esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This article introduces a multi-agent guidance law so that a formation of drones protects the ship from an incoming high-speed target. The drones generate a planar grid formation, whose center is guided to remain on the line connecting the target and the ship. Moreover, the planar formation is generated to be perpendicular to the line segment connecting the target and the formation center. Since a target heads towards its goal at least in the terminal phase, maintaining a position on this line segment is effective in protecting the ship.  
We enable slow drones to capture a fast target by letting the drones stay close to the ship while staying in the lineState at all times. This blocking strategy is desirable considering the energy consumption of the interceptor since an interceptor does not have to move far from the ship.  
We control the drone formation based on the prediction of the target's position after one sample-index in the future. Since we use a grid formation of drones, we can increase the capture rate even when the target prediction is erroneous.  
As far as we know, this article is novel in developing a ship defense approach using multiple clustered drones. In addition, our paper is novel in addressing the 3D formation control that can handle uncertainty in the target prediction. The effectiveness of our multi-agent guidance law is shown by comparing it with other state-of-the-art guidance laws under MATLAB simulations. We verify that the proposed multi-drone guidance scheme increases the capture probability significantly compared to the case where a single interceptor is used. In the future, we will do experiments to verify our multi-agent guidance law using real drones.  
In practice, the presence of wind can affect and modify a drone's path. Many papers handled how to control a drone under the effect of wind [54; 55; 56; 57; 58]. The authors of [54] improved the accelerated A-star algorithm with a converted wind vector, and [55] addressed the problem of a drone's path planning operating in complex four-dimensional (time and spatially varying) wind-fields. Additionally, refs. [56; 57; 58] handled adaptive path planning in windy conditions. The authors of [58] added a wind model to the existing path planning algorithm and combined it with a drone's control systems. In the future, we will combine the proposed guidance scheme with a wind model so that multiple drones can safely maneuver in time-varying wind-fields.  
Note that the proposed guidance scheme can be applied to protect an entity other than a ship, as long as the goal of the target is known a priori. For instance, the proposed multi-agent guidance law can be generalized to protect any vehicles, such as tanks or ground stations.  
This research was supported by the faculty research fund of Sejong university in 2023. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Grant Number: 2022R1A2C1091682).  
Not applicable.  
The author declares no conflict of interest.
# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 3 Materials and Methods
### Logistic Regression (LR)
#### 3.2.3 Algorithm Comparison  
Based on 10,646 ECG recordings, all models were trained on 6388 data items--60% of the dataset--and evaluated on the remaining 4258 (40%). Input features consisted of 11 ECG-derived variables. Dimensionality reduction output was fixed to 3D for both PCA and UMAP. The KNN classifier operated in the reduced space, while logistic regression was performed in the original 11-dimensional space. The main steps in applying the considered approaches are as follows:  
1. Use of Dimensionality Reduction Techniques (PCA or UMAP): The first step involves applying a dimensionality reduction technique, such as Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP), to project the  
Figure 3: Low-dimensional UMAP representation of ECG records colored by rhythm class.  
ECG signals into a three-dimensional space. The associated heart rhythms from the training dataset are mapped into this reduced space, which helps to create a more manageable representation of the ECG signals for further analysis. 2. Identification of the Most Likely Class for Each ECG in the Validation Group: After the dimensionality reduction step, the next step is to classify the ECG signals in the validation group. Each ECG in this group is projected into the same lower-dimensional space. Using the KNN classification algorithm, with Euclidean distance, the model predicts the most likely class or heart rhythm for each ECG in the validation group. 3. Measurement of Classification Quality: Once the heart rhythm for each ECG in the validation set has been predicted, the next step is to evaluate how accurately the model has assigned the ECGs to the correct heart rhythm classes. This is achieved by comparing the predicted labels with the true labels of the ECGs in the validation set. The evaluation metrics for this task include Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, Specificity, Accuracy, and F1-score, which provide insights into the ability of these models to correctly classify heart rhythms compared to logistic regression approaches.
