esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
###### Abstract  
Surface topography is an efficient tool for the understanding of physical phenomena, especially if multiscale roughness analysis is performed. However, the observable scale range in a topography measured with 3D optical profilometers is quite limited. Therefore, all scales linked to a physical phenomenon might not be measured, which impedes the correct analysis of the surface. Stitching of 3D topographies, a technique combining elementary topographic maps into a larger one, can be used to increase the scale range for an objective lens. A high resolution over a large field of measurement topography is then generated. A literature review of 3D topography stitching algorithm highlights the stitching procedure, and detailed explanations on in-plane registration algorithms are provided. However, some existing 3D topography stitching algorithms are not sufficiently accurate for the registration of surface, especially at smaller scales. This paper proposes a new reflectance-based multimap 3D stitching algorithm and three of its variants. These algorithm variants are compared to three existing 3D stitching algorithms (geometric, cross-correlation and global optimization of differences) on four test cases, containing measured elementary topographic maps obtained on four surfaces and with four 3D optical profilometers (two focus variation microscopes and two interferometers). Five qualitative and quantitative criteria and indicators are proposed for the comparison of 3D topography stitching algorithms: visual inspection, run time, memory usage, mean repositioning error and stitching error estimator. Lastly, two quantitative indicators and criteria are new indicators proposed in this article. Overall, the new 3D stitching algorithms based on reflectance and multimaps have a lower mean repositioning error and stitching error estimator compared to other existing algorithms. This highlights the relevance of multimap stitching algorithms in the case of 3D topographies. A new decision-helping tool, the stitching gain lift plot (SGL plot), is described for the selection of the best stitching algorithm for a given test case. The SGL plot especially highlights the higher performance of two of the variants of the novel algorithm compared to the three existing 3D stitching algorithms.  
stitching: topography; reflectance; roughness; multimap +
Footnote †: journal: Applied Sciences
## Appendix A
* ISO 9001: Quality management standard that focuses on customer satisfaction and continuous improvement. * Life-Cycle Analysis (LCC): A technique that evaluates the total cost of an asset throughout its useful life, considering all phases from acquisition to disposal. * Machine Learning (ML): Branch of AI used in predictive models to anticipate failures, optimize maintenance and analyze failure patterns in critical assets. * Minitab: Statistical software for Six Sigma. * Predictive Maintenance: Maintenance strategy that anticipates failures by analyzing historical and real-time data. * Predictive Models: Algorithms that analyze current and historical data to predict future events, such as the need for maintenance or equipment failures. * Proactive Maintenance: Maintenance strategy based on real-time data and predictive analytics that prevents failures before they occur. * Prometheus: Real-time monitoring system. * PyTorch: Deep Learning framework. * RAMS (Reliability, Availability, Maintainability, and Safety): A set of criteria that assess the reliability, availability, maintainability, and safety of an asset. * Reliability-Centered Maintenance (RCM): Maintenance methodology focused on asset reliability, with the aim of identifying and prioritizing the most critical failure modes. * Remote Monitoring: Monitoring industrial assets or processes from remote locations using connectivity technologies and cloud-based management systems. * Robotic Process Automation (RPA): Technology that uses software robots to automate repetitive, high-volume tasks, improving operational efficiency. * Salesforce: Customer relationship management platform. * Scikit-learn: Machine learning tool in Python. * Service: A service is an activity that a company provides to another company (or to an end customer) that is intangible; that is, it does not generate ownership of a good. * Simulation: Use of computational models to replicate the behavior of physical systems and evaluate hypothetical scenarios without risk. * Smart Sensors: Devices that collect data about the environment and transmit this information for real-time decision-making. * SPSS: IBM Statistical Software. * Tableau: Data visualization tool. * TensorFlow: Machine learning library. * Traceability: Ability to track the complete life cycle of a product or asset from manufacturing to operation and maintenance, improving warranty management. * Warranty: A warranty is a formal commitment that ensures the repair or replacement of a defective product within a specified period. * XGBoost: Machine learning algorithm. * Zooh CRM: Customer relationship management software.
