esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Assessment  
To assess the full classification performance of the CNN, the sensitivity (Sen), specificity (Spe), positive and negative predictive values (PPV and NPV), Accuracy (Acc) and likelihood ratios (LR+ and LR\(-\)) were calculated from the true-positive, true-negative, false-positive and false-negative predicted classifications of the test sets. The F1 score, a surrogate of accuracy, which is the harmonic mean of precision (the positive predictive value) and recall (sensitivity), was also calculated.
## 6 Current Problems and Research Difficulties
### Privacy and Security  
One of the biggest issues for any sort of network, including UWSNs, is maintaining privacy and security. Due of their distant locations, applications of UWSN are quite vulnerable to malicious assaults. Without adequate security, the UWSN platform as a whole may become compromised, rendering all efforts useless. However, protection in routing, comes with higher energy expenses.
## 2 Model Description
### Atmospheric Stability Classification  
The atmospheric thermal stability is suppressed or enhanced by the vertical temperature difference. The two atmospheric stability classification methods commonly used are those from Pasquill [23] and IAEA (International Atomic Energy Agency) [24]. The Pasquill method (abbreviated to P\(\cdot\)S) proposed in the Chinese standards includes six categories: highly unstable, moderately unstable, slightly unstable, neutral, moderately stable and extremely stable. They are denoted as A, B, C, D, E, and F.  
Taking the turbulence and thermal factors into account, there are three methods for classifying the atmospheric stability, namely the Monin-Obukhov method, Bulk Richardson number method, and Richardson Gradient method. Among them, theBulk Richardson number method does not have a uniform atmospheric stability classification standard, so it is not used in the present work. When the measured wind data only contains the wind direction and speed data, the wind direction standard deviation method and wind speed ratio method can be used to classify the atmospheric stability.
## 5 Numerical Results
### Example 3  
Here, the neural network is trained for each stress tensor component separately (Figure 5). The data set \(M=9261\) is split into 80% for training and 20% (10% for validation and 10% for test/prediction). The computed maximum of training and validation loss error, mean square error (MSE) and mean absolute error (MAE) with 84 batch size for the cases, without and with the [PERSON]'s metrics, are given in Tables 3 and 4, respectively. The above error results are presented for the stress tensor and the stiffness matrix.  
As numerical experiments have shown, the hyperparameters have a significant impact on the results. More shallower networks sometimes can perform better for the computation of partial derivatives. From the values of the training loss, validation loss, MSE, and MAE, we can see that in the neural model with the Sobolev metrics, more epochs are needed to  
Figure 12: The partial derivatives of the components of the stress tensor \(\sigma_{xy}\) after training of the neural network Figure 5 and from the data set \(M=9261\), which was obtained from representative volume elements.  
reach the desired accuracy for the stress components. The results are also compared with the third-order linear polynomial regression. In Figures 13-16, the line fitting for the computed stress components, tangent stiffness and polynomial regression are sketched. Although the polynomial regression requires less computational effort, the ANN provides more accurate approximations. In Figure 13, the residual graphs for the stress components with [50,50] neurons and 5000 epochs are sketched, and in Figures 14-16, the residual graphs for the derivatives with [30,40,30,40] neurons and 5000 epochs are illustrated.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Neurons** & **Epochs** & **Train. Loss** & **Valid. Loss** & **MSE\(\sigma\)** & **MAE\(\sigma\)** & **MSE\(\text{stiff}\)** & **MAE\(\text{stiff}\)** & **Time (min.)** \\ \hline
[50,50] & 2000 & \(4.2\times 10^{-5}\) & \(4.2\times 10^{-5}\) &  
Figure 13: The line fitting for the components of the stress tensor after training the neural network and using the polynomial regression at the prediction points of the data set (\(M=9261\)) obtained from the representative volume element.  
Figure 14: The line fitting for the partial derivatives of the component of the stress tensor \(\sigma_{xx}\) after training the neural network and using the polynomial regression at the prediction points of the data set \(M=9261\) obtained from the representative volume element.  
