esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
NCA has been successfully prepared by the co-precipitation method. After LiAlO\({}_{2}\) modification, the surface lithium compound residues and pH of the cathode material was reduced. When the molar ratio of Al(NO\({}_{3}\))\({}_{3}\).9H\({}_{2}\)O and residual Li\({}_{2}\)CO\({}_{3}\) of the NCA surface is 0.25:1, the pH is 11.80 and the surface lithium residue is 1.48%, which could improve its processing performance. Although the initial discharge specific capacity slightly decreased after LiAlO\({}_{2}\) modification, the high-rate ability  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Original**} & \multicolumn{6}{c}{**After 25 Cycles**} \\ \cline{2-9}  & **Rs** & **Rf** & **Wo** & **Rs** & **Rct** & **Rf** & **Ws** & **Wo** \\ \hline NCA & 8.28 & 327.01 & 372.60 & 9.16 & 200.20 & 13.62 & 160.90 & — \\ NCA-0.25 & 8.71 & 159.70 & 82.43 & 9.93 & 9.54 & 32.06 & 11.57 & 29.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Independent resistive components of the pristine NCA sample and NCA-0.25 samples analysed using an equivalent circuit.  
Figure 7: Electrochemical impedance spectra (EIS) of the lithium ion cells assembled with different electrodes, which were obtained before discharge (**a**) and after 25 cycles at 1 C rate (**b**). All impedance data were obtained at 4.3 V.  
and the cycling stability improved. Furthermore, the impedances of cathode material decreased and the reversibility of lithium emergence/insertion increased after LiAlO\({}_{2}\) coating Therefore, LiAlO\({}_{2}\) modification could improve NCA electrochemical performance. The capacity retention increased from 89.31% to 94.67% at 1 C between 3.0 and 4.3 V at 1 C after 100 cycles.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2076-3417/8/3/378/s1, Figure S1: The mean and standard deviation diagrams of the three discharge of NCA series(NCA, NCA-0.25-400 \({}^{\circ}\)C, NCA-0.25-500 \({}^{\circ}\)C and NCA-0.25-600 \({}^{\circ}\)C): blue, green and grey histograms correspond to discharge at 0.2 C, discharge at 1 C and discharge at 1 C after 110 cycles, respectively., Table S1: The each, mean and standard deviation of the three discharge of NCA, NCA-0.25-400 \({}^{\circ}\)C, NCA-0.25-500 \({}^{\circ}\)C and NCA-0.25-600 \({}^{\circ}\)C, respectively.  
**Acknowledgments:** We gratefully acknowledge financial supports from the Department of Science and Technology of Shandong Province (2017 CXGC0503, 2016 PT07), Shan Dong Economic and Information Technology Committee(201731917004).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Feature Extraction and Recognition Based on IPSO-VMD
### Multi-Dimension Sensitive Feature Optimization
The calculation method of \(L_{r}\) is as follows:  
\[L_{r}=\frac{\sum_{ij}\left(f_{ri}-f_{ri}\right)^{2}S_{ij}}{var(f_{r})} \tag{17}\]  
where, \(f_{r}=\left[f_{r1},f_{r2},\cdots f_{rm}\right]^{T}\), \(D=diag(SI)\), \(I=\left[1,1,\cdots 1\right]^{T}\), \(L=D-S\), matrix \(L\) is the Laplace matrix of neighborhood graph \(G\)Where  
\[ \begin{array}{rl}\sum\limits_{ij}\left(f_{ri}-f_{rj}\right)^{2}S_{ij}&=\sum \limits_{ij}\left(f_{ri}^{2}S_{ij}-2f_{ri}f_{rj}S_{ij}+f_{rj}^{2}S_{ij}\right)= \sum\limits_{ij}\left(2f_{ri}^{2}S_{ij}-2f_{ri}f_{rj}S_{ij}\right)\\ &=2\sum\limits_{ij}\left(f_{ri}^{2}S_{ij}-f_{ri}f_{rj}S_{ij}\right)=2f_{r}^{T} Df_{r}-2f_{r}^{T}Sf_{r}=2f_{r}^{T}Lf_{r}\\ \\ &\text{ }var(f_{r})=\sum\limits_{i}(f_{ri}-\mu_{r})^{2}D_{ii}\end{array}  \tag{18}\]  
