esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 4 SERS-Based MNPs Detection
### Detection of MNP Using SERS-Based Techniques
#### 4.2.4 Influence of MNP and Plasmonic Nanomaterial Size on SERS Response and Enhancement Factor
Moreover, the most promising prospects, while obviously still needing a lot of research, could be the in situ quantification (exploiting the use of portable Raman devices) and continuous analysis, which combine the use of fiber optics with the use of microfluidic circuits and real-time deep learning data processing. The goal looks promising, as do the results achieved in recent years, but there are still gaps to be filled and technical issues to be resolved. One of the most critical aspects lies in the lack of a standard methodology for sample preparation and a standardized procedure of analysis. Another key aspect is the sample matrix. The main matrix in which MNPs can be found is definitely aqueous, which is why almost all of the proposed applications have focused their testing on aqueous samples. In recent years, many research groups have tested the effectiveness of their substrates on real water samples (sea, river, tap water...) spiked with MNPs (see Table 3). However, almost all of the above studies showed the response of their devices in the presence of only one type of plastic and one size at a time, clearly not reflecting what a real sample might be. Here, a new approach to allow for the simultaneous detection of different sizes and composition of MNPs is required to achieve a universal method to characterize a real unknown sample. Regarding the role of plasmonic and SERS-based techniques in the complex landscape of MNPs detection, it is difficult to make accurate comparisons in this field. MNPs detection and quantification, indeed, still lacks recognized protocols and universal standards. So far, the legislation in the field is very limited. The most commonly used techniques are summarized in Table 1, together with their main limitations and the LODs when available. Plasmonic and SERS-based techniques could overcome some of these limitations, alone or in combination with other techniques.
## 2 Materials and Methods
### Analysis of Odorant Compounds of Wine Spirits
#### 2.3.2 Quantification of Minor Volatile Compounds by GC-FID  
Previous to the analysis by GC-FID, a volume of 100 mL of each wine spirit added of 1.6 mL of 5-methyl-2-hexanol as an internal standard (IS, 81.0 mg/L 50% ethanol solution) and 0.5 mL of 3,4-dimethylphenol (IS, 100 mg/L in ethanol) was extracted with dichloromethane (30, 10 and 10 mL) and concentrated on a rotary evaporator (Buchli rotavapor R114 at 42 \(\pm\) 0.5 \({}^{\circ}\)C, without vacuum) until a volume of about 0.25 mL according to the method previously validated [27]. Each extract was analyzed under the following chromatographic conditions: Agilent Technologies 6890 Series gas chromatograph (Wilmington, DE, USA) coupled to a flame ionization detector (FID) and equipped with a fused silica capillary column of polyethylene glycol (INNOWax of J&W Scientific, Folsom, CA, USA), 30 m, 0.32 mm i.d., 0.25 \(\upmu\)m film thickness; injection volume of 0.8 \(\upmu\)L; injector and detector temperatures (250 \({}^{\circ}\)C); carrier gas hydrogen (2.4 mL/min and split ratio 1:25); oven temperature program: 3.5 \({}^{\circ}\)C/min from 35 \({}^{\circ}\)C (6 min isothermal) to 55 \({}^{\circ}\)C, 7.5 \({}^{\circ}\)C/min to 130 \({}^{\circ}\)C, 5 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C (30 min isothermal). For each sample, the extractions were done in duplicate, and each extract was analyzed in triplicate.  
The quantification of minor volatile compounds (acetic acid, linalool, guaiacol, 4-methylguaiacol, eugenol, syringol, 4-methylsyringol, vanillin, acetovanillone, ethyl isobutyrate, ethyl butanoate, isoamyl acetate, trans-2-hexenol, ethyl octanoate, furfural, 5-methylfurfural, butanoic acid, 2-methylbutanoic acid, hexanoic acid, 2-phenylethanol, 4-ethylguaiacol, dodecanoic acid, 5-hydroxymethylfurfural, 4-allysyringol) was based on calibration curves, which were established by the extraction of hydroalcoholic solutions of standards and their chromatographic analysis under similar conditions.
