esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Intelligent Planning
#### 5.1.4 Nondeterministic Planning  
In real-world planning problems, it is often inevitable to encounter certain uncertainties. Compared to deterministic planning, planning under uncertainty generates reliable solutions in the presence of uncertain conditions [76]. For the penetration path planning problem, two main approaches have been developed to address planning under uncertainty.  
One approach involves using a probability distribution to model the uncertainty of action outcomes within the planning domain. This uncertainty is managed through techniques such as probabilistic model checking or other probabilistic planning methods. [PERSON] et al. [77] introduced a method that integrates attack probability and execution time into the model construction. They developed probabilistic attack trees from Choose and Combine primitives, which were then integrated with enhanced versions of the Floyd-Warshall algorithm and [PERSON]'s algorithm. This integration allowed for the efficient search of attack paths that minimized expected time while maximizing the probability of success. Further advancing this field, [PERSON] et al. [78] utilized the attack success probability as a measure of environmental uncertainty. He proposed the APU-D* algorithm, an adaptation of the D* algorithm, which enables rapid adjustment of penetration paths through incremental replanning in response to plan failures.  
Another approach treats the planning problem as an optimization task, utilizing MDPs and POMDPs for modeling. It employs value iteration or policy iteration methods for solving. Initially, [PERSON] et al. [79] modeled the PT process with the POMDP and solved it with an external planner. However, it requires a large number of observations in both the planning and execution phases to infer the state distribution and is mainly applicable to single-host penetration path planning. To extend the applicability beyond single hosts, [PERSON] [80] later introduced a domain-independent solving algorithm named the 4 AL decomposition algorithm. This algorithm streamlines the penetration path planning process by breaking it down into four hierarchical layers: network, subnet, host, and service. Subsequently, [PERSON] et al. [81] modeled PT as a partially observable contingent problem and developed a contingent planning tree algorithm to strategize penetration paths.  
Compared to POMDPs, MDPs are easier to solve. [PERSON] et al. [82] modeled penetration path planning as a finite-horizon MDP, which efficiently computes an action selection strategy that minimizes the expected cost of an attack through specific search and pruning techniques. [PERSON] et al. [83] proposed an online planning system called Logic for Advanced Virtual Adversaries, integrated into the Caldera red teaming platform, to automate penetration path planning in unknown environments. However, the planning system has not been released.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation
### Computation Overhead Comparison  
In this section, we report the computation overhead of our proposed methods (UL-RW, SL-RW and HL-RW) and the compared well-known methods, in the response time dataset over all cases of training set densities. The parameters in all methods are set to the default values. The running time results are given in the following Table 5.  
Figure 7: Sensitivity to \(K\). (**a**) Training set density = 5%; (**b**) Training set density = 10%; (**c**) Training set density = 15%; (**d**) Training set density = 20%.  
From Table 5, it first can be seen that service-based methods (IPCC, SL-RW) consume higher running time than user-based methods (UPCC, UL-RW). The reason is that in the dataset, the number of users (339) is much smaller than the number of services (5825), in an order of magnitude. Note that, in user-based methods and service-based methods, a necessary step is to compute the similarity between two users or two services, so the computation overhead of the two kinds of methods will be enlarged. Thus, the running time of user-based methods is clearly smaller than that of service-based methods.  
Second, compared to traditional user-based methods (e.g., UPCC  
the large whole number of all potential services. In this paper, we propose an extended similarity computation method, based on Euclidean distance, to solve the sparsity problem.  
The third motivation is to study the role of network location as context information in QoS prediction, to study whether and how network location can improve the prediction accuracy. We observe that in CPS, network location is a piece of representative context information, but has been not yet given enough attention in QoS prediction. In this paper, we propose an extended random walking model leveraging network location information, to find similar neighbors and filter fake neighbors.
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 5 Performance Evaluation and Analysis  
Based on a series of simulations, this section compares the performance of LiPa with two state-of-the-art approaches, iPath [12] and CSPR [13], to analyze the effectiveness and efficiency of our proposal in different network environments.
