esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Prediction Model Using Machine Learning Algorithms
## 2 Literature Review
Based on the above literature review, it can be noticed that this issue has been discussed in the literature from different perspectives. Most of the work in this field utilized machine learning algorithms to help farmers with crop prediction gain better yield and improve overall production. However, many research analyses did not consider a real problem for which to perform classification and performance evaluation. Further, the authors did not give clear notifications about the obtained accuracy. Exceptionally, in [26], the authors provided analysis and performance evaluation with an accuracy of 99.45%, but still on 22 selected crops only and not based on real data. With this paper, we aim to improve the performance of machine learning usage in smart farming by:  
* Presenting experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. * The research outcome recommends that farmers could make more informed verdicts about factors that affect crop growth by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors. * As per the provided analysis in this research work, the machine learning algorithms demonstrate a high level of classification accuracy. Notably, the Bayes Net algorithm achieved an impressive accuracy of 99.59%, while both the Naive Bayes Classifier and Hoeffding Tree algorithms yielded a remarkable accuracy of 99.46%. These results highlight the efficacy and reliability of these algorithms in accurately classifying the given data. * Therefore, by integrating different technologies, the achieved results can be used as an indicator for the farmers to have early and informed decisions regarding crop prediction to improve productivity, and hence the overall economy will be improved, accordingly.
## 2 Materials and Methods
### Statistical Analysis  
The OBs diameters recorded at the temperatures of 25, 50, 100, 150, 200 \({}^{\circ}\)C were determined as described in Sections 2.2.5 and 2.2.6. Then, they were analysed with descriptive statistics. Thus, we evaluated the first percentiles, the third percentiles, the medians, the means, the means, the standard deviations, and the minimum and maximum values of the diameters, which were recorded at the above-mentioned temperatures. Consequently, we determined the box plots of the particle size distribution at 25, 50, 100, 150, 200 \({}^{\circ}\)C. The characteristics of the OBs distribution were also evaluated according to the values D10, D50 and D90 (D is OBs diameter). D50, the median, is defined as the diameter where half of the population lies below this value. Similarly, 90% of the distribution lies below D90, and 10% of the population lies below D10. Additionally, we evaluated the means and the standard deviations of the circularity values. Moreover, we performed statistical tests on the OBs diameters collected in five groups corresponding to the five temperatures. The aims of these statistical tests were to assess significance and to verify if there were differences among the groups. First, the data were analysed by means of a normality test. The normality was checked using the Shapiro-Wilk test. Taking into account the Shapiro-Wilk results, we selected the non-parametric Kruskal-Wallis test for comparing the diameter groups obtained at each temperature. Furthermore, to identify which groups were different, a multiple comparison procedure with the Dunn test was applied. The tests were performed with the Bonferroni correction and were performed with Excel (version 16.37).
# Investigation of CuTi Alloy for Applications as Non-Sparking Material
## 2 Materials and Methods
The linear polarization curves were performed at a scanning rate of potential dV/dt = 0.5 mV/s, and the cyclic polarization curves at a rate of 10 mV/s. A saline solution (NaCl 3.5%) at room temperature was used as the electrolyte. The surface was analyzed by optical microscopy ([PERSON] microscope with MotiCam digital camera, Zeiss, Oberkochen, Germany), scanning electron microscopy (SEM, VegaTescan LMH II, equipped with an SE detector, TESCAN, Brno, Czech Republic) and energy-dispersive X-ray spectroscopy (EDS) (Bruker XFlash, Bruker, Karlsruhe, Germany) [18; 19]. A Rockwell diamond tip with an angle of 120\({}^{\circ}\) was used for the microhardness tests. Five determinations were performed for each sample at a distance of approximately 0.30 mm. The main characteristics of the Rockwell tip indenter are as follows: radius: 210 \(\pm\) 7 \(\upmu\)m; angle 120\({}^{\circ}\)\(\pm\) 0.25\({}^{\circ}\) and a SD of \(\pm\) 1.5 \(\upmu\)m. Based on the scratch tests, the friction coefficient was determined, and the structural behavior when subjected to an external mechanical force was analyzed.