## 5 Biosensors Developed for Detecting the VOCs of Phytopathogens  
The early detection of phytopathogens present in food is of utmost importance to prevent disease and avoid crop losses. It is estimated that between 20% and 40% of crop losses are due to these organisms. In some cases, they can be so severe that they cause total production to be lost [57]. Traditionally, the detection of phytopathogens is carried out by isolation and visual examination techniques based on manuals; however, these approaches are time-consuming and require the use of trained personnel [58].  
There are also emerging techniques for detecting phytopathogens that are more specific. These can be categorized into two methods: direct and indirect. The former refers to polymerase chain reaction (PCR) techniques and immunological methods that, despite their precision, are time-consuming and expensive since they require highly sophisticated equipment [59]. Indirect methods use biological indicators for detection in real-time since they perceive physiological and chemical changes in plants during infection or due to stress. These methods use imaging or compound detection techniques such as infrared spectroscopy, fluorescence, X-rays, thermography, and gas chromatography [60]. In this sense, new research uses techniques based on biological indicators to manufacture devices for detecting phytopathogens that meet the speed and ease of use and are of low cost. Such is the case with biosensors. In addition, they have the advantage of being automatable with multi-analysis capacity and portable; moreover, they are highly selective [61].  
A biosensor is a highly accurate and compact device that detects an analyte of interest. It includes a bioreactor formed by a nucleic acid, enzyme, protein, antibody, or cell. It is integrated into a transduction system that detects the signal emitted by the interaction between the bioreactor and the analyte of interest. This interaction causes changes in the physicochemical properties of the matrix, such as pH, color, and temperature. The transducer perceives and converts these changes into a signal with a known parameter [62; 63].  
In the agricultural field, biosensors have been developed to detect microorganisms. [PERSON] et al. [64] designed a biosensor to detect _Xanthomonas arboricola_ in walnut plants by immobilizing a monoclonal antibody on functionalized mesoporous silica SBA15 through  
Figure 1: Schematic representation of the release of VOCs in plants and some commonly used analytical techniques. Image crated with Biorender.  
covalent bonding. Through the enzymatic interaction between alkaline phosphatase and p-aminophenyl phosphate, the electrical current proportional to the level of _X. arboricola_ in plant samples was measured. Likewise, [PERSON] et al. [65] developed a device capable of detecting the presence of _Colletotrichum gloosporioides_ in papaya fruits by monitoring the chitin produced during the infection process. However, little research has been reported on detecting VOCs in the interaction between the pathogen and the plant host. Table 3 shows the relevant works on this.
## 4 Analysis of Experimental Results
[PERSON] et al., 2011, have studied the metamorphosis of the bulk wave to guided waves and its interaction with surface defects [34]. The scattering of the guided waves due to cracks, defects and surface anomalies has been studied using a scanning laser vibrometer. The wave field imaging provides dispersion and scattering behavior of ultrasonic waves in the spatial-temporal and wave-number-frequency domain [47, 48]. The above techniques are based on the complete wave field imaging, which provides detailed information regarding propagation of guided wave in anisotropic solids. However, little literature is available on guided wave propagation in anisotropic piezoelectric crystals. Also, the above technique is computationally expensive and time-consuming. The particle utility of the wave field imaging technique is limited in the laboratory environment. There is no systematic understanding and quantification process that relates this signal to the material physics. It is imperative that the quantification of the magnitude of damage could not be determined alone from the experimental results by tracking changes in amplitude and shift in frequency of the signal. Therefore, we formulated a non-local elastic wave propagation theory to relate the changes in the nonlocal material physics and quantify them reliably and consistently. We formulate nonlocal parameters, which are indicative of micro-scale aberration on surface and subsurface defects in the piezoelectric crystal.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 6 Results and Discussion
### Preview of the Performance  
In this section, there is a preview sketch of the system enhancement using spectrograms. Two audio examples from the dataset were used mixed with different noise types: baby crying and noise from call centers. There are more audio examples of the system enhancement, either on simulated or real noisy speech ([[http://dayanaribas.vivolab.es/DEMOenhancement/index.html](http://dayanaribas.vivolab.es/DEMOenhancement/index.html)]([http://dayanaribas.vivolab.es/DEMOenhancement/index.html](http://dayanaribas.vivolab.es/DEMOenhancement/index.html)) (accessed on 1 September 2022)).  
