esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 3 Results and Discussion
### Synthesis
#### 3.1.10 (E)-3-(furan-2-yl)-1-(4-((2-hydroxybenzyl)amino)phenyl)prop-2-ene-1-one (A10)  
Yellow crystals, very soluble in ethanol and methanol, insoluble in water; mp: 187-189 \({}^{\circ}\)C; EIMS (\(m/z\)): 328.1114 [M \(-\) H]\({}^{-}\), (MW = 319.12); UV (\(\lambda_{\rm max}\) nm, EtOH): 269; 285; \({}^{1}\)H-NMR (500 MHz, DMSO-\(d_{6}\)) 8 ppm: 4.29 (d, 2H, \(J\) = 5.5 Hz, CH\({}_{2}\)), 6.64 (m, 1H, H4), 6.65 (d, \(J\) = 7 Hz, H3', H5'), 6.74 (t, 1H, \(J\) = 7.5 Hz, NH), 6.84 (d, 1H, \(J\) = 8 Hz, H3\"), 6.98 (d, \(J\) = 15.5 Hz, H6\"), 7.06-7.10 (m, 2H, H4\", H5\"), 7.14 (d, 1H, \(J\) = 7.5 Hz, H5), 7.43 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\alpha}\)), 7.49 (d, 1H, \(J\) = 15.5 Hz, H\({}_{\beta}\)), 7.84 (d, 1H, \(J\) = 9 Hz, H2', H6'), 7.85 (d, 1H, \(J\) = 7.5 Hz, H3), 9.6 (s, 1H, OH); \({}^{13}\)C-NMR (125 MHz, DMSO-d\({}_{6}\)) 8 ppm: 56.1 (CH\({}_{2}\)), 111.2 (C3', C5'), 112.8 (C4), 114.9 (C5), 115.3 (C3\"), 118.8 (C6\"), 119.30 (C\({}_{\beta}\)), 124.7 (C5\"), 125.2 (C1\"), 127.8 (C1'), 128.2 (C\({}_{\alpha}\)), 128.2 (C4\"), 130.7 (C2', C6'), 145.4 (C3), 151.5 (C1), 153.1 (C4'), 155 (C\"), 185.32 (C=O).
# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 3 Proposed Method
### Heuristic Algorithm for Line-to-Line Fault Location
#### 3.2.3 Troubleshooting Scope  
Having introduced the heuristic line-to-line fault location algorithm for six possible fault structures within a PV array, attention is turned to explaining the details to consider during line-to-line short-circuit troubleshooting. In the module group voltage measurement method proposed in this paper, the measurement scope is shown in Figure 16a. When a fault is located in module group \(V_{i,j}\), it is necessary not only to inspect the three PV modules within that module group but also to examine the connecting segments between adjacent module groups, as indicated by the orange lines, to ensure that the fault is thoroughly addressed. Thus, when the fault occurs at the boundary between two module groups, as shown in Figure 16b, the fault location result may be \(V_{i,j}\) or \(V_{i,j+1}\). Regardless of which of these is indicated as the fault location, troubleshooting must be conducted at the boundary to avoid overlooking any faults.
