esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Addressing Privacy and Patient Agency through AI  
The integration of AI in healthcare brings forth complex privacy challenges, particularly in the realms of data control and usage by private corporations. As AI systems increasingly handle sensitive patient data, concerns regarding the stewardship of this information by private entities become paramount. Establishing transparent and accountable data governance frameworks that respect patient privacy and agency is essential to addressing the potential for misuse or unauthorized exploitation of health data. These challenges underscore the need for a delicate balance between technological advancement and the protection of patient rights [1].  
In the age of digital healthcare transformation, AI-driven systems are increasingly at the forefront of medical innovation. However, with this advancement come significant challenges concerning patient privacy and agency. The risk of privacy infringement due to the misuse of pseudo-anonymized patient data [7,8] is a primary concern. Sophisticated techniques can potentially re-engineer anonymized data to identify individuals, thus breaching patient confidentiality. The complexity of AI algorithms often makes it challenging to track patient data utilization within these systems [9,10], leading to potential misuse or a lack of transparency.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 1 Introduction
This dual-level representation enables the model to adapt to novel attack patterns and outperform existing models in accuracy, recall, and robustness across diverse datasets. Generally, this study aims to explore the integration of deep learning methodologies specifically tailored for the detection of malicious URLs. By delving into the nuances of URL structures, content, and behavioral patterns, the study seeks to devise an advanced deep learning-based approach capable of swiftly and accurately identifying and categorizing potentially harmful URLs. Therefore, the main contributions of this work can be summarized as follows:  
1. Development of a hybrid deep learning model (Char2B): This study introduces a novel Char2B model that fuses BERT and CharBiGRU embedding, enhanced with a Conv1D layer, to effectively extract both character-level and contextual features from URLs for accurate classification. 2. High performance in malicious URL detection: The proposed model achieved 98.50% accuracy, outperforming the baseline BERT model in all key metrics, includingprecision, recall, F1-score, and false positive rate (0.017 vs. 0.018), demonstrating its effectiveness in real-world detection tasks. 3. Comprehensive dataset and evaluation: A substantial dataset of 87,216 URLs was collected from reliable sources (DMOZ, PhishTank, and Any.Run). The study applied rigorous training, evaluation, and optimization techniques, using standard metrics to validate the model's generalizability and practical utility in cybersecurity. 4. Empirical comparison with baseline methods to highlight the effectiveness of the fusion-based approach. Lastly, this article organized into the following sections: Section 2 focuses on related works, Section 3 discusses the research methodology, Section 4 presents the results and discussion, and the final section provides the conclusion and future research directions.
## 4 Earthquake Algorithm
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (Kg/m\({}^{3}\))** & **Poisson’s Ratio** & \(v_{p}/v_{s}\) \\ \hline Sandstone & 2500 & 0.21 & 1.65 \\ Salt & 2200 & 0.17 & 1.59 \\ Limestone & 2700 & 0.19 & 1.62 \\ Granite & 2610 & 0.25 & 1.73 \\ Basalt & 2940 & 0.28 & 1.8 \\ Peridolite & 3300 & 0.29 & 1.8 \\ Dunit & 3300 & 0.29 & 1.8 \\ Pyroxenite & 3300 & 0.29 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Principal materials or geologic formation properties taken from [27]. Figure 5: Behavior of epicenters around the best solution. where \(X_{i}^{t}\) and \(X_{i}^{t-1}\) are the current and previous positions, respectively and \(V_{i}\) is the current velocity (\(v_{s}\) or \(v_{p}\)). Finally, as another heuristic freedom degree for the Earthquake Algorithm, in [16] it is shown how a random selection for an exponential distribution reduces the probability of visiting points already visited for the epicenters. The implemented expression of the distribution is originally taken from [28] (attending to its relation with the Poisson distribution explained in [15]). Therefore, the random value is generated in a range of \(\pm\) the maximum value of \(v_{p}/v_{s}\), i.e., \(\pm\) 1.91 (taken from Table 1), resulting in the relation described by Equation (9). \[X_{i}^{t}=X_{best}+Exp_{\mu}(s), \tag{9}\]  
where \(X_{best}\) is the global best solution and \(Exp_{\mu}(s)\) is the random value generated with the exponential distribution from the value of \(\mu\). After some iterations, all the epicenters begin to converge due to the small velocity around the global solution, when they enter the \(Sr\). For that reason, Equation (9) allows some randomly selected epicenters to \"escape\" the \(Sr\), in order to prevent to get trapped into a local minimum. Figure 6: Basic flow chart of the Earthquake Algorithm (EA) for inductance selection. Details of the implementation of the Earthquake Algorithm for the inductance selection will be discussed in the following section. Additionally, Figure 6 shows the flow chart of the EA, adapted for the design application, where it is important to highlight that the \(v_{s}\) and \(v_{p}\) velocities selected through the \(Sr\), represents the adaptability factor that allows optimizing solutions to problems that require both fine and aggressive searches, as explored in [17].
