esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Real-Time Quantitative PCR (qRT-PCR) Analysis  
A qPCR analysis was performed for nine genes. Briefly, total RNA was isolated from tomato tissues and extracted using the Monarch Total RNA Miniprep Kit (NEB, Europe), according to the manufacturer's guidelines. The PrimeScript first-strand cDNA synthesis kit (TaKaRa Biomedicals, Otsu, Japan) was used to perform cDNA synthesis. The following primer pairs were used to amplify the target genes: _WAT1(DUF6)-related (Solyc1 Ig012930.2,_ F: AGGCCTAAGATGACACGCAG, R: CCAACCACACAATGGA, _non-specific lipid transfer (Solyc1 Ig075070.2,_ F: AGGGAAAAGCCCTC-TAGGA, R: CGACGATGAAAAAGGGC), _Expansin (Solyc06g051800.3,_ F: TTACGGCGAAGTGATGCTT, R: GGCATGGCCTGATA), _ACS2 (Solyc1 Ig095080.3,_ F: ATGGGATTTGAGATTGCAAAGACC, R: TTAACGAACTAATGGG), _ETR1 (Solyc1 Ig01330,_ F: ATGGGATCTTTCTCCCG, R: CTAAGATTCAAGTACACCATG), _CHS2 (Solyc05g053550_, F: ATGGTCACCGTTGAGG, R: CTAAGTAGACACACATATGGAGC), _histidine decarboxylase (Solyc08g066250.3_, F: GCGATGCAGCATTATTGTGGG, R: TACAACATGTGCCATGCCTCT), _HCT (Solyc03g117600_, F: CCCTCTCCGTGTGA, R: CCCGGGTTAGTTTGAAGATTGACA) and _PE (Solyc03g083730, F: TTCGAATTCAGTTCCGCCG, R: GCTGTTTCGAGGCTAACGGA). _EF1-x (Solyc06g005060; F: GCTGCTGTAACAAAGATGATG, R: GGGGATTTTGCAGGGTTGTA) was used as a reference gene for normalization. Each reaction contained 1\(\times\) buffer, 0.2 mM dNTPs, 0.2 mM forward and reverse primers for each gene, 1\(\times\) EvaGreen(r) dye (Biotin Inc., Hayward, CA, USA), 0.5U Kapa Taq DNA polymerase (Kapa Biosystems, Wilmington, MA, USA), and 20 ng of cDNA, in a 20 uL final reaction volume. The qPCR reactions were run in a Corbett Rotor Gene 6000 Thermocycler (Corbett Research, Sydney, Australia). The protocol included an initial denaturation step at 95 \({}^{\circ}\)C for 3 min, an amplification step with 35 cycles at 95 \({}^{\circ}\)C for 20 s, 57 \({}^{\circ}\)C for 20 s, 72 \({}^{\circ}\)C for 20 s, and a final elongation step at 72 \({}^{\circ}\)C for 7 min. Three biological replicates were prepared for each genotype, and three technical replicates were carried out for each biological sample. Each run included a non-template control reaction. The relative expression gene levels of the mutants compared to the WT samples were calculated using the 2\({}^{-\Delta\Delta\text{CT}}\)[31] method. Gene expression data are expressed as the mean \(\pm\) SE of the three biological replicates. qPCR data were plotted using GraphPad Prism version 9.0.
## 5 Conclusions  
Based on experimental results and discussion, the following conclusion can be drawn:  
1. Three types of main fractures are observed, and their formation mechanism has been discussed. The longitudinal fractures and oblique fractures were caused by the pressurization of SC-CO\({}_{2}\) in perforation. The transverse fractures were caused by the combined effect of the strong heterogeneity at the transition areas of hard-soft and soft-hard rocks, the stress concentration on the tip of perforation due to discontinuity of structure, and the stagnation pressure pressing on the tip of perforation.  
2. The SC-CO\({}_{2}\) jet fracturing can be divided into two stages, the pressurization of the perforation pressure stage and fracture propagation stage, and the fractures initiation and propagation is intermittent. Through the strain curve, the fracturing time can be obtained, which can evaluate the rate of jet fracturing, and the rate of jet fracturing can be enhanced by increasing injection rate.
