esa-sceva/satcom-chunk-collection huggingface dataset:
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### AA IL Application in Wastewater Treatment and Separation Processes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method/AA IL Used** & **Pollutant/Target** & **Achieved * & **References** \\ \hline
**ligand-exchange chiral separations (CE and HPLC)** & \
\begin{tabular}{c} enantioselective \\ resolution of D, L-Phe, \\ D, L-His, D, L-Trp, \\ and D,L-Tyr \\ \end{tabular} &
\begin{tabular}{c} Rs=3.26–10.81 for HPLC; \\ Rs=1.34–4.27 for CE \\ \end{tabular} & [138] \\ \hline
**reactive extraction** & & & \\ \([\)N\({}_{4441}][\)Ser\(]\) & & & \\ \([\)N\({}_{4441}][\)Thr\(]\) & & & \\ \([\)N\({}_{4441}][\)Cys\(]\) & & & \\ \([\)N\({}_{4441}][\)Pro\(]\) & & & \\ \([\)N\({}_{4441}][\)Lys\(]\) (*) & removal of naphthalene & & (*) 46\%; \\ \([\)N\({}_{4441}][\)Val\(]\) & acid from crude oil & & (*) 46\%; \\ \([\)P\({}_{4441}][\)Ser\(]\) & acid from crude oil & & \\ \([\)P\({}_{4444}][\)Lys\(]\) (**) & & & \\ \([\)P\({}_{4441}][\)Lys\(]\) & & & \\ \([\)P\({}_{4441}][\)Thr\(]\) & & & (**) 41\%. \\ \([\)P\({}_{4444}][\)Pro\(]\) & & & \\ \([\)P\({}_{4441}][\)Cys\(]\) & & & \\ \hline
**ATPE** & & & \\ \([\)Ch\(][\)L-Met\(]\) & gold(I) recovery from & & E (\%) = 99 \\ \([\)Ch\(][\)Gly\(]\) & aurocyanide & for Au (I) & [175] \\ \([\)Ch\(][\)L-Ala\(]\) & wastewater & & \\ \([\)Ch\(][\)L-Pro\(]\) & & & \\ \hline
**magnetic dispersive SPE** & \
\begin{tabular}{c} benzimidazole \\ residue analysis in \\ fruit juice and human \\ serum \\ \end{tabular} &
\begin{tabular}{c} E (\%)\textgreater{} 97 \\ 10 recovery cycles of the \\ adsorbent \\ \end{tabular} & [158] \\ \hline
**membrane adsorption** & & & (*) highest permeability of \\ Nylon 6,6/ChOH-Gly (1:5) & Cu (II) removal & & \\ Nylon 6,6/ ChOH:Gly (1:2) (*) & & & \\ \hline
**mobile phase for ion exchange** & & & \\
**chromatography** & & & \\
**[Emim][Gly] & analysis of Mg\({}^{2+}\), & & \\
**[Bmim][Gly]** & Ca\({}^{2+}\), Zn\({}^{2+}\), and Fe\({}^{3+}\) & & (*) linear range: \\
**[Hmim][Gly]** (*) & in oral solution and & 0.01–50 mg/L, except for & \\
**[Hmim][Ala]** & water & Ca\({}^{2+}\), which is 0.1–100 mg/L; & \\
**[Hmim][Asp]** & & & (*) LOD in the range of & \\  & & 0.57–3.58 ug/L & \\ \hline
**synchronous fluorescence spectrometry** & \
\begin{tabular}{c} simultaneous \\ determination of \\ \end{tabular} &
\begin{tabular}{c} LOD =1.46 ng/mL \\ (Magnolol), LOD = \\ 0.92 ng/mL (Honokiol); \\ the recovery rates: \\ \end{tabular} & [178] \\ \([\)Omim][Ala] & Magnolol and & & \\  & Honokiol & 98.6–100.7\% (Magnolol) and & \\  & & 99.7–100.6\% (Honokiol) & \\ \hline
**capillary electrochromatography** & & & \\ poly(glycidyl methacrylate-co-ethylene & separation ability for & Rs: Trp, Phe, 5-OH-Trp: 2.231 \\ dimethacrylate)monolithic column & amino acids, & and 2.036; \(\beta\)-blockers: 2.779 & \\ modified with \([\)N\({}_{1111}][\)Arg\(]\) and graphene & \(\beta\)-blockers, and & and 2.470; and nucleotides: & \\ oxide & nucleotides & 8.345 and 3.321 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Bibliometric Trends in Industry 5.0 Research: An Updated Overview
## 3 Methodology
### Data Sources and Selection Criteria  
In this section, we describe the sources of data used in the study, including the databases, search terms, and inclusion/exclusion criteria.  