Figure 15: The line fitting for the partial derivatives of the component of the stress tensor \(\sigma_{yy}\) after training the neural network (Figure 5) and using the polynomial regression at the prediction points of the data set \(M=9261\) obtained from the representative volume element.
## 2 Materials and Methods
### Choice of the Optimization Algorithm  
The mentioned algorithms are all potential candidates for the optimization problem. The linear load case does not play an important role, as calculation times are about 30 s. With 30 min for a simple and more than 12 h for a complex crash calculation, the algorithm and its efficiency are more important for the non-linear optimization time. From the survey, and according to the experience with the framework, the following requirements are suggested:  
* Multi-objective optimization algorithm;
* Effective search strategy;
* A limited number of costly function evaluations (e.g., use of surrogates);
* Able to deal with the constraint function;
* Parallelization possible.  
As all these criteria are case-dependent and notably hard to evaluate for each optimization algorithm, a pre-selection with a weighted cost-benefit analysis is processed to choose an optimization algorithm.  
Accordingly, the three main criteria for this specific optimization scenario are defined with the weights:  
* Convergence ratio (40%);
* Parallel data evaluation (20%);
* Global search strategy (40%).  
The evaluation and ranking of all the algorithms are still subjective, but it helps quantify and compare the benefits and drawbacks of all algorithms and frameworks. Appendix A shows the detailed cost-benefit analysis in tabular format. From this analysis, the GOMORS framework is chosen and used for the following analysis due to its potentially high convergence ratio, the parallel data evaluation with up to four parallel calculations, and its global search strategy. Furthermore, GOMORS is already implemented in Python.  
Accordingly, the following paragraphs briefly describe the NSGA-II and the GOMORS to understand both methods better.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 6 Conclusions  
Sunspot detection and solar activity monitoring are very important in order to predict possible CMEs (coronal mass ejections) that can have a major effect on Earth. Automatic detection of sunspots can be very important for tracking the spots and studying their evolution along the time. The present work shows that YOLOv5s can detect sunspots in \(H\alpha\) images from the solar cycle 24, obtained with the OGAUC spectroheliograph, with up to 90% precision, which is an improvement compared to previous work with the same images.  
The YOLO networks used in the experiments work just with bounding boxes and do not perform segmentation, tracking or area calculations over time. Future work will include experiments with CNN models that perform these tasks in order to determine and follow the evolution of spots and solar activity through solar cycle 24 and other cycles as well.  
Conceptualization, M.M., T.B. and N.P.; methodology, M.M., M.M.C., A.P.C., T.B. and N.P.; software, J.S. and M.M.; validation, T.B., N.P. and C.P.; resources, T.B., N.P., A.P.C  
**Acknowledgments:** N.P. and T.B. acknowledge financial support by FCT-Fundacao para a Ciencia e a Tecnologia through Portuguese national funds and by FEDER through COMPETE2020-Programa Operacional Competitividade e Internacionaliazacao by these grants: UIDB/04434/2020; UIDP/04434/2020. The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT) for the financial support for project UIDP/00048/2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### Results Analysis  
As shown in Figure 39, these are the S parameter curves for the dual-element \"ring-slot\" structure. It is evident from the graph that, although there is a slight shift in the resonance frequencies, the structure still fully covers the operating frequency range of LTE band 42 (3.4-3.6 GHz). Even in the scenario where the rectangular ground slots are shared and the two elements are placed in complete overlap, without the addition of any decoupling structures, the isolation of this dual-element \"ring-slot\" structure remains higher than 21 dB, achieving a high level of isolation between the antennas.  
Figure 38: Current distribution diagram of the dual-element “ring-slot” MIMO antenna structure.  
Figure 39: S parameter curves of the dual-element “ring-slot” structure.  
As shown in Figure 40, based on the radiation pattern results for the E-plane and H-plane at a frequency of 3.5 GHz for the dual-element \"ring-slot\" MIMO antenna, there is no distortion in the antenna's radiation pattern. The E-plane radiation pattern exhibits a dumbbell-shaped distribution, indicating effective coverage of the entire antenna's dielectric substrate. On the other hand, the H-plane radiation pattern displays omnidirectional radiation characteristics, signifying that the dual-element \"ring-slot\" MIMO antenna structure can achieve favorable radiation characteristics, meeting the requirements for MIMO antenna design.