## 3 Results and Discussion
### Selective Detection of Chrysotile by Fluorescence Imaging  
We also studied the properties of the protein bound to chrysotile through a conventional fluorescence microscopy. The three types of asbestos, chrysotile, amosite and crocidolite, were incubated with the fluorescent dye-labeled DksA protein, and observed under the fluorescence microscope. A second control group of the asbestos samples incubated with the pure dye was also observed to check the nonspecific reactivity. Figure 4 shows the result of the fluorescently-labeled protein and asbestos binding assay. Amosite and crocidolite can be seen in the phase contrast mode (Figure 4(a,c)), but invisible in the fluorescence mode (Figure 4(b)). Chrysotile can be observed more clearly in the fluorescence mode without background noise as shown in Figure 4(b).  
Fluorescence signal was not detected in any type of asbestos samples incubated with pure dye solution (Figure 4(d)), indicating that there was no nonspecific reactivity by the fluorescent dye and that the DksA protein bound specifically to the chrysotile fibers. The phase-contrast and fluorescence images acquired with the conventional fluorescence microscopy can be used as reference images for subsequent comparison with reflection and fluorescence images obtained by the DM-HTM system. The specificity of DksA binding to chrysotile has also been tested against all five remaining types of asbestos as well as ten different kinds of non-asbestos fibers by [PERSON] _et al._[13, 16].  
A further test was conducted to confirm the selectivity of the protein against chrysotile. We created mixed samples of chrysotile and amosite at different concentration ratios, and then observed them using the DM-HTM device. We acquired both reflected light images and fluorescence images of the  
Figure 4: Protein binding test performed for three types of asbestos samples. (**a**) Phase contrast and (**b**) fluorescence images (pseudo-colored red) of asbestos fibers tagged with fluorescently-labeled DksA protein. (**c**) Phase contrast and (**d**) fluorescence images of asbestos fibers treated with fluorescent dye only without DksA protein. Bar = 100 \(\upmu\)m.  
mixed asbestos sample at the same positions on the slide. The images of the mixed asbestos samples are displayed in Figure 5.  
As the amount of chrysotile was decreased with the increasing amount of amosite, it was difficult to detect chrysotile in the reflection mode at low concentrations of chrysotile owing to the interference by amosite. However, it was straightforward to identify the chrysotile fibers in the fluorescence images.
## 3 Finite Element Models
### FEM-BEM Simulation Results  
The simulation results obtained at the center of the bottom with the three models are compared in Figures 8 and 9. In Figure 8, the results at the center of the envelope (point (a)) are plotted. The differences between the blue and the green curves indicate the influence of scattering on the structural response. The spectra obtained are similar (the same resonances); however, the response levels are different. Scattering phenomena change the cross spectra modal forces. On these results on the envelope, there is little difference between the FEM-BEM models for when the internal air volume is modeled (red curve) versus not (blue curve).  
However, in Figure 9, great differences are observed between the two FEM-BEM models at the center of the bottom. When the internal air volume is modeled (red curves), many additional resonance peaks are observed. These correspond to acoustic resonance phenomena. These acoustic resonances generate new peaks away from the main structural  
Figure 7: HB–2 Boundary Element Models in Wave6: left, model of the external air volume; and right, model of external and internal air volumes.  
resonances but may also significantly affect the response level in the vicinity of these main structural modes. Figure 9 illustrates this influence near the first bottom mode (408 Hz).  
Figure 8: HB–2 structural response (acceleration PSD) at the center of the envelope, point (a) in Figure 6, simulated using the FEM model alone (green), the BEM subsystem of the external air volume (blue) and the BEM subsystem of both internal and external air volumes (red).  
Figure 9: HB–2 structural response (acceleration PSD) at the center of the bottom, point (c) in Figure 6, simulated using the FEM model alone (green), the BEM subsystem of the external air volume (blue) and the BEM subsystem of both internal and external air volumes (red). A zoom in the vicinity of the first structural bottom mode (408 Hz) is added.  