3. The rock mechanical properties have great impact on fracture propagation. The rock strength can affect the number and complexity of fractures created and the fracturing rate. When the rock strength is low, more fractures can be created, and the fracturing rate is high. The Young's modulus of rock has an effect on fracture propagation length.
4. The fractures mainly develop near the perforation and are difficult to propagate to another layer with different mechanical properties, and the fractures are prone to develop in the transition areas of hard-soft rocks and soft-hard rocks due to the stress concentration caused by the strong heterogeneity of transition areas. When the perforation position is at the soft layer, complicated fracture networks can be created, which is conductive to the selection of perforation location in the actual field.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 3 DCP Position Error Model
### Parameter Errors of Robot Base Coordinate System  
The errors of the RBCS represents the parameter errors of the transformation model from the MICS to the RBCS. Let \(\overset{0}{0}T\) be the transformation model from the MICS to the  
Figure 3: Actual position and nominal position of DCP.  
RBCS. The nominal pose matrix of the RBCS calculated by Equation (13) in the MICS can be represented as follows:  
\[\begin{array}{c}{{}_{C}T^{N}=\left(\begin{array}{cccc}n_{x}&o_{x}&a_{x}&T_{x} \\ n_{y}&o_{y}&a_{y}&T_{y}\\ n_{z}&o_{z}&a_{z}&T_{z}\\ 0&0&0&1\end{array}\right).}\end{array} \tag{14}\]  
In Equation (14), there are 12 parameters representing position and pose. To reduce the number of parameters to be identified, it can be rewritten as a 6-parameter model composed with a translation transformation and three rotational transformations.  
\[\begin{array}{c}{{}_{C}^{0}T^{N}=T(x_{c},y_{c},z_{c})R(x_{c},\theta x_{c})R(y _{c},\theta y_{c})R(z_{c},\theta z_{c}).}\end{array} \tag{15}\]  
Therefore, the parameter errors of the RBCS are: \([\Delta x_{c},\Delta y_{c},\Delta z_{c},\Delta\theta x_{c},\Delta\theta y_{c}, \Delta\theta z_{c}]\).  
The parameter errors are added to \(\begin{array}{c}{{}_{C}^{0}T}\end{array}\) to get the actual pose model of the RBCS as  
\[\begin{array}{c}{{}_{C}^{0}T^{R}=T(x_{c}+\Delta x_{c},y_{c}+\Delta y_{c},z_{ c}+\Delta z_{c})R(x_{c},\theta x_{c}+\Delta\theta x_{c})}\\ \qquad\qquad\qquad R(y_{c},\theta y_{c}+\Delta\theta y_{c})R(z_{c},\theta z_{ c}+\Delta\theta z_{c}).\end{array} \tag{16}\]  
In the equation, the superscript \(R\) represents the actual value.
## 5 Results and Discussion  
### Optimal Metro Stations PCA for Different Zones  
In order to determine the rationality of the XGBoost model in this analysis, we compared the accuracy of the XGBoost model with other machine learning models and the comparison results of AdjR\({}^{2}\) are shown in Figure 3. As can be seen from Figure 3, the accuracy of the XGBoost model is better than others, and the AdjR\({}^{2}\) of the testing set inside the 4 th Ring Road is 0.74 and the AdjR\({}^{2}\) of the testing set outside the 4 th Ring Road is 0.72. So, XGBoost can be used for this analysis. Calculate the MAPE of PCAs for the inside and outside 4 th Ring Road metro stations based on the predicted values in the XGBoost model in the testing set and the true values in the testing set, respectively, and plot the line graph of MAPE at different PCAs. To our knowledge, the accuracy of nonlinear models has not been considered in previous studies. In addition, most scholars currently studying the nonlinear influence of the built environment factor on metro ridership have used the goodness of fit for linear models [17; 39] and experience [3; 18; 21] to determine the optimal PCA of metro stations. No one has used the accuracy of nonlinear models to determine the optimal PCA of metro stations. Figure 4 shows the MAPE folds at different PCAs of metro stations inside and outside the 4 th Ring Road. The graph shows that when the buffer zone radius is 800 m, the lowest MAPE value is reached at 9.64% for metro stations inside the 4 th Ring Road. Therefore, the optimal PCA of metro stations inside the 4 th Ring Road is the circular buffer zone of an 800 m radius. For the outside 4 th Ring Road metro stations, MAPE reaches a minimum value of 16.60% when the buffer radius is 1300 m. So, the optimal PCA of the outside 4 th Ring Road metro stations is a circular buffer of 1300 m.  
