esa-sceva/satcom-chunk-collection huggingface dataset:
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 2 Literature Review
### Simulations Validity  
The middleware that we propose for coupling multi-scale simulations is based, among other things, on the assumption of an a priori knowledge of which simulation would have the most valid behavior on certain aspects (viz. the demand, the assignment, and the travel times), i.e., the one providing the most likely correct results. This information is essential to resolve behavioral conflicts between simulations, and to know which simulation corrects the other. This information depends mostly on the relevance of the input data considered by the simulations. For example, a local simulation, working with up-to-date fine-grained local survey-based data, is a priori more relevant for the representation of local behaviors (e.g., travelers' speeds) than a regional simulation, working with long term regional data. However, the validity of a simulation also depends on the validity of the underlying model. If one of the two models has not been properly calibrated and validated, the behaviors induced by the corresponding simulation should be considered, even if the input data are less detailed.  
Calibration of mobility simulations has received varying degrees of attention in the literature, with the notable exception of the European COST Action Multitude. The most important property of a simulation model is, however, its validity [27]. Only a model that is valid enough is able to produce reliable results. Basically, validity means that the right model is used [28]. Only if the model is valid can the answers derived from its simulation be taken as answers for questions directed to the original system. Validation then is generally defined as \"the process of determining whether a simulation model is an accurate representation of the system, for the particular objectives of the study\" [29].  
Checking the validity of a model against an observed reality fits Equation (1) [30]:  
\[Prob(|Simulation-Reality|\leq d)>\alpha \tag{1}\]  
_Simulation_ corresponds to the output values of the simulation, \(Reality\) corresponds to the values observed in the real world, \(d\) is the tolerable deviation, and \(\alpha\) is the confidence level. Ideally, the values \(d\) and \(\alpha\) have been determined beforehand by the analyst.  
Only a model for which a validation process has been performed is considered valid. In the remainder of this paper, when we assume that one simulation corrects another, it means:  
* either the corrector simulation uses more relevant data than the second simulation,
* or the corrector simulation was validated and the second one wasn't, or was validated with a lower \(\alpha\).
## 2 Optimization Problem
### Determination of Gearbox Efficiency  
The gearbox efficiency is determined by:  
\[\eta_{gb}=\frac{100\cdot P_{l}}{P_{in}} \tag{30}\]  
where \(P_{l}\) is the total power loss in the gear box [25]:  
\[P_{l}=P_{lg}+P_{lb}+P_{ls} \tag{31}\]  
where \(P_{lg}\) is the power losses in the gears; \(P_{lb}\) is the power loss in the bearings; and \(P_{ls}\) is the power loss in seals. These factors can be determined as follows:  
(+) The power losses in the gears:  
Figure 2: Diagram for determining shaft dimensions.  
\[P_{lg}=\sum_{i=1}^{2}P_{\rm Igi} \tag{32}\]  
in which \(P_{lgi}\) is the the power losses in the gears of the i stage, which is found by:  
\[P_{lgi}=P_{gi}\cdot\left(1-\eta_{gi}\right) \tag{33}\]  
where \(\eta_{gi}\) is the efficieny of the i stage of the gearbox, which can be determined by [26]:  
\[\eta_{gi}=1-\left(\frac{1+1/u_{\rm i}}{\beta_{\rm ai}+\beta_{\rm ri}}\right) \cdot\frac{f_{\rm i}}{2}\cdot\left(\beta_{\rm ai}^{2}+\beta_{\rm ri}^{2}\right) \tag{34}\]  
where u\({}_{\rm i}\) is the gear ratio of i stage; \(f\) is the friction coefficient; \(\beta_{\rm ai}\) and \(\beta_{\rm ri}\) are the arc of the approach and recess on the i stage, which is calculated by [26]:  
\[\beta_{\rm ai}=\frac{\left(R_{e2i}^{2}-R_{02i}^{2}\right)^{1/2}-R_{2i}\cdot \sin\alpha}{R_{01i}} \tag{35}\]  
\[\beta_{\rm ri}=\frac{\left(R_{e1i}^{2}-R_{01i}^{2}\right)^{1/2}-R_{1i}\cdot \sin\alpha}{R_{01i}} \tag{36}\]  
in which \(R_{e1i}\) and \(R_{e2i}\) are the outside radius of the pinion and gear, respectively; \(R_{1i}\) and \(R_{2i}\) are the pitch radius of the pinion and gear, respectively; \(R_{01i}\) and \(R_{01i}\) are the base-circle radius of the pinion and gear, respectively; \(\alpha\) is the pressure angle.  
