esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Circuit Analysis and Operation Modes  
Figure 8 illustrates the schematic voltage and current waveforms of some key components. It can be inspected that both the PFC inductor current, \(i_{LPFC}\), and the resonant current, \(i_{r}\), flow through the power switch \(S_{2}\). When the input square voltage crosses zero, the resonant current becomes zero as well.  
Figure 7: Starter circuit.  
The input voltage of the resonant circuit is a square wave, and can be described by the Fourier expansion.  
\[v_{AB}(t)=\sum_{n}\left[\frac{2V_{cbus}}{n\pi}\sin(2n\,\pi\,f_{s}t)\right]\qquad n =1,\,3,\,5\ldots \tag{20}\]  
When the quality factor of the resonant circuit is high enough, almost all the harmonic contents of \(v_{AB}\) will be filtered out by the resonant circuit. Only the fundamental current at the switching frequency is present in the load resonant converter [22]. At steady-state operation, the voltage across the secondary winding is equal to the output voltage, and the load (including the output capacitance \(Co\)) is fed by a rectified sinusoidal current source. The impedance across the secondary winding is equal to the resistance of the LED string (\(R_{LED}\)). Figure 9 shows the equivalent circuit of the resonant circuit, where \(v_{1}\) represents the fundamental voltage of \(v_{AB}\). From Equation (20), the root-mean-square (rms) value of the fundamental voltage is equal to:  
\[V_{1}=\frac{\sqrt{2}V_{cbus}}{\pi} \tag{21}\]  
The impedance of the resonant circuit can be expressed as:  
\[Z_{AB}=R_{p}+j\bigg{(}2\pi f_{s}L_{r}-\frac{1}{2\pi f_{s}C_{r}}\bigg{)} \tag{22}\]  
Under this circumstance, the resonant current is sinusoidal, and can be expressed as:  
\[i_{r}(t)=\frac{2V_{cbus}}{\pi Z_{AB}}\sin(2\,\pi\,f_{s}t) \tag{23}\]  
From Equation (23), the resonant current would theoretically be zero in the case of failure and the load would go open circuit. However, in practical terms, a small current is there if the mutual inductance of the transformer is considered. On the contrary, in the case of failure where the load goes short circuit, the primary winding and the excited windings will all become short-circuited. Thus, both power switches will not be energized and the circuit will remain turned off. Besides, the LED current is not regulated in the proposed circuit since a self-excited control mechanism is used.  
Figure 8: Schematic voltage and current waveforms of some key components.  
At a steady state, the circuit operation can be divided into three modes in every high-frequency cycle. The current loops of each operation mode are shown in Figure 10, where \(v_{rec}\) represents the rectified voltage of the input line. As soon as the lower-arm power switch \(S_{2}\) starts successfully, the circuit operation initiates. The operating modes are described below.  
Figure 10: Equivalent circuit in different operation modes: (**a**) Mode I, (**b**) Mode II, and (**c**) Mode III.  
Figure 9: Equivalent circuit to the load resonant circuit.
## 3 Taxonomy and Essential Oil Production of Thyme
### Authenticity of Thyme Essential Oil
#### 3.4.2 Regulations and Standards for Thyme Essential Oil  
The methods discussed above are the best way to confirm the authenticity, detect any adulteration, or identify contamination in thyme essential oil. These can be ensured through regulations and standards governing the production and marketing of essential oils from the main industrial thyme species.  
The International Organization for Standardization (ISO)  
The ISO, recognized for its global relevance in standards, specifies the physical requirements of essential oil extracted from _Thymus vulgaris_ and _Thymus zygis_ (thymol type). It is required to have a mobile liquid appearance, with a color ranging from yellow to red, and possess an aromatic and phenolic odor with a spicy base. The required chemical profile is illustrated in Table 1, which shows the minimum and maximum percentages of active compounds in thyme. Specifically, the thymol content is specified to be a minimum of 35% and a maximum of 55% (ISO 19817:2017) (2018).  
