esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Temperature on Enzymatic Transesterification (Third Stage)  
The temperature was varied to 35 \({}^{\circ}\)C, 38 \({}^{\circ}\)C and 40 \({}^{\circ}\)C in order to analyze its effects on biodiesel production. As shown in Figure 8, highest biodiesel yield was reached at reaction temperature of 38 \({}^{\circ}\)C followed by 35 \({}^{\circ}\)C. The reaction rate increases with temperature due to the reduction of viscosity of the oil that increases its solubility in methanol. However, decrease in yields is observed after a defined temperature suggesting the deactivation of enzymes [68]. The statistical analysis reported \(p\)-value  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **[PERSON] et al. [29]** & **[PERSON] et al. [8]** & **[PERSON] et al. [8]** & **This Work** \\ \hline Oil type & Seaweed oil & WFO & WFO \\ \hline Catalyst & Enzymatic & Enzymatic & Alkaline & Enzymatic \\ \hline \multirow{4}{*}{Conditions} & 10\% of lipase & 5\% of lipase & \multirow{2}{*}{1\% of KOH} & 14\% of lipase enzyme \\  & enzyme XX split & enzyme XX 25 split & & XX 25 split \\ \cline{1-1}  & Oil-alcohol 1:3 & Oil-Alcohol 1:3 & Oil-Alcohol 1:6 & Oil-Alcohol 1:3 \\ \cline{1-1}  & 33 \({}^{\circ}\)C & 38 \({}^{\circ}\)C & 60 \({}^{\circ}\)C & 38 \({}^{\circ}\)C \\ \cline{1-1}  & 6 h & 3 h & 70 min & 8 h \\ \hline Yield & 85.49\% & 50\% & 89.40\% & 87.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Transesterification yield of different researches.  
Figure 7: Effect of pH of the reaction media on biodiesel yield. Values are mean +/– standard deviation (n = 2).  
of 0.24 (>0.05) indicating that treatments with temperature variations are not statistically significant. Similar trend was reported by [PERSON] et al. [69] for the production of biodiesel from waste sunflower frying oil. They found an increase in conversion for reaction temperature between 25-45 \({}^{\circ}\)C and a decrease in conversion after 55 \({}^{\circ}\)C.  
Table 8 summarizes the biodiesel fatty acid compositions for additional reaction temperatures considered in this assessment (35 \({}^{\circ}\)C and 40 \({}^{\circ}\)C) while FAMEs composition of biodiesel at reaction temperature of 38 \({}^{\circ}\)C was previously reported in Section 3.2.2. The composition of methyl palmit decreased from 25.4 to 26.4 when increasing temperature from 35 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. At 38 \({}^{\circ}\)C, the methyl palmit composition was 20.25% suggesting a minimum on the transformation of triglycerides on this ester. The methyl oleate showed a composition varying from 17.40 to 20.30 for 35 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, respectively. At 38 \({}^{\circ}\)C, this ester reached a composition of 17.91 suggesting that temperature contributes to increase methyl oleate production.
## 4 Performance Evaluation
### Impact of Transmission Rate from the MEC Server to the User  
Figure 7 illustrates the relationship between the user access probability and the delay violation probability of the accessed users under the proposed multiuser access control algorithm, given different bandwidths of the MEC server-connected BS \(B_{\text{MEC}}\). When the total wireless transmission bandwidth increases, the data transmission rate per user rises and the delay decreases, allowing the system to serve more users within the delay violation probability threshold. Comparing the two scenarios, \(B_{\text{MEC}}\) = 50 and 100 MHz, an interesting phenomenon can be observed: when wireless bandwidth resources are more abundant, the proposed algorithm is able to serve a larger number of users while still ensuring acceptable delay violation probabilities, albeit with a slight increase in the violation probability.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 5 Performance Evaluation  
### Simulation Environment  
In order to test our presented schema, we adopted the MATLAB with the Chinese version of R2016a simulation environment. Some relevant parameters are shown as Table 1.
