esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 3 Methodology
### Research Framework
**: _Usefulness has a significant positive impact on customer satisfaction in the context of mobile banking applications._  
Security of personal data (SEC) indicates the degree to which a client's data is protected from unauthorized access and use [48]. Banks should take major measures, such as biometric authentication, to provide secure m-banking services. A Pareto analysis con ducted by [PERSON] et al. [2] confirmed that security is one of the most important dimensions when the quality of mobile banking applications is concerned. Mobile applications with implemented security protection mechanisms make people feel safe and more relaxed when using them. In that respect, we propose the following hypothesis:  
**H8. **_The security of personal data has a significant positive impact on customer satisfaction in the context of mobile banking applications._  
Behavioral intention (BEH) refers to the extent to which clients are willing to recommend mobile banking applications to people who are important to them and continue using them [40]. Current studies uncovered that customer satisfaction has a significant impact on the profitability of an organization and behavioral intentions such as repeating purchases, saying positive things about the organization, and giving recommendations [49; 50]. Therefore, we propose the following hypothesis:  
**H9. **_Customer satisfaction has a significant positive impact on customer behavioral intention in the context of mobile banking applications._  
Figure 1 presents the research model, which consists of the aforementioned constructs and hypotheses. A proposed model was conceptualized based on the findings of a study conducted by [PERSON] and [PERSON] [19], which was focused on uncovering the key dimensions of m-banking service quality. The null hypothesis of the Kruskal-Wallis test is that the mean ranks of the groups are the same. The age of clients is a key determinant of service quality and adoption in the context of mobile banking. The findings of a study carried out by [PERSON] et al. [51] suggest that consumer behavioral intention, performance, and effort expectancy are significantly moderated by age. Thus, we propose the following hypothesis:  
**H10. **_There is no significant difference in the perceived quality of mobile banking applications among diverse age groups of their clients._  
Figure 1: Research model with corresponding hypotheses. Considering that all mobile applications share essential functionalities but also have particularities that might affect their perceived quality, we propose the following hypothesis:  
**H11**. : _There is no significant difference in the perceived quality among various mobile banking applications._
## 4 ReMouse Dataset Analysis
### Sessions Generated by Different User  
In the second stage of our ReMouse dataset study, the focus was on the pairwise analysis of sessions generated by different users. The findings of this analysis are summarized below:  
**Observation 2.1**: Different users produced different-looking sessions when completing the same/similar online task.  
The validity of this observation was confirmed by comparing all users' sessions in our dataset (i.e., by calculating the cross-user pairwise minimum DTW distance). Table 3 shows the minimum normalized cumulative DTW distance value between two sessions of two distinct users out of all users' sessions. As shown, the most similar trajectories generated by two distinct users are sessions 6 and 29 of users 1 and 2, respectively. The actual DTW distance between these sessions is 21.94, which suggests that, although similar, these two sessions are not identical. This observation can be further generalized, implying that even though sessions generated by two distinct human users while completing the same/similar online task may exhibit a high degree of similarity, they are also likely to be sufficiently distinct from each other.  
**Observation 2.2**: There are no two sessions created by two distinct users that are closer to each other than (any) two sessions created by the same user when completing the same/similar online task.  
To confirm this observation, in addition to calculating the distance between sessions generated by different users, we also computed the minimum normalized cumulative DTW distance between ANY two (not just consecutive) sessions generated by the same user in the ReMouse dataset. Table 4 summarizes these results, and it shows that out of the entire ReMouse dataset, user 1 has generated two most similar trajectories (corresponding to sessions number 16 and 28) with a respective distance of 20.376812.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Min DTW** & **Users** & **Sessions** \\ \hline
21.941833 & 1 and 2 & 6 and 29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-user pairwise DTW normalized cumulative distance calculation result.  
Figure 9: (a) Sum of cumulative DTW distance value in sessions generated by the same user, user 74; (b) Sessions 39 (blue) and 40 (orange) of user 74.  
