esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Advances in Analytical Strategies and Future Directions
### Innovations in Separation Techniques for Carotenoids Analysis  
One of the main areas of advancement in carotenoid analysis is the development of novel packing materials for chromatographic columns. Core-shell technology, which employs superficially porous particles, has significantly improved separation efficiency by reducing band broadening and enhancing resolution compared to fully porous particles [132]. Additionally, C30 stationary phases have been optimized to improve isomer separation, which is critical for accurately distinguishing structurally similar carotenoids [13; 14]. These materials allow for faster analysis times while maintaining high sensitivity, making them particularly useful in UHPLC applications [18; 19; 20].  
Another key innovation is the adoption of sustainable mobile phases. SFC has gained attention as an eco-friendly alternative to HPLC, utilizing supercritical CO\({}_{2}\) as the mobile phase, thereby reducing organic solvent consumption [9]. Similarly, DES and ILs have been investigated as greener alternatives, though further validation is required before they can be widely adopted in carotenoid research [68; 69; 70; 71; 72].  
Advancements in hyphenated techniques, such as UHPLC-QTOF-MS, SFC-MS, and bidimensional chromatography coupled with MS, offer enhanced structural characterization and quantification by integrating advanced chromatographic separation with high-resolution MS [51; 52; 53]. These approaches enable detailed profiling of carotenoids, including identifying novel derivatives and isomeric forms. Integrating IMS with MS has further enhanced the differentiation of closely related carotenoid compounds based on their collision cross-section values [32; 53; 52].
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
###### Abstract  
The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims' interactions, they are practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers' recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).  
Keywords:cyberbullying detection; tweets classification; Twitter; logistic regression; random forest; light GBM; SGD; AdaBoost; naive bayes; SVM +
Footnote †: journal:
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 3 Configuration Optimization
### Improved NSGA-III Algorithm
#### 3.3.1 Normal Distribution Crossover  
Compared to the traditional simulated binary crossover (SBX), normal distribution crossover (NDX) introduces normal distribution variables into the crossover, expanding the offspring's distribution beyond the parent vicinity. This broadens the search space and enhances the ability to escape from local optimal solutions. Additionally, NDX incorporates a discrete recombination operation inspired by evolutionary strategies, which provides new individuals with a more extensive value range, further expanding the crossover's search ability. The NDX crossover algorithm is as follows:  
\[\left\{\begin{array}{l}y_{1,2}^{j}=\frac{x_{1}^{j}+y_{2}^{j}}{2}\pm A\cdot \left|N(0,1)\right|\cdot\frac{x_{1}^{j}-x_{2}^{j}}{2},r(j)\leq 0.5\\ y_{1,2}^{j}=\frac{x_{1}^{j}+x_{2}^{j}}{2}\mp A\cdot\left|N(0,1)\right|\cdot \frac{x_{1}^{j}-x_{2}^{j}}{2},r(j)\leq 0.5\end{array}\right. \tag{15}\]  
where \(y\) represents the offspring of crossover and \(x\) represents the parent, \(N(0,1)\) is the normal distribution variable, and \(A\) is a constant. To make sure that the probability of the exploration and exploitation for NDX is equivalent to SBX, \(A\) is set to 1.481.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 2 The Principle and Theory of Rainfall Measurement by CML
### Mathematical Models
#### 2.2.2 Path-Integrated Rain Attenuation  
According to refs. [5; 10; 19], path-integrated rain attenuation \(A_{rain}\) in Equation (2) basically is a function of the specific attenuation \(k\), which can be calculated based on Equation (1), that is,  
\[A_{rain}=\int_{0}^{L}k(l)dl=\int_{0}^{L}aR(l)^{b}dl\stackrel{{ b\sim 1}}{{=}}a\overline{R}^{b}L, \tag{7}\]  
where \(R\) (mm/h) is the path-average rain rate. The attenuation caused by rainfall and the average rain rate are linearly correlated if \(b\approx 1\). Other than Equation (1), the values of the rain rate \(R\) and the specific attenuation \(k\) can be estimated based on DSD refer to [32; 33], i.e.,  
\[R=6\times 10^{-4}\pi\int_{D}v(D)D^{3}N(D)dD, \tag{8}\]\[k=\frac{1}{ln(10)}\int_{D}Cext(D,f)N(D)dD, \tag{9}\]  
where \(D\) is the raindrop diameter in mm, \(N(D)\) is the DSD's number concentration per diameter in mm\({}^{-1}\)m\({}^{-3}\), \(v(D)\) is the rain droplet final velocity in m/s, and \(Cext(D,f)\) is the extinction cross section at frequency \(f\) in m\({}^{2}\), which describes the attenuation of the signal at frequency \(f\) by each raindrop. The dependence of \(Cext(D,f)\) and \(v(D)D^{1}\) on raindrop diameter \(D\) is very similar, especially in the frequency range from 20 GHz to 35 GHz [5].