## 1 Introduction
Also, the use of simple indices would allow, e.g., the applications of low cost camera systems including narrow band optical filters serving as an alternative for cheap and widespread application. Therefore, one major aim of this study is to find the most significant and lowest dimensional spectral band combinations for extracting the LAI and WC. We have investigated two major agricultural crops in the mid-latitudes, namely sugar beet and spring barley, under different water and N-treatments in a greenhouse environment over the vegetation period of 2008. Different water and N-treatments will allow a broad spectrum of environmental conditions to be explored. Section 2 will \begin{table}
\begin{tabular}{l l l l l} \hline
17. & NPCI & Normalized Pigments Reflectance Index & NPCI \(=\) (R680 \(-\) R430) / (R680 \(+\) R430) & [14] \\
18. & PSRI & Plant Senescence Reflectance Index & PSRI \(=\) (R680 \(-\) R500) / R750 & [21, 27] \\ \hline \multicolumn{4}{l}{_Water indices:_} \\ \hline
19. & NDWI\_1241 & Normalized Difference Water Index R1241 & NDWI\_1241 \(=\) (R857 \(-\) R1241) / (R857 \(+\) R1241) & [15] \\
20. & NDWI\_1640 & Normalized Difference Water Index R1640 & NDWI\_1640 \(=\) (R857 \(-\) R1640) / (R857 \(+\) R1640) & [18] \\
21. & NDWI\_2130 & Normalized Difference Water Index R2130 & NDWI\_2130 \(=\) (R857 \(-\) R2130) / (R857 \(+\) R2130) & [18] \\
22. & WBI & Water Band Index & WBI \(=\) R900 / R970 & [28, 29] \\ \hline
23. & RATIO 975 & Three-band ratio 975 & \(\begin{array}{l}\text{RATIO\_975}=2\sum(\text{R960:R990})/\sum(\text{R920:R940}) +\\ \sum(\text{R1090:R1110}))\\ \end{array}\) & [30] \\ \hline
24. & RATIO 1200 & Three-band ratio 1200 & \(\begin{array}{l}\text{RATIO\_1200}=2\sum(\text{R1180:R1220})/\sum(\text{R1090:R1110}) \\ +\sum(\text{R1265:R1285}))\\ \end{array}\) & [30] \\ \hline
25. & MSI & Moisture Stress Index & MSI \(=\) R1599 / R819 & [16, 31] \\ \hline
26. & NDII & Normalized Difference Infrared Index & NDII \(=\) (R819 \(-\) R1649) / (R819 \(+\) R1649) & [32, 33] \\ \hline
27. & NWI\_1 & Normalized Water Index 1 & NWI \(=\) (R970 \(-\) R900) / (R970 \(+\) R900) & [23] \\ \hline
28. & NWI\_2 & Normalized Water Index 2 & NWI \(=\) (R970 \(-\) R850) / (R970\(+\)R850) & [23] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._describe the experimental setup, the used instruments, and the measured variables in more detail. Section 3 presenting the experimental results gives an extensive analysis of the temporal dynamics of differences in spectral reflectance for different experimental conditions. Here, we also try to extract the dominant spectral ranges and vegetation indices that allow soil moisture content in the root zone and N conditions to be derived from remote sensing. In particular we would like to derive and test an empirical relationship between spectral reflectance information and the soil WC and LAI. This is followed by a discussion and conclusion in Section 4.
# The Fuzzy Process Quality Evaluation Model for the STB Quality Characteristic of Machining
## 3 Fuzzy Testing Method
Therefore,  
\[x_{0}=\hat{Q}_{\text{ISS0}}\times\sqrt{\frac{\chi_{1-a/2,m(n-1)}^{2}}{m(n-1)} }+\frac{Z_{a/2}}{\sqrt{mn}}.\]  
Figure 1 presents a schematic diagram of membership function \(\eta(x)\) with vertical line \(x=k\). Figure 1: Schematic diagram of Membership function \(\eta(x)\) with vertical line \(x=k\). Let set \(A_{T}\) be the area of the membership function \(\eta(x)\), then  
\[A_{T}=\Big{\{}(x,\alpha)\Big{|}Q_{IM}\leq x\leq Q_{IR}(\alpha),0\leq\alpha\leq 1 \Big{\}}. \tag{33}\]  
