esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Solutions and the Combined Approach of AFF_CGE
### AFF_CGE Approach  
In recent years, graph-based methods for malicious traffic detection have demonstrated significant advantages in addressing the challenges posed by encrypted traffic. These methods enhance both accuracy and robustness by analyzing the structural relationships within network traffic, particularly focusing on spatiotemporal correlations. Graph neural networks (GNNs) are central to these approaches, as they map network traffic into graph structures where nodes represent traffic samples and edges denote their correlations. By capturing these structural relationships, GNNs effectively improve detection accuracy.  
[PERSON] et al. [18] developed a network trajectory graph at the application layer, where nodes represent IP hosts and edges denote network flows, to infer labels for unlabeled flows based on adjacent application labels. [PERSON] et al. [19] proposed a real-time malicious traffic detection framework that integrates spatial and temporal features through graph representation learning, efficiently identifying malicious activities in encrypted environments. [PERSON] et al. [20] introduced MalDiscovery, which constructs an attributed KNN graph with encrypted sessions as nodes and image feature similarities as edges, utilizing the GraphSAGE model for enhanced node embedding and binary classification. [PERSON] et al. [21] developed the G-TIAG method, which combines a traffic interaction attribute graph (TIAG) with a graph convolutional network (GCN), a gated recurrent unit (GRU), and a self-attention mechanism to improve detection accuracy by capturing interaction patterns, spatial features, and time series data.  
In general, both the internal characteristics of network traffic and the structural relationships between traffic are essential for detecting encrypted malicious activities. However, existing studies often concentrate on only one of these aspects. This study addresses this gap by introducing a novel approach that integrates the Attention-aware Feature Fusion mechanism (AFF) with the Communication Graph Embedding Learning method (CGE). The AFF_CGE approach combines the strengths of attention mechanisms and graph neural networks to offer a multi-level representation of encrypted traffic features. Specifically, it leverages the attention mechanism to extract deep semantic information from the internal features of encrypted traffic, while the communication graph embedding learning method captures the structural relationships between traffic components. This combined approach enhances both the accuracy and robustness of malicious traffic detection.
## 3 Artificial Intelligence Techniques for 6G Networks
### Ambient Backscatter Communications  
In 6G networks, ambient backscatter communications investigate the idea of using the present ambient radio frequencies for communication. These signals can be captured and modulated by devices to communicate data, while using little power. To establish reliablecommunication, AI approaches can be used to enhance backscatter modulation techniques, data encoding, and signal processing. For IoT devices and sensor networks, this strategy may make ultra-low-power communication possible [15].
# A Design of CGK-Based Granular Model Using Hierarchical Structure
## 2 Data Clustering
### GK Clustering  
Regardless of the data in the input space belonging to a cluster, the cluster is normally determined by the distance between the data and the center of each cluster. As described in Section 1, fuzzy clustering adopts Euclidean distance to create clusters. Euclidean distance is primarily used when circle-shaped clusters are created, and it has the problem of being unable to create clusters that are not circle-shaped. To resolve this problem, GK clustering was proposed [39; 40; 41], as it can create geometrically-shaped clusters. GK clustering employsMahalanobis distance, rather than Euclidean distance, to calculate the distance between cluster centers and data. Figure 3 illustrates clusters that were created in fuzzy and GK clustering, and Equation (11) presents the Mahalanobis distance.  
\[d_{GK}{}^{2}(x_{k},\ v_{i})\ =\ \parallel x_{k}-v_{i}\parallel_{A_{i}}{}^{2}=(x_{k} -v_{i})^{T}A_{i}(x_{k}-v_{i}) \tag{11}\]  
where \(d_{GK}{}^{2}\) denotes the square of the distance between the ith cluster's center \(v_{i}\) and the kth data \(x_{k}\), while \(A_{i}\) is the variance matrix of the ith cluster. In GK clustering, Equation (12) is used to calculate the variance matrix \(A_{i}\) in Equation (11).  