Looking at the MAPEs of the metro stations inside and outside the 4 th Ring Road, the MAPE of the metro stations outside the 4 th Ring Road is larger than those of the metro stations inside the 4 th Ring Road. It proves that the model accuracy is higher inside the 4 th Ring Road. That is consistent with existing research [39]. This is due to the fact that outside the 4 th Ring Road is a new urban area with a larger urban scale. Some passengers do not start their journey in the PCA but still choose to come to this metro station.  
Figure 4: MAPE diagram for different zones of metro station PCAs.  
Figure 3: Accuracy of different machine learning models: (**a**) Inside the 4 th Ring Road; (**b**) Outside the 4 th Ring Road.
## 3 Results
### iAPAR and RAP Model Comparison  
When comparing our empirical iAPAR model to the process-based RAP model, we found that both models tended to underpredict high values and overpredict low values of ANHP, but this bias was more pronounced for RAP than the iAPAR model (Figure 8). Overall, RAP predictions were strongly correlated with ground observations but explained less variance (R\({}^{2}\) = 0.40) compared to the iAPAR model (R\({}^{2}\) = 0.62), and RAP exhibited higher absolute and relative errors (Table 2). The RAP model tended to underpredict (MPE: \(-\)28.57\(\%\)) more than the iAPAR model (MPE: \(-\)14.97\(\%\)), and RAP consistently underpredicted when observed biomass was greater than \(\sim\)1500 kg ha\({}^{-1}\) (Figure 8).  
Figure 8: Ground-collected Above Ground Net Herbaceous Production (ANHP) values compared to satellite-based predicted ANHP using either the Rangeland Analysis Platform output (**top** panels) or our empirical model derived from iAPAR (**bottom** panels). Points represent the ground data, with colors and shapes denoting different grazing managements. Lines display linear relationships between observed (ground-based) ANHP and predicted (satellite-based) ANHP. The solid black line represents the 1:1 relationship.  
When data were broken down by ecosite, both RAP and iAPAR had lower MAE%, MPE%, and MAPE%, less biased MPE% (i.e., closer to 0), and higher r (correlation) values in the taller structured Salt Flats and Sandy Plains ecosites compared to the Loamy Plains ecosite (Table 2). The iAPAR model also had higher R\({}^{2}\) values in taller structured ecosites, whereas RAP had a higher R\({}^{2}\) in the Loamy ecosite than the other ecosites (Table 2). The two models had comparable overall error rates in Loamy and Sandy Plains ecosites, with RAP having only about 6% higher MAE% than the iAPAR model. The RAP error was noticeably higher than iAPAR error in the tallest-structured Salt Flats ecosite (Table 2; MAE 494.03 kg/ha\({}^{-1}\) vs. 295.75 kg/ha\({}^{-1}\); MAE% 33.27 vs. 19.92). As was found for the iAPAR model, the accuracy of RAP predictions varied less across grazing management classes than across ecosites (Tables S1 and S2).
