esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Additionally, the previous results of a nutritional intervention demonstrated that the presence of olive paste polyphenols may lead to decreased postprandial triacylglycerlecrols concentration [9]. The protective role of VOO is mainly attributed to its potential antioxidant bioactivity, exerted by its functional components, such as oleic acid, tyrosol, hydroxytyrosol, oleuppein, etc. [14; 15]. However, given the variable composition of olive oil, it is observed that a large share of the olive oil market is occupied by ROO, due to the low-cost technological interest from olive oil industries [16]. Taking into account these data, in order to ensure the optimal intake of polyphenols through the usual diet, and under the sustainability prism, a novel strategy supports the enhancement of ROO with bio-functional compounds, derived from the utilization of natural food by-products [13; 16]. Particularly, previous attempts to enrich the ROO with polyphenols from olive leaves and pomace, but also from wastewater, resulted in a comparable phenolic content to VOO [7; 8]. The latest strategies demonstrate the role of polyphenols intake on better management of dyslipidemia and other risk factors [17]. Moreover, the findings of our previous clinical studies have shown that the supplementation of fatty and carbohydrate-rich meals, with orange peel polyphenols, may be an effective way to modulate postprandial endothelial dysfunction and oxidative stress [10; 18; 19]. Nevertheless, the polyphenolic content of the olive oil classes may be reflected in graded cardiovascular modulation. A comparative clinical study from [PERSON] et al. demonstrated that the daily intake of ROO for six weeks led to higher levels of low-density lipoprotein cholesterol (LDL-cholesterol), C-reactive protein (CRP), and selective inflammatory biomarkers, compared to the opposite effects of EVOO intake [20]. Additionally, it has been mentioned that the consumption of ROO for three weeks led to relative higher oxidized LDL and hydroxy fatty acids levels, when compared with the corresponding effect of VOO, associated with the LDL-polyphenylic content [21]. Despite the encouraging evidence presented by previous in vitro and clinical studies on ROO enrichment, the lack of scientific data observed is noteworthy, regarding the possible effect of ROO, enhanced with bioactive compounds, in the context of a high-fat and high-carbohydrate meal, on postprandial metabolic biomarkers. So, the purpose of the present nutritional interventional study was to investigate whether the consumption of conventional ROO and ROO enhanced with 10% \(w/v\) orange peel extract, may have differential metabolic response on postprandial lipemia, glycemia, and oxidative stress biomarkers, in cardiometabolic risk participants. The postprandial response of LDL-cholesterol concentration on the test meals was set as the primary outcome of the present pilot study.
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 4 Modeling
It should be noted that an indicator function \(I_{ji}\) is used to describe the temporal relationship between the departure flight \(j\) and the arrival flight \(i\), as follows:  
\[I_{ji}= \begin{cases}0,&T_{i}^{a}-T_{j}^{d}<T^{\text{buffer}}\\ 1,&T_{i}^{a}-T_{j}^{d}\geq T^{\text{buffer}}\end{cases}  \tag{15}\]  
where \(T^{\text{buffer}}\) is a preset buffer time, and when the time interval between arrival flight \(i\) and departure flight \(j\) is greater than \(T^{\text{buffer}}\), \(I_{ji}\) is equal to 1. Otherwise, \(I_{ji}\) is equal to 0. With \(I_{ji}\) in mind, it can be determined whether two flights are allowed to use the same gate. Equation (13) is the constraint of the transferring arc, which shows that only one gate can flow through it. Equation (14) ensures that the above variables are 0-1 variables. (2) Flight exclusive constraint. The flight exclusive constraint means that each flight must be assigned a unique gate. It is formalized as follows:  
\[\sum\limits_{g\in G}(x_{Oi}^{g}+\sum\limits_{j\in F_{d}}x_{ji}^{g})=1,\forall i \in F_{a} \tag{16}\]  
where \(x_{Oi}^{g}+\sum\limits_{j\in F_{d}}x_{ji}^{g}\) is 0 or 1, indicating whether flight \(i\) is assigned to gate \(g\). (3) Gate compatible constraint. The gate compatible constraint describes the size limitation between the gate and aircraft, which means that the size of the gate should be sufficient to accommodate the size of the aircraft. It is formalized as follows:  
\[S_{i}^{\text{flight}}\leq S_{g}^{\text{gate}}+(1-x_{Oi}^{g}-\sum\limits_{j\in F _{d}}x_{ji}^{g})M,\forall i\in F_{a},\forall g\in G \tag{17}\]  
where \(M\) is an arbitrarily large integer. Note that when the value of \(x_{Oi}^{g}+\sum\limits_{j\in F_{d}}x_{ji}^{g}\) is 0, this inequality always holds.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 6 Limitations and Future Scope  
This paper proposes iWAVE-BiFBank, based on wavelet threshold denoising and FBank, which improves the birdsong classification performance, introduces a wider feature selection range, and provides technical support for biological monitoring, biodiversity strategies, and ecological protection. Although the method performs well, there are still some limitations that should be addressed in future work, mainly including the following:  
* Choosing a more effective pre-emphasis method that is closer to the birdsong acoustic attenuation model rather than using a simple high-pass filter [51].
