esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Subjects
#### 2.1.2 Time-Sequential Cytokine Release  
**TGF-\(\beta\)1**  
The TGF-\(\beta\)1 release at different time points (0 h, 24 h, 48 h, 72 h, and 96 h) in 3-chamber co-culture device was 4250.79 \(\pm\) 1441.14, 4862.89 \(\pm\) 1641.93, 4915.04 \(\pm\) 1483.96, 5324.79 \(\pm\) 1720.3, and 5390.04 \(\pm\) 1371.79 pg/mL, respectively. They were 6052.67 \(\pm\) 1035.28, 5999.44 \(\pm\) 1040.79, 6225.11 \(\pm\) 943.76, 6270.37 \(\pm\) 1371.69, and 6005.04 \(\pm\) 1011.19 pg/mL in 24-well plate (Figure 2a). There was a moderate to high correlation between TGF-\(\beta\)1 in co-culture device and 24-well plate until 96 h (Table 2).  
**PDGF-AB**  
The PDGF-AB release at different time points (0 h, 24 h, 48 h, 72 h, and 96 h) in 3-chamber co-culture device was 295.78 \(\pm\) 44.53, 319.74 \(\pm\) 89.34, 273.81 \(\pm\) 82.14, 265.16 \(\pm\) 77.69, and 267.85 \(\pm\) 64.32 pg/mL, respectively. They were 296.99 \(\pm\) 46.6, 341.84 \(\pm\) 75, 308.48 \(\pm\) 45.86, 334.76 \(\pm\) 41.28, and 350.82 \(\pm\) 47.21 pg/mL in 24-well plate (Figure 2b). There was a moderate to high correlation between PDGF-AB in a co-culture device and a 24-well plate for the first 24 h. Then it became modestly correlated from 48 to 96 h until 96 h (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Whole Blood** & **PRP-A2** & **PRP-B2** \\ \hline Sex, M; F & 10; 9 & & \\ Age, year & 58.4 \(\pm\) 16.3 & & \\ WBC, (10\({}^{3}\) cells/\(\mu\)L) & 5.4 \(\pm\) 1.8 & 0.6 \(\pm\) 0.5, (11\%) & 1.6 \(\pm\) 1.4, (29.4\%) \\ RBC, ((10\({}^{6}\) cells/\(\mu\)L)) & 4.6 \(\pm\) 1.1 & 0.04 \(\pm\) 0.03, (1\%) & 0.1 \(\pm\) 0.03, (2\%) \\ HGB, (g/dL) & 13.8 \(\pm\) 3 & 0.08 \(\pm\) 0.04 (0\%) & 0.1 \(\pm\) 0.1, (0\%) \\ HCT, (10\({}^{3}\) cells/\(\mu\)L) & 41.1 \(\pm\) 8.4 & 0.22 \(\pm\) 0.1, (1\%) & 0.5 \(\pm\) 0.2, (1.2\%) \\ PLT, (10\({}^{3}\) cells/\(\mu\)L) & 205.3 \(\pm\) 50.7 & \
\begin{tabular}{c} 452.7 \(\pm\) 313.7, \\ (221\%) \\ \end{tabular} &
\begin{tabular}{c} 687.9 \(\pm\) 367, \\ (335\%) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Demographic data of the blood content from 19 patients.  
Figure 1: The platelet concentrations after two types of PRP preparations. The platelet concentrations were significantly higher than those of whole blood. \({}^{*}\)_p_-value < 0.05. Error bars showed mean \(\pm\) standard deviation.