\[A_{i}=\frac{\sum_{k=1}^{N}u_{ik}{}^{m}(x_{k}-v_{i})(x_{k}-v_{i})^{T}}{\sum_{k= 1}^{N}u_{ik}{}^{m}} \tag{12}\]  
The variance matrix that is calculated using Equation (12) is adopted when calculating the distance between the cluster center and the data in Equation (13):  
\[D_{GK}{}^{2}=(x_{k}-v_{i})^{T}[\rho_{i}\ det(A_{i})^{\frac{1}{N}}A_{i}{}^{-1}] (x_{k}-v_{i}) \tag{13}\]  
where \(\rho_{i}\) denotes the volume of each cluster. When \(A_{i}\) is calculated in Equation (13), the matrix may become zero if the number of data is insufficient; hence, the minimum value is limited using Equation (14).  
\[(1-\gamma)A_{i}+\gamma\ det(A_{i})^{\frac{1}{N}}I\to A_{i} \tag{14}\]  
where \(A_{i}\) is the variance matrix that is calculated using all data, while \(I\) and \(\gamma\) denote the unit matrix and weight value constant, respectively. The eigen value and eigen vector can be calculated from the variance matrix. The calculated maximum eigen value is used to limit the minimum eigen value, such that the shape of the cluster can be maintained geometrically.
## 4 Experiment
### Performance Comparison  
Three evaluation indexes of recommendation algorithms are used in the experiment, including F, NDCG, and AUC [66; 67; 68]. The three indexes were calculated for top@5 and top@10 respectively. Precision measures the accuracy of predicting positive sample results, and Recall measures the recall rate of results. The formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Visual Feature** & **Style** & **Context** & **Affiliated Relation** \\ \hline BPR & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ VBPR & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ DVBPR & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) \\ ACF & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ VPOI & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ LASSO & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ VSM & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ TAUA & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between TAUA with baseline methods. \(\surd\) means that the algorithm considers the factor, \(\times\) means the opposite.  
\[R=\frac{TP}{TP+FN} \tag{18}\]  
where \(TP\) represents items that the users like among the items predicted to be of interest to the users. \(FP\) represents the items predicted to be of interest to the users that the users do not like. \(FN\) represents the items that the users like among the items that are not predicted to be of interest to the users. However, Precision and Recall are often contradictory. Therefore, \(F\) is used to comprehensively consider Precision and Recall, so that both of them can reach the highest at the same time to achieve a balance:  
\[F=\frac{2*P*R}{P+R} \tag{19}\]  
Normalized Discounted Cumulative Gain (NDCG) is an evaluation index that takes into account the return list to evaluate the accuracy of the list. The value ranges [0, 1]. The larger the value, the better the recommendation effect. AUC measures the probability that a model will predict a positive sample as a positive example rather than a negative sample as a positive example. The formula is as follows:  
\[NDCG=\frac{DCG}{IDCG} \tag{20}\]  
\[AUC=\frac{\sum\limits_{ins_{i}\in positive}rank_{ins_{i}}-M*(M+1)/2}{M*N} \tag{21}\]  
where \(DCG\) refers to Discounted Cumulative Gain, \(IDCG\) refers to optimal \(DCG\), and \(NDCG\) is used to evaluate the accuracy of ranking. \(rank_{ins_{i}}\) represents the sequence number of samples with the ith smallest probability score; \(M\) and \(N\) represent the number of positive and negative samples, respectively. AUC is used to evaluate the ranking quality of sample prediction.
## 3 System Architecture and Assumptions
### Underwater Acoustic Communication Model  
The communication methods commonly used on land, such as electromagnetic waves, infrared rays, and wireless signals, are not suitable for underwater environments due to propagation loss, multi-path interference, and spectrum limitations. Underwater acoustic propagation is suitable for underwater environments because it has characteristics such as long propagation distance and low loss, which allows underwater acoustic sensor networks to play an important role in underwater applications. The velocity of underwater acoustic propagation is affected by the properties of the medium such as temperature, pressure, salinity, and considering these factors comprehensively, the propagation velocity  
Figure 1: Network model.  
of an acoustic wave in an underwater environment can be expressed by the following equation [29]:  
\[\begin{array}{l}V=1449+4.591T-5.304\times 10^{-2}T^{2}+2.374\times 10^{-4}T^{3} \\ +1.34(S-35)+1.63\times 10^{-2}D+1.675\times 10^{-7}D^{2}\\ +1.025\times 10^{-2}T(S-35)-7.139\times 10^{-3}TD^{3}\end{array} \tag{1}\]  
where \(T\) represents the temperature in degrees Celsius (\({}^{\circ}\)C), \(S\) represents the salinity in practical salinity units (PSU), and \(D\) represents the depth in meters (m).
