esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology in Hybrid Intelligence: A Concise Literature Review
## 5 Gap Identification and Challenges
### A Quantitative Analysis  
The proposed analysis has been conducted on 56 different papers on the topic. As expected, most contributions (75%) present an application focus. However, the relatively consistent amount of more conceptual papers has provided significant insights (Section 5.2).  
The classification by macro domain (Figure 2) is relatively distributed and does not allow the identification of major patterns or trends. Almost half of the papers cannot be clearly associated with a macro-domain. There is a prevalence of contributions in business (11%), healthcare (9%) and smart systems (7%).  
Similar considerations also apply to a quantitative analysis by application (Figure 3), which presents a low percentage (13%) of unclassified papers and shows a major number of contributions in multi-agent systems (16%) and decision support systems (9%).  
Figure 3: Quantitative analysis by application.  
Figure 2: Quantitative analysis by macro-domain.  
An analysis by \"provided value\" is reported in Figure 4. In quantitative terms, the papers' objects of analysis deal with the four different identified categories uniformly. Such contributions are discussed in qualitative terms later on in the paper. An application-oriented perspective points out a high number of works on generic knowledge representation, while there is much less explicit focus on other aspects, including automatic reasoning.
# Forensic Investigation of Remnant Data on USB Storage Devices Sold in New Zealand
## 5 Discussion
### Findings of Collected Data from the Second-Hand USB Hard Drives  
In this research, 17 second-hand USB external hard drives were used to experiment with the availability and analysis of any remnant data in these hard drives. The output of the experiment revealed that 15 hard drives (88.23%) out of 17 hard drives had data after the recovering process, and only two hard drives had removed all the remnant data and were properly formatted. For detailed information from the experiments with 17 hard drives(presented in Appendix E ([[https://github.com/imdadullahunsw/forensics](https://github.com/imdadullahunsw/forensics)]([https://github.com/imdadullahunsw/forensics](https://github.com/imdadullahunsw/forensics)), accessed on 7 June 2022)), the summary results are listed as follows:  
* None of the USB external hard drives (0%) were damaged or unable to create the forensic image.
* Two hard drives (i.e., 11.76% namely 'USB-HD-08 and USB-HD-11') were formatted by using forensic methods, and no data were found from these hard drives.
* Seventeen of the purchased USB hard drives (100%) had traces of remnant data.
* Data from 15 hard drives (88.23%) were able to be recovered. Ten of these hard drives (58%) contained personal information which could help to identify the previous owners, and six hard drives (35.29%) contained the information that identified the organisations that used to own these hard drives. In addition, three hard drives (17.65%) contained viruses and malware.  
Figure 6 illustrates the type of data and information collected from the purchased hard drives.  
Based on our findings, we note that most of the purchased hard drives still contained the remnant data (e.g., bank transactions, driver licenses, and contracts). This is a high-security risk due to the amount of information related to a person's identity and the organisation's confidentiality. Only data from two hard drives out of 17 hard drives were securely deleted, which means the users in New Zealand, along with other users mentioned in previous research, are not yet aware of the security risks and threats. On the other hand, data from two hard drives were securely deleted, which means nearly 11% of the users i.e., 2 out of 17, understand the risks, and they also know the method to format the hard drive or delete data securely. The reasons for the vast majority of sensitive data being left on the storage devices are due to the lack of security awareness, organisation policies, disposal methods of second-hand storage devices, and users being unaware of the existence or the method of properly using secure erased tools [17; 47]. Therefore, there is a need to raise awareness and educate individuals and businesses about data security issues when using storage devices. These devices must be appropriately managed and processed following appropriate methods before being sold or stored.
