esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Morphology and Color  
The results of equatorial and longitudinal diameter, weight, and parameters of color are shown in Table 1. In the pear type, all the parameters measured had significant differences between cultivars. In equatorial diameter, Dolcetini obtained the highest measure (29.1 mm), and Seychelles and Santyplum were the smallest ones (22.2 and 22.5 mm, respectively). Angelle had an intermediate measure of 27.2 mm. In longitudinal diameter, Angelle, Dolcetini, and Santyplum did not show significant differences with a measure around 37.5 mm. The shape index is the ratio between the longitudinal and equatorial length; in pear fruits, Santyplum had the highest value with 1.66; whilst Angelle obtained a quotient of 1.39, not far off from Dolcetini with 1.29. These shape index results were similar to those reported by [PERSON] et al. [15] in their comparison between the morphology of different fruits, among which were found the round and pear cherry tomatoes. Regarding the weight, there were two different groups; Dolcetini and Santyplum weighed 16 and 17.2 g, respectively, while Angelle and Seychelles 10.8 and 11.4 g, respectively.  
Regarding color, in pear cherry cultivars, the \(L^{*}\) parameter (lightness) did not show significant differences, except Dolcetini, the values ranging from 38.4 to 39.4. Angelle had an intermediate value (18.9) of \(a^{*}\) parameter (red/green), lower than Santylplum and Seychelles with values of 22.2 and 22.5, respectively. In the \(b^{*}\) parameter (coordinate yellow/blue), Angelle and Seychelles obtained the lowest value (18.8, both), Dolcetini being with 21.7 the highest value. In C* (chroma), there were two different groups; on the one hand, Santylplum and Seychelles obtained the highest values (29.8 and 29.3); the other group (Angelle and Dolcetini) had 26.7 and 27.8, respectively. In the \(H\) parameter (Hue), Angelle obtained an intermediate value (44.8) showing significant differences to the rest of cultivars.  
Regarding round cultivars, equatorial diameter did not show significance differences between cultivars, while in longitudinal diameter Katalina, Sweet star, and C-95 did not have significant differences; however, Katalina showed the lowest value (27.4 mm). With respect to shape index, round cultivars did not show significance differences among cultivars, although these indexes were very similar to the results of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [9]. With respect to the weight, Katalina was the heaviest cultivar (15.5 g) compared to the other three round cultivars where no significant differences were found among them. In 4 out of 5 parameters of color (\(L^{*}\), \(a^{*}\), \(b^{*}\) and C*), Katalina obtained the highest values respective to the other cultivars, even showing differences of more than 2 units in some parameters. The only parameter in which Katalina did not obtain the highest value was in \(H\) (Hue), with C-95 being the cultivar that obtained the highest value (54.8). [PERSON] and Sweet star reached intermediate values (51.0 and 50.6, respectively).
# Challenges Associated with Implementing 5G in Manufacturing
## 4 Infrastructure Requirements for 5G
### Fog and Edge Computing
#### 4.2.2 Multi-Access Edge Computing (MEC)  
Multi-access edge computing (MEC), in the past known as mobile edge computing is a variant of edge computing that brings the capabilities of cloud computing to the edge of the network. This allows sharing of network and compute capabilities virtually anywhere, and will enable reduced deployment time and centralized management of remote compute capabilities. Whereas traditional cloud computing occurs typically on remote virtual servers that are situated far from the facility and the device, MEC allows processes to take place in aggregation points on the network, bringing decision making closer to the production environment. By opening this MEC environment up to more than just cellular users, ETSI (European Telecommunications Standards Institute) speculates that \"The capabilities and characteristics offered by a MEC platform can be leveraged in a way that will allow proximity, agility and speed to be used for broader innovation that can be translated into unique value and revenue generation [27]. According to research by [PERSON], et al. [28, 29] the use of MEC and the use of commodity server platforms to provide MEC capabilities identified five critical areas for MEC architectures to be successful; see Table 4.
## 3 Quantification of Risk Index  
### Equipment Fault Probability  
1. Normalize the proximity degree of each condition grade of the transformer. At this point, the value range of the transformer health index is [0, 100], and the calculation formula is as follows: \[d=\frac{d_{k}}{\sum\limits_{k=1}^{5}d_{k}}\] (3) where \(k\) is the number of grades and \(d_{k}\) is the proximity degree of each condition grade.