# Essential Features in a Theory of Context for Enabling Artificial General Intelligence
## 3 Research in Context-Rich Artificial Intelligence
### Commonsense Reasoning and Knowledge  
Commonsense reasoning involves processing information about a scenario in the world, and making inferences and decisions by using not only the 'explicit' information available to the senses and conscious mind, but also context and implicit information that is based on our \"commonsense knowledge\" [55,56]. Commonsense knowledge is difficult to define precisely but it may be assumed to be a broad body of knowledge of how the world works. While interesting also to cognitive scientists and philosophers, commonsense reasoning is intriguing to specialists in AI because it has also revealed potential pitfalls in the manner in which such reasoning systems are traditionally evaluated, i.e., using one or more multiple choice question answering or QA benchmarks [57]. Two assumptions have come under attack in the AI community in the last two years: first, that multiple choice questions are adequate for assessing commonsense reasoning capabilities, and second, that simple 'one-off' question answering itself is enough to evaluate commonsense reasoning, as opposed to other tasks such as storytelling and abstractive summarization [58] that are more context-rich, and also seem to rely heavily on commonsense knowledge in the real world.  
To address some of these concerns, generative QA tasks have already been proposed and are slated to grow in popularity [59]. A critical issue that the community is looking at is a reliable mode of evaluating system performance on such tasks. Because generative QA can result in open-ended answers, more innovation is needed on how to evaluate the \"goodness\" of a system's answers without always relying on human annotation that is available on-demand, such as in a crowdsourcing framework.  
True commonsense reasoning in machines requires a theory of context, especially for enabling some of the example usages we described in Section 2. Recent work has attempted to build a theory of commonsense [60], which could potentially be used for deriving a theory of context. However, recent work has shown that the theory may have to be supplemented with additional terms, since it does not always definitively map to human annotations [61]. Because commonsense includes both intuitive physics and intuitive psychology, advances in machine commonsense will likely have to draw on a broad and holistic definition of context, including understanding how events relate to one another, relational dependencies between objects and events, and appropriate framing of goals and assumptions given a task as input. We discuss some these features in more detail in Section 4.
###### Abstract  
In some high-performance applications, an LC filter must be added to the auxiliary resonant snubber inverter (ARSI) to reduce the output current ripple. However, resonance occurs due to the additional LC filter, which makes the traditional closed-loop control not suitable to be used directly. Therefore, this paper presents a double-loop digital control based on the active damping method of capacitor current feedback to stabilize the system. Most of the studies on active damping methods are focused on the grid in consideration of zero resistance. However, the load resistance should not be neglected in the drive system. Therefore, the load resistance and digital control delays are considered in this paper. Moreover, an improved loading method is proposed to improve the duty ratio range. In order to verify the effectiveness of the controller, a prototype was developed. The simulation and experimental results demonstrate that soft-switching can be realized for the entire load range. The maximum duty ratio is improved by 0.01 by using the proposed loading method. The resonance can be eliminated by using the proposed control method.  
auxiliary resonant snubber inverter (ARSI); zero-voltage switching (ZVS); LC filter; active damping; delays +
Footnote †: journal: Applied Sciences
## 1 Introduction
A 1% increase in RES is associated with a reduction in CO\({}_{2}\) emissions between \(-\)0.128% and \(-\)0.197%. These results support the assumptions of Directive 2009/28/EC and the Paris Agreement. The panel data random-effects regression model of another paper published by [PERSON] [25] reveals that the RES has a positive impact on economic growth within the EU-28, with biomass having the largest impact on economic growth. A 1% increase in biomass primary production would affect economic growth by 0.15%. Since R-squared is 0.2456, the econometric analysis shows that 24.56% of the variance of economic growth (dependent variable) is determined by RES (independent variable), and 75.46% of the variance of the endogenous variable is still determined by other factors. Previous research developed by [PERSON] and [PERSON] [26] from a panel model for the EU-16 confirms that GHG emissions and GDP have a U-shaped relationship and highlights that regulations in the EU have not really improved efforts to reduce emissions. Policy measures should be decoupled from economic growth and implemented immediately. The model  
Figure 1: U-patterned environmental Kuznets curve (EKC). with a significance level of 0.05 shows that the RES halves GHG emissions compared to fossil fuels. Per capita environmental footprint and CO2 emissions are considered in another paper published by [PERSON] and [PERSON] [27] that evaluates the EKC hypothesis with a data set of 14 EU countries. Three statistical models are adopted, and the findings at a 1% significance show that the U-shaped EKC perfectly describes the relationship between CO2 emissions and growth in Europe. Sound policies are required to accelerate the transition to RES and to reduce the environmental footprint. One study developed by [PERSON] and [PERSON] [28] models the nexus between population growth and CO2 emissions in Asia, where 60% of the world's population lives. The degree of proximity between the two curves shows values between 0.7299 and 0.9208, underlining the relationship and importance of CO2 emissions and population growth. All the studies mentioned emphasise the solid relationship between economic growth, GHG emissions and RES during the last decades and unanimously ask for stronger and more targeted policies adoption. However, none of them uses the data to estimate different future scenarios, which is the ultimate objective of this paper. In fact, this study aims to forecast GHG emissions in the coming decades at the country or regional level by means of a multifactor model based on national statistical data of (1) GDP, (2) population and (3) RES.