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 2 Analysis and Design
### Interstage Matching Network Design
To achieve the high-pass form Z21, 33 GHz is selected as the initial matching frequency point between the input impedance of driver stage 2 (Zin-diver2) and the output impedance of driver stage 1 (Zout-driver1). At 33 GHz, Zin-driver2 = 30.62 - j*72.94 and Zout-driver1 = 50.48 - j*130.14, which is equivalent to parallel R and C of (204.37 \(\Omega\) \(\mid\) 56.21 fF) and (385.99 \(\Omega\) \(\mid\) 32.21 fF), respectively, as shown in Figure 17. Actually, there are lots of alternative transformers with different parameter combinations to match Zin-driver2 and Zout-driver1 at 33 GHz. The frequency points of the two peaks of Z21 for an MCR are affected by \(\frac{1}{\sqrt{L_{1}C_{1}}}\), \(\frac{1}{\sqrt{L_{2}C_{2}}}\), and the coupling coefficient k [36]. Thus, the peak Z21 positions for MCR2 is adjusted by tuning these parameters. Figure 18 presents a group of Z21 lines with different \(L_{1}\), \(L_{2}\), and k. In all four cases, Zin-driver2 and Zout-driver1 are conjugate-matched at 33 GHz. In Figure 18, different parameter combinations of the transformers have different peak Z21 positions and different in-band ripples, resulting in different Z21 gain shapes with matching networks inserted in the amplifier. A combination of \(L_{1}\) = 585 pH, \(L_{2}\) = 320 pH, and k = 0.462 (red line in Figure 18) is selected for TF2 to achieve the high-pass Z21 shape of MCR2. The two Z21 peaks are located at 32.5 GHz and 47.5 GHz, respectively, as shown in Figure 18. Figure 19a presents the 3D view of transformer 2 (TF2) in MCR2. Figure 19b shows the Z21 of MCR2 with a similar test method to those of MCR3 and MCR4, as mentioned above, by using  
Figure 16: (**a**) S11 at the gate of power stage from 26 GHz to 36 GHz and (**b**) the Z21 of MCR3 between driver stage 2 and power stage. Figure 15: The 3D view of transformer 3 (TF3) in MCR3. the EM simulation result of TF2. Like the cases in MCR4 and MCR3, a finite Q of the inductors makes the Z21 line of MCR2 different from that using an ideal transformer (red line in Figure 18). However, the average magnitude of the Z21 from 32.5 GHz to 40 GHz is relatively higher than that from 25 GHz to 32.5 GHz. Therefore, it indicates that a high-pass form Z21 is achieved. Figure 19: (**a**) The 3D view of transformer2 (TF2) in MCR2 and (**b**) the Z21 of MCR2 between driver stage 1 and driver stage 2 with EM simulation results. Figure 17: The lumped model of MCR2 including TF2. Figure 18: The Z21 of MCR2 between driver stage 1 and driver stage 2 in four different cases.
## 4 Model-Based Optimization
### Optimization of \(h_{m}\) and \(l_{se}\)  
Dimensional optimization is also carried out to obtain large and uniformly distributed MFD as well as low AWHD. As shown in Figure 10, it can be seen that the AWHD has little variation as \(h_{m}\) and \(l_{se}\) change, while the average MFD along the air gap increases with a decreasing slope and decreases with a constant slope as \(h_{m}\) and \(l_{se}\) get larger, respectively. \(h_{m}\) should be set as big as possible to increase  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**LPMEC Results**} & \multicolumn{3}{c}{**FEM Results**} & \multicolumn{3}{c}{**Deviation (\%)\({}^{\star}\)**} \\ \cline{2-9}  & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{m}\) (\%)} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{u}\) (\%)} & \multicolumn{1}{c}{\(B_{m}\)} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{u}\)} \\ \cline{2-9}  & \multicolumn{1}{c}{**(mT)**} & \multicolumn{1}{c}{\(B_{u}\) (\%)} & \multicolumn{1}{c}{\(B_{m}\) (\(T\))} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{u}\) (\%)} & \multicolumn{1}{c}{\(B_{m}\)} & \multicolumn{1}{c}{\(B_{ave}\)} & \multicolumn{1}{c}{\(B_{u}\)} \\ \hline
1 & 1.2409 & 122.0 & 0.628 & 1.2422 & 122.0 & 0.631 & 0.10 & 0 & 0.47 \\
2 & 1.2045 & 118.6 & 0.636 & 1.2064 & 118.4 & 0.640 & 0.15 & -0.16 & 0.62 \\
3 & 1.2420 & 119.9 & 0.648 & 1.2426 & 120.4 & 0.646 & 0.04 & 0.41 & -0.30 \\
4 & 1.2394 & 125.7 & 0.620 & 1.2404 & 125.6 & 0.623 & 0.08 & -0.07 & 0.48 \\
5 & 1.2540 & 124.8 & 0.829 & 1.2549 & 124.9 & 0.821 & 0.07 & 0.08 & -0.97 \\ \hline \hline \end{tabular}  
* *. Deviation = (FEM results – LPMEC results) / FEM results \(\times\) 100%  
\end{table}
Table 3: Comparison of improved LPMEC model and FEM.  
the average MFD. However, taking into account the manufacturing capability of large block NdFeB, \(h_{m}=50\) mm is set.  