As noted by [PERSON] [29], we can calculate the double area of set \(A_{T}\) as follows:  
\[a_{T}=2\times\int\limits_{Q_{IM}}^{Q_{IR}}\eta(x)\;d_{x}. \tag{34}\]  
Let set \(A_{R}\) be the area of the membership function \(\eta(x)\) on the right side of \(x=k\), then  
\[A_{R}=\Big{\{}(x,\alpha)\Big{|}k\leq x\leq Q_{IR}(\alpha),0\leq\alpha\leq a \Big{\}} \tag{35}\]  
where \(\eta(k)=a\), and we can calculate the double area of set \(A_{R}\) as follows:  
\[a_{R}=\int\limits_{k}^{Q_{IR}}\eta(x)\;d_{x}. \tag{36}\]  
Regarding the \(a_{R}\) as the numerator and the \(a_{T}\) as the denominator, [PERSON] [30] used \(a_{R}/a_{T}\) to perform fuzzy tests. [PERSON] et al. [17] and [PERSON] [29] simplified [PERSON]'s method [30], replacing \(a_{R}/a_{T}\) with \(d_{R}/d_{T}\) to perform fuzzy tests, where \(d_{R}=Q_{IR}-k\) and \(d_{T}=2\times(Q_{IR}-Q_{IM})\) can be expressed as follows:  
\[d_{R}=\hat{Q}_{\text{LSSO}}\times\sqrt{\frac{\chi_{0.995,m(n-1)}^{2}}{m(n-1)} }+\frac{Z_{0.005}}{\sqrt{mn}}-k \tag{37}\]  
\[d_{T}=2\times\hat{Q}_{\text{LSSO}}\times\left(\sqrt{\frac{\chi_{0.995,m(n-1)}^ {2}}{m(n-1)}}-\sqrt{\frac{\chi_{0.5m(n-1)}^{2}}{m(n-1)}}\right)+2\times\frac{ Z_{0.005}}{\sqrt{mn}}. \tag{38}\]  
Obviously, when \(k<Q_{IM}\), then \(d_{R}/d_{T}=0.5\). Besides, when \(k>Q_{IR}\), then \(d_{R}/d_{T}=0\). The fuzzy testing results in these two cases are consistent with statistical testing. When \(d_{R}/d_{T}\in(0,0.5)\), then it is necessary to apply the fuzzy method proposed by this study to the evaluation. First, let \(0\;<\!\phi_{1}\!<\!\phi_{2}\!<\!0.5\), where \(\phi_{1}\) and \(\phi_{2}\) can be determined by applying a previous data analysis or experts' accumulated experience. Many researchers believe that this model is more reasonable in practice than statistical testing [27; 28; 29]. Also, based on [PERSON] et al. [17], the decision rules of fuzzy tests are listed below:  
(1) If \(d_{R}/d_{T}\leq\phi_{1}\), then reject \(H_{0}\), and conclude \(Q_{\text{LSS}}<k\). (2) If \(\phi_{1}\leq d_{R}/d_{T}\leq\phi_{2}\), then do not make a decision on reject/not reject. (3) If \(\phi_{2}\leq d_{R}/d_{T}\leq 0.5\), then do not reject \(H_{0}\), and conclude \(Q_{\text{LSS}}\geq k\).
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
## 2 Materials and Methods
### Model Selection  
Given that a wide variety of GMMs in (1) can be estimated by varying the number of mixture components and the covariances' decomposition in (2), selecting the appropriate model is a crucial matter. A popular option consists of choosing the \"best\" model using the Bayesian information criterion (BIC; [23]), which for a given model \(\mathcal{M}\) is defined as  
\[\text{BIC}_{\mathcal{M}}=2\ell_{\mathcal{M}}(\widehat{\mathbf{\theta}})-\
u_{ \mathcal{M}}\log(n),\]  
where \(\ell_{\mathcal{M}}(\widehat{\mathbf{\theta}})\) stands for the maximized log-likelihood of the data sample of size \(n\) under model \(\mathcal{M}\) and \(\
u_{\mathcal{M}}\) for the number of independent parameters to be estimated. Another available option in clustering is the Integrated Complete Likelihood (ICL; [24]) criterion given by  
\[\text{ICL}_{\mathcal{M}}=\text{BIC}_{\mathcal{M}}+2\sum_{i=1}^{n}\sum_{k=1}^{ G}c_{ik}\log(\widehat{z}_{ik}),\]  
where \(\widehat{z}_{ik}\) is the conditional probability that \(\mathbf{x}_{i}\) arises from the \(k\)th mixture component from Equation (4), and \(c_{ik}=1\) if the \(i\)th observation is assigned to cluster \(\mathcal{C}_{k}\) and \(0\) otherwise.  