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 3 Factors Affecting Grating Loss
### Directionality  
The term \(P_{clad}\) also symbolises the device directionality. On a commercial SOI, the buried oxide (BOX) thickness is optimized to reflect part of power from substrate back to waveguiding layer. However, even then, a substantial portion of power amounting to 25-30% is leaked to the substrate in a generic uniform grating coupler. We discuss the two principal ways that have been proposed to address this leakage.  
One way to improve directionality is to insert a highly reflective bottom mirror in the buried oxide layer at an optimum thickness. The mirror can either be a stack of distributed Bragg reflector (DBR) consisting of alternating high and low index films or a metal layer. Part of the light leaking to substrate will be reflected back by the mirror and interfere constructively with the upper cladding diffracted light thereby substantially improving directionality. Such bottom reflectors have been shown to improve coupling performance of uniform gratings to as high as -1.6 dB [10; 11]. Furthermore, it is possible to design sub -1 dB uniform grating couplers with bottom reflectors. This is due to the emission field profile from a uniform grating perhaps not being perfectly exponential, thus a slightly higher than expected overlap with the Gaussian fiber mode is feasible [12; 13]. However, it may be noted that incorporation of such reflectors on SOI is challenging as it involves complex processing steps like flip chip bonding [10] or backside metallization [14]. Another way to plug the substrate leakage is by way of overlay gratings. Essentially this involves the addition of an overlay region above the high index part of grating. The overlay thickness, grating period and etch depth is optimized in order to induce an additional \(\pi\) phase difference among the fields diffracted towards superstrate. This in turn causes constructive interference among the diffracted fields and hence maximises the directionality. The overlay layer can be of any material as long as the required phase change is induced. In [15], a polysilicon overlay based grating coupler was demonstrated with a -1.6 dB coupling efficiency. Use of such overlays eliminates the need for any bottom reflector and thus, simplifies processing. Figure 4a,b provide a schematic representation of the two approaches described above to improve directionality. A two dimensional (2-D) finite difference time domain (FDTD) simulation of the diffracted field intensity shows the directionality of the filed between \(Si\)-substrate, Bragg mirror, and \(Si\) overlays is showcased in Figure 4c. As described above, addition of a bottom mirror or an overlay increases directionality and coupling efficiency.  
Figure 3: Illustration of different loss channels associated with a generic grating coupler.
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 3 Proposed Model
### Objective Function  
In a conditional Generative Adversarial Network (GAN), the objective is to train a generator (\(G\)) to produce realistic images that conditioned on given inputs. These inputs includes an observed image (\(a\)) and/or a random noise vector (\(c\)). Simultaneously, the discriminator (\(D\)) is trained to differentiate between real and those generated images by the generator.  
The objective function of a conditional GAN is described as follows:  
\[L_{\text{CGAN}}(G,D)=\mathbb{E}_{a,b}[\log D(a,b)]+\mathbb{E}_{a,c}[\log{(1-D( a,G(a,c)))}], \tag{1}\]  
where \(\mathbb{E}_{a,b}\) represents the expectation over pairs of real images (\(a\)) and their corresponding labels (\(b\)), and \(\mathbb{E}_{a,c}\) denotes the expectation over pairs of real images (\(a\)) and generated images (\(G(a,c)\)) conditioned on random noise vector (\(c\)).  