The increasing of \(l_{se}\) may decrease \(R_{oc}\) according to Equation (4), and thus increase the magnetic flux leakage and decrease \(B_{ave}\). During the design of the RCDMC, four supporting blocks with a length of (\(l_{se}\) - \(h_{m}\)) in the \(x\)-direction and height of (\(T_{s}+\delta\)) in the \(y\)-direction are assembled beside the ends of the stator teeth. The length of supporting blocks is set to 100 mm to support the mass of center yoke and outer yokes. Therefore, \(l_{se}=150\) mm is set.
## 4 Simulation Experiment Results and Discussion  
This subsection first analyses the impact of the MP-WFRFT algorithm on the signal characteristics of both two-dimensional and three-dimensional constellation diagrams. Secondly, it offers a comparative analysis of the DM algorithm in conventional versus polarization modulation. Thirdly, the security performance of the MP-WFRFT algorithm for the VPAPM scheme is examined. Finally, the paper proposes directional variable polarization modulation with MP-WFRFT.
## 5 The Proposed Approach
### Notation  
We designate \(P\) a publisher and \(S\) a subscriber. The broker \(B\) manages the topics \(T_{1}\), \(T_{2}\),..., \(T_{n}\) to which \(P\) publishes messages. The solution operates in temporal rounds \(R\), i.e., time intervals. During a temporal round \(R_{i}\), \(P\) publishes over time messages \(m\) on a given topic, say \(T_{j}\).
# Performance of Polymer Composites Lubricated with Glycerol and Water as Green Lubricants
## 3 Results
### Tribological Results
#### 3.2.2 Coefficient of Friction for Different Polymers  
The average values of the measured coefficient of friction obtained from the tribological tests of the polymers are shown in Figure 5.  
In glycerol, a very low coefficient of friction was measured for all tested polymers, the lowest for PEEK CF30 and UHMWPE, around 0.023 and the highest for POM CF30, around 0.03. In glycerol, there was no significant difference between the measured coefficients of friction of the different polymers.  
In water, the coefficient of friction increased significantly for all polymers tested compared to the glycerol-lubricated conditions. The smallest coefficient of friction was measured for UHMW PE, around 0.06, with a 2.8 times increase compared to the glycerol lubrication regime. The highest increase in the coefficient of friction was measured for PEEK MOD (8 times), followed by PEEK CF 30 (7.8 times). A significantly lower (up to 3.5 times for PE) and stable coefficient of friction, indicating effective lubricating properties of glycerol compared to water-lubricated conditions, was similarly observed in a recent study on polymers for hydropower bearings [2].
## 3 Results and Discussion
### Statistical Comparison of Model Performance  
To determine whether the observed performance difference between RF and XGBoost was statistically significant, we conducted a series of comparative analyses. Although both models achieved similarly high AUC scores (RF = 0.94; XGBoost = 0.93), [PERSON]'s test [44] revealed a statistically significant difference in their ROC curves (\(Z\) = \(-\)2.20, \(p\) = 0.0278), indicating that RF more consistently ranked positive instances higher than XGBoost across the test set. These results suggest that even subtle differences in AUC can have meaningful implications for the reliability of landslide prediction.  
To assess whether the RF and XGBoost models differ significantly in their classification patterns, the McNemar test [45] was applied to the distribution of discordant predictions. Among 111 discordant pairs, RF correctly classified 59 instances that XGBoost misclassified, while XGBoost correctly classified 52 instances that RF misclassified. The resulting McNemar test statistic was \(\chi^{2}\) = 0.441, yielding a \(p\)-value of approximately 0.507. This indicates no statistically significant difference in the error patterns of the two models (\(p\) > 0.05). An exact binomial test, based on the assumption of equally likely discordant outcomes, confirmed this result. These findings suggest that while RF and XGBoost differ slightly in metrics such as AUC and area-based susceptibility zoning, their classification errors are statistically equivalent. Therefore, model selection may be better guided by factors such as interpretability, spatial generalization, or practical mapping outcomes rather than marginal differences in predictive performance.  