## 2 VIIRS Satellite Data
### VIIRS_EDR Data  
The VIIRS_EDR level 2 aerosol products are obtained from NOAA Comprehensive Large Array-data Stewardship System (CLASS) at [[http://www.nsof.class.noaa.gov](http://www.nsof.class.noaa.gov)]([http://www.nsof.class.noaa.gov](http://www.nsof.class.noaa.gov)). The VIIRS_EDR aerosol parameters are derived primarily from the M-bands of the radiometric channels covering the visible through the shortwave infrared spectral regions (412 nm to 2250 nm). As explained by [PERSON] et al. [6], the VIIRS_EDR AOD is generated from 8 \(\times\) 8 pixel aggregations of the intermediate product (IP), where in turn the IP represents retrieved AOD for each and every native resolution (e.g., 0.75 km) pixel. The pixels with clouds, cloud shadows, snow, ice, subpixel water, bright land surface, fire, sunlight, suspended sediments or shallow water, and large solar zenith angle are screened out using the internal tests and the external VIIRS VCM before proceeding with the aerosol retrieval [6]. The VIIRS_EDR product represents the statistics of the 8 \(\times\) 8 aggregation, which is a retrieve then average strategy. Consequently, the resolution of the VIIRS_EDR data is \(\sim\)6 \(\times\) 6 km\({}^{2}\) at nadir (\(\sim\)12.8 \(\times\) 12.8 km\({}^{2}\) at the edge of scan). Data screening and aggregation methods can be seen in [6].  
VIIRS_EDR AOD has been collected from 2 May 2012 to 31 March 2014 over the NCP. The data between 15 October 2012 and 27 November 2012 are rejected because of an inadvertent error introduced in the operational aerosol code during this period [6]. The AOD at 488 nm, 550 nm and 672 nm and AOD Quality Flags (QF1), as well as Land Model Aerosol Index flag (QF4), are used in this study. The values of QF1 refer to the estimated \"quality\" of the retrieval product, so that QF1 = 0, 1, 2, and 3, represent not value produced, low, medium and high quality, respectively. The values of QF4 refer to which aerosol model type was used in the AOD retrieval, where QF4 = 0, 1, 2, 3, and 4, refer to dust, high absorption smoke, low absorption smoke, clean urban, and polluted urban aerosol model, respectively.
# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 1 Introduction
\begin{table}
\begin{tabular}{c}
**Existing Issues** \\ \hline
1. In the field of marine engine fault diagnosis, this study with numerous fault types and fault feature types, which leads to the automatic feature extraction by deep learning models often containing redundant information. This increases the computational complexity of the model and affects its performance. Additionally, some systems exhibit very subtle behaviors in time series, and existing deep learning methods face difficulties in capturing such complex fault patterns.  
2. In many deep learning models, feature extraction often focuses on a single fault pattern. However, in real-world fault diagnosis tasks, faults are typically diverse and combinatorial. Existing methods perform poorly in feature fusion when dealing with data from different sensors (such as pressure, temperature, flow rate, etc.).  
3. In practical fault diagnosis, the data are often accompanied by noise and disturbances, and deep learning models are sensitive to these noises during the feature extraction process, resulting in poor model robustness.  
4. Fault data are difficult to obtain, and there is often an issue of data imbalance.  
1. In the field of marine engine fault diagnosis, this study proposes, for the first time, the combination of a CNN and TCN.  
The CNN is used to extract local spatial features, reduce redundant information, and handle noise, while the TCN captures complex temporal features, enhancing the model’s ability to recognize fault patterns. This approach effectively improves the fault feature extraction capability.  
2. By introducing the attention mechanism to optimize feature selection based on the CNN-TCN, this study addresses the issue of fusion between different fault features, adapts to various fault patterns, demonstrates the model’s generalization ability, and improves computational efficiency.  
3. Through ablation experiments, noise experiments, and comparative experiments, the reliability and robustness of the proposed method have been validated.  
4. By using simulation modeling, accurate simulations of several typical faults in large marine engines were conducted, thereby obtaining a fault dataset for large marine engines. This approach addresses the challenges of insufficient fault data and imbalanced data distribution in large marine engines.  
\end{table} 
Table 1: The existing issues and related work of this study. The contents of the other sections are as follows: Section 2 provides a detailed introduction to the theories related to the CNN-TCN-ATTENTION model. In Section 3, the marine diesel engine simulation model is developed to generate the dataset, and three diagnostic experiments and their results are presented. The conclusion is summarized in Section 4.