## 3 Methodology
### Temporal Convolutional Network Model with Channel Attention
#### 3.1.4 Model Analysis
In this study, the kernel size was set to 2, the number of channels was fixed at 25, and the expansion factor was 2. As the number of convolutional layers increased, the memory demand also increased, potentially leading to memory consumption and reduced efficiency. Thus, the experiment used a TCN network with either two or four hidden layers to integrate global contextual features while managing memory usage. The model with four hidden layers achieved an accuracy of 63.82% in emotion recognition. Adding two more hidden layers improved accuracy by 1.39%, without affecting computational resources or speed. We assessed the network model's performance with and without the scale dimension attention compression module and drew on [PERSON]'s work [30] to explore the effects of positioning these modules at various points within the network architecture. Integration of the module improved emotion recognition accuracy to 66.31%, a 2.49% enhancement over the model without it. Specifically, positioning the scale attention compression module prior to the TCN network, as illustrated in Table 3, optimized feature weighting and further increased accuracy by 1.06%. This finding underscores the significance of incorporating the module before the TCN layer. (3) Performance of Temporal Convolutional Network Model Based on Channel Attention  
In Figure 7, the \(x\)-axis signifies the false positive rate, the \(y\)-axis the true positive rate, and the AUC under the ROC curve indicates the enclosed area. Class 0 represents \"Interest\", class 1 represents \"Neutral\", and class 2 represents \"Bored\". The micro- and macro-average ROC curves offer performance measures across all classes. Figure 7 displays the confusion matrix and ROC curve for an emotion recognition experiment involving a temporal convolutional network model with channel attention. Column (a) shows the results for the scale dimension attention compression module following the TCN, while column (b) illustrates results for the scale dimension attention compression module preceding the TCN. Observing the graph, it becomes apparent that the model demonstrates \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline GRU (CWT) & 57.90\% \\ \hline TCN[25,25] + GRU (CWT) & 62.43\% \\ \hline TCN[25,25] + AS + GRU (CWT) & 64.21\% \\ \hline TCN[25,25,25,25] + GRU (CWT) & 63.82\% \\ \hline TCN[25,25,25,25] + AS + GRU (CWT) & 66.31\% \\ \hline AS + TCN[25,25,25,25] + GRU (CWT) & 67.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiment. a comparatively strong classification performance for the Interest category, achieving over 72% accuracy. Additionally, incorporating the scale dimension attention compression module before the TCN enhances the differentiation between the Neutral and Bored categories. Both the ROC curve and the AUC area suggest that the model performs better with this module. These experimental outcomes substantiate the requirement for incorporating this module.
## 4 Results and Discussion  
### Preprocessing of the Data  
In order to check the efficiency in respect of the suggested hybrid Akima-EMD-LSTM model, the daily closing price of the KSE-100 index of Pakistan Stock Exchange Limited is used. The historical data has been taken from the Yahoo Finance website ([[https://finance.yahoo.com](https://finance.yahoo.com)]([https://finance.yahoo.com](https://finance.yahoo.com))) accessed on 31 August 2022. The length of the data is between 1 January 2015 and 25 August 2022. A visual representation of the data presented in Figure 4 confirms our claim that the stock prices are nonstationary, nonlinear, and volatile. The descriptive statistics of the data are also presented in Tables 1 and 2. Furthermore, the data are sliced into training and testing in such a way that 90% of the data are used for training, whereas the remaining 10% is used for testing the model. Because of numerous factors, there exist non-trading days on the stock market, data on these days are not included, and only the data for the trading days are considered  
It can be seen from Table 1 that the minimum value of the index is 27,229, that is recorded on 25 March 2020, whereas the maximum value was 52,876, recorded on 24 May 2017. The minimum value is because of the COVID-19 pandemic that affected the majority of the well-known stock indexes across the world [57; 58]. However, the KSE-100 index of the Pakistan stock exchange recovered and soon gained potential because of the government's bold decision to keep open the business sector across the country [59].  
The Pakistan stock market showed strong resistance during the period of the pandemic, which might be the government's strategies to control the outbreak while keeping an eagle eye on the positivity rate and implementing smart lockdowns in specific areas of the country to avoid large lock-downs that crippled the economy of the most developed countries. The mean value that is observed during the seven years is 40,210, whereas the largest value of 5345.791 of the standard deviation verifies that the nature of the stock market is very chaotic.  
Furthermore, Table 2 shows a yearly descriptive summary, such as the average closing price, minimum and maximum closing price, and the number of trading days of the KSE-100 index. It is evident from Table 2 that the maximum average closing price from 2015 to 2022 is 45,959 recorded in the year 2021. The minimum closing price in these years is 33,649, recorded in the year 2015. It is worth mentioning here that the average closing price during 2020 was 38,311, which is more than the year 2015, signifying our claim that the KSE-100 index showed more resistance to the COVID-19 pandemic.
## 3 Methodology  
The model configuration follows a process illustrated in Figure 2. The data pre-processing encompassed data interpolation, Pearson correlation analysis, data division, and data normalization. The historical load data and weather data, obtained from the local website, were analyzed to assess their correlation. The load data were divided into groups based on the five-day types in the week to ensure the forecasting accuracy. The LSSVM, optimized using IBFOA, was employed for load forecasting. Finally, the proposed hybrid model is evaluated with different accuracy measures to demonstrate its performance in STLF. Further details on these processes are explained in the next subsection.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 4 Experimental Results and Discussions
### Improvement of Capacity  
Figure 6 presents a comparison of capacity between [PERSON] et al.'s scheme and the improved scheme proposed in this paper. The results demonstrate a significant increase in average capacity by a factor of 10. The experimental findings of the proposed image steganography reveal a capacity improved by 0.0080-0.0420 bits per pixel (bpp).  