Compared to previous studies such as [PERSON] et al. [14], which reported 85.7% accuracy and 0.89 AUC using RF, our model achieved improved performance (accuracy = 87.9%; AUC = 0.94 under standard and 0.89 under spatial cross-validation). These gains are attributed to the incorporation of post-seismic rainfall, spatial cross-validation, and advanced tuning. Overall, RF slightly outperformed XGBoost, but both are robust models for co-seismic landslide susceptibility modeling in mountainous terrain.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 3 Derivation of Closed Form Average BER Expression  
In this section, the equations used for calculating the average BER of the proposed scheme over the log-normal channel are discussed. The derivation of the average BER allowed us a better understanding of the proposed system, reduced long simulation time and an eased the comparison between the proposed model and other ones.
## 3 Results and Discussion
### Kinetic  
Figure 5a presents the effect of contact time on the amount of Pb(II) Sorbed by non- and acid-modified clinoptiolite-rich tuffs. It can be observed that the time to reach the adsorption equilibrium increases with increasing Si/Al ratios. This can be explained by the presence of a low number of vacant active binding sites as a consequence of the Si/Al ratio increment which limited the exchange of Pb(II) by the exchangeable ions from the zeolite network. Moreover, Figure 5a shows that the kinetic processes for the Nat-CLI, HCLI-0.1 M and HCLI-0.2 M sorbents consist of two main reaction stages: An initial fast adsorption process followed by a slow continuous sorption reaction; whereas a continuous uptake of Pb(II) from aqueous solution is observed over time for HCLI-0.5 M and HCLI-1.0 M.  
The results presented in Figure 5b show are similar in maximum Pb(II) uptake when the Si/Al ratio ranges from 4.3 to 7.91 shows. However, the sorption of Pb(II) decreased drastically as Si/Al ratio increased. This can be attributed to the decrease in exchangeable ions during the acid-treatment (Table 1) and higher competition between H\({}^{+}\) from the acid-modified clinoptiolite-rich-tuffs and Pb(II) in the solutions.  
Figure 4: pH\({}_{\text{initial}}\) vs. pH\({}_{\text{final}}\) of non- and acid-modified clinoptiolite-rich tuffs.  
Figure 5: (**a**) Pb(II) uptake of vs. time and (**b**) experimental Pb(II) uptake vs. Si/Al ratio.  
The data for Pb(II) sorption by non- and acid-modified clinoptilolite-rich tuffs were fitted to pseudo-first order, pseudo-second order, and intra-particle diffusion models. As shown in Table 2, the kinetic parameters corresponding to the sorption of Pb(II) by Nat-CLI, HCLI-0.1M, and HCLI-0.2 M fit best to the pseudo-second order model (\(R^{2}\geq 0.99\) and \(q_{\text{cal}}\approx q_{\text{exp}}\)), while for the HCLI-0.5 M and HCLI-1.0 M sorbents, the results show that the pseudo-first order model was more suitable for describing the kinetic process.  
The \(k_{2}\) diminished when the Si/Al ratio increased. However, the \(k_{1}\) slightly increased when the Si/Al increased as well. These results shows that the dealumination of the acid-modified clinoptilolite-rich tuff has repercussion on the kinetic of the Pb(II) sorption and the Si/Al ratio will determine the order of the kinetic reaction (pseudo-second order or pseudo-first order).
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Energy Model for UAV Path Planning  
The energy model plays a crucial role in understanding the energy consumption of drones during their flight. Notably, despite the significant features of UAVs, such as their versatility and agility, their flight time is still limited by the onboard battery capacity, which often leads to incomplete missions [21]. To cope with this limitation, the UAV path planning is formulated as a minimization optimization problem in which the energy consumption is taken into consideration when planning the UAV trajectory. In this regard, the path is optimized such that the energy consumption is minimum. This can be achieved by considering the energy as an objective to be minimized. The energy consumption of the generated path is calculated using the drone's energy model, as outlined by [PERSON] et al. in [53]. This model takes into account various maneuvering activities, including horizontal and vertical movements as well as hovering. In the context of path planning, the emphasis is on understanding how different movements, such as hovering, horizontal movement, and vertical movement, impact energy consumption. Therefore, the energy consumption for each operation is determined according to the methodology presented in [53]. It has to be stressed that the speeds of all UAVs are assumed to be constant in this study.  
