esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The study identified a microorganism capable of producing a biofoculant from PSW with strong flocculating properties. Additionally, the study focused on finding the best conditions for flocculation and understanding the mechanism behind the flocculation process. Isolate D2, identified as Bacillus megaterium, exhibited the highest level of flocculation activity. Analysis of the biofocculant indicated the presence of hydroxyl, amide, and carboxyl functional groups, indicating that it is mainly made of polysaccharides and proteins. The optimal storage conditions for the crude biofocculant were found to be freezing temperatures, as opposed to room temperature or refrigeration. In addition, the RSM was used to identify the optimal flocculation conditions and investigate the underlying floc formation mechanism. This was achieved by assessing the zeta potential as the response variable while considering pH and biofocculant dose as the independent factors. It has been determined that charged neutralization is not the fundamental mechanism for flocculation. The zeta potential findings yielded equivocal outcomes concerning the identification of optimal flocculation settings.  
Consequently, the flocs were examined using a microscope. The best flocculation conditions were determined based on microscopy observations, with a proposed pH of 6.5 and a biofocculant dose of 2% (\(v/v\)). Based on the results of a bonding type test, hydrogen bonding was the prevailing bonding mechanism, albeit the flocculation process was postulated to proceed in two distinct stages. The first stage involves neutralizing charges by introducing Ca\({}^{2+}\) ions, while the second stage is facilitated by the bridging action of functional groups, providing more support for this hypothesis. It is advisable to do thorough research on biofocculants to enhance our understanding of their suitability for industrial implementation, given their susceptibility to many influencing circumstances.  
Conceptualization, M.R.M., S.K.O.N. and M.B.; methodology, M.R.M.; software, M.R.M.; validation, S.K.O.N. and M.B.; formal analysis, M.R.M.; investigation, M.R.M.; resources, S.K.O.N. and M.B.; data curation, M.R.M.; writing--original draft preparation, M.R.M.; writing--review and editing, S.K.O.N., M.B. and B.S.C.; visualization, S.K.O.N.; supervision, S.K.O.N., M.B. and B.S.C.; project administration, M.R.M.; funding acquisition, M.B. and S.K.O.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation Thuthuka Funding, cost center R017, Cape Peninsula University of Technology South Africa, grant number RK45, and the CPUT Vice Chancellor Achiever's Award. Furthermore, a scholarship for [PERSON] was provided for by [PERSON] African Union scholarship scheme.  
Not applicable.  
The data presented in this study is available on request from the corresponding author.  
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.