This study obtained bibliometric data from the Scopus database (www.scopus.com) accessed on 4 February 2023. Scopus was chosen for its broader coverage compared to the Web of Science ([PERSON] and [PERSON], 2020). As noted by [PERSON] and [PERSON] (2020), p. 42, \"broader coverage is useful for mapping smaller research areas\". The broad coverage provided by Scopus is arguably crucial in the context of I5.0 as early publications on new concepts may appear outside of leading journals in a field, such as in conference proceedings that widely used in some academic fields (e.g., engineering and computer science) ([PERSON] and [PERSON], 2020) that are highly relevant to research on I5.0. In the realm of computer-related fields, it is worth noting that certain conference proceedings are regarded as being more timely, cutting edge, and rigorously peer-reviewed than certain journals ([PERSON] and [PERSON], 2020) (pp. 661-662). Although the Web of Science has a longer history of coverage, this feature was not relevant to this study due to the relatively short history of Industry 5.0 that only goes back to 2015 (Bibliometric, 2020).  
Only the search term \"Industry 5.0\" was used, and documents explicitly mentioning \"Industry 5.0\" in the abstract, title, or keywords were included to avoid excluding any potentially relevant publications. The study covers the time period from 2015 to 2023, since it is documented in the literature that the concept was first mentioned on social media in 2015 and in a scientific article from 2016 ([PERSON], 2012).  
Table 1 summarizes the considerations and choices made in this study. The search query used was: \"TITLE-ABS-KEY ('Industry 5.0') AND PUBYEAR > 2014\", meaning documents containing the term \"Industry 5.0\" in the title, abstract, or keywords were searched for, published between 2015 and 2023.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Choice** \\ \hline Database & Scopus \\ Search query & TITLE-ABS-KEY (‘industry 5.0’) AND PUBYEAR > 2014 \\ Time period & 2015–2023 \\ Date of query & 4 February 2023 \\ Categories & All \\ Sources & All \\ Languages & Any \\ Number of documents & 532 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of search procedure and choices made (Source: own elaboration).
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 2 Materials and Methods  
### Text Normalization Dataset  
As stated in Section 1, it can be challenging to obtain a valid database of normalized text. Fortunately, a huge database was built and released to the whole Machine Learning community by [PERSON] and [PERSON] [6]. This dataset occupies a total of 9.1 Gb and contains about 40 million phrases extracted from Wikipedia. It includes 1.1 billion words of English text. The expected output for the input sentences was mostly obtained with a set of hand-built rules used by a finite-state grammar [12].  
The database was prepared for their word-level model and, therefore, requires some preprocessing before being suitable for a character-level approach. Particularly, each entry on the original database is a pair of words (or special symbols) plus an additional column describing its semiotic class, as shown in Figure 2. In order to use a character-level approach, each row needs to be composed of all the words belonging to the same phrase, and information regarding each individual word (such as its semiotic class) has to be disregarded.  
