esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework
## 3 MITRE ATT&CK-Based Ship Cyberattack Analysis
Finally, [PERSON] [31] reported on IRGC cyber operations documents, which included a plan to capsize or sink a ship by attacking the marine satellite communication terminal and the BWMS. The reviewed literature is summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Threat** & **Impact** & **Reference** \\ \hline \multirow{8}{*}{AIS} & AIS malfunction, jamming, spoofing & Not mentioned & [PERSON] et al. [16] \\ \cline{2-4}  & Spoofing, replay attack, frequency & Not mentioned & [PERSON] [28] \\  & hopping attack & & \\ \cline{2-4}  & Designed without security, malicious & Hijacking, smuggling, theft & [PERSON], [PERSON] et al. [29] \\ \cline{2-4}  & False signals, represent & Collisions, pollution, & \\  & nonexistent emergencies & grounding, interruption of & [PERSON] and [PERSON] [30] \\ \hline \multirow{8}{*}{ECDIS} & Physical access Internet connection & Provides physical/logical & \\  & establishment Authorized access & access Exploitation of & \\  & Operating system support and security & well-known vulnerabilities & \\  & patches Operating system & Reduces performance and & \\  & configuration etc. & opens backdoor & \\ \cline{2-4}  & Vulnerable versions of SMB and Remote & Infection and dysfunctionality & \\  & Desktop Protocol (RDP) & & \\  & & & \\ \cline{2-4}  & Outdated Apache web server & Gain unauthorized access & \\  & Vulnerable version of SMB & Remote attacker & \\ \cline{2-4}  & Vulnerable versions of SMB and Remote & Execute arbitrary code & \\  & Desktop Protocol (RDP) & & \\ \cline{2-4}  & Designed without security, malicious & Hijacking, smuggling, theft & [PERSON] et al. [29] \\ \cline{2-4}  & version, malware & Sails to different coordinates & \\ \cline{2-4}  & GNS spoofing by malware & Crash the operator station & [PERSON] et al. [18] \\ \cline{2-4}  & Virus, DoS, spoofing & Not mentioned & [PERSON] [28] \\ \hline \multirow{8}{*}{SATCOM} & Backdoors & \multirow{2}{*}{Install malicious firmware} & \multirow{2}{*}{[PERSON] [4]} \\  & Hardcoded credentials & & \\  & Insecure protocols & Execute arbitrary code & \\  & Undocumented protocols & & \\  & Password reset weaknesses & & \\ \cline{2-4}  & Unencrypted protocols & Discloge sensitive information & [PERSON] et al. [25] \\ \cline{2-4}  & Default credentials, not updated & & \\  & software, etc. & Not mentioned & [PERSON] [28] \\ \cline{2-4}  & Cyberattack by hostile states & Discloge sensitive information & [PERSON] [31] \\ \hline \multirow{2}{*}{BWMS} & Phishing emails, malware & Ransomware, false command & [PERSON] [28] \\ \cline{2-4}  & Cyberattack by hostile states & Sinking of the ship & [PERSON] [31] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of research.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 5 Open Issues and Future Directions
It allows a UE to simultaneously connect to multiple BS's, typically a macro cell and a small cell, and use both connections to transmit data. This improves handover reliability and reduces interruption time during handover, as the device can seamlessly switch between the two connections without losing data. Additionally, it can also increase the overall network capacity, throughput and improve coverage in areas with high user density. However, there are several challenges that need to be considered, such as coordination between different base stations for fast switching during handovers, interference management, mobility, load balancing, security, energy efficiency, and QoS management between different macro cell and small cell users [25]. Data-Driven Handovers: In 4G/5G HetNets, data-driven handover approaches are crucial to ensure optimal performance and user experience. Future research will focus on developing new techniques and methods to handle the complexity of these networks, such as dealing with large amounts of data, accommodating high mobility, supporting multiple radio access technologies, adapting to dynamic environments, ensuring security, privacy and interoperability with other technologies. Overall, data-driven handover approaches are an essential aspect of 5G and B5G HetNets research and will have a significant impact on the overall performance and efficiency of these networks. * Digital Twins (DT): A digital twin in mobile networks refers to a virtual representation of a physical network infrastructure, such as a mobile device or a cellular network that can be used to simulate and optimize the performance of handovers. By creating a digital twin of a mobile device and the network infrastructure, network operators can test and optimize handover procedures in a simulated environment before deploying them in the actual network. This can help to identify and address potential issues with handovers, such as dropped connections or delays in the transfer process, bottlenecks and plan for capacity upgrades, and can lead to improved network performance and a better user experience. Digital twins can also be used in real-time monitoring, this way it can help to identify when and where handover issues are occurring in the network and take appropriate action to resolve them [183; 184; 185; 186; 187]
