esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, we have established a complete femtosecond, all-fiber laser system that used Raman-assisted FWM to generate a multi-wavelength output, delivering parametric components including 751, 864, 1134, 1203, and 1273 nm. In our case, nonlinear parametric generation was achieved in a multimode Yb-doped PCF amplifier during the amplification of the fundamental pulse. The highly intense fundamental and parametric pulses also gave rise to the process which was akin to self-similar evolution. The laser system finally produced 342 fs pulses of 241 nJ at 864 nm, 834 fs pulses of 21 nJ at 751 nm, and 1.9 ps pulses of 3.8 uJ at 1030 nm. In the future, by stronger pump power and PCFs with larger nonlinearity, a red and near-infrared laser with higher intensity and a shorter pulse duration are anticipated. Besides, we might also expect mid-infrared pulse generation at around 5 um via difference frequency by launching the parametric and fundamental pulses into crystals with high nonlinearity.  
Conceptualization, H.X. and S.Y.; methodology, H.X. and S.Y.; validation, H.X., S.Y. and Z.G.; formal analysis, S.Y., M.L. and Q.Z.; investigation, H.X.; data curation, H.X., Y.M. and Y.N.; writing--original draft preparation, H.X.; writing--review and editing, H.X., S.Y., Q.H. and K.H.; visualization, H.X.; supervision, S.Y.; project administration, S.Y. and H.Z.; funding acquisition, S.Y. and H.Z. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China, grant numbers 2018 YFB0504400 and 2018 YFB0407100; and the National Natural Science Foundation of China, grant number 11727812. Innovation Program of Shanghai Municipal Education Commission, grant number 201701070007E00007.  
We thank [PERSON] and [PERSON] for the fruitful scientific discussions.  
The authors declare no conflict of interest.
## 5 Experimental Results and Discussion
### Verification of the Health Factor
#### 5.2.2 Results Analysis of SVM  
The experimental results are analyzed.  
(1) The statistical relative error in Figure 8 shows that, compared to the relative error in the early and middle cycles, the relative error in the later cycles is relatively large. The main reason lies in the fact that, when the life of the LIB is about to reach its end, the capacity deterioration is aggravated. Even if there is a short-term capacity recovery, it decreases rapidly for relatively short life cycles.  
(2) The total number of cycle life tests is 580. A total of 518 estimation errors (i.e., 89.31%) are less than 1%, a total of 574 estimation errors (i.e., 98.97%) are less than 2% and only one relative error is more than 3%, accounting for 0.17% of the total.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative error Interval** & **Minimum** & **Maximum** & **Number of Times** \\ \hline Error \(<1\%\) & 9.992e-06 & 0.0099 & 518 \\
1\% \(<\) Error \(<2\%\) & 0.0101 & 0.0190 & 56 \\
2\% \(<\) Error \(<3\%\) & 0.0207 & 0.0299 & 5 \\
3\% \(<\) Error \(<4\%\) & 0 & 0 & 0 \\
4\% \(<\) Error \(<5\%\) & 0.0411 & 0.0411 & 1 \\
5\% \(<\) Error \(<6\%\) & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative error statistics.  
Figure 11: Relative error.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cycle Number** & **Real Available Capacity (mAr)** & **Estimated Available Capacity (mAr)** & **Real SOH** & **Estimated** & **Relative Error** \\ \hline
1 & 1495.3 & 1489.052 & 99.69\% & 99.27\% & 0.42\% \\
100 & 1486.2 & 1474.655 & 99.08\% & 98.31\% & 0.78\% \\
195 & 1458.4 & 1455.447 & 97.23\% & 97.03\% & 0.21\% \\
293 & 1412.3 & 1421.132 & 94.74\% & 95.04\% & 0.62\% \\
392 & 1365.9 & 1361.420 & 91.06\% & 90.76\% & 0.33\% \\
553 & 1267.3 & 1249.908 & 84.49\% & 83.33\% & 1.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated error of No. 1 battery in test 2.  
(3) Considered the fact that using the health factor only containing the feature extracted from the terminal voltage drop during the battery having a rest to predict the capacity and SOH of the battery, for the health factor has less dependent on the working condition, the predict result has high robustness to the working conditions. Table 5 showed the proposed method gets high precision under different working conditions.  
