esa-sceva/satcom-chunk-collection huggingface dataset:
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 1 Introduction
[17] studied the relationship between pressure field and rainfall, and stated that the pressure field was significantly correlated with weather systems producing rain. This is likely due to a short-wave trough with large temperature gradients. Low fog visibilities over the ocean are also found to be strongly related to differences between sea surface temperature (Ts) and air temperature (Ta). [PERSON] et al. [18] also studied the formation of sea fog in the Bohai Sea, and emphasized that the temperature difference between ocean surface and air temperature (Ts-Ta) (TDsa) played an important role on the formation of sea fog type and occurrence. These works suggest that meteorological parameters such as LWC, Nd, RH, Uh, Ta, Ts, and pressure should be considered in Vis calculations [7], where LWC is defined as the quality of liquid water in a unit volume of air, Nd is defined as number of droplets in a unit volume of air, and RH is defined as the ratio of absolute humidity in the air to the saturated absolute humidity at the same temperature and pressure. Considering that a relationship between Vis and both atmospheric optical thickness and meteorological parameters exists, a non-linear fitting method can be used to obtain a relationship between Vis and other parameters. The artificial neural network (ANN) method used in the analysis here utilized the satellite-based aerosol/cloud products plus reanalyzed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer gridded Vis, and generated Vis over the Indian Ocean to test the accuracy of the model. Overall, the main objective of this work, using in situ and remote sensing observations and reanalysis data, is to generate an accurate and reliable gridded horizontal Vis data set that in the past around the world was used for mainly marine environments based on an ANN analysis. Section 2 provides info on the data and ANN model description. Section 3 provides the results and discussion. Then, Section 4 provides the conclusions.
# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 5 Conclusions  
The work presented in this paper proves how SM sentiment can be utilized to help users make investment decisions and creates a base to allow further research to be conducted. To achieve this outcome, we strategically designed and implemented a prototype based on the findings in the literature review. Furthermore, a novel approach was implemented by factoring in a Twitter like weighting into the LRA's sentiment score, with the goal of accurately mapping the collective sentiment of users from single tweets.  
We found that the trend of SM sentiment had a similar pattern with the stock market performance for some of the selected assets. Based on these findings, a user would be able utilize the sentiment to make investment decisions.  
We have also shown how to build a system that can calculate SM sentiment related to financial assets, and how it can be visualised in a way to help users make investment decisions. As a result, the work of this paper can be used as a base to further explore the field of research.  
However, we also found cases in the results that did not show similarities as clearly. Going forward, we recommend a continued thorough investigation into how SM sentiment can be utilized and visualized to make stock market predictions, which would provide more insight into making investment decisions. One recommendation would be to integrate stock market data into the assets sentiment chart, so users can make comparisons between the two trends more easily. Another recommendation for the prototype would be to improve the deep learning system. To do this, different datasets should be experimented with to train the SA model. Additionally, different algorithms such as NN's should be tried and tested to improve the performance of the sentiment predictions. Furthermore, our prototype only factors in tweets written in English, and as a result, is exposed to far less SM data for each asset, reducing its effectiveness in tracking the correlated public sentiment. We suggest that further work is undertaken to update the system to accept multiple languages. Finally, this paper only tested against popular S&P500 stocks. Further analysis could be undertaken on less popular stocks to see how usable the prototype could be against a wider range of companies.  
Conceptualization, B.H. and C.C.; methodology, B.H. and C.C.; software, B.H.; validation, B.H., C.C. and N.P.; writing--original draft preparation, B.H.; writing--review and editing, B.H., C.C., N.P. and W.J.B. All authors have read and agreed to the published version of the manuscript.
## 3 Rotor-Side External Retrofit Techniques
### DC Chopper  
A DC-chopper is a resistance circuit connected in parallel with a DC-link capacitor between the RSC and GSC, as shown in Figure 6. A braking resistor is used to maintain DC-link voltage by accepting transient rotor overcurrent [24]. The insulated gate bipolar transistor (IGBT), in series with resistors, controls the inserting and quitting time of the DC chopper circuit. Whenever grid faults occur, the rapid increase in rotor current will lead to DC-link overvoltage by charging the DC-link capacitor. When DC-link voltage exceeds the threshold value, the IGBT should be closed to short the link with braking resistors [25; 26]. As a result, DC-link voltage can be adjusted to an acceptable level. However, the rotor transient overcurrent and DC-link overcurrent cannot be restrained by a conventional DC-chopper. In this situation, the RSC switching is stopped. A modified DC-chopper was proposed not only to restrain DC-link voltage on a desirable level, but also restrict the overcurrent on both the rotor side and the stator side in Reference [27]. The modified DC-chopper is placed between the DC-link capacitor and RSC, and DC chopper resistance can be inserted through three extra semiconductors. By employing the modified DC chopper, rotor current and stator current, as well as DC-link voltage, can be controlled without utilizing the extra fault-current-limit method and voltage sag compensation approach [27].  
