esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
```
Algorithm: CS. Input: \(x=\left(x_{1},\ldots,x_{d}\right)^{T}\)--A sequence of training data. Output: \(x_{best}\)--The returned value with the best fitness in the search domain. Parameters: \(n\)--Number of nests. \(p_{a}\)--Discovery rate of alien eggs/solutions. \(Ub\)--Upper bounds of the search domain. \(Lb\)--Lower bounds of the search domain. \(F(x)\)--Objective function. MaxGeneration--Maximum number of generations. 1: /* Generate an initial population of \(n\) host nests \(x_{i}(i=1,\ldots,n)^{*}\)/ */2: FOR EACH: \(1\leq i\leq n\) DO
3: \(x_{i}=Lb+(Ub-Lb)*rand()\);
4: END FOR
5: iter = 1;
6: WHILE (iter<MaxGeneration) DO
7: /* Get a cuckoo randomly (say\(i\)) by Levy flights. */
8: /* Evaluate its quality/fitness \(F_{i}\). */
9: /* Choose a nest among \(n\) (say \(j\)) randomly. */
10: IF (\(F_{i}>F_{j}\)) THEN11
11: /* Replace \(j\) by the new solution. */
12: END IF
13: /* Abandon a fraction (\(p_{a}\)) of the worse nests. */
14: /* Build new ones at new locations via Levy flights. */
15: /* Keep the best solutions. */
16: /* Rank the solutions and find the current best. */
17: iter = iter + 1;
18: END WHILE
19: RETURN \(x_{best}\)
```  
**Algorithm A1** The shortened process of the cuckoo search algorithm.  
Figure A2: Fitting curves and forecasting for the upper-middle income group.  
Figure A3: Fitting and forecasting curves for the lower-middle income group.
# Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror
###### Abstract  
During the processing of an optical mirror, the performance parameters of the bottom support system would affect the surface forming accuracy of the mirror. The traditional bottom support system has a large unadjusted support stiffness, which increases the difficulty of unloading the impact force generated by the grinding disc. In response to this scenario, a flexible support system (FSS) consisting of 36 support cylinders with beryllium bronze reeds (BBRs) and rolling diaphragms (RDs) as key components is designed. It is necessary to analyze the key components of the support cylinder to reduce its axial movement resistance, ensure a consistent force output of each support point. First, the internal resistance model of a flexible support cylinder is established, and the main factors of internal resistance are then analyzed. Thereafter, the multi-objective structural parameters of the BBR and RD are simulated in ANSYS using the control variable method. The optimal structural parameters of BBR and RD are determined by simulation. Finally, experiments are performed on the RD ultimate pressure, internal resistance of the support cylinder, and consistency of the force output of the FSS. The experimental results show that the support cylinder with the optimized design has good force output consistency, which provides a theoretical basis for the application of FSS in optical mirror processing.  
optical mirror processing; flexible support system; beryllium bronze reed; rolling diaphragm; resistance to movement; multi-objective parameters optimization +
Footnote †: journal: Applied Sciences  
**Keywords:** optical mirror processing; flexible support system; beryllium bronze reed; rolling diaphragm; resistance to movement; multi-objective parameters optimization
# Systematic Literature Review of Challenges in Blockchain Scalability
## 7 RQ2: What Vital Root Factors Are Causing Scalability Issue in Blockchain?
### Block Size  
Typically, the size of one block is around 1 MB in Bitcoin [5; 117]. This is considered very small and limits the number of transactions that can be stored. It is apparent that large block size is able to accumulate more transactions and directly increase the throughput and lower the latency [5], but the larger block would prompt an increase in block propagation time because heavier block needs more time to be transmitted over the network. In addition, handling more transactions in one block would demand more computational resources [117].
## 4 Results
### Testing the Effects of Fungi Bed Aging  
The amount and type of VCs vary with the age of FBs, crucial for reusing waste FBs as biostimulants source. To assess the effects of different-aged shitiake FBs on rice seedling growth, 60 g of YSFB, MSFB, and WSFB were used. Dry weight, plant height, and root length were measured, as shown in Figure 5.  
