esa-sceva/satcom-chunk-collection huggingface dataset:
# A Polarity Capturing Sphere for Word to Vector Representation
## 5 Results and Discussion
### Opinion Mining  
Opinion mining application refers to classifying a review as positive or negative. We applied our approach to learn vectors for words that have bias from which sentiments can be inferred. We have begun with a small set of such words then grow our sphere by obtaining synonyms and antonyms for these words. The generated word vectors are used to compute the sentiment that the review reflects. Every word in the review has two sub-scores that are obtained by respectively the dot product between this word and a number of positive words on one hand, and a number of negative words  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F-Score** & **Accuracy** \\ \hline Sphere (our approach) & **0.72** & **0.72** & **0.72** & **72.39\%** \\ word2 vec & 0.65 & 0.58 & 0.51 & 57.77\% \\ GloVe & 0.67 & 0.62 & 0.58 & 61.61\% \\ fastText & 0.67 & 0.66 & 0.65 & 65.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Antonym/synonym discrimination results—nouns.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F-Score** & **Accuracy** \\ \hline Sphere (our approach) & **0.83** & **0.81** & **0.81** & **81.49\%** \\ word2 vec & 0.70 & 0.54 & 0.43 & 53.80\% \\ GloVe & 0.69 & 0.59 & 0.53 & 59.02\% \\ fastText & 0.66 & 0.63 & 0.61 & 62.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Antonym/synonym discrimination results—verbs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Precision** & **Recall** & **F-Score** & **Accuracy** \\ \hline Sphere (our approach) & **0.86** & **0.83** & **0.83** & **82.98\%** \\ word2 vec & 0.61 & 0.56 & 0.50 & 55.96\% \\ GloVe & 0.65 & 0.60 & 0.57 & 60.43\% \\ fastText & 0.67 & 0.67 & 0.67 & 66.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Antonym/synonym discrimination results—adjectives.  
on the other hand, which are specifically collected to gauge sentiment. We applied the approach to 20,000 hotel reviews [59] from \"515K Hotel Reviews Data in Europe\" dataset [60] that are collected from booking.com where each review is annotated as positive or negative. Furthermore, we used vectors from other models to compare the results. We achieved the highest F-score compared to word2 vec [17], GloVe [19], and fastText [20]. The scores obtained are 0.81, 0.79, 0.74, and 0.79 respectively [61].
## 3 Results and Discussion
### Differential Capacitance Calculation and Analysis
It is also observed that, despite whether the electrode is charged positively or negatively, there is a more significant increase in capacitance in the rough cases than in the non-rough cases. This phenomenon can be attributed to the fact that the rough electrode increases the  
Figure 8: Correlation between the electrode charge density and the potential drop across the electrical double layers (EDLs) on the non-rough and rough electrodes. effective contact area between the ions and electrode, which attracts more counterions to the electrode surface compared to the non-rough electrode. In addition, the double-hump-shaped C-V curve for the rough electrode is asymmetric with respect to the symmetric one for the non-rough electrode, where the capacitance is increased more significantly when the electrode is positively charged. This must be caused by the different impact of the grooved electrode surface on the accumulation of [EMIM]\({}^{+}\) cations and [BF4]\({}^{-}\) anions. Clearly, when the electrode is charged, it is harder for the larger-sized [EMIM]\({}^{+}\) cations to accumulate in the narrow grooves on the rough electrode when compared with the smaller [BF4]\({}^{-}\). Consequently, a smaller capacitance appears on the negatively charged rough electrode. The comparisons between the C-V curves of the EDLs on the non-rough and rough electrodes suggest that roughness on the electrode can increase the capacitance in the EDL, and thus be beneficial for improving the energy density of the EDL supercapacitor in real applications. We believe this work can provide useful information to improve the energy density of the EDL supercapacitor via optimizing the micro-structure of the electrode. In addition, the charging/discharging rate, which is a crucial parameter for the super-capacitor system, will be considered in our future work.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 5 Discussion  
In this study, we first estimated the UBD and LST using Luojia1-01 NTL imagery and then used the GWR model in the central city zone of Hefei as an example to study the correlation between the two. The following conclusions were obtained:  
1. According to the accuracy verification, the absolute error between the estimated and actual values of UBD was only 3.58%, which proves that Luojia1-01 NTL imagery has strong potential for UBD estimation. The UBD can better reflect the aggregation degree in the built-up area of Hefei and is a full expression of urbanization. Its main feature is that UBD decreases from the interior to the periphery. The areas with high UBD are concentrated in the interior. Where the buildings are dense, the UBD index is large and concentrated, while the lower UBD areas are mostly scattered in the outer suburbs with more vegetation.
2. UBD and LST were found to be positively spatially correlated at all four scales examined, and the larger the spatial scale, the greater the correlation found.
