esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 2 Variable Valve Timing (VVT) System
### Electrical VVTT (EVVT)  
The issues associated with HVVT systems discussed above led to the development of EVVT systems, which was also made possible by recent advances in permanent magnetic motor technology and dramatically reduced motor drive cost. Toyota variable valve timing--intelligent electric (VVT-iE), for example, is a variation of dual VVT-i, by replacing the hydraulic cam phaser with an electric cam phaser for the intake camshaft timing [(47)]. The exhaust camshaft timing is still controlled using a hydraulic cam phaser. This technology was first introduced on the 2007 MY Lexus LS 460 as a 1 UR engine [(18)]. In operation, the electric motor in the cam phaser spins with the intake camshaft, running at the same speed to maintain camshaft timing. To advance or retard the camshaft timing, the actuator motor rotates slightly faster or slower, respectively, than the camshaft speed. The speed difference between the actuator motor and camshaft timing is used to operate a mechanism that varies the camshaft timing.  
The performance of an EVVT is less dependent on engine oil temperature and pressure [47], thus providing better control precision and improving engine performance over a wider operational range. The control accuracy and fast response of a VVT system is more critical for advanced combustion such as HCCI, especially for the combustion mode transition control between spark ignition (SI) and HCCI combustion, where the engine cam timing needs to follow a desired trajectory to accurately control the engine charge and recompression process, as articulated by [PERSON] and [PERSON] [48].
## 2 Related Works  
Recent research on performance analysis and optimal design methods for multicopter UAVs has been earnest. Initially, studies on small electric-powered fixed-wing UAVs mainly focused on performance analysis and modeling methods for multicopter platforms, owing to their similar configurations.  
[PERSON] et al. [7] analyzed the modeling of a simple propulsion system consisting of a single motor and a propeller for the design automation of flying wing micro aerial vehicles (MAV). The study shows a typical form of the electric propulsion system model for small UAVs; however, the reliability of the model is not very high, because the dynamics could not sufficiently explain the behavior of the components.  
[PERSON] et al. [8] performed an optimization study of an electric propulsion system for small fixed-wing UAVs. For multicopter platforms, [PERSON] et al. [9] conducted similar studies. Both of the studies presented a multidisciplinary design optimization (MDO) framework for the initial sizing process based on statistical analysis and dynamics of the key performance variables of each component of the electric propulsion system. The two studies highlighted the same problem of low reliability of some statistical data. This is because the sample population for statistics targeted overly generic products, and the correlation between some variables was incorrectly analyzed. In the case of [PERSON] et al., this problem might have been inevitable at the time of the study, because there were not enough commercially available multicopter UAVs.  
[PERSON] et al. [10; 11; 12] presented a more detailed model for fixed-wing aircrafts and demonstrated that the accuracy of the model can be effectively improved by focusing on the detailed characteristics of each component of the electric propulsion system.  
One recent notable study is the Conceptual Layout Optimization for Universal Drone Systems (CLOUDS), a multicopter performance analysis and conceptual design program that was developed by Seoul National University through a joint study with the Korea Aerospace Research Institute [13; 14]. CLOUDS provides performance analysis and conceptual design tools for small multicopter platforms weighing 25 kg or less, based on the blade element momentum theory (BEMT) and electric propulsion system model.  
However, most of the above studies were conducted on small UAVs and did not include factors that should be considered in the high-power operation of large motors, such as armature reaction or thermal effects. Thus, errors are common when comparing the model results with performance data or experimental results in high-power operations.
# A Neural N-Gram-Based Classifier for Chinese Clinical Named Entity Recognition
## 5 Conclusions  
In this study, we propose a neural model based on n-gram character embeddings to learn more semantic information of Chinese characters and address the problem of Chinese clinical named entity recognition. This method avoids relying on external resources and knowledge bases. We conduct the experiments on CCKS-2017 Shared Task 2 dataset with five categories of clinical named entities. The empirical studies show that our approach performs better than other CNN and LSTM based baselines.  