Dry weight (Figure 5B) showed the most significant improvements across treatments. Seedlings exposed to YSFB-VCs achieved the highest dry weight (41.2 \(\pm\) 6.37 mg), with a significant mean difference of \(-\)29.40 mg compared to the control (21.8 \(\pm\) 1.64 mg, \(p\) = 0.0001, 95% CI: \(-\)43.62 to \(-\)15.18, labeled b, c). Similarly, seedlings treated with MSFB-VCs exhibited a significant increase in dry weight (45.6 \(\pm\) 9.52 mg), with a mean difference of \(-\)20.00 mg relative to the control (\(p\) = 0.0049, 95% CI: \(-\)34.22 to \(-\)5.78, labeled b, c). Exposure to WSFB-VCs also resulted in a significant increase (35.0 \(\pm\) 7.57 mg) compared to the control, with a mean difference of \(-\)15.80 mg (\(p\) = 0.0268, 95% CI: \(-\)30.02 to \(-\)1.58, labeled b, c).  
Plant height (Figure 5C) was significantly enhanced under YSFB-VCs and MSFB-VCs. Seedlings treated with YSFB-VCs displayed the greatest plant height (42.96 \(\pm\) 7.72 cm) compared to the control (26.12 \(\pm\) 2.77 cm) with a significant mean difference of \(-\)16.84 cm (\(p\) = 0.0002, 95% CI: \(-\)25.22 to \(-\)8.46, labeled a, b). Similarly, seedlings exposed to MSFB-VCs showed significant improvements in plant height (24.9 \(\pm\) 3.34 cm) with a mean difference of \(-\)13.68 cm (\(p\) = 0.0013, 95% CI: \(-\)22.06 to \(-\)5.30, labeled a, b). Seedlings treated with WSFB-derived VCs exhibited a moderate increase in plant height (26.6 \(\pm\) 3.10 cm), with a mean difference of \(-\)13.40 cm (\(p\) = 0.0016, 95% CI: \(-\)21.78 to \(-\)5.02, labeled a, b).  
In contrast, root length (Figure 5D) did not show any significant differences across treatments. [PERSON]'s multiple comparisons test confirmed that seedlings treated with YSFB-VCs exhibited a mean root length of 8.74 \(\pm\) 2.00 cm, which was not significantly different from the control (7.12 \(\pm\) 2.00 cm, \(p\) = 0.7242, labeled a). Similarly, seedlings exposed to MSFB-VCs displayed a root length of 9.72 \(\pm\) 1.89 cm (\(p\) = 0.9926, labeled a), while seedlings treated with WSFB-VCs showed a root length of 8.36 \(\pm\) 1.05 cm, which also did not differ significantly from the control (\(p\) = 0.5764, labeled a).  
Overall, results showed that YSFB and MSFB produced the most pronounced growth enhancement, significantly increasing dry weight and plant height (\(p\) < 0.05). However, WSFB also promoted moderate but significant growth, confirming that even after harvesting mushrooms, FBs retain bioactivity (Figure 5).
## 2 Related Work
### Multi-UAV Coordination and Coverage  
Successful swarm coordination relies on region decomposition and task allocation. Voronoi partitioning [11] and NSBKA [12] reduce overlapping coverage, whereas E2M2 CPP [13] equalizes energy consumption over non-overlapping regions. Vehicle-drone collaboration models [14] alleviate energy constraints but do not facilitate adaptive reallocation when the mission is updated. Multi-agent systems like CEL [15] employ experience sharing; yet, scalability under highly dynamic conditions remains limited. The quantum robot Darwinian PSO (QRDPSO) [16] accelerates task allocation in drone swarms, and the composite-directional raid strategy (CDR-DRS-WPOA) [17] enhances patrol efficiency. Most approaches do not address heterogeneous coordination, e.g., weeding robots and drones in MOTLBO [18], or fuse attention mechanisms to cover adaptation [19].