Therefore, according to the relative error statistics, it is feasible to estimate the available capacity and SOH of a LIB using the information on the terminal voltage drop when the full charged battery is resting.
## 2 Results and Discussion
### Antioxidant Activity  
Several studies reported a wide range of biological properties associated with \(R\). _coriaria_ fruit extracts such as antimicrobial, antiproliferative, antidiabetic, and, most prominently, antioxidant activity [7]. In this light, all samples were investigated by ORAC assay, one of the most common methods used to estimate the antioxidant capacity in food. Results, expressed as Trolox equivalents (\(\mu\)mol TE/100 g of extract), are presented in Figure 5a. Among extracts, Sample 3 (226,661.42 \(\pm\) 22,867.89 \(\mu\)mol TE/100 g) and Sample 4 (225,836.14 \(\pm\) 23,427.64 \(\mu\)mol TE/100 g) showed the highest antioxidant capacity, followed by Sample 1, Sample 2, Sample 5, and Sample 6 (respectively 208,709.88 \(\pm\) 22,104.72, 181,393.61 \(\pm\) 28,287.22, 44,978.42 \(\pm\) 4717.30, and 34,321.05 \(\pm\) 4456.08 \(\mu\)mol TE/100 g).  
Furthermore, the total phenolic content (TPC) of \(R\). _coriaria_ extracts was determined by the Folin-Ciocalteau method. Results, expressed as milligrams of gallic acid equivalents (GAE) per gram of extract (mg GAE/g), are shown in Figure 5b. TPC of \(R\). _coriaria_ extracts well correlate with the antioxidant capacity. In general, the oven dried samples collected in  
Figure 5: Antioxidant activity (**a**) and total phenolic content (**b**) of \(R\). _coriaria_ extracts. Antioxidant activity and total phenolic content were assessed by ORAC and Folin-Ciocalteau methods respectively; results are the average of three independent experiments and are expressed as \(\mu\)mol TE/100 g of extract (**a**) and as milligrams of gallic acid equivalents per gram of extract (mg GAE/g, (**b**). Letters labels indicate significant statistical differences among samples (\(p<0.05\)) according to analysis of variance (ANOVA) and the Tukey’s (HSD) multiple range test. For samples description refer to Section 3.1.  
Sicily, Sample 4, Sample 2, (respectively 174.24 \(\pm\) 18.10, 130.28 \(\pm\) 2.65 mg GAE/g), had a lower TCP when compared to their undried counterparts: Sample 3 (230.85 \(\pm\) 7.37 mg GAE/g) and Sample 1 (194.72 \(\pm\) 4.79 mg GAE/g). The lowest TCP was reported for the commercial samples, Sample 5 and Sample 6 (15.38 \(\pm\) 1.40 and 7.25 \(\pm\) 0.57 mg GAE/g, respectively).
# Sentiment Analysis of Twitter Data
## 6 The Approaches for Twitter Sentiment Analysis
### Machine Learning-Based Approach
#### 6.1.1 Probabilistic Classifier  
Mathematical models are used to predict the categorization based on the input [42]. Probabilistic classifiers such as the Naive Bayes classifier (NB), Bayesian Network (BN), and Maximum Entropy classifier (ME) are often used in data analysis [43; 44]. To determine the best class match, the Bayes theorem-based NB classifier is one of the most extensively used techniques. BN is another probabilistic model that employs Bayesian inference to calculate probability. Directed Acyclic Graph (DAG) is used to depict the variables and their conditional interdependencies [45]. The probability of a feature belonging to a specific category is computed using ME.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Objective and Algorithm Used** & **Data Scope** & **Dataset** \\ \hline
[46] & Feature selection, particle swarm optimization (PSO), CRF & Restaurants and laptop reviews & SemEval-2014 \\
[47] & Feature subset selection, discrete PSO, logistic regression model & Financial, spambase, Nursery, etc. & UCI ML Respository \\
[48] & Feature selection, Binary PSO, CART, NB, SVM & Handwritten digits & UCI benchmark datasets \\
[49] & Selecting emotional features, multi-swarm PSO, SVM & Course review & Datasets from MOOC \\ Feature weighting, optimization-based weighted voting scheme, NB, SVM, LR, Bayesian logistic regression, linear discriminant & Camera, doctor, drug, radio, TV, etc. & Datasets extracted from websites \\
[51] & Binary classification, SVM & Movie review & Own \\ Feature weighting, adaptative & Movie review, newspaper article, & Polarity dataset, Subjectivity dataset, MPQA dataset \\ score, SVM & Feature selection and weighting, NB, SVM & Movie review & IMDb \\