From the data in [26], the friction coefficient can be determined by the folowing regression equations:  
* When the sliding velocity is \(v\leq 0.424\) (m/s), the friction coefficient is calculated by (with \(R^{2}=0.9958\)): \[f=-0.0877\cdot v+0.0525\] (37)
* When the sliding velocity is \(v>0.424\) (m/s), the friction coefficient is calculated by (with \(R^{2}=0.9796\)): \[f=0.0028\cdot v+0.0104\] (38)  
(+) The power losses in the bearings [25]:  
The power losses in rolling bearings can be found by:  
\[P_{lb}=\sum_{i=1}^{6}f_{b}\cdot F_{i}\cdot v_{i} \tag{39}\]  
where \(f_{b}\) is the coefficient of the friction of the bearing; as the radial ball bearings with angular contact were used, \(f_{b}=0.0011\)[25]; F denotes the bearing load in Newtons (N) while v represents the peripheral speed. Additionally, i represents the ordinal number of the bearing, ranging from 1 to 6.  
(+) The total power losses in the seals are determined by [25]:  
\[P_{\rm s}=\sum_{i=1}^{2}P_{\rm si} \tag{40}\]  
in which i is the ordinal number of the seal (i = 1 \div\) 2); \(P_{si}\) represents the power loss caused by the sealing for a single seal (w), which can be calculated by:  
\[P_{si}=[145-1.6\cdot t_{lli}+350\cdot loglog(VG_{40}+0.8)]\cdot d_{s}^{2}\cdot n \cdot 10^{-7} \tag{41}\]  
where \(VG_{40}\) is the ISO viscosity grades number.
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 2 Surface Enhanced Raman Scattering: A Brief Tutorial
According to the EM theory [42], the SERS intensity (\(I\)) is directly proportional to the fourth power of the local electromagnetic field strength (\(E^{4}\)). By contrast, \(E\) varies inversely to the distance (\(d\)) between analyte and NP surface, that is, \(E\propto(1/d)^{12}\). Based on these mathematical expressions, EM is distance-dependent, and small modification in \(d\) and \(E\) results in exponential changes in \(I\). Ideally, the optimum SERS is achieved when an optimal number of analytes are within regions of strong LSPR, within the interstices of aggregated NPs [43]. The second, chemical enhancement (CM) or charge-transfer mechanism relies on resonance Raman scattering-like effect. It is thought to involve electronic excitation of covalently bound coupled electron clouds within chemical bonds formed between analytes and the NP surface. CM increases polarisability of adsorbed molecules and contributes up to \(10^{3}\) orders of magnitude to the overall SERS EF [43]. Unlike EM, CM is only significant when molecules are chemically bonded to NPs typically at monolayer coverage. It is also noteworthy that the Raman effect can be tuned further to \(10^{14}\) orders of magnitude to reach fluorescence-like cross-section [44]. This is achieved when incident frequency excites electronic energy states of specific chromophores near or bonded to the NP surface, giving rise to surface-enhanced resonance Raman scattering (SERRS). With this large EF, ultralow limit of detection (LOD) to the extent of single molecule detection were reported by [PERSON] and coworkers [45]. Overall, to achieve this optimally huge SERS amplification, several experimental parameters need to be manipulated including laser wavelength, morphology of NPs and dispersion medium. For more detailed information on experimental considerations for optimal SERS, readers are directed to excellent reviews by [PERSON] et al. and [PERSON] et al. [42; 46]. Quite importantly, optimally aggregated NPs, excitation frequency overlapped with plasmonic band of NPs and enhancing media with small refractive index collectively yield large and reproducible EFs [47]. In terms of technical aspect, flow injection and microfluidics technologies combined with SERS show good SERS reproducibility and reliability [48]. Microfluidics devices consist of microchannels where a minute volume of a sample is mixed uniformly with NPs at constant and automated flow velocity. Tandem microfluidics-SERS overcomes effects of localised heating, photodissociation and variations in scattering geometry of NPs. A considerable focus of recent research has been on developing microfluidic SERS to improve in situ bioanalysis, which will play a decisive role for clinical utility of SERS in healthcare systems [49].
