esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Generation of Attributed Bipartite Graph  
We first extract the user-location interactive behavior from app records. Subsequently, user and location attributes based on app usage behavior are calculated as supplementary information. Then, an user-location attributed bipartite graph is constructed as the input of the representation model.  
Specifically, the usage frequency of different apps is taken as users' app preferences. We use the maximum normalization to constrain the values. User \(u\)'s app preference \(\mathbf{x}_{u}\) can be denoted as  
\[\mathbf{x}_{u}=\left\{<\frac{c_{ua}}{\text{M}_{\text{UA}}}>\right\}_{a=1}^{N_{A}}, \tag{4}\]  
where \(c_{ua}\) represents the frequency of user \(u\) using app \(a\). M\({}_{\text{UA}}\) is the maximum value of usage frequencies among all apps, and \(N_{A}\) is the total number of apps.  
Similarly, the usage preference of app in each location \(l\) is extracted as  
\[\mathbf{x}_{l}=\left\{<\frac{c_{la}}{\text{M}_{\text{LA}}}>\right\}_{a=1}^{N_{A}}, \tag{5}\]  
where \(c_{la}\) represents the total frequency of app \(a\) used in the location \(l\). M\({}_{\text{LA}}\) is the maximum frequency.  
Based on the extracted features, a bipartite graph \(G\) with node attributes is constructed. Users and locations are both set as nodes. Graph edges are established based on the interactive relationship between users and locations. Since there is no direction for visiting interactions, an undirected graph is built. In order to distinguish the different visits on various locations, we take users' preferences of different locations as the edge weights. To be specific, user \(u\)'s preference \(J_{ul}\) of the location \(l\) is  
\[J_{ul}=\frac{c_{ul}}{\text{M}_{\text{UL}}}, \tag{6}\]  
where \(c_{ul}\) represents the visiting frequency of user \(u\) to location \(l\). M\({}_{\text{UL}}\) is the maximum value among all users' visits. In addition, each node also has its own attributes as we mentioned above. The attributes of user \(u\) is \(\mathbf{x}_{u}\), and the attributes of the location \(l\) is \(\mathbf{x}_{l}\). Then the attributed graph \(G\) is contructed. The schematic diagram of \(G\) is shown in Figure 3a.  
Figure 3: The “User-Location” Bipartite Graph \(G\) and the Information Propagation in Graph Convolution. (**a**) The Schematic of “User-Location” Bipartite Graph. (**b**) The Information Propagation of Graph Convolution.  
After constructing the bipartite graph with node attributes, an attributed-graph representation model via graph convolution network is proposed to obtain representations of nodes.
## 4 Design of the Equalizers
### Zero Forcing Equalizer  
The term zero forcing (ZF) signifies reducing ISI to zero. The filtering operation usually represents the transmission of a signal x(n) through a time invariant linear system, whose weight function h(n) is known.  
When applying ZF, the purpose is to determine the equalizer impulse response c(n), in such a way that using the convolution operation with the weight function of the system h(n) can obtain the unit impulse (which takes the value 1 for index 0 and 0 otherwise between \(-\infty\) and \(+\infty\), as Figure 6 depicts), thus canceling the interference caused by the channel [43], according to  
\[h(n)*c(n)=\delta(n), \tag{9}\]  
Figure 5: Diagram of a nonlinear equalizer [15].  
Figure 6: Unit impulse or unit sample.  
Equation (9) can be transcribed as a group of linear equations, obtaining  
\[q_{m}=\sum_{n}c_{n}h_{m-n}=\begin{cases}1;&m=0\\ 0;&m=\pm 1,\pm 2,\ldots,\pm N\end{cases} \tag{10}\]  
This formula can be written as a matrix equation, respectively  
\[Tc=q_{r} \tag{11}\]  
where \(T\) is a Toeplitz matrix whose elements match the following statement:  
\[T_{ij}=t_{i-j}, \tag{12}\]  
otherwise, it can be written as  
\[T=\begin{bmatrix}t_{0}&t_{-1}&t_{-2}&\ldots&t_{1-n}\\ t_{1}&t_{0}&t_{-1}&\ldots&t_{2-n}\\ t_{2}&t_{1}&t_{0}&\ldots&t_{3-n}\\ \ldots&\ldots&\ldots&\ldots&\ldots\\ t_{n-1}&t_{n-2}&t_{n-3}&\ldots&t_{0}\end{bmatrix}, \tag{13}\]  
Thus, the coefficients of the equalizer are determined by reversing the square matrix \(T\), according to:  
\[c=qT^{-1}, \tag{14}\]  
It can be noticed that noise is neglected in the development of the ZF criterion. In practice, however, noise is always present, and although the ISI terms are eliminated, there is the possibility that the equalizer amplifies the effect of the noise and therefore degrades the performance [44].  
