esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library  
The VEOR classifier proposed for this study was evaluated together with ten machine learning algorithms implemented within the Python scikit-learn (former sklearn) library [75] version 0.24.2, released in April 2021 (Table 1).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Module/Routine** \\ \hline Multi-layer perceptron classifier that optimizes \\ the log-loss function using the limited memory \\ Broyden–Fletcher–Goldfarb–Shanno (LBFGS) \\ gradient descent algorithm [76; 77] \\ \hline K-nearest neighbor classifier [78] & sklearn.neighbors/KneighborsClassifier \\ \hline Support vector classification with a linear \\ kernel [79] & sklearn.svm/LinearSVC \\ \hline Support vector classification with a Radial \\ Basis Function (RBF) kernel [79] \\ \hline Gaussian process classification (GPC) based on \\ the Laplace approximation [80] & sklearn.gaussian_process/ \\ \hline Decision tree classifier [43] & sklearn.tree/DecisionTreeClassifier \\ \hline Random forest classifier [81] & sklearn.ensemble/RandomForestClassifier \\ \hline AdaBoost-SAMME classifier [82; 83] & sklearn.ensemble/AdaBoostClassifier \\ \hline Gaussian Naive Bayes classifier [84] & sklearn.naive_bayes/GaussianNB \\ \hline Quadratic Discriminant Analysis [85] & sklearn.discriminant_analysis/ \\  & Quadratic DiscriminantAnalysis \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of scikit-learn classification routines used for benchmarking with the VEOR algorithm.  
The scikit-learn library is very well established within the machine learning community (developed for >10 years). However, it has to be emphasized that the numerical implementation of some classifiers might still be suboptimal or erroneous. Moreover, this library makes external calls to other libraries implemented in Python (i.e., Numpy version 1.21.1) or C (i.e., BLAS version 3.7.1), which adds another source of potential software bugs. Nevertheless, debugging of the scikit-learn classification routines is beyond the scope of this work and the library was used as it was. Besides classifiers, the scikit-learn library comes with a set of routines used within this study for (1) generation of synthetic datasets, (2) computation of classification performance metrics, (3) data pre-processing and standardization, (4) hyper-parameter optimization, (5) feature selection and (6) visualization of classifiers' performance (plot_classifier_comparison.py). In the following subsections, a short summary of each classifier is given in order to facilitate further understanding and interpretation of the results.
## 3 Results and Discussion
### External Quality Assurance  
Well-validated and checked methods are essential if used to determine an abnormal amount of trace elements in the human body. Therefore, participation in a recognized external quality assurance has several benefits, including information on the relative performance of different methods, and knowledge about one's own ability to perform tests and report results accurately. Doing so also gains the confidence of clinicians and patients [60]. We partook in an external quality assurance using our method given the extensive benefits--including timesaving, low material costs, using only a micro volume of sample, and good accuracy and repeatability values--of our new method in comparison to other published methods, as shown in Table 8. A batch of ten analytes was tested during the INSTAND interlaboratory comparative program of the Institute for Standardization and Documentation in Clinical Laboratories.  
Laboratories partake in external quality assessment or proficiency testing schemes in order to evaluate the quality of their results and to fulfil accreditation requirements. Moreover, in some jurisdictions, it is mandated by law to participate in external quality assessments to guarantee a high standard of analysis and credibility with health authorities [61; 62]. External quality assurance assessment scheme results for ten analytes are listed in Table 8. The measured and recommended concentrations are in good agreement with the interlaboratory comparative program of the Institute for Standardization and Documentation in Clinical Laboratories. This satisfactory correspondence was also found for extremely low concentrations (Cd value 1.02 \(\mathrm{\SIUnitSymbolMicro L}^{-1}\)) as well as very high concentrations (Zn value 0.43 \(\mathrm{mg}\)\(\mathrm{L}^{-1}\)) in urine. The results from the external quality assurance program demonstrate that the described method is valid for the quantification of the elements in urine, requiring a simple one-step dilute and shot method using ICP-MS.