* Developing a method to separate overlapping vocalizations across the time domain, frequency domain, and spectrogram as well as attempting to recognize different bird vocalizations within overlapping audio.
* Exploring more handcrafted and deep-learning-based features and constructing a multi-feature fusion method to enhance the classification performance of bird vocalizations in future studies. In addition, experiments could be conducted with different acoustic datasets containing more species of birds as well as different animal species to enhance the generalization of the method.
## 3 A Post-Processing Algorithm to Enforce Traction Continuity along the Interface
### Inverting for the Polynomial Coefficients
Although the differences between polynomials centered at two neighboring columns are small, they may be enough to cause some discontinuities in tractions at the interface. To ensure the continuity of tractions, we computed strains and stresses within the \(2\times n_{x2}\) band, where displacements are updated directly as the algorithm goes over each column of pixels (i.e., we calculated the displacement derivatives for column \(j\) with respect to \(x_{1}\) using the displacements in columns \(j\)-1 and \(j+1\) obtained from the polynomial centered at column \(j\). Full-field maps of stresses modified by the algorithm using \(n_{x1}=7\), \(n_{x2,inter}=12\), and \(n_{x2,cent}=11\) (Figure 2c,d) demonstrated the capability of this approach to generate more realistic and continuous stress fields near the interface. Interestingly, because of the symmetry of the problem, the enforcement of TC led to normal stresses that are close to the background value (\(\sim\)17.6 MPa) at the interface.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
###### Abstract  
Multi-label text classification refers to a text divided into multiple categories simultaneously, which corresponds to a text associated with multiple topics in the real world. The feature space generated by text data has the characteristics of high dimensionality and sparsity. Feature selection is an efficient technology that removes useless and redundant features, reduces the dimension of the feature space, and avoids dimension disaster. A feature selection method for multi-label text based on feature importance is proposed in this paper. Firstly, multi-label texts are transformed into single-label texts using the label assignment method. Secondly, the importance of each feature is calculated using the method based on Category Contribution (CC). Finally, features with higher importance are selected to construct the feature space. In the proposed method, the feature importance is calculated from the perspective of the category, which ensures the selected features have strong category discrimination ability. Specifically, the contributions of the features to each category from two aspects of inter-category and intra-category are calculated, then the importance of the features is obtained with the combination of them. The proposed method is tested on six public data sets and the experimental results are good, which demonstrates the effectiveness of the proposed method.  
Keywords:feature selection; multi-label text classification; category contribution; feature importance +
Footnote †: journal: Applied Sciences
# Efficient Filtering for Edge Extraction under Perspective Effect
## 2 Optimal Scale and Orientation Assignment
### Angular Accuracy with Pre-Known Localized Target Orientation
Steering a steerable filter involves rotating the original filter to an arbitrary angle using a linear combination of rotated versions of the same filter's impulse response. Although it is very efficient, the steerable filters always suffer from the trade-off between the number of basic filters and the angular resolution. For the filter to be steerable, it must have certain constraints regarding its shape and angular resolution. We argue that angular resolution is an important performance factor for the extraction of orientated features. To design an oriented filter with a higher angular resolution, we need its shape to be freely adjustable. For this reason, we prefer to rotate the same filter to many orientations. All possible orientations are evenly divided and a filter bank is designed with one filter for each orientation. Here, the angle between adjacent orientations can be seen as the angular resolution of the filter bank. The angular resolution is connected tightly with the selectivity of the filter's angular response. We can use this as the definition of selectivity. For DoB in Equation (14), this is still the case. We plot out the local filter response with \(\lambda=10^{-1}\), \(\lambda=10^{-\frac{1}{2}}\), \(\lambda=10^{0}\), \(\lambda=10^{\frac{1}{2}}\) and \(\lambda=10^{1}\), as shown in Figure 5a. The orientation mismatching angle \(\theta\) varying from \(-\frac{\pi}{2}\) to \(\frac{\pi}{2}\). Similarly, we plot the angular selectivity figure according to the aspect ratio of the filter.
