esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Limitations, Final Discussion and Conclusions
### Safety Considerations  
#### 6.2.1 Respiratory Flow Visualization  
As opposed to other respiratory activities (speaking, singing, coughing and sneezing), the involved respiratory flow of ECA is visible because the carried submicron droplets (ECA and respiratory) act effectively as visual tracers of the carrier fluid (see Section 2.3 and further details in [22]). Besides the evident psychological dimension of this flow visualization, there are safety implications: vapers and those surrounding them have a clear, instinctive and immediate delineation of the flow's horizontal distance reach and spreading direction along the exhaled jet. From the outcomes of our hydrodynamical analysis (Section 5.2), we can recommend as a basic safety measure to avoid direct exposure (irrespective of face mask wearing) by keeping a 2 m distance away from the vapor (when vaping) in the direction of the visible jet. In other directions, the exposure would be indirect, but nevertheless it is prudent to maintain 2 m of separation in all directions from anyone vaping when not wearing a face mask. Notice that these recommended safety measures coincide with the standard social separation recommendations adopted worldwide [121]. Since vapers often thrust their jets in varying downward angles, children and pets might be exposed (because of their shorter height) at distances and positions that could be safe for adults. While vaping starting jets are quite collimated, and thus involve direct exposure mostly in the jet direction, ventilation and motion of the vapers might disperse the emissions in other directions. However, as emphasized above, the fact that these emissions are visible is a valuable safety feature that allows bystanders to avoid exposure.
## 3 Tunnel Stability Based of In-Site Monitoring
### Monitoring and Analysis of Subsidence of Vaults and Horizontal Convergence
According to the displacement fitting curve, the subsidence rate of vault is as follows:  
\[du/dt=7.443e^{-0.140t} \tag{1}\]  
From Equation (1), the rate for vault subsidence is 0.198 mm/d, which is less than 0.2 mm/d during the 26 th day after tunnel excavation, and the change rate is as follows:  
\[d^{2}u/dt^{2}\Big{|}_{t=26}=-1.042e^{-0.140t}<0 \tag{2}\]  
The current measured value of vault subsidence reaching 97% of the final subsidence indicates that the surrounding rock has essentially stabilized and meets the criteria for secondary lining construction. In the same way, because the final horizontal convergence displacement of the monitored section is 24.77 mm whereas the tunnel excavation width is about 12.76 m, the relative displacement of horizontal convergence is 0.20%, which meets the requirements of allowable relative displacement. The horizontal convergence rate is as follows:  
\[du/dt=2.621e^{-0.099t} \tag{3}\]  
Figure 8: Fitting curve of horizontal convergence. Figure 7: Fitting curve of vault subsidence. Therefore, the obtained rate for horizontal convergence is 0.182 mm/d, which is less than 0.2 mm/d on the 27 th day after tunnel excavation, and its change rate is as follows:  
\[d^{2}u/dt^{2}\Big{|}_{t=27}=-0.260e^{-0.099t}<0 \tag{4}\]  
At this time, the measured horizontal convergence value has finally reached 94%, which demonstrates that the surrounding rock of the tunnel has stabilized and met the requirements of secondary lining construction. To sum up, the optimal time [22] for secondary lining construction of this shallow soft-rock tunnel is 26-27 days after tunnel excavation, and the results are consistent with the on-site construction time.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 5 Conclusions  
A comprehensive analytical approach is implemented with two sub-studies: first, a segmentation study, and second, an emotion comparison and evaluation in addition to comparisons of selected segments in a more detailed way. This was performed in addition to comparing the effect of class separation levels and classification accuracies for both unbalanced and balanced feature sets. The results obtained from the second part of this study are compared, evaluated, and commented on with this information. Eight emotional states are considered and inferred for VAD space.  
This study is critical because it compares segmentation, class separation levels, and the data balancing effect with SMOTE. The segmentation effect is significant because it is conducted with 3D VAD space with handcrafted features, including the dominance dimension in the evaluation, while also considering the impact of each of the three dimensions together. In conclusion, it is emphasized that emotion evaluation can be conducted with EEG signals successfully. It is believed that because facial expressions or speech could be misleading in some conditions that could change from person to person, a physiological signal obtained from the brain may give closer results to the emerging emotion. Consequently, the results emphasize that emotional states occur due to stimuli, such as music videos, which can change the physiological signals and can be predicted by analyzing the EEG data.  
