esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Data
### Input Variables Using Dimensionless Numbers or Primitive Parameters  
In an ML model, the input and output variables should be determined first. In the three dimensionless solution sets, \(\pi_{13}\) and \(\pi_{14}\) were the outputs of the variable corresponding to \(L_{d}\) and \(f\), respectively. In addition, the input variables of the algorithms were determined by \(\pi_{1}\)\(\sim\)\(\pi_{12}\) to obey the rules. (1) They should have all specimen characteristics to represent the different experiments. (2) They should have less information redundancy to decrease the amount of computation. (3) Some of them should be ignored for the dimensionality reduction if they were invariable [38]. In order to testify the generalized variable system, this paper implemented 2 group experiments with the different two-phase flows, which were gas-liquid and liquid-liquid in the tapered co-flow microchannel. Each group experiment endured hundreds of individual experiments with different parameters. In Experiment 1 of the gas-liquid two-phase flow, the continuous phase was the solution with sodium dodecyl sulfate aqueous, and the dispersed phase was argon. In Experiment 2 of liquid-liquid two-phase flow, the continuous phase was the lubricating oil, and the dispersed phase was the deionized water. The primitive parameters of gas-liquid two-phase flow in Experiment 1 were \(\alpha\), \(\rho_{c}\), \(\mu_{c}\), \(\sigma\), \(u_{c}\), \(u_{d}\), and the primitive parameters of liquid-liquid two-phase flow in Experiment 2 were \(X\), \(\alpha\), \(u_{c}\), \(u_{d}\). Table 3 lists the four different sets for the ML model in 2 group experiments. There were the dimensionless numbers in Set1, Set2, and Set3 and the primitive parameters in Set4 as the input variables in the ML model. In Experiment 1, the number of input variables with the dimensionless numbers was less than 4, and it with the primitive parameters was 6. In Experiment 2, the number of input variables with dimensionless and primitive parameters was 4.
## 2 Coded Aperture Compressive Temporal Imaging (CACTI)
### Estimation of Motion Vector by Scale-Invariant Feature Transform (SIFT) Flow algorithm  
Based on the preliminary estimation of reconfiguration results, we can estimate the motion vectors between different frames. The typical optical-flow method may be applicable for motion estimation and compensation, but is faced with challenges from large-scale movement, sheltering judgment and processing, non-rigid movement, changing illumination, small object, blurred motion, noise of camera system, and so on [27]. To overcome the weaknesses in the optical-flow method, [PERSON] proposed the SIFT Flow velocity field theory [28] to estimate the dense correspondences across complex scenes such as illumination changes. This method can help to match the images involving different objects under varied visual angles, altered spatial positions, or scales.  
Based on the Bayesian model and Equation (13), if the SIFT Flow is represented as \(\mathbf{w}(p)=\left[u(p),v(p)\right]^{T}\) for point \(p\), the SIFT Flow \(\mathbf{w}(p)\) for point \(p\) should be expressed as  
\[\begin{array}{c}\hat{\mathbf{w}}=\underset{\mathbf{w}}{\mathrm{argmin}}\sum \limits_{p}\left(\left\|I_{1}(p)-I_{2}(p+\mathbf{w}(p))\right\|_{2}^{2},t \right)+\eta_{1}\underset{p}{\mathrm{\Sigma}}(|u|+|v|)\\ +\eta_{2}\underset{(p,q)\in\epsilon}{\sum}\left(\left(\alpha|u(p)-u(q)|,d \right)+\left(\alpha|v(p)-v(q)|,d\right)\right)\end{array} \tag{24}\]  
The first constraint term refers to the constancy of SIFT descriptors; the second to sparsity of motion vectors; the third to the motion consistency. Since the truncated \(\ell_{1}\) norms are adopted in this objective function, the sequential belief propagation of distance transform function may further ease the complexity and achieve better convergence. \(\eta_{1},\eta_{2},\alpha,d\) are set to values as described in the original literature [28].
## 4 Numerical Results and Discussion
### Defect Case Study 1: Defects Placed at 5.65 m for Both Web and Flange  
Five defect scenarios listed in Table 2 were modelled on both the web and flange of a 7 m U-pile. Figures 16 and 17 show the superposed results obtained from defects located at 5.65 m on the web and flange, respectively. These results were normalized to the excitation signal as displayed in Figures 16a and 17a. Figures 16b and 17b show defect reflection, i.e., the percentage of excitation signal reflecting from the defect. It was noted that the defect reflection increased with an increase in the size of the defect.  