## Appendix A Training, Validation, and Test Results of the LSTM Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Performance**} & \multicolumn{8}{c}{**Number of Neurons in the Hidden Layer**} \\ \cline{3-10}  & & \multicolumn{1}{c}{**1**} & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.657 & 0.893 & 0.887 & 0.908 & 0.921 & 0.923 & 0.929 & 0.925 \\  & & RMSE & 0.420 & 0.260 & 0.225 & 0.200 & 0.183 & 0.180 & 0.173 & 0.179 \\  & & \(R^{2}\) & 0.354 & 0.517 & 0.593 & 0.715 & 0.759 & 0.703 & 0.703 & 0.660 \\  & & RMSE & 1.319 & 1.181 & 1.086 & 0.956 & 0.857 & 0.939 & 0.941 & 0.985 \\  & & \(R^{2}\) & 0.475 & 0.787 & 0.794 & 0.810 & 0.835 & 0.832 & 0.842 & 0.846 \\  & & RMSE & 0.590 & 0.370 & 0.343 & 0.326 & 0.303 & 0.306 & 0.298 & 0.294 \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.877 & 0.895 & 0.900 & 0.917 & 0.921 & 0.927 & 0.924 & 0.916 \\  & & RMSE & 0.281 & 0.234 & 0.216 & 0.190 & 0.183 & 0.178 & 0.179 & 0.192 \\  & & \(R^{2}\) & 0.650 & 0.718 & 0.708 & 0.709 & 0.740 & 0.732 & 0.696 & 0.738 \\  & & RMSE & 1.096 & 1.015 & 0.976 & 0.942 & 0.909 & 0.929 & 0.959 & 0.894 \\  & & \(R^{2}\) & 0.771 & 0.783 & 0.795 & 0.817 & 0.828 & 0.836 & 0.838 & 0.844 \\  & & RMSE & 0.397 & 0.363 & 0.344 & 0.319 & 0.310 & 0.303 & 0.301 & 0.296 \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.896 & 0.892 & 0.908 & 0.911 & 0.925 & 0.925 & 0.927 & 0.931 \\  & & RMSE & 0.266 & 0.236 & 0.205 & 0.196 & 0.180 & 0.180 & 0.177 & 0.171 \\  & & \(R^{2}\) & 0.691 & 0.734 & 0.649 & 0.692 & 0.754 & 0.731 & 0.689 & 0.689 \\  & & RMSE & 1.049 & 0.932 & 1.021 & 0.956 & 0.879 & 0.919 & 0.948 & 0.931 \\  & & \(R^{2}\) & 0.780 & 0.785 & 0.802 & 0.815 & 0.834 & 0.844 & 0.846 & 0.847 \\  & & RMSE & 0.389 & 0.360 & 0.337 & 0.323 & 0.304 & 0.296 & 0.294 & 0.294 \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.894 & 0.910 & 0.911 & 0.907 & 0.929 & 0.930 & 0.930 & 0.926 \\  & & RMSE & 0.260 & 0.215 & 0.206 & 0.201 & 0.174 & 0.174 & 0.171 & 0.177 \\  & & \(R^{2}\) & 0.654 & 0.788 & 0.754 & 0.691 & 0.773 & 0.711 & 0.711 & 0.747 \\  & & RMSE & 1.076 & 0.856 & 0.897 & 0.943 & 0.797 & 0.871 & 0.884 & 0.830 \\  & & \(R^{2}\) & 0.784 & 0.820 & 0.828 & 0.810 & 0.843 & 0.845 & 0.848 & 0.840 \\  & & RMSE & 0.374 & 0.325 & 0.315 & 0.326 & 0.297 & 0.299 & 0.294 & 0.291 \\ \hline \multirow{4}{*}{RT} & Train & \(R^{2}\) & 0.896 & 0.896 & 0.901 & 0.905 & 0.911 & 0.918 & 0.923 & 0.923 \\  & & RMSE & 0.262 & 0.231 & 0.213 & 0.201 & 0.195 & 0.186 & 0.181 & 0.181 \\ \cline{1-1}  & & \(R^{2}\) & 0.648 & 0.692 & 0.712 & 0.643 & 0.729 & 0.728 & 0.701 & 0.712 \\ \cline{1-1}  & & RMSE & 1.091 & 0.988 & 0.940 & 0.985 & 0.886 & 0.900 & 0.918 & 0.894 \\ \cline{1-1}  & & \(R^{2}\) & 0.779 & 0.779 & 0.786 & 0.790 & 0.808 & 0.826 & 0.833 & 0.832 \\ \cline{1-1}  & & RMSE & 0.383 & 0.362 & 0.348 & 0.342 & 0.328 & 0.312 & 0.306 & 0.310 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance for generating synthetic Vp using four input variables.
## 3 Methodology
### Feature Extraction
#### 3.3.2 Local Binary Pattern (LBP)  
LBP is a texture feature descriptor that extracts pixel-wise information from the acquired images. The LBP feature descriptor considers the number of neighbors (\(p\)) in each pixel (\(c\)) within the radius (\(r\)). If the grey-level value of \(p\) is higher than or equal to \(c\), the value of one is assigned, or zero otherwise. By summing up the values to the power of two, the LBP of every pixel is calculated using Equation (5) [36]:  
\[LBP_{p,r}=\sum_{p=0}^{p-1}s\big{(}g_{p}-g_{c}\big{)}2^{p},s(x)=\big{\{}\begin{matrix} 1&if\ x\geq 0\\ 0&if\ x<0\end{matrix} \tag{5}\]  
where \(p\) indicates the number of neighbors, \(g_{p}\) represents the value of the \(p^{th}\) neighbor, and \(g_{c}\) denotes the center pixel value; the LBP feature descriptor extracts 3470 texture feature vectors. Using the feature-level fusion method, the SURF and LBP feature vectors are integrated; thus, the model becomes multidimensional, increasing its complexity.