It is also worth noting that the channel is assumed to be perfectly known, consequently, the errors in the estimation of the channel impulse response will then affect the equalizer coefficients and will lead to a degradation of performance.  
The zero-force principle (ZF) cancels the inter-symbol interference but does not achieve good performance, because it does not take into account the noise through the channel.
## 3 Test and Result Analysis of Three-Dimensional Bending Test
### Data Processing Analysis  
This experiment measured three-dimensional curvature fiber Bragg grating sensors in a three-dimensional linear state, three-dimensional bending state 1 and three-dimensional bending state 2.  
In this experiment, the data of the 3D curvature sensor of FBG in the three-dimensional linear state, three-dimensional bending state 1 and three-dimensional bending state 2 were measured, respectively. The perceived data were measured twice, and the measured data were measured once. According to the three-dimensional spatial curve reconstruction method described in this paper based on curvature information of discrete points, Matlab was used for programming fitting. The actual and perceived curves under various states were compared and analyzed, as shown in Figure 9.  
The data processing and analysis results show that the shape of the measured curve is basically consistent with the shape of the perceived curve in the three bending forms. In the three-dimensional bending perception state, the error of each coordinate axis direction is generally no more than 15 mm. In engineering practice, it is generally required that the error should not exceed 10%. Therefore, the precision of the designed optical fiber three-dimensional curvature sensor meets the error range of the straightening of the scraper conveyor in coal mine engineering. The error between the sensing data and the actual data of the three-dimensional curvature sensor of the FBG does not exceed 1.6% of the maximum bending deformation, and it shows good repeatable test stability in adapting to different bending forms.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 5 Results and Discussions
### Evaluation of Mixed Vehicle Applications versus Vehicle Speed
In Figure 11a, we measure the average delay against the vehicle's speed. When the vehicle was moving at a speed of under 80 km/h, the proposed AQAGS scheme could maintain the QoS requirement for both applications. However, only the FTP application delay requirement, which is below 0.3 s, was maintained when the speed was increased to 120 km/h (i.e., 300 ms). The AQAGS delay rose to 0.45 s when the vehicle speeds reached 160 km/h. In comparison to the CMGM and Adroit scheme, the AQAGS method exhibited a lower average delay of roughly 13.52% and 32.93%. Additionally, AQAGS's delay for slower-moving vehicles was very small, recorded at 0.00234 s (or 2.3 ms). When compared with the delay reported by the CMGM and Adroit schemes, this delay was lower by 7.52%  
Figure 10: Evaluation of mixed application types (FTP vs. VoIP) vs. vehicle speed: (**a**) The vehicle speed vs. the PDR; (**b**) the vehicle speed vs. the throughput. and 20.14%, respectively. This is because low-speed vehicles experience shorter search times for new MGWs than high-speed ones because of the longer link lifetimes between the IV and MGW. In high-speed vehicles, links frequently break and require repair by finding the new or existing MGW, which lengthens the packet delay. When the vehicle's speed was increased, the Adroit scheme exhibited the worst average delay. The delay increased from 0.05 s to 0.6 s, which was 17.12% longer than in CMGM. Because the best MGW is not chosen based on vehicle speed, the delay in the Adroit method rapidly increases. To transmit the final data packets, it is necessary to frequently search for a new MGW. The application's QoS is affected by this process, which increases the average delay. Figure 10(b) illustrates the impact of the changing vehicle speed on the CPO. The CPO of cluster-based schemes, such as CMGM, was higher than that of non-cluster-based schemes. In comparison to AQAGS and Adroit, CMGM's CPO was higher by 9.61% and 5.0%, respectively. These findings show that cluster deformation is required when the vehicle speed increases frequently, and the CPO value rises due to the necessity of circulating a large number of control packets through the VANET. No cluster is required for a non-cluster-based scheme, so no control packets need to be injected into the network to initiate cluster formation and CH election. However, the MGW discovery phase is when the non-cluster-based schemes produce the majority of their control packets. However, this was prevented by AQAGS, which only required the GWreq message to be transmitted when the TTL was set to one. There are no broadcast storms in the AQAGS scheme. However, as the vehicle's speed rises, the LLT between the IV and MGW becomes shorter. As a result, the MGW is frequently reselected, which raises the CPO. In comparison to CMGM and Adroit, the AQAGS generally displayed a lower CPO by 9.62% and 4.63%, respectively.
# Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment
## 4 BUVC under Limited Communication Distance in Non-Convex Environment
### BUVC under Limited Communication Distance  
Considering that the communication range of the robots in practice is limited rather than assuming that robots can communicate mutually in a given region as in the traditional CVT, the normal communication between a robot and its neighbors and the aforementioned discrete analog sensors is affected. Consequently, incorrect Voronoi regions may be developed around some robots, and the cost function may deteriorate. When the robots cannot move to the correct regional centroid, the cost function does not converge. Moreover, the robots may collide. A limited communication distance conforms to the characteristics of actual robots and reduces the communication and computing costs of the robots. To solve the abovementioned problems after constructing the division \(V_{o}\) of the BUVC under non-convex environment constraints, a limited communication distance is introduced. When calculating the points \(q\in Q\) contained in the Voronoi region of \(p_{i}\), only the satisfying analog sensors \(q\) and \(\parallel p_{i}-p_{j}\parallel\leq r_{c,i}\) robots in \(p_{i}\) are considered, where \(r_{c}\) is the communication radius of each robot. The division \(V_{o}\) of the BUVC under a limited communication distance can be obtained as follows:  
\[ \begin{cases}V_{o}=\{v_{o,i}^{b,u}\}_{i=1}^{n}\\ v_{o,i}^{b,u}=\{q\in Q,q\cap\mathbb{C}_{Q}O\ ||\ p_{i}-q\parallel\leq\frac{r_{c,i}}{2}\}. \\ \parallel p_{i}-q\parallel^{2}\leq\parallel p_{j}-q\parallel^{2}-\omega_{ij} \end{cases}  \tag{11}\]  
The BUVC before and after introducing the limited communication range is shown in Figure 5. As shown in Figure 5b, after the constraint of the limited communication range is introduced, the Voronoi region of each robot is reduced, and its centroid is correspondingly shifted. For several common centroid invisibility problems under BUVCs in non-convex environments during the division process, a centroid can be located in the invisible area relative to a robot, resulting in a collision with the non-convex part or the inability of the robot  
Figure 4: BUVC in non-convex environment. Figure 5: BUVC before and after introducing limited communication range. to reach the centroid. The concept of the invisible region \(S_{i,q}\), \(q\in Q\) is introduced as the region \(S_{i}\) comprises all the relative and invisible points \(q\) corresponding to the robot \(p_{i}\) in \(Q\), as shown in Figure 6.  
When the positions of the robots and their centroids are as shown in Figure 6, the robots can safely avoid obstacles and thus reach the final target by introducing the following obstacle avoidance strategies. When the robot \(p_{i}\) is in the safe area \(O+r_{i}\) of the non-convex part \(O\), and the centroid \(c_{v_{i}}\) of the Voronoi cell \(v_{o,j}^{b,u}\) of the robot is in the inaccessible area \(S_{i,q}\cup O\) at time \(k\), the following conditions appear. 1 and 2 represents two different robots.
## 1 Introduction
* Based on evaluation, an over 13% reduction in flow rejection rate and a 50% improved load distribution compared to baseline routing methods are demonstrated. Also, strong generalisation and transfer capabilities of 3 DQR agents are demonstrated, which enable their effective exploitation in previously unseen topologies. The paper is structured as follows. Section 2 outlines key routing challenges in 3D networks. Section 3 describes related works on TE focusing on SDN-based frameworks, QoS enforcement, and DRL applications. Section 4 presents the 3 DQR architecture, principles, and E2E path establishment workflow. Section 5 presents the details of the 3 DQR algorithm, including mathematical formulation, DRL problem setup, agent architecture, and E2E operation. Section 6 describes the evaluation and obtained results. Section 7 outlines open issues and potential future works. Finally, Section 8 summarises and concludes the paper.