Both criteria evaluate the fit of a GMM to a given set of data by considering both the likelihood of the data given the model and the complexity of the model itself, representedby the number of parameters to be estimated. Compared to the BIC, the ICL introduces a further penalization for the overlap of the clusters. For this reason, the ICL tends to select models with well-separated clusters.
## 3 Results and Discussion
### Microstructure of Geopolymers
#### 3.2.2 Morphology Analysis  
The changes in the amount of calcium hydroxide should influence the microstructure of the materials prepared. The morphology of geopolymer materials containing different calcium hydroxide content was observed through SEM investigation. SEM pictures of materials prepared using an 8 M NaOH activator and calcium hydroxide of 0-75 g at 28 days of curing time are shown in Figure 5. It can be seen that raw materials are embedded together in geopolymers. However, some particles or fragments with relatively weak adhesion on the surface were also observed for all samples. More unreacted and nonactive particles were detected for the geopolymer sample prepared without calcium hydroxide addition (Figure 5a). It is clearly seen that as more calcium hydroxide was added, the geopolymer materials became denser, with fewer voids or holes present. This result is due to the formation of geopolymeric products and C-S-H, C-A-S-H, which can help bridge the gaps between the unreacted or nonactive particles. Significantly, the gel form, which could result from a geopolymerization reaction under an alkaline activator, was obviously seen in the sample prepared with 45 g Ca(OH)2. This finding is greatly consistent with the enhancement of its compressive strength analyzed above.  
Figure 4: XRD diffractograms of geopolymer materials prepared using 8 M NaOH activator and Ca(OH)2 content of 15 g (a), 30 g (b), 45 g (c), 60 g (d), 75 g (e) at 28 days of curing time (Q- quartz; K: kaolinite; I: illite; H: hematite; C-A-S-H: calcium aluminosilicate hydrate; C-S-H: calcium silicate hydrate).
## 4 Results and Discussions
### Technical Feasibility Analysis on HFUVAS of Quadrilateral Microlens Array  
A high-quality machined structure with sub-micrometer form accuracy and nanometer-scale surface roughness is the premise of optical application. Obtaining the error map of the designed structure is a reasonable method to evaluate the form accuracy of a 3D structure. To investigate the technical feasibility of HFUVAS in the machining of the quadrilateral microlens array. Figures 12 and 13 present the measured 3D structure, the surface morphology of a single lenslet after removing the structure form, the iterative closest point (ICP) matching result, and the obtained error map from matching for the 3 \(\times\) 3 lenslets machined by CS and HFUVAS, respectively. The form accuracy and surface finish of the machined microlens array were significantly improved for HFUVAS compared with CS. Single lenslet machined by HFUVAS achieved an optical application level with a surface finish of 4 nm \(Sa\) and a form error \(Sz\) within 0.13 \(\upmu\)m, compared with 37 nm and 1.2 \(\upmu\)m obtained by CS correspondingly. The form error of 3 \(\times\) 3 lenslets was much larger than that of the single lenslet, which could be more accurate and reliable to evaluate the form accuracy of a 3D structure. The form error of 3 \(\times\) 3 lenslets machined by HFUVAS and CS were within 0.6 \(\upmu\)m and 1.5 \(\upmu\)m, respectively. The surface roughness and form accuracy of the microlens array machined by HFUVAS can meet the requirement of the optical application. This indicates that HFUVAS is a technically feasible method to fabricate 3D structures with a sharp edge on steel materials for molding of optical products.  
Figure 11: (**a,b**): SEM of the cutting tool after CS of the microlens array, (**c,d**): EDS analysis results of (**b,e,f**): SEM of the cutting tool after HFUVAS of the microlens array.
# Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
## 4 Experiments
### Implementation Details  
As far as we know, we are the first to design a multimodal GAN for data augmentation in the problem of audio-visual emotion recognition. Under different experimental settings, we compare our approach (Ours) with the following methods to show the superiority of our framework: (1) Baseline (A), which only uses the audio modality of the original training set for emotion classification. (2) Baseline (V), which only uses the visual modality of the original training set for emotion classification. (3) Baseline (A+V), which only uses the multimodal data of the original training set for emotion classification without data augmentation. In addition, some representative previous works of audio-visual emotion recognition are also compared, including (4) Attention Mechanism [97], (5) Canonical Correlation Analysis (CCA) [98], and (6) Tensor Fusion [99]. Moreover, we report the performance of (7) Ours (wo corr), which first uses our multimodal conditional GAN without the correlation loss to generate data, and then augment the original dataset with the generated data, finally performing emotion classification with the augmented training set. By comparing the performance of Ours and Ours (wo corr), we can show the importance of the proposed correlation loss in our framework.  
In the training stage, we use the segment-level samples to perform classification. After obtaining the segment-level classification results, we use the majority rule to predict the video-level emotion labels. To ensure the reliability of the experimental result, we repeat each experiment five times and use the average classification accuracy (%) as the final result. For eNTERFACE'05, RAVDESS, and CMEW, we set \(\lambda\) to 0.001, 0.01, and 0.1, respectively. In addition, Adam optimizer [100] with the learning rate of 0.0002 is adopted, and the batch size is set to 100. It is worth noting that our architecture is a GAN conditional on the class label. It can generate samples of different categories, which can internally prevent mode collapse [40,101]. In addition, inspired by [102,103], the above hyperparameters and network weights are carefully set to deal with other training problems of our multimodal conditional GAN. Pytorch [104] is used to implement our architecture on an NVIDIA TITAN V GPU card.
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 1 Introduction
The Radio Frequency Mass Gauge (RFMG) method has shown rapid progress during recent years and the proven uncertainty in space is approximately 0.5-2%. The RFMG method [25; 26; 27; 28; 29] is based on the frequency response of the tank to certain incentives given at different propellant fulfillments. The tank should be reconstructed before the RFMG method is used. [PERSON] [30] introduced the advantages and disadvantages of the RFMG method via theoretical analysis. From a recent report, the RFMG method has been verified in the SHIIVER project, and the reported uncertainties were approximately 0.5%. Though many attempts have been made, the vital problem of MG has still not been solved completely. Among the above methods, NASA listed four promising solution methods for future study: CMG, TMG, PGS, and RFMG. The PGS method is one of the traditional residual propellant mass gauge methods [31; 32; 33; 34; 35; 36; 37; 38], which has the advantages of high accuracy, high success rate, and no need for engine maintenance. [PERSON] [16; 17] has applied the PGS method in China's satellites' residual propellant mass gauge by advancing the propellant pipes in the FY-4 satellite. This paper will discuss the effect factor of the PGS method and the measuring uncertainty in applications. Some suggestions on the modification of precision will be proposed, and the results of the ground tests are given.
## 3 Results  
The results section consists of two main parts. First, results from the network of IGS stations are analyzed through a comparison of dual-frequency, triple-frequency, and mixed dual-/triple-frequency results. The latter results (simultaneous processing of dual-frequency and triple-frequency satellites) are analyzed in more detail in terms of convergence time and accuracy, with sample results from a single dataset being provided as well. The second part of the results section focuses on the kinematic vehicle datasets and analyzes the performance that can be achieved in real kinematic scenarios. Results are discussed as they are presented, and where applicable, compared to published research.
## 6 Fuzzy Based Adaptive Resource Allocation
### Experimental Results and Discussions
Figures 15 and 16 depict clearly the performance improvement achieved as a result of employing the fuzzy-rule based resource allocation scheme. With reduced blocking probability, more video and data requests can be admitted and potential revenue increase can accrue for service providers whilst maintaining high level of quality of user experience. From Figure 17, the cumulative distribution functions of the total bandwidth utilization during a single experimental run with \(\rho=0.2\) for the three scenarios are shown. The plots reveal performance improvement of the FRB system over the other two scenarios in terms of bandwidth utilization. Indeed, the system performance (service availability/blocking probability) can be tuned by changing the rules in the fuzzy system to attain desired performance goals and/or optimize the utilization of one or more of the service classes over others if for example there is a particular incentive in doing so, say cost reduction or revenue improvement. Figure 14: Class 1 (voice) services blocking probabilities for the three scenarios. Figure 13: Overall service availability plot for the three scenarios. Figure 16: Class 3 (data) services blocking probabilities for the three scenarios. Figure 17: Cumulative Distribution Functions of total bandwidth utilization for \(\rho=0.2\). Figure 15: Class 2 (video) services blocking probabilities for the three scenarios.