## 4 Empirical Analysis
### Evaluation of Services
#### 4.3.1 Calculation Process and Analysis Results of All Respondents
Service models ranked low included the detection services for night security incidents, which had, violence-related incident detection (A-2-1, CC\({}_{i}\): 0.479), detection of crime fear (A-2-2, CC\({}_{i}\): 0.355), and intrusion status detection (A-2-3, CC\({}_{i}\): 0.521) ranked 13 th, 18 th, and 11 th, respectively. This may be due to the technological development of general CCTVs, which has made it easier to detect objects at night even with RGB images. Detection services for emissions in business places (C-3-1, CC\({}_{i}\): 0.517), and explosives at large events (C-3-2, CC\({}_{i}\): 0.370) were ranked 12 th and 17 th, respectively. These services can be replaced with other measurement sensors which need not be thermography cameras. Road surface hazard detection (B-2) and building status detection (C-1) services ranked the lowest, ranging from 16 th to 21 st. These services cannot cover the control blind areas with fixed equipment. Although mobile equipment, like drones, may be utilized, the results may be insufficient and not reliable enough for evaluators.
# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 4 Semantic Correspondence Likelihood
### Segment Shape, Size, and Position Similarity  
The shape, size, and position similarity of two segments is measured by comparing their CTI descriptors. In order to reduce computational complexity, the CTI descriptors, \(d\), are projected onto a lower dimensional latent space, as proposed in [19]. Thereby, descriptors \(q\) of \(n_{q}<n_{d}\) elements are obtained by:  
\[q=O^{T}d, \tag{2}\]  
where \(O\) represents an orthonormal basis defining a latent space computed by performing the principal component analysis (PCA) of the CTI descriptors \(d\) extracted from the segments of a training set. Another reason for computing the latent vectors is to decouple the shape from the position information. The first three elements of \(q\), denoted in this paper by \(q^{t}\), represent the position of the segment in the object RF, while the other 21 elements, denoted in this paper by \(q^{s}\), describe its shape and size. Let usconsider a model database \(M\) of \(n_{M}\) models. The segments of the \(k^{\text{th}}\) model are represented by latent vectors \(q_{ik}^{M},i=1,...,n_{M,k}\). Analogously, segments of the query object are represented by the latent vector \(q_{j}^{Q},j=1,...,n_{Q}\).  
The similarity between two segments taking into account their position (\(t\)), shape (\(s\)), and size (\(a\)) can be measured by Gaussian function:  
\[y_{ijk}^{C}=\exp\left(-\frac{1}{2}\left(\frac{e_{ijk}^{t}}{\sigma_{t}^{2}}+ \frac{e_{ijk}^{s}}{\sigma_{s}^{2}}+\frac{e_{ijk}^{a}}{\sigma_{a}^{2}}\right) \right), \tag{3}\]  
where \(\sigma_{t}\), \(\sigma_{s}\), and \(\sigma_{a}\) represent parameters that define the contribution of the difference between the segment position, shape and size to the total similarity measure, respectively. The values of the algorithm parameters used in the experiments reported in Section 7 are given in that section. Translation, shape, and scale differences are computed by the following three equations.  
\[e_{ijk}^{t}=\frac{||q_{j}^{Q,t}-q_{ik}^{M,t}||^{2}}{||q_{j}^{Q,s}||||q_{ik}^{M,s}||}, \tag{4}\]  
\[e_{ijk}^{s}=1-\left(\left(\frac{q_{j}^{Q,s}}{||q_{j}^{Q,s}||}\right)^{T}\left( \frac{q_{ik}^{M,s}}{||q_{ik}^{M,s}||}\right)\right)^{2}, \tag{5}\]  
\[e_{ijk}^{a}=\frac{\left(||q_{j}^{Q,s}||-||q_{ik}^{M,s}||\right)^{2}}{||q_{j}^{ Q,s}||||q_{ik}^{M,s}||}. \tag{6}\]  
The norm of vector \(q^{s}\) represents a measure of the segment size. In (4), the position difference is normalized by the size of the segments, therefore allowing bigger segments to have greater distance in order to achieve the same similarity measure values. Equation (5) contains a scalar product of two unit vectors. The greater the value of the scalar product, the more similar the shapes are. Equation (6) represents a segment size difference measure.