Figure 5: Parallel R-L configuration crowbar.  
Figure 6: Direct current (DC) chopper protection circuit.
## 3 Results and Discussion
### Spectroscopic Analysis  
#### 3.4.1 NMR Spectroscopy  
\({}^{1}\)H NMR spectroscopy was employed to determine the qualitative composition of a mixture of substances contained in extracts obtained after washing with dichloromethane (DCM) and isopropanol (iPrOH) (Figure 11). The spectra showed significant similarities, but with differences in their intensity. All the spectra obtained exhibited intense signals from aliphatic chains in the range of 0-3 ppm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**CZT2** & **T\({}_{\text{max1}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max2}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max3}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max4}}\)**[\({}^{\circ}\)C]** \\ \hline \textless{25} \(\upmu\)m & 67.9 & 316.3 & 435.0 & 700.3 \\ \textless{25}-40 \(\upmu\)m & 69.8 & 319.1 & 438.2 & 703.2 \\ \textless{40}-100 \(\upmu\)m & 72.6 & 326.3 & 442.3 & 680.0 \\ \textless{100}-250 \(\upmu\)m & 66.5 & 336.0 & 448.0 & 691.3 \\ \textless{250} \(\upmu\)m–1 mm & 66.2 & 335.9 & 449.4 & 686.4 \\ \hline
**BIS** & **T\({}_{\text{max1}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max2}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max3}}\)**[\({}^{\circ}\)C]** & **T\({}_{\text{max4}}\)**[\({}^{\circ}\)C]** \\ \hline \textless{25} \(\upmu\)m & 71.9 & 310.6 & 428.6 & 670.6 \\ \textless{25}-40 \(\upmu\)m & 70.5 & 317.0 & 433.0 & 668.4 \\ \textless{40}-100 \(\upmu\)m & 71.5 & 323.0 & 437.0 & 669.5 \\ \textless{100}-250 \(\upmu\)m & 70.6 & 327.3 & 431.7 & 669.7 \\ \textless{250} \(\upmu\)m–1 mm & 69.3 & 324.4 & 433.7 & 678.0 \\ \hline \end{tabular}
\end{table}
Table 7: TGA results data for the CZT2 and BIS samples.  
Figure 11: NMR spectroscopy of extracts obtained from fractions extracted with DCM (red) and iPrOH (blue).  
Furthermore, the region from 3 to 8 ppm can be divided into distinct signal groups: (a) 3.4-4.4 ppm from oxygenated groups and oil components; (b) 4.6-5.6 ppm from butadiene derivatives; (c) 6.4-7.8 ppm from aromatic groups.  
The qualitative composition indicates the presence of organic residues that contain substances that are part of the composition of tires. The data presented here are consistent with those available in the literature, suggesting a commonality in the composition of such extracts [23].
## 3 Results and Discussions
### Adsorption Mechanism  
For the sake of further clarifying the adsorption mechanism of PVKAP to U(VI), XPS spectra were used to analyze the changes of surface functional groups after enrichment of U(VI) by PVKAP. Figure 9 shows that the specific enrichment of U(VI) by specific functional groups was deduced from the O 1s and P 2p fine XPS spectra of PVKAP. In general, there are two possible ways for the adsorption process between the phosphonic acid group and U(VI). One way is that the H\({}^{+}\) of the phosphonic acid group is exchanged with U(VI) by ion-exchanged process, and the other way is by coordination  
Figure 8: Reusability studies of PVK and PVKAP on the removal of U(VI) from aqueous solution, \(T\) = 293 K, pH = 5.0 \(\pm\) 0.1, C \({}_{\text{U(VI)Initial}}\) = 36.0 mg L\({}^{-1}\), m/V = 0.1 g L\({}^{-1}\).  