As shown in Figure 2, there are special symbols in the original dataset, namely: (1) <eos>, denoting the end of the current sentence; (2) <sil>, marking a silence (comma, colon, and so on); and (3) <self>, meaning that the output in that entry is the same as the input. Since these symbols cannot be used in a character-level approach (due to the alignment problem), they were removed in the following way: <eos> disappears once the sentence has been recomposed; whereas <sil> and <self> are substituted by the corresponding input.  
Other minor changes have been made on the original dataset to speed up the training process, obtaining a new dataset as shown in Figure 3. The process consists of the following steps:  
Figure 2: Sample text from the original dataset ([[https://github.com/rwsproat/text-normalization-data](https://github.com/rwsproat/text-normalization-data)]([https://github.com/rwsproat/text-normalization-data](https://github.com/rwsproat/text-normalization-data))).  
1. Concatenation of the words belonging to the same phrase and removal of special symbols, as mentioned before.
2. Phrases with non-permitted characters are discarded, keeping an alphabet of \(v=127\) characters, including numbers, basic arithmetic symbols, currency, and the English alphabet.
3. Entries with an output longer than 177 characters are discarded as well, which corresponds to removing only 0.01% of the sentences.
4. Entries are sorted in descending order with respect to their output length. This way, the padding introduced in batches is minimized and, as described by [PERSON] et al. [13], convergence speed is increased without a significant loss in accuracy.
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
## 5 Conclusions  
In this paper, four shapes of the flat tube and four shapes of the oval tube have been investigated and compared with the conventional circular tube for the case of annular FTHEs. Furthermore, the findings were presented and compared in terms of performance evaluation criterion and global performance criterion \(G_{pc}\) vs. Reynolds number. For a range of Re of (2600 \(\leq\) Re \(\leq\)10,200), the impact of the tube design on hydrothermal behavior for a three-row staggered bank was investigated. From the analysis of the numerical results obtained, the following main conclusions may be drawn:  
* For both oval and flat tubes, the heat transfer rate and the Colburn factor increases with the axis ratio and the flatness, whereas O1 and F1 provide the highest values.
* Among the oval tubes, O1 produces the lowest friction factor values at all Re. However, the friction factor of F4 is lower than that for the other flat tubes (F4 provides 13.2-18.5% lower than those of the other tube shapes).
* All the studied tubes (both oval and flat tubes) outperformed the traditional circular tube (O5) in terms of performance evaluation criterion, where O1 and F1 reached the greatest values at all Reynolds numbers.
* Annular fins with the O1 tube shape outdo all the other oval tubes in terms of global performance criterion \(G_{pc}\) (higher by about 9.6%, 35.2%, and 45.9% than that of O2, O4, and O5, respectively, at lower values of Re). An increase in the flatness of flat tubes increases the \(G_{pc}\) values, where F1 shows highest values of \(G_{pc}\) among the four flat-tube geometries.  
Figure 13: Influence of flat tube shapes and Reynolds number: (**a**) performance evaluation criterion and (**b**) global performance criterion.  
At all Reynolds numbers, F1 beats all other tube patterns. As a result, this tube geometry is suggested to be used in energy systems.  