[54] & Supervised term weighting, SVM, kNN & Newsgroup message, Economic news & 20 Newsgroups, Reuters-21578, TanCorp \\ Feature selection, dynamic relevance, and joint mutual & Vehicle, Madelon, USPS, etc. & UCI Repository \\
[55] & Information maximization, SVM with RBF kernel, NB, 3-Nearest Neighbors (3 NN) & News message, HTML documents & 20 Newsgroups, data from open directory project \\ Feature clustering, divisive algorithm, NB, SVM & & wide range of domains & UCI datasets \\
[56] & Discriminatively weighted NB, NB, IWNB, BNB, DNB & wide range of domains & Datasets in WEKA \\
[57] & Adaptive feature weighting, MNB, CNB, OVA & Economic news, Newsgroup message & Reuters 21578, 20 Newsgroups \\ Feature weighting and ranking, SVM, kNN, RBF & wide range of domains & UCI ML Respository \\
[58] & Content-based recommendation system, feature weighting, Iterative RELIEF for feature weighting, kNN & Movie review & IMDb \\
[59] & Effective feature weighting, improved NB, GRFWNB, RFWNB, DTFWNB, CFSFWNB, CFSNB, and DFWNB. & Wide range of domains & UCI and Microarray datasets \\ \hline \hline \end{tabular}
\end{table}
Table 4: The list of machine learning-based approaches for sentiment analysis.
###### Abstract  
The droughts striking the Colorado Plateau, where the Hopi Tribe and Navajo Nation Native American reservation lands are located, and their impacts have appeared slowly and relatively unnoticed in conventional national drought monitoring efforts like the National Drought Monitor. To understand the effect of drought-based drivers on vegetation productivity in the Hopi Tribe and Navajo Nation reservation lands, an assessment approach was developed integrating climate, land cover types, and topographical data with annual geospatially explicit normalized difference vegetation index (NDVI)-related productivity from 1989 to 2014 derived from 15-day composite multi-sensor NDVI time series data. We studied vegetation-environment relationships by conducting multiple linear regression analysis to explain the driver of vegetation productivity changes. Our results suggest that the interannual change of vegetation productivity showed high variability in middle elevations where needleleaf forest is the dominant vegetation cover type. Our analysis also shows that the spatial variation in interannual variability of vegetation productivity was more driven by climate drivers than by topography ones. Specifically, the interannual variability in spring precipitation and fall temperature seems to be the most significant factor that correlated with the interannual variability in vegetation productivity during the last two and a half decades.  
drought; NDVI time series; vegetation productivity; Hopi and Navajo Nation 2018 10.3390/sul0051643 10.3390/sul0051643
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 4 Experimental Results
### Diagnostic Accuracy Assessment  
This study proposes a CNN-based super-resolution technique incorporating an attention mechanism to enhance the resolution and quality of medical images. However, evaluating the impact of super-resolution techniques on diagnostic accuracy in real clinical settings is essential. In this section, we discuss diagnostic accuracy based on previous research findings.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 5 Discussion  
### Advantages of the High-Density Gauge Observations  
To assess the SPPs in this study, a high-density network of 1381 gauge stations was used, covering essentially the entire study area (Figure 1b). Only the IMERG grids containing more than one station were involved in the evaluation. These evaluated grids were distributed evenly across the study area (Figure S10), enabling a spatial evaluation of IMERG. The gauge density is approximately 2.49 stations/100 km\({}^{2}\), well above the threshold of 1.5 stations/100 km\({}^{2}\) which defines a high-density network [83]. Previous studies have reported that the sparse distribution of ground reference stations can lead to an underestimation of SPP performance [61]. Thus, such high-density gauge observations can yield more accurate and dependable evaluation results.  