## 4 Discussion
Changes in the expression of **miRRNA150-5p** (\(p\) = 0.056) and **miRNA155-5p** (\(p\) = 0.051) allowed for the speculation that these miRNAs could also be useful as biomarkers of sepsis, in line with previous results [31], and that in the case of miRNA155-5p, a role as biomarker of resuscitation is worth pursuing in future studies. Given their role in the immune response, our findings of increased serum expression of miR-146a-5p and miR-34a-5p were expected. However, these findings have not been previously reported in a clinically relevant large animal model of sepsis nor has the association with resuscitation been described. The role of the miRNAs measured herein in other in vitro models, as well as other biomarkers of sepsis, is discussed in further detail in the Supplementary Materials [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Changes in miRNA expression are expected to occur in sepsis. Conditions that take place in sepsis, such as inflammation and hypoxia, are known to regulate miRNA expression, which is under the control of transcription factors that play roles in the inflammatory response [54]. Our findings on the relationship between the serum expression of certain miRNAs and the intensity of the resuscitation is of great clinical interest. Currently, the serum lactate concentration is widely used for the diagnosis of tissue hypoperfusion and to monitor the response to resuscitation [1; 2]. However, elevations of serum lactate could be due to reduced clearance in the context of sepsis and liver dysfunction and thus not always reflect tissue hypoperfusion [4; 5; 6]. The serial measurement of several hemodynamic variables can monitor the achievement of predefined hemodynamic objectives during resuscitation. However, a biochemical biomarker of resuscitation would be a noninvasive biomarker of resuscitation complementary to the currently available hemodynamic measurements and could provide information about cellular changes sensitive to hypoperfusion.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
The authors use a total of 197,566 comments from four platforms: YouTube, Reddit, Wikipedia, and Twitter, with 80% of the comments labelled non-hateful, and the remaining 20% was hateful. The experiments were conducted using several machine learning algorithms to test each feature separately to evaluate their accuracy based on features selection. In addition to machine learning classifiers, [PERSON] et al. [68] suggested an appropriate strategy combining roles typical to cyberbullying, content-based, and user-based. The results showed better performance with the combined use of all features. [PERSON] et al. [69] developed the corpus of Dutch social media messages and annotated the same in different categories of cyberbullying, such as threats and insults. The authors also added the comprehensive details that the participants involved in bullying (victim, cyber predator, and bystander identification). [PERSON] et al. [70] extended the insult to create bullying features based on word embedding and obtained an f-measure of 0.78 with an SVM classifier. In addition, the novel features were derived from a dictionary of standard terms used by neurotics in social networks. The authors in [71] have used the Word2 Vec embedding model-based neural network, which was utilized to represent textual health data with a semantic context. Moreover, unique domain ontologies are incorporated into the Word2 Vec model. These ontologies provide additional details on a neural network model that recognizes the semantic sense of uncommon words. New semantic information utilizing the Bi-LSTM model is employed to precisely distinguish unstructured and structured health data. A different wok is used the decision tree C4.5 classifier based on TF-IDF weighting method to detect and classify hoax news on Twitter. N-gram is also utilized to extract features to the suggested C4.5 classifiers [72]. In [73], authors have suggested a novel model that incorporates the most relevant documents, reviews, and tweets from social media and news articles. In addition, they integrated a topic2 vec with Word2 Vec and created a word embedding model representing each word in a document with a semantic meaning and a low-dimensional vector. The authors also used ML to classify the data using the models as mentioned earlier. Table 1 summarizes and shows the comparison results of the related studies. As cyberbullying is considered a classification issue (i.e., categorizing an instance as offensive or non-offensive), several supervised learning algorithms have been employed in this study for the further evolution of their classification accuracy and performance in detecting cyberbullying in SM, in particular on Twitter. The classifiers adopted in the current study are as follows:
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 1 Introduction
Our significant contributions highlight a superiority, compared to previous designs, in both gain and radiation efficiency when comparing our proposed designs of a 2-port MIMO system using two 1 \(\times\) 2 arrays and a 2-port MIMO system using two 1 \(\times\) 4 arrays. The 2-port MIMO system with two 1 \(\times\) 2 arrays achieves a maximum gain of 8.3 dBi and a maximum radiation efficiency of 95%, with an effective envelope correlation coefficient (ECC) of less than 0.006. In contrast, the 2-port MIMO system utilizing two 1 \(\times\) 4 arrays reaches a maximum gain of 10.9 dBi, a maximum radiation efficiency of 97.46%, and an ECC of less than 0.003. Additionally, MIMO antennas were experimentally tested and implemented using the MIMO Application Framework for 5G systems operating in the sub-6 GHz range, demonstrating effective performance tailored for cutting-edge 5G applications. These antennas operate seamlessly in the sub-6 GHz spectrum. The structure of this paper is outlined as follows: Section 1 provides an introduction to 5G technology and its applications within the sub-6 GHz frequency band, accompanied by a review of previous research. Section 2 details the development and evolution of a single antenna element, presenting findings from a simulated parametric study and results obtained from the fabricated prototype. Section 3 introduces a 2-port MIMO system using two flipped parallel 1 \(\times\) 2 arrays configuration, arranged with two flipped parallel elements, and includes results from simulation, fabrication, and an evaluation of its MIMO performance. Section 4 describes a 2-port MIMO system using two flipped parallel 1 \(\times\) 4 arrays setup, where the array elements are positioned opposite each other, and provides results from simulation, fabrication, and an assessment of its MIMO performance. Section 5 demonstrates an experimentally tested evaluation of the three fabricated antennas on the MIMO Application Framework for 5G systems. Finally, Section 6 contains the paper's conclusion.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 5 Simulations
### Simulating Modeling Inaccuracies  
In this section, the modeling inaccuracies and disturbance are considered in the course of controlling the system. The fault scenario, system parameter and other controller parameter settings are the same as case B. At \(t=25\) s, A disturbance is imposed to the load at bus 3, bus 4, bus 6 and bus7 with 5% increase of the active and reactive power.  