where, \(var(f_{r})\) is the variance of the \(r\)-th feature \(f_{r}\), and standardization \(f_{r}\) can obtain the following:  
\[\overset{\sim}{f}=f_{r}-\left(\frac{f_{r}^{T}D\ I}{I^{T}D\ I}\right)I \tag{20}\]  
Further, we can find the Laplacian score of the \(r\)-th feature:  
\[L_{r}=\frac{\overset{\sim}{f_{r}}L\overset{\sim}{f_{r}}}{\overset{\sim}{f_{ r}}L\overset{\sim}{f_{r}}} \tag{21}\]  
Step 5: Sort \(L_{r}\) in ascending order and output \(L_{r}\) in turn. According to Equation (14), the smaller the molecular \(\left(f_{ri}-f_{rj}\right)\), the smaller is the feature difference within the representative sample. The greater the variance of the \(r\)-th feature \(f_{r}\), the greater is the feature difference between samples and the higher is the separability. Therefore, the smaller the Laplace score \(L_{r}\) corresponding to the feature, the more important it is. The final multi-dimensional sensitive feature vector \(T_{multi}\) can be obtained by combining the first five order IMF component ratios of feature vector \(T\) constructed earlier and the five feature parameters extracted in this section:  
\[T_{multi}=\left[\frac{imf\ E_{1}}{E},\frac{imf\ E_{2}}{E},\frac{imf\ E_{3}}{E},\frac{imf\ E_{4}}{E},\frac{imf\ E_{5}}{E},S_{c},\text{{Teo}},Sdf,\overline{x},S\text{{cf}}\right] \tag{22}\]
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
## 2 Materials and Methods
### Thermal Conductivity and Chemical Composition of Aggregate  
The thermal conductivities of BS-1, BOF-1 and FER-1 were tested via the hot-wire method with a QTM-500 thermal conductivity measuring apparatus (ASTM D 5334). The basic principle of this method is to place an electric resistance wire in the sample to be tested. When it emits heat at a constant power, the temperature of the hot wire and the sample near it will increase with time. The thermal conductivity of the sample can be calculated according to the relationship between temperature and time. The schematic diagram of the measuring circuit is shown in Figure 1a.  
For the testing of particle materials, two boxes with internal dimensions of 80 mm \(\times\) 114 mm \(\times\) 40 mm were used, as presented in Figure 1b. The box below has a bottom, and the aggregate to be tested was filled into this box until it was flush with the upper edge. Then, the measuring probe was placed on the aggregates. The upper box was the same size as the lower box, but without a bottom. Next, the upper box was put on the lower box and the material to be tested filled the upper box. Finally, the box was covered with a lid.  
An infrared camera was used to detect the change of temperature during the temperature-fall period and the cooling rate can be calculated by infrared images. Firstly, 100 g of aggregate was placed on the aluminum plate with a diameter of 200 mm and a thickness of 5 mm, as shown in Figure 2. Next, aggregates were kept in an oven for four hours at 150 \({}^{\circ}\)C. Then, the samples were taken out and pictured during the cooling process at room temperature (25.9 \({}^{\circ}\)C). The total shooting time for each sample was 20 min, and the interval between two shootings was 4 min.  
Figure 1: (**a**) Schematic diagram of measuring circuit and (**b**) sample boxes.  
Moreover, the elementary composition and physical phases of the aggregates and slags were evaluated by Axios XRF (PANalytical B.V, Almelo, The Netherlands) and D8 Advance XRD (Bruker, Mannheim, Germany), respectively.