Usually when gauge stations are sparse or insufficient, only a single station and the corresponding SPP grid are used for evaluation. However, the spatial representativeness of an individual point-scale station is limited and cannot fully account for the precipitation over a certain area. It has been suggested that the grid-average value of station observations can better reflect the mixing of precipitation at different localities within the grid cell [84]. Based on the high-density network in this study, the SPP estimates for grids covering more than one gauge can be compared with the grid-average values of gauge data. Since IMERG FR is calibrated against GPCC observations, it is necessary to consider the overlap between the reference gauge stations and GPCC data to ensure a meaningful comparison between the three IMERG products. However, the GPCC does not provide the exact location of its stations, and only releases the number of stations per 1\({}^{\circ}\) grid worldwide. Thus, the method suggested by [PERSON] et al. [46] was used to analyze the number of GPCC stations as a proportion of the number of reference stations in each 1\({}^{\circ}\) grid. The result showed that this proportion is less than 10% in most 1\({}^{\circ}\) grids, with a few of the highest not exceeding 30% (Figure S11). This illustrated the independence of most local gauges versus the GPCC observations, thus the comparison results for the three IMERG runs were valid in the study.
## 1 Introduction
However, in most of the dynamic load tests, the confining pressure and static deviator stress are maintained at a constant level throughout the cyclic loading process, and the impact of stress disturbance caused by adjacent construction is not taken into account. The effect of stress disturbance on soil deformation under long-term cyclic loading has only been considered in a few studies. [PERSON] et al. (2021) studied the influence of lateral unloading between cycles on the deformation of kaolin clay, and the results showed that the accumulated axial strain increased significantly with the increase in lateral unloading [21]. [PERSON] et al. (2021) investigated the effect of axial and lateral stress disturbances on the undrained dynamic response characteristics of kaolin clay. The results showed that the accumulated axial strain is controlled by the static deviator stress ratio (\(q/p_{0}\)) and is independent of the stress disturbance schemes [22]. The related research is mainly focused on kaolin clay, and the effect of stress disturbance on permanent strain and pore water pressure of silt under cyclic loading needs to be further investigated. In this paper, a series of undrained cyclic triaxial tests of silt with varying fine particle content and stress disturbance are presented. The static axial stress of the soil samples is reduced by various magnitudes to simulate the impact of foundation pit excavation and structural construction above the tunnel. By analyzing the accumulated axial strain and excess pore water pressure (EPWP) of the soil samples under long-term cyclic loading, the influence of fine particle content and axial unloading strength on the dynamic response of silt is discussed. The findings of the study can be used to develop a theoretical framework for assessing the long-term deformation of the existing tunnel due to the combined effects of adjacent construction and train loads.
## 2 Related Work
### CT Reconstruction Based on PICCS  
By using a reconstruction algorithm, CT reconstruction collects X-ray projection data recorded by the detector at different views to obtain the linear attenuation coefficient of the scanned object. In the sparse-view reconstruction, in order to suppress noise and artifacts in the reconstructed image and obtain a reasonable approximate solution, it is usually necessary to introduce a regularization term to optimize the model, and the specific optimization model can be expressed as follows:  
\[Ax=Y \tag{1}\]  
\(A=\{a_{ij}\}\in R^{|x|}\) is a system matrix, \(I\) and \(J\) stand for the number of X-rays and image pixels, respectively. \(x\) represents the reconstructed image, in whose value is the X-ray's linear attenuation coefficient. \(Y\) stands for the projection data. Hence, the core problem in CT reconstruction is commonly regarded as an inverse problem, which reconstructs the image \(x\) from \(A\) and \(Y\). In practice, \(x\) can be calculated by addressing the least squares problem:  
\[\underset{x}{\operatorname{argmin}}\|Ax-Y\|_{2}^{2} \tag{2}\]  
To solve the ill-posed inverse problem and acquire reasonable approximate solutions, especially for SVCT, appropriate regularization term should be introduced. For simplicity, the problem can be represented as follows:  
\[\underset{x}{\operatorname{argmin}}\|Ax-Y\|_{2}^{2}+\mu R(x) \tag{3}\]  
The first and second term are data fidelity and a regularization term such as TV, TGV, and wavelet framework, etc., respectively. \(\mu\) is a scale parameter, which balances the regularization term and data fidelity term. When a prior image is available, it could be appended to the second regularization term. Therefore, the PICCS image reconstruction is achieved by resolving the following minimization problem [34]:  
\[\underset{x}{\text{argmin}}a||x||_{TV}+(1-a)||(x-x_{prior})||_{TV},s.t.Ax=Y \tag{4}\]  
where \(a\) is the empirical parameter and \(x_{prior}\) is a prior image. TV denotes the image gradient \(L_{1}\)-norm, which is expressed as follows:  
\[\left\|x\right\|_{TV}=\sum_{ij}\sqrt{\left(x_{ij}-x_{i-1,j}\right)^{2}+\left( x_{i,j}-x_{i,j-1}\right)^{2}} \tag{5}\]  
where \(i\) and \(j\) stand for the pixel position of image. \(x_{i,j}\) are the pixel values at the position.