The observations of this section can be further generalized and put in the context of session-replay bots. Namely, the numerical results obtained through the analysis of ReMouse dataset imply that no two sessions (i.e., mouse trajectories) generated on a static web-site--regardless of whether they are generated by the same or two distinct users--can be identical. Based on this, we further hypothesize that only pre-programmed session-replay bots are theoretically able to produce identical browsing sessions (i.e., mouse trajectories). Or, put another way, any occurrence/observation of 'identical' or 'almost identical' browsing sessions (i.e., mouse trajectories) in a web-site should be taken with caution, potentially warranting further investigation for the presence of session-replay bots.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attacks on CC
#### 5.2.3 Ping of Death  
A ping of death (PoD) attack involves an adversary using unusually large packets to cripple the CC's VM or resources. The adversary changed the ping instruction by modifying the Fragment Offset field in the IP header to create a packet larger than the maximum permissible value for that field, which is 65,536 bytes. A ping packet with a size larger than the limit set by TCP/IP could overflow the buffer of the destination OS [53], affecting the victim's computer connected to the CC networks and influencing the CC services linked to those networks. However, nowadays, all modern network equipment and OS ignore 65,535-byte IP packets that may cause a crash or slowdown of the OS, making today's network and machines less susceptible to this attack. Figure 6 illustrates the PoD attack.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 2 Design of TEG Simulation System  
### Working Principle of TEG  
In 1821, the German scientist [PERSON] discovered during his research that when two different conductor materials form a closed loop and there is a temperature difference between the two contact points of the loop, a electromotive force will be generated in the loop. This phenomenon is called the Seebeck effect, and TEG is based on the Seebeck effect to directly convert heat into electricity [25; 26].  
As shown in Figure 1, A and B represent different conductor materials, and \(T_{h}\) and \(T_{c}\) represent the temperature at the junction of two materials. Due to the considerably greater Seebeck effect of semiconductors in comparison to metallic conductors, semiconductor material is commonly utilized in the construction of thermoelectric plates.  
In light of the modest amount of electromotive force produced by a solitary TEG plate, it is customary to join several TEG plates in series or parallel configuration to create a TEG module. The TEG module itself has internal resistance, so its equivalent circuit can be represented by a voltage source and equivalent resistance, as shown in Figure 2. \(V_{\mathrm{OC}}\) is the open circuit voltage of the TEG module, \(RTEG\) is the internal equivalent resistance of the TEG module, and \(RL\) is the equivalent load resistance.  
Figure 1: Schematic diagram of Seebeck effect.  
Figure 2: Equivalent circuit of thermoelectric module.  
In Figure 2, \(V_{OC}\) can be expressed as shown in Equation (1).  
\[V_{OC}=S_{\text{pn}}(T_{h}-T_{c}) \tag{1}\]  
\(S_{\text{pn}}\) is the Seebeck coefficient, \(T_{h}\) and \(T_{c}\) are the temperatures of the hot end and the cold end respectively. The relationship between load voltage \(V_{L}\) and open circuit voltage \(V_{OC}\) is shown in formula (2)  
\[V_{L}=V_{OC}-IR_{TEG}=S_{pn}\Delta T-IR_{TEG} \tag{2}\]  
In Equation (2), \(I\) is the output current of the TEG module. The load voltage \(V_{L}\) can be expressed as:  
\[V_{L}=IR_{L} \tag{3}\]  
\[\text{I}=\frac{S_{pn}\Delta T}{R_{L}+R_{TEG}} \tag{4}\]  
The output power of the TEG module can be expressed as:  
\[P_{TEG}=V_{L}I=I^{2}R_{L} \tag{5}\]  
Substituting Equations (4) into (5), we can get the following results:  
\[P_{TEG}=\big{(}S_{\text{pn}}\Delta T\big{)}^{2}\frac{R_{L}}{(R_{L}+R_{TEG})^{ 2}} \tag{6}\]  
According to formula (6), when the load resistance is equal to the equivalent resistance of the TEG module, the output power of the TEG module reaches the maximum value:  
\[P_{TEG}=\frac{\big{(}S_{\text{pn}}\Delta T\big{)}^{2}}{4R_{TEG}} \tag{7}\]
## 2 Data
### Propagation of Uncertainty  
The calculation of the second term in Equations (1) and (3) requires the determination of \(\gamma\). For realistic plasma measurements with a certain level of uncertainty, the index \(\gamma\) is derived with uncertainty \(\delta_{\gamma}\). We use the error propagation formula in order to estimate the  
Figure 4: (**a**) Histograms of the relative standard deviations (standard deviations over the absolute mean values) of the Bernoulli integral within short time intervals observed in 1995. We calculate the Bernoulli integral assuming (magenta) \(\gamma=0\), (blue) \(\gamma=5/6\), and (red) \(\gamma=5/4\). The grey histogram shows the relative deviation of the dynamic term \(\frac{1}{2}v^{2}\). (**b**) histograms of the differences between the Bernoulli integral relative deviation and the relative deviation of the dynamic term within the selected intervals.  