## 5 Modelling the Communication Solutions
### Modelling an IoT Network Using Physical Features  
In this section a procedure is presented to model the IoT network based on its physical features with the aim to give insight into the best solution for a particular deployment. A model for predicting network performance is built for each communication solution based on these physical features. The model is then tested to determine if it can be effectively used to compare communication solutions.  
A dataset taken from the testing procedure described in Section 7 is used to build and evaluate a model that describes the response the solutions in the network. Experiments are conducted using three network solutions each based on the RPL routing protocol. Each solution differs in choice of routing metric with Expected Transmission Count (ETX) [30], Expected Transmissions with Neighbourhood Heuristics (ETX-NH) [31; 32] and Expected Transmission Time (ETT) [33] used in the comparisons. Each solution is tested over a range of the physical characteristics used to describe the network. The dataset is split into two distinct sets, a training set and a test set. The training set is used to train and build a model that describes the response of the communications solution. The test set is used in the evaluation process when determining the accuracy of the models. Both the training set and test set contain equal number of data points spread over the whole range of each physical feature. This ensures the model is built from and tested over the widest range available in the dataset. The results suggest that IoT network communications can be modelled using key physical characteristics of the network.  
Figure 6: Mean and standard deviation for the maximum percentage load incurred using the probing technique over normal network load.  
Using multiple linear regression techniques over training data set a model is built that represents the response of a communications solution in the network. The eventual model will present heuristics which inform the developer. The heuristics can be used in the form: _Solution A_ shows _effect on metric B_ for _physical scenario C_, e.g., _ETX_ shows _superior packet reliability_ for networks with _network radius greater than 5 hops_. In summary the following contributions are made from the modelling process:  
* Define a set of features of a physical network that are best disposed to characterise the response from the network for a given communication solution.
* Build a model for each communication solution which presents the trends seen in the network response, in terms of latency, reliability, energy efficiency and stability, as each physical feature changes.
* Test the model and compare communication solutions so that the findings can be used to direct decision making in future deployments.  
Section 5.2 introduces the techniques used to model the network using physical feature data set and provides an evaluation of the models accuracy.
# Several Cases for the Validation of Turbulence Models Implementation
## 2 Physical Model
### Turbulence Models  
Turbulence models are essential for producing accurate results when using Reynolds averaging. They provide the closure needed for the Reynolds-averaged Navier-Stokes (RANS) equations. The following sections describe the two turbulence models used in this work: the Shear Stress Transport (SST) turbulence model and the Spalart-Allmaras (S-A) turbulence model. Both turbulence models employ the Boussinesq eddy-viscosity hypothesis, where the Favre-averaged turbulent shear stresses, \(\mathbf{\tau}^{F}\), is defined by  
\[\mathbf{\tau}^{F}_{ij}=2\mu_{T}S_{ij}-\left(\frac{2\mu_{T}}{3}\right)\frac{ \partial\delta_{k}}{\partial x_{k}}\delta_{ij}-\frac{2}{3}\rho\kappa\delta_{ ij}. \tag{4}\]  
where \(\mu_{T}\) is the turbulent eddy viscosity, \(S_{ij}\) is the strain rate tensor, \(\kappa\) is the kinetic energy of the turbulent fluctuations, and \(\delta_{ij}\) is the Kronecker delta.