The European Pharmacopeia (Ph. Eur)  
The Ph. Eur (11.0), recognized as a legal reference for standards and quality control of pharmaceutical products, also establishes standards for thyme products. _Thymi herba_, which refers to the leaves and flower parts separated from the dried stems of _Thymus vulgaris_ L. and _Thymus zygis_ L., are required to contain a minimum of 12 mL/kg of essential oil (anhydrous drug), of which 40% should comprise the sum of thymol and carvacrol contents (0865 (07/2014)) [78]. For _Serphylli herba_ (_Thymus serphyllum_ L.), known as wild thyme, a minimum of 3.0 mL/kg of essential oil (dried drug) is set as the standard requirement (1891 (07/2014)) [78].  
The Ph. Eur also sets standards for the content of active compounds for _Thymi typo thymolo aetheroleum_, which describes the essential oil obtained from the fresh flowering aerial parts of _Thymus vulgaris_ L. and _Thymus zygis_. The required chemical profile for Ph. Eur. 11.0 is presented in Table 1, where minimum standards slightly differ from those set by ISO 19817:2017 [78]. Specifically, the minimum thymol content is determined at 35% for ISO (19817:2017), whereas it is 37% for Ph. Eur (11.0).
## 3 Results and Discussion
### Stability and Reusability of Catalyst  
The reusage of catalyst is highly preferable for reducing cost of the production and potential environmental pollution. The PEK-C-ILs catalyst could be fully separated from hydrostate solution by filtration for recycling. After drying under vacuum at 50 \({}^{\circ}\)C, it was subjected to the next new hydrolysis solutions. After used five times, the catalyst powder was washed with deionized water. Then it was used for next five rounds of hydrolysis experiment. The catalytic performance of the recycled PEK-C-ILs was shown in Figure 12. After the first and fifth rounds of hydrolysis, the TRS yields were 99.7% and 94.5%, respectively. The results indicated that the catalyst could be reused five times with slightly decrease of 5.2% in catalytic activity. The reason for the decrease of the TRS seems to be primarily due to the active sites covered by reducing sugar in the catalysis process and a little shrinkage which resulted from oven dry after each hydrolysis. The recycled PEK-C-ILs powder after five rounds of use was washed with deionized water. After PEK-C-ILs powder dried, the hydrolysis reaction was conducted in the same conditions. Its catalytic activity recovered by 1.7%. After the sixth and tenth rounds of hydrolysis, the TRS yields were 96.2% and 91.5%, respectively. Compared with the original PEK-C-ILs powder catalyst, the PEK-C-ILs catalyst after 10 rounds hydrolysis showed only 8.2% loss of catalytic activity (Figure 12).  
These results were consistent with the FT-IR and XPS analysis (showed in Supplemental Material). The FT-IR and XPS spectrum indicated that the PEK-C-ILs catalyst after 10 rounds was very stable and without obvious change in chemical structure after the catalysis operation. So, it was clearly that the ILs ([SO\({}_{3}\)H-PMIM][HSO\({}_{4}\)]) catalysts was firmly immobilized on the polymer PEK-C material. Therefore, it can be concluded that the PEK-C-ILs was efficient, stable and recyclable powder catalyst for the hydrolysis process of inulin.  
Figure 11: Reducing sugars yield surfaces as (**a**) a function of temperature and process time at 4.0% PEK-C-ILs concentration, (**b**) a function of PEK-C-ILs concentration and process time at 65 \({}^{\circ}\)C. Inulin concentration: 20 (\(w/w\))%.
