esa-sceva/satcom-chunk-collection huggingface dataset:
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 4 Decision Tree and Important Predictors  
### Initial Decision Tree and 11 Important Predictors  
The Decision Tree machine-learning model was evaluated to confirm the prediction quality and list important predictors before exploring other models later using MATLAB 2022b Classification Learner. This method was chosen because it is easy to create by programming via MATLAB and easy to understand. It also does not require standardizing or Z-score all predictors before training the model; hence, there is no need to worry about whether data is categorical or number. It also has a flexible boundary with good prediction quality.  
For this model, all 59 predictors were studied. The Entropy method was used to determine the most informative information, and the binary split method was used to improve the prediction quality. To find the best Number of Splits for the Decision Tree model, 1000 different Decision Tree models with the Number of Splits ranging from 9 to 1000 were created. The minimum Number of Splits must be the total number of classes  
Figure 6: Frequency Domain Data across all 10 classes (**a**) Underhang Accelerometer in the x-axis (**b**) Underhang Accelerometer in the y-axis (**c**) Underhang Accelerometer in the z-axis (**d**) Overhang Accelerometer in the x-axis (**e**) Overhang Accelerometer in the y-axis (**f**) Overhang Accelerometer in the z-axis.  
subtracted by 1. A higher maximum Number of Splits could be explored. However, 1000 is good enough for this study. Then, the average F-score of all ten classes for each model was compared, and the best Number of Splits was chosen.  
The best average F-score is 99.8% at the Number of Splits of 30. From Figure 7, as the Number of Splits increases, the average F-score increases but flat out at the Number of Splits of 30. The prediction quality of the Decision Tree with the Number of Split at 30 is very good, with all the outcomes staying close to 99% or 100% as shown in Table 4. Both the Confusion Matrix and ROC curves also show excellent performance as it can be seen in Table 4 and Figures 8 and 9.  
band 6 in overhang accelerometer in the y-axis, predictor 11 th as the variance of underhang accelerometer in the x-axis, predictor 25 th as power band 1 of underhang accelerometer n z-axis, predictor 16 th as the variance of overhang accelerometer in the z-axis, predictor 37 th as power band 3 in underhang accelerometer in the z-axis, and predictor 13 th as variance in underhang accelerometer in the z-axis.  
All 11 predictors are not involved with tachometer data as expected because they only provide the same rotating frequency. This indicates that microphone data is not necessary.
## 7 Performance Comparison
### Security Functionalities  
Table 4 presents a brief comparison between the proposed scheme and relevant existing schemes in terms of security functionality. It is worth noting, from Table 4, that the related schemes are not validated through formal security validation tools, such as AVISPA, and none of them guarantee replay attack (RA) and integrity (I). Our proposed scheme is shown to be resistant against various attacks through formal analysis using the widely-accepted automated validation for internet security validation and application (AVISPA) tool as shown in Appendix A.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 2 Remote Sensing for Agriculture
### Sentinel-2 Data Access, Secondary Products, and Support Tools
Another relevant point is data itself, its plan for continuity, and its access to both raw data and pre-calibrated and pre-processed satellite images that provide value added benefits to end-users. Sentinel-2 products consist of Level-1B, which provides radiometrically corrected granulates in Top-Of-Atmospher radiance covering (\(25\times 23\)) km\({}^{2}\). Sentinel-2 also offers Level-1C imagery which consists of tiles of \(100\times 100\) km\({}^{2}\) that provide geo-coded Top-Of-Atmosphere reflectance with a sub-pixel multi-spectral and multi-date registration. It additionally includes cloud masks, supplying an indication about the presence of cirrus clouds. Moreover, it provides Level-2A, a tile of \(100\times 100\) km\({}^{2}\) which provides Bottom-Of-Atmosphere reflectance in cartographic geometry [41]. Since December 2018 Level-2A products are generated globally and are ready for users to download. Level-2A can be also generated using Level-1C with Sentinel-2 Toolbox. ESA frequently provides specific access tools, algorithms, and software in support of the use and processing of their satellites, such as the Sentinel-2 Toolbox within the Sentinel Application Platform (SNAP, [[https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-2](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-2)]([https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-2](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-2))). On SNAP Sen2 Cor is the atmospheric correction processor to generate Level-2A data; it also includes algorithms for cloud and cloud shadow detection, cirrus detection, and slope effect correction. Beyond SNAP, Sentinel-2 imagery can also be atmospherically corrected using MAJA (developed by CESBIO/CNES) or i-COR (developed by VITO), among other options. In addition, the further pre-processing chain of Level-2A products can be done on SNAP: resampling and subsetting followed by calculations using the bands. Besides vegetation indices, more deterministic biophysical parameters, such as LAI or FAPAR, can be calculated on SNAP with Sentinel-2 data following the algorithms developed by [PERSON] and colleagues [42]. It is relevant to point that as an experimental Sentinel-2 approach done with simulated Sentinel-2 imagery in 2015 [43], even before its launch, it already showed potential for retrieving LAI and with actual Sentinel-2 data it is currently fully operational.