## 2 Literature Review
### Ensemble Learning
#### 2.1.2 Boosting  
Boosting is a method of changing the target value to the extent that the previous model did not fit. In the case of AdaBoost, it is a method of adjusting the weight of data selection. The boosting method uses some measurement, ensuring that it is substantially different from the other members. A typical gradient boosting machine (GBM) [23] fits an additive model (ensemble) in a forward stage-wise manner. In each stage, a weak learner is introduced to compensate for the shortcomings of existing weak learners. In GBM, \"shortcomings\" are identified by gradients. Both high-weight data points and gradients tell us how to improve our model. Gradient boosting iteratively builds a sequence of approximations. Boosting aims to increase the accuracy of the model by sequentially combining weak learners, but boosting is sensitive to noisy data and outliers and is susceptible to overfitting because it focuses on data that previous models did not predict well. Common approaches to reducing variance are cross-validation and bagging (bootstrapped aggregated ensemble). On the other hand, reducing bias is commonly carried out with boosting [22].  
In relation to boosting machine learning, improved algorithms were presented in the order of XGBoost [24], LightGBM [25] and CatBoost [26]. XGBoost is a technique to quickly process performance for large amounts of data based on GBM. XGBoost introduces methods such as parallelized tree building, tree pruning using a depth-first approach, cache awareness and out-of-core computing, regularization for avoiding overfitting, in-built cross-validation capability, etc.  
LightGBM uses some techniques such as gradient-based one-side sampling and exclusive feature bundling. Conventional GBM needs to, for every feature, scan all the data instances to estimate the information gain of all the possible split points. It takes a lot of time because it must scan every feature and all the data instances. LightGBM introduces a gradient-based one-side sampling that targets objects with large gradients and excludes objects with small gradients from sampling. It also uses exclusive feature bundling that combines exclusive features into one variable to reduce the number of features.  
CatBoost introduces techniques such as ordered target statistics and ordered boosting to improve the conventional boosting method. The ordered target statistics technique is used to improve the target information leaking problem, by which the target value of an instance is used to compute its feature value when the categorical feature is replaced with target statistics. The ordered boosting technique is used to avoid the prediction shift problem; the dataset used for training in each step of boosting should be independent.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods  
The RTK UAV-based weather radar radial velocity validation consists of five key steps (Figure 1). Firstly, the radar environment is checked to confirm the weather conditions and radar configuration parameters. Then, based on the radar position and the far-field region, the geodetic coordinates and altitude of the two endpoints of the UAV routes are theoretically calculated. After completing the preparatory work, the UAV begins to fly along the route at a preset constant velocity, and the radar is set to a tracking mode which performs a synchronized scan of the UAV to check its position and stability. Finally, using time synchronization as a benchmark, the measured radar velocity values are examined in conjunction with the UAV intensity values, and the results of the validation are evaluated both qualitatively and quantitatively and compared with the results of the internal and external instrument calibration.
## 5 Conclusions  
The ACMD method has a good effect on processing chirp signals. The parameter \(\alpha\) controls the smoothness of the output signal. Too small an \(\alpha\) will cause a smooth output signal. The parameter \(\beta\) controls the IFs. If too small a \(\beta\) is used, the output IF will be very smooth. However, rapidly changing IFs require relatively large \(\beta\). The choice of \(\alpha,\beta\) is artificially set, and there are errors in practical applications. Therefore, it employs STA to adaptively optimize \(\alpha,\beta\). As the number of species in the STA is an artificial error, this paper proposes the Rastrigin optimization test equation to optimize the number of different species. Finally, the ACMD method optimized in this paper is applied to the chirp signal from marine animals, which verifies the feasibility of the proposed method. The following conclusions can be drawn in this paper:  
1. This article applies the intelligent algorithms of Ant Lion Optimizer (ALO), Grey Wolf Optimizer, Lightning Search Algorithm (LSA), Particle Swarm Optimization (PSO), STA, and Whale Optimization Algorithm (WOA) to the optimization process of Rastrigin optimization test equations. The comparison shows that the fastest STA is 13.196 s. Through a large number of simulation experiments, it is learned that when the population is 9, the STA algorithm runs fastest.