Conceptualization, U.I. and A.G.; methodology, U.I. and A.G.; software, U.I. and T.B.; investigation, U.I., A.G. and T.B.; writing--original draft preparation, U.I.; writing--review and editing, A.G. and T.B.; supervision, A.G.; project administration, A.G. and U.I.; funding acquisition, TUBITAK. All authors have read and agreed to the published version of the manuscript.  
This research is a part of an ongoing project related to a PhD thesis funded by TUBITAK (The Scientific and Technological Research Council of Turkishye), 1002A Short Term R&D Funding Program (Project No: 122E592, Project Title: Transfer Entropy, Moments & Artificial Intelligence Methods based Emotion Analysis).  
Not applicable.  
The DEAP dataset used in this study is available at: [[http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/)]([http://www.eecs.qmul.ac.uk/mmv/datasets/deap/](http://www.eecs.qmul.ac.uk/mmv/datasets/deap/)) and can be accessed upon approval (accessed on 14 September 2021).  
We would like to express our gratitude to TUBITAK (Project No: 122E592) for the funding support and the DEAP dataset authorities for providing access to the dataset.  
The authors declare no conflict of interest.
## 6 Experimentation
### Discussion and Limitations of the Validation  
The GESTAC validation focused on the resolution of 10 incidents logged by users in different IT areas within a business domain by two different groups of technicians. One group (Technicians A) did not use GESTAC, whereas the other group applied GESTAC (Technicians B) (see Figure 1). Briefly the results were as follows:  
1. The incident resolution times for Group B were shorter than for Group A.
2. The mean time was greater for Group A than for Group B, which means that the center of the distribution will be greater for Group A than for Group B.
3. The standard deviation for Group A (43) is greater than for Group B (23.026). Therefore, the data for Group A are less reliable than for Group B as their dispersion with respect to the mean covers a wider range of values.
4. There is evidence to reject H0 as the statistical value is less than the possible t-value at a significance level of 0.10. There is no evidence to reject H0 at the 0.05 significance level (Figure 17).
5. Applying the Mann-Whitney test, the \(p\)-value increases as the number of cases decreases. This means that, in this case, up to 7 cases have to be taken into account to gather evidence to reject H0 (Table 9).  
This is the extent of the results of the validation until the model can be used in more real business situations.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## Appendix C Number of Training Samples  
In our investigation, we considered 200 different permeability scenarios in which to calculate the trace of the posterior covariance matrix for a given sensor layout. There is a trade-off to this number. If this number is increased, it allows the ensemble of scenarios to better represent all the potential scenarios that could possibly occur, making the sensor design far more robust. Clearly, this is beneficial for the design of locations that work well regardless of the exact permeability scenario. However, increasing this number also increases the computation time significantly.  