2. Transform the transformer condition assessment vector information into the corresponding health index value [23]: \[HI=D_{m}(Z_{1})\times 0+D_{m}(Z_{2})\times 25+D_{m}(Z_{3})\times 5+D_{m}(Z_{4}) \times 75+D_{m}(Z_{5})\times 100\] (4) where \(D_{m}\) is the condition evaluation vector and \(D_{m}\) (\(Z_{n}\)) is the proximity degree of evaluation vector support for each grade.
3. The equipment health index is converted into equipment fault probability [24]; the specific formula is as follows: \[P=Ke^{HI*C}\] (5) where \(P\) is the equipment fault probability, \(C\) is the proportionality coefficient, and \(HI\) is the health index. \(K\) and \(C\) can be determined by the inversion calculation of Formula (6), whereby \(K=0.0112\) and \(C=0.0451\). \[P=\frac{q}{L}\times 100\%=\sum\limits_{i=1}^{10}L_{i}Ke^{HI*C}\times 100\%\] (6)  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Risk Grades**} & **Cloud Model Parameters** \\ \cline{2-3}  & \((E_{x},E_{n},H_{e})\) \\ \hline Low \(N_{1}\) & (6.00, 3.55, 0.36) \\ Normal \(N_{2}\) & (30.00, 10.64, 1.06) \\ Medium \(N_{3}\) & (78.40, 17.74, 1.78) \\ Large \(N_{4}\) & (150.00, 24.83, 2.48) \\ Major \(N_{5}\) & (246.00, 31.93, 3.19) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Normal cloud model parameters of transformer risk grades.  
where \(q\) is the number of faulty transformers; \(L\) is the total number of transformers; \(i\) = 1, 2,..., 10, divided into 10 categories; \(L_{n}\) is the number of transformers of the \(n\) category; and \(HI_{i}\) is the average value of the upper and lower limits of the corresponding \(HI\).
###### Abstract  
During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.  
ultra-high arch dam; panel data clustering; deformation analysis; dynamic zoning; CRITIC method; CSP index +
Footnote †: journal: Applied Sciences
# Recent Advances in Muconic Acid Extraction Process
## 4 Muconic Acid Extraction
### Direct Extraction Processes for MA Separation  
A highly effective and biocompatible reactive extraction system (12.5% (\(v/v\)) CYTOP 503 in canola oil) for muonic acid from _Saccharomyces cerevisiaeMDS130_ fermentation broth (the MA strong inhibiting effect limits the product titers) was created by [PERSON] et al. [51], providing the groundwork for the manufacture of MA sustainably. Biomass growth and the titer of muonic acid were boosted by 44% and 18%, respectively, by incorporating the reactive extraction into the fermentation at the shake flask level. With a final MA titer of 4.33 g/L and a maximum productivity of 0.053 g/L, the process was successfully converted to fed-batch fermentation in a 10 L bioreactor, highlighting the potential of this approach to improve difficult fermentation processes for industrial biochemicals.  
Using 6% glucose and 4.5% xylose as carbon sources, [PERSON] et al. (2021) [50] successfully developed a second-generation yeast cell factory for MA production while completely inhibiting ethanol production and minimizing protocatechuic acid (intermediate) production, with a maximum titer of 4.5 g/L (highest achieved in batch yeast fermentations). In  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solvent** & **Extractant** & \
\begin{tabular}{c} **Extractant** \\ **Conc., mol/kg** \\ \end{tabular} &
\begin{tabular}{c} **Extraction** \\ **Efficiency, \%** \\ \end{tabular} & **Ref.** \\ \hline Ethyl propionate & TOPO & 0.12 & 84.07 & [49] \\  & TOPO & 0.25 & 88.43 & [49] \\  & & 0.39 & 88.98 & [49] \\  & & 0.55 & 91.01 & [49] \\  & & 0.41 & 79.43 & [49] \\ Ethyl propionate & TBP & 0.82 & 82.62 & [49] \\  & & 1.20 & 84.55 & [49] \\  & & 1.57 & 89.08 & [49] \\  & & 1.98 & 90.37 & [49] \\ Ethyl oleate & DOA & 0.01 & 85.14 & [52] \\  & & 0.032 & 96.04 & [52] \\  & & 0.055 & 97.3 & [52] \\  & & 0.01 & 15.45 & [52] \\ Ethyl oleate & TOA & 0.25 & 95.04 & [52] \\  & & 0.5 & 95.81 & [52] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Extractant concentration influence on MA reactive extraction.  
this process, MA shows a significant product inhibitory effect at low pH, which inhibits biomass growth and has a significant impact on MA production. The authors developed a direct extraction process (ISPRR--in situ product removal) using PPG (polypropylene glycol 4000) as solvent (1:2 ratio), but the concentration of MA was decreased (from 4030 to 1500 mg/L), even if the solvent (PPG) was effective in removing half of the biosynthesized MA from the fermentation broth [50].