Modeling the air volumes surrounding the structure thus implies clear effects on the structural responses; yet, the main resonances and the RMS levels remain similar. The RMS levels corresponding to the three simulations of Figure 9 are 10.86 m\(\cdot\)s\({}^{-2}\) (FEM model, green curve), 9.43 m\(\cdot\)s\({}^{-2}\) (FEM-BEM model with external air volume modeled, blue curve) and 9.07 m\(\cdot\)s\({}^{-2}\) (FEM-BEM model with external and internal air volumes modeled, red curve).  
The differences between the simulation results obtained with the three models aids in understanding the effects of each vibroacoustic phenomenon in the structure response. The modeling method chosen for the comparison with experiments is the most complete: the FEM-BEM model with external and internal air volumes modeled. In the next section, these simulation results are compared with the experiments.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 5 Design of High-Resolution Multispectral Polarization Imaging Scheme  
### Composition and Function  
The system composition is shown in Figure 5. The system consists of four parts: power subsystem, electronics subsystem, optical detection subsystem and servo subsystem. The power supply subsystem is used to power and drive the device; the electronics subsystem is used for video image processing and storage; the optical detection subsystem is used to obtain multi-dimensional information about the target; and the servo subsystem is used for image stabilization and cross-roll pitch control.  
Figure 4: Simulation results. (**a**) S0 image; (**b**) DOP image; (**c**) fused image.  
Figure 5: Schematic diagram of system composition.  
Figure 3: Image fusion process based on two-dimensional wavelet transform.  
The optical detection subsystem includes a visible polarization imaging module, an infrared imaging module, and a laser active illumination module. The visible polarization module is used to obtain the polarization information of the visible band of the target during the day; the infrared imaging module is used to obtain the long-wave infrared radiation information of the target at night; the laser active illumination module is used to assist the illumination in the low-light environment to enhance the imaging effect.
## 4 Results and Discussion
### HSR (Heliostat Solar Receiver) Results  
Figure 6 is dedicated to reporting the monthly average optic efficiency and energy and exergy efficiencies of the heliostat solar receiver in each month of a year. This parameter mainly depends on the field layout and spillage efficiency in comparison to changes associated with different months. This fact can justify the slight changes in this parameter during a year. As can be seen, the changes were between 46.3% and 55.9%. The highest values of average heliostat optic efficiency belong to May, June, and July, which were respectively 53.4%, 54.2%, and 55.9%. Therefore, July had the highest amount. While December had the minimum average heliostat optic efficiency about 46.3% followed by February and November for which the values were about 49.8% and 49.9%.  
Figure 6: Monthly average optic efficiency of the heliostat in different months of a year.  
For energy efficiency, slight changes were observed during a year from 40.8% in December, up to 47.3% in July. According to Equation (23), these slight changes were because both numerator and denominator of energy efficiency were increased by the rising of solar radiation. The exergy efficiency of the heliostat solar receiver was generally lower than the energy efficiency of the system. Based on the equations presented in Table 7, this is because of the \(\left(1-\frac{T_{0}}{T_{ex}}\right)\) coefficient that leads to a reduction in the numerator, and also the \(\left[1-\frac{4}{3}\big{(}\frac{T_{0}}{T_{Sun}}\big{)}+\frac{4}{3}\big{(} \frac{T_{0}}{T_{Sun}}\big{)}^{4}\right]\) coefficient that leads to an increase of the denominator. The variations of exergy efficiency range from 25.3% in December up to 33.5% in July, which the reason for these small changes during a year is similar to the reason discussed for energy efficiency variations.  