## 2 Materials and Methods
### Body Composition Analysis  
Body composition was measured in all participants at the beginning of the experiment. The bioelectric impedance analysis (BIA) was performed using an InBodyS10 (BioSpace; Seoul, Republic of Korea) device. The electricity parameters during the bioimpedance measurement were as follows: Impedance Z (\(\Omega\)): 1 kHz, 5 kHz, 50 kHz, 250 kHz, 500 kHz, and 1 MHz; Reactance X (\(\Omega\)): 5 kHz, 50 kHz, and 250 kHz; Whole Body Phase Angle O (\({}^{\circ}\)): 50 kHz. BIA measures the electrical properties of body tissues and allows us to estimating body composition parameters. The BIA assessment of body composition was based on the volume of body fluids and the BIA resistance value. Bioelectrical impedance is defined as the opposition of a conductor to the flow of alternating electric current applied to it. The bioimpedance varies depending on the composition of the tissue as well as the frequency of the applied current. BIA can be performed by applying electric current of different frequencies simultaneously. At low frequencies (1-5 kHz), the electric current does not penetrate the cell membrane, so it is assumed to flow through the extracellular fluid. Conversely, at higher frequencies (\(>\)50 kHz), the current flows across cell membranes and is associated with both intracellular and extracellular fluid compartments.  
Before starting the body composition measurement, the height and body mass of the participants were measured. The height and weight of the participants were measured using a Momert 5967 electronic column scales with a height rod (Hungary), with an accuracy of d = 100 g and maximum capacity = 200 kg. The subjects had not consumed a meal for at least 4 h prior to the examination. Immediately before the measurement, participants were asked to empty their bladder and then remained supine for 15 min. After this time, electrodes were placed to the thumb and middle finger of the right and left hands and the ankles of the right and left lower limbs, and body composition was measured.  
Measured BIA parameters included fat mass (FM, kg), percent body fat (PBF, %), visceral fat area (VFA, cm\({}^{2}\)), soft lean mass (SLM, kg), fat-free mass (FFM, kg), skeletal muscle mass (SMM, kg), body cell mass (BCM, kg), protein (kg), minerals (kg), bone mineral contents (BMC, kg), intracellular water (IW, I), extracellular water (EW, I), total body water (TBW, I), extracellular water/total body water (EW/TBW). Six weeks after the first measurement, all participants underwent the body composition analysis again.
###### Abstract  
In this paper, the all-solid dual-concentric-core microstructure fiber (MSF) with ultra-broadband dispersion compensation characteristics is designed. The effects of microstructure fiber structure parameters on dispersion, phase-matching wavelength, and kappa value are analyzed by the multi-pole method and mode coupling theory. The average dispersion compensation multiple is 18.45, that is, 1 km long dispersion compensated MSF can compensate for the cumulative dispersion of standard single-mode fiber of 18.45 km in the wavelength range of 1385-1575 nm by optimizing MSF parameters. The change range of residual dispersion is within \(\pm\)0.72 ps/(nm\(\cdot\)km), and the splicing loss with standard single-mode fiber is controlled below 5 dB within the compensation bandwidth of 190 nm. Compared with the air hole-quartz structure dual-concentric-core microstructure fiber, the designed fiber reduces the difficulty of fiber drawing, is easy to splice with standard single-mode fiber, and has wider compensation bandwidth as well as larger compensation multiple than the existing microstructure fiber. This lays a solid foundation for the optimization of dense wavelength division multiplexing networks and the construction of all-optical networks.  