# Information Consumer Experience: A Systematic Review
## 5 Answering Research Questions
### What Is ICX? (Information Consumer Experience)
The lack of a definition for ICX or a similar concept (besides the CX approach) represents an opportunity for us to introduce the first definition of ICX to the body of knowledge and open a new research trend to explore the information consumption within an organization with a CX perspective, in order to generate a broader assessment of the interactions between specific organization members with internal (or external for internal use) information products, systems, and services, across the touchpoints of an information consumer journey (inherited from CX domain). Therefore, ICX can be considered a particular instance of the CX concept and could be addressed using analytical methods from the CX approach. Our proposed definition of ICX is: \"Information Consumer Experience (ICX) involves all the interactions between employees (consumers) and information products, systems and services inside an organization, including tasks such as information usage, information generation, information management, information inter-department sharing, information usage, based team work and decision making\".
# Design, Implementation and Power Analysis of
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Light**} & \multirow{2}{*}{**No of Light**} & \multicolumn{2}{c}{**Base Scenario**} & \multicolumn{2}{c}{**Manually Controlled Scenario**} & \multicolumn{2}{c}{**Smart Scenario**} \\ \cline{3-8}  & & \multicolumn{1}{c}{**Solb**} & \multicolumn{1}{c}{**Cost in Rupees**} & \multicolumn{2}{c}{**Cost in Rupees**} & \multicolumn{2}{c}{**Cost in Rupees**} \\ \cline{3-8}  & & **Dom** & **Com** & **Dom** & **Com** & **Dom** & **Com** \\ \hline \multirow{8}{*}{Incandescent} & 1 & 0.972 & 1.992 & 0.486 & 0.996 & 0.40824 & 0.83664 \\  & 2 & 1.944 & 3.984 & 0.972 & 1.992 & 0.81405 & 1.6683 \\  & 4 & 3.888 & 7.968 & 1.944 & 3.984 & 1.62567 & 3.33162 \\  & 8 & 7.776 & 15.936 & 3.888 & 7.968 & 3.24891 & 6.65826 \\  & 12 & 11.664 & 23.904 & 5.832 & 11.952 & 4.87215 & 9.9849 \\ \hline \multirow{8}{*}{8 W LED} & 1 & 0.1296 & 0.2656 & 0.0648 & 0.1328 & 0.05508 & 0.11288 \\  & 2 & 0.2592 & 0.5312 & 0.1296 & 0.2656 & 0.11016 & 0.22576 \\  & 4 & 0.5184 & 1.0624 & 0.2592 & 0.5312 & 0.22032 & 0.45152 \\  & 8 & 1.0368 & 2.1248 & 0.5184 & 1.0624 & 0.440235 & 0.90221 \\  & 12 & 1.5552 & 3.1872 & 0.7776 & 1.5936 & 0.66015 & 1.3529 \\ \hline \multirow{8}{*}{0.5 W LED} & 1 & 0.0081 & 0.0166 & 0.00405 & 0.0083 & **0.005265** & **0.01079** \\  & 2 & 0.0162 & 0.0332 & 0.0081 & 0.0166 & **0.009315** & **0.01909** \\ \cline{1-1}  & 4 & 0.0324 & 0.0664 & 0.0162 & 0.0332 & **0.017051** & **0.034943** \\ \cline{1-1}  & 8 & 0.0648 & 0.1328 & 0.0324 & 0.0664 & _0.0324_ & _0.0664_ \\ \cline{1-1}  & 12 & 0.0972 & 0.1992 & 0.0486 & 0.0996 & _0.047831_ & _0.098023_ \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Cost consumption analysis of light bulbs under diverse conditions and scenarios. tailored to control more apparatus in the future to automatically satisfy the needs of users with large power savings. Conceptualization, P.S.R. ; Formal analysis, M.M. ; Investigation, P.S.R. and R.A.R. ; Methodology, P.S.R. ; Supervision, M.M. and R.A.R. ; Writing--original draft, P.S.R. ; Writing--review & editing, M.M. and R.A.R. The authors would like to thank the Editor and the anonymous reviewers for their perceptive comments and productive suggestions that certainly enhanced the quality of this paper. The authors declare no conflict of interest.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### Repetitive Training Analysis  
To more clearly observe the effects of different GAN sample sizes on accuracy, the number of GAN-generated samples was progressively reduced from 60 to 5, with each reduction followed by 50 training sessions. The average accuracy was recorded after each reduction. Table 5 illustrates how the accuracy varied depending on the number of GAN samples generated for Case 1. The results indicate that accuracy initially improved with more GAN samples but then declined, possibly due to the model overfitting to the generated data, as Figure 3 shows.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.19 Mobile Phones  
Malicious hackers also make use of a method known as phone phishing. Phone phishing is a criminal activity in which the malicious hacker performs a social engineering attack through a telephone or mobile phone to obtain sensitive and private information; this information is then used for the attacker's gain. Phone phishing has several forms of attack and the most popular are:  
* SMS phishing: Phishers send victims texts with a fraudulent URL, which is disguised as a legitimate source and instructs users to send their personal information or to download a specific app.