## 4 Results
### Error Characteristics
In particular, the tendency of the CNN model to overestimate was more pronounced as the visibility decreased, and a visibility of lower than 2 km showed a positive bias of 0.037 MJ m\({}^{-2}\) and a high nRMSE of 0.404. As the visibility increased, the RMSE increased, and at a visibility of more than 20 km, the RMSE and nRMSE were 0.198 MJ m\({}^{-2}\) and 0.158, respectively. In situ daylight refers to the amount of time during which direct solar radiation arrives at the station over the course of an hour; a daylight of 0.5 means that there is direct solar radiation incident to the station for 30 min or 0.5 h. In the terms of in situ daylight, as the daylight increased, the bias and nRMSE decreased (Figure 10c). In high-daylight conditions of more than 0.8 h, the model tended to underestimate, and its bias was less than \(-\)0.02 MJ m\({}^{-2}\). The nRMSE was0.437 at a low daylight of 0 h and 0.092 at a high daylight of 1 h. The in situ cloud amount (unitless variable) indicates the fraction of the sky covered by clouds over the regions around the station; a cloud amount of 5 specifies that half of the sky is covered by clouds. As the cloud amount increased, the RMSE increased, although for specific cloud amounts of more than 9 the nRMSE clearly increased (Figure 10d). As the cloud amount increases, the proportion of direct SSI and scattered SSI in the global SSI generally decreases and increases, respectively, depending on cloud distribution. Conversely, satellites estimate the SSI by calculating the degree of attenuation of the ESR by atmospheric elements, including clouds and aerosols, in the corresponding pixel. Thus, the accuracy of the GK2A/AMI-derived SSI decreases when the proportion of scattered radiation increases due to high-cloud-amount conditions [48,49]. In high-cloud-amount conditions of more than 9, however, the RMSE and nRMSE decreased and increased, respectively. This was not because the accuracy of the CNN model increased, but because the amount of SSI decreased. The accuracy had an RMSE of 0.141 MJ m\({}^{-2}\) and an nRMSE of 0.335 at a high cloud amount of 10. Figure 9: Variation of accuracy by comparison between GK2A/AMI-derived SSI estimates using the CNN model as the reference model and in situ SSI from ASOS stations operated by KMA with respect to (**a**) observation local time, (**b**) observation month, and (**c**) solar zenith angle; blue, green, and red lines represent RMSE, bias, and nRMSE, respectively, and the gray bars denote the number of matchups.
## 2 Materials and Methods
### Classification Methods
#### 2.2.2 Decision Tree Classifier  
The decision tree (DT) classifier [7] is a versatile algorithm that structures a dataset into a tree-like model based on various features. This hierarchical approach allows the data to be split at multiple levels, enabling the efficient handling of large volumes of information. The decision tree method involves constructing nodes and branches that represent different features and their corresponding test cases. Each node in the tree evaluates a specific feature, with branches representing the possible outcomes of the test. This recursive process continues for each subsequent subtree. Decision trees utilize metrics such as information gain to determine the optimal way to split nodes, enhancing the model's effectiveness for both regression and classification tasks.
# Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
## 2 Methodology
### Importance and Choice of Personality Traits  
The university LMS contained several tables that each provided different information. We checked each table's appropriateness in modelling any of the OCEAN traits. The Forums and Logins Tables contained logs with details about student interaction, and were, thus, chosen as primary tables from which to source behavioural information for our proxies for Conscientiousness and Extraversion. By comparison, Openness, Agreeableness and Neuroticism were more complex to model, given the available data and the lack of validation of a link to academic performance within the literature.  
Our data linked closest to the dutifulness facet of Conscientiousness and the gregariousness facet of Extraversion. Alternative formulations of each trait were considered and are described in Section 5.1. We used quantitative proxies to model Conscientiousness (Dutifulness) and Extraversion (Gregariousness).
## 5 The Driving Mechanism and Key Driving Factors of Green Residences in China
### Regression Analysis  
Through State 16.0 software, this paper employed the panel Tobit model to quantitatively study the driving effect of the above factors on the G\({}^{\prime}\)-score, and further adopted the average marginal effect value to quantitatively analyze the influence of each unit change of a factor on G\({}^{\prime}\)-score under the condition that other factors are relatively unchanged, according to Equations (7)-(9). Table 5 reports the results obtained from the empirical estimations. The LR test is used to judge whether the model has individual effects. Since the \(p\) values of all the LR test results were zero, this indicated that the null hypothesis, \"H\({}_{0}\) : \(\sigma_{\text{u}}=0\)\", was strongly rejected. Hence, the model had significant individual effects. Because this paper could not get consistent fixed effect estimators whether using fixed effects or mixed Tobit regression (45), the random effects panel Tobit regression model was selected. The specific analysis of each influencing factor is as follows. First, the columns one and two present the coefficients and average marginal effect values of G\({}^{\prime}\)-score regression results for one-star green residences, respectively. Of the 20 influencing factors (excluding climatic conditions), only 11 had a value greater than zero and played positive roles in promoting the development of green residences. The three factors with the greatest positive impact were GDP, GE, and NEIP. With other factors remaining unchanged, for each unit change in them, the corresponding G\({}^{\prime}\)-scores changed by 2.69, 1.53, and 1.27%, respectively. This showed that the development level of one-star green residences in cities with a relatively developed economy, introduced standards, and noneconomic incentive policy was higher than in other cities. On the contrary, for each unit of change in PPEU and PRP, the G\({}^{\prime}\)-scores changed by \(-5.11\) and \(-2.63\%\), respectively.
# Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
###### Abstract
Locating these indicative fish parts allows the study of the color and texture of the fish, and these properties can be used as health indicators. This facility is not offered in other approaches, or at least not in the referenced ones. Two approaches are combined for estimating the aforementioned morphological parameters from photographs that have been captured using a single camera: (1) image processing based on novel edge detection, pattern matching and shape rotation techniques and (2) deep learning techniques (mask region-CNN, GrabCut [24]) that lead to the estimation of eight landmarks. The results of these approaches differ in their speed and the achieved accuracy, depending on the conditions of the photograph (e.g., background complexity, light exposure and existence of multiple fish), but may operate in a complementary way, as will be described in the following sections. Using the (relative) fish length as a comparison metric, which is also measured in many referenced approaches, it can be stated that the employed image processing techniques can achieve an average error of 4.9%. Deep learning techniques can be used in fish part localization but cannot achieve a high accuracy in measuring the dimensions. This is owed to the small dataset used. Using a larger dataset can also reduce the error in the estimation of the fish dimensions with deep learning methods. The latency of the proposed processing methods ranges from 2 to 50 s or more, depending on the employed configuration. The attributes of the contour, the landmark distances and the color or texture of the located fish parts can be used to extend the developed application for the recognition of fish species in the wild. The contribution of this work can be summarized as follows: (1) the combination of both image processing and deep learning techniques to increase the accuracy in a diverse set of conditions, (2) new facilities being offered such as coloring the recognized fish parts, (3) being appropriate for several application domains and mobile platforms, (4) novel techniques being employed for edge and corner detection, noise filtering, background complexity detection and pattern stretching and (5) detailed extensions being proposed to estimate the absolute dimensions, correct fish rotation and slant and to detect obscured fish. The dataset, tools, employed image processing and deep learning methods and how the absolute dimensions can be measured are described in Section 2. The experimental results are presented in Section 3. The conclusions and future work are described in Section 4.
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 2 Related Work
proposed a model to describe the behavior of the platoon. Based on the analysis results, the authors proposed an efficient control algorithm for the platoon based vehicular cyber-physical systems. In [22], [PERSON] et al. proposed a TripSense scheme in a platoon trust-based system to perceive and process vehicles' data. The data are collected by the member vehicles and aggregated by the head vehicle before they are transmitted to a server. In [23], [PERSON] et al. proposed a beacon-based medium access control (MAC) protocol in a platoon. The simulations have demonstrated that the proposed protocol can enhance reliability and ensure the low-delay transmission for safety applications in intelligent traffic system (ITS). From the related works mentioned above, we can see few works have considered the multi-platoon scenario while no studies have been conducted to optimize the one-hop delay of inter-platoon communications. A long and unbalanced one-hop delay of inter-platoon communication may affect the end-to-end delay and thus may pose a potential threat to safe driving, especially for a highway scenario. To solve this problem, we propose a swarming approach to reduce and balance the one-hop delay in inter-platoon communications.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis  
The measurements were continuously taken over a 24 h period and divided into daylight hours (06:30-18:40) and night hours (18:40-6:30). The conditions during the  
Figure 8: Total bytes uploaded are recorded to determine the bandwidth consumed over the same testing period.  
Figure 7: (**a**) The Jetson Nano enclosure which houses all the electrical components. (**b**) RealSense camera installation location. (**c**) Platform view of the installation site.  
measurements included clear skies, overcast and heavy precipitation. The measurement period was scattered over a few weeks to allow the system to be tested under diverse weather conditions.  