3. The simulation effect of GWR was significantly better than that of OLS. GWR had the smallest \(AIC_{\text{c}}\) and Sigma, and the largest \(R^{2}\), while the regression residual of OLS was higher than that of GWR. OLS overestimated or underestimated the heating or cooling capacity of different UBDs. GWR can well reflect the influence of UBD on the LST in different spatial locations, and the results showed excellent visualization effects. Therefore, when studying the relationship between UBD and LST in the city, GWR is more intuitive and accurate.  
The results show that there is a significant correlation between UBD and LST as a whole. The LST was higher in regions with higher UBD, and vice versa. In addition, the GWR's simulated effect and regression coefficient in the central city were lower than those in the suburbs because the underlying surface of the central city is dominated by buildings, while in the suburbs, vegetation is abundant. This indicates that the LST is more affected by vegetation than by buildings.
## 4 Modeling
The 3D deformation pattern depicted by integrating GNSS and DInSAR data from December 2 to 14 clearly defines a radial contraction of the entire edifice, accompanied by a general subsidence centered on the summit part, indicating an evident deflation of the edifice. A circular slight uplift at a lower altitude permits hypothesizing that a deep source was pressurizing, able to produce a wider smooth effect, underlying the more evident and stronger deflation. However, this uplift was not accompanied by radial horizontal expansion, the dominating deformation being the centripetal direction of horizontal displacements imputable to the deflation. For this reason, the deep pressurizing source cannot be constrained by a clear deformation and we concentrated our efforts on modeling the deflating source. In order to define the source of the observed cumulative deflation, we inverted the 3D SISTEM integrated data using a pressure source model, as formulated by [33], and implemented it into the dMODELS Matlab(r) software package [34]. To search the minimum of the residuals, we used an optimization routine based on the genetic algorithms approach, as modified by [5], using the same procedure described in [23] by considering also the volcano topography. The goodness of the SISTEM integration, depends on the number of GPS points and on the geometry of the GPS network, and this issue was largely investigated in [26]. Before using the integrated data, the deformation calculated by the integration has been checked and validated at all corresponding GPS points, to ensure that it reproduces what really observed by GPS; we also revert the SISTEM output into LOS displacements for calculating the fit with the original DInSAR data (see supplementary material). Once being sure that SISTEM integration does not introduce artifacts and unreal signals, we think that inverting the integrated and validated data provides a better-constrained model than inverting only the original GPS and DInSAR data. In this way, we gain millions of points with 3D information instead of few tens with 3D and millions with only 1D. The solution converged to a final fitness value of 75% with an average misfit of 1.4 and 4 mm for the horizontal and vertical components, respectively. The distribution of the residuals (Figure 10) shows a general good fit of the expected deformation with the observed one all over the volcano. Larger residuals can be observed on the vertical component, where the uplift at a lower altitude remains unmodeled, while the subsidence due to the deflation is well matched by the model. The general solution for a prolate spheroid depends on eight parameters: the dimensionless pressure change \(\Delta\)P/ \(\mu\); the semimajor axis; the geometric aspect ratio A between the length of the semimajor axis a and that of the two semiminor axes b; the source location (x,y,z); the dip angle \(\theta\) (measured from the free surface) and the azimuth angle \(\varphi\) (measured clockwise from the positive North direction). In addition, we calculated the volume change of the spheroid, according to [35].
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 2 Related Work  
### Target Detection Framework Based on Deep Learning  
Target detection aims to locate and identify each target instance using a bounding box. Traditional target detection algorithms include edge detection [8], module matching [9], Hough transform [10], etc. These target detection algorithms use edges, contours, and textures for target detection. These features rely on a priori knowledge and have limited expressiveness, limiting the accuracy of target detection algorithms. With the rapid development of deep learning, the field of computer vision has achieved remarkable success in target detection tasks using deep-learning algorithms [11]. Deep-learning-based target detection frameworks have also been proposed one after another [12; 13].  
Target detection frameworks based on deep learning mainly fall into two categories: two-level detection framework and single-level detection framework [14]. The two-level detection framework includes a pre-processing step for region recommendations. That is, candidate regions are selected and then classified. Such representatives include Faster R-CNN and Mask R-CNN [15], etc. They adopt the R-CNN proposed by [PERSON] et al. [16] as the target suggestion method [17], which significantly reduces the amount of calculation compared with the traditional method. The conventional method usually uses superpixel, edge [18], and shape to score Windows, containing objects to generate region suggestion boxes. Deepbox [19] used a lightweight ConvNet model for training, which rearranged the regional suggestion boxes generated by the Edge box. Compared with R-CNN, these traditional methods often require a more extensive calculation. The single-stage detection framework adopts the regression method to directly regress the position and type of the target from the feature graph. The grid method or convolution of different scales is used to operate the feature graph to obtain the position and classification information of the target directly. Such representatives include YOLO and SSD. Generally speaking, the two-stage detection frame has higher accuracy, and the single-pole detection frame is faster. In order to improve the performance of CMF-3 DLSTM, we use an infrared image target detection model based on multiscale feature fusion and context analysis proposed in this paper, and we adopt the target detection framework of Faster R-CNN as its basic framework.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 9 Dataset Collection  
To train the deep learning model to recognize malicious URLs, a dataset of URLs will first have to be collected. Three sources of data were identified, namely PhishTank, PhishStats, and OpenPhish. PhishTank contained both benign and malicious URLs while PhishStats and OpenPhish only contained malicious URLs. These platforms have APIs for the data, but they are paid services. Therefore, customized scripts, e.g., web scrappers, parsers, and downloaders, were used to obtain the data.
