esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Taxonomy for the Threat Model
### Threats against Confidentiality  
We shortlist eight attacks in this domain, including MitM, eavesdropping, snooping, chosen text, impersonation, stalking, collaboration and disclosure. The most venerable area is related to MitM threats. MitM threat is considered a realistic vulnerability to mobile communication where attackers can take a middle position between two communicating parties. Various challenges have prevailed in the vulnerabilities related to False Base Station Attacks (FBSA)-based MitM in modern cellular networks [32]. Most of the solutions use public-key cryptographic techniques for countering different threats related to MitM. Likewise, asymmetric information-based intelligent routing and collision avoidance techniques are also suitable for countering MitM attacks in modern 6G networks based on UAV communication [33; 34]. Various unconventional techniques such as Artificial Intelligence (AI)-based Machine Learning (ML) can be employed for ensuring security in 6G network-based smart cities, which counters many vulnerabilities, including MitM [35]. Blockchain-based authentication mechanisms are also widely researched for protection against issues related to MitM attacks in 6G-based ad hoc networks. For example, eavesdropping results in compromised confidentiality through data transmission on unsecured channels. In [36], the authors presented a blockchain-based confidentiality probability matrix to estimate data confidentiality level in 6G networks with a specific focus on eavesdropping.  
6G communication is vulnerable to edge server snooping attacks, resulting in compromised data confidentiality in the Cybertwin architecture [37]. Confidential information can be compromised through statistically chosen text attacks in wireless networks [38]. Therefore, data aggregation through multiple statistical functions is used to obtain a single result of an information unit. This technique provides security against several types of chosen text attacks against confidentiality [39]. Impersonation attacks can trick users into revealing vital information on unsecured wireless channels. A massive communication paradigm in 6G can result in several possibilities of impersonation at various levels in the communication chain. Likewise, the emerging 6G communication environment will result in a huge challenge for regulatory authorities to assist common users against bullying/stalking attacks concerning confidential information [40]. Similarly, these massive  
Figure 3: Taxonomy of threats in 6G Mobile Networks.  
communication scenarios may create a wide span of vulnerabilities related to collaborative attacks on confidential information in 6G networks. In [41], the authors suggested blockchain-based transparent data management for 6G-enabled networks for countering vulnerabilities related to intentional or unintentional disclosure of confidential data.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 4 Problem Definition
### Spatial-Temporal Uncertainty Problem  
To accurately solve the collision issue in terrestrial wireless networks and due to its short propagation delay, it is sufficient to limit the interfering sensor nodes from sending concurrently. In UWSNs, however, it is necessary to consider the transmission time and the location of the node because of the long propagation delay of acoustic waves. The spatial-temporal uncertainty problem can be described as a 'two-dimensional uncertainty', which is defined as follows:  
Figure 2: Impact of the hidden terminal problem and the long propagation delay on underwater MAC protocols.  
* The collision in the destination node is dependent on the propagation delay and transmission time; thus, it can be shown as a duality that differs between both the transmission time and the location of the sensor nodes.
* The distance between sensor nodes changes based on the uncertainty of current channel status and a data packet may collide even if no other nodes transmit concurrently.  
Figure 2 shows how the high propagation delay in UWSNs could make a spatial-temporal uncertainty problem. This figure illustrates two cases of the spatial-temporal uncertainty problem. Firstly, when nodes B and C transmit packets with varying times of transmission, a collision might occur at node A. In the second case, when both nodes B and C start transmitting to node A simultaneously, there is no collision as their packets arrive at node A at different times. This is mainly because of various propagation delays.
_Article_  
**Coupled-Cavity VCSEL with an Integrated Electro-Absorption Modulator: Small- and Large-Signal Modulation Analysis**  
**[PERSON] 1,2,*,+(c) and [PERSON] 3,*,+(c)**  
1 Department of Applied Physics and Photonics (IR-TONA), Vrije Universiteit Brussels, Pleinlaan 2, B-1050 Brussels, Belgium  
2 Institute of Solid State Physics, Bulgarian Academy of Sciences, 72 Tzarigradsko Chaussee Blvd., 1784 Sofia, Bulgaria  
3 KTH Royal Institute of Technology, 164-40 Kista, Sweden  
* Correspondence: [PERSON][PERSON]DDRESS] (K.P.); [PERSON]ADDRESS] (R.S.)  
