [PERSON] et al. presented measurement results for land-to-ship offshore wireless channels [19]. The frequency used in this channel model was 2.4 GHz. The model was investigated through an implementation of a narrowband channel measurement system between the quay and a sailing patrol boat. For large-scale fading, different existing path loss models were compared with the empirical path loss data. The free space path loss model, two-ray path loss model, and ITU-R P.1546 model [43] were taken into consideration for comparative analysis. Destructive and constructive interferences around distances ranging from 200 to 500 m were observed from empirical path loss data, which led the authors of [19] to conclude that the two-ray path loss model was the best fit for their scenario. The Kolmogorov-Smirnov (K-S) test [44] was conducted to verify the goodness of the fit. The received field intensities of direct and reflected rays were formulated using the following two equations in the channel model as follows:  
\[E_{LOS}=\frac{E_{0}}{d_{1}}\exp\left(-\frac{j2\pi fd_{1}}{c}\right) \tag{36}\]  
and  
\[E_{R}=\Gamma\frac{E_{0}}{d_{2}}\exp\left(-\frac{j2\pi fd_{2}}{c}\right) \tag{37}\]  
where \(d_{1}\) and \(d_{1}\) are the distances between the transmitter and receiver for direct path and reflected path, subsequently, \(E_{LOS}\) is the field intensity of the direct rays, \(E_{R}\) is the field intensity of the reflected ray, \(E_{0}\) is the free space electric field, \(f\) is the carrier frequency, and \(\Gamma\) is the reflection coefficient. The total electric field intensity was the summation of Equations (36) and (37), as represented below:  
\[E_{tot}=E_{LOS}+E_{R} \tag{38}\]  
The measurements for the channel model were conducted in the Incheon Maritime Police Port, located on the Yellow Sea in South Korea. Experiments were conducted for the cases when the ship was in static mode and moving with high speed. Propagation loss data was obtained in real time using received signal strength. For small scale fading, authors of [19] considered multipath propagation, speed of the mobile vehicle, changes of surrounding objects, and transmission bandwidth of the signal. Rayleigh distributions are widely known to model the functions of small-scale fading. In [19]; however, it was found suitable to use the Rician distribution.  
This is the only channel model that analyzes different propagation loss models comparatively, with the practical data obtained manually. Additionally, the channel model was designed to consider both small- and large-scale fading. Unfortunately, the fade margin calculation was not done in [19].
## 3 Key Enabling Technologies for AI-Driven 6G Networks
### Native Security  
With the emergence of advanced capabilities in 6G networks, robust and comprehensive security mechanisms have become imperative. Thus, the concept of native security has been proposed and widely studied. Native security typically refers to the principle of embedding security mechanisms directly into the foundational architecture of the network, which leverages AI, blockchain, and quantum cryptography to create a resilient and trustworthy communication infrastructure [28].  
By integrating AI technologies into the core network, AI-driven native security can provide autonomous, intelligent, and adaptive security solutions to the 6G system which leverage advanced AI techniques to identify anomalies, predict vulnerabilities, and respond to attacks dynamically. Specifically, by analyzing vast numbers of data, AI techniques such as anomaly detection and supervised learning can identify unusual patterns and help the system take corrective actions automatically without human intervention [29]. Additionally, the anomaly detection and blockchain-based authentication mechanisms are also key enablers for AI-driven system security [30; 31].  
Benefiting from AI-driven native security, diverse applications have been developed. AI-empowered intrusion detection and prevention systems can monitor network traffic and identify malicious activities such as distributed denial-of-service (DDoS) attacks and unauthorized access, with AI algorithms providing the ability to classify normal and abnormal behavior with high accuracy [32]. Moreover, AI-driven native security systems can detect and mitigate malware by analyzing code signatures and behavioral patterns, employing convolutional neural networks to identify sophisticated malware that evades traditional signature-based detection methods [33]. In addition, quantum-resistantcryptography has been regarded as a promising solution for 6G networks. In [34], an AI-driven system is proposed to analyze quantum algorithms and their potential impact on traditional encryption which can design robust cryptographic techniques that withstand quantum threats.  
However, despite the potentials illustrated, AI-driven native security still faces several challenges. AI systems are vulnerable to adversarial attacks where attackers manipulate input data to deceive the AI models and prompt false decisions. Moreover, AI models require access to large datasets for training and operation, and thus data privacy concerns escalate. The high computation and communication complexity of AI models also becomes a heavy burden, especially for the resource-constrained edge devices in 6G networks.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
## 2 Theoretical Background
Therefore, it is a nonlinear and space-dependent model. According to the electromagnetic theory, the refractive index of a medium is \(n=\sqrt{\varepsilon_{\rm r}\mu_{\rm r}}\), where \(\varepsilon_{\rm r}\) and \(\mu_{\rm r}\) are the relative permittivity and relative permeability of the medium, respectively. Since the relative permeability of plasma is usually defaulted to unit one \(\mu_{\rm r}=1\) in the regime of optical frequencies, the complex refractive index of plasma is accordingly expressed as  
\[E_{10}(r,z)=\frac{E_{0}\sqrt{2}w_{0}r}{w^{2}(z)}\exp\left[-\mathrm{i}\left( \frac{kr^{2}}{2q(z)}+kz+\phi-2\tan^{-1}(\frac{z}{z_{\mathrm{R}}})\right)\right] \tag{2}\]0  
which also reflects the dispersion and dissipation characteristics of the plasma.