# Control-Flow Integrity: Attacks and Protections
## 5 Protection Techniques
### Control Flow Guard (C-Guard)  
Control Flow Guard is a highly implemented security mechanism developed by Microsoft to defend against different types of memory error vulnerabilities [66]. It protects against memory error exploitations by enhancing strong security where the program executes its codes. While Control Flow Guard is placed, arbitrary code execution is quite hard to initiate through common vulnerabilities such as buffer overflows.  
C-Guard is unable to verify when a function returns to some unauthorized destination [49].
## 8 The Local Navigation Satellite Systems (LNSS)  
Positioning is the most important task of AUV navigation. Navigation Dead Reckoning [19] and INS [20] are traditional methods of localizing and navigating UUVs, but have problems with decreasing position accuracy over time. In addition to traditional technologies, this problem is addressed with acoustic technologies such as LNSS based on Long Baseline (LBL, Figure 9).  
Three or more acoustic buoys are placed in a specific area on the ocean surface or along the sides of a ship. The distances between the UUV and the buoys are calculated using signal delays and the speed of sound in the water. The UUV position is calculated using trilateration in the buoy reference frame. The estimated position is transmitted to the parent ship.  
Buoys (Figure 10) have three modes of operation. At the first, the product receives information via satellite communication channels, memorizes it and, at the request of the robot, transmits it. In the second mode--\"dialogue\"--the buoy connects the coastal, aerial, sea control centers with underwater robots over the VHF radio channel in real-time mode. Such data exchange allows not only knowing where the robot is and what tasks it solves, but also to continuously control it. The third mode is the easiest. The robot operates completely autonomously and only checks its coordinates with buoys, adjusting the course. In an emergency, the drone can give an SOS signal, reporting the termination of a deep-sea mission.  
Figure 10: The main idea of LNSS.  
Figure 9: Method of the operation of a long baseline (LBL) acoustic positioning system [9].
## 3 Materials and Methods
### Dynamic Attention  
The dynamic attention module computes attention weights for the shared representation generated by the cross-modal learning module. It employs a self-attention mechanism to assess the importance of each feature in the shared representation. The self-attention mechanism calculates the relevance of each feature by measuring its interaction with other features in the representation. These attention weights are then used to produce an attention vector, which is a weighted sum of the shared representation features. The attention vector captures the most relevant information across all modalities, emphasizing the features that contribute the most to the anomaly detection task.  
GRUs are employed in the dynamic attention module to compute attention weights and generate the attention vector. GRUs are a type of recurrent neural network that can capture temporal dependencies and adapt to changes over time. By using GRUs, the model can dynamically adjust attention weights based on the current state and input sequence, improving the model's ability to focus on relevant features and to adapt to changes in the input data over time. It calculates attention weights for each position in the input data based on the model's current state.  
The attention mechanism is a location-based attention mechanism that uses the position of the input features in the sequence of real-time data streams to calculate the attention weights. The attention mechanism is a hybrid approach that combines content-based and location-based attention. Content-based attention uses the input features to calculate the attention weights. In contrast, location-based attention uses the position of the input features in the sequence to calculate the attention weights. The attention weights are adaptive and can be adjusted at each time step to give more or less importance to different parts of the input sequence depending on their relevance to the task.
## 4 Experiments and Results
By integrating multiple metrics, a more comprehensive evaluation of model performance can be achieved. This approach helps to mitigate the shortcomings of individual metrics and provides a more holistic view of the model's capabilities and accuracy. \(Corr\) is not a measure of the deviation between SR results and original data but is used to assess the degree of linear correlation between the two, typically calculated using the Pearson correlation coefficient [51]. It reflects the correlation between the model's output and the true values. \(PSNR\) is an objective criterion for evaluating images and is often used to calculate the visualization error of two images and to measure the degree of distortion of an image [52]. Max is a constant indicating the maximum possible pixel value of the image. \(SSIM\) is a measure of the similarity of two images, mainly used to detect the similarity of two images of the same size or to detect the degree of distortion of an image. The algorithm is based on comparing the brightness, contrast, and structure of two images and weighing these three elements [53]. \(\mu_{x}\) and \(\mu_{y}\) represent the average brightness of images \(x\) and \(y\); \(\sigma_{x}\) and \(\sigma_{y}\) denote the standard deviation (contrast), respectively; \(\sigma_{xy}\) denotes the covariance between \(x\) and \(y\) (structural).