## 4 Experimental Results and Analysis
### Comparison of the Methods  
We compare our KBDSP method with several state-of-the-art methods, including SC [17], KSSC [30], KLRR [14], IBDLR [40], SLKEs [35], SLKEr [35], SLSPs [36], and SLSPr [36]. These state-of-the-art methods can be classified into three types: clustering methods based on similarity preservation, clustering methods based on kernel self-expression, and clustering methods based on block diagonal representation. To show faithful comparison, all parameters of these state-of-the-art methods are manually tuned to be the best. The parameter settings of all experimental methods have been shown in Table 2, in which the recommended parameters are indicated in bold.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 1 Introduction
Motivated by previous work, DeepRaman [23], which primarily extracts features from individual spectra for pairwise comparison and overlooks the relationship between pairs, we leverage a three-branch feature fusion framework to address the limitations of existing algorithms, offering a more robust solution for practical applications where the accuracy of mixture identification is paramount. The potential impact of this approach extends to a variety of fields. In the pharmaceutical industry [7], it could improve the quality control process by enabling the rapid and precise identification of active ingredients in complex formulations. In food safety [9], it could help detect contaminants or adulterants in products, ensuring consumer protection. Additionally, in environmental monitoring [8], this framework could be used to detect and quantify pollutants in complex mixtures, providing more reliable data for assessing environmental risks. Ultimately, the advancements made in this study could broaden the applicability of Raman spectroscopy, making it a more powerful tool for chemical analysis across diverse sectors [24]. In the following sections, this article presents a comprehensive framework for the identification of mixture components using Raman spectroscopy enhanced by deep learning techniques. Section 2 delves into data augmentation methods, including the use of traditional techniques combined with DCGAN, to overcome the challenges posed by limited Raman spectral data. Section 3 describes the proposed deep learning network architecture, focusing on the three-branch feature fusion framework designed to capture both local and global features of the Raman spectrum. Section 4 outlines the experimental setup and results, where the performance of the proposed model is compared with existing models. Section 5 discusses the model's advantages in improving accuracy for component identification, while acknowledging challenges related to computational complexity, synthetic data reliance, and interpretability. Finally, Section 6 concludes with remarks on the broader implications of the study, suggesting directions for future research, including model efficiency improvements, alternative architectures, dataset expansion, and refined augmentation techniques.
## 2 Materials and Methods
### Sample Measurements
#### 2.3.2 Optical Characterization+  
The optical absorption properties of the perovskite films were measured using a UV-Visible (UV-Visible) spectrophotometer (Agilent 8453, Santa Clara, CA, USA). Tauc plot analysis was used to estimate the optical bandgap of the films, where (\(\alpha\)tv)\({}^{2}\) was plotted against the photon energy (hv) based on the absorption spectra.  
Figure 1: (**a**) Manufacturing process used to obtain the triple mixed-cation perovskite CsFAMAPbI3; (**b**) schematic of the device architecture for the perovskite-based PD.  
2.3.3. Electrical and PD Performance  
The electrical and optoelectronic performances of the perovskite-based UVC PDs were measured using a source-measure unit (Keithley 2400, Cleveland, OH, USA). All the measurements were conducted under ambient air conditions at room temperature (18-25 \({}^{\circ}\)C) inside a dark chamber to minimize the interference from external light.  
A portable UVC lamp (UVGL-58, Analytik Jena, Jena, Germany) with a wavelength of 254 nm was used as the excitation source. The lamp was positioned approximately 10 cm away from the device surface. According to the manufacturer's specifications, the light intensity at this distance was estimated to be approximately 1.2-1.5 mW/cm\({}^{2}\). The active area of each device was approximately 2.0 cm \(\times\) 1.5 cm.  
The devices were mounted onto a custom-designed test jig to ensure stable and reproducible electrical contacts during the measurement. The current-voltage (I-V) characteristics were obtained by sweeping the bias voltage from +1 V to -1 V in 0.1 V increments.  