# Transverse and Longitudinal Energy Flows in a Sharp Focus of Vortex and Cylindrical Vector Beams
## 4 Simulation  
### Energy Flux near the Focus for a Vortex Beam with Right-Handed Circular Polarization  
The simulation is carried out using Richards-Wolf integrals [1] of the initial field (1), the wavelength is 633 nm and 0.95 is the numerical aperture.  
It is seen from Figure 3 (\(m=3\)) that, before the focal plane (a), the transverse flux is directed towards the coordinate center (the beam converges), after the focal plane (c), the energy flow is directed from the coordinate center (the beam diverges) and, in the focal plane (b), the energy flow rotates along closed trajectories (clockwise and counterclockwise). Blue arrows are also seen in Figure 3 to show the backward energy flux near the focus.  
Figure 4 shows that, even at \(m=0\) (no vortex), the transverse energy flux is directed towards the coordinate center before the focal plane (a), from the optical axis after the focus (b), and, in the focal plane, the transverse energy flux due to the spin-orbital conversion rotates along circular trajectories around the optical axis (b). Moreover, at different distances from the optical axis, the energy flow rotates in different directions. Many blue arrows are seen in Figure 4 to indicate the reverse energy flux directions near the focal spot.  
Figure 4: The same as in Figure 3, but for \(m=0\). The dotted circles are the boundaries of the zones of positive and negative longitudinal energy flow. Transverse planes are \(z=-\lambda\) (before the focus) (**a**), \(z=0\) (at the focus) (**b**) and \(z=+\lambda\) (behind the focus) (**c**).  
Figure 3: Intensity distributions (color) and energy fluxes (arrows) near the sharp focus of an optical vortex with right-handed circular polarization. Here are the simulation parameters: the wavelength is \(\lambda=633\) nm, the focal length is \(f=10\) μm, \(m=3\) is the vortex order, the numerical aperture is NA = 0.95. An amplitude radial distribution of the focused field is uniform (\(B(\theta)=1\)). Transverse planes are \(z=-\lambda\) (before the focus) (**a**), \(z=0\) (at the focus) (**b**) and \(z=+\lambda\) (behind the focus) (**c**). The red, blue and white colors of the arrows mean, respectively, positive, negative and close-to-zero longitudinal energy flow.  
The topological charge in Figure 5 at \(m=-2\) has changed its sign, and, therefore, the energy rotation in the focal plane in the region of maximum intensity is clockwise (b), and not counterclockwise, like in Figure 3. In addition, the energy flow is backward inside the light intensity ring (blue arrows on the optical axis shown in Figure 5). Where the white arrows are, there is only a transverse energy flow and almost no longitudinal.
## 3 Experimental Results
### Creep Tests Results
#### 3.2.2. Creep Tests on Hourglass Specim  
Calculating the deformation values is undertaken in the DIC software (Alpha software from X-Sight, s.r.o. version 2.1.52). The entire strain tensor is available to each calculation point of the DIC network. Figure 2b schematically shows the positions of the cross-sections in which the creep strain evaluation was performed (for solder SAC305, hourglass sample).  
The creep curves for the SAC305 and SACX0807 alloys obtained by using DIC measurements on the hourglass specimens are shown in Figure 6a,b. The first number of the legend refers to the distance of the evaluated point from the location of the smallest cross-section, while the second number indicates the corresponding nominal stress value. The HMH index means the equivalent value calculated according to [PERSON] theory.
## 2 Proposed Architecture
### Proof-of-Concept Demonstration  
The experimental setup for the feasibility test of the proposed system is illustrated in Figure 3. DCO-OFDM signal is generated by an arbitrary waveform generator (Tektronix, 70002B) and adjusted by an amplifier (Mini-Circuits, LZY-22+ and ZPUL-30P+). A  
Figure 1: Illustration of the proposed V2V-VLC system.  
Figure 2: (**a**) Design of license-plate-shaped receiver; (**b**) schematic diagram of the physical process of a fluorescent concentrator.  
bias-T (Mini-circuit, ZFBT-6 GW+) is used to combine a suitable DC offset to ensure the positive polarity from the bipolar signal created by the AWG. Two types of transmitters are implemented: one with headlamps using high-power WLEDs and the other with a blue LD. The APD (Hamamatsu, C12702-04) at the receiver module detects the optical signal concentrated through the FC. Then, the corresponding electrical signal is captured by an oscilloscope (Keysight, MSO6104), followed by offline signal processing via MATLAB for communication performance analysis.  
Next, two LED taillights (used as beacons) provide location information to two cameras (Pixy2) at the transmitter module. The information is used for beam steering and tracking. The steering angle is calculated by the values given from a microcontroller (Arduino Mega 2560) connected to the cameras. It also controls the steerable mirror (Optotune, MR-E-2) accordingly.
# MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
## 3 Methods
### Log Parsing Method  
The primary task of log anomaly detection is converting log contents into features the machine can recognize and understand. According to LogRobust [32], the processing noise in log data primarily comes from the lack of log parsing. Incorrect log parsing may result in several misidentified log templates, which hinders the model's performance. Our proposed parsing model does not use existing log parsers, but directly processes the original logs to avoid the noise generated by the log analysis.  
The first step of log parsing is to split each log message \(l_{i}\) by the delimiter, breaking the log into words and numeric values. In order to segment the log more accurately, the \"space\", etc., are regarded as separators, for a given  
\[C=\{d_{1},d_{2},\ldots d_{n}\}, \tag{1}\]  
where \(d_{i}\) denotes a valid word. After splitting the log \(l_{i}\), first, check whether the words it contains are in dictionary \(C\). The word set of \(l_{i}\) can be expressed as  
\[ld_{i}=\{d_{a},d_{b},\ldots d_{m}\},\quad\forall d_{i}\in C. \tag{2}\]  
Because some words appear more times in the log message, this will affect subsequent processing. Thus, we count the number of times the word \(d_{i}\) in the word set \(ld_{i}\), \(lf_{i}\) is used.  
The second step is to aggregate log \(l_{i}\) with the same \(ld_{i}\) and \(lf_{i}\) into a novel log cluster \(L_{i}\).  
The third step is to use the LCS to mask the log variables in \(L_{i}\). When using the LCS for masking, not only effective words, but also words outside the dictionary are considered. For example, a cluster contains \"PacketResponder 1 for block blk_-1608999687919862906 terminating\" and \"PacketResponder 2 for block blk_-1608999687919862906 terminating\". The masking result of its cluster is \"PacketResponder <\"> for block <\"> terminating\". \"<\">\" is a sign used to cover up the variables.  
After completing the above three steps, the event template \(lt_{i}\) of all logs is obtained. This way, parsing logs reduces the noise caused and parses event templates for rare logs.  
Figure 1: Overview structure of MDFULog. Split, clustering, and mask are used to parse the log, extract its valid information, and capture the semantic and temporal features for vectorization, and an Informer-based classification model is used for anomaly detection.
## 5 Determination of Extrusion Deformation Limit Value of Tunnel Face
### Analysis of Test Results  
Taking the central point on the central axis of the tunnel face as the monitoring point, we compared the difference of horizontal displacement of the tunnel face under three conditions, as shown in Figure 3. After the start of the test, with the withdrawal of the pilot tunnel block, the horizontal displacement of the monitoring point increases gradually under the three conditions, and the displacement rate is also increasing. During the whole monitoring period, the absolute value of horizontal displacement and displacement growth rate of monitoring points in condition \(\Gamma_{1}+\Gamma_{2}>\) condition \(\Gamma_{1}\) > condition \(\Gamma_{2}\). After 13 min, the growth rate of horizontal displacement at the monitoring points of condition \(\Gamma_{1}\) and condition \(\Gamma_{2}\) slows down and gradually converges. The maximum horizontal displacement of the monitoring point in condition \(\Gamma_{1}\) is 3.3 mm, and the maximum horizontal displacement of the monitoring point in condition \(\Gamma_{2}\) is 2.34 mm. The growth rate of the horizontal displacement of the monitoring point in condition \(\Gamma_{1}+\Gamma_{2}\) does not decrease but increases. At 16 min, the maximum displacement reaches 5.6 mm. Then the sand on the vault suddenly falls, causing the tunnel face to collapse, with a large collapse range. The maximum sliding distance of the horizontal sand is 20.7 cm, and the vertical collapse height is 76.9 cm, 54.2 cm beyond the boundary of the tunnel excavation contour line, as shown in Figure 4.  
According to the results of the model test, the maximum horizontal displacement of 5.6 mm at the monitoring point of condition \(\Gamma_{1}+\Gamma_{2}\) can be taken as the limit deformation value of tunnel face collapse, and the conversion relationship between tunnel face extrusion deformation and collapse risk probability can be obtained, as shown in Table 5.  
Figure 3: Variation curve of horizontal displacement of measuring points on tunnel face under three conditions. Combined with Figure 4 and Table 5, the risk probability and risk level of tunnel face collapse under three conditions can be evaluated in real time, as shown in Table 6.  
The factor causing the instability of the tunnel face in condition \(\Gamma_{1}\) is the change of excavation method, from CRD excavation to full-face excavation. It can be seen from Table 6 that with the passage of time, the risk probability of tunnel instability gradually increases, and finally it is 2.14%, and the risk level increases from level 2 to level 3. The factor causing the instability of the tunnel face in condition \(\Gamma_{2}\) is that the initial support is not closed in time. It has little impact on the instability of the tunnel face, and the risk level is always Level 2. When two factors exist at the same time, such as condition \(\Gamma_{1}+\Gamma_{2}\), it can be seen that with the passage of time, the risk probability of tunnel instability increases rapidly, and the risk level increases from level 2 to level 4.