## 3 System Model and Assumptions
### Proposed Slice Allocation System
#### 3.2.3 Proposed Delay Aware Adaptive Slice Allocation System (DA-ASA)
\begin{table}
\begin{tabular}{c c} \hline \hline
**5G Slice** & \(\textbf{ASP}_{i}\) \\ \hline URLLC & \(ASP_{1}=\frac{R^{1}}{P_{\text{URLLC}}\times R}\times(MTD_{1}-D_{1})\) \\ \hline eMBB & \(ASP_{2}=\frac{R^{2}}{P_{\text{eMBB}}\times R}\times(MTD_{2}-D_{2})\) \\ \hline mMTC & \(ASP_{3}=\frac{R^{3}}{P_{\text{URLLC}}\times R}\times(MTD_{3}-D_{3})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The ASP of each slice’s request \(i\) in the DA-ASA system. Figure 7: The system model for the DA-ASA system. mMTC slice request will have the highest priority and obtain the service first since it has the lowest ASP value. Table 8 summarizes the CAC of the DA-ASA system. The Pseudo code for the AR-RIVAL and DEPARTURE event of the DA-ASA system is the same as the ASA system, except that the ASP is calculated using the equation shown in (17).
## 2 Materials and Methods  
The major purpose of MVD is to separate water vapor from the air without a vapor-liquid phase change, i.e., using gas-gas separation membranes. Gas separation membranes selectively separate the target gas from a mixture via the chemical potential difference between the two sides of the membrane. While the membranes can be fabricated using various materials such as metals, ceramics, and liquids, polymers are commonly used as they are easy to prepare. Membranes are broadly divided into porous and dense membranes as illustrated in Figure 1, where the latter have no physical porosity that allows a substance to pass through the membrane.  
In the case of dense membranes for water vapor separation, hydrophilic materials are usually bonded to hydrophobic polymer-based materials to form ion nanochannels in which water vapor molecules can diffuse. Therefore, the solution-diffusion model can explain the movement of the substances, where the water vapor pressure difference between the two sides of the membrane acts as a driving force.  
For the validation of membrane performance, three main performance factors for gas separation membrane are discussed: permeability, permeance, and selectivity [11]. Permeability is a normalized measure of the flux of the target substance that penetrates the membrane with a certain thickness under a driving force, with units of Barrer (1 Barrer = 10\({}^{-10}\) cm\({}^{3}\) (STP):cm/cm\({}^{2}\cdot\)s:cmHg), where the unit 'cm\({}^{3}\) (STP)' means a volume measured at standard temperature and pressure (SPT) condition at 0 \({}^{\circ}\)C and 1 bar. In contrast, the permeance is used as a membrane performance indicator when the membrane is structured in mixed channels or when the performance of the membrane itself is determined. Considering that the gas-gas separation membrane is relatively thinner than other types--liquid-liquid or liquid-gas separation types--of membrane, permeance is preferred as an index to measure the performance rather than permeability. The permeance is the permeability removing membrane thickness and is expressed in gas permeation units (GPU = 10\({}^{-6}\) cm\({}^{3}\) (STP)/cm\({}^{2}\cdot\)s-cmHg). Selectivity refers to the ratio of permeabilities of the substances that pass through the membrane. The selectivity is calculated as the percentage of each substance's permeability or permeance. When both selectivity and permeability of the target substance are high, the membrane is assumed to have good separation performance. However, since permeability and selectivity have a tradeoff relationship, it is important to produce a membrane with values optimized for the application.
# Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation
## 8 Discussion
Meanwhile, it helps to increase the computational efficiency and reduce the algorithmic complexity in the process of determining \(T\). In the following studies, we will consider the separate contribution of each sparse structure and integrate this factor into the process of determining the threshold value \(T\), in order to analyze its influence on the resulting sparse structure. The algorithm presented in this study has the potential to be applied in reality. In particular, the fabrication of metal wire grid plates has been studied and applied for a long-time using techniques such as welding or 3D metal printing. These methods can be easily implemented in industrial production at a relatively low cost and increase the feasibility of the algorithm in practice. Once the process of creating a metal grid plate is completed, the presented algorithm can be used to identify and remove wires that have low influence on the scattering field. Finally, these unneeded wires in the original WG structure can be removed using a simple cutting technique without causing deformation of the structure surface, which is a significant advantage compared to creating a perforated structure. This not only helps to optimize the structure parameters but also saves materials. To demonstrate this conclusion, in this paper, we considered the actual sparse horn antenna and measured its characteristics. This antenna can be considered as representing complex structures due to its design, which involves the assembly of rectangular and trapezoidal facets with different angles and dimensions. Furthermore, the analysis of sparse structures with different shapes, when GEET and \(T\) threshold are varied, also leads to complex shapes. These shapes arise from the combination of many cells of different shapes in different sparse configurations. The result of using OCGA for horn antenna is that the mass of sparse antennas has been reduced by about 1.4 times compared to WG original through a simple low-cost wire cutting process without deforming the surface, which increases the practicality of the proposed method. Even some actual sparse structures achieve better characteristics than the original WG structure. The obtained actual model contributes to increasing the scientific basis of the paper and helps to strengthen the reliability and capability of the algorithm under real conditions, making the algorithm performance obvious and fully applicable in fields requiring high precision such as telecommunications and military purposes. Although the measurement results for the sparse scatterer structures are not presented in this paper, the agreement between the WG-model results and the measured BSCS for the original structure has demonstrated that our scattering characteristic analysis software achieves an acceptable level of accuracy. The measurement results for the sparse scatterer structures will be presented in future work.
## 2 Materials and Methods
### Methods  
#### 2.4.1 Entropy-CRITIC Weight Method  
The entropy weight method and CRITIC method are both commonly used to assign weights to indicators. By combing these two methods, we can effectively capture the diversity, dispersion, and correlation among different features and enhance the accuracy of the weight assigning process [60].  
Standardization:  
Positive indicators:  
\[x_{ij}^{\prime}=\frac{x_{ij}-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
Negative indicators:  
\[x_{ij}^{\prime}=\frac{x_{max}-x_{ij}}{x_{max}-x_{min}} \tag{2}\]  
where \(x_{ij}^{\prime}\) is the standardized value of \(x_{ij}\), and \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of the \(j\)th indicator, respectively.  
Calculate the weight using the Entropy-CRITIC method:  
\[p_{ij}=\frac{x_{ij}^{\prime}}{\sum_{i=1}^{m}x_{ij}^{\prime}} \tag{3}\]  
\[E_{j}=-\frac{1}{ln~{}m}\sum_{i=1}^{m}p_{ij}ln~{}p_{ij} \tag{4}\]  
\[W_{1}=\frac{1-E_{j}}{\sum_{j=1}^{m}(1-E_{j})} \tag{5}\]  
\[W_{2}=\frac{\sigma_{j}\sum_{i=1}^{m}\left(1-r_{ij}\right)}{\sum_{j=1}^{m} \left[\sigma_{j}\sum_{i=1}^{m}\left(1-r_{ij}\right)\right]} \tag{6}\]  
\[W_{j}=\alpha W_{1}+(1-\alpha)W_{2} \tag{7}\]  
where \(W_{1}\) is the weight obtained by the entropy weight method; \(p_{ij}\) is the probability of the occurrence of a particular indicator value; \(E_{j}\) is the entropy value of the \(j\)th indicator; \(W_{2}\) is the weight obtained by the CRITIC method; \(\sigma_{j}\) is the standard deviation; \(r_{ij}\) denotes the linear correlation coefficient between \(i\) and \(j\); \(\sigma_{j}\sum_{i=1}^{m}\left(1-r_{ij}\right)\) is the information given by the \(j\)th indicator; \(W_{j}\) is the weight of obtained by the Entropy-CRITIC weight method; and \(\alpha=0.5\)[42].
## 3 Results and Discussion  
### Consumers' Demographic Information  
A total of 120 consumers participated and of these, 58 were male and 103 (86%) were aged 19-25 years old and 111 participants were students. Consumers' consumption behaviors are shown inTable 2. About 76% of participants responded that they consumed cold black coffee more than once a week. Evaluation was conducted in August (average high 33.4 \({}^{\circ}\)C in Busan), so there may be the possibility of seasonal influence on the frequency of cold black coffee consumption.  
