esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 3 BGP Anomalies and Attacks
### Interception Attacks  
Interception attacks, particularly those executed as adversary-in-the-middle (AitM) operations within the BGP ecosystem, represent a sophisticated evolution of prefix hijacking techniques. Unlike traditional hijacks that merely redirect traffic, interception attacks are characterized by their ability to both divert and subsequently forward traffic, ensuring that communications reach their intended destinations, albeit via the attacker's network. This dual action allows the attacker to remain undetected, preserving the connectivity and functionality of the network while clandestinely monitoring or manipulating data.  
At the heart of an interception attack is an attacker's ability to insert themselves as a clandestine intermediary within the communication path between a source and its intended  
Figure 5: AS path forgery hijacks.  
destination. By exploiting vulnerabilities in BGP routing, attackers announce routes that not only attract traffic towards their systems but also cleverly reroute it back to the legitimate path after inspection or alteration. This sophisticated strategy involves announcing IP prefixes--either exact matches or more specific subprefixes not owned by the attacker--to mislead routers into sending traffic through the attacker's network.  
The execution of an interception attack typically involves two critical phases: the initial diversion of traffic through the attacker's network and the subsequent forwarding of this traffic back to its original path. This process requires a nuanced understanding of BGP routing mechanisms and the ability to manipulate route announcements in a way that remains invisible to both the source and destination ASs. By maintaining the appearance of normalcy, the attacker can intercept, inspect, and potentially alter data packets without raising alarms, making interception attacks particularly insidious and difficult to detect. The DEFCON attack is another well-known experiment that demonstrates interception attacks on the Internet.  
An example of the application of an interception attack might involve compromising the integrity of CAs (illustrated in Figure 6). CAs are pivotal in Internet security, issuing digital certificates that verify the identity of websites and ensure encrypted connections. However, by manipulating BGP routes, attackers can intercept the validation checks performed by CAs [36; 41; 43]. This interception enables the attacker to fraudulently obtain CAs for domains they do not own, facilitating a range of malicious activities. There have also been a number of suspected geopolitical BGP interception incidents in recent years that include Internet traffic being compromised and rerouted through state-owned telecommunications companies and ISPs [6; 31; 32].
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 2 Background and Motivation  
### Assumption on Fat-Tree  
In this paper, we consider packet forwarding in fat-trees. We use the three-tier \(k\)-ary fat-tree architecture proposed in [1]. A \(k\)-ary fat-tree has \(k\) pods, each containing two layers of \(k/2\) switches. Each \(k\)-port switch (ToR switch) in the lower layer is directly connected to \(k/2\) hosts. Each of the remaining \(k/2\) ports is connected to \(k/2\) of the \(k\) ports in the aggregation layer of the hierarchy. There are \((k/2)^{2}\)\(k\)-port core switches. Each core switch has one port connected to each of \(k\) pods. The \(i\)th port of any core switch is connected to pod \(i\) such that consecutive ports in the aggregation layer of each pod switch are connected to core switches on \((k/2)\) strides. We note that each ToR switch is connected with \(k/2\) aggregation switches and each aggregation switch with \(k/2\) core switches. In general, a fat-tree built with \(k\)-port switches supports up to \(k^{3}/4\) hosts. A fat-tree architecture in [1] can achieve the full bisection bandwidth of a network. In this paper, we assume that \(k\geq 8\) is a power of 2. The links between ToR switches and aggregation switches are homogeneous and the links between aggregation switches and core switches are homogeneous. In a fat-tree, path selection is made only in the stage of upward forwarding because downward forwarding is deterministic once upward forwarding is determined.