+ These authors contributed equally to this work.  
Received: 4 August 2020; Accepted: 1 September 2020; Published: 3 September 2020  
**Featurered Application: VCSELs are widely employed in Fiber to the Home links, computer networks and optical interconnects because of their low power consumption, small footprint, high reliability, easy packaging, single longitudinal mode emission, high modulation speed, low cost and circular output beam. Short- and medium-range optical communication links demand for continuous increase of VCSEL modulation speed. VCSEL direct modulation speed is however limited by the relaxation oscillation frequency and new approaches as, e.g., integrating VCSEL with an electro-absorption modulator (EAM) are required. Hereby, we demonstrate that an integrated EAM-VCSEL is capable of producing a resonantly enhanced, flat modulation response with ultrahigh bandwidth of 100 Gbs for applications in optical communication links.**  
**Abstract: We consider an integrated electro-absorption modulator within a coupled-cavity VCSEL structure (EAM-VCSEL). We derive expressions for the modulation transfer function (MTF) of the EAM-VCSEL for small-signal modulation of either VCSEL injection current or EAM losses. For current modulation, the cut-off frequency remains limited by relaxation oscillation frequency. For EAM loss modulation, the MTF curve is much flatter and its shape around the relaxation oscillation frequency displays either a well-pronounced maximum, both a maximum and a minimum or a sharp minimum only depending on the bias point of the EAM losses. Such features have been found experimentally in [PERSON] et al., J. Physiscs: Photonics, 1, 2019, but remained unexplained hitherto. Furthermore, the cut-off frequency remains beyond 100 GHz for moderate and week coupling between the VCSEL and EAM cavities. Such ultrahigh bandwidth modulation is due to the fact that the changes of EAM impact much less the optical power distribution along the EAM-VCSEL and, consequently, the confinement factor and photon density in the VCSEL cavity. The three cases of strong, intermediate and weak coupling are also considered when carrying out the large-signal modulation response of the EAM-VCSEL and a clear open-eye diagram is demonstrated at 100 Gbs for an optimal EAM cavity length.**  
**Keywords: VCSELs; optical modulation; electro-absorption; electro-optic modulators; intensity modulation**
## 4 Performance Evaluation
### Evaluation of FL-ECC/D
In other words, they tend to die in groups rather than individually. This is contrary to the case of less balancing of remaining energy between different sensors. Referring to Figure 18, we see that the line representing the live nodes of FL-EEC/D takes the form of a step function with a sudden drop, while the line representing the live nodes of LEACH tends to decrease gradually, and the drops are larger in the earlier period of network lifetime. Thus, most of the nodes in LEACH die through the earlier period of network lifetime. In contrast, the FL-EEC/D overcomes this shortcoming and always works to prolong sensors lifetime when it is worthy to keep them alive. To analyze the energy consumption in FL-EEC/D versus the other techniques, we take a WSN consisting of 200 nodes distributed randomly over an area of \(200\times 200\) meters using the same simulation parameters listed in Figure 7:2 The BS is located in the area at position \((100,100)\). The efficiency of managing energy by minimizing total consumed energy and balancing it among the sensors is the idea behind prolonging the network lifetime. Moreover, as observed earlier, minimizing the total consumed energy without balancing the energy reserves of the nodes does not necessarily result in better network lifetime. It is more important to minimize the differences between remaining energies among the different nodes. Figure 19 depicts the percentage of total remaining energy for the rounds before HND. The figure shows that the FL-EEC/D conserves more total energy than the other three schemes. It also shows that the FL-EEC/D tends to consume energy gradually per round in equal amounts. Figure 7:3 Live nodes vs. rounds for FL-EEC/D and LEACH. Figure 20 shows that the proposed FL-EEC/D achieves significantly lesser Gini index values for the remaining energy via all the rounds before HND occurrence. This means that the FL-EEC/D keeps the remaining energy among the nodes more equally balanced compared to the three other protocols.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 2 Review Methodology  