To determine what this number should be, an experiment was conducted using Model 1. We considered solving the optimal sensor location problem, using a different amount of permeability scenarios (values for \(M\)). We then consider the average trace of the posterior covariance matrix for 100 different scenarios. These are testing scenarios, which are fixed as we increase \(M\), and the optimal sensor locations potentially change. We compare the setup against scenarios other than what the setup was 'trained' on in order to determine whether the sensor locations are still optimal in conditions outside of what it was directly optimised on. This is the objective, as it shows that the optimal sensor design is robust, regardless of permeability. This is calculated as follows (a variation of Equation (42)):  
\[\frac{1}{100}\sum_{i=1}^{100}\text{tr}(\Gamma_{\text{post}}^{S}(\mathbf{k}_{i})) \tag{11}\]  
The results of conducting this investigation four times from \(M=10\) to \(M=500\) are shown in blue in Figure A3. With small numbers for \(M\), the location of the sensors changes greatly because the configuration is not yet robust, and adding more training samples causes the optimal locations to change. This causes rapid changes in the testing metric. The sensor locations developed using these low numbers of samples perform worse in the test set compared to those trained on a larger amount of samples. When the optimal locations are based on only a few samples, they will be over-fitted to those samples and perform poorly in the test set. The testing metric appears to remain relatively constant (i.e., the optimal sensor locations are changing minimally) after around 75-100 samples. In this paper, we use \(M=200\) to be safe while not creating any excessive run time.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 3 Discussion
### Identification of the Candidate Rubellin Biosynthetic Candidate Cluster
In the closely related wheat pathogen _Zymoseptoria tritici_, the expression profile of SM gene clusters was also studied. Two clusters organised around PKSs exhibit a similar expression profile to that of the _R. collo-cygni rccG_ cluster and were hypothesised to be involved in the conidial germination and stomatal penetration stage of disease development [49]. Six other clusters organised around PKSs and NRPSs showed the highest transcript levels during the mid and late stages of disease development and were associated with the initiation of tissue necrosis, host cell death, and sporulation. Considering that the rubellin candidate cluster is mostly expressed during the early stages of disease development, these results suggest that, unlike cladofulvin, rubellins may not beinvolved in sporulation but are more likely to play a role in mediating the host-pathogen interactions, be it through the induction of symptom formation or through the protection of an ecological niche against other pathogenic fungi.
## Case Study
### Results and Discussion
#### 5.2.2 Case 2: Optimization Results with ESS  
In this case, ESS is added to the available generation units to enhance the flexibility of the energy production department. The optimal power flow considering ESS is shown in Figure 6. The charging and discharging power of ESS is shown in Figure 7.  
It can be concluded from Figure 6 that the output power of DE in this case is the same as that in Case 1. The participation of ESS helps explain the increase of the power exchanged with the main grid (both purchasing and selling). The microgrid purchases energy from the main grid and stores the excess energy in the ESS when the electricity prices are low. Conversely, the ESS releases the stored  
Figure 6: Optimal power flow with ESS.  
Figure 7: Charging/Discharging power of ESS.  
power to reduce the power purchased from the main grid in order to reduce the operation cost when the electricity prices are high.  
In addition, Figure 8 shows the SOC of the ESS. In order to extend the battery life, the SOC is always between 25% and 95%.  
When \(\omega 1=\omega 2=0.5\), the financial results are listed in Table 4.  
It can be seen from Table 4 that the total cost of the microgrid system in Case 2 is lower than that in Case 1 when the microgrid has no access to ESS, indicating that the ESS discharges to supply partial load at peak load times and peak price times from 8:00 a.m. to 10:00 a.m., 12:00 p.m. to 1:00 p.m. and 6:00 p.m. to 8:00 p.m., which is conductive to decreasing the total cost of the microgrid. The pollutant treatment cost in Case 1 is higher than that in Case 2, which is attributed to the fact that the pollutant treatment cost of the main grid is higher compared to DE. It is calculated that, when the microgrid has access to ESS, the total cost is reduced by 2.67% and the operating cost by 4.14%, although the pollutant treatment cost is increased by 1.02%. Therefore, in general, Case 2 is preferable to Case 1.
