esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Detecting Abnormal Activity in a Group of Mobile Robots
## 1 Introduction
G denotes the goals that are assigned to the group, Cr is the information received from other nodes, CS\({}_{\text{i}}\) is the control system of robot R\({}_{\text{i}}\), E is the environment in which the goals are performed; (**b**) The mobile robotic control system. C\({}_{\text{R}}\) is the set of connections of the given robot with other robots of the group, x\({}_{\text{d}}\) is the vector of the coordinates of the next point of the robots trajectory, and x\({}_{\text{g}}\) denotes the coordinates of the chosen goal. The formation unit and path planning unit determine the position of the nodes in space, relative to other nodes at certain points in time. The clustering unit is responsible for establishing communication within the cluster. The lower-level unit control performs the interaction of the robot control system with the execution mechanisms. Thus, the main objectives of this paper are as follows:  
* Development of a wireless network model for a group of robots and simulation of group control. * Development of methods for detecting abnormal behavior based on the analysis of cyber parameters and in-depth traffic analysis. To achieve the above objectives, we executed the following steps:  
* Analysis of structural and functional characteristics of robotic systems. * Development of an experimental stand simulating the work of the robot group with the group control system. * Development and implementation of attack scenarios for the robot group. * Analysis of node characteristics that are changed under the influence of the attack. * Development of a set of metrics that identify anomalous behavior. * Software development for the implementation of the methodology. * Analysis of the results of an experimental study, and determination of sets of metrics that are affected by a particular attack. The article is organized as follows: Section 2 presents a review of papers on this topic; Section 3 describes the network model that was developed to collect statistical information; Section 4 contains a methodology for conducting attacks against the network in order to identify the parameters affected by the attack; Section 5 provides a method for identifying anomalies based on the collected statistical information and confirming the effectiveness of the method.
# Compressive Strength Comparison of Different Orthodontic Metal Bracket Systems
###### Abstract  
Rebonding of orthodontic brackets to new positions during treatment with fixed orthodontic appliances is a common practice and it is important not to cause plastic deformation during bracket removal. The aim of this study was to evaluate the resistance of various brackets to compression and assess their thresholds for plastic deformation. 5 different groups of metal brackets (BioQuick, Damon Q, Experience, Mini Diamond, Mini Sprint II) were bonded to 85 extracted human central incisor teeth utilizing the same adhesive system (Transbond XT). Compressive forces were applied via Weingart forceps in order to mimic clinical setting with the Shimadzu Universal tester. Kruskal-Wallis and Mann-Whitney U tests were used for comparing groups. Damon Q group exhibited the highest yield point value (549.35 N), the highest ultimate strength value (764.50 N) and the highest failure/debonding point value (721.89 N). The lowest yield point value (211.73 N), the lowest ultimate strength value (224.07 N) and the lowest failure/debonding point value (121.71 N) were found in the Mini Diamond group. The ultimate strength point values of Damon Q and Experience brackets were higher compared to Mini Diamond, BioQuick and Mini Sprint II brackets (_p_ < 0.05). No statistically significant difference between Damon Q and Experience brackets in terms of yield strength and ultimate strength values (_p_ > 0.05) were observed. Adhesive Remnant Index (ARI) score was 3 for all debonded samples. It may be concluded that Damon Q brackets were more resistant to plastic deformation than Mini Diamond, BioQuick and Mini Sprint II brackets (_p_ < 0.05).  
plastic deformation; yield point; orthodontic debonding
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 2 Research Objectives and Contributions  
The main objectives of this article are to achieve the following:  
* Propose a novel data-driven PHM-based method for battery State-of-Charge (SOC) estimation, demonstrating enhanced model significance relative to existing state-of-the-art approaches.
* Test the selected models on a real-world dataset to verify the model's accuracy and significance, validate the proposed framework and measure the overall system classification.
* Provide, with the selected PHM model, a real-time inference for the system's health condition monitoring as well as multi-step ahead forecasting for long-term predictions. The key contributions of this work can be summarized as follows:
* A novel real-time PHM methodology based on bidirectional LSTM for battery system state monitoring and control is proposed.