# Effect of High-Speed Train-Induced Wind on Trackside UAV Thrust Near Railway Bridge
## 3 Analysis of the Effect of High-Speed Train-induced Wind on UAV Thrust along a Track
### Analysis of the UAV Thrust Change Caused by Train-Induced Wind  
Figure 8 shows the UAV thrust and vibration acceleration measured at 1.8 m from the train surface during the passage of a KTX train. Before the passage of the train, the thrust of the UAV motor was maintained at 0.5 kgf. The thrust sharply increased to 0.6 kgf when the leading power car of the train passed and then rapidly decreased to 0.46 kgf. The thrust showed \(\pm\)8.8% change during the passage of the passenger cars and sharply rose to 0.8 kgf when the trailing power car passed. After the passage of the train, the thrust slowly decreased and returned to 0.5 kgf in approximately 5 s.  
The UAV thrust increased by 20% when the leading power car of a high-speed train passed and by up to 60% when the trailing power car passed. Even after the passage of the train, the pressure change and wake, which are factors affecting the thrust, were observed. This indicates that it takes several seconds for the thrust to stabilize. The wind velocity increased up to 10.89 and 22.19 m/s when the leading and trailing power cars of the KTX train passed, respectively, as shown in Figure 4. As observed, the thrust of the UAV increased significantly when the trailing car passed, which is attributed to the influence of the maximum wind velocity at this time.  
Figure 5: Train-induced wind velocity over time measured at 0.8 m from the train surface in each direction. (**a**) Train travel direction; (**b**) train width direction; (**c**) height direction.  
Figure 6: Maximum train-induced wind velocity according to the distance from the train surface during the passage of KTX trains.  
Figure 7: Train-induced wind velocity measured at 0.8, 1.3, 2.3, and 2.8 m distances from the train surface in each direction. (**a**) Train travel direction; (**b**) train width direction; (**c**) height direction.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 2 Adaptive Neuro-Fuzzy Inference System (ANFIS)  
Fuzzy inference [40] is effective in processing ambiguous and uncertain information by processing it in a similar way to human thinking, but it does not have learning ability. Artificial neural networks [41] have excellent learning ability, but they do not produce rule-based knowledge. A neuro-fuzzy inference system is constructed by fusing these fuzzy inferences and artificial neural networks to complement the disadvantages of each method. The neuro-fuzzy inference system generates an if-then fuzzy rule through fuzzy inference and optimizes the prediction performance by updating the parameters used in the fuzzy rule by applying the learning ability of the neural network. Because of these advantages, the neuro-fuzzy inference system is widely applied to various fields such as system control, pattern recognition, disease diagnosis, and energy efficiency prediction.