If the disturbance can be measured and sent to the control center, the voltage behavior based on AH-MPC is shown in Figure 14. The system voltage drops dramatically due to the disturbance at \(t=25\) s. At the next sampling point \(t=30\) s, the optimization is carried out based on the updated system measurements.  
If the disturbance is ignored by the control center, there will be modeling inaccuracy problems at all future control steps after \(t=25\) s. The voltage behavior based on AH-MPC is shown in Figure 15. Although the prediction model is not completely updated after the disturbance, the controller will get  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n\) & \(I_{\text{s1}}\) (p.u.) & \(I_{\text{s2}}\) (p.u.) & \(I_{\text{s3}}\) (p.u.) & \
\begin{tabular}{c} **Control** \\ **Horizon (s)** \\ \end{tabular} &
\begin{tabular}{c} **Prediction** \\ **Horizon (s)** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & \(\begin{array}{c}(-0.0211,\,-0.0301,\\ -0.0275,\,-0.0198)\end{array}\) & \(\begin{array}{c}(0.0013,\,-0.0045,\\ -0.0019,\,0.0201)\end{array}\) & \(\begin{array}{c}(0.0196,\,0.0152,\\ 0.0177,\,0.0510)\end{array}\) & 30 & 60 \\ \  
the actual voltage responses according to measured feedback signals at each future sampling point, and the structure of receding-horizon multi-step optimization offers chance to correct the consequences of modeling errors.  
From Figures 14 and 15, we can see that the proposed control scheme is still valid. This is mainly because the disturbance and modeling inaccuracy problems are compensated by the closed-loop feature of the centralized controller.
## 5 Results of Calculations  
### Parameters of the GTN Model  
As already mentioned, the parameters of the GTN model were determined by comparing several calculated J-R curves for CT specimens to the experimentally determined CT-based J-R curve (Figure 4). In this figure, the magnitudes of fracture toughness parameters, namely J\({}_{\rm in}\) and J\({}_{0.2}\), are also marked.  
Figure 3: The network of elements for the SENT specimen—the clamped variant.  
Figure 2: A detail of the network ahead of the crack tip in the CT specimen.  
It was found from all these calculations that the most expedient values of the GTN parameters were those listed in Table 2. It was also found from the calculations that there was no need to consider the nucleation of new voids according to the relation (5).  
The experimental J-R curve for the CT specimens was described by the power function (6)  
\[J=785.45(\Delta a)^{0.5653} \tag{6}\]  
with the blunting line (7)  
\[J=4\sigma_{fs}(\Delta a)=2052(\Delta a) \tag{7}\]  
and the 0.2 mm offset line (8)  
\[J=4\sigma_{fs}(\Delta a-0.2)=2052(\Delta a-0.2) \tag{8}\]  
Using relations (6) through (8), the following magnitudes of the quantities \(\rm{J_{in}}\) and \(\rm{J_{0.2}}\) were obtained: \(\rm{J_{in}}=225.3\) N/mm; \(\rm{J_{0.2}}=494.4\) N/mm.  
The J-R curves for the CT specimens, obtained both experimentally and numerically, are compared in Figure 5. The power functions describing the R-curves are also given.  
Due to the small difference between the magnitudes of the exponents in the power functions, the R-curves intersect at the point where \(\Delta\)a\(\sim\)1 mm. For \(\Delta\)a < 1 mm, the calculated R-curve lies above the experimental one, and for \(\Delta\)a > 1 mm, it lies below. The difference in J integrals, as determined by FEM and by experiments, decreases from 27.1 N/mm at \(\Delta\)a = 0.2 mm to zero at \(\Delta\)a\(\sim\)1 mm and then increases gradually in absolute values to 32.6 N/mm at \(\Delta\)a = 1.8 mm. In terms of the relative inaccuracy, it is \(\sim\)8.5% at \(\Delta\)a = 0.2 mm and \(\sim\) 3% at \(\Delta\)a = 1.8 mm. Owing to these moderate relative inaccuracies, it can be considered that these two R-curves are close to one another, and therefore the Gurson parameters in Table 2 can be considered to be appropriate.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Magnitude** \\ \hline \(q_{1}\) & 2.00 \\ \(q_{2}\) & 0.77 \\ \(q_{3}\) & 4.00 \\ \(f_{0}\) (initial volume of the voids) & 0.007 \\ \(f_{\rm{F}}\) (final volume of the voids) & 0.10 \\ \(f_{N}\) & 0.00 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the GTN model as used for the simulation of crack growth in the CT and SENT specimens.  