Figure 15: (**a**) Shear wave propagation in the flange of the U-pile, (**b**,**c**) the excitation signal when it reached the distances of 5.65 m and 7.12 m, respectively.  
In the case of the defects located on the web, defect reflection from Defect A was 8.5% of excitation signal, which increased to 44.5% of excitation signal for the case of Defect E. A similar trend was observed for defects placed on the flange of the pile at the same location. Defect reflection increased from 6% of excitation signal for the case of Defect A to 32.5% of excitation signal for the case of Defect E. In both the web and flange of the U-pile, Defect E had a much higher defect reflection compared to the rest of the defect scenarios. This is because of the shape of this defect, as a larger portion of the defect was perpendicular to the wave propagation direction.
## 3 Results
### Microplastic Particles Classification by SWIR-HSI  
#### 3.2.1 Average Raw Reflectance Spectra  
The average raw reflectance spectra of the microplastic particles in the training dataset are plotted in Figure 4. Polymer molecules absorb light due to various vibrational modes and their overtones. This absorption occurs in the SWIR range, making it particularly informative for studying polymers, in the same way as it is for satellite detection of floating plastic [62]. This spectral interval captures the unique vibrational signatures of polymer molecules, providing detailed insights into their composition and structure [63]. The absorption of CH\({}_{2}\) and CH\({}_{3}\) in the third overtone region (1024, 1157, 1195 and 1220 nm), CH\({}_{2}\) in the second overtone region (1403, 1497, 1642 and 1774 nm), and low absorption at 2193 nm because of CH stretch in the first combination region were the characteristics of the PP spectrum. In the meantime, the PE spectrum reveals that CH\({}_{2}\) is absorbed in the third overtone region (1050, 1119, 1182 and 1220 nm), CH is absorbed in the second overtone region (1308, 1396, 1422, 1547 and 1714 nm), and CH stretch + CH deformation is absorbed in the first combination area (2018, 2149 nm). The PS spectrum is identified by the absorption of CH\({}_{2}\) and CH in the first combination area (1817, 1868, 1918, 2012, 2074, 2149, 2174, 2199, 2312 and 2361 nm), CH\({}_{2}\) and \(\delta\)C-H\({}_{2}\) in the second overtone region (1352, 1415 and 1648 nm), and CH\({}_{2}\) and CH in the third overtone region (1043, 1151, 1214 and 1308 nm). The fingerprints of the EPS and PS are similar; the primary distinctions are in the reflectance values and the lower intensity absorptions of the EPS relative to the PS. The absorption of CH\({}_{2}\) and CH in the third overtone area (1151 nm), CH\({}_{2}\) and \(\delta\)C-H\({}_{2}\) in the second overtone region (1686 nm) and the CH group in the first combination region (1817, 2149, 2174, 2199, 2305 and 2361 nm) are the characteristics of the EPS spectrum.  
Figure 3: Composition of the training dataset of microplastics. Polyethylene (PE), polypropylene (PP), polystyrene (PS) and expanded polystyrene (EPS).
## 2 Experimental Section
### Instrument and Characterization  
\({}^{1}\)H NMR and \({}^{13}\)C NMR spectra were recorded in chloroform-\(d\) (CDCl\({}_{3}\)) solution at 400 MHz on Bruker DRX-400 spectrometer (Bruker Taiwan Co., Taipei, Taiwan). All NMR spectra were calibrated by CDCl\({}_{3}\) where \({}^{1}\)H NMR chemical shifts of CDCl\({}_{3}\) is 7.23 ppm; in addition, \({}^{13}\)CNMR of CDCl\({}_{3}\) is 77.0 ppm. Microwave assisted Stille cross-coupling reactions were performed in Anton Paar Monowave 300 microwave reactor (Graz, Austria). Ultraviolet-visible (UV-vis) absorption spectra in solution or solid thin film were recorded on a JASCO MD-2010 spectrometer (JASCO, Tokyo, Japan). Gel permeation chromatography (GPC) was conducted at 40 \({}^{\circ}\)C using two Jordi DVB mixed-bed columns (250 mm (length) \(\times\) 10 mm (inner diameter, ID); suitable for separating polymers with molecular weights from 1 \(\times\) 10\({}^{2}\) to 1 \(\times\) 10\({}^{7}\) g\(\cdot\)mol\({}^{-1}\)) using THF as the eluent at a flow rate of 1.0 mL/min on a JASCO instrument (JASCO, Tokyo, Japan) that was equipped with UV-vis and refractive index (RI) detectors connected in series. The work functions of materials were measured using an AC2 photoelectron spectrometer (Riken Keiki Co., Tokyo, Japan). Atomic force microscopy (AFM) images were captured using taping model in Digital Instruments Nanoscope III (Eugene, OR, USA). The AFM samples were prepared by spin-coating the blend solution on a ITO/PEDOT:PSS  
Figure 2: Chemical Structures of PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP (R\({}_{1}\) = 2-ethylhexyl; R\({}_{2}\) = \(n\)-octyl).  