###### Abstract  
It is critical to use scientific methods to track the performance degradation of in-service buildings over time and avoid accidents. In recent years, both supervised and unsupervised learning methods have yielded positive results in structural health monitoring (SHM). Supervised learning approaches require data from the entire structure and various damage scenarios for training. However, it is impractical to obtain adequate training data from various damage situations in service facilities. In addition, most known unsupervised approaches for training only take response data from the entire structure. In these situations, contaminated data containing both undamaged and damaged samples, typical in real-world applications, prevent the models from fitting undamaged data, resulting in performance loss. This work provides an unsupervised technique for detecting structural damage for the reasons stated above. This approach trains on contaminated data, with the anomaly score of the data serving as the model's output. First, we devised a score-guided regularization approach for damage detection to expand the score difference between undamaged and damaged data. Then, multi-task learning is incorporated to make parameter adjustment easier. The experimental phase II of the SHM benchmark data and data from the Qatar University grandstand simulator are used to validate this strategy. The suggested algorithm has the most excellent mean AUC of 0.708 and 0.998 on the two datasets compared to the classical algorithm.  
unsupervised learning; deep auto-encoder; score-guided regularization; multi-task learning; structural damage detection
## 4 Test Results: Analysis and Interpretation
### Signal Extraction  
During the uniaxial compression experiment, the collected signal is susceptible to interference, primarily stemming from instrument vibrations. Following noise reduction in the anticipated signal data, energy, frequency, and amplitude are systematically compared and analyzed. Distinct AE, ME, and EMR signals are evident in the data collected from the four rock specimen groups.  
To accurately isolate genuine signals generated during the experiment and the on-site sandstone fracture process, the experiment employs the STA/LTA algorithm for data identification and extraction [23; 24].
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion  
The proposed RNN model was implemented in Python using the open source TensorFlow deep learning framework developed by Google [34]. TensorFlow has automatic differentiation and parameter sharing capabilities, which allows a wide range of architectures to be easily defined and executed [34]. The proposed network was trained with 791 samples and validated with 339 samples using the Tensorflow framework. The SGD optimization algorithm was applied, with a batch size of 32 and a learning rate of 0.01.
## 4 Experimental Results
### Model Analysis
#### 4.6.2 Hyper-Parameter Study  
In this experiment, we evaluate the impact of the number of synthesized unseen visual features and the loss of weight, \(\beta\), on the semantic decoder, \(Dec\). Figure 5 shows the harmonic mean (\(H\)) when changing the \(L_{R}\) weight \(\beta\) from 0.01 to 100 and the number of synthesized unseen visual features from 10 to 1600. For the loss weight \(\beta\) of semantic decoder \(Dec\), when \(\beta\) equals 1, our model achieves the best \(H\) result (77.4%), and when \(\beta\) becomes larger, the \(H\) result becomes lower. For the number of synthesized unseen visual features, it produces the best \(H\) result (77.4%) in both 800 and 1200.
## 6 Conclusions  
In this paper, a novel algorithm for PQD classification based on UKF and KELM is proposed. The UKF is used to detect and process the original disturbances, and the performance of the signal detection methods is compared by analyzing the indices of both KF and UKF in tracking and detecting voltage swell disturbances. The results show that UKF outperforms KF, providing better robustness against interference and noise. For  
\begin{table}
\begin{tabular}{c c} \hline
**Label** & **UKF-KELM/\%** \\ \hline C1 & 97 \\ C2 & 98 \\ C5 & 97 \\ C7 & 100 \\ C1 + C7 & 96 \\ C2 + C7 & 100 \\ C5 + C7 & 98 \\ Average rate & 98.00 \\ \hline \end{tabular}
\end{table}
Table 7: Success rate of hardware platform classification.  
Figure 10: The test results of the hardware platform. (**a**) Harmonic disturbance; (**b**) voltage sag; (**c**) harmonics and voltage swell; (**d**) voltage swell; (**e**) voltage pulse; (**f**) harmonics and voltage sag; (**g**) harmonics and voltage pulse.  
feature extraction, real-time tracking of the amplitudes of the fundamental wave, third harmonic, fifth harmonic, seventh harmonic, and oscillatory wave is performed, with the minimum and peak indices being selected as the optimal feature vector set. The combination of KF, UKF, ELM, and KELM is applied to classify fourteen types of PQDs in simulation, achieving an average classification success rate of 98.14%. The experimental results further demonstrate the method's effectiveness and feasibility, with an average classification success rate of 98%.  
