esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Self-Born Weighted Least Square Method for Cylindicity Error Evaluation
## 5 Experimental Verification and Literature Verification
### Literature Verification  
To verify the method proposed in this paper, data samples of the literature [4] were used in this paper to evaluate the cylindricity error. The 24 data points are shown in Table 5. Reference [4] used the genetic algorithm to evaluate three cylindricity error model methods, including the minimum area method, the minimum circumscribed cylinder method, and the maximum inscribed cylinder method. The error evaluation results based on the MZC, MCC, and MIC methods in the literature [4] are shown in Table 6. The ISWLS, LS, PSO, and GA methods were used to evaluate the error of the least square cylinder method for the sampling point data, and the setting parameters were consistent with the above. The error evaluation results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **X** [mm] & **Y** [mm] & **Z** [mm] & **No.** & **X** [mm] & **Y** [mm] & **Z** [mm] \\ \hline
1 & 11.0943 & 0.4522 & 65.2328 & 13 & 10.815 & 0.5918 & 85.2307 \\
2 & 5.094 & 10.845 & 65.0765 & 14 & 4.8148 & 10.9846 & 85.074 \\
3 & \(-\)6.9063 & 10.8439 & 65.0089 & 15 & \(-\)7.1855 & 10.9835 & 85.0641 \\
4 & \(-\)12.9065 & 0.4498 & 65.0897 & 16 & \(-\)13.185 & 0.5894 & 84.8952 \\
5 & \(-\)6.9063 & \(-\)9.9429 & 65.054 & 17 & \(-\)7.1855 & \(-\)9.8033 & 85.0516 \\
6 & 5.094 & \(-\)9.9418 & 65.2216 & 18 & 4.8149 & \(-\)9.8022 & 85.2171 \\
7 & 10.9546 & 0.522 & 75.2316 & 19 & 10.6754 & 0.6616 & 95.2291 \\
8 & 4.9544 & 10.9148 & 75.0752 & 20 & 4.6752 & 11.0544 & 95.0728 \\
9 & \(-\)7.0459 & 10.9137 & 75.077 & 21 & \(-\)7.3253 & 11.0533 & 94.9077 \\
10 & \(-\)13.0461 & 0.5196 & 74.8964 & 22 & \(-\)13.3254 & 0.6592 & 95.094 \\
11 & \(-\)7.0459 & \(-\)9.8731 & 75.0528 & 23 & \(-\)7.3252 & \(-\)9.7335 & 95.0504 \\
12 & 4.95447 & \(-\)9.872 & 75.2204 & 24 & 4.6752 & \(-\)9.7323 & 95.2179 \\ \hline \end{tabular}
\end{table}
Table 5: Coordinates of points in reference [4]. Reprinted with permission from Ref. [4]. 2004, [PERSON]; [PERSON].  
Comparing Tables 6 and 7, it can be seen that the method proposed in this paper was consistent with the results obtained in the literature, and the evaluation error was smaller.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 4 Wireless Communication System Using Chaotic Filter
### Threshold Calculation
#### 4.3.1 BER Performance Analysis
Figure 11a shows the comparison of different methods under this quasi-static channel with the suboptimal threshold, where the BER performance deteriorates due to the inaccurate estimation of channel state information. However, the proposed threshold calculation method is still 2.2 dB better than traditional MMSE equalization. In addition to BPSK, the proposed method can also be used in high-order modulation. Figure 11b shows the comparison of different methods for 16 QAM, where BER performance degrades more than 3 dB compared with BPSK due to the distance between constellation points being reduced. The proposed method outperforms the CBWCS and RRC filters by 0.4 dB and 0.56 dB for BER = \(10^{-3}\), respectively. Figure 11: BER comparison under time-varying multi-path wireless channel with suboptimal threshold for (**a**) binary-phase shift keying (BPSK) and (**b**) 16 quadrature amplitude modulation (QAM). Figure 10: BER comparison under static multi-path wireless channel with suboptimal threshold.
# Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot
## 2 Angel Legs: A Powered Exoskeleton with Torque-Mode Actuators
### Actuation System of Angel Legs  
An actuation module of Angel Legs, called Angel Joint v1, is shown in Figure 2. For accurate control of the assistive torque transferred to the human joint, a structural spring is utilized, as shown in Figure 2b, which realizes the series elastic actuation. The spring plays the roles of an energy buffer and a torque sensor [22; 23]. By measuring the spring deflection, the assistive torque can be measured. Therefore, the precise torque control can be achieved by controlling the spring deflection. In addition, the spring is helpful in improving the safety of the actuation module.  
Angel Joint v1 consists of a brush-less direct-current (BLDC) motor (Maxon motor company, 70 W, EC45-flat), spring-embedded gears, sensors for measuring the spring deflection, as well as the absolute angle of the human joint. However, the actuation power is not very meaningful because the motor is operated at low speeds, where the stall state takes a place frequently. After the gears and a spring, the magnitude of an assistive torque is about 14.2 Nm in a continuous assistance condition and 35.5 Nm in a short-term assistance condition. The other mechanical specifications are shown in Table 1.  
The actuation performance of Angel Joint v1 is determined by the design of the spring-embedded gears as well as a control algorithm controlling the spring deflection. As shown in Figure 2b, Angel Joint v1 is designed to have two spring-embedded gears. For the sake of minimal weight and volume, the \"spring-embedded\" gears embed springs, such that the gears exhibit elasticity without any additional springs. As the spring-embedded gears should be made of a strong material to prevent the failure of gear teeth, the spring constant is unavoidably high. Therefore, Angeloint v1 has two spring-embedded gears to realize the desired spring constant.  
Figure 3 shows the linearity of springs in the Angel Joint. It was measured by a dynamometer. The spring constant was measured by 302.34 Nm/rad and the backlash, i.e., the dead-zone at the zero-torque shown in Figure 3 was measured as 0.14 degrees (i.e., 0.0024 rad. The backlash was caused by the tolerance of gear teeth, and the torque measurement error due to the backlash was about 0.74 Nm, i.e., 302.34 Nm/rad \(\times\) 0.0024 rad. This error is small enough compared with the magnitude of desired assistive torque (i.e., 14.2 Nm).
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Algorithm Development  
A genetic algorithm (GA) is employed for local searching in the proposed system. This algorithm determines the passing sequence of trains through a junction. Unlike traditional GAs, our approach incorporates two additional operators: modification and classification.  
* **Modification Operator**: This post-correction operator ensures that the solutions generated by the GA are feasible within the local context of each junction. It addresses any inconsistencies or infeasibilities that may arise in the solution space, ensuring that the results adhere to operational constraints.
* **Classification Operator**: This operator classifies the final generation of solutions based on the decision variable \(i_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t}_{ \overline{t}_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t}_{ \overline{t}}}}}}}\,}}}}^{m}\). Classification helps to organize and manage solutions, facilitating a more structured and efficient evaluation process.  
The detailed implementation of these operators is discussed in the following sections. The pseudo-code for the designed GA is provided below, illustrating how these operators are integrated into Algorithm 1.  
```
1. Initialization: generate \(N_{gen}\) of individuals from \(\left[{t_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t}_{\overline{t} }}}}^{O}},{t_{\overline{t}_{\overline{t}_{\overline{t}}}}^{O}}+t_{slot}}\right]\)
2. While (! The local terminating condition is satisfied)
3. Crossover: two-point crossover;
4. Mutation: extended-line mutation;
5. Modification: ensures all the operational constraints;
6. Sorting;
7. Selection: Maximum keeps \(2N_{gen}\) individuals;
8. End;
9. Classification;
10. Output the solution table to collaborative agent.
```  
**Algorithm 1**GA for local searching in MAS
## 3 Datasets and Methods
### Machine Learning  
This section examines in detail the ML techniques used for Sentiment Analysis. In addition, it is important to emphasise that in these experiments we used sparse vectors resulting from the application of BOW and TF-IDF feature extraction.  
The first model tested on the dataset was the Logistic Regression classifier to obtain a quick overview of the dataset. In fact, it is easy to implement and interpret, and very efficient to train, as well as having no hyperparameters to adjust. Another advantage of this model is that the Logistic Regression coefficients can be interpreted as indicators of the importance of the features. This aspect will also be discussed in Section 5 about model explainability.  