substrate. The two-dimensional grazing incident X-ray diffraction (2D-GIXRD) samples were prepared by drop-casting from CB solution (10 g/L) on 10 mm \(\times\) 10 mm Si wafers substrate and then thermal annealing at 120 \({}^{\circ}\)C, 20 min. 2D-GIXRD measurements were carried out on the wiggler beamline BL17A1 of the National Synchrotron Radiation Research Center (NSRRC, Hsinchu, Taiwan), with a wavelength of 1.333 A delivered from a superconducting wavelength-shifting magnet and a Si (111) triangular crystal monochromator (TCM) [26; 33]. The data were recorded by a Mar3450 image plate with exposure duration 60 s. The two-dimensional diffraction pattern was converted to a one-dimensional powder diffraction profile by fit-2D program. Furthermore, the diffraction patterns are fitted into the one-dimensional powder diffraction spectra using Gauss function of OriginPro 8 (Northampton, MA, USA) to locate the peak positions and calculate the full-width at half-maximum.
_Applied_  
_sciences_  
**Revisiting the Sodiation Mechanism of TiO2 via**  
_Operando_ X-ray Absorption Spectroscopy**  
**[PERSON] 1,*\({}^{\text{\textdagger}}\)**, [PERSON] 2, [PERSON] 3\({}^{\text{\textdagger}}\)**, [PERSON] 2\({}^{\text{\textdagger}}\)**, [PERSON] 2,4\({}^{\text{\textdagger}}\) and [PERSON] 2,4\({}^{\text{\textdagger}}\)**  
1 CIC Energigune, Parque Tecnologico de Alava, [PERSON] 48, ED. CIC, 01510 Minano, Spain  
2 Institut Charles Gerhardt Montpellier (ICGM), Univ. Montpellier, CNRS, 34095 Montpellier, France; [EMAIL_ADDRESS] (A.H.); [EMAIL_ADDRESS] (B.B.); [EMAIL_ADDRESS] (N.L.)  
3 Synchrotron SOLEIL, L'orme des Merisiers, BP 48 Saint Aubin, 91192 Gif-sur-Yvette, France; [EMAIL_ADDRESS]  
4 Reseau sur le Stockage Electrochimique de l'Energie (RS2E), CNRS, 80039 Amiens, France  
5 Correspondence: [EMAIL_ADDRESS] (M.F.); [EMAIL_ADDRESS] (L.S.); Tel.: +34-911-922-137-365 (M.F.); +33-46714-3346 (L.S.)  