## 4 Conclusions
In future research, different fatigue life stages will be investigated by means of scanning electron microscopy and X-ray imaging to better understand the frequency-induced microstructure evolution, which is crucial for identifying frequency-related damage mechanisms and may also be implemented into the lifetime prediction method. The goal is to obtain S-N curves for further test frequencies from the StressLife\({}_{\text{HCF}}\) calculation using only just one test frequency without having to execute additional tests at other frequencies. To enable this conversion, a factor could be determined based on the frequency difference. Since the electrical resistance-temperature hysteresis is acquired without dynamic stress in a quasistatic system, the behavior may vary in a dynamic system. For this reason, air cooling will be installed for future tests to counteract the forming heat build-up and to reduce the temperature effects. Also, the test bench must be adapted to obtain higher temperatures in the hysteresis by using a more powerful or comparable heat source. The StressLife\({}_{\text{HCF}}\) method at high test frequencies should also be verified with different heat treatment conditions, austenitic materials, and further material groups. As a result of the increasing importance of additively manufactured materials, an application to this type of material appears highly relevant. Conceptualization, J.A.Z. ; methodology, J.A.Z. ; validation, J.A.Z. ; formal analysis, J.A.Z. and F.W. ([PERSON]); investigation, J.A.Z., J.L.O., J.K. and L.M.S. ; resources, F.W. ([PERSON]) and P.S. ; data curation, J.A.Z. and J.K.; writing--original draft preparation, J.A.Z. ; writing--review and editing, J.A.Z., F.W. ([PERSON]), J.K., J.L.O., L.M.S., F.W. ([PERSON]) and P.S. ; visualization, J.A.Z. and J.K.; supervision, F.W. ([PERSON]) and P.S. ; project administration, J.A.Z. and J.L.O. ; funding acquisition, F.W. ([PERSON]) and P.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG), project no. 518776466 (STA 1133/22-1, WA 1672/120-1). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors would like to thank the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) for their financial support of this research project (project no. 518776466) and for providing the resonant test rig (project no. 468976436, INST 252/21-1 FUGG) as well as the scanning electron microscope (project no. 514139564, INST 252/27-1) at the University of Applied Sciences Kaiserslautern. Additional thanks to the University of Applied Sciences Kaiserslautern for their financial support in procuring the test infrastructure. Thanks to [PERSON] and [PERSON] for their support in the experimental investigations within their masters' theses. The authors are grateful to Russenberger Profmashchinen AG for their technical support. The authors declare no conflicts of interest.
# NMR Analysis of Pulegone in Food Products
## 2 Results and Discussion
### Method Development and Optimization
#### 2.1.4 Validation of the NMR Method  
The recovery rate of pulegone in peppermint oil is 85.0%, in Chinese mint oil 99.8%, in corn mint oil 98.3%, in buchu oil 86.4%, in pennyroyal oil 86.1%, and in muna oil 95.1%. The coefficient of determination for the standard addition of pulegone to peppermint, Chinese mint, corn mint, buchu, pennyroyal, and muna oil is R\({}^{2}\) = 0.998, 0.999, 0.998, 0.997, 0.997, and 0.998, respectively.  
For the essential oils obtained via hydrodistillation, the coefficient of determination of the standard addition of pulegone to the respective oil was R\({}^{2}\) = 0.994 for peppermint, R\({}^{2}\) = 0.999 for pennyroyal, R\({}^{2}\) = 0.996 for spearmint, R\({}^{2}\) = 0.996 for mint-flavored candies, and R\({}^{2}\) = 1 for mint-flavored candies.  
The recovery rates of pulegone in peppermint are 91.2%, 96.7%, and 84.3% in peppermint, pennyroyal, and spearmint, respectively. The recovery rate of pulegone in mint-flavored pastilles was 90.1%, whereas that in dragees was 92.1%. Furthermore, a calibration curve based on higher pulegone concentration levels was applied to the spearmint. This resulted in an improved recovery rate of 102%.  
The LOD of pulegone in peppermint is 59.9 mg/kg, and the LOQ is 214 mg/kg (see Table 2). The LOD of pulegone in pennyroyal, spearmint, pastille, and candy is 134 mg/kg, 371 mg/kg, 14.5 mg/kg, and 7.15 mg/kg, and the LOQ is 491 mg/kg, 1363 mg/kg, 51.5 mg/kg, and 26.2 mg/kg.