The climbing energy for a distance \(\Delta h\) and speed \(v_{acmlb}\) is given by  
\[E_{clmb}(\Delta h)=P_{clmb}\frac{\Delta h}{v_{clmb}}, \tag{1}\]  
where \(P_{clmb}\) is the power required for climbing.  
The descent energy for a distance of \(\Delta h\) and velocity \(v_{desc}\) is given by  
\[E_{desc}(\Delta h)=P_{desc}\frac{\Delta h}{v_{desc}}, \tag{2}\]  
where \(P_{desc}\) is the power required for descending.  
The hovering-in-place energy for a duration of time from \(t1\) to \(t2\) is given by  
\[E_{how}=P_{how}(t2-t1), \tag{3}\]  
where \(P_{how}\) is the power required for hovering.  
Flying horizontally is given as a function of speed and can be expressed as follows:  
\[E_{horz}=P_{v}\frac{h_{dis}}{v}, \tag{4}\]  
where \(h_{dis}\) is the horizontal distance, \(P_{v}\) is the power required for flying horizontally, and \(v\) is the horizontal speed.  
Therefore, the energy can be written as follow:  
\[E=E_{clmb}+E_{desc}+E_{hov}+E_{horz}. \tag{5}\]
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 4 Performance Validation and Discussion  
As the MIT-BIH AF database is self-annotated, it is essential to validate the performance of the proposed algorithm over a standard annotated database. Thus, the well-known QT database [26] was used for this purpose. The algorithm was applied on the automatically annotated records of the QT database, and the performance was compared to [45]. The authors in [45] combined four different deep recurrent neural networks (RNNs), for example, long-short term memory (LSTM), and each was trained to extract useful features from ECG signals and determine the absence or presence of P-waves. The outputs of these networks were then combined for the final detection of P-waves using a five-fold cross-validation method. This output was then evaluated using the automatically annotated QT database. In addition, the proposed algorithm was applied to the manually annotated beats in the QT database and the results were compared to [35; 37; 39; 40; 44]. The authors in [35; 37] detected P-waves depending on wavelet transform. In [39; 40], the authors proposed automated P-wave detection algorithms based on second-order derivatives and low-pass differentiation. These techniques are mathematically simple. However, they only detect monophasic P-waves and are not suitable for biphasic waves. In [44], three different deep neural network architectures were implemented and their results were compared to each other's, showing that the best P-wave detection architecture is the ConvNet.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Record** & **TP** & **FP** & **FN** & **SE (\%)** & **+P (\%)** \\ \hline  
18 & 94.73 \\ \hline  & 2920 & 74 & 39 & 98.78 & 97.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: P-wave detection performance over the annotated MIT-BIH Atrial Fibrillation database.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 1 Introduction and Related Works
### Contribution and Article Structure  
This article describes an approach to the determination of the subject-specific Quality of Experience (QoE) for spherical (immersive) still images in Virtual Reality (VR) presentations. Our approach follows practical real-world scenario considerations within the confines of current commercially available off-the-shelf equipment in a practical, non-clinical setting. The main contribution we describe in this article is the prediction approach to the QoE of individual human subjects in virtual reality settings employing EEG measurements.  
The remainder of this paper is structured as follows. In the succeeding section, we review our general approach and methodology before presenting the utilization of the data in Section 3 and describe the obtained results in Section 4 before we discuss the implications in Section 5 and conclude in Section 6.
## 3 System Design
### Reputation Calculation Model  
In this system, each node maintains a reputation value that reflects its reliability and performance in the network. The reputation value is computed based on the node's historical interaction data, including successful and failed transmissions, as well as feedback from other nodes. This approach allows the system to dynamically adjust the topology based on node reliability, ensuring that high-reputation nodes are prioritized for critical transmission tasks. This model employs Bayesian inference to compute node reputation values:  
\[R_{i}(t+1)=\alpha\cdot R_{i}(t)+(1-\alpha)\cdot\frac{S_{i}(t)}{S_{i}(t)+F_{i}(t)} \tag{8}\]  
where \(R_{i}(t)\) is the reputation value of node \(i\) at time \(t\), and \(S_{i}(t)\) and \(F_{i}(t)\) represent the counts of successful and failed interactions, respectively. This model effectively identifies and penalizes malicious nodes, thereby enhancing overall system reliability. In our system, reputation values are dynamically used to adjust a node's position within the chain topology, prioritizing high-reputation nodes for critical transmission tasks.