## 4 How to Experiment with the Database
### Statistical Modeling  
The generalized extreme value is a family that combines three different class types of distributions according to their tails, that correspond to the limiting distribution of block maxima. The types are:  
* Type I (Gumbel domain): Distributions whose tails decrease exponentially.
* Type II (Frechet domain): Distributions whose tails decrease as a polynomial.
* Type III (Weibull domain): Distributions whose tails are finite.  
The generalized extreme value distribution has three parameters, called scale (\(\sigma>0\)), shape (\(-\infty\leq x\leq\infty\)), and location (\(-\infty\leq\mu\leq\infty\)), and its probability density function (PDF) is given by  
\[f(x,\mu,\sigma,\kappa)=\frac{1}{\sigma}\ t(x)^{\kappa+1}\exp^{-t(x)} \tag{1}\]  
where  
\[t(x)=\begin{cases}(1+\kappa(\frac{\tau-\mu}{\sigma}))^{-1/\kappa}&,\text{if } \kappa\
eq 0\\ \exp^{\frac{\tau-\mu}{\sigma}}&,\text{if }\kappa=0\end{cases} \tag{2}\]  
\(\kappa\) determines the domain of the PDF as follows:  
\[x\in\left[\frac{\mu-\sigma}{\kappa},+\infty\right)\text{, when }\kappa>0 \text{, type II} \tag{3}\] \[x\in(-\infty,+\infty)\text{, when }\kappa=0\text{, type I}\] \[x\in\left(-\infty,\frac{\mu-\sigma}{\kappa}\right]\text{, when }\kappa<0 \text{, type III}\]The parameters \(\mu\) and \(\sigma\) can be calculated using the maximum likelihood estimators  
\[\hat{\mu} = -\sigma\log\left(\frac{1}{n}\sum_{i=1}^{n}\exp^{\frac{X_{i}}{\hat{ \sigma}}}\right) \tag{4}\] \[\hat{\sigma} = \bar{X}-\frac{\sum_{i=1}^{n}X_{i}\exp^{-\frac{X_{i}}{\hat{\sigma} }}}{\sum_{i=1}^{n}\exp^{-\frac{X_{i}}{\hat{\sigma}}}} \tag{5}\]  
The cumulative density function (CDF) for \(x\in(2)\) and (3), is given by  
\[f(x,\mu,\sigma,\kappa)=\exp^{-t(x)} \tag{6}\]  
The generalized extreme value distribution has the following advantages: (1) It can be adapted to the types of non-homogeneous patterns of extreme value variation observed in the dataset. (2) It is easy to incorporate all relevant information into an inference, and (3) it is beneficial to quantify uncertainties in estimation. Second, this distribution has the following disadvantages: (1) the model is developed using asymptotic arguments, which implies that necessary to take care is needed in treating them as exact results for finite samples. (2) The model is derived under an idealized circumstance, which may be reasonable, not exact, or only for a process under study. (3) The model may lead to wastage of information when implemented in practice because in the worst case, it can store only the maximum observed value over a specified period [23].
# Mycoremediation of PCBs by _Pleurotus ostreatus_: Possibilities and Prospects
## 4 Mechanism of Bioremediation by _P. ostreatus_  
It is known that extracts from WRF or their lacases catalyze the degradation of hydroxylated PCBs. However, as it stands, little is known about the in vivo mechanisms of PCB degradation [PERSON] et al. (2000); [PERSON] et al. (2000). Lacases degrade isolated PCBs congeners [PERSON] et al. (2000) or PCBs in commercial mixes such as Delor 103, Delor 106, and Arochlors 1242, 1254, and 1260 [PERSON] et al. (2000); [PERSON] et al. (2008), but the limitation was that higher chlorination levels reduced the degradation efficiency. Most of these studies have been applied on low PCB concentrations only (1-2000 ppm). [PERSON] et al., 2012 [PERSON] et al., 2012 [PERSON] et al., 2012, determined the correlation of _P. ostreatus_ lacase activity through their conformation on its transcript expression with the removal of high concentrations(7100 ppm) of PCBs from Arochlor 1242 in liquid culture. This ability of _P. ostreatus_ was significantly marked in that it was not influenced by the chlorination levels.  