# Blur Kernel Estimation by Structure Sparse Prior
## 1 Introduction
Since most nature images can meet the demand for sparsity, the SRM-based image deblurring methods can get promising performance. Moreover, by adapting the dictionary to the image content, the deblurring performance can be promoted. However, the SRM-based approaches usually utilize the \(l_{1}\)-sparsity penalty term, which is proved to be an unstable model and may produce unsatisfactory results [12]. To preserve the image structures, a blind image deconvolution method is proposed to incorporate the sparsity regularization and the locality constraint. The proposed method exploits sparsity prior to model the relationships among pixels in the input image. By utilizing the sparsity regularization and the locality constraint, the proposed method can ensure that similar patches have similar representations and preserve the structure information. As shown in Figure 1, the proposed method consists of two phases: the blur kernel estimation and the final image recovery. In the kernel estimation phase, the sparse representation framework is adopted, and locality constraint is used to take place the traditional \(l_{1}\) norm penalty term to estimate the sparse coefficient. In the final image recovery phase, once the blur kernel is estimated, the final sharp image is recovered through an existing non-blind deblurring method [8]. The major contributions of this paper can be summarized as follows:  
* A new blur kernel estimation method is proposed by considering the structure information of the image, which can effectively estimate the blur kernel. Both the sparsity regularization and the locality constraint are incorporated to exploit the structure relationships among pixels. Figure 1: Diagram of the proposed blur kernel estimation method. * A structure sparse prior is proposed by introducing the locality constraint into sparse representation framework. The structure sparse prior can preserve the inherent attribute of the sharp image. The rest of the paper is organized as follows. In Section 2, the progress of single image blind deblurring is addressed. In Section 3, the formulation of our method is designed. Section 4 shows experimental results compared with other blind deblurring algorithms. Finally, the discussion and conclusions are presented in Sections 5 and 6.
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 4 Experimental Results  
### Dataset  
Because no datasets on airport small targets are publicly available, we could not compare our results with those reported in other studies under the same standard. In this regard, we created a small target dataset simulating the airport and applied our method using this dataset. We also performed several experiments using this dataset.  
The sizes of the small targets used in this experiment were based on the definition of relative scale. The width and height of the targets are generally considered to be less than one-tenth of the image, and the specific area is generally 32 \(\times\) 32 pixels. In other words, if the area covered by the target is less than 0.25% of the entire image, it can be considered a small target. We took 300 high-quality images of an airport surface, which included 1500 small targets, by constructing a simulated airport experimental platform (Figure 7). All the images were calibrated pixel-by-pixel using LabelMe, and the dataset was split in a 7:3 ratio between the training set and test set. The format of our dataset was the same as that of the COCO dataset. According to the pixel size statistics of the targets, the maximum pixel was 45 \(\times\) 21, and the maximum proportion of the target size was 0.12%; therefore, all targets were small. Owing to the different pixel proportions of different targets, we divided the small targets into 11 proportional intervals from 0 to 0.25%, and the number of  
Figure 6: Classifier head.  
correctly identified targets in each interval was computed to compare the correct rate of identification using different methods. The target number of statistics for each interval was as shown in Figure 8:
## 1 Introduction  
Protonic ceramic fuel cells (PCFCs) form a class of fuel cells (FCs) providing highly efficient and economically advantageous chemical-to-electrical energy conversion at elevated temperatures [1; 2; 3; 4; 5]. A proton-conducting electrolyte membrane as a PCFC's heart transports proton charge carriers with high levels of ionic conductivity compared with those of oxygen-ionic electrolytes of the traditional solid oxide fuel cells (SOFCs), owing to the corresponding high mobility/concentration of protons [6; 7; 8; 9]. This allows the operational temperatures of the FCs to be decreased by \(\sim\)100-300 \({}^{\circ}\)C to reach low- (300-500 \({}^{\circ}\)C) and intermediate-temperature (500-700 \({}^{\circ}\)C) ranges [10; 11; 12; 13]. As a consequence, temperature-determined processes (electrode sintering, material interaction, thermal misbalance, poisoning) occurring in PCFCs become less adverse factors in terms of their effect on overall performance and FC degradation over a long-term period of operation [14; 15; 16].  