As HDR images cannot be accurately displayed on conventional monitors, in Figure 2, we present stego-images that have been processed using a tone mapping operator (TMO) to convert the HDR images into LDR images. The first column shows the original images without any secret image embedding, while the second and third columns display the stego-images embedded using [PERSON] et al.'s scheme and the enhanced scheme proposed in this paper, respectively.  
The peak signal-to-noise ratio (\(PSNR\)) analysis is a common analysis of LDR image authentication schemes and is defined as  
\[PSNR=10\times\log_{10}\frac{255^{2}}{MSE}\ (\text{dB})\ and\]  
\[MSE=\frac{1}{3\times M\times M}\sum_{k\in\{R,G,B\}}\sum_{i=1}^{M}\sum_{j=1}^{ M}\left(I_{i,j}^{k}-\widetilde{I}_{i,j}^{k}\right)^{2}.\]  
where \(I_{i,j}^{k}\) and \(\widetilde{I}_{i,j}^{k}\) are the original image and the embedded version. The larger the \(PSNR\), the better the quality of the image.  
The \(PSNR\) values are provided below the images in the second and third columns. The \(PSNR\) values for the enhanced scheme are lower than those for [PERSON] et al.'s scheme, indicating that our scheme has greater distortion. However, the \(PSNR\) value in the range of 51.77-78.60 is acceptable and greater than 30, as shown in Figure 7. This level of distortion is typically imperceptible to the human eye.  
Figure 5: The occurrence rate of “irregular” pixels of \(\max(R,G,B)=127\) (a) in [PERSON] et al.’s scheme, and (b) in the proposed scheme.  
Figure 6: The comparison of capacity between [PERSON] et al.’s scheme (**left**) and the enhanced scheme proposed in this paper (**right**).  
Due to space constraints, one HDR image was randomly chosen from a set of 101 images to serve as an illustrative example (Figure 8a). The resulting steganographic image (Figure 8b) achieved a _PSNR_ value of 72.66, demonstrating an excellent preservation of image quality. Moreover, its capacity of 0.0212 greatly surpasses the capacity of 0.0013 achieved in the prior study [13].  
According to the experimental findings, among the 101 HDR images examined, three images were found to lack \"_promising_\" pixels for embedding the secret message by [PERSON] et al.'s scheme. This limitation is not present in the proposed enhanced scheme.
## 1 Introduction
The manufacturer of this GIC claims that this product possesses the strength and durability of amalgam as well as the protective benefits of GICs without mercury. Zirconia and/or zirconium oxide has been used as an indirect restorative material since 1998. The strength of zirconia allows it to be used for crown and bridge restoration in all areas of the mouth [12]. On the other hand, by combining the characteristics of the resin composites and GICs, hybrid products known as goimers have been obtained. In this formula, pre-reacted glass ionomer technology was employed to form the glass ionomer phase. Giomers contain fillers that are produced by the reaction of ion-leachable glasses with polyalkenoic acids. Due to the pre-reacted glass fillers, acidic resin monomers are not essential for in situ acid-base reactions. Giomers offer both protection against caries and functional and esthetic results [13; 14]. From the clinical perspective, the preferred restorative materials are those that not only prevent the growth of oral bacteria, but also inhibit the colonization of bacteria on the tooth surfaces. The antibacterial efficacy of GICs has been investigated extensively in in vitro studies [5; 12; 15; 16; 17; 18; 19], and some of these studies showed that conventional GICs had potential antibacterial activity and also reduced formation of biofilm by _S. mutans_ strains [5; 12; 15; 17; 18; 19; 20]. On the other hand, very few studies have been published evaluating the antibacterial properties of contemporary fluoride-releasing materials [11; 21]. The lack of bacterial growth inhibition of previously developed fluoride-releasing restoratives has also been shown in the literature [20; 22]. Studies investigating the antibacterial efficacy and biofilm inhibition of these newly developed fluoride-releasing/hybrid restorative materials are also needed to minimize the frequency and severity of secondary caries. Therefore, the aim of this study was to investigate the antibacterial properties and biofilm formation of five different commercially available new-generation hybrid/fluoride-releasing materials: two bulk-fill GHs (Equia Forte HT Fil and Riva Self-Cure), a silver-reinforced GIC (Riva Silver), a zirconia-reinforced GIC (Zirconomer), and a giomer (Beautiful II) against _S. mutans_ and _L. casei_. Additionally, surface roughness of these hybrid/fluoride-releasing materials were examined. A nanohybrid resin composite (G-aenial A'Chord) was used for comparison. The null hypothesis stated that there would be no differences: (1) in the antibacterial effect of different hybrid/fluoride-releasing materials on _S. mutans_ and _L. Casei_, (2) in the biofilm formation on the surfaces, and (3) in the surface roughness of these materials.