esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Hierarchical Representations for Explainable Chemical Reaction Prediction
## 2 Related Work  
### Chemical Reaction Representation  
In recent years, several studies utilizing machine-learning techniques to represent chemical reactions have been conducted for various tasks, such as reaction classification and yield prediction. These studies can be roughly divided into two categories: molecular fingerprints based and graph neural network based.  
The molecular fingerprints are mathematical representations capturing the structural and electronic features of the chemical reactions and can be used to train machine learning models. [PERSON] et al. [11] introduced the differential reaction fingerprint method which generates a unique fingerprint representation capturing the changes in molecular structures for each reaction, and successfully adopted the representations of both the reaction classification and yield prediction tasks. Compared with traditional methods, this approach has the potential to accelerate the discovery and optimization of new chemical reactions with less time cost. [PERSON] et al. [12] developed a deep model that links molecule structure and biomedical text to learn chemical reaction representation and achieved a level of results comparable to human professionals. [PERSON] et al. [13] investigated various data augmentation strategies to learn reaction representations for enhancing the yield-prediction performance and providing a better understanding of the associated uncertainties. [PERSON] et al. [20] applied neural message passing algorithms to model the interactions between atoms in a molecule and predict various molecular properties such as energy, stability, and reactivity, and showed a high accuracy and efficiency compared to other state-of-the-art methods.  
Another category is using the graph-based neural network to encode molecular structures and their associated properties. [PERSON] et al. learned chemical reaction representations for the tasks of reaction classification [9] and yield prediction [10] via a Transformer-based deep model with a BERT classifier and achieved superior performances on both tasks, in comparison to traditional methods. [PERSON] et al. [21] utilized molecular graphs and reaction condition graphs to model the reaction and estimate the yield of reaction products by introducing uncertainty distributions. [PERSON] et al. [22] captured the graph structures of chemical reactions and their associated properties to predict yield.  
Even though large achievements have been made by these methods, in comparison to traditional hand-crafted representations, these studies are limited either in the requirements of large amounts of annotated training data, or by a lack of interpretability and generalizability. In contrast, this paper proposes an explainable coarse-fine level representation model which only needs a small amount of easily available expert knowledge regarding the reaction classification and yield tasks.
## 4 Experimental Results
### Experimental Results without ABC Algorithm

Table 2: Experimental results using all the DMI values—1.48 kW motor. situation, the Kappa coefficient is 0.84, which confirms the results obtained. It is noteworthy that the C4.5 DT presents promising results since the classifier model's construction time is lower than for the MLP network. When the G110 inverter feeds Motor 2, the MLP classifier has the best classification accuracy. 99.7% of the global samples are properly diagnosed. Healthy and faulty classification rates above 99.7% are achieved, and the Kappa index is greater than 0.99, confirming the results. The C4.5 classifier diagnoses adequately 92.6% of the total samples, as shown in Table 2. Therefore, the MLP is the most suitable classifier. In this section, the experimental results have shown the proposed methodology's capability based on information measurements and intelligent classifiers in diagnosing bearing faults in TIMs fed by frequency inverters and subject to various operating conditions. The following section evaluates the system's performance when the ABC algorithm is used to select the relevant DMI attributes and reduce the input data matrix of the pattern classifiers, decreasing the calculation time to build the methodology model.
## 3 Results: Verification of the Proposed Methodology
### Application Outputs by Industry
#### 3.2.3 Electrical Engineering Enterprises  
The last analyzed sector was companies from the electrical industry. Five companies were evaluated, which dealt with, for example, the production of electronic components, connectors for printed circuit boards, or manufactured OLED and LCD TVs. The aim was to have an evaluated sample from this group of companies, as it was assumed that these companies had implemented a basic degree of automation in the field of internal logistics and would be better prepared to meet the principles of Industry 4.0, especially in logistics processes related to handling within the company.  
Only 5 companies were evaluated; however, the average level of preparedness in the electrical engineering sector was 2.55. Figure 13 is a graph showing the average level values for subdimensions and, unlike the previous two industries, all relevant subdimensions for dimension D1: Handling were better rated, and subdimensions SD3: Handling and SD2: Handling units also had very good values.  
As in previous industries, analyses were again performed on the total number of occurrences of the assigned level values for the whole area for all five enterprises. Level 2 (101\(\times\)) occurred the most, while level 3 (72\(\times\)) and level 4 (37\(\times\)) had the second highest frequency. The graphical representation is in Figure 14.  
As with other industries, the graph in Figure 15 was processed, and the individual levels are differentiated in color.  
Figure 12: Total number of occurrences of level values for subdimensions—mechanical engineering.  