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
## 1 Introduction
### Microneedling  
Microneedling is a minimally invasive procedure that causes microorganisms in the skin using very thin needles. Micro-injuries stimulate neocollagenesis, neo-elastogenesis, and transcutaneous elimination of melanin [13]. Over the past years, this technique, alone or in combination with topical agents, has demonstrated efficacy and safety in melasma treatment, with a growing body of literature supporting its effectiveness. After a single session of microneedling using a dermaroller with 1.5 mm needles, a reduction in melanin density and pendulous melanocyte was observed, along with an improvement in Melasna Area and Severity Index (MASI) and quality of life [14]. Moreover, microneedling improves the effectiveness of topical therapies by increasing their transcutaneous penetration [15]. Using needles of micron size, hydrophilic high molecular weight compounds can penetrate the stratum corneum, which limits transcutaneous absorption of topical creams. This method administers the drug directly into the epidermis or upper dermis layer, ensuring the delivery of 100% of the product [16; 17]. The application of topical TXA and a depigmenting solution containing TXA, N-acetyl glucosamine, vitamin C, and idebenone, in conjunction with microneedling, led to further reductions in MASI compared to microneedling alone [18; 19]. The benefits of microneedling include a low complication rate and a very low risk of post-inflammatory hyperpigmentation, making it safe even for darker phototypes [20]. Additionally, microneedles do not penetrate deeply enough to reach the pain receptors located in the lower dermis, making the treatment less painful compared to techniques such as hypodermic needles [17].
## 4 Results and Discussion
### Performance of Asphalt Binders  
#### 4.2.1 Polymer Phase Morphology  
Figure 8A-E shows the SEM images of CaCO\({}_{3}\) NPs, CaCO\({}_{3}\) NFs, base asphalt binder, 5% CaCO\({}_{3}\)-NPs-modified asphalt binder, and 5% CaCO\({}_{3}\)-NFs-modified asphalt binder, respectively. As clearly seen from Figure 8A,B, the agglomeration phenomena of the CaCO\({}_{3}\) NPs were severe, and there were a large number of voids, which became dense after surface functionalization.  
As evident in Figure 8D, the agglomeration of the CaCO\({}_{3}\) NPs in asphalt binder was very obvious regardless of the high or low magnification compared with Figure 8C. The main reason for this was that the surface energy of CaCO\({}_{3}\) NPs was relatively high, which led to the strong interaction between particles and the consequent formation of agglomeration [40, 41]. In addition, many NPs were substantially embedded in the surface of the asphalt binder rather than deeper into it. This can be explained by the fact that the interface bonding force between CaCO\({}_{3}\) NPs and asphalt binder is weak, the compatibility is poor, and interface defects between organic and inorganic phases are easily formed. Compared with Figure 8D, in Figure 8E, CaCO\({}_{3}\) NFs basically exhibited nano-scale dispersion in asphalt binder after stirring, and formed a stable blending system with asphalt binder, which had no agglomeration phenomenon and showed a more uniform dispersion effect. It can also be observed that the CaCO\({}_{3}\) NFs were almost entirely embedded in asphalt binder.  
These results suggest that the compatibility of the CaCO\({}_{3}\)-NFs-modified binders was better, and the interfacial force was stronger. This was mainly due to the following reasons. On the one hand, the surface energy of the nano-CaCO\({}_{3}\) was lowered, thus increasing the mutual repulsion among the particles. Therefore, the agglomeration weakened, and the dispersion uniformity improved. On the other hand, the organic material on the CaCO\({}_{3}\) NFs' surface had better fluidity, and the compatibility with asphalt binder was also better, further promoting the dispersion therein.  
Figure 7: TG and DSC curves of solvent-free CaCO\({}_{3}\) NFs.
## 1 Introduction
Recently, the applications of AI technologies have been used to analyze and control the stability of the SG and provide effective smart solutions for complex systems, and they have received enormous attention [8]. The recent applications of AI technologies in power systems have mainly included fault detection and diagnosis, energy planning, energy forecasting, and power system optimization (e.g., economic scheduling, power optimization process and problem formulation, optimization of neural network applications, and reactive power optimization) [9]. In light of the above, this survey focuses on reviewing the applications of AI and optimization for the SG in smart manufacturing. Firstly, a comprehensive survey on AI and optimization applications for the SG in smart manufacturing is carried out, and then discussion and future challenges along this line of research are provided. The main contributions of this survey are as follows:  
* This survey reviews recent works on the SG as well as the AI and optimization applications using the SG in smart manufacturing. * To achieve smart manufacturing and sustainability, this survey collects recent works on the applications of AI and optimization technologies for SG in manufacturing operations. Figure 1: Major technologies and characteristics of smart grids [4]. The rest of this survey is organized as follows. Section 2 gives an overview of the SG operations. Sections 3 and 4 give the AI and optimization applications for the SG in smart manufacturing, respectively. Section 5 gives the discussion and future challenges. Finally, Section 6 gives the conclusion.
# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
## 3 Results
### Sensor-Based Solutions  
Sensor-based technologies enable real-time injury event monitoring in sports settings. Three studies [13; 15; 20] assessed iMGs and Head Impact Telemetry (HIT) in rugby and American football. iMGs with machine learning achieved high accuracy (CatBoost AUROC: 0.900) in classifying head impacts [13], surpassing HIT, which showed weaker correlations (Spearman's r = 0.254-0.262, \(p\) = 0.02-0.03) [20]. Helmet-based HIT detected impacts but lacked robust validation [15]. When worn during games, iMGs offer practical field data, though further standardisation for comparability between devices is needed. These real-time solutions are more aligned with the sports industry's need for real-time injury detection. With the correct supervision and further development, these technologies may deliver insights for real-time, sport injury detection.
# Helium Atmospheric Pressure Plasma Jet Source Treatment of White Grapes Juice for Winemaking
## 2 Materials and Methods
### Plasma Source and Electro-Optical Diagnosis  
The discharge configuration consisted of a 100 mm long quartz tube, with inner and outer diameter of 4 and 6.1 mm respectively, and two 10 mm long copper tape electrodes. One power electrode (HV) and a grounded electrode (Gr) were wrapped on the glass tube, with an electrode gap width of 10 mm, similar to that reported by [8; 11; 16]. The discharge was driven by a PVM500 plasma power generator (Information Unlimited)with independent voltage, current, and frequency adjustment: voltage 1-40 kV, frequency 20-70 kHz, output power 1-300 W. The applied sinusoidal voltage Ua (up to 16 kVpp, @ 50 kHz) and the total current of the discharge Id, were monitored using voltage and current probes (Testec HVP-15 HF, Pico TA131, a 50 \(\Omega\) resistor) and a 200 MHz digital oscilloscope (Picoscope 2208A, two channels, 1 GS/s, 8 to 12 bits resolution, function generator + AWG 1 MHz). The working gas, supplied through the discharge tube, was pure helium (spectral helium, He 5.0, Siad Romania). The gas flow rate of 2 slm was regulated using a needle valve rotameter (Platon 0-5 slm NGVS312 series). Long exposure photos of the plasma interacting with samples were captured using a Canon 600D (18 Mpx, 400-750 nm spectral sensitivity) and Tamron 18-200 mm Di II lenses; IR photos of plasma-surface interaction was captured using a FLIR TG165 camera (8-14 \(\upmu\)m spectral range, \(\pm\)1.5\({}^{\circ}\)). The spectral emission of the discharge in the UV-Vis-NIR range (200-1100 nm) was analyzed by using a ASEQ Instruments LR1 broad range spectrometer (monochromator with a 50 \(\upmu\)m entrance slit, a 600 gr/mm diffraction grating blazed at 300 nm, and a CCD Toshiba TCD1304 DG linear array detector, calibrated for Absolute Irradiation Measurements), via a 0.4 mm diameter and 1 m long optical fiber (for 200-1400 nm, kevar reinforced, cosine corrected adapter, Thorlabs), placed at 5 mm from the plasma.  
The must samples were plasma treated (marked from now on '**PB**' and '**PH**') as follows: 50 mL of must was placed in Petri dishes and treated for 3 min. The discharge tube was positioned centrally to the Petri dish (25 mm height, 150 mm diameter soda glass plate), 10 mm above the must level, as in Figure 1. The procedure was repeated for 750 mL, for the two sets of must samples (the one from Barzesti, respectively from Husj). The control samples (marked with '**MB**' and '**MH**') were also placed in identical volumes in Petri dishes and kept for 3 min each outdoors.