For photocurrent response measurements, the UVC light was periodically modulated at 10 s on/off intervals using an Arduino-controlled servomotor system. Each measurement was repeated five times to ensure reproducibility.
## 3 Materials and Methods
### Design and Manufacturing
#### 3.1.5 Energy-Harvesting System
Figure 12 shows that the voltage that was produced by the piezoelectric sensors and temporarily stored in the capacitor passed through the circuit and fed the sensor's batteries. The diode was used to rectify half of the voltage that was going through the circuit. After the sinusoidal signal was halved when it went through the capacitor, it rectified the signal into an average of the signal voltage. This allowed the system to be charged by the voltage exiting the capacitor. To increase the amount of harvested energy, we added two more piezoelectric sensors in parallel [44]. The improved solution is shown in Figure 13.  
The next step was to design and implement a protective case for the energy-harvesting subsystem. The container was also designed in CAD software (3D Autodesk Inventor CAD) and 3D-printed with PLA technology. When the CAD was generated, it was ensured that the rubber plates were in contact with the case to allow the piezoelectric sensor and rubber plate to take advantage of the vibrations and thereby increase the quantity of harvested energy. The case was designed with holes on the outside of the box to tie the case to the chassis using belts. The CAD and manufactured solutions are shown in Figure 14.  
Figure 12: Preliminary circuit to store and deliver harvested energy. Figure 13: Improved circuit to store and deliver energy.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### Smart Mobility
In the first one, the RAN scheduling has two stages: one slice-specific and another cell-specific. Slice-specific scheduling works on virtual resources and is in charge of the scheduling of resources among the users of the slice. Each slice can adopt a different scheduling algorithm. Virtual resources are then mapped into physical resources by the cell-specific scheduler that is in charge of the division of the resources among the slices, in general guaranteeing a minimum capacity to each slice [27]. The second solution is a multiservice scheduling that jointly performs intra- and inter-slices allocations. This has to take into account users' capacity requests, channel conditions, and priority (depending on the slice), so it requires the solution of complex multiobjective problems, even if it can provide higher flexibility and efficiency. In order to investigate RAN slicing challenges and solutions, some studies have been carried out by the University of Florence. We show here some achieved results. Figure 3 shows the performance in terms of the outage rate (i.e., the total amount of users' data rate requests not satisfied) obtained by using a multiservice scheduler and two two-stage schedulers that first divide the resources among slices proportionally (i) to the mean number of users and (ii) to the priority, respectively, and then allocate intra slice resources using a proportional fair approach. One slice is devoted to vehicular traffic and has higher priority, and the mean data rate of the users is half of that of the other slice that is devoted to general broadband (BB) data traffic. Results are given as a function of the ratio of the number of users. In detail, the number of vehicular users is a random Poisson-distributed variable with a mean value in the range [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37  
is a typical legal traffic light. The traffic light was placed in a regular road at a typical distance from the lanes. A photodiode was used as the receiver, and a lens collected the light. The photodiode was wired to a high precision oscilloscope for the display and record of the VLC signal. The receiver was moved on a virtual grid from 2 to 40 m along the lanes road. In each point of the grid, the received VLC signal was recorded for 40 s. The measurements led to a propagation model and a detailed performance evaluation.
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### PGPB Support Nutrient Acquisition in Plants  
Plant nutrition is one of the most serious issues that excite researchers, agronomists, nutritionists, technologists, and plant protection specialists. It is related in the first place to the quantity of the crop yield, but also to its quality, especially under environmental stress. Salinity stress is one of the most damaging stresses in terms of quality and quantity of production because it affects the transport of nutrients and water to the roots. Low levels of some important elements, such as nitrogen, phosphorus, and potassium, are some of the main reasons for reduced growth and crop quality. Soil salinity causes a reduction of essential nutrient uptake because of ion toxicity and osmotic pressure. There are micronutrients that are essential for the plants, although they are needed in small quantities [37; 54; 89]. These microelements, such as copper, zinc, iron, magnesium, sulfur, an molybdenum, are very important as they participate in the composition of various proteins, DNA, enzymes (nitrate reductase, catalases, hydrogenases), amino acids (methionine, tryptophan, threonine), and photosynthetic processes [89]. At the same time, soil bacteria can improve plant nutrient status under salinity stress through different ways using the proper traits that define them as plant-growth promoters [42; 54; 90].