Figure 14: Number of values’ occurrences—electrical engineering.  
Figure 13: Average values of sublevels—electrical engineering.  
The level most assigned to the indicators was level 2, then the next was level 3, and several times level 4 was also assigned. The frequency of level 1 was significantly reduced and the lowest level 0 practically did not appear in the selection.
## 4 Experimental Results
### Dynamic Measurement  
As shown in Figure 13, a wooden movable model was chosen as the tested object. There are four circular buckles of 33 mm in diameter fixed on the model. The movable part consists of four buckles of 39 mm in diameter which can be manually pushed out and pulled back.  
Throughout the measurement, 630 frames of deformed fringe patterns had been captured. The moment \(t_{0}=8\) was selected as the initial moment whose wrapped phase can be successfully unwrapped in 2D spatial domain. Then, the unwrapped, continuous and natural phase value on each pixel of the initial moment were taken as the starting point, from which the 3D wrapped phase values were unwrapped along the temporal axis pixel by pixel. The moments \(t=8\), 51, 94, 137, 180 are taken as examples. The deformed fringe patterns, corresponding wrapped phase distribution and 3D reconstruction of five different moments are illustrated in Figure 14a-c, respectively.  
Figure 14: Experimental results of a moving wooden model. (**a**) Deformed fringe patterns, (**b**) wrapped phase distribution and (**c**) 3D reconstruction of 5 different moments.  
Figure 13: Tested object whose circular buckles can be manually pushed out and pulled back.  
Throughout the entire reconstruction process, the phase value of each pixel was calculated independently along the temporal axis. This method successfully maintained the idea that every single deformed fringe pattern corresponded to one 3D reconstruction result. The experimental results show that TFTP can be applicable for measuring the isolated dynamic object.
## Abbreviations  
\begin{tabular}{l l} FAR & false alarm rate \\ FDR & fault detection rate \\ GPR & Gaussian Process Regression \\ IMS & Inductive Monitoring System \\ KL & Kullback-Leibler \\ KLD & KL divergence \\ LOPV & local optimum PV \\ LSTM & Long Short-Term Memory \\ OS-SVM & One Class Support Vector Machine \\ PCA & principal component analysis \\ PDF & probability density function \\ PV & projection vector \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & \multicolumn{2}{c}{**PCA + KLD1**} & \multicolumn{2}{c}{**PCA + KLD2**} & \multicolumn{2}{c}{**Proposed Method**} \\ \hline
**Significance Level** & **0.05** & **0.025** & **0.01** & **0.05** & **0.025** & **0.01** & **0.01** \\ \hline FDR & 76.84\% & 84.21\% & 92.63\% & 76.84\% & 84.21\% & 92.63\% & 92.63\% \\ FAR & 0\% & 3.13\% & 23.44\% & 0\% & 3.13\% & 23.44\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of three different methods at detecting the spread-spectrum transponder fault.
## 4 Results and Discussion
### Discussion
Even though the DL-based approach could be useful for the BCR measuring, preparing a suitable training dataset is not straightforward [43]. Creating a DL model from scratch is time-consuming and requires specialized skills. This method might be suitable for those who already have a well-trained DL model, or they may choose to utilize a technique that produces training samples automatically [44]. Transfer learning could be another way to avoid the need to develop site-specific training data; this approach would allow users to save resources and improve efficiency when training a new model. Besides, the performance of the DL model is also crucial in the BCR extraction. Basically, the degradation in model prediction leading to a decrease in the BCR measuring accuracy. In specific BCR blocks, or lots where the building is significantly modest and blends with the surroundings, the DL model is unable to generate the building footprint, leading to a significant issue in the BCR measuring. Additionally, a few building typologies might not be included in the training dataset, which can again lead to missing detection. Each type of building has a different profile; for instance, factories look distinctly different from other building typologies [45]. This means that a well-trained model that detects residential buildings with high accuracy may not turn in a good performance for other building types. Obviously, the training model plays a crucial role in building extraction; therefore, the training dataset should include as many different building typologies as possible to achieve a good result and in order to optimize the building detection process. In addition, the building height results in the distortion effect, which also affects the accuracy of the BCR measurements. While a conventional building detection task simply requires the segmentation of buildings located on a spatial image, the information needed for the BCR calculations is more challenging. Although orthodox imagery was used for the current study to avoid distortion effect, there remains a problem. Namely, in some cases when generating the footprints for high-rise buildings, the DL model generated the building footprints that were bigger than the ground truth shapes, due to vertical structures being included. Consequently, the BCR values extracted from these buildings tend to be higher than is actually the case. In other words, to compute the correct BCR, a building's segmentation must be similar to its footprint. However, the distortion effect is increased in buildings that have high heights, leading to the visibility of buildings that include vertical structures rather than just the roofs. This might be caused by the limitations of precautions during orthoimage generation [36] causing issues in the building footprint generation for the BCR calculation. Further research should consider this issue to find a way to deliver more reliable results.