Figure 7: Competitive adsorption of coexistent ions by PVKAP, \(C_{0}\) = 0.5 mmol L\({}^{-1}\) for all cations, pH = 5.0 \(\pm\) 0.1, m/V = 0.05 g L\({}^{-1}\).  
between the phosphonic acid group (P=O) and UO\({}_{2}\)\({}^{2+}\)[63]. The peaks at 134.05 (P 2p\({}_{1/2}\)) and 133.05 (P 2p\({}_{3/2}\)) eV shown in the P 2p high-resolution spectrum of PVKAP reveal that P 2p can be attributed to phosphorus atoms in two phosphine-containing groups.  
When PVKAP is loaded with U(VI), the peak intensities of P 2p\({}_{3/2}\) and P 2p\({}_{1/2}\) are significantly reduced, and the binding energies of P 2p\({}_{3/2}\) and P 2p\({}_{1/2}\) are increased from 133.05 and 134.05 eV to 133.55 and 134.65 eV, respectively. These results indicate that the phosphonic acid group forms a new chemical bond with UO\({}_{2}\)\({}^{2+}\)[41].  
In addition, the O 1s high resolution spectrum from PVKAP can be divided into four peaks, namely peaks of 531.48, 532.38, 533.08, and 534.18 eV, respectively, corresponding to P=O, C=O, C-O-C, and -OH [11, 64]. When the PVKAP is loaded with U(VI), the characteristic peaks at P=O and -OH move from 531.48 and 534.18 eV to 531.18 and 533.78 eV, respectively, and the relative peak area decreases significantly, indicating a coordination of P=O and -OH with U(VI) was formed. However, after PVKAP loading with U(VI), the positions of the peaks for C-O-C and C=O remained unchanged, indicating that C-O-C and C=O did not participate in the coordination. Therefore, the enrichment of U(VI) by PVKAP can be mainly ascribed to the coordination of phosphonic acid groups rather than carboxyl sites.
# Required Moment Sets: Enhanced Controllability Analysis for Nonlinear Aircraft Models
## 2 Materials and Methods
### Calculation of Required Maneuver Set and Assembly of Total Required Set
#### 2.5.2 Controllability Evaluation  
Now that attainable and required acceleration sets are determined, the easiest way to assess the controllability is just to check if, in any direction, the required moment set exceeds what is attainable:  
\[\min(\mathbf{a}_{AMS}-\mathbf{a}_{RMS})\geq 0.. \tag{31}\]  
According to this definition, Equation (31) is a condition for fulfilling the controllability requirements. Note that this simple comparison contributes to two major objectives of this paper. First of all, a very simple final evaluation of controllability is possible. Second of all, the RMS is obtained from a simple sum of individual sets, therefore it is always traceable what the corresponding contributions of individual sets are.  
In order to obtain continuous instead of binary controllability measures we can assemble a margin vector, which contains the differences of attainable and required accelerations. In order to obtain a more relative controllability measure, the margin can be normalized by the required moment set that obtains a relative margin vector \(\mathbf{m}\in\mathbb{R}^{1\times n_{\beta}}\).  
\[\mathbf{m}=(\mathbf{a}_{AMS}-\mathbf{a}_{RMS})\oslash\mathbf{a}_{AMS}. \tag{32}\]  
In order to assess the overall controllability, various norms can be used to transform the vector margin to a scalar controllability measure.
# Coordination and Private Information Revelation
## 5 Purification of Agent B's Mixed Strategies
### Analysis: Non-Revelation by the A Agent in Stage 1
Please note that \(\frac{\partial[\pi_{A}(NR)-\pi_{A}(R)]}{\partial\theta_{NR}}=1/2>0\), and at \(\theta=\theta_{h}\), this difference \(\pi_{A}(NR)-\pi_{A}(R)=\frac{\theta_{h}}{2}-c[\frac{1}{2}-G(\hat{b}_{NR})]\). This expression is positive, iff \(\hat{b}_{NR}>G^{-1}(\frac{c-\theta_{h}}{2c})\). Finally, from Equation (16), \(\hat{b}_{NR}=G^{-1}(\frac{c-\hat{\theta}_{NR}}{2c})>G^{-1}(\frac{c-\theta_{h} }{2})\), since \(\hat{\theta}_{NR}<\theta_{h}\) and \(G(B)\) is strictly increasing. As discussed earlier, \(\pi_{A}(R)=\pi_{A}(NR)\) at \(\theta=\hat{\theta}_{NR}\).Therefore, in the range \((\hat{\theta}_{NR},\theta_{h}]\), \(\pi_{A}(R)<\pi_{A}(NR)\). 3. Next, consider \(\theta\) in the range \([\theta_{l},\theta)\). Over this range \(\pi_{A}(NR)\) is independent of \(\theta\), whereas \(\pi_{A}(R)\) is strictly decreasing in \(\theta\). Given that \(\pi_{A}(NR)\) equals \(\pi_{A}(R)\) at \(\hat{\theta}_{NR}\), it follows that \(\pi_{A}(NR)>\pi_{A}(R),\forall\theta\in[\theta_{l},\hat{\theta}_{NR})\). Therefore, whenever \(\hat{\theta}_{NR}>\theta_{l}\), A's expected payoff from non-revelation is greater than that from revelation \(\forall\theta\in\ [\theta_{l},\theta_{h}]\), except for \(\theta=\hat{\theta}_{NR}\), where these payoffs are equal. Finally, consider the case where \(\hat{\theta}_{NR}=\theta_{l}\) in the non-revelation equilibrium. Please note that for any \(\theta\in\Theta\), \(\pi_{A}(R)=\frac{\theta+c}{2}<\theta+\frac{c-\theta_{l}}{2}=\pi_{A}(NR)\). 1. Given that \(F(\theta)+\frac{\theta}{2c}+\frac{\xi^{-1}((c-\theta)/2c)}{c}\) is monotonic in \(\theta\), from (17) it follows that \(\hat{\theta}_{NR}\) is unique. This in turn ensures that \(\hat{b}_{NR}\) is unique.