## 3 Processes for Bioenergy Production
### Biochemical Methods  
#### 3.2.1 Alcoholic Fermentation  
This process is based on the transformation of carbohydrates into ethanol by microorganisms (_Saccharomyces cerevisiae_). The optimal activity of the yeast occurs at 33-35 \({}^{\circ}\)C and pH 3-5. The maximum concentration of bioethanol in the growth medium must not exceed 11% by volume, since higher concentrations can inhibit the activity of the yeast [8]. During continuous fermentation, the concentration of bioethanol is maintained at levels close to the microorganisms' tolerance values [11].  
At the industrial level, fed-batch fermentation and semi-discontinuous fermentation are widely used to fulfill the dual purposes of maintaining the concentration of bioethanol below the threshold for inhibition of yeast and operating under variable volume conditions. In fed-batch fermentation, there is an initial batch phase followed by the fed-batch process once an adequate amount of microbial biomass is reached. This technique is ideal for processes in which either cell growth or product formation is sensitive to the concentration of the limiting substrate [26]. Finally, semi-discontinuous fermentation can be considered as a combination of the fed-batch and continuous processes. In this operation, the feed solution is fed at constant intervals, while the effluent is removed discontinuously. The advantage of this system is that the intermittent feeding of the substrate, which has an inhibitory effect on the production of the catabolites, keeps its concentration low, improving fermentation productivity. Both methods have the advantage that there is no non-productive idle time for cleaning and re-sterilization and not much control is required. However, there is a high risk of contamination and mutation due to the long periods of cultivation and handling, and since larger reactor volumes are required, investment costs are higher [26].  
Several studies have focused on enhancing the digestibility of lignocellulosic biomass through physical, chemical, biological, and hybrid pretreatments in the production of liquid fuels (primarily ethanol) via biochemical pathways [27; 28; 29].
# Contribution of Etalon Observation to Earth Rotation Parameters under a New Observation Scenario
## 3 Results
### Orbit
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellites**} & \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**2018**} & \multicolumn{3}{c}{**2019**} \\ \cline{3-8}  & & **R (cm)** & **T (cm)** & **N (cm)** & **R (cm)** & **T (cm)** & **N (cm)** \\ \hline \multirow{4}{*}{Etalon-1} & Maximum & 6.73 & 27.82 & 37.64 & 5.71 & 21.80 & 33.39 \\  & Minimum & \(-\)8.38 & \(-\)37.68 & \(-\)43.17 & \(-\)5.64 & \(-\)38.56 & \(-\)32.08 \\  & Mean & 0.03 & 2.85 & \(-\)0.05 & \(-\)0.004 & \(-\)0.41 & \(-\)0.19 \\  & RMS & 1.49 & 7.85 & 10.55 & 1.46 & 6.64 & 8.22 \\ \hline \multirow{4}{*}{Etalon-2} & Maximum & 5.99 & 41.15 & 48.85 & 5.46 & 20.66 & 27.26 \\  & Minimum & \(-\)6.88 & \(-\)31.77 & \(-\)47.61 & \(-\)5.18 & \(-\)41.41 & \(-\)23.46 \\ \cline{1-1}  & Mean & \(-\)0.03 & 0.30 & 0.57 & \(-\)0.01 & \(-\)1.25 & 0.22 \\ \cline{1-1}  & RMS & 1.68 & 8.61 & 10.06 & 1.48 & 7.15 & 7.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristics of orbital error for Etalon-1/2 satellites in 2018 and 2019. Figure 17: Orbit overlap differences in the R, T, N directions for Etalon-2 in 2019.