* Call phishing: Phishers pretend to be a legitimate organization such as a bank or tax agency and instruct the user to share their personal and sensitive information.  
Figure 8: Outline of a man-in-the-middle attack [70].  
* Social media phishing: Phishers create fake profiles to entice victims to take part in giveaways and romantic scams and then proceed to ask the victims to send large amounts of money and share their personal information.
* Application phishing: Many legitimate applications and games use advertisements as a means for users to earn rewards or increase profit. Malicious hackers can therefore use this to display their own advertisements which, when clicked on by a user, lead to opening a malicious link or downloading a malicious application.  
Examples of these social engineering attacks include the following: (1) The attacker calls the victim and informs them that they have won a substantial amount of money; if the victim wants to claim the reward, they must send their personal information, including their account credentials. (2) An attacker calls the victim and pretends to be an authority from the victim's bank. The attacker claims there is an issue with the victim's account credentials and advises that the account will be blocked unless the victim confirms their sensitive information, such as bank account and card details.
# An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0
## 1 Introduction  
The implementation of the Industry 4.0 concept within manufacturing enterprises was, and still is, the objective of many research papers [1; 2; 3; 4]. Maintenance in manufacturing companies plays a crucial role in improving their competitiveness [5]. Moreover, companies should develop and implement those models that have already been employed and that can predict reliable production in operation, according to the Industry 4.0 paradigm [6].  
Human operators are key resources within a smart manufacturing company [7], since such workers are aware of specific technological processes; however, one can still observe the need to obtain support in the selection of appropriate employees, in order for them to undertake given activities. Maintenance department managers expect that, in the event of a breakdown, they will receive a list of those employees whose competence will guarantee that the machine will be repaired in the shortest time in real time.  
The approach to the selection of expert workers needs to be computerised and codified, while using data of a specified format, in order that it may prove to be useful. According to [8], the formal representation of the competence of workers is the key factor of the model's effectiveness. Moreover, they stated that there exists a lack of the representation of competence for \"Diagnosis\" as well as\"Management\" tasks that were carried out in maintenance departments based on the literature review of 74 research papers; it is clear that these competences are primarily associated with employees performing these processes in the company.  
Our proposed approach focusses on supporting management tasks that were carried out in the maintenance department and it includes the following elements: (1) Defining the type of failure for each manufacturing resource, (2) Defining the parameters of each type of failure for each maintenance worker, (3) Defining the competences of each maintenance worker, (4) Defining the importance of competence while using the Fuzzy Analytic Hierarchy Process (FAHP) method, (5) Defining the \"state of nature\" for each manufacturing resource regarding the employee, and (6) Selecting the maintenance expert for the repair of the manufacturing resource. Our approach is also partly investigated in the form of a web-application and is presented, based on a case study. The problem in this paper is how to assess the competences of maintenance workers in manufacturing companies and how to select the employees with the appropriate competences to undertake the repair of a given machine in companies.
## 5 Open Challenges and Future Directions
### Model Generalization and Transferability  
Generalization remains a major barrier to field deployment. Many AI models perform well in one environment but poorly in another due to differences in soil conductivity, antenna frequency, or survey geometry. Domain adaptation and transfer learning techniques offer a partial solution by enabling models trained in one domain to adapt to others. However, further work is needed to ensure robustness across diverse conditions. Incorporating meta-learning and continual learning strategies may allow models to update incrementally as new data become available, improving adaptability without requiring full retraining [79; 80; 81; 82; 83; 84].