Figure 4: The CT-based J-R curve determined experimentally.  
The CT J-R curve calculated by the GTN model is represented by Equation (9):  
\[J=785.33(\Delta a)^{0.5141} \tag{9}\]
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 4 Proposed Rapid Estimation Method  
With all the information collected and the analysis reviewed in previous sections, we developed a method for rapid estimation and visualization of TVWS. In Figure 10, we observe the diagram of the proposed REM. It shows the flow graph necessary to process the information obtained by using the MSSS. Once the signal of the \(W(p)\), \(X(p)\), and \(GPS\) coordinates were collected and combined, these were fed into three blocks, namely, the Energy Detection (ED) block, the Statistical Analysis (SA) block, and the ROC Analysis (RA) block:  
* The **ED** block calculates the optimal value for \(T\) and identifies the channels in which PU signals are detected, marking them as used channels; otherwise they are identified as TVWS channels.
* The **SA** block obtains the statistical parameters of the sensed signals (i.e., \(\overline{W}\), \(\overline{X}\), \(\sigma_{W}\), and \(\sigma_{X}\)) used to identify \(P_{fa}\) and \(P_{d}\) for each case. With this information, it creates a plot of the PDF curves.
* The **RA** block plots the ROC curves for each point sensed and identifies tendencies or patterns for each case.  
The output of these three blocks are introduced to the REM block, where the results are combined to obtain an estimation of the TVWS (E-TVWS) channels available in the sensed point. To explain how the REM works, we used two well-differentiated sensed points; these are P1 that has the lowest PU detected, and P6 that has the higher PU detected. These points were taken from the ones shown in previous sections and will provide a didactic visual explanation to understand our proposed method.
## 7 Conclusions  
This paper has investigated energy-efficient transmission strategies in IHS networks for emergency communications. Firstly, a system model has been established, which consists of a space segment, a near space segment and a ground segment. Secondly, assuming that the channel exhibits a slow flat Rician fading, the concept of LSA has been proposed. The optimal transmission powers \(\hat{P}_{s,1}\), \(\hat{P}_{s,2}\) and \(\hat{P}_{s,3}\) have been derived using the mathematical methods of gradient descent and differential equations. In addition, energy consumption has been modeled, and each step of the adaptive algorithm for the energy-efficient path selection has been described. Finally, the simulations have further proven that the proposed algorithm can improve the quality of communication and reduce the energy consumption. Most significantly, the simulation results show that, at the source level, the energy consumption of the \(i\)-th terminal for the \(T_{i}\to HAP_{1}\to Sat\). path, \(T_{i}\to HAP_{2}\to Sat\). path, \(T_{i}\to HAP_{3}\to Sat\). path, \(T_{i}\to HAP_{4}\to Sat\). path and the adaptive algorithm path is \(78.99\%,\ 73.62\%,\ 69.19\%,\ 69.98\%,\ \mathrm{and}\,84.36\%\) lower, respectively, than that for the \(T_{i}\to Sat\). link. We also found that, at the system level, the energy consumption for the adaptive algorithm path is \(43.34\%,19.54\%,2.25\%,13.81\%\ \mathrm{and}\ 22.44\%\) lower than that for the \(T_{i}\to Sat\). path, \(T_{i}\to HAP_{1}\to Sat\). path, \(T_{i}\to HAP_{2}\to Sat\). path, \(T_{i}\to HAP_{3}\to Sat\). path and the \(T_{i}\to HAP_{4}\to Sat\). path, respectively. Additionally, in a multiple-user, multiple-HAP and single-satellite scenario, the simulation results also show that the proposed adaptive algorithm is energy efficient with various numbers of terminals, numbers of HAPs and Rician factors. We conclude that the proposed energy-efficient transmissions and adaptive path selection algorithm are promising solutions for remote WSNs for emergency scenarios. In the paper, a deterministic method is used to formulate the solution. However, a more reliable and interesting method is to estimate the parameters in the system using Bayesian or Kalman filtering (KF) at the terminal. Therefore, further studies will extend the results in a nondeterministic way.