## 5 Nanocarriers as a Strategy to Fight Biofilms
### Destabilization of EPS  
In addition to effectively penetrating biofilms, breaking down EPS can naturally facilitate the delivery of antimicrobial substances. Nanocarriers can perform multiple functions: targeting bacteria and simultaneously breaking down EPS [84]. One key characteristic of nanocarriers is their ability to target multiple sites, allowing certain formulations to act on both EPS and bacteria simultaneously [85]. Breaking down EPS helps deliver a higher concentration of antibacterial drugs to the bacteria and leads to a more efficient removal of the biofilm [86].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.1 Bioplume Iii  
BIOPLUME III is a 2D model for simulating the sequential biodegradation of hydrocarbons in groundwater using a series of aerobic and anaerobic electron acceptors: oxygen, nitrate, iron (III), sulphate, and carbon dioxide. It was developed by modifying a two-dimensional transport model using the method of characteristics (MOC) [96]. The output of the model is the distribution of oxygen and pollutant concentrations. This tool has already been described and used in detail. For more information, see [150; 387; 650; 651; 652; 653; 654]. Raei et al. [653], for example, coupled BIOPLUME III and the Non-dominating Sorting Genetic Algorithm (NSGA-II) to optimise the remediation of groundwater contaminated by hydrocarbons, taking into account stakeholder feedback. The model and algorithm enabled the localisation of extraction and injection wells in the hypothetical BTEX-contaminated area. The optimisation function was used to minimise the fragmentation of the contaminant plume and increase the integration of the plume for the desired contaminant concentrations.
# A Transformer-Less Buck-Boost Grid-Tied Inverter with Low Leakage-Current and High Voltage-Gain
## 6 Discussions  
According to the experimental waveforms above, the feasibility of the proposed inverter has indeed been verified. In order to further summarize its main contributions and discuss the performance in terms of loss, efficiency, cost, and harmonic distortions, the following discussions are carried out.  
The traditional PV grid-tied system is composed of a boost converter and a H-bridge inverter. Due to the two-stage energy processing, the system efficiency is reduced. The proposed inverter only needs single energy processing, and only one power switch performs high frequency switching, improving the conversion efficiency effectively. By adding coupled inductors, the voltage gain of the inverter can be increased, which is suitable for low input voltage applications. The inverter has both step-up and step-down functions so that it is suitable for PV systems with wide-range input-voltage fluctuation.  
Based on the results of the previous loss analysis, the expected full load efficiency should be 96.3%. The measured full load efficiency of 100V input shown in Figure 18 is about 94.7%, which is slightly lower than the expected result. This is because only the power losses of the main power components at room temperature is considered in the loss analysis. Long-time operation increases the junction temperature and on-resistance of power semiconductor components, resulting in increase of power loss. In addition, the conduction loss of the copper wire in the circuit layout should be of considerable proportion, but it is not included in the loss analysis.  
Table 5 shows comparisons between the proposed buck-boost inverter and the traditional two-stage grid-tied system. Since the proposed inverter requires only a single stage of energy processing, its measured full-load efficiency can be as high as 94.7%. In the two-stage system, even if the efficiencies of the boost converter and the inverter are both up to 97%, the overall efficiency is only about 94.1%, which is lower than the proposed inverter. The proposed inverter has the beneficial feature of reduced voltage stress of power switches, which is favorable for reducing the cost of power components. Although the proposed inverter requires a higher number of components, the overall cost is only slightly higher than the two-stage system. Since the proposed inverter has both step-up and step-down capabilities, and it has high voltage gain, its operable input voltage range is relatively wide. In addition, since the proposed inverter and the traditional two-stage system both have an output low-pass filter, their total harmonic distortions can be lower than 2% to meet electrical specifications.  
Figure 19: The picture with the experimental test bench.
## 5 Conclusions  
In this study, we proposed a standardized methodology to design a storm drainage system for a basin in Culiacan, Mexico, using UAV, geographic information systems, and hydrological methods. The urban basin was delineated using a high-resolution DEM obtained from a photogrammetric process by using a UAV for image acquisition. The basin had an area of 0.42 km\({}^{2}\), a perimeter of 2.8 km, a length of 1.1 km, a slope of 0.02, and a time of concentration of 11.56 min.  
The probability distribution that best fitted the rainfall data was Gumbel of two populations, and this was used to estimate the maximum daily precipitation for different return periods. The high-resolution orthophoto was used to identify different land uses in the study area, which were then used to calculate the runoff coefficients for each return period. The intensity-duration-frequency curves were obtained using the isohyet curves and [PERSON]'s formula, and a design storm was defined with a return period of 10 years and a duration of 11.56 min. The resulting rainfall intensity was 166.9 mm/h.  