During the initial stage of data collection, it was found that some of the images captured by the sensor were affected by sun glare. The issue was tackled by applying polarising filters on the stereo imagers to eliminate the reflection from the water surface. The results presented were collected from the setup with the polarising filter applied.  
The testing results were compiled and analysed from two different angles. Firstly, the overall results (Figures 9-12) were collectively analysed to understand the measurement performance during the daylight and night hours. The results in the graphs are segmented into day and night periods separated by the dashed line. Secondly, a separate analysis was performed on different scenarios to understand the effect of weather conditions on the measurement performance. Four sets of results in the form of graphs are presented in Figures 9-12 where different weather conditions are depicted in different colour shades accordingly. The variation in weather provided a natural and automatic diversification
## 2 Related Work
It uses operators such as selection, crossover, and mutation to evolve a population of solutions towards an optimal solution. Hybridization of GA with other algorithms can enhance convergence speed and solution quality by combining exploration (global search) and exploitation (local refinement) capabilities. In the same way, FA [29] is inspired by the flashing behavior of fireflies, where brighter fireflies attract others. The brightness is associated with the fitness function, and the movement of less bright fireflies toward brighter ones represents the search for optimal solutions. Similarly, LFA [30] is a variation in the Firefly Algorithm that incorporates the Levy flight distribution, allowing for more random, long-distance movements in the search space. This enhances the exploration ability of FA, making it less likely to become trapped in local optima, especially in complex optimization problems. Meanwhile, the BFA [31] is inspired by the foraging behavior of _E. coli_ bacteria, where bacteria \"swim\" or \"tumble\" in search of nutrients (optimal solutions). It involves chemotaxis, reproduction, and elimination-dispersal processes. Moreover, BPSO [32] is a variant of the Particle Swarm Optimization (BPSO) adapted for binary search spaces. It is used for discrete optimization problems where variables take binary values. Hybridizing BPSO with other metaheuristics like EHO enhances the exploration of the solution space while maintaining robustness in finding optimal or near-optimal solutions. EHO is a nature-inspired optimization algorithm based on the social behavior of elephant herds. It uses clan updating and separating operators to optimize solutions. When hybridized with algorithms like [28; 29; 30; 31; 32], the EHO [7] can enhance the exploration-exploitation balance and improve global search capabilities, leading to faster and more accurate convergence. It is worth noting that each of these algorithms can be hybridized with Elephant Herding Optimization (EHO) to combine the strengths of various approaches, leveraging the global search capabilities of one method with the local refinement potential of another for more effective optimization.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 2 Problem Formulation and Tracking Strategy
### Guidance Method in Pursuit-Evasion Games  
Suppose at time \(k\) that the pursuer \(P(x^{k},y^{k})\) has perceived the escaping target \(E(x^{k}_{E},y^{k}_{E})\), and the line \(l\) formed by the pursuer pointing to the escaping target \(E\) is called the line of sight. The angle formed by the line of sight and the \(x\)-axis is the line of sight angle. The angle between the target speed \(V_{E}\) and \(l\) is \(\psi_{E}\), and the angle between the pursuer speed and \(l\) is \(\psi_{P}\). The angle between the line connecting \(P\) and the virtual hunting point \(E^{\prime}\), and the \(x\)-axis is the desired heading angle \(\psi_{d}\). A schematic diagram of the guidance method is shown in Figure 1.  
The relative motion equation of the pursuer and the evader is as follows:  
\[\left\{\begin{array}{l}\dot{l}=V_{P}\cos\psi_{p}-V_{E}\cos\psi_{E}\\ l\dot{\theta}=V_{P}\sin\psi_{p}-V_{E}\sin\psi_{E}\end{array}\right. \tag{1}\]  
The normal speed of the pursuer needs to be equal to the escaping target, that is \(\dot{\theta}=0\); substituting it into the Formula (1), we have the following:  
\[V_{P}\sin\psi_{p}=V_{E}\sin\psi_{E} \tag{2}\]  
According to the geometric relationship, the desired heading angle is as follows:  
\[\psi_{d}=\sin^{-1}(\frac{V_{E}}{V_{P}}\sin\psi_{E})+\theta \tag{3}\]