## 7 Simulation Results and Analysis
### Verification of the RAWC Algorithm on the Six-Node Network Topology  
Figure 6 shows the performance of this scheme on the six-node network topology. The Dijkstra routing algorithm and the resource-adaptive (RA) algorithm are used as two benchmark schemes to verify the performance improvement in the RAWC algorithm in terms of the service request success rate and resource utilization. In this paper, the Dijkstra routing algorithm takes the link length as the link weight, and the RA algorithm does not consider wavelength conflicts. Figure 6a,b compare SRs of different algorithms, and the percentage improvement in the RAWC algorithm in SR compared with other algorithms, respectively. As the traffic load increases, the SRs of all three algorithms decrease. Due to limited network resources, the increase in traffic load reduces the ability of QKDON to provide wavelength channels and QKP to provide key services. Additionally, it can be observed that the lowest SR is achieved under the Dijkstra algorithm, and the highest SR under the RAWC algorithm. At a traffic load of 80 Erlang, the proposed RAWC algorithm improves SR by up to 13.5% compared with the Dijkstra algorithm. This is because using only the path length as the link weight increases the link load pressure, which causes service blocking. Even if a dynamic routing scheme is adopted, not taking into account the competition for wavelength resources blocks services. Figure 6c,d show the wavelength resource utilization (WRU) and quantum key utilization (QKU) of the three schemes, respectively. From the figures, it can be seen that both WRU and QKU increase with the increase in traffic load, which is the result of increased network resource consumption. When the traffic load is 40 Erlang, the advantages of the RAWC algorithm are not observable because the traffic is low. As the traffic load increases gradually, WRU and QKU of the proposed RAWC algorithm are consistently found to be the highest. The RAWC algorithm gives priority to the links with more remaining resources when selecting the communication path. The above simulation results show that the RAWC algorithm is the optimal choice for achieving load balancing.  
Figure 6: Performance comparison of different algorithms on the six-node network topology. (**a**) SR; (**b**) Improvement rate; (**c**) WRU; (**d**) QKU.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 4 Results
### Modeling
#### 4.1.1 Two Locations Relationship  
In the first case performed during the modeling phase, the behavior of the wind speed in two locations is evaluated. The calculation time of the algorithm until the correlations are reached is 0.60 s in the case of Fragavella and Labrada and 0.59 s in the case of Fragavella and Lanzos. The difference in the computation time between both studies is practically negligible, and, initially, there does not seem to be any relationship between distance and calculation time.  
Comparing Tables 3 and 4, the spatial correlations and cross-correlations have remarkably high values between Fragavella and Labrada and exceptionally low between Fragavella and Lanzos. Therefore, it can be deduced that the distance between the places influences the correlations inversely, i.e., the greater the distance is, the lower the correlations are and vice versa. On the other hand, the values of the autocorrelations in both cases are exceedingly high, and thus, it can be deduced that there is a temporal influence among the values of the series in each of the locations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Friction Factor** & **Weather Station Height (m)** \\ \hline Fragavella & 0.21 & 10 \\ Labrada & 0.17 & 2 \\ Lanzös & 0.23 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Friction factor of the terrain and meteorological station height in each location.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **c** & **k** \\ \hline Fragavella & 7.8580 & 1.4572 \\ Labrada & 6.6059 & 1.9478 \\ Lanzös & 2.9162 & 1.4014 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weibull parameters for each of the studied locations.
# Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence
## 5 Conclusions  
The present study investigated the objective measurement of auditory presence using EEG, on the basis of the previous study. It was found in the previous study that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio, while in this study it was found that an increase in the subjective presence level was correlated with a decrease in the peak alpha frequency (PAF). These results indicate that EEG is likely to reflect changes in the perceived presence level. Though limited with regard to the number of participants, these findings may shed light on research into the objective measurement of presence. Nevertheless, the neurological mechanism underlying the correlation between presence and EEG should be addressed in future work. In addition, future work should involve more participants and possibly develop a model of presence level estimation using EEG indices.  