To identify the literature relevant to this review, we employed a widely used review methodology following previous studies [21; 22]. First, we selected a set of keywords and data sources to gather papers potentially related to our review. Next, we refined the selection based on specific eligibility criteria and expanded the literature pool through citation tracking. Finally, we classified the papers in the final collection. Figure 2 illustrates our literature screening flowchart, presenting the specific steps of the screening process:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Map Type** & **Elements** & **Advantages** & **Weaknesses** \\ \hline Point Cloud [12; 13; 14] & Points with 3D coordinates & • High precision  • Sensor-direct acquisition  • Detailed feature capture & • Computationally intensive  • May include noise \\ \hline
2.5D Grid [15; 16] & Grid cells with elevation values & • Simple generation • Easy integration  • Good for flat terrains & • Limited vertical complexity  • Loss of detailed features  • Less effective for complex terrain \\ \hline
2.5D Grid [15; 16] & Triangles & • Accurate surface representation • Efficient for terrain analysis  • Handles large-scale terrains well & • Complex to create/update  • Less dynamic adaptability  • High computational cost for detail \\ \hline
2.5 Voxel Map [19; 20] & Voxels & • Detailed 3D representation • Supports dynamic updates Direct path-planning compatibility & • High storage requirements  • Less precision than point clouds Computationally demanding \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between typical map types in navigation.  
Figure 2: Flowchart illustrating the methodology of this literature review, adapted from PRISMA.
## 4 Discussion  
A considerable optimization for the dual excitation technique for QEPAS has been introduced that ensures an automated adaption of the modulation frequency of the excitation laser to the resonance frequency of the tuning fork as well as signal retrieval at the optimum phase between electrical and photoacoustic excitation. Thus the pure photoacoustic signal is measured even though a simultaneous electrical driving is present. The performance was demonstrated using high methane concentrations varying from 0% to 100% in carbon dioxide. As for conventional QEPAS a linear dependence of the signal on the concentration is found for small concentration, which was demonstrated by the measurement of ammonia in synthetic air. The derived limit of detection is worse than for conventional QEPAS with acoustic resonator; however, the technique enables continuous measurement because no interruptions to check or realign the modulation frequency are needed.  
**Author Contributions**: Conceptualization, M.M. and U.W.; data curation, M.M.; formal analysis, M.M. and U.W.; funding acquisition, M.K. and U.W.; investigation, M.M.; methodology, M.M. and U.W.; project administration, M.K. and U.W.; software, S.E. and M.K.; supervision, M.K., W.S., and U.W.; validation, M.M., S.E., and U.W.; visualization, M.M.; writing--original draft, M.M. and U.W.; writing--review and editing, S.E., M.K., and W.S. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Determination of the limit of detection of NH\({}_{3}\) in synthetic air. The blue line marks the sum of the mean value of the noise floor and 3 times its standard deviation.  
**Funding:** This research was funded by Bundesministerium fur Bildung und Forschung (German Federal Ministry of Education and Research), grant numbers 13N13826 and 13N13824.  
**Acknowledgments:** We acknowledge support by Open Access Publishing Fund of Clausthal University of Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Tennessee Eastman Process (TEP)  
In an attempt to scrutinize the usefulness of employing the KPCA-KDE-SSGLR graph for tracking mechanism mean variations, its consistency needs to be measured utilizing actual data. Many writers use the Tennessee Eastman Process (TEP) to test the efficiency of their procedures [12,25,35]. The Tennessee Eastman Process is a practical model of the natural chemical compound consisting of a generator, condenser, stripper, compressor, and separator, and is generally recognized as a paradigm for fault identification [25].  