Future work will focus on optimizing the UKF-KELM algorithm to handle more complex disturbances and improve its real-time performance. Expanding the classification to cover more PQD types and exploring multi-modal data fusion are also potential directions. Additionally, the real-time implementation of the proposed method in industrial PQD monitoring systems will be investigated to assess its scalability and practical application.  
Conceptualization, H.H., L.W. and Y.J.; Methodology, H.H. and L.W.; Investigation, H.H., H.C., L.W. and Y.J.; Resources, J.W. and Y.Z.; Writing--original draft, L.W. and J.W.; Writing--review and editing, H.H., H.C., L.W., J.W., Y.Z. and Y.J.; Supervision, H.H. and L.W.; Funding acquisition, H.H. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the [Degree and postgraduate education reform and research project of XUST] funded number [Y]G202412].  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
Author [PERSON] was employed by the company Avic Shaanxi Aero Electric Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 5 Conclusions  
The increasing number of studies published in the literature over the past two years on ChatGPT, and large language models in general, show a real interest in this tool and its application in diverse areas of dentistry:  
* Dental research: It has been shown to be useful in study designing, abstract generation, draft correction, syntax error correction, translations, and reference formatting. At present, ChatGPT cannot be trusted to generate bibliographic references from a text due to frequent \"hallucinations\", errors, and/or outdated information.
* Clinical application: ChatGPT can be used, only if properly trained by the operator, to assist the diagnostic workflow. However, it is not yet possible to use it directly as a diagnostic tool, and answers should not be accepted without question, primarily because there is a chance that an outdated version of the model might be in use.  
* Administrative applications: Through inputted clinical findings and treatment plans, the tool is able to generate high-quality reports, spot trends, and manage patient follow-up appointments.
* Educational enhancements: By simulating a realistic conversation, it is capable of assisting students in generating scenarios, questions, answers, and explanations in a quick and engaging way. It can compose ever-changing tests and grade them.
* Ethical and practical considerations: The processing and storage of patients' sensitive data raises significant privacy concerns. This is a crucial aspect to consider in the deployment of such tools in a clinical setting.  
The results of this narrative review show embryonic results with little clinical application with patients; thus, ChatGPT is still far from being used on a daily basis. Nonetheless, ChatGPT stands as a pivotal innovation in dentistry, promising to enhance research capabilities, clinical accuracy, administrative efficiency, and educational processes. Effective integration, coupled with stringent ethical practices and ongoing validation, is essential for realizing the full potential of AI in dentistry.  
Conceptualization, F.P. and A.M.B.; methodology, G.L.G.; validation, A.M.B.; formal analysis, F.P.; writing--original draft preparation, F.P. and G.L.G.; writing--review and editing, G.L.G. and C.E.B.; project administration, R.L.G. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the European Union (NextGenerationEU), through the MUR_PNRR project SAMOTHRACE.  
Not applicable.  
The authors declare no conflicts of interest.
# Improvement of Intake Structures in a Two-Way Pumping Station with Experimental Analysis
## 4 Data Process Methods
### Time-Frequency Analysis Method  
The signal processing in the frequency domain allows finding and analyzing the spectral components contained in the whole-time domain, but it does not allow an assignment of these spectral components to time. Time-frequency analysis has been proved to be suitable for analyzing non-stationary signals, especially when the signal is corrupted by transients. The appearance and disappearance of these transients often have physical significance. Therefore, it is important to be able to localize these transients in addition to characterizing oscillatory components in the signal. The wavelet transform is a linear transform that uses a series of oscillating functions with different frequencies as window functions. When at high frequencies, the wavelet reaches a high time resolution but a low-frequency resolution, whereas at low frequencies, a low time resolution and a high-frequency  
Figure 6: Distribution of pressure monitors.  