## 2 Materials and Methods
### Distance Limit of Target Detection and Target Resolution  
A one-way repeated ANOVA measure and Pearson Correlations were used to understand the effect of target size on single target detection. Post-hoc testing via the Bonferroni method was used to identify significant differences between each group. The significant level was set at 0.05. The R-value was used to understand the relationship between target size and distance between target and smartphone.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 3 Model
### Approach 1: Use Estimated Trust
As shown in lines 10-14 of Algorithm 2, the stochastic gradient descent (SGD) is used to estimate the model parameters \(Pu\), \(Qi\), \(TPu\), and \(TQi\). ```
1:procedureGenerateLatentModel(\(RatingMatrix\), InitialTrustMatrix\(,\alpha,\lambda\), n_epoch)
2:\(n\_users=length(RatingMatrix.row)\), \(n\_items=length(RatingMatrix.row)\)
3:\(Pu\), \(Qi=Initialize(n\_users\), \(n\_items,\ k)\)
4:\(TPu\), \(TQi=Initialize(n\_users\), \(n\_items,\ k)\)
5:for\(n=0,n++\), \(n<n\_epoch\)do
6:for\(u=0,u++\), \(u<n\_users\)do
7:for\(i=0,i++\), \(i<n\_items\)do
8:\(p_{u}=Pu(u)\), \(q_{i}=Qi(i)\), \(tp_{u}=TPu(u)\), \(tq_{i}=TQi(i)\), \(r_{u,i}=RatingMatrix(u,i)\)
9:\(l_{u}=RatingMatrix(u,:)\), \(U_{i}=RatingMatrix(:,i)\)
10:\(\mu=mean(RatingMatrix)\), \(b_{i}=\mu-mean(U_{i})\)
11:\(b_{u}=\mu-mean(l_{u})\), \(t_{u,i}=InitialTrustMatrix(u,i)\)
12:\(ImpFactor=sqr(len(l_{u}))\times sum(l_{u})\)
13:\(reg=\lambda\left(len(l_{u})^{2}+len(U_{i})^{2}+{{b_{u}}^{2}}+{{b_{i}}^{2}}\right)\)
14:\(\delta q=-q_{i}\times\left(r_{u,i}-\mu-b_{i}-b_{u}-p_{u}.q_{i}-reg\right)\)
15:\(\delta p=-p_{u}\times\left(r_{u,i}-\mu-b_{i}-\ b_{u}-(p_{u}+ImpFactor)q_{i}- reg\right)\)
16:\(q_{i}\gets q_{i}+\ \alpha\ \times\ \delta q\)
17:\(p_{u}\gets p_{u}+\ \alpha\ \times\ \delta p\)
18://trust estimation
19:\(\delta tq=-tq_{i}\ \times\left(t_{u,i}-tp_{u}.tq_{i}-\lambda\right)\)
20:\(\delta tp=-tp_{u}\times\left(t_{u,i}-tp_{u}.tq_{i}-\lambda\right)\)
21:\(tq_{i}\ \gets tq_{i}+\ \alpha\ \times\ \delta tq\)
22:\(tp_{u}\gets tp_{u}+\ \alpha\ \times\ \delta tp\)
23:\(Pu(u)=p_{u}\), \(Qi(i)=q_{i}\), \(TPu(u)=tp_{u}\), \(TQi(i)=tq_{i}\)
24:endfor
25:endfor
26:endfor
27:return\(Pu\), \(Qi\), \(TPu\), \(TQi\)
28:endprocedure
```  
**Algorithm 2** Pseudocode for latent vector approximation. Since both the trust and latent features are estimated together, they are trained with the same number of epochs. Then, the outputs of Algorithm 1 are combined using any of the three combination methods mentioned earlier.
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 3 Method  
Sensor fusion means combining data coming from different sensors measuring the same phenomenon (direct fusion), or from sensors measuring different quantities (indirect fusion). In both cases, the action of merging the data is useful to decrease the uncertainty of the observed phenomenon. In particular, in this work we propose to merge in-vehicle sensor data with these coming from a set of external sensor (added ad hoc to the vehicle) in order to improve the identification of unsafe driver behavior through machine learning techniques. This is an approach that can be brought back to the so called feature-level fusion, where features from multiple data sources (e.g., a single embedded platform node with multiple sensors) are composed (i.e., fused) into a new vector (with typically higher dimension) to be given as input, for instance, to a subsequent classifier in order to improve its performance [21].  