With the proposed methodology, it is possible to perform standardized hydrologic studies to calculate design surface water flow rates in urban basins with little basic information. Such standardization would facilitate the review of hydrologic studies by researchers or authorities to analyze surface water flow or to authorize or deny construction permits. The most appropriate methods existing in the literature were selected for urban areas with scarce basic information, such as the case study area. This methodology includes the delineation of urban basins with precision, considering the layout of roads and urban and real estate storm drainage works. In addition, it emphasizes the study of urban surface flow patterns with a basin vision instead of an urban development zone. This would allow for the integration of results from sub-basins with the results for larger basins.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.2 ANN in Network Measurement  
An ANN is made up of a lot of connected processing units. The network processes information by progressively altering and increasing the weights of the neuron connections to replicate the relationship between the input and output through repeated learning and training of known information. Each neuron and its connection can only represent a portion of the information in an artificial neural network, which can imitate the complex nonlinear relationship well. Therefore, when there is a node break, the overall operation effect of the subnetwork is not affected, and it has strong robustness and fault tolerance. As a result, the subnetwork has excellent robustness and fault tolerance, and when a node breaks, the influence on its overall operation is unaffected. One branch of machine learning techniques, ANN, has frequently been employed in future prediction and image recognition processing due to its self-learning function, high-speed search for optimal solutions, and simultaneous processing of enormous volumes of data, addressing classification and regression issues [39].  
To anticipate the possible throughput in a non-independent 5g network based on network slices, [PERSON] et al. [24] suggested a non-network intrusive ML model. To comprehend the decision-making procedure of root cause analysis, a decision tree model was employed. Verification yielded an accuracy rate of 93% using an ANN as the optimal method for processing the tabular dataset. An ANN has the abilities of self-adaptation, self-organization, and real-time learning and can carry out a huge number of operations quickly as a parallel and distributed processing approach. The failure to guarantee a superior effect and total reliability, however, results from extensive training and data renewal, which is unsuitable for high-precision computations.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### The Floodwater/Permanent Water Mapping Algorithm
#### 3.2.4 Flooded Vegetation
Through Equations (5)-(7), maximum membership (equal to 1) to the class of flooded vegetation is given to objects having \(\overline{\Delta\sigma^{0}_{VV}}>4\ \mathrm{dB}\), \(\overline{\Delta\sigma^{0}_{VV}}-\overline{\Delta\sigma^{0}_{VH}}>6\ \mathrm{dB}\) and totally surrounded by open water. The combination of the membership's degrees is carried out by computing their average and if the average is larger than 0.5, an object is labelled as flooded vegetation (defuzzification box in Figure 11).
## 3 Methodology
### Technological Integration  
The third phase is focused on integrating the various technological components of the SCADA system, including hardware, communication networks, and data servers. Remote Terminal Units (RTUs) were installed at key locations, such as wells and pumping stations, to collect data from sensors and control local equipment [3]. A hybrid communication system was implemented, combining radio frequency (RF), satellite, and cellular networks to ensure reliable data transmission across the geographically dispersed infrastructure [7]. For data management, a centralized data server was set up to store and process the collected data. The server was equipped with advanced analytics tools to generate insights and alerts for operators [10]. Finally, robust cybersecurity protocols, including encryption and access control, were included to protect the system from cyber threats [19].
# Scale-Adaptive Simulation of Unsteady Cavitation Around a Naca66 Hydrofoil
## 3 Hydrofoil Geometry and Computational Domain  
The geometry of the NACA66 hydrofoil and the main computational domain are outlined in Figure 1a,b respectively and it is noted that the surface roughness of NACA66 body was considered as a no-slip wall condition. The chord length (\(c\)) is equal to 0.15 m with an attack angle (\(\tilde{AOA}\)) of 6\({}^{\circ}\), which were based on experimental studies carried out by [PERSON] [22]. According to [PERSON] et al. [33], boundary conditions for inlet and outlet are 5.48 m/s and 20.3 kPa, respectively, and the value of cavitation number (\(\sigma\)) is 1.2.  
A structured grid mesh was obtained using GMSH, which is a free open source software [34]. For that, an exponential criteria close to the boundary layer was applied to improve the CFD performance, and the resulting grid is composed by 801628 cells with 38 divisions on the span-wise direction as indicated in Figure 2a, which is based on the mesh independence analysis carried out by [PERSON] et al. [33] and [PERSON] et al. [35]. The mesh requirements of ILES and SAS have been achieved with a \(y^{+}\) equal to 2 [4; 17]. Further mesh analysis was considered using the quality filter of Paraview [14], and results show values between 0.7 and 1, which are acceptable according to previous studies [4; 14] as indicated in Figure 2b.  