2. The artificial \(\alpha,\beta\) in ACMD has an error when processing the signal, and different signals require different \(\alpha,\beta\) values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  
**Author Contributions:** [PERSON] and [PERSON] performed the simulation experiment; [PERSON], [PERSON] and [PERSON] analyzed the data and contributed reagents/materials/analysis tools; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (51905496), Shanxi Applied Basic Research Project (Grant No. 201801D221237), the project supported by Science Foundation of North University of China (NO. XJJ201802), the Open Fund of Key Laboratory of Advanced Manufacturing Technology of Shanxi Province (XJZZ201802).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 3 Proposed Noise-Level Detection  
The flow chart of the noise-level detection is shown in Figure 2. The noise level of each video frame is first estimated, i.e., the standard deviation of Gaussian distribution, and generates the standard deviation sequence \(\sigma[t]\). Then, the fast Fourier transform (FFT) [15] is used to calculate the amplitude spectrum of \(\sigma[t]\), and the periodicity of \(\sigma[t]\) is determined by detecting whether there is a large spike in the amplitude spectrum. Finally, in order to realize automatic detection, we calculate the ratio of the maximum and mean value of the amplitude spectrum, and set the threshold to determine whether the video is forged by FR. The following describes the proposed noise-level detection in detail.
# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 3 The Proposed Local Event Detection Scheme
### Data Collection  
In order to detect local events in SNSs, we should collect SNS data generated by users in real time. Data preprocessing is also required to accurately detect local events since the collected data can contain unnecessary and redundant information such as stop words, buzzwords, special characters, abbreviations, and emoticons. The data collection not only collects SNS data but also removes redundant and unnecessary data in order to extract meaningful keywords.  
Figure 2 shows the processing procedure of the data collection. It collects SNS data such as posts (i.e., tweets), comments, retweets, and threads in a particular time window. We extract regional information from the collected data through a geographical dictionary by using geo-tag information. We assume that the geographical dictionary is preconstructed. The geographical dictionary is defined according to the administrative district such as village, town, district, county, city, province, metropolitan city, special city, and state; the administrative district has hierarchical features. After that, it gets rid of redundant data from the collected data through the lexical analyzer and the stop-word dictionary. The lexical analyzer splits a sentence into words, and then it removes words that are considered stop words such as verbs, adjectives, adverbs, prepositions, etc. We only utilize nouns for the event detection. Lastly, the data collection sends extracted nouns and geo-tagged data to the module graph modeling for modeling and clustering to extract local events.  
Figure 1: Overall architecture of the proposed local event detection scheme.  
Figure 2: Processing procedure of the data collection.
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
## Appendix C Additional Numerical Results for Various Side Crack Morphologies  
To utilize the solution of \(K\) directly, only cracks lying along the centreline of the plates are considered in this study. However, as corner functions are involved in the admissible functions of the RRM, the current methodology can be used to calculate the natural frequencies and mode shapes of cracked rectangular plates when the side crack has varying location, length, and orientation. To demonstrate this, additional numerical results are presented for unloaded cracked plates with various crack configurations.  
To characterize a slant side crack, Figure 3 has been revised as shown below (Figure A2).  
The free vibration of unloaded square plates with edge cracks featuring different angles and lengths has been studied. Dimensionless natural frequency results are listed in Tables A1 and A2 for a completely free square cracked plate and a simply supported square cracked plate, respectively, when \(I=J=8\) and \(N=3\). The mode shape results for selected crack configurations are prese  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\alpha\) (Degrees)} & \multirow{2}{*}{_d/b_} & \multirow{2}{*}{_cl/a_} & \multicolumn{6}{c}{**Mode**} \\ \cline{3-8}  & & & **1** & **2** & **3** & **4** & **5** \\ \hline
15 & 0.5 & 0.1 & 13.34 & 19.45 & 24.12 & 34.34 & 34.34 \\  & & 0.2 & 12.91 & 18.99 & 23.73 & 32.63 & 33.31 \\  & & 0.3 & 12.03 & 18.21 & 23.12 & 29.05 & 32.26 \\  & & 0.4 & 10.59 & 17.17 & 21.94 & 25.31 & 31.43 \\  & & 0.5 & 8.76 & 16.03 & 20.12 & 23.71 & 30.80 \\ \hline
30 & 0.75 & 0.1 & 13.38 & 19.51 & 24.20 & 34.32 & 34.63 \\  & & 0.2 & 13.04 & 19.06 & 23.87 & 32.29 & 34.04 \\  & & 0.3 & 12.35 & 17.68 & 23.24 & 28.95 & 33.28 \\  & & 0.4 & 11.12 & 15.45 & 22.67 & 26.38 & 32.69 \\  & & 0.5 & 9.39 & 13.59 & 22.18 & 24.86 & 32.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensionless frequency parameter \(\Omega=\omega a^{2}\sqrt{\rho h/D}\) for a completely free square plate with a slant side crack.