# Context-Based Model for Browsing the Web Through Voice
## 7 Results and Discussion
### Impact of Context on Web Search Results  
This study compares the search results obtained using the prototype with the search results in a popular web browser. The objective is to evaluate how the user's context and browsing history influence the results obtained. The following methodology was applied:  
* Definition of search requests: a set of four representative requests covering different types of content, such as images, web pages, videos, and specific terms, was selected;
* User context consideration: to assess the impact of context on the results, each query was carried out under a different user profile. The profiles used are defined in Table 9;
* Participants: a total of four participants were part of this evaluation, each corresponding to one of the defined user profiles;
* Searching: each request was executed two times:
* The former using the prototype, which takes into account the user context to filter and sort the results;
* The latter, through Google incognito mode, as a standard search reference without explicit customization. Users reviewed the first five results from each set and provided a subjective rating. They were asked to rate the match of the results with their profile using a scale of 1 to 3, where 1 indicated \"no match\", 2 \"slightly match\", and 3 \"match\". Table 10 shows the first five results obtained for the query \"search for current music, please\", made by a user with Profile A, and their respective ratings. Figure 16: Matches between web search results and user profiles and requests. (**a**) Image search results. (**b**) Video search results. (**c**) Web page search results. (**d**) Term search results. Each user made a different query to rate the results. In particular, they requested:  
* Profile A: \"Search for current music, please\";
* Profile B: \"What is chemistry?\";
* Profile C: \"I want to see the CINVESTAV website\";
* Profile D: \"Show me images of the Raiders\". These requests allowed us to analyze the ability of each tool to respond to different types of searches. Figure 17 shows the comparison of the average match rate of the five results per request obtained in Google and in our prototype according to the evaluation of users with different profiles. It is observed that our prototype outperforms Google for requests from users with profiles A and D, who searched for videos and images, respectively.
# Shear Tests of Deep Hollow Core Slabs Strengthened by Core-Filling
## 1 Introduction
In structural design codes, such as ACI 318-14 [16], CSA-A23.3-14 [22], and Eurocode2 [26], estimating the shear strength of PHCSs with core-filling concrete is not provided. In this study, experimental research was carried out to investigate the shear resistance mechanism of PHCS strengthened by the core-filling method. A total of five PHCS specimens with a thickness of 400 mm was fabricated by using the extrusion method, in which a machine extrudes low-slump concrete and compacts it to form a hollowed section along the long-line prestressing bed. The main test variables were set to the number of filled cores and the shear reinforcement ratio. During the test, the member behavior and crack patterns of PHCS specimens were measured in detail. After the test, the members were cut with a wire saw to observe the crack patterns that occurred in the filled cores, and to identify whether the filled cores effectively contributed to the shear resistance mechanism. Based on the test results, this study also examined whether the current design codes adequately evaluate the shear strengths of PHCS with filled cores, and proposed a modified equation to accurately estimate the shear strengths of PHCS members strengthened by the core-filling method. The significance of this research is summarized, as follows:  
1. Investigating the shear reinforcing effect of core-filling concrete. 2. Identifying the effects of shear reinforcement placed in core-filling concrete. 3. Investigating the composite action between the PHCS unit and core-filling concrete by observing their shear crack patterns. 4. Developing a simple equation to accurately estimate the shear strengths of PHCS reinforced by the core-filling method.
## 5 Simulation Experiments
### Network Throughput (NT)  
We all know that if the number of messages in the network increases upto a great extent, then it gives rise to a huge amount of message contention and collision, yielding a high rate of packet loss. Since the competitors of SD-EAR suffer from much more message overhead than our proposed protocol SD-EAR, the network throughput produced by SD-EAR is much higher than others, as shown in Figures 13 and 18. It is seen in the figures that for all of the protocols, network throughput initially increases with the number of nodes; then, it starts decreasing. The reason is that with the increase in the number of nodes, more links are formed, and more data packets are delivered to their respective destinations. However, when the network becomes saturated with nodes, message contention and collision increases, reducing network throughput.