uncertainty of the function \(F(\gamma)\equiv\frac{\gamma}{\gamma-1}\), which is part of the second term in the Bernoulli integral as  
\[\delta_{F}=\bigg{|}\frac{\partial F}{\partial\gamma}\bigg{|}\delta_{\gamma} \Rightarrow\delta_{F}=\frac{\delta_{\gamma}}{\left(\gamma-1\right)^{2}}. \tag{5}\]  
The relative error \(\frac{\delta_{F}}{F}\) is  
\[\frac{\delta_{F}}{F}=\frac{1}{\left(\gamma-1\right)}\frac{\delta_{\gamma}}{ \gamma}. \tag{6}\]  
In Figure 5, we show the relative error \(\frac{\delta_{F}}{F}\) as a function of \(\gamma\) for different values of the relative error of the polytropic index \(\frac{\delta_{\gamma}}{\gamma}\). The relative error \(\frac{\delta_{F}}{F}\) increases rapidly as \(\gamma\to 1\). The grey solid bar shows the range \(5/6<\gamma<5/4\), for which the absolute value of \(F\) is larger than 5, and the histogram of the thermal term of the Bernoulli integral overlaps with the histogram of the dynamic term (see Figure 3). According to the diagrams in Figure 5, even for a small error in the calculated polytropic index such as \(\frac{\delta_{\gamma}}{\gamma}=0.1\), the relative error of \(F(\gamma)\) exceeds \(\sim\)50% for \(\gamma\) values between \(5/6\) and \(5/4\).  
Our analysis shows that the second term of the non-isothermal Bernoulli integral and its uncertainty may become significant for values \(\gamma\to 1\). In typical analyses however, we may use the non-isothermal expression when \(\left|\gamma-1\right|\) is bigger than a threshold value we choose arbitrarily. Then, the significance of the second term in the Bernoulli integral depends on the threshold we choose. For instance, if we choose to use the isothermal expression for \(\left|\gamma-1\right|<0.3\), then according to Equations (2) and (4), the significance of the thermal term depends on the plasma density. It is then reasonable to ask: how do we choose the appropriate integral expression when \(\gamma\) is close to unity? In the Section 4 we re-examine the formula of the Bernoulli integral.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
###### Abstract  
With the evolution towards the interconnected future internet spanning satellites, aerial systems, terrestrial infrastructure, and oceanic networks, availability modeling becomes imperative to ensure reliable service. This paper presents a methodology to assess end-to-end availability in complex multi-tiered architectures using a Markov model tailored to the unique characteristics of cloud, fog, edge, and IoT layers. By quantifying individual tier reliability and combinations thereof, the approach enables setting availability targets during the design and evaluation of operational systems. In the paper, a methodology is proposed to construct a Markov model for the reliability of discrete tiers and end-to-end service availability in heterogeneous multi-tier cloud-fog-edge networks, and the model is demonstrated through numerical examples assessing availability in multi-tier networks. The numerical examples demonstrate the adaptability of the model to various topologies from conventional three-tier to arbitrary multi-level architectures. As connectivity becomes ubiquitous across heterogeneous devices and networks, the proposed approach and availability modeling provide an effective tool for reinforcing the future internet's fault tolerance and service quality.  
multi-tier networks; availability; cloud computing; internet of things; Markov models +
Footnote †: journal:  
0000-0001-669-22000
## 4 Empirical Study
### Experimental Discussion  
To demonstrate the effectiveness of and satisfaction with the proposed system, a number of objective and subjective evaluations were conducted. Here, a set of insightful discovery is discussed as follows.  