Received: 30 June 2020; Accepted: 5 August 2020; Published: 11 August 2020  
**Featured Application: Owing to the large availability of sodium, Na-ion batteries could be a viable and cheap alternative to Li-ion batteries, and the development of new electrode materials for this promising technology, such as TiO2, is necessary to accelerate their adoption in large-scale applications.**  
**Abstract: The sodiation mechanism of TiO2 anatase was thoroughly investigated via X-ray absorption spectroscopy under _operando_ conditions. The data set was analysed via an innovative and smart approach based on chemometric tools that allows the unbiased and reliable extraction of the maximum amount of meaningful information. The resulting data analysis reveals that the electrochemical sodiation mechanism is mainly based on the reduction of Ti\({}^{4+}\) to Ti\({}^{3+}\), going along with the irreversible amorphisation of the pristine anatase structure. At least one semi-amorphous intermediate is formed during the first discharge, whose local structure resembles those obtained at the end of the charge.**  
**Keywords: TiO2; anatase; sodium-ion batteries; X-ray absorption spectroscopy; chemometrics**
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 1 Introduction  
Efficient use of energy resources is one of the most important challenges of modern society. In the context of Internet of Things (IoT) technologies, which are now actively implemented for air quality monitoring, the issue of energy efficiency is gaining particular relevance. The implementation of energy-efficient IoT systems allows for the long-term autonomous operation of devices, thereby reducing electricity costs and minimizing environmental impact. Reducing electricity consumption, which is generated mostly fromfossil fuels, helps to reduce greenhouse gas emissions and slow down climate change, and optimizes the use of natural resources [1; 2]. Energy-efficient solutions allow for significant reductions in operating costs, which is important for large-scale monitoring systems. In addition, reducing energy consumption extends the service life of electronic components, reducing the cost of their replacement. From a technical point of view, energy-efficient solutions expand the functionality of monitoring systems by increasing the number of sensors without also increasing the overall energy consumption. Reducing the heating level of components increases their reliability and contributes to long-term autonomous operation, which is especially important for mobile and remote systems [3]. In addition, energy-efficient IoT systems have a significant social impact. They provide up-to-date and accurate data on air quality, which allow policymakers to make informed decisions about the health and safety of the population. These systems are also an important element of the infrastructure of smart cities, contributing to an increase in the quality of life and sustainable development. Therefore, the research and implementation of energy-efficient IoT solutions for air quality monitoring is an important step towards ensuring environmental safety and economic efficiency and improving the quality of life.  
This study aims to propose an energy-efficient IoT architecture and introduce an IEI for sensor evaluation.
# Application of Virtual Reality in Learning Quantum Mechanics
## 1 Introduction
Cognitive load theory indicates that before the new knowledge schema of the learner is formed, there are three types of cognitive load generated in the working memory, namely, intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. The correlation between cognitive load theory and schema is that schema plays an important role in learning and memory, while cognitive load theory explores how learners deal with the mental resources needed for various complex tasks. The basic assumptions related to the schema in cognitive load theory can be found in [28]. Quantum mechanics is an important foundation of modern physics. At present, there are few studies using virtual reality for teaching quantum mechanics, and few empirical studies have been conducted to explore its learning effectiveness. Therefore, this study used virtual reality to develop the teaching modules of quantum mechanics suitable for university and high school physics courses, and the objective is to provide a scaffold for self-learning to promote conceptual change by making the abstract and complex concepts of wave-particle duality more concrete and observable. This study aims at the popularization of science education through promotional activities. Learners can wear a head-mounted display (HMD) for immersive learning or use a mobile device such as a tablet computer to facilitate independent learning, which is of great help to physics teaching in high schools and colleges. Learners may find that understanding quantum mechanics is not very difficult, and they would like to learn and have the ability to apply new knowledge in solving problems. According to the above research goals, there are four research questions in this study:  
* What is the impact of virtual reality on the learning outcome of quantum mechanics? * What is the impact of virtual reality on the learning motivation of quantum mechanics? * What is the impact of virtual reality on the cognitive load of quantum mechanics? * What is the system satisfaction of learners after using the virtual teaching modules? The rest of the study is organized as follows. Section 2 describes the virtual teaching modules developed in this study and their design principles and operation procedures. Section 3 describes the teaching experiment and the analysis result. Section 4 provides the discussion and findings of this study. Section 5 is the conclusion and future suggestions.