* A comprehensive dataflow is established including offline and online processing that can be extended for other nonlinear systems.
* A noise reduction strategy is implemented, which can also be deployed in real-time to cope with noisy measurements' systems.
* The data-driven PHM SOC estimation consistently outperforms the state-of-the-art Kalman filters throughout different conditions, demonstrating its accuracy and usability.
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 2 Materials and Methods
### Artificial Intelligence Modeling
#### 2.5.2 Deep Learning Training  
The computing resources employed for training the aforementioned model consist of an Intel (R) Core (TM) i7-9700 CPU with 3.00 GHz and 16 GB of 2667 MHz RAM on a Windows 11 Pro OS Version 22H2 (Microsoft, Redmond, Washington, U.S.). No CUDA compatible GPU was utilized, so the AI training was conducted completely on the CPU. The created feature dataset consists of 2401 trials: 1234 samples for the Fatigued class and 1167 for the Rested class. Even though all subjects completed the same number of tasks for each state, only correct responses were considered for the creation of the dataset, introducing a minor class imbalance. However, the differences were negligible and, henceforth, it cannot be suggested that they contributed to the corresponding bias. To train the designed deep learning model, a combined-subject training strategy was employed in order to exploit features that provide generalized (subject independent) information on mental fatigue. As such, all the observations of Fatigued and Rested data points of all the subjects were combined and randomly partitioned into the three data subsets; i.e., the training, the validation and the test subsets. In detail, the full dataset was split into a training subset (containing 75% of the full dataset; 925 samples of Fatigued class and 875 of Rested class), a validation set (containing 15% of the full dataset; 185 of Fatigued class and 175 of Rested) and a test set (containing 10% of the full dataset; 124 of Fatigued class and 117 of Rested). Assignment was performed randomly to avoid any selection bias among participants. The neural network model was trained repeatedly using the training dataset for a specific number of epochs. Each epoch (which is a hyperparameter of the training process) is a complete pass of the entire training dataset from the learning algorithm. This is required for a model until the learning process converges. Usually, the number of epochs is in hundreds or thousands. However, our model converged in almost 150 epochs.  
Figure 1: A schematic of the proposed framework. The * symbolizes the convolution function.
# Assessing Voltage Stability in Distribution Networks:
## 2 Modeling of Stochastic Variables Considering Correlation
### Stochastic Variable Correlation Modeling
The calculation equation is as follows:  
\[\tau=4\int_{0}^{1}\int_{0}^{1}C(u,v)\text{d}C(u,v)-1 \tag{12}\]  
To establish the correlation model for distribution network variables based on the Copula function, follow these specific steps:  
Step 1: Obtain original data samples of distributed PV generators and loads;  
Step 2: Choose the Frank Copula function to describe the correlations between the stochastic variables, based on the uncertainty of positive and negative correlations between PV and load data;  
Step 3: Use the maximum likelihood estimation method derived from the initial sample's probability distribution, and apply Equations (10) and (11) to calculate the parameter \(\theta\) of the Frank Copula function;  
Step 4: Calculate the Kendall rank correlation coefficient \(\tau\) using Equation (12), which serves as an index to measure the correlation between variables. Figure 1: Types of Copula Functions. Probabilistic Assessment of Voltage Stability in Distribution Network Considering the Correlation of Stochastic Variables  
To accurately characterize the influence of stochastic factors on SVS in the distribution network, the critical state of distribution network is first obtained through the CPF method [35], and then the probability distribution of LMI [7] is analyzed. The basic idea is: First, handle the correlated samples of input variables. Second, compute the sample values and weights using the 3 PEM, conducting CPF calculations and obtaining the probabilistic distribution of the LMI using the corresponding series expansion method. Finally, establish the degree of risk of exceeding the index, and thus assess the SVS of the distribution network considering the correlation of stochastic variables.