According to the previous section results, the system used was the one based on OMLSA with \(Gmin=0.0562\). For comparison purposes, we used _Deepxi_[22; 23], a recent enhancement method from the state of the art with very competitive results (see the benchmarking table in the following link ([[https://github.com/anicolson/DeepXi](https://github.com/anicolson/DeepXi)]([https://github.com/anicolson/DeepXi](https://github.com/anicolson/DeepXi)) (accessed on 1 September 2022)). Moreover, this method has common points with the proposal; for instance, it is also based on the combination of deep learning with classical speech estimators, and the version \(DeepXi-ResNet(1.1c)\) uses Resnet with causal convolutions.  
Figure 4a shows the spectrogram of an utterance corrupted with noise from a call center, while Figure 4b shows other speech sample mixed with the sound of a baby crying. In both examples, SNR = 5 dB and the speech is immersed into the noise, so it is difficult to distinguish the spectral structures of speech. Below the noisy speech are the spectrogram corresponding to the method proposed in this paper and the method for comparison purposes _deepxi_. At a glance, we appreciate that the spectrograms of both enhancement methods are very similar. In order to highlight some difference, we see that the structure of harmonics and the speech formants in example (a) are more defined with the method proposed than with _deepxi_, which spectrogram is softer. There are also some artifacts from the noisy speech (indicated with red arrows in the figure) that remain in the _deepxi_ spectrogram, while the method proposed was able to remove them. For example (b), the harmonic structure introduced by the crying is more pronounced in the spectrogram of  
Figure 3: SNR, distortion, PESQ, STOI, CBAK and OVL for LSA and OMLSA speech estimators averaging throughout all noises.  
deepxi._ So when listening to this example, the baby crying is still a bit stronger in the background of the _deepxi_ sample than the proposal.
## 4 Results and Discussions
### Seasonal Variation of Global Solar Radiation  
Figure 5 shows the monthly average value of the daily global solar radiation of Harbin, Shenyang, Beijing, Wuhan, Shanghai, and Guangzhou during the ten years from January 2011 to December 2020. It can be observed that the highest monthly average values of daily global solar radiation for these cities were recorded in June (20.52 MJ/m\({}^{2}\)), May (21.52 MJ/m\({}^{2}\)), May (22.11 MJ/m\({}^{2}\)), July (18.45 MJ/m\({}^{2}\)), July (17.28 MJ/m\({}^{2}\)), and July (16.79 MJ/m\({}^{2}\)), respectively, throughout the year. Notably, Beijing experienced the maximum monthly average value of daily global solar radiation among all six cities in May. The lowest monthly average values of the daily global solar radiation in six cities over a ten-year period were as follows: Harbin in December (5.70 MJ/m\({}^{2}\)), Shenyang in December (6.91 MJ/m\({}^{2}\)), Beijing in December (7.45 MJ/m\({}^{2}\)), Wuhan in December (6.27 MJ/m\({}^{2}\)), Shanghai in January (7.48 MJ/m\({}^{2}\)), and Guangzhou in March (8.70 MJ/m\({}^{2}\)). Among these cities, Harbin had the lowest monthly average value of daily global solar radiation during the month of December. Except for Shanghai, which exhibited a relatively pronounced fluctuation in June, the monthly average value of daily global solar radiation in the remaining five cities demonstrated almost continuous variations. This is consistent with the semi-annual cyclical fluctuation shown in the wavelet coefficients variance graph for Shanghai in Figure 4. The primary challenge for achieving optimal utilization of solar energy resources lies in integrating energy usage during months characterized by low average solar radiation but high energy demand. Therefore, Harbin, which experiences the lowest monthly average daily global solar radiation among these six cities, and a strong demand for energy consumption in the cold winter climate, was selected for further investigation.  
Figure 4: The variance of Morlet wavelet coefficients for daily global solar radiation in (**a**) Harbin, (**b**) Shenyang, (**c**) Beijing, (**d**) Wuhan, (**e**) Shanghai, and (**f**) Guangzhou.