## 2 Materials and Methods
### Optimization Model Formulation
Instead, it assumes that the potential location of a microgrid is the center of each \(Geo_{i}\) and definesd the distance from a microgrid placed in \(Geo_{i}\) to node \(v^{l}\) as \(d_{i\
u^{l}}\). Furthermore, \(d_{max}\) is defined as the maximum distance that a node can be located from a microgrid. In this case, the microgrid placement problem can be formulated as problem \(P2\). \[P2:\;\;Min\;\;\alpha\sum_{i=1}^{K}\sum_{l=1}^{L}\sum_{v^{\prime}=1}^{N^{l}}-C_{p} (v^{l})y_{iv^{\prime}}PC_{iv^{\prime}}TWV(v^{l})+\beta\sum_{i=1}^{K}F_{i}*x_{i} \tag{14}\]  
\[\sum_{l=1}^{L}\sum_{v^{\prime}=1}^{N^{l}}C_{p}(v^{l})y_{iv^{\prime}}\leq S_{i}*x _{i}\qquad\forall\;i \tag{15}\]  
\[\sum_{v^{\prime}=1}^{N^{l}}C_{p}(v^{l})y_{iv^{\prime}}\leq\eta^{l}S_{i}*x_{i} \qquad\forall\;l \tag{16}\]  
\[\sum_{i=1}^{K}x_{i}=1 \tag{17}\]  
\[P1:\;\;Min\;\;\alpha\sum_{l=1}^{K}\sum_{l=1}^{L}\sum_{v^{l}\in Geo_{i}}-C_{p} (v^{l})y_{i\
u^{l}}PC_{i\
u^{l}}TW(v^{l})+\beta\sum_{l=1}^{K}F_{i}*x_{i} \tag{9}\]0  
\[P1:\;\;Min\;\;\alpha\sum_{l=1}^{K}\sum_{l=1}^{L}\sum_{v^{l}\in Geo_{i}}-C_{p} (v^{l})y_{i\
u^{l}}PC_{i\
u^{l}}TW(v^{l})+\beta\sum_{l=1}^{K}F_{i}*x_{i} \tag{9}\]1  
The cost of the microgrid is minimized while connecting nodes with greater importance in the infrastructures considered to the microgrid. The first three sets of constraints serve the same function as in model \(P1\), that is, (12) ensures that the capacity of the microgrid is not exceeded, (13) limits the fraction of power that a single infrastructure can use, and (14) requires that only a single location for the microgrid is selected. In addition, the constraints defined by (15) limit the maximum distance that a node can be from the microgrid, thereby ensuring a practical geographic span for the system. Lastly, constraints (16) define the restrictions on the decision variables. The optimization models \(P1\) and \(P2\) are mixed integer linear programming (MILP) problems that the bound and branch model can solve for undersized problem instances using standard optimization software (e.g., CPLEX, Gurobi). The outcomes of the models show the optimal location for a microgrid and have the benefit of selecting which infrastructure nodes attach to the pre-selected microgrid. In general, given the regulatory constraints on the size and ownership of microgrids, we expect that if multiple microgrids are deployed, they are built sequentially and support different consortia of infrastructure owners and community groups. In this scenario, the optimization models can be applied iteratively by re-running the model while modifying the power requirement and value of TWV, excluding nodes linked to a previously deployed microgrid.
## 3 Feature Extraction Method of an Impulse Fault
### Time-Frequency Distribution Construction  
The time-frequency analysis techniques, including short time Fourier transform, wavelet transform, and Wigner distribution, provide solutions to mapping vibration signals on a time-frequency plane that incorporates the information from both time-domain and frequency-domain. Due to the convenient operation and good interpretability, STFT is employed to obtain the time-frequency distributions for every signal segment (or phase point vector), as discussed in the previous section.  
STFT, in essence performing Fourier transform on the windowed signal segments, is defined in discrete form as follows:  
\[STFT_{x}(n,m)=\sum_{k=nT}^{nT+N-1}x(k)w(k-nT)e^{-j\frac{2\text{int}n}{N}} \tag{23}\]  
where \(n\), \(m\) represents the index of time sequence and frequency sequence, \(T\) denotes the sliding interval of the window \(\omega(k)\), and \(N\) is the window length.  
As for the window function, to strike a balance between the frequency resolution and the energy leakage, let us select the Hanning window. Yet subject to the Heisenberg uncertainty principle, the pitfall of STFT is the inevitable tradeoff between time and frequency resolution, for which an appropriate width of the window function should be determined to obtain the best time-frequency concentration. With respect to the existing norm-based measures, such as Renyi entropy, and Jones-Parks norm ratio, the Stankovic measure integrates the window energy with the norm of STFT spectrogram to provide an automatic determination of the best window length [27]. This concentration measure is given by  
\[H(N)=\left(\sum_{n=1}^{N}\sum_{m=1}^{N}\left(\frac{1}{E(N)}|STFT(n,m)|^{2} \right)^{\frac{1}{2}}\right)^{2}, \tag{24}\]  
where the window energy can be written simply using the window function:  
\[E(N)=\sum_{k=0}^{N-1}|\omega(k)|^{2}. \tag{25}\]  
In this way, the Stankovic measure finds an optimal window length \(N\) when \(H(N)\) reaches a minimum. As a shorter window has less energy and a higher value of \(H(N)\), while a longer window does the opposite, the introduction of \(E(N)\) seemingly strikes a balance between the measure of short windows and long windows. We finally select the length \(N\), according to Stankovic measure, but empirically at which the slope of \(H(N)\) starts to turn flat.  