# Application of Neural Networks to Explore Manufacturing Sales Prediction
## 2 Literature Review
### Sales Prediction  
Sales predictions have been used in research and practical application for many years, with the various prediction models broadly divided into qualitative and quantitative approaches. Quantitative approaches collect historical data and analyze developing trends to provide useful input for management decision making. Prediction methods include case based reasoning, ANNs incorporating artificial intelligence and regression analysis, moving average methods, and exponential smoothing methods based on statistical logic. Analyzing key product characteristics and combining previous sales history data helps to provide better sales predictions from limited data volumes.  
Sales prediction is also important for e-commerce, critically impacting wise decision making, and helping to manage workforce, cash flow and resources; and optimize manufacturer supply chains. Thus, sales prediction is generally somewhat challenging because sales are affected by many factors, including promotions, price changes, user preferences, etc. [9], and yet impacts significantly on important company policies and plans, with accurate predictions helping to reduce costs, optimize inventory management, and improve product competitiveness [10].
## 5 Feature Parameter Selection
### Correlation Coefficients among Feature Parameters
The SVM model, which uses the transmission coefficient and Tr wave WP entropy as the feature parameters, is defined as B. The results show that B has lower prediction errors than A. It is known that the correlation coefficient of the two feature parameters in B is smaller than that of A (0.811 < 0.951). Thus, the correlation between feature parameters significantly affects the prediction accuracy of the SVM algorithm. Therefore, the feature parameters should be chosen for the SVM model by comprehensively considering the selection standard in Part A and Part B. The detailed rule is that when the correlation coefficients between feature parameters and crack geometry information are larger and the correlation coefficients between feature parameters \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline A & 1 & & & & & & \\ B & 0.979 & 1 & & & & & \\ C & 0.951 & 0.929 & 1 & & & & \\ D & 0.811 & 0.848 & 0.679 & 1 & & & \\ E & \(-\)0.285 & \(-\)0.301 & \(-\)0.162 & \(-\)0.347 & 1 & & \\ F & \(-\)0.338 & \(-\)0.335 & \(-\)0.256 & \(-\)0.362 & 0.943 & 1 & \\ G & 0.004 & 0.044 & \(-\)0.091 & 0.071 & \(-\)0.734 & \(-\)0.597 & 1 \\ \hline \end{tabular} Note: A: Transmission coefficient, B: transmitted wave peak, C: deconvolution sum, D: Tr wave WP entropy, E: reflection coefficient, F: root mean square of reflected wave, G: Rr wave WP energy peak.  
\end{table} 
Table 4: Correlation coefficients among the selected parameters. themselves are smaller, a higher prediction accuracy is achieved. The reason is that the feature parameter, which has a larger coefficient with crack geometry information according to the selection standard in Part A, can provide a more accurate prediction. Higher correlation coefficients among feature parameters imply that these feature parameters contain similar prediction information.
## 2 Literature Review
### Affecting Factors for the Implementation of Industry 4.0
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Method** & **Factors Affecting the Implementation of Industry 4.0** \\ \hline [PERSON] et al. [19] & Case study & CSFs: Continuous improvement/lean culture, top management leadership, inter-functional teams, pilot projects, etc. \\ \hline [PERSON] et al. [80] & Delphi method & CSFs: Employee training, prior study, regular use of available data, stronger presence of a manager, etc. \\ \hline [PERSON] et al. [20] & Survey & CSFs: Top management support, sustainability, alignment with organizational strategy, change management, etc. \\ \hline [PERSON] and [PERSON] [78] & Review & CSFs: Alignment with organizational strategy, top management support, employee skills, change management, cybersecurity, etc. \\ \hline [PERSON] et al. [81] & Review & Key factors: Political support, cooperation, IT infrastructure, knowledge and skills, lean manufacturing, etc. \\ \hline [PERSON] et al. [77] & BWM & Risks: Lack of standards and methodical approach, capital risk, insufficient talent, potential delay to manufacturing, etc. \\ \hline [PERSON] et al. [22] & GTMA & Roadblocks: Management roadblock, operational roadblock, human resource roadblock, procedural roadblock, behavioral roadblock. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies on the factors affecting the implementation of Industry 4.0. The factors listed in Table 2 are numerous and varied. To identify the most relevant success factors for investigation, further research was conducted on the implementation of advanced manufacturing technology (AMT) and information technology/system (IT/IS). Industry 4.0, being a form of AMT, is fundamentally based on information technologies [31]. As illustrated in Table 3, critical success factors consistently appearing across these three fields include complexity, compatibility, top management support, technology competence, supplier support, and pilot projects.