The second step was to test the performance of the Random Forest (RF). It is an ensemble method (based on the divide-and-conquer intuition) of decision trees generated on a randomly divided data set, where each tree is voted on and the most popular class is chosen as the final result. It is simpler and more powerful than other non-linear classification algorithms.  
The third test was to apply the Latent Semantic Analysis (LSA) technique [15] in combination with the Support Vector Machine (SVM) model. The idea is to use LSA to obtain a dimensional reduction of the dataset, as it is able to discover latent themes in a set of documents and to successively separate the two classes using an SVC model with a radial kernel.  
Finally, we have applied the Latent Dirichlet Allocation (LDA) technique [16] and tested local models. The idea here is to divide our set of documents into subsets of related  
Figure 2: Dataset cleaning functions flowchart.  
tweets and to treat each resulting subset separately in order to use each one with local models such as Logistic Regression and Random Forest.
## 2 Results and Discussion
### Comparison between BA\(\mu\)E(PS-DVB) and SBSE(PDMS)  
To demonstrate the advantages of the proposed analytical methodology (BA\(\mu\)E(PS-DVB)) over other static microextraction approaches, SBSE(PDMS) was evaluated under similar experimental conditions. Figure 1b depicts the comparison between BA\(\mu\)E(PS-DVB) and SBSE(PDMS), where must better performance can be observed with the former. The data obtained from both methodologies proved that the recovery yields obtained by BA\(\mu\)E(PS-DVB) method is higher than 80% to recover 4-NOP, 3-NOP and BPA, whereas SBSE(PDMS) is not efficient at all. Nevertheless for OP and NP both recoveries are similar since these two analytes present non-polar characteristics, evidenced by octanol-water partition coefficients (log \(K_{\mathrm{O/W}}\)) higher than 5.50, which is definitely in agreement with the SBSE(PDMS) theoretical predictions [22]. This observation reinforced the unsuitability of the PDMS polymeric phase for compounds with this type of polarity characteristics, present the limitation of SBSE to several groups of polar compounds. On opposition, BA\(\mu\)E shows great ability for trace level analysis of polar compounds, having the advantage for allowing to choose the most indicated sorbent phase for each particular type of application.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Animal Dataset  
The visual dataset used in this study was selected from the open animal images of Kaggle, Roboflow, and Ultralytics [21; 22; 23]. In this study, a total of 2462 animal images that could potentially be present around farms were used. Care was taken to ensure that these images represented the most common species that could threaten farm animals. These species included birds of prey, pigs, cattle, sheep, foxes, jackals, and dogs. Images were collected from the free image dataset by choosing copyright-free ones (CC0) and compiled for the study. To ensure the efficient functioning of image detection, the species were included in the dataset in roughly equal numbers. Attention was paid to sourcing images taken in natural habitats. The dataset will be made available to researchers on sharing platforms. The raw form of the dataset images is presented in Figure 1 and the distribution of animal types within the dataset is shown in Figure 2.  
After acquiring the dataset, the raw images underwent preprocessing steps for analysis using deep learning-based image processing techniques. These preprocessing steps included the below:  
* Resizing the images to a resolution of 640 \(\times\) 640.
* Figure 3 shows the distribution of image data into training, validation, and testing sets. The division was made with 1721 (70%) images for training, 491 (20%) images for validation, and 250 (10%) images for testing.
* For the training and validation data, the target objects within the images were marked, and their coordinates were defined, meaning a labeling process was conducted. Figure 4 provides examples of the labeling process.
* Additionally, an Auto-Orient preprocessing step, which involved shuffling the data, was applied to increase the difficulty of detection for some data. Figure 5 presents example images of the Auto-Orient process.  
Figure 1: Dataset raw images.  
Figure 2: Dataset animal species and quantity.
# Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning
## 3 Materials and Methods
### Candidate Algorithms  
As explained in the beginning of this section, in this study, we will explore the potential of the four following BERT-based approaches:  
* Translating the comments into English and using the classical BERT [4]. In the following, the approach will be referred to as \"BERT\({}_{\text{EN}}\)\";
* Using the Multilingual BERT (mBERT) [4] on the translated comments. In the following, the approach will be referred to as \"mBERT\({}_{\text{EN}}\)\";
* Using mBERT on the original comments in Arabic. In the following, the approach will be referred to as \"mBERT\({}_{\text{AR}}\)\";
* Using AraBERT [30], a BERT-based model trained on Arabic text, on the original comments in Arabic. In the following, the approach will be referred to as \"AraBERT\".  
In a previous study [43], we compared the performance of the following Shallow and Deep Learning algorithms:  
* Logistic Regression (LR);
* Naive Bayes (NB);
* Random Forests (RF);
* Support Vector Machines (SVM);
* Long Short-Term Memory (LSTM).  
In that study, we found that LSTM provided the best performance, so we will keep it as a candidate in this current study.  
We will also take the Linear Support Vector Classification (LinearSVC) based on the approach presented by [PERSON] et al. [44], in which they demonstrated that LinearSVC can reach a 90% accuracy, which is higher than our LSTM-based approach, reaching an accuracy of 82%.  
To measure the performance of the abovementioned approaches, we will rely on the usual classification metrics, i.e., Accuracy, Precision, Recall, F1-Score, and the Confusion Matrix.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 2 System Model
### Partially Connected Structure  
Figure 2 shows PCS hybrid beamforming system model. Unlike the FCS hybrid beamforming system model, a transmitter is equipped with \(N_{t}^{RF}\) RF chains and each of RF chains is connected to one sub-array with \(N_{t}/N_{t}^{RF}\). The total number of phase shifters is \(N_{t}\). Because the total number of phase shifters is \(N_{t}N_{t}^{RF}\) and \(N_{t}\) at the FCS, PCS hybrid beamforming systems respectively, the PCS hybrid beamforming system has low hardware complexity of the analog beamformer comparing the FCS hybrid beamforming system. The remaining parameters and equations are same with FCS hybrid beamforming system.
## 1 Introduction
The above models only consider the dynamics of the workpiece, while the cutter is assumed to have a rigid body. It is essential to establish a multi-DOF milling system when neither the tool nor the workpiece dynamics can be ignored [30; 31; 32]. However, the 3-DOF stability prediction model for bull-nose end milling has been rarely explored. Especially for the cylindrical part, which has a ring-shaped and closed geometry structure. [PERSON] et al. [33] modeled a 3-DOF milling system by taking into account the tool dynamics along the \(x\) and \(y\) directions and the workpiece dynamics along the \(z\) direction. [PERSON] et al. [34] suggested a 3-DOF milling stability model for thin-floor components, in which the static and dynamic deflections of the tool and the workpiece are considered together. In the chatter stability model, the cutting force coefficients are very important for the calculation of SLD [3; 35]. Currently, most models ignore the effect of the cutter diameter variation and treat the cutting force coefficients as constants for the bull-nose or ball end milling [36], which can result in inaccurate stability predictions. Based on previous research [29], this article presents a 3-DOF chatter stability model that takes into account the effects of cutter diameter variation on the cutting speed. In Section 2, the 3-DOF milling system for the bull-nose end milling of a thin-walled cylinder is established. The dynamic cutting force model, CWE calculation, identification of layered cutting force coefficients, and SLD calculation with SDM are also conducted in this section. In Section 3, a group of milling tests are performed to validate the accuracy and reliability of the 3-DOF chatter stability model. Finally, some conclusions are proposed.