* Algorithm 2 shows that \(k\) is the factor for predicting the course degree. Notably, if \(k\) is smaller than 50, the effectiveness decreases. Although the best effectiveness appears when \(k>50\), \(k\) cannot be too large or efficiency is affected. This is why we set \(k\) to 50.
* The levels 1 and 2 questions can be viewed as the motivation evaluations of the proposed system. Figures 3 and 4 show that over 80% of testers want an intelligent system that can provide course suggestions and course degree prediction services. This result provides evidence that the proposed system is necessary and valuable for student learning.
* Figure 6 summarizes the ratings for all three levels of questions. First, 87.611% and 86.283% of testers assigned positive ratings for levels 1 and 2 questions, respectively. These results show that most testers agreed that the existing system lacks the ability to suggest courses and predict course degrees. Second, 95.664% assigned positive ratings for the level 3 questions. In detail, the proposed  
Figure 4: Ratings for level 2 questions.  
Figure 5: Ratings for level 3 questions.  
system can provide an effective course selection support. Further, the high positive ratings for the level 3 questions give an evidence that the data mining technique based on References [5; 37] works well for bridging personalities, preferences and achievements [9; 23; 24; 27].  
In summary, the basic idea of our proposed idea is based on an educational aspect that personality is an impact factor for preferences, and preferences will have an impact on learning results. Accordingly, our intent is to mine the confident rules between personalities and achievements by data mining techniques. To evaluate the proposed idea, a number of experiments were made. The subjective and objective evaluation results show that the proposed idea is effective by combining data mining techniques and educational aspects.
## 2 System Model and Problem Formulation
### System Model
It is presumed that the \(l\)-th [PERSON] is solely intercepting the data intended for [PERSON], who is situated within the same vicinity. Then, for the independent or coupled phase scheme, the signal-to-interference-plus-noise ratio (SINR) at the Bob\({}_{l}\) and [PERSON]\({}_{l}\) can be expressed as  
\[\mathrm{SINR}_{[PERSON]_{l}} =\frac{\left|(\mathbf{h}_{Bl}^{H}+\mathbf{\theta}^{H}\mathbf{H}_ {I})\mathbf{w}_{I}\right|^{2}}{\left|(\mathbf{h}_{Bl}^{H}+\mathbf{\theta}_{I} ^{H}\mathbf{H}_{I})\mathbf{w}_{I}\right|^{2}+\sigma^{2}} \tag{3}\] \[\mathrm{SINR}_{Eve_{l}} =\frac{\left|(\mathbf{h}_{I}^{H}+\mathbf{\theta}_{I}^{H}\mathbf{G }_{I})\mathbf{w}_{I}\right|^{2}}{\left|(\mathbf{h}_{I}^{H}+\mathbf{\theta}_{I }^{H}\mathbf{G}_{I})\mathbf{w}_{I}\right|^{2}+\sigma^{2}} \tag{4}\]  
Figure 1: STAR-RIS-aided secure communication from a BS to a user in the presence of an eavesdropper. respectively, where \(l=r\), \(l^{{}^{\prime}}=t\), and vice versa. Thus, for the independent or coupled phase scheme, the secrecy rate for the \(l\)-th Bob is represented as  
\[R_{l}^{SEC}=[\ln(1+\text{SINR}_{Bob_{l}})-\ln(1+\text{SINR}_{Eve_{l}})]^{+}. \tag{5}\]  
where \([x]^{+}\triangleq max\{x,0\}\).