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm
### Optimization Problem for SF Selection
#### 5.1.3 Problem Solution  
We denote by \(\eta^{*}\) the optimal solution of the original problem (\(\mathcal{P}\)). It can be expressed by:  
\[\begin{split}\eta^{*}&=\max_{p(s)}\,(\frac{ \mathcal{R}}{\mathcal{E}})\\ &=\frac{\mathcal{R}(p^{*}(s))}{\mathcal{E}(p^{*}(s))}\end{split} \tag{24}\]  
\(\eta^{*}\) is the maximum of the problem (\(\mathcal{P}\)) if and only if:  
\[\begin{split} F(\eta^{*})&=\max_{p(s)}\,(\mathcal{ R}-\eta^{*}\mathcal{E})\\ &=0\end{split} \tag{25}\]  
\(F(\eta)\) is continuous and strictly decreasing in \(\eta\). Then, we can derive its unique root \(\eta^{*}\) using the Dinkelbach iterative algorithm, summarized in Algorithm 1. At each iteration, we solve the inner problem (\(\hat{\mathcal{P}}\)) (Line 3) using the nonlinear programming solver provided in MATLAB fmincon. It is a gradient-based method that is designed to work on problems where the objective and constraint functions are both continuous and have continuous first derivatives. Then, we update the \(\eta\) value in (Line 6) until \(F^{n}\) becomes \(\leq\epsilon\), where \(\epsilon\) represents a maximum given tolerance. Note that the obtained local optimum will be considered global since we work in the concavity domain of \(\mathcal{R}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(s\) & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(D(s)\) & 5612 & 3264 & 1831 & 1043 & 561 & 303 \\ \hline \end{tabular}
\end{table}
Table 7: Maximum number of nodes at each SF, \(\lambda=6\) packets/h, \(k=40\) bytes.
## 3 Results and Discussion
### Droplets Deposition Characteristics
#### 3.2.2 Simulation of the Influence of Different Factors on Droplets Deposition  
According to the droplet's spatial distribution, the droplet concentration in the calibration line was obtained by the fluent post-processing. The maximum concentration point was regarded as the droplet deposition center. Therefore, the influences on droplet deposition were analyzed by comparing the drifting distance of the droplet's deposition center.  
1. [label=()]
2. Influence of crosswind wind speed on droplet deposition  
When the V-shaped wind speed was 20 m/s, and the spray pressure was 0.5 MPa, the crosswind wind was changed from 2 m/s to 4 m/s at an equal interval of 1 m/s. The droplet's spatial distribution and the droplet concentration in the calibration line are shown in Figure 11.  
Figure 10: Droplet spatial distribution with different crosswind speed: 2 m/s (**a**); 4 m/s (**b**).  
As shown in Figure 12, when crosswind wind speed and the spray pressure are the same, the faster the V-shaped wind speed is, the smaller \(\beta\) is, the bigger the droplet concentration in the calibration line is, and the nearer the drifting distance of droplet deposition center is. When the V-shaped wind speed remains constant, the droplet concentration in the calibration line increases first and then decreases. When the V-shaped wind speed is 25 m/s, the drifting distance of the droplet's deposition center is only 1.2 cm, which is 1.8 times smaller than that of 15 m/s.
3. Influence of spray pressure on droplet deposition  
When the crosswind wind was 3 m/s, and the V-shaped wind speed was 20 m/s, the spray pressure was changed from 0.4 MPa to 0.6 MPa at an equal interval of 0.1 MPa. The droplet's spatial distribution and the droplet concentration in the calibration line are shown in Figure 13.  
Figure 12: Droplet spatial distribution and the droplet concentration in the calibration line with different V-shaped wind speed: 15 m/s (**a**); 20 m/s (**b**); 25 m/s (**c**).  
As shown in Figure 13, when the V-shaped wind speed and the crosswind wind are the same, the bigger the spray pressure is, the bigger \(\beta\) is, the bigger the droplet concentration in the calibration line is, and the further the drifting distance of the droplet deposition center is. When the spray pressure remains constant, the droplet concentration in the calibration line increases first and then decreases. When the spray pressure is 0.6 MPa, the drifting distance of the droplet deposition center is 9.0 cm, which was 6.2 times larger than that of 2 m/s.