Figure 1 shows the algorithmic workflow of the proposed approach which is, essentially, composed of 4 stages. The first stage, called data gathering, entails data collection from several in-vehicle sensors, using an OBD dongle, and from the motion sensors installed on a common smartphone. A second step is then performed to preprocess the collected data in order to temporally align the waveforms collected from the two different devices. Then, the waveforms are divided into separated windows of fixed size, and for each window, a label reporting whether the driving behavior was safe or unsafe is generated using lateral and longitudinal accelerations. The third step involves the feature extraction from in-vehicle and motion sensors waveforms (except for the acceleration data, which are used only to generate a reference label representing an objective ground truth). Finally, the last phase uses machine learning tools, in particular, by training and testing two different classifiers, namely, a support vector machine and an artificial neural network.
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
## 4 Numerical Procedure  
The model that describes the energy conversion process in the PV-Th panels, integrated desalination by flashing chamber process concept using nanofluids is presented in Equations (1)-(16). The flow diagram in Figure 2 was established to solve the aforementioned system of equations for mass and energy taking place during the flashing chamber process. The calculation starts with the input of the parameters of the PV-Thermal solar panel, thermal tubes, desalination chamber parameters, nano particles, \(\text{A}_{2}\text{O}_{3}\), \(\text{CuO}\), \(\text{Fe}_{3}\text{O}_{4}\) and \(\text{SiO}_{2}\) and the heat transfer fluid. The system of equations was integrated using the finite-difference formulations to determine the behavior of the process at each flashing chamber. Iterations were performed using MATLAB iteration techniques until a converged solution was reached with less than 0.05. With the knowledge of the solar radiation, the mass flow rate of the nanofluid circulating in the thin tubes welded to the PV solar collector can be determined. Then, the thermophysical properties and the heat transfer characteristics of the base fluid, water, and nanofluids at different concentrations are determined, as well as the mass and energy in the flashing chamber control volume. This was accomplished by solving the finite-difference formulation of the aforementioned system of equations. Then, the parameters describing the behavior of PV-Thermal solar panels, flashing chamber process were determined at different conditions. Finally, the individual and hybrid system efficiencies, availability and irreversibility, which represents the availability destruction in the process, were calculated.  
Figure 2: Logic diagram for the numerical solution.
## 3 Application Examples
### Collection and Nontargeted Analysis of Hairy Root Exudates of  
Sesamum indicum  
Growth medium of hairy root cultures grown as described above was cleared by centrifugation for 10 min at 2900\(\times\)_g_ to remove root fragments. The medium was used for HPLC-ELSD analysis  
Figure 5: HPLC analysis of root exudates of rose (_Rosa canina_ L.) collected in the bioreactor. (**A**) HPLC chromatogram of root exudates separated on a reverse-phase column, eluted with a methanol gradient and detected by light absorption at 254 nm. (**B**) UV absorption spectra of major phenolic compounds recorded using a diode array detector coupled to HPLC.  
Figure 6: Hairy root cultures generated from several accessions of _Hyoscyamus niger_ in bioreactors operated in a bubble aeration mode.  
without cleanup. Figure 8 shows a comparison of HPLC-ELSD chromatograms of hairy root exudates obtained from the bioreactor and from a flask culture for one accession of sesame.  
The amount and diversity of hairy root exudates obtained from a bioreactor were higher than the amount and diversity of exudates obtained from a shaking flask culture (Figure 8). Better oxygen supply in the bioreactors was the likely reason. Most importantly, all metabolic signals recorded in exudates from the flask culture were also found in exudates from the bioreactor, indicating that that few (if any) metabolites will be lost by replacing culture flasks with bioreactors.  
Figure 8: Comparison of HPLC-ELSD chromatograms of hairy roots exudates of _Sesamum indicum_ (accession Japan) grown in the bioreactor and a flask culture. Culture fluid clarified by centrifugation was directly analyzed by HPLC-ELSD.  
Figure 7: Biomass of hairy roots grown in bioreactors and flask cultures. Hairy root cultures originating from three accessions of each plant species were grown for 30 days and the dry weight of harvested biomass was determined. Asterisks denote significant differences between means (two-tailed \(t\)-test, n = 3, p < 0.05).