## 4 Simulation Experiments  
In order to verify the effectiveness of the IMOGWO algorithm proposed in this paper for solving green manufacturing collaborative optimization problems of the semiconductor wafer distributed heterogeneous factory, the NSGA-II algorithm proposed in literature [36], the MODGWO algorithm proposed in literature [38], and the MOGWO algorithm proposed in literature [26] are used as comparative algorithms in the experimental stage. Among them, MODGWO is an algorithm that improved the GWO algorithm and achieved better experimental results. MOGWO is the most original multi-objective Grey Wolf algorithm. The NSGA-II algorithm is a classical and effective algorithm for solving multi-objective optimization problems. In the experimental stage, the three objective functions are first normalized, according to the min-max standard, so that they are in the same order of magnitude, which is suitable for comprehensive comparative evaluation. The experimental simulation environment is set to Windows 10 operating system, the processor is Intel(R) Core (TM) i7-4770 CPU @3.40 GHz, the memory is 8 GB, and the algorithm is programmed with MATLAB R2017a.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 5 Experimental Results
### Comparison with State of Art
(2019), underscoring SBNNR's superior reliability in producing precise predictions, even when handling limited data points. Figure 3 visually illustrates the alignment between predicted and actual values for the BONE dataset using the SBNNR method. In this figure, red points represent predictions on the test dataset, while blue points show predictions on the training dataset. The green line marks the actual values, providing a benchmark for comparison. Both the red and blue points closely cluster around this line, indicating that SBNNR can effectively capture the dataset's underlying trends and generalize well across unseen data without overfitting. This close alignment is consistent with the high \(R^{2}\) score and low error rates reported in Tables 4 and 5, suggesting that the model successfully balances accuracy with generalizability. Similarly, Figure 4 depicts the alignment of predicted versus actual values for the FAT dataset under the SBNNR method. The red and blue points remain closely grouped around the green line, showing that SBNNR consistently predicts values that adhere closely to the true data trends. This clustering of predicted values near the actual value line reflects the method's robustness, affirming its suitability for small-data regression tasks by effectively preventing excessive variance in predictions. Together with the observed low error rates and high \(R^{2}\) score, this alignment indicates that SBNNR can manage data limitations without compromising on accuracy. Table 6 presents the cross-validation results for the \(R^{2}\) values of the BONE and FAT datasets. This table summarizes the performance stability of the small-size bat-optimized nearest neighbor regression (SBNNR) model across multiple cross-validation folds. For the BONE dataset, the mean cross-validation \(R^{2}\) score is reported as 0.9694, with a standard deviation of 0.01465, indicating high predictive accuracy and minimal variance across folds, suggesting strong generalizability. The FAT dataset shows an even higher mean cross-validation \(R^{2}\) score of 0.9915, with a lower standard deviation of 0.00882, reinforcing the model's robustness and precision, especially in small-data contexts. These results highlight the efficacy of SBNNR in capturing underlying trends within limited datasets. The lower standard deviations imply consistent performance across the different cross-validation splits, which is crucial for applications where data availability is restricted and reliability across samples is critical.
## 3 Structure and Phase Error Analysis of Dual Antenna Tracking Loop  
The proposed dual antenna tracking architecture utilizes the tracking information from master channels to help the signal processing in slave channels. Similar to A-GNSS, the information obtained from the master antenna could be used to improve the signal acquisition in slave channels. Moreover, the Doppler frequency estimated from the master tracking loop could be used to assist the weak signal tracking in the slave loop. The aiding loop structure of the dual antenna receiver is shown in Figure 2.  