resolution are achieved, which makes them more suitable for non-stationary signal analysis. For this purpose, the one-dimensional continuous wavelet transform (CWT) of the discrete sampled pressure signal was computed to determine the continuity in time [18]:  
\[C\langle a,b;f(t),\psi(t)\rangle\ =\ \frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}f(t) \psi^{*}\left(\frac{t-b}{a}\right)dt \tag{8}\]  
where \(\psi\left(\frac{t-b}{a}\right)\) is the wavelet function at the scale dilation \(a\) (\(a\)\(\in\)R+) and translation \(b\) (\(b\)\(\in\)R) of the mother wavelet function \(\psi(t)\). The asterisk (*) represents the complex conjugate.  
In practice, the constant signal is often obtained by the discrete sampling data \(x(j)\)\(j\) = 1, 2,... \(M\), where \(M\) is number of samples of signal, Thus, the discretized variant of CWT can be redefined as [19]:  
\[W\ =\ \sum_{j\ =\ 1}^{M}x(j)\frac{1}{\sqrt{a}}\psi^{*}\left[\frac{(j-b)}{a}\right] \tag{9}\]  
In this research, the complex Morlet wavelet was chosen, since it provided a good balance between the time and frequency localization and it returned information about both amplitude and phase. The complex Morlet wavelet both in time and frequency domain are mathematically defined as [20]:  
\[\psi(t)\ =\ \frac{1}{\sqrt{\pi f_{b}}}e^{2\pi f_{c}(t-t^{2}/f_{b})} \tag{10}\]  
\[\psi(f)\ =\ e^{-\pi^{2}f_{b}(f-f_{c})^{2}} \tag{11}\]  
where \(f_{c}\) is the wavelet center frequency and \(f_{b}\) is the bandwidth parameter is the inverse of the variance in frequency, Therefore, increasing \(f_{b}\) results in a narrower concentration of energy around the center frequency. In this research, \(f_{c}\) and \(f_{b}\) were all taken to be 3 (cmor3-3), which can approximately satisfy the condition admissibility. Figure 7 showss the image of the cmor3-3.
## 4 Results and Discussion
### I-V Characteristic and Static Resistance Analysis
#### 4.1.3 Summary of I-V Characteristic and Static Resistance  
The commercial semiconductor diode, no. 4007 IN, was chosen to verify the MSM diode in this work [24]. It is seen from the results that the I-V characteristic and static resistance obtained by testing the MSM diode with the local indigo show similar results to those of the commercial diode, although values are not as large as the commercial one. However, the I-V characteristic and static resistance of this proposed device suffer from a low-temperature burning point of the indigo layer when applying a high V\({}_{\text{MSM}}\). It may be said that the MSM diode with local indigo blue performs a suitable operation between \(-1\) and \(+3\) V\({}_{\text{MSM}}\). In addition, in the forward bias, there is an obvious linear operation at \(+2\) to \(+3\) V\({}_{\text{MSM}}\). This operating range is thought to be because this indigo blue layer has a suitable number of electrons and holes with low energy of band gap, and when it is burnt, this will be no longer suitable [15]. Thus, it is summarized that the electrons and holes characteristics and band gap energy of the indigo blue layer have an effect on the behavior of the I-V characteristic and static resistance. The improvement of these factors depends on the preparation of the indigo blue pigment, including planting, harvest, fermentation, and extraction.
## 4 Factors Influencing Chitosan Properties
### Molecular Weight (MW)  
The physicochemical properties (viscosity, solubility, adsorption on solids, elasticity, tear strength, bio-functional activities, crystal size and morphological character) are affected by the MW of chitosan [27; 28; 29]. The crystallinity of the membrane could be decreased by increasing MW. The MW of chitosan plays an important role in its performance as a polymer flocculant. The larger the relative MW is the smaller the solubility and greater the degree of entanglement between molecules.  