With the help of STFT, all column vectors in the phase space are transformed into multiple TFDs, which are then stacked up to create a three-way tensor. By synthesizing the PSR technique and STFT, the vibration signal is circularly and completely utilized to generate a third order time-frequency representation.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 2 Related Work
### Machine Learning-Based Routing
It primarily consists of two components: a crossroads-optimal road-segment-selection routing strategy based on multidimensional Q-learning and a greedy routing strategy for selecting the best relay node within the chosen road segment. In IV2 XQ, the state space represents all the network intersections, which effectively reduces the number of states for Q-learning, thereby making it more feasible. However, in this approach, a central server explores the network environment based on historical traffic data and may not adapt well to highly stochastic real-world environments. In [41], the IRQ also employs a central server for training. To prevent network congestion, the IRQ controls traffic through traffic management. Roadside units (RSUs) monitor road conditions to understand traffic situations. This approach uses both global and local views during routing, thereby creating a traffic propagation mechanism. This mechanism helps the IRQ update traffic information and provides a fresh global view to the network server. The global view is used for designing Q-learning-based routing techniques, while the local view designs a greedy routing strategy for each road segment to discover the best next-hop nodes.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Comparative Analysis  
#### 4.6.1 Comparison with Machine Learning Models  
The current study seeks to investigate the application of integrated learning methods in greater detail through a systematic comparison of their effectiveness in enhancing the overall accuracy of the model. This research specifically examines the disparity between the final accuracy of the overall model, achieved through the implementation of the integrated learning algorithm, and the individual accuracy of each base learner (i.e., meta-model) that comprises the algorithm. The pertinent comparison results are illustrated in Figure 17.  
The findings of the comparative analysis indicate that the integrated learning approach attains a superior final accuracy compared to any individual meta-model, as it leverages the predictive strengths of multiple meta-models.
## 5 Conclusions  
The results obtained in this study show that implementing an IoMT system in a medical clinic can improve patient care by allowing the monitoring of their vital signs in real-time and from anywhere. In addition, medical personnel achieved high accuracy in collecting vital signs data and a fast response time in case of system alerts. The frequency of use of the system by patients was moderate, suggesting that patients may benefit from increased knowledge and understanding of the benefits of using the system.  
However, limitations were also identified in this study, such as the lack of long-term data on the resolution rate of health problems detected by the system and the need for a more significant effort in educating patients on the use of the system. In addition, although a high success rate was obtained in the transmission of data from the devices to the cloud system, it is recommended to continue improving the devices' connectivity and the cloud platform's stability.  
Regarding future work, it is suggested that a longitudinal study be carried out to evaluate the long-term effectiveness of the IoMT system in improving patient care and disease prevention. In addition, new data analysis techniques can be explored to improve the accuracy of detecting health problems. Improvements to the system interface can also be considered for a better user experience and greater acceptance of the system by patients. This study provides a solid foundation for implementing an IoMT system in a medical clinic and highlights its potential to improve patient care and prevent disease.  
Conceptualization, W.V.-C.; methodology, W.V.-C.; software, J.G.-O.; validation, I.U.-C.; formal analysis, W.V.-C.; investigation, J.G.-O.; data curation, W.V.-C. and I.U.-C.; writing--original draft preparation, W.V.-C.; writing--review and editing, J.G.-O.; visualization, J.G.-O.; and supervision, W.V.-C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research study uses data that have already been collected and anonymized so that participants cannot be identified; therefore, ethical review and approval are considered waived.  
Written informed consent has been obtained from the patient(s) to publish this paper.  
The data can be sent to the interested parties, for which they must be communicated to the email of the corresponding author.  
The authors declare no conflict of interest.