Future work will investigate to obtain a more contextualized representation for named entity recognition. Joint learning trains a single model to handle multiple tasks for the purpose of improving performance on all tasks. We plan to exploit the recognition of EMR sections (general items, medical history, diagnosis and treatment, discharge summary) and jointly train with the Chinese CNER to boost the performance. In addition to the Embeddings and N-gram layers which are used to preprocess Chinese characters, other layers of our approach are expected to be applicable to different languages. Another possible avenue of future work might be to extend the model to other languages in order to maximize the usefulness of our method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Body** & **Exam** & **Disease** & **Symptom** & **Treatment** \\ \hline
1-g & **84.47** & 92.87 & 75.76 & 95.04 & 74.75 \\ \hline
2-g & 84.06 & 93.22 & 75.55 & 95.13 & 73.71 \\ \hline
3-g & 83.39 & 92.99 & 75.13 & 94.82 & 74.27 \\ \hline Our model & 83.99 & **93.82** & **77.89** & **95.23** & **75.88** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed F-score comparison for all entity categories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **P** & **R** & **F** \\ \hline
1-g & 87.88 & 89.98 & 88.92 \\ \hline
2-g & 87.30 & **90.47** & 88.86 \\ \hline
3-g & 87.21 & 89.84 & 88.50 \\ \hline Our model & **88.53** & 90.13 & **89.33** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between different n-gram lengths with our model.  
**Author Contributions:** Supervision, J.-S.J.; methodology, J.-S.J., C.-S.L. and C.-H.L.; investigation, C.-S.L. and C.-H.L.; writing--review and editing, C.-S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Background
### Application Scenario  
The oil and gas industry involves processes such as exploration, extraction, storage and marketing to finally obtain petroleum products [46]. The focus of this paper is on gathering and transmitting the wireless information during oil exploration, extraction, transportation and storage. A scenario of such an application is illustrated in Figure 2. It is shown that sensor nodes can be installed on different facilities to collect different signals. With the help of these sensor nodes, the following tasks can be achieved.  
Figure 2: Application scenario.  
Firstly, the sensors may aid in monitoring the production process, to either detect fault issues or to enhance production [47; 48]. For example, the sensor node on a pump-jack of an oil well can gather the electric parameters, temperature, pressure and payload of this pump-jack. Such parameters are important in the fault detection of a pump-jack. By diagnosing and solving the faults in time, the reduction of oil production can be prevented.  
Secondly, the transportation system for oil or natural gas consists of a complex network of pipelines, and is designed to transport oil or natural gas quickly and efficiently from its origin to areas of high demand. Sensors along the pipeline can be used for state-monitoring of the transportation process. By this means, pipeline leakages can be detected once they happen. The leakage points can be further located with the signals gathered from different sensors along the pipeline. In addition, in order to prevent oil/gas leakage in advance, it is highly desirable to perform pipeline inspection regularly. Mobile sensors are typically required for such situations.  
Finally, one of the key imperatives of the oil storage process is that the safety of the oil depot must be guaranteed. By monitoring the equipment condition and the reservoir real-time states, disasters such as explosions and fires can be prevented. The technical requirements for the deployment of WSNs in oil and gas industries are typically more critical than for those in other fields. According to recent reports [49; 50], these technical requirements include:  
1. [label=(0)]
2. Operate in hostile areas where environmental and platform conditions may be very harsh. Because oil facilities are often installed far away from the cities and towns, it is very possible that no cellular mobile communication services are available.
3. Meet the requirement of temporary sensor installation.
4. Reduce maintenance difficulty with limited battery consumption.
## 3 Simulation and Experimental Validation
### Energy Efficiency of Control Strategies  
In this brief section, a comparative analysis of the classical PID controller and the hybrid PID controllers with decay rates \(\alpha=0.5\), \(\alpha=1\), and \(\alpha=2\) is presented, with a focus on energy consumption. The analysis is based on numerical simulations conducted on the quadrotor model described in the previous section. To this end, the rotor power expression considered is as follows:  
\[P_{i}=k_{p}\cdot u_{i}^{3}, \tag{14}\]  
where \(u_{i}\) represents the angular velocity of rotor \(i\) in radians per second, and \(k_{p}\) is a power coefficient that depends on rotor characteristics and air density. For simplicity, \(k_{p}\) is assumed to be equal to 1 in this study.  
The relative energy savings achieved by the hybrid approaches, in comparison to the classical PID, are summarized in Table 1. Additionally, a graphical comparison of the cumulative energy consumption is provided in Figure 28.  
As expected, the hybrid controller with \(\alpha=0.5\) exhibits the highest energy efficiency as its slower dynamics reduce the effort on the rotors more effectively compared to higher values of \(\alpha\). It is important to note that these results may vary depending on the quality of the tuning of the classical PID controller.  
Figure 28: Cumulative energy consumption for classical PID and hybrid PID controllers with \(\alpha=0.5\), \(\alpha=1\), and \(\alpha=2\), based on numerical simulation results.  
Figure 27: Behavior of the rotor input \(u_{4}\) under the influence of different PID controllers.
## 1 Introduction  
Surveillance and air navigation systems are fundamental for the surveillance of commercial air traffic within their radar coverage. Countries use these processes for the acquisition and implementation of their transportable radar systems.  