# Special Issue Advances in High-Performance of Eco-Efficient Concrete
## 13 Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes  
[PERSON], [PERSON], [PERSON] and [PERSON] [12] show in their paper that the research presented in this article analyzed the influence of incorporating precast concrete waste as an alternative to coarse aggregate in self-compacting concrete to generate new precast elements. The experimental study involved the characterization of recycled aggregate and the design of the mix of the new self-compacting concrete (SCC). The experimental study evaluates the physical processes that affect the durability of concrete with percentages of incorporation such as 20%, 50% and 100% of recycled aggregate. Two types of SCC were manufactured with a minimum compressive strength of 30 MPa and 45 MPa. The properties analyzed were the density of hardened SCC, shrinkage cracking, freeze-thaw resistance, resistance to ageing by thermal shock and abrasion resistance. The results obtained were compared with those of the control concrete, and it was observed that the SCC under physical aggressions has a great capacity that affects durability. The results of this research show that it is possible to use the recycled aggregate coming from precast pieces in order to manufacture self-compacting recycled concrete in the same precast industry. However, a high loss of proprieties occurs for a 100% substitution, whereas for 20% and 50%, the variations with respect to the control concrete are smaller. In addition, taking advantage of this waste to incorporate it back into the production chain contributes to more sustainable construction.  
Design Optimization of Rubber-Basalt Fiber- Modified Concrete Mix Ratios Based on a Response Surface Method  
[PERSON] et al. [13] show in their paper that rubber aggregates produced from waste rubber materials and environmentally friendly basalt fibers are excellent concrete modification materials, which significantly improve the working performance and mechanical properties of concrete. This paper studied the influences of the water-binder ratio, basalt fiber content and rubber content on the properties of rubber-basalt fiber-modified concrete (RBFC). Based on the response surface method (RSM), optimization schemes of three preparation parameters were designed. The results showed that all preparation parameters have significant impacts on the slump. The rubber content has a closer relationship with the compressive strength and the quadratic term of the basalt fiber content has a significant impact on the flexural strength. According to the analysis, the optimal mix ratio which possesses a reliable accuracy compared with experimental results includes a water-binder ratio of 0.39, a basalt fiber content of 4.56 kg/m\({}^{3}\) and a rubber content of 10%.  
Thanks are due to all the authors and peer reviewers for their valuable contributions to this Special Issue. The MDPI management and staff are also to be congratulated for their untiring editorial support and for the success of this project.  
The authors declare no conflict of interest.
# Classification of Valvular Regurgitation Using Echocardiography
## 4 Experimental Results
### Classification Comparison with Existing Methodologies  
To classify AR, MR and TR, the results are compared with SVM and PCA-SVM, which were previously used to classify normal and abnormal images or types of regurgitations. The results of our model is compared with the two methodologies as shown in Table 8. For comparison with other methodologies, taking the average best accuracy of our method, it can be observed that the proposed method is better than the existing method as shown in Table 8. Other parameters are not compared as they are not available in other papers. Moreover, using our data, a comparison is made with PCA + SVM and SVM as a method. Classification is performed using five folds for the existing methodologies, based on the \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fold**} & \multirow{2}{*}{