Conventional FCs, including SOFCs and PCFCs, are comprised of a minimum of three functional components (layers): electrolyte, cathode and anode. Each layer requires its own unique processing regime [17; 18; 19]. In terms of the electrode-supported configuration of FCs, a thin electrolyte layer is formed onto a Ni-based anode and then these two layers are jointly fired at high temperatures to form a half-cell, combining the porous anode substrate and dense electrolyte. The next step consists of cathode formation onto the electrolyte surface followed by its sintering at a lower temperature, which produces the final FC [20; 21; 22; 23]. As can be seen, the provided scheme includes several temperature regimes along with their various parameters (temperatures, heating/cooling rates, soaking times). Since the multi-stage preparation process of conventional FCs is time-, energy- and resource-intensive, the possibility of transition to a single-stage preparation process is of high interest.  
The one-step sintering of FCs represents an important technological strategy, allowing the entire FC to be produced using a single temperature regime. This strategy has recently been successfully used in the fabrication of both SOFCs [24; 25] and PCFCs [26; 27]. However, it should be noted that the FCs in the referenced studies were prepared by means of a co-pressing method requiring high pressing pressures. Although such a method can be quite easily used for fabricating the single cells with a small size, its adaptation for large-scale production presents severe challenges. Therefore, as an alternative approach, we herein propose a tape calendering method as a simple, scalable and efficient means for one-step fabrication of PCFCs. The possibility of one-step fabrication is based on our recent achievements in materials science (the selection of chemically and thermally compatible materials [28; 29]) and technological (the rational parameter tuning of the tape calendering method [30; 31]) fields.
## 4 Experiment and Result Analysis
### Feature Selection  
The feature selection component consists of two phases, such as the lasso method and multicollinearity analysis. It is well known that the lasso method helps to increase the prediction of the model by removing irrelevant features that are not related to target classes. The lasso feature selection method is identified irrelevant features as \"Residence area\", \"Employment condition\", \"Average sleep time per day\", \"Stress level\" and \"Long term depression (more than two weeks)\" that eliminated by assigning them a coefficient equal to zero. On the contrary, the remaining 17 of the 22 features estimated along with non-zero coefficients, which were selected by sufficiently representative features.  
We directly verify to check the collinearity between selected features using multicollinearity in regression analysis after eliminating the non-significant features, which are passed in the lasso method. Figure 2 illustrates the result of the Variance Inflation Factor (VIF) value for all independent features. Generally, it is suspected that multicollinearity will present if the VIF lies between 5 and 10. If the VIF value is greater than those values, it investigates a high correlation among features that remains problematic. As can be seen, none of the features have detected the presence of multicollinearity issue  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Features** & **Former Smoker (\%)** & **Current Smoker (\%)** \\ \hline Diabetes & & \\ Yes & 878 (23.8) & 2111 (57.2) \\ No & 73 (2.0) & 630 (17.1) \\ \hline Asthma & & \\ Yes & 139 (3.8) & 285 (7.7) \\ No & 812 (22.0) & 2456 (66.5) \\ \hline Average sleep time per day & & \\ Less than 6 h & 60 (1.6) & 165 (4.5) \\
7–8 h & 1129 (30.6) & 1447 (39.2) \\ More than 9 h & 383 (10.4) & 508 (13.8) \\ \hline Stress level & & \\ Very high & 30 (0.8) & 148 (4.0) \\ Moderate & 197 (5.3) & 713 (19.3) \\ Low & 587 (15.9) & 1576 (42.7) \\ Rarely & 137 (3.7) & 304 (8.2) \\ \hline Long term depression (more than & & \\ two weeks) & & \\ Yes & 87 (2.4) & 272 (7.4) \\ No & 864 (23.4) & 2469 (66.9) \\ \hline Age of smoking initiation (years) & & \\ Less than 18 & 516 (14.0) & 1394 (37.8) \\
19–24 & 327 (8.9) & 1070 (29.0) \\ More than 25 & 108 (2.9) & 277 (7.5) \\ \hline Secondhand smoke in the & & \\ workplace & 468 (12.7) & 1178 (31.9) \\ Yes & 394 (10.7) & 1146 (31.0) \\ No working place & 89 (2.4) & 417 (11.3) \\ \hline Daily smokers at home & & \\ Yes & 49 (1.3) & 320 (8.7) \\ No & 902 & 2741 (74.2) \\ \hline Attendance in smoking prevention & & \\ or smoking cessation education & & \\ Yes & 55 (1.5) & 296 (8.0) \\ No & 896 (24.3) & 2445 (66.2) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._in the multiple linear regression models. In total, 17 features and 3682 subjects remained in the end of this component and are is used as inputs to the next components.