Conceptualization, F.D. and L.B.; methodology, R.R. and M.I.; software, H.A. ([PERSON]) and F.T.; validation, H.A. ([PERSON]) and G.L.; formal analysis, Y.M.; funding, S.K.E. and T.M.J. investigation, F.D. and R.R.; data curation, L.B.; writing-original draft preparation, M.I. and F.T.; writing-review and editing, H.A. ([PERSON]) and Y.M.; resources, H.A. ([PERSON]) and G.L. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/318), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Simulation Experiments
#### 2.3.4 Comparison of Simulations with a Reference Baseline  
In addition to comparing algorithms with each other, we compared the selected algorithms against a baseline. The reason for this comparison was to note what would happen without the algorithms, and so assess the role of the RL algorithms in the achievement of results. To make this basis for comparison, we ran another series of simulations with the same problem settings and the same thresholds for user agents but without RL algorithms for the governing agent. Instead, in these simulations the governing agent produced _signals_ randomly in each time-step. We then compared the results of this new model, which we call the baseline, with the selected RL algorithms.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 2 Related Work
Several studies reported a successful application of various multivariate machine learning models for the estimation of surface-level emissions. For example, in [28], the authors created a multivariate machine learning model for the estimation of NO\({}_{2}\) emission over Germany [28], while in [29], the O\({}_{3}\) concentrations were estimated for California. The areas of analysis of the above-mentioned studies, however, are restricted to over-land territories. In [30] was reported the first attempt to extend near-surface concentration predictions to an ocean region. The authors acknowledged that the performance of emission estimations over the ocean is significantly more challenging than the equivalent task for the over-land areas, mainly due to the absence of in situ measurements' possibilities. In addition, the sources of the detected emission levels have not been studied in the above-mentioned paper. In this study, we present a pipeline that, for the first time, allows the application of multivariate machine learning models for the estimation of NO\({}_{2}\) emission from seagoing ships. The developed method uses TROPOMI satellite data, AIS data on ship positions, as well as ECMWF wind data for the segmentation of NO\({}_{2}\) plumes from individual ships, allowing differentiation between the plume produced by the ship of interest from all the other NO\({}_{2}\) plumes in the ship neighborhood. In the following section, the data sources used and the developed methodology are presented in detail.
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 4 Results and Discussions
### Experiment Performance
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scenario** & **k\_clusters** & **F\_score** & **\(P\)** & **\(R\)** \\ \hline A & 10 & 0.634750567 & 0.707801418 & 0.707801418 \\ A & 20 & 0.759527691 & 0.782370821 & 0.782370821 \\ A & 30 & 0.774174454 & 0.797163121 & 0.797163121 \\ A & 40 & 0.791222723 & 0.81337386 & 0.81337386 \\ A & 50 & 0.796625203 & 0.818439716 & 0.818439716 \\ A & 60 & 0.807701369 & 0.826545086 & 0.826545086 \\ A & 70 & 0.82940477 & 0.840932118 & 0.840932118 \\ A & 80 & 0.856617997 & 0.860182371 & 0.860182371 \\ A & 90 & **0.856617997** & 0.860182371 & 0.860182371 \\ A & 100 & 0.856576486 & **0.860992908** & **0.860992908** \\ \hline B & 10 & 0.631232209 & 0.702567865 & 0.702567865 \\ B & 20 & 0.656807596 & 0.719148936 & 0.719148936 \\ B & 30 & 0.663706266 & 0.724578136 & 0.724578136 \\ B & 40 & 0.677842173 & 0.731327953 & 0.731327953 \\ B & 50 & 0.698839937 & 0.739858326 & 0.739985326 \\ B & 60 & 0.713056204 & 0.7540719 & 0.7540719 \\ B & 70 & 0.723907056 & 0.760674982 & 0.760674982 \\ B & 80 & 0.73798451 & 0.769332355 & 0.769332355 \\ B & 90 & 0.751859908 & 0.777696258 & 0.777696258 \\ B & 100 & **0.769164772** & **0.786353632** & **0.786353632** \\ \hline C & 10 & 0.361244359 & 0.437019101 & 0.437019101 \\ C & 20 & 0.531886447 & 0.543395211 & 0.543395211 \\ C & 30 & 0.578844264 & 0.581113801 & 0.581113801 \\ C & 40 & 0.591782961 & 0.594834544 & 0.594834544 \\ C & 50 & 0.611349266 & 0.61393597 & 0.61393597 \\ C & 60 & 0.668733435 & 0.664911552 & 0.644911552 \\ C & 70 & 0.681865684 & 0.679096045 & 0.679096045 \\ C & 80 & 0.710997885 & 0.71401668 & 0.71401668 \\ C & 90 & 0.697500494 & 0.708528383 & 0.708528383 \\ C & 100 & **0.741288262** & **0.743287598** & **0.743287598** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of the proposed self-supervised learning-based identification model for unknown Internet traffic.