**Author Contributions:** Conceptualization, S.Z.; methodology, S.Z. and X.F.; software, S.Z.; formal analysis, S.Z.; investigation, S.Z. and X.F.; resources, X.F. and Y.S.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, X.F. and Y.S.; visualization, S.Z.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Key R&D Program of China 2018 YFB1403800.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank all listeners who participated in the listening test.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks
### LTE Multi-Connectivity Solutions (Non-3 GPP)  
There are some standards and methods (separate from 3 GPP) that use the notions ITU-vertical multihoming (ITU-VMH) and the coordinated multipoint (CoMP) [60; 61; 62]. In light of the discussion presented in Table 2 of Section 3, we identified the following advantages and drawbacks of multi-homing and COMP approaches concerning their usage in 5G and future networks. These are as follows.  
**Advantages of Multi-Homing Support**  
* There is the possibility of channel-based granularity of service.
* At any instance of time, there is the possibility of connection to more than one access point [63].
* Redundancy can also be attained [63]  
**Drawbacks of Multi-Homing Support**  
* The protocol needs to be updated [63].  
**Advantages of COMP**  
* At any instance of time, there is the possibility of connection to more than one access point [64; 60].
* There is the possibility of channel-based granularity of service.  
Redundancy can also be attained [64].
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Comparative Experiments
#### 4.4.1 Comparison on the ISIC2018 Dataset
Table 4 shows the number of parameters and training duration for each segmentation method, as well as the test time for a single dermoscopic image. Both the training and testing times are measured in seconds. Due to the addition of different modules, there is a small increase in the number of parameters compared to the other models, but the segmentation of the skin lesion region can be performed in less than 1 s, which is still viable in future medical practice. As an additional comparative analysis of our method with other methods in this specific field, we included the most recent methods from the past two years of research on skin lesion segmentation for quantitative comparison, as shown in Table 5. It should be noted that the dataset used to train the model is the ISIC2018 dataset, and mIoU is considered the most important evaluation metric. Compared with other methods, our method achieves a significant improvement on the ISIC2018 dataset. By calculating _mIoU_, we found that our method outperforms other methods in skin lesion segmentation. Specifically, our method has a greater improvement in both specificity and _mIoU_ at the pixel level of the lesion region, proving its better segmentation effect and differentiation ability.
## 4 Security of Computer Network Users
### TAI-Based Securing Against Intrusion Attacks
[168; 169] proposed a new adversarial attack structure that assesses the resistance of IDSs based on ML algorithms. The created structure considered the automatic mutation of malicious network traffic while maintaining its functionality. Using this structure's Generative Adversarial Network (GAN), the authors generated opposing features. Then, using Particle Swarm Optimization (PSO), they searched for mutants of the mysterious movement. Also, they proposed a system for IDSs' verification. Also, [PERSON] et al. in [170] considered an adversarial attack problem. Firstly, the authors proposed an algorithm for generating adversarial samples. Secondly, they offered an IDS for an IoT network called the Feature Grouping and Multi-model Fusion Detector. The authors tested their model using publicly available datasets for the IoT. Also, the authors obtained better performance of the proposed system than another method against adversarial attacks. [PERSON] et al. concluded that their approach allowed for resisting adversarial attacks with high precision. Another problem related to users' security is malicious software, for example malware in intelligent devices. Usually, the attacker prepares malware to steal the data, but also, it can contain code that will harm the operating system [171]. [PERSON] et al. in [172] considered the malware-detection problem. The authors proposed the DeepAMD system for Android-based devices. The proposed method used Deep Artificial Neural Networks to identify and detect malware applications on the Android system. The test results confirmed the high precision of malicious software detection. In turn, [PERSON] et al. in [173] used FL for malware detection in IoT devices. The authors employed Multi-Layer Perceptron and auto-encoder-based models. The authors used a dataset that models malware-affected network traffic for tests. They compared the federated approach with a non-privacy-preserving setup and a local setup. They concluded that federated methods positively impact malware detection in IoT devices.