# Novel Methodology for Condition Monitoring of
## 3 Methodology
### Calculation of Features  
Commonly, condition monitoring strategies are based on the calculation of a high-dimensional set of features from different acquired physical magnitudes [37]. However, in this proposed work, we consider the estimation of a specific set of significant features to characterize the thermal behavior of the gearbox transmission system under assessment; thus, the set of features is: maximum temperature, minimum temperature, and average temperature; and the analysis of the asymmetry of thermographic images is also considered based on the calculation of characteristics' stated features of first statistical order (average histogram, medium, standard deviation, variance, skewness, kurtosis, entropy, and energy). In addition, these features have the ability to adapt to changes according to the operating conditions of the system.  
Table 2 summarizes the mathematical equations of the set of considered features to characterize the thermal behavior of the gearbox transmission system. In these equations, \(f_{x}\) indicates the number of pixels per column (width of the image), and \(g_{y}\) the number of pixels per row (height of the image); \(p\) is the number of distinct gray levels in the quantized image, \(h\)(\(p\)) is the intensity of the pixel in the histogram, and \(q\) is the level of intensity of the image.  
On the other hand, the average intensity determines the brightness or darkness of the image, skewness defines the degree of asymmetrical property of the histogram with respect to average intensity, kurtosis measures the peakness or flatness of the intensity distribution with respect to the normal distribution, entropy measures the randomness of the input image, and variance outlines the deviation of gray-level pixels from the mean [32].  
Figure 2: Thermal matrix of the infrared thermography.
## 3 Materials and Methods
### VOC Analysis
#### 3.6.1 GC-MS Conditions  
The VOCs were analyzed using an Agilent 7890B GC coupled to an Agilent MSD 5977A quadrupole mass spectrometer (Agilent Technologies, Beijing, China). The chromatographic separation was carried out with 30 m \(\times\) 0.25 mm L.D. \(\times\) 0.25 \(\upmu\)m SOLGEL-WAX column (SGE Analytical Science, Ringwood, Australia) with hydrogen as the carrier gas at a flow rate of 1.6 mL/min. The initial pressure was 4.6 psi with the average velocity being 26 cm/s. The oven temperature was held at 40 \({}^{\circ}\)C for 3 min, increased at a rate of 3 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C, then increased at 4 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, and held for 5 min. The transfer line to the MS and the quadrupole were set to 230 and 150 \({}^{\circ}\)C, respectively. The mass spectrometer was operated at a scan speed of 5.1 scans/s and mass spectra recorded in the range of 30-300 m/z. Carryover between GC runs was evaluated using empty vials as blanks before and after every 31 samples.
## 3 Enabling the Enhancements of Language Features and Domain Knowledge
### Integrating Domain Information
After obtaining the multiple-hot vector \(v_{tag}\), we can obtain the fixed-dimensional representation integrated with domain information through a fully connected layer whose activation function is ReLU. The Introduction of a Domain Description of the Dependency Library. The simplest way to vectorize words is to use one-hot encoding; however, the dimension of the word vector overgrows with the dictionary size, incurring high computational cost. The LibRepLayer avoids the curse of dimensionality by mapping the word identifier to a low-dimensional vector through a small neural network. The bag-of-words (BoW) model is also a common document representation method. The shortcoming of this method is that it ignores the word order and is insensitive to negative words, which results in two sentences with opposite meanings being considered \"highly similar\". To cope with the word order problem, EMGSCC uses a long short-term memory recurrent neural network (LSTM) [22] to convert a dependency library's functional description _desc_ into a fixed-dimensional representation \(v_{desc}\). LSTM has been proven to be effective in dealing with sequence data and addresses the long-term dependency problem by selectively memorizing valuable information and discarding useless information.