## 1 Introduction
In this paper, a deep learning-based ultrasonic diffraction data analysis method is proposed to overcome the above difficulties. It can automate the interpretation of ultrasonic data to accurately size defects for assisting in decision-making. Firstly, a convolutional neural network (CNN) architecture is developed to automatically measure the length of the defect. Using the architecture, the population of A-scan signals in the scanning path is classified. The defect region is extracted and its size in the scanning direction is obtained by the connected region solution algorithm based on the classification results. Secondly, in determining the height of defects (size in the direction of ultrasonic propagation), the arrival time of diffraction waves is accurately identified by the intelligent denoising framework previously proposed, combined with Hilbert transform, and then the height of defects is calculated by corresponding geometric relations. Two welded joints, with five embedded artificial cracks in the middle and at the root, respectively, were prepared to verify the effectiveness of the method. The estimation results demonstrate that the measurement method can be considered as a useful technique for crack sizing in industrial structures, even in the case of complex noise. Figure 3: Schematic diagram of the conventional method to determine the length of defect in the ultrasonic diffraction image.
## 2 Materials and Methods
### Extraction Process Modelling and Statistical Analysis  
The dependent variables \(Y_{1}\) (extraction yield, %, _w/w_), \(Y_{2}\) (B-type (epi)catechin dimer content, mg/g), \(Y_{3}\) (epicatechin content, mg/g), \(Y_{4}\) (quercetin-3-\(O\)-glucose content, mg/g), \(Y_{5}\) (quercetin-3-\(O\)-rhannoside content, mg/g), and \(Y_{6}\) (total flavonoids content, mg/g) were used in the extraction process optimization. The response surface models were fitted by means of least squares calculation using the following second-order polynomial equation:  
\[Y=b_{0}+\sum_{i=1}^{n}b_{i}X_{i}+\sum_{\begin{array}{c}i=1\\ j>i\end{array}}^{n-1}\sum_{j=2}^{n}b_{ij}X_{i}X_{j}+\sum_{i=1}^{n}b_{ii}X_{i} ^{2} \tag{1}\]  
where \(Y\) corresponds to the dependent variable to be modelled, \(X_{i}\) and \(X_{j}\) define the independent variables, \(b_{0}\) is the constant coefficient, \(b_{i}\) is the coefficient of the linear effect, \(b_{ij}\) is the coefficient of the interaction effect, \(b_{ii}\) is the coefficient of the quadratic effect, and \(n\) is the number of variables (\(n\) = 3).  
Fitting procedures, coefficient estimates, and statistical analysis were performed using the Design-Expert software. Analysis of variance (ANOVA) was used to assess the significance of the models and of all the terms that make up the models, as well as the lack-of-fit. Only the statistically significant terms (\(p\) < 0.05) were used in the models' construction (except those required to ensure hierarchy). Coefficient of determination (R\({}^{2}\)), adjusted coefficient of determination (R\({}^{2}\),dj), and adequate precision were used to estimate the adequacy of the polynomial equation to the response. The lack-of-fit measures the quality of the model's fit to the experimental data; thus, it must be non-significant (\(p\) > 0.05).  
Models Validation and Evaluation of the Bioactivity of the Extract Produced under Optimized Extraction Conditions  
The optimized global UAE conditions that maximize the recovery of flavonoids from kiwi peel were applied to obtain a flavonoid-rich extract, following the proceduredescribed above. This extract was used for experimental validation of the theoretical models, performed through the analysis of the experimental responses (extraction yield and flavonoid content) of this new extract and comparison with the model-predicted values. The in vitro bioactivity of this extract was also evaluated as described below.
# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 3 Materials and Methods
### Machine Learning Models  
Machine learning models were built using the Orange Data Mining tool v3.38 [41]. In total, seven models were built using the KNN, random forest, gradient boosting (scikit-learn), SVM, neural network (MLP), logistic regression, and CatBoost (a variation of the gradient boosting algorithm) algorithms. The model was initially trained using stratified cross-validation (K = 10). It was then tested with the test dataset. Figure 2 illustrates the methodology adopted to build the classification models using machine learning.  
The hyperparameters were manually selected, and the parameters with the best results based on training accuracy were chosen (details are available in the Supplementary Material--Supplementary Figures S1-S8). The following parameters were used in the final version:  
* KNN--k = 5; metric: Euclidean, Weight: by distances;
* Random Forest--number of trees: 10; do not split subsets smaller than five;
* Gradient Boosting (scikit-learn)--number of trees: 500; learning rate: 0.9; limit of the depth of individual trees: three; do not split subsets smaller than two;
* SVM--cost (c): 1.00; regression loss epsilon: 0.1; kernel: linear; numerical tolerance: 0.001; iteration limit: 100;
* Neural Network (MLP)--neurons in hidden layers: \"100\"; activation: ReLu; solver: Adam; regularization alpha = 0.0001; the maximal number of iterations 200;
* Logistic Regression--regularization type: ridge (L2); strength C1;
* CatBoost (Gradient Boosting variation)--number of trees: 200; learning rate: 0.4; regularization lambda 0.09; limit depth of individual trees: 5.  
Figure 2: Representation of the constructed machine learning models. Training and testing were randomly separated in a previous step using Python scripts. Generated using Orange Data Mining.  
The results were evaluated using five metrics: accuracy, F1-score, precision, recall, and specificity.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics
### MKIDs for Neutrino Physics Experiments
#### 4.1.2 Calder  
CALDER (the Cryogenic wide-Area Light Detectors with Excellent Resolution) is an experiment based in Rome and LNGS and its main goal is to develop sensitive MKIDs in order to identify rare events, in particular the group is working on answering two different fundamental questions [127]1. Is the neutrino a standard particle or is it its own anti-particle and therefore a Majorana particle? This information can be obtained by the detection of the particular rare neutrino-less double beta decay.
2. What is the nature of the dark matter that fills our universe?  
CALDER uses CUORE's former experimental setup with TeO\({}_{2}\) to develop large area phonon mediated MKIDs whose main task is the detection of ultraviolet and visible Cherenkov photons emitted in the TeO\({}_{2}\) crystals. The detection of such photons would allow to significantly improve background rejection: \(\alpha\)-particles from natural background radiation do not produce Cherenkov radiation in TeO\({}_{2}\) crystals, while electrons produced by a double-beta decay of Te would. The correct detection of said Cherenkov light could therefore be beneficial to distinguish the nature of these two different events that otherwise produce very similar signals.  
CALDER is investigating the feasibility of fabricating phonon mediated MKIDs out of Al/Ti/Al tri-layers whose critical temperature can be tuned accurately by varying the thickness of the three superconducting layers. These devices produced an improvement in the RMS baseline resolution from 105 eV down to 26 eV, getting close to the target resolution of 20 eV [128]. Particularly interesting results have been shown by [PERSON] et. al. [126] who utilized MKIDs to simultaneously detect photons and phonons in Li\({}_{2}\)MoO\({}_{4}\) crystals and demonstrated that their MKIDs can achieve very high detection speeds and immense sensitive areas. This combination is very promising for further background reduction as it allows to reject pile-up events of 2-neutrino double beta decays, a potentially significant background in \({}^{100}\)Mo based experiments [126]. Currently, the CALDER collaboration is working on further improving these promising results and increasing the sensitive area of the detector from 2 cm x 2 cm to 5 cm x 5 cm while also working on developing further detectors such as BULLKID [129], which we will describe later. It is also important to note that other materials such as TiN\({}_{\rm x}\) and Ti/TiN multilayers are being taken into consideration for the sake of the optimization of the CALDER detectors [130; 131].