Monthly average heat absorbed by the solar receiver, total solar energy input, defined as a tot in the figure, and thermal power losses are shown in Figure 7. As presented in Equation (14), thermal power losses occur by radiation and convection. Based on Equation (13), the average heat absorbed by the solar receiver can be obtained by subtracting the thermal power losses from the total solar energy input presented by Equation (17). Generally, the thermal power losses are enormously lower than the total solar energy input, which is because of a significant amount of solar radiation reflected by large areas of heliostats in comparison to comparatively low thermal power losses from small areas of the solar receiver. As can be seen in Figure 7, the thermal power losses are relatively high in May, June, and July since this parameter is considerably increased by the increase of the wind velocity and air temperature. On the other hand, because of high average solar radiation in May, June, and July, total solar energy input and, as a result, the average heat absorbed by the solar receiver are both high. The maximum average heat absorbed by the solar receiver was about 23.98 MW in July, and the minimum was 10.29 MW occurs in December.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### pH Value  
Growth and enzymatic activities of the microorganisms and solubilization of organic matter are affected by pH value [127; 139; 140; 141; 142; 143]. Providing an optimal pH value during the different phases was the key reason some reactors were divided into two phases, with an acidogenic and methanogenic phase [144; 145]. Moreover, pH affects the distribution of total ammonia nitrogen (TAN) between toxic NH\({}_{3}\) and innocuous NH\({}_{4}\)\({}^{+}\)[146]. Optimum performance of the anaerobic microorganisms will be reached with neutral pH (6.8-7.2) [147]. Importantly, pH affects the distribution of total ammonia. It was noticed that at a higher pH of 8.5, unionized ammonia was highly (2473 mg/L) associated with poor biogas production, which confirms the ammonia toxicity, in particular for leather fleshing waste [148]. By contrast, it was reported that at pH of 4.5 the ammonia nitrogen was minimal (510 mg/L), but high VFA (26,803 mg/L) inhibited the methanogens [6].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Substrate** & **C/N Ratio** & **Substrate** & **C/N** \\ \hline Kitchen waste & 26:30 & Grass cutting waste & 11:15 \\ Food waste & 2:18 & Rice straw & 50:68 \\ Cattle manure & 15:26 & Wheat straw & 51:151 \\ Poultry manure & 4:16 & Corn straw & 51:57 \\ Sheep manure & 20:34 & Sawdust & 199:501 \\ Vegetable waste & 8:36 & Algae & 74:101 \\ Slaughterhouse waste & 21:36 & Sugar cane waste & 139:151 \\ \hline \hline \end{tabular}
\end{table}
Table 2: C/N ratio of some substrates [137].
## 2 Materials and Methods
### Metagenome Sequencing and Bioinformatics Analysis  
Metagenomic amplicon sequencing, a methodology centered on the targeted sequencing of specific genomic regions, was applied in this study. The construction and subsequent sequencing of metagenomic libraries were outsourced to Macrogen Inc. (Seoul, Republic of Korea). Bacterial metagenomic libraries were generated utilizing primers designed to amplify the V3-V4 hypervariable region of the bacterial 16S rRNA gene (primers 341F-805R). Similarly, archaeal metagenomic libraries were constructed using primers specific to the archaeal 16S rRNA gene (primers 21F-516R). The quantitative and qualitative assessment of DNA within the samples was performed via DNA QC-Picogreen. Library preparation involved the utilization of the LightCycler qPCR system, the JANUS liquid handling system for library pooling, and the Herculase II Fusion DNA Polymerase in conjunction with the Nextera XT Index V2 Kit. Sequencing was conducted on the Illumina MiSeq platform, employing a 301 base-pair paired-end (PE) protocol, targeting approximately 200,000 reads per sample. Raw data statistics revealed the following: the Bioreactor 1 16S sample yielded 641,636 reads, totaling 193.1 Mbp, with a GC content of 53.2% and a Q30 score of 82.7%; and the Bioreactor 2 16S sample produced 635,562 reads, amounting to 191.3 Mbp, with a GC content of 52.9% and a Q30 score of 83.5%. For the Bioreactor 1 Arc sample, a total of 592,956 reads were generated, equating to 178.4 Mbp, with a GC content of 56.4% and a Q30 score of 71.0%. The Bioreactor 2 Arc sample yielded 934,736 reads, totaling 281.3 Mbp, with a GC content of 55.4% and a Q30 score of 72.1%. Downstream analysis involved the generation of Amplicon Sequence Variants (ASVs) for both bacterial and archaeal datasets. ASV generation relies on algorithmic approaches designed to model and mitigate sequencing errors, thereby yielding high-resolution operational taxonomic units that effectively differentiate genuine biological sequences from background noise.
# Analysis of Powertrain Loading Dynamic Characteristics and the Effects on Fatigue Damage
## 5 Conclusions  
With the studies of the PG testing loading histories, the powertrain dynamic characteristics and the fatigue damage of shaft parts and gear parts, the following conclusions are drawn:  
1. The 5-axle vehicle powertrain loading are mainly decided by three factors: the mean engine torque, the 1 st and 4 th harmonic engine torque, and the elasticity of the powertrain system.