## 4 Discussion
The results also help to categorize spatial de-speckling filters as spectral filters, system resolution enhancement filters, and image resolution improvement applications, while their objective image quality metrics play an important role for their selection and implementation. Future studies might include other types of de-speckling filters, such as transform domain filters with more objective quality metric verifications based on time-domain, frequency-domain and time-frequency domain analysis, however the texture and the application purpose remain very important.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 1 Introduction
The latest studies, though limited in number, assert that visible boundaries, such as hedges, land cover boundaries, etc., which might indicate cadastral boundaries, could be automatically extracted using methods such as algorithms that detect object boundaries in images [26; 27; 28; 29]. In fact, not all visible cadastral boundaries can be automatically detected--certain boundaries would require a semi-automatic approach, especially in urban areas where the morphology of cadastral boundaries is complex [7]. Nevertheless, the potential of computer vision methods for automatic detection and extraction of visible objects in the images is promising for cadastral applications, especially due to the urgent global need for accelerating and facilitating cadastral mapping as a basis for registration of land rights and following the dynamics of land tenure and land use.
## 2 Previous Works
### System Modeling  
Modeling SPVSs is essential for understanding their performance and optimizing their design and operation. One of the primary purposes of modeling is to predict system performance under varying environmental conditions. Accurate predictions enable stakeholders to assess energy output and efficiency before installation. Ref. [24] emphasize that tools such as PVsyst and System Advisor Model (SAM) facilitate energy generation simulations, which are crucial for project feasibility assessments and investment decisions.  
In addition to performance prediction, modeling serves to compare different SPVS designs. Simulation tools allow researchers to evaluate various configurations, such as panel orientations, tilt angles, and system capacities. [PERSON] and [PERSON] [25] conducted a study comparing fixed tilt versus tracking systems in various climatic regions, finding that tracking systems could significantly enhance energy output. Such comparative analyses inform design choices that maximize efficiency and effectiveness.  
Another vital purpose of modeling is to demonstrate the technical and economic feasibility of SPVSs, particularly regarding energy savings and sustainability. Modeling enables the evaluation of life cycle costs and Return on Investment (ROI), which are essential for determining project viability. NPV Solar [26] highlighted that detailed modeling reveals potential savings from reduced energy costs and improved energy yield, thereby supporting better decision-making and investment strategies aligned with sustainability goals.  
Moreover, modeling can facilitate the integration of SPVS into existing energy infrastructures, promoting energy efficiency and supporting the transition to sustainable energy systems. [PERSON] et al. [27] noted that comprehensive modeling of hybrid energy systems, which combine SPVSs with other renewable sources, optimizes energy dispatch and en hances reliability. Such models are crucial for designing systems that are economically viable and environmentally sustainable.
## 5 Conclusions  
The effect of sugarcane bagasse, softwood, and their extracted cellulose on the mechanical, thermal, and morphological properties of polypropylene/polyethylene (PP/PE) blends was investigated in this study. TGA results showed that the introduction of cellulose fibers did not enhance the thermal stability of the PP/PE blend. The cellulose blend composites appeared to be the least thermally stable compared to untreated fibers. The blend composite materials had a higher storage modulus compared to the PP/PE blend, with the PP/PE/SCB untreated blend displaying the highest storage modulus. The current results showed that the mechanical properties of the PP/PE blend could be improved with addition of cellulosic fibers, even at lower filler content. DSC analysis revealed that the introduction of untreated fiber and extracted cellulose did not change the melting and crystallization behavior of the PP/PE blend. The crystallinity index decreased with the introduction of fiber and cellulose, as confirmed by DSC and XRD results. The fractured surfaces of the PP/PE blend showed poor interfacial adhesion between the PP and PE, signaling immiscibility and poor interaction. Moreover, SEM analysis revealed less interaction and poor dispersion of SCB and SW filler with the PP/PE blend. These results show the dependence of morphology on the fabrication method and interfacial interaction. The introduction of a compatibilizer may significantly enhance the interaction between the PP/PE blend and the cellulosic fibers. Lastly, it is evident that the modification of the fabrication method has the potential for large-scale production of immiscible PP/PE blends with improved properties. The surface modification of cellulose fibers with various reactive agents needs to be explored further if the compatibilizers are not utilized.  
Conceptualization, T.E.M.; Investigation, S.M.M. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the National Research Fund (NRF) of South Africa.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to this is a development research in our research group.  
The authors declare no conflict of interest.