# Planning and Scheduling with Uncertainty in the Steel Sector: A Review
## 2 Uncertainty in Scheduling: Definition and Approaches
### Reactive Scheduling  
Techniques based on the reactive scheduling approach are mainly used in those scenarios in which there is not enough information to allow a preventive action which would avoid the uncertainty from occurring. Hence a change in the schedule must be proposed to readapt to the new scenario whenever an unexpected event appears [3]. [PERSON] and [PERSON] [12] propose four main categories to classify dynamic scheduling solutions, one being the robust scheduling, which refers to proactive scheduling approach, and the other three representing the reactive approaches:  
* Completely reactive: Tasks are directly scheduled in real time, applying mainly dispatching rules or heuristics that evaluate the status of the scenario, considering aspects like process priority or processing time.
* Predictive reactive: A basic preventive schedule is generated for a deterministic scenario. According to different rescheduling strategies (on a periodic basis, each time a new job arrives or a disturbance appears) the model can propose modifications to the initial schedule or generate a completely new schedule.
* Robust predictive reactive: In this approach, the reactive schedules proposed whenever an unexpected event appears try to minimize the effect of the disruption upon the initial schedule. This is done by considering not only the schedule efficiency criteria, but also the deviation from the original preventive schedule (stability).  
[PERSON] et al. [6] propose additional criteria to classify these models differentiating between distributed solutions and centralized approaches and according to the usage of priority rules for the reassignment of the tasks after the disturbance: they can be static (they do not depend on the time) or dynamic (they depend on the time and the status of the system).  
In terms of the techniques employed to solve the reactive scheduling problem, the solutions can be sorted by [12]:  
* Dispatching rules and simulation: Dispatching rules for dynamic environments are usually combined with simulation techniques to evaluate and select the best suited rule for the current scenario. Most of the completely reactive approaches rely on this technique.
* Heuristics: They are mainly employed to define the schedule repairing strategies for the initial schedules according to the type of disturbance produced.
* Metaheuristics: In recent years, metaheuristics have developed an increasing presence in the scheduling literature. Some of the most common include: genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colonies (ABC), differential evolution (DE) or tabu search (TS).
* Multi-agents and other Artificial Intelligence techniques: A multi-agent framework is used for distributed approaches where the manufacturing system is split into separate agents that negotiate to achieve global optimal results. Other Artificial Intelligence-based techniques also employed are knowledge-based-systems, neural networks, case-based reasoning.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 8 Conclusions  
Protein is an indispensable constituent of organism and plays a wide spectrum of functions in an organism. Generally, proteins need undergo different types of post-translational modifications for maturation and activation, and glycosylation is one of the most important among them [10]. It is employed mainly in six aspects in the host-pathogen interaction: (I) host glycoproteins separate pathogens from host as barriers; (II) host glycoproteins entrap the pathogen by specific interaction via carbohydrates with adhesins of the pathogen; (III) pathogen glycoproteins act as weapons to attack the host; (IV) hosts sense pathogen glycoproteins to induce resistance; (V) pathogen proteins are glycosylated in the host ER and turn pathogenic; (VI) pathogen proteins can glycoslate host proteins to cause disease (Figure 2). The activities of glycosylation on the host-pathogen interaction are attributed to the glycans that frequently alter the properties of proteins. The spatial structure of carbohydrate residues affects the conformation of proteins and masks specific sites of proteins or the hydrophilic of carbohydrate residues. The protein solubility is subsequently affected, resulting in a change of the protein activity, stability, or antigenicity. The glycans also act as nutrients for microbes or a mimic of cell wall components to directly inhibit pathogens. Investigating the role of protein glycosylation in host-pathogen interaction is significant in basic science since it provides a better understanding of host-pathogen interaction mechanism and its evolutionary inference, while it also sounds in applied science as it can be used to vaccine development, disease diagnosis, and drug therapy.  
**Author Contributions:** [PERSON] and [PERSON] contributed to the conception and design of the layout and prepared the manuscript; [PERSON] and [PERSON] contributed to revising the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by grants from the National Natural Science Foundation of China (31772136, 31972246 and 31601614).  
**Conflicts of Interest:** The authors declare no conflict of interest.