microstructure fiber; broadband dispersion compensation; all-solid; dual-concentric-core; residual dispersion Article
# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 2
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **MCC** & **KAPPA** & **F1 Score** & **AUC** & **FDR** & **FNR** & **FPR** & **NVP** \\ \hline Decision Trees & 0.8298 & 0.8383 & 0.8383 & 0.8383 & 0.1000 & 0.1000 & 0.8300 & 0.5400 & 0.1700 & 0.1700 & 0.1700 & 0.8300 \\ K-Nearest Neighbor & 0.8769 & 0.8800 & 0.8800 & 0.8800 & 0.0000 & 0.0000 & 0.8800 & 0.5000 & 0.1200 & 0.1200 & 0.1200 & 0.8800 \\ Logistic regression & 0.8769 & 0.8800 & 0.8800 & 0.8800 & 0.0000 & 0.0000 & 0.8800 & 0.5000 & 0.1200 & 0.1200 & 0.1200 & 0.8800 \\ Gaussian Naive Bayes & 0.7981 & 0.8000 & 0.8000 & 0.8000 & 0.0100 & 0.0100 & 0.8000 & 0.5100 & 0.2000 & 0.2000 & 0.2000 & 0.8000 \\ Neural Network & 0.8731 & 0.8700 & 0.8700 & 0.8700 & 0.1100 & 0.0700 & 0.8700 & 0.5200 & 0.1300 & 0.1300 & 0.1300 & 0.8700 \\ Quadratic Discriminant & 0.1298 & 0.1300 & 0.1300 & 0.1300 & 0.0300 & 0.0000 & 0.1300 & 0.5000 & 0.8700 & 0.8700 & 0.8700 & 0.1300 \\ Analysis SVM & 0.8769 & 0.8800 & 0.8800 & 0.8800 & 0.0000 & 0.0000 & 0.8800 & 0.5000 & 0.1200 & 0.1200 & 0.1200 & 0.1200 & 0.8800 \\ AdaBoost & 0.8654 & 0.8700 & 0.8700 & 0.8700 & 0.1800 & 0.1600 & 0.8700 & 0.5600 & 0.1300 & 0.1300 & 0.1300 & 0.8700 \\ Gradient Boosting & 0.8769 & 0.8800 & 0.8800 & 0.8800 & 0.1100 & 0.0600 & 0.8800 & 0.5200 & 0.1200 & 0.1200 & 0.1200 & 0.8800 \\ Random Forest Max Voting & 0.8779 & 0.8800 & 0.8800 & 0.8800 & 0.0800 & 0.0100 & 0.8800 & 0.5000 & 0.1200 & 0.1200 & 0.1200 & 0.8800 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of Dataset 2 using the Machine Learning Algorithms and Ensemble learning methods. Figure 4: Graphical representation of Evaluation Metrics against Ensemble Learning Models for Dataset 2. Figure 3: Graphical representations of Evaluation Metrics against Machine Learning Algorithms and ensemble learning for Dataset 2.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 2 Methods
### Post-Processing  
The results after several time registrations are less distorted but with serval misalignments and remaining sparse noises. For producing an undistorted sequence \(V_{f}=\left\{I_{f1},I_{f2},\ldots I_{fn}\right\}\), the PCA algorithm is applied to dispel random noises. Furthermore, to reconstruct a single dewarped image, the lucky patches fusion is employed on \(V_{f}\) again.
###### Abstract  
Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-delta environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson's Ratio attribute. By comparing with the Scaled Poisson's Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. [PERSON]'s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.  