###### Abstract  
Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The \(k\)-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.  
wind energy; wind turbines; power curve; SCADA; data analysis; multivariate regression Article
## 1 Introduction
In addition to its transformative impact on the nuclear industry, artificial intelligence (AI) has also been recognized as a key driver in the digitization of maritime transportation, leading to significant improvements in operational efficiency. Intelligent tools have already been developed and applied across various domains of maritime vessel operations [20]. One critical area where AI has proven valuable is the predictive maintenance of systems and components [21]. Fault detection [22], prognostics, and health management [23] are among the most prominent applications, enabling the early identification of issues and reducing downtime. AI has also revolutionized ship routing, particularly in complex environments such as maneuvering in small and congested ports [24] and optimizing navigation routes [25]. In addition, intelligent methods have enhanced the efficiency of maritime operations through accurate predictions of operational parameters [26], localized weather conditions [27], vessel sailing times [28], and emissions related to fuel consumption [29]. Other notable applications of AI in the maritime sector include enhancing situational awareness [30], enabling autonomous control for collision avoidance [31], and improving cybersecurity in vessel communication systems [32; 33]. This paper aims to review nuclear-powered ships, examine the challenges associated with adopting nuclear technology in the commercial maritime sector, and explore the potential opportunities in synergism with artificial intelligence it presents. The methodology we followed is based on existing AI applications in both land-based nuclear energy systemsand conventionally powered maritime vessels, using these as a foundation to identify key areas where AI can enable more efficient operation in nuclear-powered maritime vessels. Nuclear-powered ships present a significant opportunity to achieve decarbonization goals by 2050 [34]. This paper examines how AI tools can support these objectives by enhancing the efficient application of nuclear technology in maritime vessels. By addressing key challenges and alleviating concerns surrounding nuclear propulsion, AI could serve as a critical enabler for the adoption of nuclear-powered ships while securing the sustainability of international transportation and trade [35, 36]. The current lack of operational experience at the intersection of AI and nuclear maritime systems is also evident in the literature: although AI has been increasingly applied in maritime contexts, there is a noticeable scarcity of studies focused on nuclear-powered ships. This paper seeks to fill that gap by exploring how AI can act as an enabler for the adoption and optimization of nuclear maritime systems. The roadmap for this paper is as follows: Section 2 presents a brief history of nuclear-powered maritime vessels. Section 3 explores the application of artificial intelligence technologies in supporting the operation of nuclear maritime ships, while it examines future challenges and opportunities in the field. Finally, Section 4 concludes this paper by summarizing its key points.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 2 Material and Methods
### Data Processing and Analysis
The average net radiation that occurred in the 24-h period (\(\mathrm{R_{n24h}}\), in W\(\cdot\)m\({}^{-2}\), is obtained using Equation (5), developed by [PERSON] [37]:  
\[\mathrm{Rn_{24h}}=(1-\alpha)\mathrm{R_{S\downarrow 24h}}-\alpha\tau_{\mathrm{sw24h}} \tag{5}\]  
where \(\alpha\) is the surface albedo, \(\mathrm{R_{S\downarrow 24h}}\) is the daily average incident solar radiation (W\(\cdot\)m\({}^{-2}\)), and a is the regression coefficient between the daily longwave net radiation and the daily atmospheric transmissivity. The surface albedo is assumed to be equal to the albedo during the satellite overpass time, estimated as proposed by [38]. The daily solar radiation and transmissivity were obtained from ground measurements [39]. The coefficient a was set to 110, according to [39], and then, for comparison purposes, the value of 98.208 was used, calibrated by [40] for the northeastern region of Brazil. The daily average atmospheric transmissivity was obtained as:  
\[\mathrm{t_{sw24h}}=\frac{\mathrm{R_{S_{1/24h}}}}{\mathrm{R_{S_{1/TOA24h}}}} \tag{6}\]  
where \(\mathrm{R_{S_{1/TOA}}}\) is the daily solar radiation incident at the top of the atmosphere (W\(\cdot\)m\({}^{-2}\)), calculated according to procedures described in [41].