\begin{tabular}{c} **Performance** \\ **Metrics** \\ \end{tabular} } & \multicolumn{2}{c}{**With Segmentation**} & \multicolumn{6}{c}{**Without Segmentation**} \\  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{2-folds} & Accuracy & 67.64 & 76.47 & 76.47 & 82.35 & 85.29 & 91.17 & 88.23 & 70.58 & 79.41 & 88.23 \\  & Precision & 72.85 & 76.77 & 76.51 & 82.57 & 86.11 & 92.85 & 87.46 & 70.95 & 79.54 & 87.46 \\  & Recall & 76.85 & 79.05 & 80.60 & 82.57 & 87.22 & 92.30 & 87.45 & 71.11 & 82.03 & 87.46 \\  & F1-score & 65.89 & 76.25 & 75.70 & 82.57 & 84.08 & 91.64 & 87.46 & 70.92 & 79.83 & 87.46 \\ \hline \multirow{4}{*}{3-folds} & Accuracy & 78.26 & 82.60 & 100 & 86.95 & 95.65 & 82.60 & 86.95 & 82.60 & 100 & 82.60 \\  & Precision & 74.24 & 79.62 & 100 & 88.88 & 95.83 & 83.33 & 84.72 & 78.33 & 100 & 82.14 \\  & Recall & 84.24 & 87.20 & 100 & 91.66 & 96.29 & 83.33 & 85.92 & 90.47 & 100 & 82.14 \\  & F1-score & 81.65 & 80.25 & 100 & 88.56 & 95.81 & 77.77 & 85.02 & 81.34 & 100 & 82.14 \\ \hline \multirow{4}{*}{5-folds} & Accuracy & 100 & 69.23 & 92.30 & 84.61 & 100 & 90.00 & 100 & 100 & 100 & 92.30 \\  & Precision & 100 & 55.55 & 91.66 & 86.66 & 100 & 88.88 & 100 & 100 & 100 & 95.23 \\  & Recall & 100 & 51.85 & 96.57 & 88.88 & 100 & 86.66 & 100 & 100 & 100 & 93.33 \\  & F1-score & 100 & 50.47 & 93.42 & 84.99 & 100 & 84.99 & 100 & 100 & 100 & 93.72 \\ \hline \multirow{4}{*}{8-folds} & Accuracy & 75.00 & 100 & 50.00 & 87.50 & 75.00 & 87.50 & 100 & 100 & 100 & 87.50 \\  & Precision & 77.77 & 100 & 77.77 & 91.66 & 83.33 & 66.66 & 100 & 100 & 100 & 83.33 \\  & Recall & 77.77 & 100 & 73.33 & 88.88 & 83.33 & 58.33 & 100 & 100 & 100 & 88.88 \\  & F1-score & 66.66 & 100 & 49.99 & 88.56 & 77.77 & 61.90 & 100 & 100 & 100 & 82.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics for 10%, 20%, 30%, 40% and 50% testing data using Reference frame extraction for with and without segmentation for two, three, five, and eight folds.
## 2 Enzymatic Cascade Reactions to Produce Natural and Modified 5\({}^{\prime}\)-NTPs
### Other Interesting Approaches to Produce 5\({}^{\prime}\)-NtPs  
An innovative approach to synthesizing 5\({}^{\prime}\)-NtPs involved the development of a large enzyme cascade inspired by the de novo purine biosynthesis pathway. Using simple building blocks like glucose, CO\({}_{2}\), NH\({}_{3}\), and serine, natural purine nucleotides ATP and GTP were synthesized with up to 66% isolated yield [102]. This complex process includes CK in the final step and allows for specific isotopic labeling at various positions of the 5\({}^{\prime}\)-NTP products. Similarly, the same group developed an 18-enzyme cascade to produce isotopically labeled pyrimidines CTP and UTP from glucose, hydrogen carbonate, ammonia,  
Figure 7: Enzymatic synthesis of 5\({}^{\prime}\)-NtPs using NKs and PPKs as biocatalysts. Nucleosides were the starting material. (**A**) Reaction scheme. (**B**) Reported conversion to ATP [97]. (**C**) Reported conversions for various 5\({}^{\prime}\)-NtPs [79]. AtADK: _Arabidopsis thaliana_ adenosine kinase, ScADK: _Saccharomyces cerevisiae_ adenosine kinase, MJNK: _Methanocaldococcus jannaschi_ NK, LhPPK: _Lampropedia hyalina_ PPK, SIPPK: _Sulfnrowum lithotrophicum_ PPK, AjPPK: _Acintebocter jolnsonii_ PPK, SmPPK: _Sinorthizobium mellidi_ PPK.  
and aspartate [103]. Although this method relies on simple starting materials, the use of over 15 enzymes makes it practical mainly for introducing specific isotopic labels.  
A different approach for 5\({}^{\prime}\)-dNTP synthesis involves a mutated nucleoside 2\({}^{\prime}\)-deoxyribosyltransferase (NdT), engineered to transfer nucleobases not only between deoxynucleosides but also between 5 '-dNMPs, 5 '-dNMPs and 5 '-dNMPs [104]. This enzyme was used to produce base-modified purine and pyrimidine 5\({}^{\prime}\)-dNTTPs from their respective nucleobases and CTP (Figure 8). However, this method is limited to \(N\)-glycosidic 5 '-dNTTPs and has so far resulted in low conversion rates.