# Research on Robust Day-Ahead Dispatch Considering Primary Frequency Response of Wind Turbine
## 3 Optimization Model Considering Primary Frequency Response
### Solution  
It would be very difficult to solve the model directly. For the min-max-min problem, we introduce the auxiliary variable \(\beta\) for the max-min problem, so that:  
\[\min\sum_{t=1}^{T}\sum_{i=1}^{N_{G}}\left(C_{G,i}P^{0}_{G,i,t}+C^{ LI}_{R,i}R^{LU,0}_{i,t}+C^{D}_{R,i}R^{D,0}_{i,t}\right)+\beta \tag{31}\]  
We also add the constraint:  
\[\beta_{s\in\Omega}\geq\sum_{t=1}^{T}\sum_{i=1}^{N_{G}}C_{G,i}\left(r^{U,s}_{i, t}-r^{D,s}_{i,t}\right)+\sum_{t=1}^{T}\sum_{i=1}^{N_{G}+N_{W}}C^{F}_{R,i}R^{F,s}_ {i,t}+\sum_{t=1}^{T}\sum_{i=1}^{N_{B}}C_{L,i}P^{non}_{i,t} \tag{32}\]  
The model after the introduction of the auxiliary variable \(\beta\) is equivalent to that from before the change. The three-layer optimization problem is reduced to a single-layer one. However, if all of the scenes in the set \(\Omega\) are taken into consideration with the corresponding real-time operation constraints, the problem would become intimidating and could hardly be solved. Therefore, [PERSON], et al. [23] proposes an iterative method where the master problem focuses on the key subset \(\Omega_{m}\), which is found through iteration in the sub-problem. As \(\Omega_{m}\) contains all of the information of \(\Omega\), the dispatch strategy and reserve considering \(\Omega_{m}\) will meet the output re-adjustment demands of all of the scenes. The process is as follows:  
1. Define the starting key subset \(\Omega_{m}=\{P^{m,s1}_{W,w,t}\}\);
2. Master problem: solve the above model to obtain the dispatch strategy \(X_{m}\) and the adjustment cost \(\beta_{M}\) for Stage 2;
3. Sub-problem: based on \(X_{m}\), calculate the goals for each and every scenario in \(\Omega\backslash\Omega_{m}\); \[\min\beta_{s}=\sum_{t=1}^{T}\sum_{i=1}^{N_{G}}C_{G,i}\left(r^{U,s}_{i,t}-r^{D, s}_{i,t}\right)+\sum_{t=1}^{T}\sum_{i=1}^{N_{G}+N_{W}}C^{F}_{R,i}R^{F,s}_{i,t}+ \sum_{t=1}^{T}\sum_{i=1}^{N_{B}}C_{L,i}P^{non}_{i,t}\] (33) The optimization goal during the real-time operation is restricted by Equations (20) to (29). For the results obtained in 3, the maximum \(\beta_{S}=\max\limits_{s\in\Omega\backslash\Omega_{m}}\beta_{s}\), and the corresponding scenario is \(P^{m,s^{\prime}}_{W,w,t^{\prime}}\);
4. If \(\beta_{S}>\beta_{M}\), \(\Omega_{m}=\{\Omega_{m}\), \(P^{m,s^{\prime}}_{W,w,t^{\prime}}\}\), then we go back to 2; otherwise, we get the dispatch strategy for the master problem, and the iteration ends.  
Through the above process, we can obtain the dispatch strategy and reserve considering the key subset \(\Omega_{m}\). As \(\Omega_{m}\) contains all of the information of the scenes of \(\Omega\), the maximum dispensable power caused by the unit failure could be obtained by solving the key subset \(\Omega_{m}\), if the operator needs that. The flow chart is shown in Figure 3.
## 2 Materials and Methods
### Biological Activity of GEs  
The antimicrobial, insecticidal and anticancer activities of the synthesized GEs were determined using the protocols described in [PERSON] et al. [36]. Briefly, the antimicrobial activity of GEs (used at a concentration of 40 \(\upmu\)g/mL) was evaluated in vitro using the agar well diffusion assay [37], the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) [38] against human pathogens including the Gram-negative _Escherichia coli_ (ATCC 25922), _Klebsiella pneumoniae_ (ATCC 700603), _Pseudomonas aeruginosa_ (ATCC 15442) and _Salmonella typhimurium_ (ATCC 14028); the Gram-positive bacteria _Bacillus subtilis_ (ATCC 6633), MRSA _Staphylococcus aureus_ (ATCC 4330) and _S. aureus_ (ATCC 25923); and the unicellular fungus _Candida albicans_ (ATCC 10221). The insecticidal activity was evaluated by exposing early 4 th instar larvae of a field strain of _Aedes aegypti_ to different concentrations of GEs (up to 100 ppm) for 48 h, in glass beakers containing 100 mL of tap water and GE solutions. Calculation of statistical parameters was performed using the Finney method [39]. The apoptotic activity of the SKOV-3 ovarian cancer cell line in response to the tested compounds (both FAMEs and GEs) was determined by Annexin FITC, as per the manufacturer's instructions (BD Biosciences, San Jose, CA, USA).