## 8 Changes in Section 5. Range Versus Data Rate: Results
#### 4.2.2 Changes in Section 6. Discussion  
In paragraph 2 line 6, the sentence \"For instance, a 5 dB fade margin decreases the maximum urban range from 867 m to 576 m, while a 10 dB fade margin further decreases the range to 369 m.\" should be changed to \"For instance, a 5 dB fade margin decreases the maximum urban range from 443 m to 277 m, while a 10 dB fade margin further decreases the range to 164 m.\"  
In paragraph 3 line 1, the sentence \"The free space line-of-sight scenario resulted in a theoretical maximum range of 333 km when transmitting at the highest SF and using the lowest bandwidth.\" should be changed to \"The free space line-of-sight scenario resulted in a theoretical maximum range of 133 km when transmitting at the highest SF and using the lowest bandwidth.\"  
In paragraph 3 line 6, the sentence \"For instance, the maximum range of LoRa at 868 MHz calculated with the FSPL model equals 921 km, which is almost three times the range of LoRa at 2.4 GHz.\" should be changed to \"For instance, the maximum range of LoRa at 868 MHz calculated with the FSPL model equals 921 km, which is almost seven times the range of LoRa at 2.4 GHz.\"  
In paragraph 4 line 4, the sentence \"The estimated range varies from 26 m to 107 m, depending on the SF and BW.\" should be changed to \"The estimated range varies from 18 m to 74 m, depending on the SF and BW.\"  
In paragraph 6 line 4, the sentence \"This data rate can be achieved if the distance between the transmitter and receiver is not greater than 9393 m, 26 m and 25 m in a free space, indoor and urban environment, respectively.\" should be changed to \"This data rate can be achieved if the distance between the transmitter and receiver is not greater than 3739 m, 18 m and 3 m in a free space, indoor and urban environment, respectively.\"  
In paragraph 7 line 4, the sentence \"Thus, the outdoor range of LoRa is more than five times larger than the outdoor range of BLE 5 and more than eight times larger compared to typical IEEE 802.11 networks.\" should be changed to \"Thus, the outdoor range of LoRa is almost three times larger than the outdoor range of BLE 5 and more than four times larger compared to typical IEEE 802.11 networks.\"  
These changes have no material impact on the conclusions of our paper. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected.  
Figure 4: Communication range and data rate for every combination of spreading factor (SF) and bandwidth (BW) in an urban environment.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
## 6 Conclusions  
We have proposed a cloud-based distributed architecture to accelerate video encoders that could be offered by cloud providers as a coding platform to their users or can be deployed on premises. Its main features are: elasticity, fault-tolerance, hyperconvergence, adaptability to different video codecs, and automation.  
The architecture relies on three node types (master node that schedules jobs, media node that stores raw and coded videos, and worker nodes that perform the coding) to be deployed on a cloud infrastructure. This paper has also detailed the deployment of our proposal on a private cloud to fine tune the infrastructure. The goal is to achieve hyperconvergence and near bare metal network performance by using SR-IOV in a provider network. Moreover, an application based on a message middleware is deployed on the top of this virtual infrastructure to parallelize the execution of video encoders x265 and libvpx-vp9. The proposal has been evaluated in terms of scalability, workload, and job distribution ranging the number of workers from 1 to 15 at different bitrates and chunk sizes.  
According to the quality evaluation, in terms of PSNR, the proposed cloud-based encoding system provides a similar quality to the sequential encoding process while improving encoding time, which can decrease up to 89% when using 15 workers. Moreover, the speedup is almost ideal until the use of 9 workers with an efficiency greater than 80%.  
As future work, we are testing the behavior of the architecture with AV1 codec and the impact on performance and quality of other non-uniform video splitting schemes and other higher resolutions.  
Conceptualization, J.G.-A. and M.G.-P.; methodology, J.G.-A. and J.M.C.; software, J.G.-A. and R.P.-O.; validation, J.G.-A., R.P.-O. and M.G.-P.; investigation, all authors; resources, J.M.C.; data curation, R.P.-O.; writing--original draft preparation, all authors; writing--review and editing, all authors; supervision, J.M.C.; funding acquisition, J.M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Research and Development Grants RTI2018-098156-B-C55 and the Generalitat Valenciana under the project GV-2016-002.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.