## 4 Discussion
### The Role of VRT in the Design Process
It might be possible to provide users with a template in which virtual reality is already provided and basic interactions can be prototyped. The study's findings suggest that VR is most effective when used in the embodiment phase of the product innovation cycle. In this phase, VR can be utilized to refine and validate design concepts before final decisions are made. The ability to visualize and interact with the design in a simulated environment allows for more informed decision-making and can prevent costly changes later in the development process. The VR prototype was also useful to produce content for the experience since the final product was not ready in time. So, this experience could replace the real one, potentially saving time in the development process. However, the VR prototype was being developed in parallel with other parts of the project, which made it, on the one hand, independent of other parties, but difficult to assume time-saving at the end of the process. Iterative VR testing could also have taken place several months earlier, during the development phase of the 3D model, so the feedback could be implemented in an earlier design. However, such an approach was not applied in this study. VR cannot be used in the last stages of the NPD cycle, since the product must become physical at some point. Moreover, VR is also known as a bad choice for detail design. Feedback for changes should be provided when parts of the experience do not work as intended. Thus, waiting to test for problems when building the final box would have left no opportunity for revising the development. Finally, we still consider the main issue to be the neglect of the social context in this test, rendering VR still suitable during the embodiment phase of the product innovation cycle for its spatial aspects and user flow. A multi-person experience (e.g., VR arcades) would have been useful, but did not fit within the budget. Another option, building a physical dev kit, would have implied more work and thus a higher cost and lead time. This was an advantage of VR over physical prototype development. This implies that if the cost of VR development decreases or becomes more predictable, it could be easier to implement VR in the NPD process. These findings could also indicate that VR might be used in more technical or practical use cases in contrast to social or open-ended scenarios.
## 1 Introduction
### Contributions  
The resource allocation problem for an IRS-aided underlay D2D cellular network is studied in this paper. The main contributions are as follows:  
* The objective of this paper is to maximize the network's SE, i.e., sum rate of both the CUs and the D2D pairs, by jointly optimizing the transmit power for both the CUs and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. The optimization problem is subjected to the signal-to-interference-plus-noise ratio (SINR) constraints to meet the minimum data rate requirements to ensure the QoS for both the CUs and the D2D radio links. Since the constructed problem in this paper is a mixed integer non-linear optimization problem and poses challenges to being solved optimally, RL-based approaches are utilized.
* The IRS-assisted D2D underlay cellular network is structured by a Markov Decision Process (MDP) in the RL framework. At first, a Q-learning-based solution scheme is utilized. Then, to make a scalable solution in the large dimensional state and action spaces, a deep Q-learning-based solution scheme using experience replay is adopted. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem with the consideration of the continuous-valued state and action spaces.
* Simulation outcomes under various representative parameters are provided to prove the effectiveness of the proposed RL-based solution schemes, and an analysis of the impact of different parameters on the system performance is also provided. It is certainly observed that the proposed RL-based solution schemes can provide significantly higher SE compared to the traditional underlay D2D network without IRS and without RL under different network parameters.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 6 Challenging Issues for Marine Internet
### Mobility Management with Mobile BSs  
In terrestrial mobile systems, mobility management include user's location management and handoff support. The former aims to maintain connectivity and services when a user changes his/herlocation on a large scale such as moving from one city to another. The latter aims to maintain the continuation of a communication in-progress when the user moves in the course of communication due to switching contact points, i.e., a BS/AP [21]. Here, the user connects to the network through a BS/AP, which is stationary (except satellites, which however their motion traces are predictable), and connects with other networks such as Internet. The basic idea of maintaining the connectivity of a mobile user is to assign a temporary network identity to the user after it moves out of the home network to a foreign one. Since this temporary identity is corresponding to the network address of the BS/AP (e.g., IP address) that the user is attached to, the mobile user can be connected via the BS/AP.  
In most cases, marine Internet users are carried by moving vessels each equipped with a BS/AP, via which the user connects with other networks. If a marine Internet user is assigned with a temporary network identity similar to the terrestrial mobile user, the connectivity cannot be maintained because the onboard BS/AP has to connect other networks such an ICN, which however changes relatively to the vessel's motion. In this case, the temporary identify can be reached by the onboard BS/AP but not by the other networks. This issue becomes more complex when a vessel cannot contact an ICN or a satellite but via other relaying vessels instead.  
As a vessel is moving, switching of contact points between the onboard BS/AP and the one for other networks causes handoff to occur in batch (i.e., for all on-going communication sessions of the users in the vessel). This is different from per-call handoff in terrestrial mobile networks, resulting in handoff processing more challenging, especially when different types of sessions (e.g., voice, data and video) are present in the same batch. Furthermore, as mentioned above, both broadcast and unicast communications may be used; as vessels move, a user originally under broadcast communication may require unicast communication, and vice versa. This feature further complicates handoff process.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 3 Malware Propagation Modeling in Multi-Layer Networks
### Topologies
#### 3.1.3 Random Graph Topology  
Random graphs can be described by their probability distribution [54]. Its practical applications are found in all areas where complex network models are needed, so many random graph models are known to reflect the different types of complex networks found in different areas.  