The generator (\(G\)) aims to minimize the loss function, where the discriminator (\(D\)) seeks to maximize it. Hence, the optimal generator (\(G^{*}\)) is obtained by solving the minimax problem:  
\[G^{*}=\arg\min_{G}\arg\max_{D}L_{\text{CGAN}}(G,D). \tag{2}\]  
In practice, the \(L1\) distance measure is often preferred over \(L2\) because it results in less blurring in the generated images. Therefore, the final objective of training a conditional GAN includes the \(L1\) distance term, given by  
\[G^{*}=\arg\min_{G}\arg\max_{D}L_{\text{CGAN}}(G,D)+\lambda L_{L1}(G), \tag{3}\]  
where \(\lambda\) is a hyper-parameter controlling the importance of the \(L1\) loss term.
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 2 Materials and Methods
### Algorithm for Processing the Signals from IR Sensors  
The 32 IR sensors attached on the circuit were responsible for receiving the signals from the IR transmitter, and the analog signals from these sensors were sent to the computer via MCU#2. Calculation of the signal values for the 32 IR sensors is presented in Appendix E. Figure 8 shows the sensor arrays and their signal values (\(v_{i}\)). The distance between two sensors is 3.001 mm. To have the parameters for simulating welding processes from these signals, an algorithm for processing the sensor's signals was proposed as shown in Figure 9. From the signals, a median filter was performed to remove the noise. The median filter function from the signal values of the 32 IR sensors can be seen in Appendix F. Then, the five largest signal values that were close together were filtered. From these values, the sensor's current position of the welding torch was determined as follows:  
\[S=\frac{\sum_{i=k}^{k+5}i.v_{i}}{\sum_{i=k}^{k+5}v_{i}} \tag{1}\]  
The function to determine the \(S\) position was programmed as detailed in Appendix G. From the value of the sensor's position (\(S\)), the distance from the start point to the current position of the welding torch (\(x\)) was calculated. In order to simulate the welding process, in addition to the \(x\) value, in this work, the dependent of the welding energy on the distance from the nozzle to the plates was also considered.  
In the real welding process, the distance from the electrode to the welded plates affects the arc energy formation. With a large distance, small arc energy is established and vice versa. Applying this principle to the developed VR system, the distance from the IR transmitter attached on the welding torch to the IR receivers attached on the circuit boards and the angle between the welding torch and the plates affected the weld formation. So, to establish the weld joint in consideration of the arc welding energy, the angle of torch with the base metal was selected from 70 to 80 degrees (or from 10 to 20 degrees perpendicular to the base metal), and the distance was from 1.5 to 5 mm. The angle between the filler rod with the base metal was selected at 20 degrees [37]. In this range of the angle and distance values, the receivers could recognize the energy of signals from the IR transmitters.  
Figure 7: Architecture of module #2.  
Figure 10 depicts parameters for simulating the welding process, which are the current position of the welding torch (\(x\)), the welding energy (\(E_{\mathrm{w}}\)), and the distance from the nozzle to the plates (r). For simulating, the value of the distance from the nozzle to the plates was assumed to equal \(1/5\) of the welding energy. The function for calculating the welding torch position, distance from the welding torch to the plates, and the welding energy is shown in Appendix H.  
Figure 8: Sensor positions and signals.  
Figure 9: Algorithm for processing signals from sensors.
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 4 The PM-QKD with Discrete Phase Modulation of Coherent State Sources  
In this section, we introduce the security analysis of discrete phase randomized PM-QKD. Then, we apply the decoy-state method to derive the single photon yield formula. Finally, we compare the yield difference between continuous phase randomization and discrete phase randomization.
## 3 Results and Discussion
### Feature Importance  
Once the Shapley value is computed for each feature in a machine learning model, the information can be used to determine the importance of each feature. The feature with the highest Shapley value is deemed the most significant because it contributes the most to the prediction outcome. Shapley values can also be used to plot feature importance graphs, which provide a visual representation of the importance of a feature in a model [66]. Based on the Shapley analysis of the bagged decision trees, the importance of the features (input variables or predictors) was ranked as follows: urbanization rate, GDP, PCI, energy consumption, population, and FDI (Figure 8).  
Figure 8: Shapley explanation of the predictors.