## 5 Performance Evaluation  
In this section, the proposed algorithm EBRA-NLE is verified by numerous simulation experiments in MATLAB 2019, and compared with DS-EERA [22] and MCRP [19]. In order to avoid the contingency of the experimental results, all sensor nodes are randomly distributed in the monitoring area, and the average of the experimental results are adopted. The simulation parameters are shown in Table 1.  
Figure 4 shows an example of data transmission path established by EBRA-NLE algorithm, where 100 nodes (i.e., network size N = 100) randomly distributed in \(100\times 100\) m\({}^{2}\) monitoring area.
To mitigate uncertainties with the interpretation of the ray-path averaged characteristics of RO, these PRO constellation observations are examined in Section 5 in terms of their spatial and temporal proximity to existing passive microwave (MW) observations taken by operationally sustained low-Earth orbiting satellites, such as the Advanced Technology Microwave Sounder (ATMS) onboard the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS). While the wide-swath (2200 km) ATMS radiometers are exploited for RO assessment [22; 23], their observations can also provide additional precipitation context [24] to the constellation PRO observations.
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Inference-Time Re-Parameterization of MRAB  
During inference, the multi-branch convolutions and the following BN are converted into a single-path \(3\times 3\) convolution through the technique of re-parameterization. Let \(X\) denote the input of the MRAB, and \(Y\) denote the output of BN. The process of re-parameterization can be described as:  
\[Y=BN(W^{3}*X+W^{1}*X+X), \tag{2}\]  
where \(W^{3}\) denotes the kernel of the \(3\times 3\) convolution branch, and \(W^{1}\) denotes the kernel of the \(1\times 1\) convolution branch. Since the identity branch can be expressed as a particular \(1\times 1\) convolution, and the \(1\times 1\) convolution can be considered a \(3\times 3\) convolution with zero padding, the re-parameterization during inference is:  
\[Y =BN(W^{3}*X+W^{1}*X+X) \tag{3}\] \[=BN(W^{3}*X+W_{1}^{3}*X+W_{identity}^{3}*X)\] \[=BN((W^{3}+W_{1}^{3}+W_{identity}^{3})*X),\]  
Figure 3: Structures of MRAB during training (**left**) and during inference (**right**).  
where \(W_{1}^{3}\) and \(W_{identity}^{3}\) denote the equivalent kernels of the \(1\times 1\) convolution branch and the identity branch after re-parameterization.  
Furthermore, \(BN\) in the above equation can also be integrated into convolution during inference. Let \(\{W,b\}\) represent the \(3\times 3\) convolution kernel and bias obtained by the three-way branch fusion in Equation (3), and \(\mu,\sigma,\gamma,\beta\), respectively, represent the accumulated mean, standard deviation, learned scaling factor, and bias of the \(BN\) layer. Then, the convolution kernel and bias obtained after integrating \(BN\) are:  
\[\begin{split} W^{\prime}&=\frac{\gamma}{\sigma}W, \\ b^{\prime}&=\frac{\gamma(b-\mu)}{\sigma}+\beta. \end{split} \tag{4}\]  
Since the haze distribution often has a non-homogeneous character in many real scenes, a spatial attention mechanism is advantageous for dealing with such a situation. We design an improved spatial attention module, where 3D MaxPool, \(1\times 1\) convolution, ReLU, and \(1\times 1\) convolution are applied subsequently, and then a Sigmoid operation is used to output the attention weights:  
\[Y_{sa}=\sigma(Conv(Relu(Conv(MaxPool(X))))), \tag{5}\]  
where \(X\) is the input feature, \(\sigma\) is the Sigmoid function, and \(Y_{sa}\) is the spatial attention weights. The output \(Y\) is derived as the element-wise product of the input \(X\) and the spatial attention weights \(Y_{sa}\):  
\[Y=Y_{sa}\circ X. \tag{6}\]  
Different channels of a feature map often have different degrees of importance; thus, the channel attention mechanism is necessary for low-level vision tasks, such as dehazing. We follow the channel attention design in [13].