The Tennessee Eastman Method includes a database of predetermined flaws that researchers can use to test the efficiency of their designed flaw detection techniques. Further details on the Tennessee Eastman Process, the overview of the procedure, and the accessible defect database can be found in the literary works [4,12,25,35]. This research investigates the fault situation Fault 12 of the TE method. Fault 12 (IDV12) is a random variation in the condenser cooling water inlet temperature.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **FDR (\%)** & **FAR (\%)** & **ARL\({}_{1}\)** \\ \hline KPCA-KDE-based T\({}^{2}\) & 14.47 & 2.71 & 1.025 \\ KPCA-KDE-based Q & 40.58 & 5.87 & 5.50 \\ KPCA-KDE-SSGLR & 99.50 & 0.07 & 3.55 \\ KPCA-based T\({}^{2}\) & 11.58 & 2.50 & 1.025 \\ KPCA-based Q & 40.58 & 9.46 & 5.50 \\ KPCA-based GLR & 96.40 & 13.80 & 3.55 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of fault detection results.  
Fault Situation: IDV 12, Random Variation in the Cooling Water Inlet Temperature  
This particular fault scenario implemented the 3 h of purposeful alteration in the inlet cooling water temperature. The KPCA-KDE-based T\({}^{2}\) and Q charts and the KPCA-KDE-SSGLR statistics are shown in Figure 7a-c, and the failure conclusions designation is tabulated in Table 2.  
From Figure 7b,c, it can be confirmed that perhaps the T\({}^{2}\) and Q-based KPCA-KDE statistics are not worthy of tracking the entire fault. Fascinatingly, the KPCA-KDE-SSGLR chart developed primarily to examine mean alterations (see Figure 7a) can identify a considerable number of defects with the lowest missing identification accuracy.  
From the outcomes displayed in Table 2, it can be observed that the KPCA-based Q statistic has a few low false alarm rate, 0.25%, but its false detection rates are too low compared to all other statistics. The false detection rate (FDR) of KPCA-KDE-SSGLR is 81.35%, which is much higher compared to all other charts. Therefore, it can be perceived from Table 2 that the KPCA-KDE-SSGLR process is much superior to its counterparts, such as KPCA-based T\({}^{2}\) and Q statistics.  
The KPCA-KDE-based T\({}^{2}\) statistic has a much smaller ARL\({}_{1}\) value but cannot adequately locate a number of the visible faults, and so the smaller ARL\({}_{1}\) value can be related to the noise signal.
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 3 Experimental Results
### Thrust Performance Measurements  
For calculation of TTPR, the energy dissipated into the arc is measured and compared to the achieved thrust level [8; 9; 15]. Thus, the TTPR was calculated from the above measurements of the thrust and the energy dissipated into the arc and is defined as:  
\[TTPR=\frac{\hat{T}}{P_{pulse,eff}}=\frac{\hat{T}}{E_{pulse,eff}.f_{p}} \tag{5}\]  
with \(P_{pulse,eff}\) (respectively, \(E_{pulse,eff}\)) the effective input electrical power per pulse (respectively the effective input electrical energy per pulse) and \(f_{p}\) the pulse frequency.  
The TTPR calculated for different arc energies is presented in Figure 8. It can be seen, that the TTPR in the presence of magnetic field is higher than without magnetic field. The best TTPR is achieved for \(E=1.3747\) J. Here, higher capacitance values (\(C_{1}=436.9\) uF) together with the magnetic field extend the pulse lengths. This results in a more stable arc [16]. The rotation of the arc due to magnetic field leads to more homogeneous erosion [17; 18].  
Figure 7: Average thrust in dependence of different energies dissipated in the arc with (red) and without (blue) magnetic field. The error bars indicate the standard deviation of the measurement.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 5 Conclusions  
This paper reviewed the developments in the position estimation of SRMs, with a focus on the application of position sensorless methods. Via the discussions, it is found that there are many obvious constraints and potential opportunities for the sensorless technology, with the development of advanced control theory and the in-depth study of electromagnetic signature by FEA. Besides the requirements of efficient rotor position estimation in the whole-speed range, there are some challenging objectives for the design  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Features of Application** & **Future Development** \\ \hline Pulse injection methods & Outstanding startup performance & Hybrid with other methods \\ \hline Additional components methods & Similar to the novel position sensor & Smaller additional components \\ \hline Flux-current-based methods & Excellent real-time performance & More accurate analytical models \\ \hline Inductance-based methods & Simple detection of special positions & Enhance real-time \\ \hline Intelligent-control-based methods & For the acquisition of electromagnetic values & Mate position sensorless method \\ \hline Observer-based methods & Outstanding real-time performance, decoupling from speed control strategy & Improvements at startup and low speed \\ \hline Hybrid method & Suitable for whole-speed & Smoother mode switching \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction of the development of existing methods.  