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
## 3 Results and Discussion
### Influence of pH  
First, Alg-Mel sorbent (about 100 mg) was added into 40 mL of 0.48 mM the Pb\({}^{2+}\) solution with different pH values (range from 1 to 8), stirred at room temperature (25 \({}^{\circ}\)C) for 6 h, filtered, and then the amount of unadsorbed Pb\({}^{2+}\) in the filtrate was measured by ICP-OES. As shown in Figure 4, the influence of pH on the adsorption capacity of Alg-Mel can be divided into three stages: (1) when the pH value of the solution is less than 1, the Alg-Mel can only adsorb less than 25% of Pb\({}^{2+}\). This may be due to the pH value being lower than pH\({}_{\text{pzc}}\) (the pH\({}_{\text{pzc}}\) value of Alg-Mel is 1.83), the surface of Alg-Mel is positively charged and exhibits electrostatic repulsion towards Pb\({}^{2+}\); (2) when the solution pH in the range of 1-5, the adsorption capacity of Alg-Mel for Pb\({}^{2+}\) increases rapidly with increasing of pH. This can be ascribed to the negative surface charge (when the pH value is higher than pH\({}_{\text{pzc}}\)) and strong chelating groups on the surface of the Alg-Mel; (3) when the solution pH is in the range of 5-8, the adsorption capacity of Alg-Mel remains stable. On another hand, according to the rule of solubility product, Pb\({}^{2+}\) ions (0.48 mM) will start to form Pb(OH)\({}_{2}\) precipitation when the pH value of solution is greater than 5.74. Therefore, we suggest that the optimal adsorption condition for the Alg-Mel is the solution pH in the range of 5-5.74. Within this range, neither the amino group is protonated nor Pb\({}^{2+}\) ions are precipitated. Therefore, pH 5.0 of the metal solution was chosen for the adsorption experiment in this study.
Applied sciences Article  
Application of Response Surface Methodology to Optimize the Extraction Process of Bioactive Compounds Obtained from Coffee Silverskin  
[PERSON] *\({}^{\copyright}\), [PERSON] *\({}^{\copyright}\), [PERSON] \({}^{\copyright}\) and [PERSON] \({}^{\copyright}\)  
Department of Chemistry, Institute of Food Science, Warsaw University of Life Sciences, Nowouvsynowska St. 159c, 02-78 Warsaw, Poland; [PERSON]SS] (L.P.); [PERSON]SS] (A.G.)  
Correspondence: [PERSON](R.B.); [PERSON].-W.)  
The present research focused on the extraction optimization of bioactive compounds from coffee silverskin (CS), a by-product generated in large amounts worldwide during the coffee roasting process. The effect of the different extraction conditions has been investigated by the exploitation of the response surface methodology (RSM). The antioxidant activity assays, such as ABTS and FRAP, total phenolics content (TPC), borwning index (BI), and chromatographic analysis of caffeine and chlorogenic acids contents have been performed to evaluate the CS extracts characteristics. The most favorable extraction conditions on the maximum recovery of antioxidant bioactive compounds were found to be as follows: 50% aqueous solution of ethanol (\(\sigma/v\)) in solvent solid ratio of 45 mL/g CS, during 30 min at 60 \({}^{\circ}\)C. The CS extract prepared in this extraction variant reached the values for ABTS, FRAP, TPC, and BI approximately 101.6 \(\mu\)mol Trolox/g d.m. CS, 132.3 \(\mu\)mol Fe (II)/g d.m. CS, 52.3 \(\mu\)mol GAE (gallic acid equivalent) per g d.m. CS, and 0.3, respectively. Additionally, this extract is rich in caffeine (6 mg/g d.m. CS) and chlorogenic acids (2.2 mg/g d.m. CS). In conclusion, this research demonstrates that CS could be considered as a valuable by-product of bioactive compounds with potential applications in the food industry.  
phenolic compounds; antioxidant activity; caffeine; chlorogenic acids; borwning index
## 6 Conclusions
### Data Acquisition Step  
Previously, images or time series data were simply copied into the digital form for data acquisition. However, for R&D with commercialization purposes or being conducted in specific professional fields, it is difficult to indiscriminately use existing data due tocopyrights and personal information protection, and a process to check the data individually after acquisition is mandatory. A tool to automatize this process exists but cannot be considers as completed; thus, human interference is necessary throughout all processes.