## 5 Application of eNO\({}_{3}\)R to Brackish Groundwater: A Case Study
### Nitrate Concentration  
The variation in nitrate concentration also caused changes in the kinetic order of the electrode during electrochemical reduction. In acidic solutions, [PERSON] et al. [39] studied the electrocatalytic reduction in nitrate at low concentrations using noble and transition-metal electrodes. They found that cathodic currents increased as the concentration of nitrate augmented. The Tafel slopes indicated that the rate-determining step involved the first electron transfer and was close to or greater than 120 mV. dec\({}^{-1}\). The nitrate reaction order was found to be approximately 0.51 for Pt and 0.34 for Rh from the logarithm linear fitting of the current densities versus the logarithm of nitrate concentration [39]. Nitrate adsorbates occupied the active surface area, causing the butterfly-like hydrogen adsorption and desorption pattern to disappear. Also, [PERSON] et al. [215] assessed the impact of nitrate concentration on the cyclic voltammogram of Pt(110) in 0.1 M HClO\({}_{4}\) + 10 mM KNO\({}_{3}\), and concluded that the reaction follows a zero-order dependence on nitrate concentration. The adsorbed NO\({}_{3}^{-}\) is believed to be in a state of quasi-equilibrium with the NO\({}_{3}^{-}\) in the bulk solution before the rate-determining step of the reduction reaction. The average Tafel slope value of \(-66\pm 2\) mV. dec\({}^{-1}\) indicating that the RDS does not involve any electron transfer. Instead, a pure chemical reaction occurs on Pt(100), wherein NO\({}_{3}^{-}\) reacts with H\({}_{\text{ads}}\)[215]. In another study, [PERSON] et al. [122] studied the kinetics of nitrate reduction at high concentrations and determined that the reduction follows Langmuir-Hinshelwood kinetics. Whereas the reduction is characterized by first-order kinetics when the concentration isbelow 0.3 M. However, at higher concentrations, zero-order kinetics dominate due to the blockage of surface sites, indicating that the regeneration of active sites controls the reaction. Also, the selectivity towards N\({}_{2}\) increased from 70% to 83% with an increase in nitrate concentration from 100 to 1500 mg L\({}^{-1}\) and remained relatively stable at higher nitrate concentrations. Conversely, the selectivity of ammonia showed a declining trend from 25% to 11%. Furthermore, the faradic efficiency increased from 25% to 78% with an increase in nitrate concentration, and this trend was observed when 95% of nitrate was reduced [122].
## 2 Materials and Methods
### Statistical Analysis  
Partial least squares discriminant analysis (PLS-DA) was performed on the Metabo-Analyst 5.0 platform. Differential metabolites, lipids and volatiles were identified based on univariate analysis criteria, specifically with variable importance in projection (VIP) > 1 and \(p\) value < 0.05. The VIP value was extracted from the results of PLS-DA. Using pheatmap (V1.0.12), a heatmap was generated, and hierarchical cluster analysis of the differential metabolites among samples was conducted. For the mapping of metabolic pathways related to differential metabolites, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was employed.  
SPSS 17.0 was employed for the analysis of meat quality data, and the outcomes were expressed as the mean \(\pm\) SD. Significance levels were established at \(p\) < 0.05. To assess correlations between differential metabolites and meat quality indices, the Spearman correlation test was executed.  
The R package DESeq2 was used to screen differentially expressed genes (DEGs) with \(|\log_{2}\) (fold change) \(|\geq\) 1 and \(p\) \(\leq\) 0.05. Utilizing DAVID tools, Gene Ontology (GO) and the KEGG pathway enrichment analyses of DEGs were conducted. The correlations between differential lipids and DEGs were calculated using Spearman's method. The differential lipid and DEG pairs with r > 0.4 and \(p\) < 0.05 were screened. Using Cytoscape (V3.8.2), the network of differential lipids and DEGs was constructed.