of sensorless control, including high detection accuracy, high robustness, and improved algorithm versatility. To address these constraints, some advanced control theories, such as sliding mode observers and hybrid solutions that fuse multiple methods, are used for position estimation. Due to their excellent suitability for modeling nonlinear characteristics, reduced dependence on motor parameters and application in a wider speed range are expected in the future.  
Conceptualization, X.S. and M.Y.; methodology, X.T.; software, X.T.; validation, X.S., M.Y. and X.T.; formal analysis, M.Y.; writing--original draft preparation, X.T.; writing--review and editing, X.T.; visualization, X.S.; supervision, X.S.; project administration, M.Y.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data set used in the current study will be made available on reasonable request.  
The authors declare no conflict of interest.
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 3 Algorithms Used for Dimensionality Reduction
### Univariate Feature Selection  
To select the most relevant features, univariate feature selection method utilizes univariate statistical tests to return a list of features which are ranked depending on the scoring function used. This can be an effective pre-processing step to retrieve the most significant features in a dataset that contribute to prediction accuracy significantly. To perform the feature selection process, a one-way ANOVA F-test was performed. Like Naive Bayes Classifier, the one-way ANOVA (Analysis of variance)F-test ensures that there is no relationship between the feature attributes used to accurately classify the dependent attribute. There is higher degree of variance if the means obtained from groups of data is different from the global mean derived from the dataset. Thus, it successfully returns the ratio of the inter-group to intra-group variability in a sample. ANOVA was selected over the T-test to give more stability and reduce the type 1 error while comparing population means of multiple groups. ANOVA is very effective in determining the difference of means of two or more groups at the same time. This also offers a significant advantage over the common T-test which conducts a repeating set of comparison between two attributes at a time [32]. Features with a score higher than a percentile value of 97 were considered useful in this analysis.
## 3 System Models
### Flexible Resource Models
#### 3.1.2 Demand Response  
The electric-gas integrated energy demand load can be divided into rigid load not participating in the demand response and flexible load participating in the demand response. The electric and gas loads after participating in the demand response are as follows:  
\[{L_{e}}^{t}={L_{e,o}}^{t}+\Delta{L_{e}}^{t} \tag{7}\]  
\[{L_{g}}^{t}={L_{g,o}}^{t}+\Delta{L_{g}}^{t} \tag{8}\]  
where \({L_{e}}^{t}\) and \({L_{g}}^{t}\), respectively, represent the electric and gas loads after participating in the demand response; \({L_{e,o}}^{t}\) and \({L_{g,o}}^{t}\), respectively, represent the initial predicted values of the electric and gas loads before participating in the demand response; and \(\Delta{L_{e}}^{t}\) and \(\Delta{L_{g}}^{t}\), respectively, represent the changes of the electric and gas load response.  
The total amount of flexible load remains unchanged during the demand response process:  
\[\sum_{t=1}^{T}\Delta{L_{e}}^{t}=\sum_{t=1}^{T}\Delta{L_{g}}^{t}=0 \tag{9}\]  
The percentage of decrease/increase in the electric and gas loads shall meet the following constraints:  
\[-\Delta{L_{e}}^{\max}\leq\Delta{L_{e}}^{t}\leq\Delta{L_{e}}^{\max} \tag{10}\]  
\[-\Delta{L_{g}}^{\max}\leq\Delta{L_{g}}^{t}\leq\Delta{L_{g}}^{\max} \tag{11}\]  
where \(\Delta{L_{e}}^{max}\) and \(\Delta{L_{g}}^{max}\), respectively, represent the maximum allowable changes of the electric and gas loads.