## 3 Simulation Results  
This section discusses the simulation results of the ladder-shaped multibeam extended interaction resonant cavities (with four drift tubes) and the RF section using commercial electromagnetic simulators [39; 40]. The initial geometrical design parameters, such as the dimensions of the cavity, gap lengths, drift tube parameters, and intercavity distances, are obtained from the analytical approach presented in the previous section. The cavity geometry and the RF section are then modeled and simulated in the three-dimensional electromagnetic simulation tools. The electromagnetic simulations regarding the device performance have been validated via the overall agreement between two well-established commercial software using two different computational methods for electromagnetic simulations. The CST studio tool uses the hexahedral volumetric meshing scheme together with a finite integration technique (similar to the finite difference technique) while the HFSS employs a triangular surface meshing with a finite element method for the simulations. The agreement between the simulation results obtained using these two simulation tools employing different numerical techniques represents the accuracy of the simulation. The small variation in the results can be attributed to the different meshing schemes employed in the two simulation tools. In the different meshing schemes, the dimensions (the area and volume) of the unit mesh cells are approximated differently. Therefore, the calculated resonant frequencies, electromagnetic fields inside the cells, and RF losses come out to be slightly different. Consequently, the quality factor (which depends upon the resonant frequency and RF losses) and the R over Q (which depends upon the developed electric field across the accelerating gaps and quality factor) also become slightly different.  
Besides the electromagnetic field of the operating mode of the RF cavities and their resonant frequencies, the shunt impedance and R over Q have also been obtained. The intermediate cavities, input and output cavities, and full RF section have been modeled and simulated for the beam-wave interaction studies. The simulation results are given in the following sections.
# Evaluation of High-Speed Railway Bridges Based on a Nondestructive Monitoring System
## 3 Evaluation of the Bridge Condition Using SHM Monitoring Data  
[PERSON] _et al._[5] designed a 3-D finite element model (FEM) of the Kaya Bridge using ANSYS V10.0 software (ANSYS, Canonsburg, PA, USA, 2005) to support the proposed movement analysis of different train's speeds. Train speeds from 200 to 450 km/h were studied. The FEM model results are concluded as follows: (i) the natural frequency modes of the bridge are 3.186 (1 st bending), 3.689 (2 nd bending) and 5.913 (1 st torsion) Hz for the first, second and third modes, respectively; (ii) the maximum vertical acceleration and displacement are 3.5 m/s\({}^{2}\) and 6 mm occurred with train speed 250 and 280 km/h, respectively; (iii) the maximum acceleration and displacement for the train 400 km/h speed are 1.6 m/s\({}^{2}\) and 3.8 mm, respectively. Therefore, comparing the FEM results and the bridge design criteria in Table 1 shows that the bridge is safe under applied loads.  
The current study utilizes the real monitoring data that is collected from the SHM system of Kaya Bridge to assess and evaluate the real bridge condition in terms of its vibration, static strain, torsional and fatigue behavior of the steel deck as well as evaluating and comparing the frequency contents for the measurements of the monitoring sensors.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 4 Policy for Manufacturing Industry
### Manufacturing Flying Taxis Can Benefit the Telecommunications Industry  
Manufacturing flying taxis can benefit the telecommunications industry in several ways. First, the development of flying taxis will require the integration of advanced communication and navigation technologies to ensure safe and efficient operations. This will create opportunities for telecommunication companies to develop and provide these technologies to the aviation industry. Additionally, the use of flying taxis will require reliable and efficient communication networks to support the booking and management of flights, as well as the tracking and monitoring of aircraft. Telecommunication companies can partner with flying taxi manufacturers to provide these services and help establish a reliable and secure communication infrastructure for the aviation industry. Moreover, the increased use of flying taxis could also drive the need for improved telecommunication infrastructure in areas where the demand for air travel is high but road infrastructure is limited. This can create new opportunities for telecommunication companies to expand their services and reach new customers in these regions [208].