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 3 Preparation of Molecularly Imprinted Microspheres
### Suspension Polymerisation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Template**} & \multirow{2}{*}{**Monomer**} & \multicolumn{2}{c}{**PP**} & \multicolumn{2}{c}{**ATRPP**} & \multicolumn{2}{c}{**RAFTPP**} & \multicolumn{2}{c}{**PE**} \\ \cline{3-10}  & & **Binding Affinity** & **IF** & **Binding Affinity** & **IF** & **Binding Affinity** & **IF** & **Binding Affinity** & **IF** \\ \hline \multirow{3}{*}{17\(\beta\)-estradiol} & \multirow{3}{*}{MAA} & More than 50\% & 2.3 [40] & - & - & - & - & 30–60\% analyte [50] & 3.0 [50] \\ \cline{2-10}  & & 4-VP & 0.75 mg/g [10] & 4.55 [10] & 180.65 mg/g [10] & 6.67–7.38 [10] & - & - & - & - \\ \hline \multirow{3}{*}{Nicotine} & \multirow{3}{*}{MAA} & Not mention in & 10.5 [56] & - & - & Not mention in & 3.33 [57] & - & - \\  & & the article [56] & & 3.91 [52] & Not mention in & 10 [1] & - & - & 1.32 mg/g [87] & 6.5 [87] \\ \hline \multirow{3}{*}{Bisphenol A} & \multirow{3}{*}{4-VP} & Not mention in & 3.91 [52] & \multirow{3}{*}{Not mention in the article} & \multirow{3}{*}{10 [1]} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{1.32 mg/g [87]} & \multirow{3}{*}{6.5 [87]} \\  & & the article & 4.83 [88] & & & & & \\ \hline \hline \end{tabular}  
* PP, precipitation polymerisation; ATRPP, atom transfer radical precipitation polymerisation; RAFTPP, reversible addition-fragmentation chain transfer precipitation polymerisation; PE, Pokerring emulsion polymerisation; IE, imprinting factor; MAA, networkic acid; 4-VP, 4-VP, 4-vinylpyridine; -, no resource found in related technique.  
\end{table} 
Table 3: Comparison of different polymerisation in preparation of molecularly imprinted microspheres.
## 3 Materials and Methods
### Validation Metrics  
Well known metrics as accuracy, precision, recall, F-measure and AUC (area under the ROC curve) [76], were used to validate the classifiers in this work. In addition, other metrics indicated to evaluate models induced from imbalanced data were used: G-mean, optimal precision (OP) and generalized index of balanced accuracy (IBA).  
G-mean is the geometric mean of TPR (true positive rate) and TNR (true negative rate), thus, this metric considers the correct classification of instances of both positive and negative classes.  
A drawback of AUC and G-mean is the fact that they do not differentiate the contribution of each class to the overall accuracy. [PERSON] and [PERSON] [15] addressed this problem and proposed a new metric called optimized precision (OP).  
\[\text{OP}=\text{Accuracy}-\text{I}\text{TNR}-\text{TPR}\text{I}/(\text{TNR}+ \text{TPR}) \tag{1}\]  
OP takes the optimal value for a given overall accuracy when true negative rate and true positive rate are very close to each other.  
More recently, a new performance measure called generalized index of balanced accuracy (IBA) [16], has been proposed. It can be used and defined for any performance metric (m) as:  
\[\text{IBA}\alpha(m)=\text{ }(1+\alpha\cdot Dom)\cdot m \tag{2}\]where \(Dom\), called dominance, encloses the degree of prevalence of the dominant class over the other. It is defined as:  
\[(Dom=TPR-TNR) \tag{3}\]  
Given that the values of TPR and TNR are in the range [0, +1], Dom values are within the range [-1, +1]. Values of Dom close to zero, obtained from similar values of true positive rate and true negative rate, indicate that these rates are balanced. In general, the value of Dom is weighted by \(\alpha\) (\(\alpha\geq 0\)) to reduce its influence on the result of the metric used (m). In practice, a suitable value of \(\alpha\) for a given performance metric must be selected since its value is sensitive to m [77] and affects the accurate of overall IBA result.