In recent years, there has been an exponential increase in air traffic, generating congestion problems, areas without air surveillance, nearby obstacles that generate the duplication of aircraft runways, and a lack of replacement of radar equipment that leads to equipment degradation. This research work answers the following question: What will operational impact will be caused by the loss of stationary radar when an MSSR-S transportable radar is not implemented for Peruvian aeronautical surveillance?  
The importance of implementing a transportable radar arises from the need to cover the gaps that exist in Peruvian air surveillance, which prevent 100% coverage of the airspace or the need for a backup system for Lima airport in emergencies. The Mode S radar system [1] offers high precision, low cost, and great flexibility to adapt to customer needs, complying with the international standards and requirements of a state-of-the-art technological surveillance radar station.  
The implementation of the transportable radar consists of an LVA antenna, which is a fundamental part of the interrogation and acquisition of targets. Reference [2] evaluated the impact of the other determining factors, such as the reflection of the signal and the atmospheric refraction, on the coverage of the radar. Reference [3] found that each existing target will generate several responses from the position that will be obtained, similar to the final one; thus, the objects are called plots, which are displayed on the screen.  
This implementation was executed using the Agile SCRUM methodological framework, ensuring compliance with the international standards for its operationalization and showing a configuration related to the modern architecture of an unmanned radar station, with easy-to-use interfaces that allow for local management and a fully remote nature, optimizing the balance between performance, rapid deployment, reliability, security, and cost-effectiveness.  
The transportability of the elements is ensured to allow for their movement by different methods; therefore, the radar is ready to operate when integrated with the remote team for radar visualization, control, and monitoring functions [4], which must be carried out using the meeting coordination and Scrum team model according to the distribution of the cascading method through three iterations of SPRINT. The success of the project is based on the scope, time, and cost aspects during the execution of the transportable radar.  
The goal of the study is as follows: the implementation of a transportable MSSR-S radar for Peruvian civil aeronautical surveillance to minimize the operational impact and improve air safety throughout the Peruvian territory.
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 7 Discussion
### Lessons Learned  
The AV-Teller framework proved effective in evaluating antivirus detection mechanisms, offering flexibility for experimental setups across various browser and antivirus configurations. Its adaptability facilitates testing under diverse system environments and addressing emerging security challenges. Enhanced real-time data collection and analysis capabilities improved the detection of vulnerabilities and refinement of detection scenarios.  
Notably, minimal browser interactions revealed insight into antivirus system behaviors, exposing inherent weaknesses in client-side security and underscoring the necessity for more robust detection mechanisms to mitigate information leakage risks. Additionally, the observed inconsistency in antivirus performance across scenarios, especially in phishing detection and DOM-based monitoring, indicates current algorithmic limitations in addressing diverse threat vectors. These findings highlight the need for advancements in antivirus algorithms to ensure resilience across a broader spectrum of attack scenarios.  
The scalability of the AV-Teller framework offers substantial potential for further research. Although this study primarily addressed desktop environments, the methods are adaptable to mobile and cloud-based systems. This adaptability establishes AV-Teller as a robust foundation for investigating detection mechanisms across diverse emerging technologies, ensuring sustained relevance in future security evaluations.  
Variations in antivirus performance observed during testing likely stem from inherent differences in detection capabilities and vendor-specific strategic priorities. AV-Teller evaluates three distinct detection vectors--behavioral (DOM manipulation), static (EICAR signature), and heuristic or URL-based (phishing emulation). While certain antivirus solutions demonstrated consistent efficacy across all vectors, others exhibited gaps in specific scenarios. For instance, products without real-time DOM analysis failed to detect Scenario 1, and limited URL filtering hindered the detection of phishing attempts. Such discrepancies reflect architectural and policy-driven differences rather than inherent deficiencies.
## 2 Methodology
### Hilbert-Huang Transform
#### 2.4.2 Hilbert Spectral Analysis (Hilbert Transform)  
After extracting the IMFs via EMD, the Hilbert transform is applied to each IMF to extract instantaneous frequency and amplitude information [47; 48; 49].  
Given a real-valued signal \(x(t)\), its analytic representation is defined as:  
\[\hat{x}(t)=x(t)+j\hat{x}(t) \tag{6}\]  
where \(\hat{x}(t)\) is the Hilbert transform of \(x(t)\):  
\[\hat{x}(t)=\frac{1}{\pi}\,\mathcal{P}\int_{-\infty}^{\infty}\frac{x(\tau)}{t- \tau}\,d\tau \tag{7}\]  
and \(\mathcal{P}\) denotes the Cauchy principal value.  