A random graph is achieved by starting with a set of \(n\) isolated vertices and adding successive ends randomly to each other. Different random graph models produce different probability distributions. The most commonly studied model is the one proposed by [PERSON], called \(G(n,p)\), where each possible edge appears with a probability of \(0<p<1\). The probability of obtaining a particular random graph with \(m\) edges is: \(p^{m}(1-p)^{N-m}\), where \(N=\begin{pmatrix}n\\ 2\end{pmatrix}\)[55].  
Random graph theory studies the typical properties of random graphs, that is, those that are most likely to apply to graphs derived from a particular distribution. Researchers often focus on the asymptotic behavior of random graphs and the fact that different probabilities converge as \(n\) becomes very large. Filtration theory characterizes the degree of connection of random graphs, especially infinitely large graphs. Local infiltration has been particularly studied by [PERSON] [56] and refers to the removal of a node by its neighbors and subsequent nearest neighbors.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 3 Results
### Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case** & **Ship Type** & **Lon** & **Lat** & **Course** & **Speed** & \
\begin{tabular}{c} **Timestamp** \\ **(s)** \\ \end{tabular} & **TCPA** & **DCPA** & **DCPA** & **
\begin{tabular}{c} **Turning** \\ **Direction** \\ \end{tabular} \\ \hline \multirow{2}{*}{Scenario 1} & First ship & 123.028005 & 30.227079 & 205 & 8.9 & 1122 \\  & Target ship & 123.002815 & 30.159317 & 20.6 & 11.4 & 1122 \\ \hline \multirow{2}{*}{Scenario 2} & First ship & 123.084828 & 30.241897 & 308 & 9 & 1902 \\  & Target ship & 123.072669 & 30.303739 & 160 & 8 & 1902 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The collision avoidance turning point information for the first ship and the target ship. information of both scenario 1 and scenario 2. In scenario 1, when the two vessels were 4.26 nautical miles apart and in a head-on situation, the first ship executed a port-turn collision avoidance maneuver. In scenario 2, when the two vessels were 3.77 nautical miles apart and in a crossing situation with a small crossing angle, the first ship deviated from the COLREGs, executing a port-turn collision avoidance maneuver. In summary, through comparative experiments using case trajectories, the proposed model, compared to the DP algorithm, accurately identifies the earliest collision avoidance turning point of the vessel while avoiding data interference caused by wind, waves, and currents. Furthermore, based on the encounter scenario classification standards, the model correctly identifies the encounter scenario between the vessels, thereby verifying the effectiveness of the model.
## 5 Conclusions  
A novel deraining network is presented; it mitigates different rain effects--rain streaks with different directions and intensities and rain accumulations--and targets images captured under rainy driving scenes. The proposed network comprises of three main phases. The first phase performs rain streak removal to remove most visible rain streaks in an image; the second phase is rain accumulation removal which is similar to a defogging operation; lastly, we perform a second rain streak removal operation in order to remove any left rain streaks that could be boosted by the previous rain accumulation removal phase.  
The network is based on baseline lightweight network in order to maintain real-time computational performance, as this is a crucial constraint for the performance of deraining networks along with other subsequent operations performed by AVs.  
Experiments showed that the proposed network has higher efficiency when compared with SOTA deraining networks, regarding both qualitative and quantitative evaluation metrics, and running time. Moreover, we evaluate the proposed network in addition to the SOTA deraining networks regarding object-detection performance. The presented methodology is considered to be the first to deal with both real-time efficiencies and enhanced performance.  
Further research direction includes the investigation of enhancing the model to mitigate more diverse weather conditions such as snow, sun glare, and lightning. Moreover, the proposed network performs removal of both rain streaks and rain streaks accumulation even if only one of these degradations exist. Therefore, another direction to our research is to add initial step of detecting the type of degradation exists in the captured images. Additionally, we plan to test the network on different hardware in order to evaluate the speedup enhancement that could be achieved using more powerful hardware.  