_P. ostreatus_, in the presence of a fungal growth substrate (e.g., lignocellulosic matrices), could transform PCBs in spiked and actually contaminated soils [26; 27]. Myco-augmentation of contaminated matrices by this fungus is operational via substrate-unspecific extracellular and intracellular oxido reductases, lacases, and Mn-dependent and -independent peroxididases [28; 29; 30], enabling them to transform PCBs [24]. Additionally, _P. ostreatus_ produces ligninolytic enzymes, which is an added asset, whereby it orchestrates the transformation of a plethora of waste substrates [31; 32; 33] including PCBs [34; 35; 36]. The exact mechanism whereby _P. ostreatus_ degrades PCB is not worked out, but with the rich reservoirs of enzymes such as Lignin Peroxidases, manganese peroxididases, and lacases, there is no doubt that these enzymes will play a synergistic role, if not for individual contributions (Figure 1).
## 4 Results and Discussion
Hence, the effect of NW length on the short-circuit current density and PCE is calculated with a change in the NW length from 3 to 6 \(\upmu\)m as given in Table 7. The best performance has been obtained at 5 \(\upmu\)m length, as the increase in the length will increase the electron relaxation due to changing the NW bandgap. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Period (nm)** & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **PCE (\%)** \\ \hline
**350** & 33.0 & 15.0 \\
**400** & 27.8 & 12.7 \\
**450** & 35.9 & 16.4 \\
**500** & 40.6 & 18.5 \\
**550** & 41.7 & 19.0 \\
**600** & 33.8 & 15.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Short-circuit current density and overall efficiency for different distances between NWs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**R (nm)** & **Au NP Density** & **J\({}_{\text{sc}}\) (mA/cm\({}^{2}\))** & **PCE (\%)** \\ \hline
**30** & 26.0\% & 34.0 & 15.5 \\
**40** & 46.0\% & 34.6 & 14.4 \\
**50** & 73.0\% & 41.7 & 19.0 \\
**60** & 88.4\% & 33.2 & 15.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Au NP density, short-circuit current density, and overall efficiency for different NP materials. Figure 9: Au NP density for different radii: 30, 40, 50, and 60 nm.
# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
###### Abstract  
This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy.  
imbalanced classification; ensemble learning; large earthquake prediction Article
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 2 Methodology
### Models Employed
#### 2.1.3 U-Net 3+  
[PERSON] et al. [23] introduced a novel architecture called U-Net 3+, which is based on the U-shaped design. This architecture uses full-scale skip connections and deep supervision. U-Net3+ was developed to address the limitations of previous U-Net variants and improve the performance of segmentation tasks, particularly in medical imaging. It has found successful applications in various real-world scenarios, including medical imaging, satellite imagery analysis, and industrial automation. This network architecture has shown promising results in these fields, showcasing its versatility and effectiveness in different domains. The extensibility and symmetry of the U-Net framework allow for a variety of possible designs. One such design is U-Net3+, which incorporates advanced features and optimizations to improve segmentation performance. U-Net3+ introduces advanced features that optimize feature utilization and incorporate attention-gate techniques for improved segmentation performance. These features allow U-Net3+ to surpass its predecessors in terms of accuracy and efficiency, making it a powerful tool for medical imaging and other applications.  
According to [23], the low-level detailed feature maps capture intricate spatial information, highlighting organ boundaries, while the high-level semantic feature maps provide positional information about the organ locations. To fully harness the advantages of these multi-scale features, U-Net 3+ integrates low-level details with high-level semantics from the full-scale feature maps. Importantly, it accomplishes this with fewer parameters,leading to improved computational efficiency. U-Net 3+ achieves this by redesigning skip connections and incorporating full-scale deep supervision.  