AVO attribute; Scaled Poisson's Ratio; SQp; SQs; facies; fluid +
Footnote †: journal: Applied Soft. 2020, 10, 7786; doi:10.3390/app10217786
_Article_  
**Influence of Laser Intensity Fluctuation on Single-Cesium Atom Trapping Lifetime in a 1064-nm Microscopic Optical Tweezer**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1,2 and [PERSON] 1,2,***  
Received: 16 December 2019; Accepted: 13 January 2020; Published: 16 January 2020  
**Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.**  
**Abstract:** An optical tweezer composed of a strongly focused single-spatial-mode Gaussian beam of a red-detuned 1064-nm laser can confine a single-cesium (Cs) atom at the strongest point of the light intensity. We can use this for coherent manipulation of single-quantum bits and single-photon sources. The trapping lifetime of the atoms in the optical tweezers is very short due to the impact of the background atoms, the parametric heating of the optical tweezer and the residual thermal motion of the atoms. In this paper, we analyzed the influence of the background pressure, the trap frequency of optical tweezers and the laser intensity fluctuation of optical tweezers on the atomic trapping lifetime. Combined with the external feedback loop based on an acousto-optical modulator (AOM), the intensity fluctuation of the 1064-nm laser in the time domain was suppressed from \(\pm\)3.360% to \(\pm\)0.064%, and the suppression bandwidth in the frequency domain reached approximately 33 kHz. The trapping lifetime of a single-Cs atom in the microscopic optical tweezers was extended from 4.04 s to 6.34 s.  
**Keywords:** optical tweezer; atomic trapping lifetime; parametric heating; suppression of laser intensity fluctuation
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 5 Methodology
### Evolutionary Algorithm  
Given a CFG \(\mathcal{G}\) and a fitness function \(f:L(\mathcal{G})\rightarrow\mathbb{R}\), CFGGP works as shown in Algorithm 1. After the initialization of the population \(P\), CFGGP repeats \(n_{\text{gen}}\) times the following three steps.  
1. It builds the offspring population \(P^{\prime}\), with \(|P^{\prime}|=n_{\text{pop}}\), by iteratively selecting one (mutation, with \(1-p_{\text{xover}}\) probability) or two (crossover, with \(p_{\text{xover}}\) probability) parents chosen with tournament selection of size \(n_{\text{tour}}\) and then applying the genetic operator. If the resulting solution \(\varphi_{c}\) is already part of the offspring \(P^{\prime}\) or parent population \(P\), a new solution is generated, and the process is repeated for a maximum number of \(n_{\text{atts}}\) attempts; otherwise \(s_{c}\) is added to \(P^{\prime}\) and its fitness \(f(\varphi)\) is computed.
2. It merges the parent and offspring populations \(P^{\prime}\) and \(P\).
3. It shrinks the resulting new population \(P\), until its size is \(n_{\text{pop}}\), by iteratively removing the worst solution.  
The initial population is built with the ramped half-and-half method [27]. Let a range \(\{d_{\text{min}},\ldots,d_{\text{max}}\}\) for the depth of the derivation trees be given and let \(n_{\text{pop}}\) be the number of trees to be generated. For each \(d\) in the range, we build \(k\) random approximately full derivation trees (i.e., where each leaf node is at depth \(d\)) and \(k\) random trees with the deepest leaf at depth \(d\), with \(k=\frac{n_{\text{pop}}}{2(d_{\text{max}}-d_{\text{min}}+1)}\). We write \"approximately\" because it is not possible, in general, to build a derivation tree of a grammar \(\mathcal{G}\) where each leaf is exactly at depth \(d\). This procedure ensures that the size, and hence, the complexity of the generated formulas is evenly distributed in a predefined range.  
The genetic operators are defined over the space of derivation trees of the grammar \(\mathcal{G}\). We used the standard CFGGP mutation and crossover. The former \"replaces\" a random subtree of the derivation tree with a randomly generated subtree that is appropriate according to \(\mathcal{G}\). The crossover \"replaces\" a random subtree of one parent with an appropriate random subtree of the other parent. In both cases, it is ensured that the resulting derivation tree is at most \(d_{\text{max}}\) deep.  
Figure 1: A derivation tree of the grammar of Figure 2 for the formula \((a_{1}<r)\text{S}_{[h,t_{2}]}\
eg(a_{2}>r)\).