The Yplus, \(y^{+}\), was calculated as  
\[y^{+}=\frac{u_{\rm\tau}y}{\
u}, \tag{15}\]  
where \(u_{\rm\tau}\) is the friction velocity and \(y\) is the distance to the nearest hydrofoil wall. Furthermore, the cavitation number, \(\sigma\), was calculated as  
\[\sigma=\frac{p_{\rm\tau}-p_{\rm v}}{0.5\rho U_{\infty}^{2}}, \tag{16}\]  
where \(U_{\infty}^{2}\) is the undisturbed flow velocity, \(p_{\rm\tau}\) and \(p_{\rm v}\) are the reference and saturation pressures, respectively.  
Numerical simulations were run in parallel using the simple decomposition method of OpenFOAM and OpenMPI. For that, the computational domain was decomposed in eight subdomains to run in eight cores of the Dell Precision 3430 workstation with 64 RAM memory at Laboratorio de Informatica-Mecanica of Escuela Politecnica Nacional. Moreover, the mean computational time required to run SAS and ILES simulations were 4 and 6 days, respectively.
## 3 Results
### Differences between Infected and Non-Infected Trees  
For examining the variations and distinctions observed between centennial olive trees affected by the OLS and those not affected, we conducted a series of \(t\)-tests across a range of examined variables. Results showed statistically significant differences between almost all the phenotypic traits, excluding height (Table 5). It was observed that trees with OLS (mean values: OLS presence/OLS absence) exhibit a higher diameter, which consequently leads to a higher estimated age (251.35 \(\pm\) 22.29 years; 169.32 \(\pm\) 31.87 years), lower shoot ratio (0.60 \(\pm\) 0.26; 0.78 \(\pm\) 0.14), reduced crown area (58.15 \(\pm\) 32.14 m\({}^{2}\); 85.37 \(\pm\) 34.96 m\({}^{2}\)) and healthy crown area (30.98 \(\pm\) 27.74 m\({}^{2}\); 77.20 \(\pm\) 33.41 m\({}^{2}\)), a lower cavities ratio (0.02 \(\pm\) 0.01; 0.01 \(\pm\) 0.01), decreased productivity (27.69 \(\pm\) 11.01 kg; 46.63 \(\pm\) 5.92 kg), and a narrower productivity range (26.42 \(\pm\) 11.78 kg; 34.66 \(\pm\) 11.85 kg).  
In addition to phenotypic traits, differences extended beyond them and were also noticeable in trunk thermal and humidity indices. Specifically, significant disparities were identified in just one humidity index, H\({}_{\text{OPR}}\) Side A. However, variations were more pronounced in all thermal indices, as illustrated in Figure 11.  
Finally, significant differences were observed in both T\({}_{\text{IQR}}\) canopy and CWSI [T\({}_{\text{IQR}}\) canopy: t (42) = \(-\)3.063, \(p\) = 0.004; CWSI: t (42) = \(-\)2.1, \(p\) = 0.042]. Notably, trees affected by the olive leaf spot exhibited higher values, while those unaffected displayed lower values in both instances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phenotypic Traits** & **t-Value** & **df** & **\(p\)-Value** \\ \hline Height & \(-\)0.299 & 42 & \(>\)0.05 \\ Diameter & \(-\)3.722 & 42 & \(<\)0.05 \\ Shoots ratio & 2.522 & 42 & \(<\)0.01 \\ Crown area & 2.665 & 42 & \(<\)0.01 \\ Healthy crown & 4.997 & 42 & \(<\)0.001 \\ Age & \(-\)3.722 & 42 & \(<\)0.05 \\ Cavities ratio & \(-\)2.938 & 42 & \(<\)0.01 \\ Productivity & 6.645 & 42 & \(<\)0.001 \\ Productivity range & 2.124 & 42 & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical comparisons between trees with and without OLS the phenotypic traits. The table shows t-values and \(p\)-values for each trait, indicating significant differences between the two groups. Significant differences are denoted by \(p<0.05\).
## 2 Materials and Methods
### Design  
The design of the Cryologger platform takes into consideration several key principles, including cost-effectiveness, power efficiency, modularity, ruggedness, and ease of use. The use of low-cost, off-the-shelf components that do not require specialized tools or training to assemble makes the platform accessible to researchers, citizen scientists, and hobbyists alike. An emphasis on modularity and extensibility enables the system to be interfaced with a wide variety of easily interchangeable sensors and provides control over design complexity.  
The selection of components that are resistant to harsh environmental conditions also ensures continuous cold-weather operation. These design decisions, in turn, allow for the Cryologger platform to be customized for the specific research needs of the end-user, in this case as an ice tracking beacon, and suitable for deployment within a broad range of real-time environmental monitoring applications.
# Digital Twin Framework for Road Infrastructure Management
###### Abstract  
Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework's implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain.  
digital twin; road infrastructure digital twin; digital twin framework; digital twin reference model; digital twin standardization; intelligent road infrastructure management; road infrastructure digitalization Article