## 3 Results
### Antioxidant Activity Analysis of ZnO-NPs  
The capacity of ZnO-NPs to scavenge free radicals was assessed using the DPPH test. Free radicals such as DPPH have been widely employed to examine the antioxidant strength of inorganic substances and nanomaterials [38]. The DPPH solution was made with a deep violet hue that, after the addition of ZnO-NPs, turned pale yellow, which often denotes the antioxidant capacity of ZnO-NPs [39]. ZnO NPs and _C. spinosa_ L. extract were evaluated for their antioxidant activity using the DPPH technique, with ascorbic acid acting as the standard reference. According to Table 1 and Figure 9, the biosynthesis of ZnO NPs using _C. spinosa_ L. extract displayed good antioxidant activity at dosages of 7.5-300 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, which varied from 23% to 96%. IC50 was about 43.68 \(\pm\) 0.04 \(\mathrm{\SIUnitSymbolMicro g}\)/mL compared with 81 \(\pm\) 0.054 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for plant extract. However, there was a difference in the antioxidant potential between _C. spinosa_ L. extract and the ZnO NPs. While both samples reacted directly and reduced the wide range of free radicals of DPPH, there was a difference in antioxidant capability between the extract and ZnO NPs, which may be caused by the chemical composition of each sample analyzed [40]. Our results are consistent with those of a recent work by [PERSON], [PERSON] et al. [41] that investigated the antioxidant activity of Ag NPs using _C. spinosa_ fruit extract. Previous studies have demonstrated the significant antioxidant activity of _C. spinosa_ L. extract [42].  
Figure 8: Hemolytic activity of fruit extract and bio-synthesized ZnO nanoparticles.  
The antioxidant potential of biosynthesized ZnO NPs may be connected to the complex of various antioxidant metabolites that are found within cells in plants and protect biological substances such as flavonoids and phenolic chemicals from oxidation and damage. After the physicochemical interaction of the metal ions with the functional groups of the plant extract, these compounds created a coating covering for the ZnO NPs, giving them a spherical morphology and a greater surface area [43].
## 7 Challenges and Limitations
### Model Complexity  
Advanced models like LSNet [51] and YOLO-SCD [36] exhibit high computational complexity, making them less accessible for industries with limited technological infrastructure.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 2 Related Works  
### Overview of the Network Traffic Classification  
Network traffic classification research is the study of analyzing the traffic generated by computer communications, which is essential for the effective management, monitoring, and security of computer networks. As shown in Figure 1, network traffic classification research is broadly classified according to the field of research, methodology, classification level, and data units processed.  
First, in terms of research areas, it consists of various subfields, including application classification [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], malicious traffic detection [29, 30, 31, 32], user behavior profiling [27, 28, 29, 30], and web fingerprinting [44, 45, 46], of which application classification and malicious traffic detection are the most widely studied. Second, in terms of methodologies, methods such as port-based and payload-based methods have traditionally been widely used. Port-based classification categorizes traffic based on known port numbers, which is inapplicable because many applications use dynamic ports. Payload-based methods classify applications based on fixed payload content. Signature-based methods extend the mechanisms of payload-based methods to various traffic characteristics, defining common statistical, header, and behavioral characteristics of traffic as signatures and classifying based on them. Both payload-based and signature-based methods perform poorly on encrypted traffic. To solve these limitations, learning-based methods using machine learning and deep learning are the most active, and recently, methods using transformer models have also been performed. Third, in terms of classification level, it consists of the following levels: application classification, which distinguishes each application; service classification, which  
Figure 1: Overview of the network traffic classification.  
categorizes the detailed features, services, and behaviors of the application; application type classification, which categorizes the characteristics of the application such as Chat or File Transfer; and encryption classification, which categorizes the presence or absence of encryption. Fourth, in terms of data units, it is categorized into unidirectional and bidirectional flows, packets, and bursts. A flow is a set of packets with the same 5-tuples of information in the packet header, and a burst is a set of time-adjacent network packets originating from either the request or the response in a single-session flow [34].  
As mentioned before, we propose a multi-task classification method for encrypted traffic using DistilBERT to perform encapsulation, application type, and application classification on ISCX 2016 VPN/Non-VPN data. In Figure 1, the green-colored parts represent the four aspects of our proposed method.