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Dataset  
The data set used in this study is the Jigsaw Unntended Bias in Toxicity Classification dataset [50], a large-scale public dataset released by the Conversation AI team, an initiative founded by Jigsaw and Google. This data set contains 1,971,916 instances of online comments, annotated with toxicity scores and additional attributes. It was designed to enable machine learning models to better understand toxicity in comments while addressing bias and fairness challenges.  
To facilitate training, validation, and testing, the dataset is divided into three subsets:  
* Training set: comprising 1,804,874 instances, this subset is used to train and fine-tune the model.
* Validation set: containing 70,180 instances, this subset is used for hyperparameter tuning and intermediate evaluations during model development.
* Test set: consisting of 96,862 instances, this subset is reserved for evaluating the model's performance on unseen data.  
Table 1 provides an overview of the corrected dataset distribution.  
The data set includes a variety of toxicity types, ranging from subtle to explicit forms, and is annotated with demographic and contextual metadata, allowing robust bias analysis. This diversity makes it an ideal choice to evaluate the fairness and accuracy of sentiment analysis models.  
To further enhance the utility of the dataset, we added sentiment polarity labels to adapt it to sentiment analysis tasks. The toxicity score of each comment was used to classify it into one of three sentiment categories: positive, neutral, or negative. This modification ensured that the data set aligned with the objectives of this study, facilitating the development and testing of our bias-aware sentiment analysis framework.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Lightning  
#### 2.4.1 WWLLN  
The Worldwide Lightning Location Network (WWLLN) is a ground-based network that detects radio-frequency (RF) emissions from lightning on a global basis [28]. The network is most sensitive to powerful cloud-to-ground (CG) flashes, and thus only detects a small fraction of total lightning [29]. However, it does enable continuous monitoring of thunderstorms over the global oceans, and thus is a useful dataset for comparison with satellite observations of oceanic winds. For this study, the only data used were from 1 August through 31 October of 2018 and 2019 (6 months of data), as they were obtained in support of field campaigns that operated during those periods [30]. However, because of the continuous global observations, this short time period still provided the most matchups of lightning flashes with the CYGNSS tracks, of the three lightning datasets examined in this study.  
WWLLN data were matched to CYGNSS tracks in the following manner. Only lightning occurring during a given track's duration (typically several minutes) was considered. Contiguous portions of the track with nonzero IMERG precipitation were automatically identified, and if lightning was identified within 25 km of a specular point in the raining portion of a track, that wind measurement was included in the \"winds with lightning\" category. The flash rate related to that wind measurement was calculated by totaling all flashes within 25 km of the specular point, and dividing by the duration of the track itself. To limit cross-contamination, matching lightning anywhere along a track led to the exclusion of that track from contributing to the \"winds without lightning\" category (even if the track featured some raining areas without lightning). For tracks that had no lightning within 25 km anywhere along it, but did have raining portions, winds within the raining portion were added to the \"winds without lightning\" category. Typically \(<\) 10% of the overall raining dataset featured lightning.  
The advantage of using WWLLN is that the dataset is global and the temporal coverage is continuous. The disadvantage is that it does not detect all lightning, and thus many wind regions without lightning may in fact be near thunderstorms with undetected lightning. This will tend to cause some overlap between winds associated with lightning, and those without. Figure 1 shows the analysis region for WWLLN, which includes all of the CYGNSS sampling region.
# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 4 Research Methodology
### Data Analysis Methods  
The analytical approaches used to explore consumer attitudes towards ultra-processed foods (UPFs) can be broadly categorised into three groups: statistical methods, ML techniques, and MCDM approaches.  
The first group, statistical methods, focuses on measuring, summarising, and visualising the key characteristics of multi-dimensional datasets. This includes techniques such as normality tests, t-tests, analysis of variance (ANOVA), chi-squared tests, and regression analysis. A prominent method within this category is PLS-SEM, which is well suited to analysing complex models with latent variables, particularly when working with smaller sample sizes.  