2. During the shifting process, the shock torque generated by the clutch produces a greater impact on the engine and the transmission output shafts, and the amplitude of transmission output shaft torque is larger. The 1 st harmonic engine torque has effects on both the engine and the transmission output shaft loading. The 4 th harmonic engine torque has a great influence on the engine output shaft loading, but little impact on the transmission output shaft loading.
3. Considering the effects of the stiffness and damping of the elastic coupling, the torsional damper, and the tires, dynamic analysis of the powertrain shows that the 1 st order natural frequency coincides with the vibration frequency during shifting process, and the 2 nd and 3 rd order natural frequencies are not obvious. With the superposition of the mean engine torque, the 1 st and 4 th harmonic engine torque as system inputs, and the vehicle resistances in driving as system loads, the responses have a good agreement with the experimental data.
4. For shaft parts, the 4 th harmonic engine torque has a great influence on the engine output shaft. The large amount of small loading cycles contribute 80% pseudo-fatigue damage but have little impact on the transmission output shaft. The torque oscillations during shifting process have a greater impact on the fatigue damage of the transmission output shaft torque than the engine output shaft. For gear parts, the mean engine torque constitutes the majority of the pseudo-damage. Regardless of the factors of the harmonic engine torque and the powertrain system, elasticity only results in less than 5% errors.
5. As the harmonic engine torque and the elastic components have a significant impact on the powertrain loading dynamic characteristics and fatigue damage for shaft parts, further research in optimizing the stiffness and damping of the elastic coupling and torsional damper can be made to reduce the vibration of the system and extend lifetime of components in the future work, while the elasticity of the tire is also non-negligible in analyzing the system characteristics.  
[PERSON] and [PERSON] conceived the idea, guide the work and revised the manuscript together; [PERSON] designed the work, performed the experiment and the simulation, analyzed the data, make the figures and tables, and wrote the paper; [PERSON] contributed to materials and lab facilities, and proof-read the manuscript.  
The authors declare no conflict of interest.
## 2 Collisions and the Coefficient of Restitution (\(CoR\), \(e\))
### Methods for Determining the Coefficient of Restitution (CoR, e)
#### 2.2.2 Height-Based (Drop-Test) Method  
When rebound velocity cannot be measured reliably, a simpler height-based method is often chosen [22; 23]. In this approach, grains are released from known heights onto surfaces such as steel, plexiglass, seed plates or rubber sheets, and the rebound height is recorded.  
Because impact velocity \(u\) in free fall is uniquely related to drop height \(h_{u}\), the coefficient of restitution is  
Figure 3: Force–time relationship for different material characteristics (adapted from [4; 7]).  
\[CoR=e=\frac{v}{u}=\frac{\sqrt{2 gh_{v}}}{\sqrt{2 gh_{u}}}=\sqrt{\frac{h_{v}}{h_{u}}} \tag{4}\]  
where the following apply:  
* \(e\)--coefficient of restitution (CoR);
* \(u,v\)--velocities just before and after impact;
* \(h_{u}\)--drop height (release height);
* \(h_{v}\)--rebound height.  
This height-based test works best with large, nearly spherical kernels, whose predictable bounces minimise angular error [22; 24; 25]. For irregular kernels such as wheat, however, a full rebound-velocity vector (\(v_{x}v_{y}v_{z}\)) is required [20]:  
\[CoR=e=\frac{v}{u}=\frac{\sqrt{v_{x}^{2}+v_{y}^{2}+v_{z}^{2}}}{\sqrt{2 gh_{u}}} \tag{5}\]  
Uncertainty in rebound direction increases measurement error, which is why we use the impulse method described below.
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 3 Test Rig, Gearbox States, and Data Processing  
A test rig comprising a speed reducer (easy to break down into components) has been designed for testing helical gears and bearings. Section 3.1 presents the test rig components, gearbox states, and data acquisition system for the fault detection task. The processing using CWT (continuous wavelet transform) of the measured raw vibration signals is described in Section 3.2.