## 3 Results and Discussion
### Ion-Exchange  
#### 3.2.1 Isothermal Adsorption Models  
In this study, the different initial concentrations of Ca2+ (10, 20, 50, 100, 200 and 400 ppm) and the relationship between C\({}_{\mathrm{e}}\) (concentration of adsorbate in the liquid when adsorption is in equilibrium) and q\({}_{\mathrm{e}}\) (equilibrium adsorption capacity of the adsorbent), were used to create the isothermal adsorption curve (Figure 4). From the results, the saturated adsorption amount was 80 mg/g.  
In order to get a high accuracy of the adsorption model, Langmuir and Freundlich equations were used to create the figures. Equation (6) and Figure 5 show the Langmuir equation [19] and indicate that they use the relationship between C\({}_{\mathrm{e}}\) and C\({}_{\mathrm{e}}\)/q\({}_{\mathrm{e}}\), to get the maximum adsorption capacity q\({}_{\mathrm{m}}\) and adsorption equilibrium constant K\({}_{\mathrm{L}}\). On the other hand, Equation (7) and Figure 6 show the Freundlich equation [20] and indicate that they use the relationship between lnC\({}_{\mathrm{e}}\) and lnq\({}_{\mathrm{e}}\), to get the empirical constant n and the adsorption equilibrium constant K\({}_{\mathrm{F}}\). By R\({}^{2}\) in Figures 5 and 6, the adsorption behavior of Dowex G26 fits with the Langmuir model. It showed that the resin has a uniform adsorption position, on the surface.  
\[\frac{C_{e}}{q_{e}}=\frac{C_{e}}{q_{m}}+\frac{1}{q_{m}K_{L}} \tag{6}\]  
\[\ln q_{e}=\ln K_{\mathrm{F}}+\frac{1}{n}\ln C_{e} \tag{7}\]
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Battery-Based Supply Techniques
#### 2.2.2 Swapping Method Algorithm
No flight tests were conducted to evaluate the performance of the hardware platform. Additionally, the study did not focus on discussing the specific improvements in endurance achieved through the implemented technology. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Contribution** & **Advantages** & **Limitations** \\ \hline An aerial prototype utilizing multiple rotors designed for surveillance missions of extended duration by employing battery health monitoring. & The hardware prototype enables uninterrupted operation by taking into account battery health considerations. & The system does not include autonomous swapping, and its operation is managed using a laptop instead of an embedded controller. \\ \hline The design, testing, and construction of an autonomous ground recharge station were carried out, incorporating a balancer and safer electrical contacts. & A hardware platform was developed that enabled autonomous swapping, utilizing an embedded controller. An optimization algorithm was implemented to minimize the recharge duration. & Although no flight test was conducted, the hardware platform focused on autonomous swapping and did not consider battery health as a factor in its design. \\ \hline A planning and learning algorithm was developed and tested during a 3 h long persistent flight involving 3 UAVs and over 100 battery swaps. & The design and hardware implementation of an automated refueling system was carried out, and flight tests were conducted to evaluate its performance. \\ \hline An automated swapping mechanism was implemented, incorporating online algorithms for energy management, vehicle health monitoring, and accurate landing. The system aimed to optimize the swapping process by efficiently managing energy resources, monitoring the health of the vehicles, and ensuring precise landing during the swapping operation. & The constructed platform is designed to sustain the continuous operation of a single UAV indefinitely. It incorporates algorithms for precision landing, ensuring accurate and safe landing during the swapping process. Additionally, the platform includes battery health tracking algorithms to test involving the complete system was not performed. \\ \hline A design for an autonomous docking platform was developed, incorporating the use of active infrared imaging. The system utilized a combination of a camera and an infrared filter to achieve precise and accurate docking between the unmanned aerial vehicle (UAV) and the docking platform. The active infrared imaging technology enabled the system to operate effectively both during the day and at night, enhancing the reliability and versatility of the docking process. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The critical evaluation of available swapping studies [78; 79; 80; 84].