From the analytic signal \(\hat{x}(t)\), the following quantities are derived:  
Instantaneous amplitude:  
\[A(t)=|\hat{x}(t)|=\sqrt{x^{2}(t)+\hat{x}^{2}(t)} \tag{8}\]  
Instantaneous phase:  
\[\phi(t)=\tan^{-1}\biggl{(}\frac{\hat{x}(t)}{x(t)}\biggr{)} \tag{9}\]  
Instantaneous frequency:  
\[f(t)=\frac{1}{2\pi}\frac{d\phi(t)}{dt} \tag{10}\]  
These quantities provide a high-resolution time-frequency representation of the original signal, enabling the identification of time-localized spectral features.
## 2 Materials and Methods
### In Silico Evaluation  
Docking studies and molecular dynamics simulations were conducted using a comprehensive in silico evaluation to determine the binding energy and stability of the interaction between forchlorferuron (FCF) and the gibberellin (GA3 Ox2) and auxin (IAA7) receptors.  
The 3D structures of the ligands were obtained from two publicly accessible databases. The PubChem database provided the ligand molecules: forchlorferneuron (FCF, ID: 93379), indole-3-acetic acid (IAA, ID: 802), and gibberellin 3A (GA3, ID: 6466), available at [[https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)]([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)), accessed on 13 April 2023 [16].  
Protein structures were sourced from the Protein Data Bank (PDB), specifically the auxin receptor (IAA7, ID: 2 PIN) and gibberellin receptor (GA3 Ox2, ID: 7 EKD), accessible at [[https://www.rcsb.org/](https://www.rcsb.org/)]([https://www.rcsb.org/](https://www.rcsb.org/)), accessed on 2 May 2023 [17].  
Several software tools facilitated this analysis: Avogadro version 2.0 (2022) was used to visualize and optimize ligand molecules [18]. Chimera v.38.43.1129 was utilized to visualize and refine receptor proteins [19]. AutoDock Vina v.1.1.2 calculated the binding affinity between receptors and ligands [20,21]. After docking, the resulting data were visualized and analyzed with AutoDockTools v.4.2.6 [22]. The best docking configuration was then used in molecular dynamics simulations via Gromacs v.2022.4. Notably, all software used in this process is publicly accessible [23].  
Molecular dynamic inputs were generated using the CHARMM-GUI v.3.8 server, accessible at [[https://charmm-gui.org/](https://charmm-gui.org/)]([https://charmm-gui.org/](https://charmm-gui.org/)), accessed on 18 January 2024 [24]. The simulation box was cubic, encompassing the entire protein, and the system was neutralized with a 0.15 M concentration of KCl and NaCl ions. Following this, all systems underwent an NVT (constant number of particles, volume, and temperature) equilibration phase for 125,000 steps. Molecular dynamics simulations were conducted over 100 ns using the CHARMM36m force field, a widely used force field in biomolecular simulations due to its accuracy and reliability. Gromacs utilities evaluated parameters such as the root mean square deviation (RMSD) of the complexes, radius of gyration (RG), fluctuation (RMSF), and hydrogen bonding. The resulting data were plotted using the GRACE program.
# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 1 Introduction
### Novel Contribution  
In this work, we propose a novel approach that complements a NN architecture with Granular Computing techniques. To the best of our knowledge, this is the first study to integrate these two paradigms for time series prediction and classification, specifically within the context of GH microclimate analysis. While previous studies have explored the potential of Adaptive Neuro-Fuzzy Inference Systems (ANFISs) for modeling nonlinear systems and reducing computational complexity [15], as well as hybrid ANFIS frameworks for processing agricultural data [16], our approach differs both methodologically and structurally. In contrast with standard ANFIS models, our framework applies fuzzy logic in a post-processing cascade, directly to the outputs of the NN. This layered integration allows us to exploit the representational power of NNs in the first stage and then refine the outputs through granular fuzzy reasoning, improving their interpretability and robustness against uncertainty in environmental time series data. This structure offers to two key advantages:  
* It reduces the computational complexity of the system while maintaining the same accuracy performance as standard NN models.
* It enhances the interpretability of classification results for farmers by providing an alert to the user when the system's output has low reliability.  
Our contribution is based on real datasets containing temperature and humidity values collected from four different GHs. Additionally, we propose a network scenario that enables the practical application of the obtained results. This scenario also facilitates the deployment of distributed NN models, both directly on GH installed sensors where complexity reduction techniques are applied for low-latency prediction and actuation and at the network edge, where higher computational power allows for the execution of a more complex NN model that maximizes decision accuracy. This design represents a novel and unexplored direction in the domain of smart agriculture, opening new opportunities for hybrid intelligent systems that combine data-driven learning with human-interpretable reasoning.