## 4 Key Outcomes and Discussion  
The review reveals several significant findings that shape the current understanding and future development of MC:  
* Evolution and maturity of MC: MC has evolved from a niche concept to a mainstream production strategy, enabled by digitalization and I4.0. The convergence of customization with technologies such as CPS, IoT, and AI marks a shift from traditional modularity-based approaches to real-time, data-driven customization paradigms.
* Three major research streams identified around three key domains: \(\circ\) RS1--MC general concepts and metrics: focusing on MC definitions, strategic principles, and performance measurement, including sustainability metrics. \(\circ\) RS2--MC technologies and methods: covering enablers such as product configurators, modular product design, and flexible production systems; structured into three sub-streams--customer interaction, product design, and production architecture. \(\circ\) RS3--MC and supply chain management: emphasizing supply chain agility, integration, postponement strategies, and supplier collaboration as key to supporting MC systems.
* Technological enablers and integration: enabling technologies such as product configurators, recommender systems, additive manufacturing, and digital twins play a pivotal role in making MC feasible and scalable. Their integration is essential to overcoming the operational complexity introduced by high product variety and dynamic customer preferences.
* Customer involvement and co-design: a recurring theme in the literature is the centrality of customer involvement. Co-design, co-creation, and preference elicitation are key to ensuring that customized offerings deliver value. Tools such as AR, mobile interfaces, and interactive web platforms are widely used to support this involvement and reduce decision fatigue or \"mass confusion\".
* Barriers to adoption: despite its advantages, MC implementation is hindered by multiple challenges. These include high initial costs, lack of integration with legacy systems,difficulties in managing real-time data, and low digital readiness--particularly in SMEs. Moreover, aligning the customization strategy across product, process, and supply chain levels remains a complex endeavor.
* Sustainability considerations: MC holds potential for environmental and economic sustainability through reduction in overproduction, minimized waste, and localized manufacturing. However, the operationalization of sustainability in MC environments is still in its infancy, with limited standardized metrics and few empirical validations.
* Shift toward personalization: A growing body of literature suggests that MC is transitioning toward \"mass personalization,\" where AI-driven systems and real-time customer data are used to anticipate needs and deliver one-to-one tailored experiences. This paradigm shift implies a more granular, flexible, and intelligent MC framework aligned with the principles of I4.0.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Applications Beyond Bacteria: Mycotoxins, Viruses, and Metabolomics  
While current MALDI-TOF MS applications in food microbiology are largely limited to bacterial and fungal identification, future iterations may expand into other domains, such as the following:  
1. Mycotoxin detection: by modifying matrix chemistry and ionization protocols, MALDI-TOF MS could be adapted for the rapid screening of fungal toxins (e.g.,aflatoxins, ortharooxins) in grain, nut, and dairy products. However, it is still needed to develop methods for simultaneous detection of multiple mycotoxins [3; 151; 152]. 2. Viral diagnostics: although viruses pose unique challenges due to their small size and lack of abundant proteins, preliminary research suggests potential for detecting viral capsid proteins or host-cell biomarkers using MALDI-based methods [72; 153; 154]. 3. Microbial metabolomics: high-resolution MALDI-TOF/TOF instruments may facilitate the profiling of volatile and non-volatile microbial metabolites, supporting spoilage prediction, fermentation monitoring, and identification of food fraud [33; 39; 102].