## 4 Results and Discussion
Figure 11 shows the accuracies of the accumulative ensemble models indicated by the blue stars and curve as well as the accuracies of the unique standalone models indicated by the red circles. When there is one model, the results of the ensemble model and the single model are always identical, and as the size of the model increases, the advantages of the ensemble are clearly revealed. The traditional ensemble method, such as random forests [44, 45], typically consists of 30 decision trees, and in many cases the number of decision trees is over 100. However, the number of models in a CNN ensemble is normally from 5 to 10 because more models make it more time consuming and computationally expensive to train. As shown in Figure 11, when there are five or more models, the blue curves of the ensembles showed accuracies that are better or comparable to those of single models in almost all cases. Fold 1, fold 2, and fold 5 had the same characteristics. The use of an ensemble helps to achieve high accuracies and keep accuracies stabilized better than randomly selected single models whose accuracies are sometimes high and sometimes a little lower. In fold 3, increasing the model number of the ensemble does not only increase accuracy, but it also maintains a stable state. In the case of fold 4, when the number of models in the ensemble increased, the accuracy also is increased; however, many single models with lower accuracies added make the accuracy of the ensemble models a little lower. Nonetheless, the ensemble accuracy is still higher than almost any randomly selected single models. Figure 11: _Cont._  
Figure 10: Effect of batch size: (**a**) Training accuracy plot; (**b**) Validation accuracy plot; (**c**) Training loss plot; (**d**) Validation loss plot. Table 5 reports the comparison of the accuracy, F1 score, and Cohen's kappa of our approach and other methods. In this study, we compare our experimental result with all other previous studies that experimented with the same dataset (SWIMCAT). The performance metrics are calculated using confusion matrix data that were published in previous research (in the case of [PERSON] et al. [24], the detail confusion matrix data was not provided; only accuracy is compared). The first three papers [20], [16], and [19] applied traditional machine learning approaches and they obtained accuracies of 91.1%, 95.1%, and 98.3%, respectively. The other papers [26], [27], and [24] applied deep learning with CNN approaches. These deep learning approaches got better results than the traditional machine learning ones, achieving accuracies of 98.6%-98.7%. Along with accuracy, the F1 score and the [PERSON]'s kappa of the deep learning approaches are also better than the traditional machine learning approaches. Our proposed method is an optimized CNN model with regularization techniques and involves the ensemble method. The accuracy of our model is greater than that of all other methods and it reaches an almost perfect accuracy of 99.5%, both F1 score and [PERSON]'s kappa got 0.993.
## 3 Probabilistic Analysis
### Fragility Analysis
#### 3.3.2 Fragility Curves  
It can be observed from Figure 9 that the capacity of the bridge decreases with each increment of the discharge value, which directly increases the mean value of the scour depth. Therefore, higher values may cause problems with extensive settlements of the bridge, which may induce instability on the arch barrel and consequently bridge collapse [5]. As expected, the loss of bearing capacity for discharge values where most of the scour depth values are lower than the \(F_{t}\) is minor, thus causing a slight decrease in the bridge reliability index [5]. For the symmetric scour process, it can be observed that for discharge values below 50 m\({}^{3}\)/s, the failure probability is rather small, and it only reaches an exceedance probability near 1 for discharge values higher than 400 m\({}^{3}\)/s.  
Figure 8: Collapse mechanisms at different stages of the scouring process with respect to the footing thickness (\(F_{t}\)): (**a**) Stage 0: no scour; (**b**) Stage 1: scour depth smaller than \(F_{t}\); (**c**) Stage 2: scour depth equal to \(F_{t}\); (**d**) scour depth larger than \(F_{t}\).  
Figure 9: Fragility curve for the case study for a flow angle of attack of 0\({}^{\circ}\).  
Before analyzing the fragility curves for flow angles different from 0\({}^{\circ}\), it is important to understand that the effect of a skewed flow not only affects the bridge stability due to the increased rate of scour around the bridge but also due to the uncertainties associated with changes in flood flow [43; 57]. Figure 10 shows the fragility surface for the case study, in which it can be observed that the failure probability increases with the discharge and with the angle of attack. For the asymmetric scouring process, when compared with the symmetric scouring process, there is an increased failure probability, particularly for the range when the latter has a small failure probability (i.e., discharge values below 50 m\({}^{3}\)/s). Even so, for the most critical scenarios, where the angle of attack is higher than 20\({}^{\circ}\), the failure probability is closer to 1 for the same range. It can be concluded that the angle of attack decreases the bridge reliability and that it is more sensitive to changes in discharge. This is expected due to asymmetric scouring having higher mean values of scour depth than symmetric scour [39].