Conceptualization, E.K. and A.O.; data curation, E.K.; formal analysis, E.K.; funding acquisition, A.O. and M.V.; investigation, E.K.; methodology, E.K.; project administration, A.O. and M.V.; resources, A.O.; software, E.K.; supervision, A.O.; validation, E.K., A.O. and M.V.; visualization, A.A.; writing--original draft, E.K.; writing--review and editing, A.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
In this work, a novel deep learning-based model for classifying fabric faults is developed. We developed our own dataset to test the suggested concept empirically. The noise was manually added to the data to assess the noise resistance of the algorithms proposed in this study. The classical DRSN technique performed best with 96.86% accuracy and 765.0 s, DRSN based on the leaky activation performed best with 97.85% accuracy and 773.0 s, whereas the suggested Log-DRSN model performed best with 99.17% accuracy and 781.0 s. There is not much difference between the complexity times of the Leaky activation ReLU function and the Log-Leaky activation ReLU function, only a small amount of computation time due to the log operation is high in the log-leaky function. However, the accuracy rate of the log-leaky activation ReLU is higher than leaky activation, so the accuracy rate criterion is more critical for our study. It has been discovered that the proposed novel model is an effective strategy for both lowering computation time and providing good outcomes in terms of performance metrics.  
However, it is important to acknowledge certain limitations and challenges associated with the proposed model. The experiments were conducted on a relatively small, custom dataset with synthetic noise, which may not fully represent the diversity of real-world fabric defects and environmental conditions. Moreover, the slightly increased computation time, while acceptable in offline or batch-processing contexts, could pose challenges for real-time inspection systems. It is also anticipated that utilizing higher computational resources--such as more powerful GPUs or parallel processing--would further improve both the training speed and overall performance of the model. Future work should focus on testing the model on larger, more diverse datasets and exploring optimization techniques to reduce inference time without compromising accuracy. Addressing these aspects will be essential for the successful deployment of the model in industrial fabric inspection settings.  
C.T.: conceptualization, investigation, design, analysis, writing--original draft; E.A.: data collection, analysis, writing--original draft and supervision; M.A.: writing and data collection. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ablation Settings**} & \multicolumn{3}{c}{**Metrics**} \\ \hline
**RSN-1** & **RSN-2** & **RSN-3** & **Attn** & **ST** & **Acc (\%)** & **Time (s)** \\ \hline ✓ & ✓ & ✓ & ✓ & ✗ & 98.75 & 705.0 \\ ✓ & ✓ & ✓ & ✗ & ✓ & 26.48 & 365.0 \\ ✓ & ✓ & ✗ & ✓ & ✓ & 96.40 & 580.0 \\ ✓ & ✗ & ✓ & ✓ & ✓ & 98.29 & 605.0 \\ ✗ & ✓ & ✓ & ✓ & ✓ & 99.03 & 626.0 \\ ✓ & ✓ & ✓ & ✓ & ✓ & 99.17 & 781.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics of different ablation studies.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 1 Introduction
### Assessment of Recent Work
Several approaches that do consider the type II error use it in a different context, e.g., as an economic aspect in the sense of customer risk, or limit their analysis to a specific test type, such as sudden death tests (SD tests). In the work of [PERSON] et al. [28], the accuracy of an uncensored EoL test is discussed. As an assessment criterion, the confidence interval width is used as a ratio of upper and lower confidence bounds at a certain lifetime quantile. MCS are used to determine the required sample size, which results in the desired confidence interval width for a Weibull distribution stemming from prior knowledge. [PERSON] et al. [29] present a method to determine an SD test configuration that allows a mean time to failure (MTTF) to be estimated most economically through MCS. In a certain parameter space of the Weibull shape parameter, sample size, and inspection lot size, a decision is made as to whether the inspection lot is accepted or rejected. The Weibull evaluation is limited to the MTTF, whereby smaller failure probabilities might be more interesting in practical application. [PERSON] and [PERSON] (2018) focus their work on time-censored EoL test plans, which are approximated using exponential distributions, and in which test specimens are observed in intervals. The entire approach is limited to exponential distribution and time-censored tests, which results in limited applicability. [PERSON] et al. (2018) compare the SD test with non-censored, time-censored, and failure-censored EoL tests with regard to time-saving potential. Various test configurations are generated using MCS. The average duration of failure-censored tests is discussed by [PERSON] (2018). The expected test time values are calculated and compared using a binomial distribution. This means that the time benefits of failure-censored tests, which depend on the Weibull shape parameter, the sample size, and the censoring proportion, can be worked out. [PERSON] et al. (2018) evaluate failure-censored tests under limited test infrastructure. The investigated influencing variables are the sample size, censoring proportion, number of sequential tests, and the acceptance probability of an inspection lot on the basis of a failure distribution specified as already known.