## 4 Stray Fields Compensation
Under these conditions the width of the cloud also changes noticeably, but the 1 s long dynamics within gray molasses is very non-trivial and the cloud's shape becomes distorted, making the width an unreliable measure. The maximum of the atom number within a chosen region of interest allows us to identify the magnetic field that compensates stray fields the best. We want to emphasize that the long gray molasses hold time exceeds typical timescales used for efficient cooling by nearly two orders of magnitude and we believe that it is a result of careful balancing of intensities and polarizations of cooling beams. During the hold time, the cloud disperses significantly due to heating of the sample and diffusion. For compensated stray fields, exactly on two-photon resonance, the temperature increases from \(\sim\)8 u to \(\sim\)100 uK after 1 s, and the increase is higher away from the two-photon resonance or when stray fields are not well compensated. Several articles on sub-Doppler cooling (e.g., [18, 19, 28]) have reported a quadratic increase in the cloud's temperature with magnetic field during gray molasses cooling. From the inset of Figure 3c, we extract the dependence of temperature on the magnetic field, obtaining \(\Delta T=225(10)B_{\rm net}^{2}\) uK/G\({}^{2}\), a result comparable to \(\Delta T=300(100)B_{\rm net}^{2}\) uK/G\({}^{2}\) observed by [PERSON] et al. [19]. We plot \(\Delta T\) for varying angles \(\phi\) and \(\theta\) (i.e., for varying net field at the location of atoms) in Figure 3d, which shows qualitative agreement with data obtained to prepare Figure 3b. Due to the uncertainty in determining \(\phi_{\rm min}\) and \(\theta_{\rm min}\), for the stray field estimated at \(\sim\)700 mG, we put a conservative upper limit of \(\sim\)100 mG on the remaining, uncompensated field.
## 2 Impact Factors on Power Quality
### Types of Variation  
The impact of the electrical environment results in variations of different periods, which are displayed in Figure 1[8].  
Short-term variations are mainly influenced by the consumer usage and generation behavior of the connected devices. Certain consumer topologies show a distinct behavior with different daily patterns of current quality parameters (e.g., working days vs. weekends in office buildings). This repetitive behavior can be used to derive emission profiles, e.g., for current harmonics as shown in [9], to model their impact on residential loads [10] or identify load patterns for single households [11]. Considering harmonic currents at the bus bar of public LV networks can provide valuable qualitative information about the dominant circuit topologies for different consumer configurations [12]. Generation in German public LV grids is mainly dominated by small PV installations and occasionally by micro combined heat and power (micro-CHP) installations, which are usually located in residential areas. While PV installations always require an inverter, micro-CHPs can be connected either directly, or via an inverter, to the grid. In the future, it is expected that converter-based storage applications will also continuously increase, especially in residential areas. Both inverters and converters contribute, due to the power electronics, to harmonic emission. The network topology normally does not contribute to short-term variations.  
Medium-term variations, over periods of several months, are likely related to general changes in consumer and generation behavior, which are mostly linked to seasonal effects [7]. Early nightfall during winter months can lead to an increased use of equipment like illumination, and subsequently result in a higher emission of harmonics. A more detailed methodology for the analysis of seasonal variations can be found in [8].  
Long-term variations are usually characterized by trends, which can only be identified over several years [13]. One possible reason is the introduction of new device technologies at a larger scale. For example, the introduction of electrical vehicle chargers might lead to an increased emission level of current harmonics or a decreased emission level, due to cancelation effects between the existing and newly introduced device topologies [14].  
Nevertheless, the consumer topology is the dominant impact factor for public LV grids in Germany. The impact is highly visible due to distinctive daily and weekly cycles, especially in the emission of harmonic currents.