## 6 Misincorporation and Conflicts between Transcription and Replication
The fact that DksA/ppGpp plays a role in the resolution of conflicts between elongation complexes and replication forks suggests that misincorporation could be the major cause of the formation of backtracked complexes. Estimated frequency of misincorporation is between \(10^{-4}\) to \(10^{-5}\) mistakes per nucleotide, depending on the sequence [79, 84, 85]. In single-molecule experiments, putative misincorporation pauses were shown to be very long-lived, with a duration up to 30 min, even under saturated nucleotide conditions [86]. Consequently, if not prevented or resolved, misincorporation events may account for 40-400 strong pauses per genome (four Mbp in _E. coli_). Furthermore, taking into account the potential for paused RNAP to cause transcription \"traffic jams\", relatively few misincorporation events could lead to multiple potent obstacles for the replication fork. Therefore, even the modest observed decrease in fidelity (approximately four-fold in the absence of DksA), may significantly diminish the viability of cells.
# Stationary Phases for Green Liquid Chromatography
## 3 Stationary Phases Used in Pure Water Conditions
### Instability of Stationary Phases--A Motivation to Search for Solutions
#### 3.1.2 Polymer-Based Stationary Phases  
Polymeric materials usually exhibit higher thermal stability. Such fillings are successfully used in elevated-temperature chromatography, e.g., for size-exclusion chromatography; thus, it is reasonable to expect them to be the most thermally stable chromatographic materials for analyses using superheated water as a mobile phase. Among polymer materials, two of them are most commonly used in SHWC: polystyrene divinylbenzene PLRP-S (PS-DVB) [55; 57; 81; 83; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101], and crosslinked polymer PRP-1 by [PERSON] [35; 36; 77; 87; 89; 102; 103]. All polymeric stationary phases and the groups of compounds separated on them are summarized in Table 2. The thermal stability of these phases is between 100 \({}^{\circ}\)C and 200 \({}^{\circ}\)C. Polymer-based phases are more thermally stable than silica-based phases; they are therefore used in conditions of prolonged operation at temperatures higher than 200 \({}^{\circ}\)C. However, when comparing retention times and temperature, using the silica phase is sometimes more advantageous, because the analysis time is shorter and does not require such high temperatures. Of course, the limiting parameter for using these phases is their lifetime, so for long-term use, polymeric phases are mostly used [90]. The literature also mentions new polymeric phases that contain attached amino acids that allow them to operate in heated water up to 150 \({}^{\circ}\)C for 500 h [104]. Despite better thermal stability, polymeric stationary phases also have some disadvantages compared to silica-based materials, e.g., lower column efficiency; this is likely related to the high retention capacity; thus, groups of moderately polar compounds such as alkyl- and aryl ketones can be eluted only at elevated temperatures.
## 3 Results and Discussion
### UV-Visible Spectroscopy  
It is of utmost significance to know about the speciation of dissolved copper in choline chloride/EG system. After metallic bulk copper's dissolution, electrochemically, into Ethaline, a coloured solution of dissolved copper was generated. Of great importance to the dissolved bulk metal is the determination of the identity of the species that arose from copper dissolution into a chloride rich electrolyte, as that can be regarded as a key factor in dealing with the kinetics of the electrochemical processes. Herein, a facile, non-destructive technique was used to conduct this task: the UV-Vis spectroscopic technique. In the visible wavelength range, to gain information about any species, the solution must be coloured. To fulfil this condition, the chromophores/electron transition within the d-d orbitals have to exist [22].  
From Figure 4, one can see similar UV-Vis spectra of 0.1 mM CuCl\({}_{2}\):2H\({}_{2}\)O in ethaline and the solution of dissolved copper obtained electrochemically by holding a copper disc electrode in ethaline at a potential of 1.2 V for 1 h, from which dissolved copper ion in the electrolyte, i.e.,ethaline, was obtained at 20 \({}^{\circ}\)C. However, there are two observations to make: first, there is a large peak at around 220 nm present in the copper dissolved electrochemically, which is entirely missing in the control solution; next, the peak at 281 nm in the control solution underwent blue-shifting in the solution of the copper product dissolved electrochemically. These can be associated to the impurities which might be present in the used copper wire (99.9%).  
The spectra obtained in both cases were identical, indicating that the speciation of stripped copper from the bulk copper metal into ethaline is exactly the same as that for the solution species obtained by dissolving CuCl\({}_{2}\).2H\({}_{2}\)O in solution. The formation of [CuCl\({}_{4}\)]\({}^{2-}\) was emphasised from the spectra where three distinct peaks were seen at 233, 281, and 407 nm and this was confirmed using EXAFS [20; 22].  
Figure 3: The influence of electrolyte composition on the equilibrium passivation potential of copper disc electrodes in ethaline ChCl:EG, 1:2 (solid line); ChCl:EG, 1:3 (dashed line); and ChCl:EG, 1:4 (dotted line).