The second group, ML methods, encompasses techniques like cluster analysis, predictive modelling, and sentiment analysis. These methods help uncover hidden patterns and relationships within data. For example, cluster analysis identifies groups of consumers with similar attitudes, while sentiment analysis evaluates subjective opinions from text data. Unlike traditional statistical approaches that emphasise hypothesis testing and causal inference, ML methods are data-driven, focusing on pattern recognition and predictive analytics based on historical data.  
The third category, MCDM methods, offers a structured framework for evaluating complex decisions where multiple factors must be considered simultaneously. In the context of UPF research, MCDM techniques can be applied to assess consumer preferences, rank influencing factors, and support policy-making decisions related to public health and nutrition.  
Statistical methods such as SEM are suitable for hypothesis testing and examining theoretical models, while ML techniques excel at identifying trends and making predictions from large datasets. Alternatively, MCDM approaches provide a comprehensive framework for analysing multiple factors simultaneously, offering multi-faceted insights into consumer behaviour and preferences.  
The integration of these diverse analytical methods enhances UPF research by providing a holistic view, improving the accuracy of findings, enabling detailed consumer segmentation, supporting data-driven decision-making, and fostering more effective health policy recommendations.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### [PERSON] et al. [200] found that the optimal pH for _Trichoderma_ sp. development was 6. In addition, the results showed that in an alkaline medium with pH 8, the mycelium is reduced and less developed than in an acidic environment with pH 4. According to [PERSON] et al. [19] as the acidity of the medium increases, the ability of the microorganisms to germinate increases, while alkaline conditions limit the growth of _Trichoderma_ sp. [PERSON] et al. [201] documented that the _Trichoderma_ sp. were highly adapted to growing in a medium with a pH value between 5.5 and 7.5. In contrast, minimal quantities of their mycelium were obtained at pH 4 and 4.5. [PERSON] et al. [189] reported that the optimal pH for the development of _T. asperellum_, _T. hamatum_, _T. harzianum_, and _T. viride_ ranged from 4.6 to 7.6. According to [PERSON] et al. [191], _T. harzianum_, _T. koningii_, and _T. viride_ grew not only in a wide temperature range but also in a wide pH range from 3 to 13. However, the greatest increase in mycelium was observed at pH 5.5.  
A study by [PERSON] and [PERSON] [202] showed that the pH of a medium also affected the antagonistic properties of _Trichoderma_ sp. against plant pathogens. According to the above-mentioned authors, the analyzed strains limited the development of _Fusarium_ sp. in the pH range from 4 to 7, with the highest inhibition rate occurring in a medium with a pH of 6 and amounting to 56.79%. [PERSON] et al. [196] documented that _T. atroviride_, _T. hurzianum_, and _T. sulpureum_ limited the growth of _R. solani_ at pH 5.5 to a greater extent than at pH 7 and 8.5. Similarly, [PERSON] et al. [190] reported that the rates of inhibition of _R. solani_ by _T. pseudokoningii_ and _T. viride_ were significantly higher at pH 4.5 than 8.5.
## 2 Materials and Methods
### Monte Carlo Calculations  
In the present study the absorbed dose per unit administered activity was assessed using Monte Carlo calculations in a simplified geometry. MC code MCNP4C has been used for this purpose [23]. MCNP is a general-purpose, continuous-energy, generalized-geometry, time-dependent, coupled neutron/photon/electron Monte Carlo transport code. For photon transport, the code takes into account photoelectric absorption, with the possibility of K- and L-shell fluorescent emission or Auger electron, coherent and incoherent scattering and pair production. The photoelectric cross sections are based on Storm and Israel [24] whereas the scattering cross sections are taken from ENDF tabulations [25]. The continuous slowing down approximation energy loss model is used for electron transport.  
Spherical lesions of different size (diameter in the range 0.5-20 cm) were simulated for two different densities: \(\rho=1.00\ \mathrm{g/cm^{3}}\) (water density) and \(\rho=1.05\ \mathrm{g/cm^{3}}\) (liver density). In both scenarios, spheres were assumed to be immersed in a semi-infinite medium with the same density of the sphere. The spheres are representative of tumor regions and are supposed to be uniformly filled with \({}^{90}\)Y, while the surrounding medium is assumed to contain no radioactivity.  
Calculations were performed in coupled electron-photon mode [MODE P E] using the el03 electron interaction data library (ELIB = 03E) and the mcnplip2 photon interaction data library (PLIB = 02P). Simulations were carried out taking into account all the available advanced options such as electron production by photons, Bremsstrahlung effect and knock-on electron production. MCNP simulations were run for an adequate time to get a statistical uncertainty on the absorbed dose below 0.01%.