# Design and Simulation Analysis of a New Type of Assembled UHPC Collision Avoidance
## 5 Discussion  
The results of the impacts under the three conditions and the calculated values of the impact forces under the different specifications are summarized in Table 4. From Table 4, the following rules can be found: the deviation of the impact force between the numerical simulation results of the collision avoidance device under the forward collision 0\({}^{\circ}\) Condition and calculation results based on JTG D60-2015 [43], TB10002-2017 [44], AASHTO [45], and Eurocode [46] are 3.1%, 103%, 16.8%, and 30.3%, respectively. The large deviation of the impact force calculated based on \"Code for Design of Railway Bridges and Culverts\" is mainly due to the small kinetic energy reduction coefficient gamma and the difficulty in mastering the elastic deformation coefficients, \(C_{1}\) and \(C_{2}\), in the empirical formula. The results show that the impact force calculated based on the \"General Specification for Highway Bridge and Culvert Design\" is in good agreement with the numerical simulation results.  
Figure 34: Ship energy diagram at 0\({}^{\circ}\) forward collision.  
Figure 35: Time history curve of impact force of piers under different collision angles.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & & & & & & & & & \\ \multirow{3}{*}{**Image**} & \multirow{3}{*}{**Highway**} & \multirow{3}{*}{**Railway**} & \multirow{3}{*}{**Euroco**} & \multirow{3}{*}{**P\({}_{\text{d}}\)/MN**} & \multirow{3}{*}{**Euroco**} & \multirow{3}{*}{  
For the Honghe Bridge in Zhuhai, the design resistance of the pier is 27.2 MN, and the maximum impact force without the collision avoidance device is 35.31 MN, which exceeds the design resistance by 30.0%, seriously endangering the safety of the bridge. However, the maximum impact force with the collision avoidance device decreases to 20.57 MN, which is lower than the design resistance by 24.4%, effectively protecting the bridge structure.  
It can be seen that this newly assembled UHPC collision avoidance device has good energy absorption performance and design performance. Moreover, the size of the longitudinal and transverse direction of the collision avoidance device can be adjusted by setting the number of floating tanks of different specifications to serve as the collision avoidance protection structure of different types of pairs, which provides an important reference for future bridge construction safety.
## 4 Results
### Comparative Analysis with External Models  
Comparison with external Hugging-Face models underscores the significant advantage of fine-tuning domain-specific models for the Mediavida dataset. External models without fine-tuning, specifically piuba-bigdata/beto-contextualized-hate-speech and unitary/multilingual-toxic-xlm-roberta, exhibited consistent shortcomings when applied to our dataset.  
For sexism detection, the piuba-bigdata/beto-contextualized-hate-speech model achieved high precision--86% in the single-turn variant and perfect precision (100%) in the contextual variant--but struggled considerably with recall, achieving only 13% and 8%, respectively. Consequently, the F1 scores were notably low at 23% (ST) and 15% (CTX), despite relatively high ROC-AUC scores (0.84 and 0.82, respectively). The multilingual unitary/multilingual-toxic-xlm-roberta model performed even less effectively, with a precision of 30% (ST) and 50% (CTX), and similarly low recall scores of 7% and 12%, resulting in F1 scores of merely 11% (ST) and 20% (CTX), accompanied by modest ROC-AUC scores of 0.72 and 0.62.  
In racism detection, similar patterns emerged. The piuba-bigdata/beto-contextualized-hate-speech model maintained excellent precision (100%) in the single-turn scenario but failed with recall (7%), yielding an F1 score of 13%. Its contextual variant dropped significantly in performance, attaining only 50% precision and 2% recall, resulting in a minimal F1 score of 4% and a ROC-AUC of 0.79. Meanwhile, the unitary/multilingual-toxic-xlm-roberta model displayed limited precision (40% in ST and 79% in CTX) coupled with very low recall rates of 10% (ST) and 20% (CTX), producing F1 scores of 16% and 32%, and ROC-AUC values of 0.70 and 0.65, respectively.  