## 3 Methods
### Optimization Algorithm
#### 3.3.3 Performance Comparison
The actual mean, minimum, and standard deviation values of the optimal values of 25 repetitions obtained by PSO-CK, SPSO, and SDPSO for each benchmark optimization problem are shown in Table 3. The bold values in the table indicate the best results among the three optimization algorithms. From Table 3, we can see that: (1) all the bold values are distributed in the column of SDPSO. That is, the optimization capability of SDPSO is better than that of PSO-CK and SPSO. (2) When considering the minimum optimal values of 25 repetitions, both BOP1 and BOP2 show that SDPSO performs better than PSO-CK and SPSO. That is, SDPSO has a more significant potential to find the globally optimal solution. (3) When considering the standard deviation of the optimal values of the 25 repetitions, the two problems show that SDPSO is more robust than PSO-CK and SPSO. Figure 7 shows the convergence history of the mean of the present optimal values of the 25 repetitions obtained by the three optimization algorithms, respectively. We can see that SDPSO could find better results with a faster convergence speed when compared with PSO-CK and SPSO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problems** & **Criteria** & **PSO-CK** & **SPSO** & **SDPSO** \\ \hline \multirow{3}{*}{BOP1} & Mean & \(-0.5513\) & \(-0.5469\) & \(\mathbf{-0.6344}\) \\  & Minimum & \(-0.6675\) & \(-0.6586\) & \(\mathbf{-0.6684}\) \\  & Std.Dev & \(0.0578\) & \(0.0602\) & \(\mathbf{0.0195}\) \\ \hline \multirow{3}{*}{BOP2} & Mean & \(-9.4227\) & \(-9.4800\) & \(\mathbf{-13.1658}\) \\  & Minimum & \(-12.00\) & \(-12.00\) & \(\mathbf{-14.4300}\) \\ \cline{1-1}  & Std.Dev & \(2.0889\) & \(2.0640\) & \(\mathbf{1.3983}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean, minimum, and standard deviation values of the optimal values of 25 repetitions, obtained by PSO-CK, SPSO, and SDPSO for each benchmark optimization problem, respectively. Figure 6: Boxplots of the optimal values of 25 repetitions obtained by PSO-CK, SPSO, and SDPSO, respectively, for each benchmark optimization problem. (**a**) BOP1; (**b**) BOP2. Figure 7: Convergence history of the mean of the present optimal values of the 25 repetitions obtained by PSO-CK, SPSO, and SDPSO, respectively. (**a**) BOP1; (**b**) BOP2. In summary, SDPSO is superior to PSO-CK and SPSO in optimization capability, numerical robustness, and convergence speed for optimization problems with constraints. It has a more significant potential to find the globally optimal solution.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.3 IoT Applications
Twenty subjects tested the six-command semiautomatic mode, and the performance metrics employed for this experiment were classification accuracy, ITR, and the average time required to reach each designated target. [PERSON] et al. [38] developed a fast recognition method based on a separable convolutional neural network (SepCNN) in order to improve the accuracy and ITR of AR-BCI systems. An EEG cap with 32 electrodes was combined with HoloLens HMD in order to display the nine-target experiment. In their comparison, SepCNN was tested against four common ML classifiers: Bayesian LDA, LDA, SVM, and SWLDA. The results revealed that SepCNN demonstrated significant improvements in both ITR and classification accuracy. [PERSON] et al. [40] improved the classification accuracy of SSVEPs by combining the use of FFT and CCA in the time domain. To acquire the EEG data, the researchers employed the Olimex EEG-SMT system, equipped with two active electrodes. For the AR device comparison, they evaluated three different HMDs: Epson Moverio BT-350, Oculus Rift S, and Microsoft HoloLens. The proposed algorithm demonstrated higher performance in terms of classification accuracy compared to the classic CCA method. Among the AR devices evaluated, HoloLens exhibited the best overall performance. [PERSON] et al. [42] introduced a BMI system designed to determine the user's focus or attention on an object, enabling them to grasp it within the physical environment. An eight-channel EEG cap was combined with HMZ-T3 HMD to carry out the study. To assess the performance of the experiment, eight subjects took part in the experimental process, and the study utilized classification accuracy as the evaluation metric.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 5 Experimental Results
### End-to-End Delay
The synchronous E-MQTT has no additional response procedure like the MQTT but needs a certain degree of additional overhead for synchronous communication. With E-MQTT introducing the mechanism of collecting subscriber responses at the broker, the net outcome is a reduction in the number of packets exchanged between the broker and the publisher. Since the time taken for the additional response process of MQTT consistently outweighs other delay factors, the conventional MQTT emerges as the slowest performer across all cases.