Both hot and cold black coffee were consumed at similar frequencies: 52% and 64% of participants consumed hot or cold black coffee in the afternoon, respectively. When selecting coffee, consumers tended to care about taste of coffee the most. A total of 90% cared about its flavor, and 77% about its aroma. Degree of bitterness and price were also important factors. Finally, most consumers (about 87%) responded that their favorite type of coffee was coffee shop coffee.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & **Response (\(n=120\))** & **Response (\%)** \\ \hline
**Frequency of consuming hot (cold) black coffee** & & \\  More than twice a day & 6 (15) & 5.0 (12.5) \\  Once a day & 9 (37) & 7.5 (30.8) \\  2 or 3 times a week & 25 (39) & 20.8 (32.5) \\  Once a week & 18 (20) & 15.0 (16.7) \\  Once a month & 32 (5) & 26.7 (4.2) \\  Once every three months & 10 (2) & 8.3 (1.7) \\  Once every six months & 7 (2) & 5.8 (1.7) \\  Do not drink & 13 (0) & 10.8 (0.0) \\ \hline
**Time of hot (cold) black coffee consumption** & & \\  In the morning & 36 (33) & 30.0 (27.5) \\  In the afternoon & 62 (77) & 51.7 (64.2) \\  In the evening (after 6 pm) & 21 (4) & 17.5 (3.3) \\  Others & 1 (6) & 0.8 (5.0) \\ \hline
**Reason to drink cold black coffee** & & \\ **(check-all-that-apply)** & & \\  Cool & 105 & \\  Little bitter taste & 15 & \\  High amount of caffeine & 14 & \\  Good coffee flavor & 44 & \\  Others & 0 & \\ \hline
**Important aspects when select coffee** & & \\ **(check-all-that-apply)** & & \\  Taste & 108 & \\  Aroma & 92 & \\  Degree of bitterness & 80 & \\  Price & 90 & \\  Origin of coffee bean & 11 & \\  Brand & 6 & \\  Extraction method & 10 & \\  Fair trade & 5 & \\  Organic and environment-friendly & 1 & \\  Others & 0 & \\ \hline
**Type of favorite coffee** & & \\  Coffee shop & 104 & 86.7 \\  Instant coffee & 5 & 4.2 \\  Bottled/Canned coffee & 8 & 6.7 \\  Vending machine coffee & 0 & 0.0 \\  Capsule coffee & 3 & 2.5 \\  Others & 0 & 0.0 \\ \hline
**Usually drink when** & & \\  Talk with other people & 15 & 12.5 \\  After the meal & 44 & 36.7 \\  After the go to work/school & 23 & 19.2 \\  When distracted & 32 & 26.7 \\  Others & 6 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Consumers’ coffee consumption behavior.
## 6 Future Directions
### Security and Secrecy
introduced a novel approach by integrating Distributed Artificial Intelligence (DAI) with neural networks to ensure energy-efficient routing and address the challenges of fast communication within intra-clusters of nodes in intelligent transportation systems (ITSs). Additionally, they propose a new method to combine DAI with Self-Organizing Maps (SOMs). This technique aims to minimize overall energy consumption and reduce computational challenges within the network [146]. A summary of the current articles on routing approaches in 6G and satellite networks is listed chronologically in Table 5. It reveals distinct routing priorities for 6G and satellite networks. The reviewed articles highlight distinct routing paradigms for 6G and satellite networks, each addressing unique challenges. In 6G networks, AI-driven approaches dominate: ref. [143] employs Deep CNNs for real-time traffic control, optimizing scalability but facing computational overhead, while ref. [144] identifies interoperability and latency hurdles in ML integration. Ref. [145] provides an RIRM algorithm to tackle congestion in 5G Core networks, ensuring reliability for URLLC applications like autonomous vehicles. Conversely, satellite routing [122] prioritizes robustness through hybrid RF/FSO solutions, where RF withstands weather disruptions, but FSO offers higher bandwidth, albeit with atmospheric interference. Cross-cutting themes emerge in [146] regarding a Distributed AI framework for energy-efficient routing in ITS, which is relevant to both domains due to shared challenges like dynamic topologies and energy constraints. Notably, 6G emphasizes low-latency terrestrial routing, while satellite networks focus on long-distance, delay-tolerant solutions. Future integration of these paradigms (e.g., 6G-satellite NTNs)could leverage AI to harmonize terrestrial speed with satellite coverage, though handover management and resource allocation remain critical gaps.