These results confirm a substantial performance gap when relying on general-purpose models without domain-specific fine-tuning, highlighting the necessity of training models specifically for the characteristics, linguistic nuances, and interactional complexity inherent to forum-based dialogues like those found on Mediavida.
# Numerical Simulation of Interaction between Plasma and Azithromycin Based on Molecular Dynamics
###### Abstract  
Growing attention has been paid to nonthermal plasma treatment technology and its effects on the degradation of organic matter, especially for antibiotics. However, the majority of the conducted research has focused on the experimental results. Rare attempts were made to analyze the reaction mechanism at the microscopic level. In this paper, molecular dynamics simulation and reactive forcefields were used to investigate the reaction mechanism of different plasma particle interactions with azithromycin molecules. The simulation results indicated that the degradation of azithromycin was caused by the destruction of C-H and C-C bonds, followed by the formation of C=C and C=O bonds when reacted with the active particles. It was also found that the ability of degrading azithromycin varied among the different types of active particles. The oxygen atoms had the strongest ability to decompose the azithromycin molecule, with 38.61% of the C-H bonds broken as compared with other oxygenated species. The findings from this computational simulation could provide theoretical support and guidance for subsequent practical experiments.  
molecular dynamics simulation; azithromycin; nonthermal plasma; reactive forcefields; active oxygen group +
Footnote †: journal: Applied sciences
## 3 Results
### Abandoned Farmland Data Extraction  
First, the NDVI values were equally divided into ten intervals and then extracted to the sampling points using ArcGIS software. Then, the proportion of abandoned sampling points to all points in each interval was calculated separately, and the thresholds were determined by the inflection point of the proportional change curve. The proportional change curves and their inflection points for 2021 and 2022 are shown in Figure 9a,b. The inflection point was located in the range of [0.21, 0.29) in 2021 and [0.14, 0.21) in 2022.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year of Time Series Dataset** & **Date of GF-6** & **Date of STARFM-Fused Data** \\ \hline
2021 & 25 March & 30 April \\
2022 & 26 November & 21 September \\ \hline  & 24 February & \\
2022 & 5 May & 10 October \\  & 1 July & \\  & 26 November & \\ \hline \end{tabular}
\end{table}
Table 4: Types and dates of NDVI used to construct time series datasets.  
Figure 7: Correlation graphs between Landsat 8 NDVI and STARFM-fused NDVI: (**a**) correlation graph of data 30 April 2021, (**b**) correlation graph of data 21 September 2021, and (**c**) correlation graph of data 10 October 2022.  
The threshold was set to 0.29 in 2021 and 0.21 in 2022, with abandoned land showing a value less than the threshold and cultivated farmland exhibiting a value greater than the threshold during the year. The two-year abandoned farmland area data were overlain to distinguish between permanent abandonment and seasonal abandonment.  
The abandoned farmland for 2021 is shown in Figure 10a, and that of 2022 is shown in Figure 10b. The area of permanently abandoned farmland was obtained by overlaying the extraction results for 2021 and 2022, as shown in Figure 10c.  
Figure 8: Annual NDVI difference for 2021 and 2022. (**a**) NDVI difference for 2021 and (**b**) NDVI difference for 2022.  
Figure 9: Annual abandoned samples’ proportional change curves: (**a**) proportional change curve for 2021 and (**b**) proportional change curve for 2022.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Software Characteristics  
The CR software design and applications can be divided into multiple categories, from MAC protocols and routing algorithms to machine learning approaches used to predict channel occupancy. In this work, we summarize three of those categories: (i) spectrum sensing algorithms, focusing mainly on transmitter detection and identification of spectrum holes [52; 53]; (ii) methods to perform the spectrum handover [54]; and (iii) and simulation software [55].