# Displays Based on Dynamic Phase-Only Holography
## Head-Up Display  
The first commercial application of this technology has been the holographic head-up display (HUD) [23]. The holographic system is capable of delivering identical levels of performance at much lower power levels or significantly brighter images than those currently possible with LED backlights [23] and those that use LED front illumination. Advantageously, the holographic display allows defects in the display device to be annealed, software control of the pixellation of the display, and holographic gain. The LCOS SLMs allow the largest format of all 2D display technologies for the required power levels of the HUD. Over the past 10 years, patents in this area of display technology have increased sixteen folds.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 3 Congenital Heart Disease
### Defects Amenable to Stenting
#### 3.2.4 Pulmonary Artery Stenosis  
Pulmonary artery stenosis is a narrowing of the pulmonary artery connecting the right ventricle with the lungs (Figure 3d). Depending on the location of the stenosis, the pathology is classified into four different types [60]: type I consists of a single stenosis site affecting the main, the left, or the right pulmonary artery; in type II morphology, the pulmonary artery bifurcation is affected; type III lesion is a stenosis of multiple segmental pulmonary arteries with normal central arteries; in the type IV variant, both peripheral pulmonary arteries and the central ones are stenotic. This pathology accounts for 2-3% of all CHD malformations [54; 60; 61]. It may generally have a congenital origin or it can be acquired after surgical intervention; however, only the former case is considered in this review. Moreover, it may develop as an isolated defect or in conjunction with other CHD forms. While during in utero life this condition can be tolerated thanks to the parallel circulations, as in the case of pulmonary atresia, at birth, intervention is required in case of right ventricular hypertension associated with the increased work of the right heart, strong asymmetry of the pulmonary blood flow, right ventricle dysfunction, and pulmonary artery hypertension [62]. The goal is to restore a homogeneous pulmonary blood flow distribution,avoiding hypoplasia of the affected segments due to low perfusion [54; 61]. Transcatheter therapies constitute the first-line choice, while the surgical approach is restricted to a limited number of cases such as well-localized lesions of the main pulmonary artery or the pulmonary bifurcation [61]. Transcatheter treatments are basically the same adopted for CoA, namely balloon angioplasty and pulmonary artery stenting. Stenting is typically preferred in cases of proximal or distal branch pulmonary artery stenosis, stenosis due to kinking, tension, or external compression, and in cases of compliant obstructions [61].
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
###### Abstract  
This paper focuses on a channel feed-forward software defined equalization (FSDE) of visible light communication (VLC) multistate quadrature amplitude modulation (M-QAM) based system, implemented in the LabVIEW programming environment. A highly modular platform is introduced; the whole experiment is simulated in software and then thoroughly explored and analyzed during practical measurements in the laboratory, simulating real-world situations. The whole platform is based on modified National Instruments software defined radios (NI SDR) and a commercially available Philips light source, often used in Czech government institutions. Three FSDE algorithms were tested: least mean squares (LMS), normalized least mean squares (NLMS), and QR decomposition based RLS (QR-RLS). Based on measurements, QR-RLS provides the best results, improving measured values by up to 10%. The experiments also show that the simulated results are very similar to real measurements, thus proving the validity of the chosen approach. The whole platform manages to improve measured data simply by making changes to the software side of the testing prototype.  
feed-forward software defined equalization (FSDE); visible light communication (VLC); software-defined radio (SDR); multistate quadrature amplitude modulation (M-QAM); adaptive equalizers; QR decomposition based recursive least squares (RLS) +
Footnote †: journal: Sensors
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 4 The Proposed Algorithm
### Attention-Based LSTM  
In this section, for the hidden states of the LSTM output, the feed-forward attention mechanism was utilized to adaptively calculate the weight of every frame. The weights are denoted by \(\alpha=\{\alpha_{1},\cdots,\alpha_{i},\cdots,\alpha_{N}\}\) for the hidden states, \(\mathrm{h}=\{h_{1},\cdots,h_{i},\cdots,h_{N}\}\), of frames 1, 2,..., \(N\), where \(\{\alpha_{i}\geq 0,i=1,\cdots,N\}\). \(h_{i}^{(j)}\) represents the \(j\)-th entry of the frame \(i\) in the hidden state \(h\). \(w^{(j)}\) represents the trainable parameters. Then the weights, computed by the attention mechanism using sigmoid function, can be defined as,  
\[u_{i}=h_{i}^{(j)}\cdot w^{(j)}, \tag{7}\]  
and  
\[\alpha_{i}=\frac{e^{\frac{1}{1+e^{-\mathrm{i}\mu}}}}{\sum_{i=1}^{N}e^{\frac{1}{ 1+e^{-\mathrm{i}\mu}}}+\varepsilon}, \tag{8}\]  
where the weighted, hidden state, \(\overline{h}_{i}\), and the super vector, \(c\), are obtained by,  
\[\overline{h}_{i}=\alpha_{i}\cdot h_{i}^{\mathrm{T}}, \tag{9}\]  
and  
\[\mathrm{c}=\sum_{i=1}^{N}\overline{h}_{i}. \tag{10}\]