## 4 Combined Applications of NCs and BCs for Elimination of Hydrocarbon Pollutants
In membrane bioreactors, biological purification is successfully combined with the microfiltration/ultrafiltration/nanofliltration/direct osmosis of effluents. At the tertiary stage of purification, membrane bioreactors are combined with membrane distillation and electrodialysis [100]. To improve the operation of membrane bioreactors, the use of various nanomaterials is being actively mastered [101]. Membrane systems based on nanomaterials provide an increase in the available flow rate per square unit of the membrane and contribute to the even more efficient removal of target contaminants with a longer period of membrane operation [100]. It is obvious that the development of membrane technologies in combination with nanomaterials with NCs and BCs is a fundamental trend in the current development of technologies for the elimination of hydrocarbon pollutants. The successful application of a membrane bioreactor was demonstrated during the purification of petroleum refinery wastewater using a hybrid approach combining the intimate coupling of photocatalysis and biodegradation (ICPB) [99]. ICPB assumes the simultaneous carrying out of physical-chemical treatment and biodegradation in one reactor and ensures the achievement of good results in the decomposition of PAHs [97]. When combining different processes in one reactor, biodegradable intermediate products obtained as a result of the physical-chemical treatment of waste are immediately biodegradable. Thus, during the photocatalytic destruction of toluene, its partial transformation to biodegradable intermediates was ensured. Then, the appeared product was immediately mineralized by microorganisms [102]. Today, hybrid processes such as ICPB are known for the degradation of hydrocarbon pollutants (Table 3) [99,102,103,104,105,106,107,108,109,110] and some persistent and toxic organic pollutants [97]. At the same time, it can be argued that research in this direction is currently almost at the initial stage of development but will actively advance in the near future.
## 3 Simulation Results
### Depolarization of Reflection and Transmission  
Depolarization ratio \(\delta\) is shown in Figure 12 for different incident zenith angles. The high DoP in Figure 6d,h and Figure 6d,h corresponded to a little \(\delta\) value. The \(\delta\) value near one was consistent with a low DoP. Figure 12a shows the depolarization ratio of reflection of air-incident light. The depolarization ratio was positively correlated with wind speed. The reflected light would become unpolarized with too high speed. In some cases, the \(\delta\) value beyond one indicated that parallel-polarized lidar was transformed into partially vertically polarized light. Only in certain conditions, where wind is gentle and incident angle is between 20\({}^{\circ}\) to 40\({}^{\circ}\) or beyond 60\({}^{\circ}\), can the depolarization of reflected light be negligible. Figure 12b presents the depolarization ratio of transmission of air-incident light. When the wind speed was beyond 20\({}^{\circ}\), the depolarization was nearly zero no matter how strong the wind was. The depolarization ratio of reflection for water-incident light had the same effects as air-incident light. The depolarized influence of transmission for water-incident light, shown in Figure 12d, was negligible with large zenith angles. We find an unusual depolarization phenomenon whereby polarized lidar light was severely depolarized when the zenith angle was small, even in the case of transmission. From this point, the depolarization effects of sea surface were not negligible. To minimize the effects of sea surfaces, it would be better if the incident angle of lidar was bigger than 20\({}^{\circ}\) and less than 40\({}^{\circ}\). Alternatively, if the depolarization rate of sea surfaces is calculated based on the geometry of lidar and wind speed, then the depolarization rate of the echo signal minus depolarization rate caused by the ocean-air interface could be regarded as the rectified depolarization rate of ocean constituents.  
Figure 11: Transmitted energy pattern for polarized water-incident lidar corresponding to Figure 10. The viewing direction is looking downward, facing the specular refraction direction. (**a**) I of reflectance; (**b**) _O/I_, horizontal polarization (\(>\)0) and vertical polarization (\(<\)0); (**c**) _U/I_, \(+45^{\circ}\) polarization (\(>\)0) and \(-45^{\circ}\) polarization (\(<\)0); (**d**) circular polarization.