# Load-Bearing Performance of a Reinforced Fill Structure with Pile Penetration
## 6 Conclusions  
This study was on the bearing and reinforcement characteristics of a pile-penetrating reinforced structure through an indoor model test. The study yielded the following main conclusions:  
1. By measuring the lateral displacements of the soil on the slope we can find that the reinforced fill structure with pile penetration enhances the ability to limit lateral displacement through the synergistic effect of vertical piles, the cap of piles, and horizontal reinforcement. This results in a more reasonable distribution of the bending moment in the pile and improved distribution of the vertical earth pressure.  
Figure 12: Change diagram of bending moment of reinforced piles with different slope rates.  
2. Increasing the pile length and the number of reinforcement layers within a certain range enhances the effect of limiting lateral displacements in the middle and at the bottom of the slope of the reinforced fill structure with pile penetration. This reduces the extreme value of the bending moment and makes the distribution of the bending moment in the pile more uniform. The appropriate spacing of the reinforcement maximizes the lateral confinement effect on the soil body.
3. Within a certain range, decreasing the slope rate reduces the extreme value of the bending moment, improves the distribution of the bending moment in the pile, and avoids localized force concentration. However, if the slope rate is too small, the absolute amplitude of bending moment increases. Moreover, increasing the pile diameter significantly reduces the vertical soil pressure below the cap of pile and between the cap of piles.  
Q.M.: Funding acquisition, Supervision, Data curation. H.Y.: Formal analysis, Writing--original draft, Investigation, Visualization. Y.Y.: Conceptualization, Methodology, Writing--review & editing, Investigation. L.X.: resources. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support of the National Natural Science Foundation of China (Grant Nos. 52078194), the National Young Top-notch Talent of \"Ten Thousand Talents Program\", the Science Fund for Distinguished Young Scholars of Hubei Province (2022 CFA043), the Young Top-notch Talent Cultivation Program of Hubei Province, the Outstanding Young and middle-aged Science and Technology Innovation Team of colleges and universities in Hubei Province (No. T2022010), and the Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes (No. 2020 EJB004).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Sinohydro Bureau 8 Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results
### Test Turbine 1

Table 1: Parameters of the fingerprint algorithm for sensors on Test Turbines 1 and 2.  
only visible in averaged spectrograms by a detailed close-up view; hence, the fingerprint algorithm can be used for automatic extraction. A comparison of both fingerprints is displayed in Figure 12. All Type 2 vibrations could successfully be measured and detected by both sensors. For Sensor \(S2\), the highest vibration (first from top) was detected for a larger range of rotation frequencies than for Sensor \(S1\). This is represented by coloured error bars at those rotation frequencies, for which peaks were assigned to the respective vibration. The majority of Type 1 vibrations were detected by both sensors. Those vibrations exclusively being detected by Sensor \(S1\) indicate eigenfrequencies with small amplitudes, which are hidden in noise for Sensor \(S2\). In contrast, Type 1 vibrations exclusively being detected for Sensor \(S2\) indicates a detection of sensor noise in distinct narrow-band frequencies. To sum this up, fingerprints of both sensors overlap for the majority of vibrations since the radial difference between both sensors is small. However, higher vibrations at the blade tip, e.g., due to fluttering, are reflected in the fingerprint of Sensor \(S2\) by the location of detected Type 1 vibrations and the bandwidth of Type 2 vibrations.
## 2 Overview of the L-PBF Process  
The L-PBF process comprises a powder chamber and a build chamber placed on movable stages, as illustrated in Figure 1. The process is started by evacuating the chamber from oxygen and filling the chamber with inert gas, thus preventing heated metals from oxidation [29]. Then, the building platform is heated if required, and the first layer of powder is spread on the building platform using a recoater. This recoater can have different geometries (flat, round, or sharp) [30] and materials (ceramic, high-speed steel, or carbon fiber brush) [31]. Then, the laser starts scanning specific locations in the powder bed according to the part's geometry. As a result, the powder gains energy and starts transferring from a solid state to a liquid state, forming a small molten pool referred to as meltpool [32]. The cooling rate of this meltpool is around 10\({}^{3}\)-10\({}^{8}\) K/s. Therefore, the layer being melted cools down very rapidly, forming a fine uniform microstructure [12]. The build platform then moves one layer downwards, while the powder dispenser moves one layer upwards. The recoater then spreads a fresh layer of powder on the previously solidified layer, and the process repeats until the whole part is manufactured layer by layer. The following sections are dedicated to discussing the process parameters, the process signatures, and the most common defects resulting from the L-PBF process.