The relative MW of chitin in marine organisms is \(1\times 10^{6}\)-\(2\times 10^{6}\), after extraction, about \(3\times 10^{5}\)-\(7\times 10^{5}\). The relative MW of chitosan is lower, about \(2\times 10^{5}\)-\(5\times 10^{5}\). Chitin, chitosan and chitosan oligosaccharidesaride are all called chitin substances [30]. Chitin is not soluble in alkali, water, general acid and organic solvent and is only partially soluble in concentrated acid. It can be  
Figure 2: The crystalline structure of chitosan and its main marine organisms sources.  
partially decomposed by chitinase and lysozyme in the human gastrointestinal tract, so its absorption rate is low, the dosage is large and the taking reaction is over 70%. Chitosan can be dissolved in dilute acid, which is better than chitin. However, chitosan is still a large molecule and insoluble in water, degraded into small molecule chitosan oligosaccharide (Figure 1), which can be directly soluble in water, thus the absorption rate is greatly increased, the dosage and the reaction after taking are greatly reduced. Chitosan with MW 10,000 possesses many excellent functions, such as inhibiting the growth of tumor cells, reducing cholesterol, blood sugar and blood lipids in serum and liver, enhancing body immunity, strengthening liver function, promoting the generation of spleen antibodies, promoting the proliferation of Bifidobacterium, inhibiting the growth of _Escherichia coli_ and absorbing and resenting moisture.  
The deacetylation process of chitosan brings about the change of MW. In the manufacturing process, the relative MW of chitosan is generally expressed by the value of viscosity, and the products with different viscosity have different purposes. A high concentration and degree of deacetylation increase the viscosity, whereas high temperature decreases it. It is used as a tackifier because of its high molecular weight and linear and unbranched structure [31].
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 7 Conclusions  
In the last decade, various nature-inspired optimization algorithms have been proposed for route discovery in IoT-based VANETs. However, the existing algorithms have become stuck with local optimal solutions due to unsustainable selection strategies, parameter tuning, and exploitation maintenance. Thus, the proposed Java macaque algorithm based on the social behavior of Java macaque monkeys amply shows its efficiency in striking a balance between exploration and exploitation using dominance hierarchy-based selection, male replacement and the learning process which suits the solution of route discovery in dynamic network topology well. In experimentation, the performance of the proposed Java macaque algorithm clearly illustrates its efficiency over the existing algorithm in three different scenarios. Therefore, the natural behavior of Java macaque suits the solution of route problems in real-world IoT-based VANETs well.  
For future work, the proposed algorithm should be fine-tuned to solve wide varieties of real-world problems, such as multi-objective engineering, image processing, and cloud computing domains. Furthermore, it could be extended to solve complex problems such as knapsack, scheduling, routing, and feature selection.  
Conceptualization, D.K., R.R. and A.D.; Methodology, R.S., I.A. and D.A.; Validation, H.H. and M.I; Formal Analysis, D.K. and R.R.; Investigation, I.A.; Resources, H.H.; Data curation, D.A.; Writing--Original Draft, D.K., R.R. and A.D.; Writing--Review Editing, D.A. and I.A., Supervision, H.H.; Project Administration, I.A. and M.I. All authors have read and agreed to the published version of the manuscript.  
The project was supported by University of Moncton, Canada.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
JMA Java Macaque Algorithm  
IoT Internet of Things  
VANETs Vehicular Ad hoc Networks  
DCSO Discrete Cuckoo Search  
GWO Grey Wolf Optimization  
PSO Particle Swarm Optimization  
GA Genetic Algorithm  
NIOA Nature-Inspired Optimization Algorithm  
V2V Vehicle-to-Vehicle  
V2I Vehicle-to-Infrastructure  
ACO Ant Colony Optimization  
MLA Modified Lion Algorithm  
BA Bat Algorithm  
FA Firefly Algorithm  
AODV Ad hoc On-demand Distance Vector  
RSS Received Signal Strength  
\(\Psi\) Individual  
\(\mathbb{G}\) Feasible solution search space  
\(\mathbb{X}\) Problem space  
\(\delta\) Learning rate of individual  
\(N_{p}\) Number of population  
\(M_{R}\) Male Replacement rate