By incorporating multi-scale features, redesigning skip connections, and utilizing multi-scale depth supervision, U-Net3+ can produce more accurate location-aware and boundary-enhanced segmentation maps. The use of multi-scale features in U-Net3+ enables the network to capture information at different resolutions, enhancing its ability to detect objects and boundaries of interest. Additionally, U-Net3+ leverages attention-gate techniques to focus on local semantic features, allowing the network to prioritize important regions of the image while suppressing non-interesting regions.
###### Abstract  
Small leaks in natural gas pipelines are hard to detect, and there are few studies on this problem in the literature. In this paper, a method based on the extended Kalman filter (EKF) is proposed to detect and locate small leaks in natural gas pipelines. First, the method of a characteristic line is used to establish a discrete model of transient pipeline flow. At the same time, according to the basic idea of EKF, a leakage rate is distributed to each segment of the discrete model to obtain a model with virtual multi-point leakage. As such, the virtual leakage rate becomes a component of the state variables in the model. Secondly, system noise and measurement noise are considered, and the optimal hydraulic factors such as leakage rate are estimated using EKF. Finally, by using the idea of an equivalent pipeline, the actual leakage rate is calculated and the location of leakage on the pipeline is assessed. Simulation and experimental results show that this method can consistently predict the leakage rate and location and is sensitive to small leakages in a natural gas pipeline.  
Keywords:natural gas pipeline; small leaks; pipeline leakage detection; extended Kalman filter +
Footnote †: journal: Applied Sciences
# Susceptibility to Expansive Reactions of a _Greener_ Uhpc: Micro to Macro-Scale Study
## 3 Results and Discussion
### External Sulphates Attack  
UHPC showed outstanding performance in the sulphate-rich environment. As can be seen in Table 4, no expansion occurs. According to LNEC E\(-\)462, the cement is resistant to sulphates if the expansion after 26 weeks of immersion is lower than 0.1%. The visual examination of UHPC specimens exposed to sodium sulphate solution, depicted in Figure 10, reveals no damage.  
Susceptibility to sulfate attack depends on both physical and chemical factors. Magnesium sulfate and sodium sulfate are particularly active salts [61]. Sodium sulphate reacts with the free calcium hydroxide and forms calcium sulphate (gypsum), which then reacts with tri-calcium aluminate to form calcium sulpho-aluminates (Ettringite) [61]. The rate of attack by sulphate solutions depends on the solution's concentration and the cement composition [61]. The permeability of the cementitious-based matrix is a major factor affecting the attack rate [68]. The low w/b ratio and the ultra-dense matrix of UHPC avoided the sulphate entrance. In addition, the pozzolanic activity of ECat and the SF, translated into the consumption of calcium hydroxide from cement hydration and thus was no longer available for reaction with external sulphates, mitigating the formation of gypsum. Moreover, the pozzolanic reaction also decreases the cementitious matrix porosity and strengthens the cementitious paste-aggregate interface.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{_expCa(OH)\({}_{2}\) (\%)_} & \multicolumn{2}{c}{_expSO\({}_{3}\)Na\({}_{2}\) (\%)_} & \multirow{2}{*}{_exp\({}_{sulphate}\) (\%)_} \\ \cline{1-1}
**Time (d)** & **Average** & **Std Dev** & **Average** & **Std Dev** & \\ \hline
0 & 0 & 0 & 0 & 0 & 0 \\
14 & \(-\)0.0001 & 0.0001 & \(-\)0.0006 & 0.0000 & \(-\)0.0005 \\
28 & \(-\)0.0035 & 0.0001 & \(-\)0.0033 & 0.0000 & 0.  
After sulphate solution exposure, samples were taken from bar specimens and examined by SEM. Even though there was no expansion and no visual damage (see Figure 10), a few products from sulphate reaction were found on UHPC samples after exposure to sulphate solution, namely, ettringite (calcium sulfoaluminate) needles, as can be observed in Figure 11.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Augmentation of Neural Machine Translation  
Applying NMT to low-resource languages is a challenge. NMT is a data-driven task. Given the lack of parallel corpora, it is difficult to train a machine translation model to a high level of quality for the English-Bengali language pair. To address this, we employ strategies that focus on leveraging monolingual data and auxiliary language data, such as back-translation (round-trip translation) [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], pre-training on monolingual data [75; 76], Pivot Language Technique [30; 31; 32], Leveraging Multi-Modal Data [33], and others.