Since the assistance is from the main loop to the slave loop, the transfer function and the performance of the main loop are the same as those of a standard loop described in the previous section. The slave loop is a multiple inputs single output (MISO) system, due to the Doppler assistance from the master loop. Therefore the performance of the slave loop is different from the traditional one. In this section, the output phase error is analyzed in details with regard to each error source mentioned above. As described in the last section, the dynamic stress and clock noise prevent loop bandwidth reduction. Thus, the dynamic stress and clock phase noise will be discussed first below, followed by the thermal noise effect. In addition, the differential phase error induced by each error source is also analyzed as a factor, due to its importance of the DGNSS kinematic positioning.  
Figure 2: Tracking loop structure of the dual antenna receiver.
## 4 Experimental Results
### Evaluation Criteria  
Ten-fold cross-validation is used in our studies, which splits the dataset into ten subgroups (folds). Each fold includes a tenth of the documents in the collection. The remaining folds are used as training sets, while one is utilized as a test set. The findings are reported using micro-f1 (also known as F1) and macro-f1 measurements.  
Micro-averaging considers all categorization judgments in the dataset without discriminating between classes. If a dataset's categories are skewed, large categories will outnumber tiny ones. The following formula is used to calculate this metric:  
\[F1=\frac{2\times P\times R}{P+R} \tag{16}\]  
where a pair of (\(r_{k}\),\(p_{k}\)) represents the recall and precision of categories \(k\), respectively.  
Macro-averaging is calculated for each category among the dataset, and so the average overall categories are found. In this manner, equal weight is assigned to each category without considering the categories distributions. The equation of macro-f1 can be written as  
\[F1=\frac{\sum_{j=1}^{c}F_{j}}{C},\ F_{j}=\frac{2\times P\times P_{j}\times r_ {j}}{P_{j}+r_{j}} \tag{17}\]  
where a pair of (\(r_{k}\),\(p_{k}\)) represents recall and precision of class \(k\), respectively.
## 5 Antenna Processing and Testing
Figure 9: Comparison data of measured axial ratio and gain of the antenna: **(a)** 1–3 GHz; **(b)** 1.56–1.62 GHz; **(c)** 2.44–2.62 GHz. As demonstrated in Table 2, the performance comparison between the proposed waveguide slot antenna and previous designs with a similar architecture revealed the following characteristics: Compared with the directional rectangular slot antenna with radiation pattern optimization in Ref. [8], the current design exhibits a measurable gain discrepancy (\(\Delta\)G = 1.2 dBi) within the operational bandwidth, while maintaining comparable impedance matching characteristics (\(\Delta\)BW = 0.4%, \(\mid\) S111 \(<\) -10 dB). When benchmarked against the omnidirectional radiator in Ref. [14], a 5 dBi improvement in 3D pattern uniformity was achieved, albeit with a 1.8% reduction in impedance bandwidth (BW = 4.7% vs. 6.5%). For directional radiators, although Ref. [16] (\(\Delta\)G = \(-\)0.7 dBi, \(\Delta\)BW = \(-\)1.8%) and Ref. [17] (\(\Delta\)G = \(-\)3.7 dBi) demonstrate superior main lobe gain, their beam scanning capabilities are constrained by limited angular coverage. Notably, the proposed design showed significant broadband advantages, achieving 12.8% wider impedance bandwidth (BW = 4.7% vs. 3.9%) and 4.3 dBi higher omnidirectional gain compared to the narrowband resonant structure in Ref. [18]. In addition, according to the principle analysis of decreasing the operating frequency band and increasing the antenna size, the operating frequency band of other designs is higher than the antenna proposed in this paper, and this design has certain advantages in size. These results validate the comprehensive performance advantages of the proposed antenna in broadband matching and omnidirectional radiation, highlighting its potential for practical engineering applications. Figure 11: Polarization gain of the antenna at 2.49 GHz (**a**) XOZ (**b**) YOZ. Figure 10: Polarization gain of the antenna at 1.62 GHz (**a**) XOZ (**b**) YOZ.