# Biosynthetic Pathways of Hormones in Plants
## 11 Strigolactones
ported from the chloroplast to the cytoplasm, where the reactions related to SL biosynthesis occur (Figure 13) [190]. The oxidation of CL results in the formation of carlactanoic acid (CLA) (Figure 13). This reaction is catalyzed by the More Axillary Growth 1 (MAX1) enzyme, which is a member of the CYP711A subfamily. The structure and biochemical properties of MAX1 have been well characterized. It is a conserved protein that possesses many homologs in a number of plant species. CLA can be treated as the precursor of strigol and orobanchol-type SL, i.e., 5-deoxystrigol (5 DS) and 4-deoxyorobanchol (4 DO). Enzymes identified in plants, such as Os900/OsCYP711A2 in rice (_Oryza sativa_), VtCYP722C in cowpea (_Vigna unguiculata_), SICYP722C in tomato (_Solanum lycopersicum_), and GaCYP722C in cotton (_Gossypium arboreum_), take part in the synthesis of organic compounds 4-deoxyorobanchol, orobanchol, and 5-deoxystrigol, respectively, from CLA [190; 192; 193; 194]. In the next reaction, CLA is transformed to methyl carlactonoate (MeCLA) by an unknown enzyme [189]. Next, Lateral Branching Oxidoreductase (LBO) converts methyl carlactonoate (MeCLA) into hydroxymethyl carlactonoate (I\({}^{\prime}\)-OH-MeCLA) and CLA [195]. LBO can produce CLA via direct demethylation of MeCLA or indirect spontaneous reversion of 1\({}^{\prime}\)-OH-MeCLA to CLA [196]. MeCLA is considered to be a precursor used for the synthesis of non-canonical SLs. Feeding experiments showed that MeCLA can be converted into heliolactone in sunflower and tobacco [193; 194]. The enzyme responsible for the production of heliolactone has not yet been identified to confirm this theory, and it is also important to study the possibility that MeCLA is an important intermediate for the synthesis of other non-canonical SLs (e.g., avenaol and zealactone) (Figure 13) [196]. The enzymes that lay downstream and upstream of CL may be important for the production of various compounds that belong to the SL family. Carotenoid isomerase, CCD7, and CCD8 are able to transform all-_trans_-\(\beta\)-carotene into CL and convert 3-hydroxy-carlactone (3-OH-CL) through zeaxanthin [191]. Hydroxy-carlactone derivatives are the main SL in _A. thaliana_[195]. However, their roles in the regulation of plant growth and development have not yet been confirmed [196]. These non-canonical SLs are characterized by lower stability in relation to canonical SLs. Thus, studies of the biological functions of non-canonical SLs are difficult to carry out, and the results are incomplete [197].
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
###### Abstract  
Surface phenomena play the crucial role in the behavior of sulfide minerals in mineral processing of base and precious metal ores, including flotation, leaching, and environmental concerns. X-ray photoelectron spectroscopy (XPS) is the main experimental technique for surface characterization at present. However, there exist a number of problems related with complex composition of natural mineral systems, and instability of surface species and mineral/aqueous phase interfaces in the spectrometer vacuum. This overview describes contemporary XPS methods in terms of categorization and quantitative analysis of oxidation products, adsorbates and non-stoichiometric layers of sulfide phases, depth and lateral spatial resolution for minerals and ones under conditions related to mineral processing and hydrometallurgy. Specific practices allowing to preserve volatile species, e.g., elemental sulfur, polysulfide anions and flotation collectors, as well as solid/liquid interfaces are surveyed; in particular, the prospects of ambient pressure XPS and cryo-XPS of fast-frozen wet mineral pastes are discussed. It is also emphasized that further insights into the surface characteristics of individual minerals in technological slurries need new protocols of sample preparation in conjunction with high spatial resolution photoelectron spectroscopy that is still unavailable or unutilized in practice.  
X-ray photoelectron spectroscopy; sulfide minerals; surface; oxidation; flotation; leaching +
Footnote †: journal: Applied Sciences
## 4 Experimental Procedure
### Monitoring Campaign  
The monitoring campaign was developed with the objective of capturing the building's dynamic responses under various loading conditions, including ambient, seismic, and construction-induced vibrations from an adjacent structure [15]. Accelerometers were installed on levels \(-1\) (basement), 3, 4, 5, and 6 of the building, allowing for comprehensive coverage from the basement to the upper floors. To adapt to the operational conditions and optimize data capture, datasets were generated with configurations of two, three, and four synchronized accelerometers. Figure 5 shows acceleration records for ambient vibration, a seismic event, and vibrations from adjacent construction.  
While the monitoring campaign was conducted with only four accelerometers due to resource limitations, the placement of the sensors was carefully planned to ensure sufficient spatial coverage of the building's dynamic response. This approach aligns with previous  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline Standard range & 2 G \\ ADC resolution & 32 bits \\ Dynamic range & 144 dB \\ Sampling frequency & 400 HZ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the SARA SL06 ACEBOX.  
Figure 4: SL06 ACEBOX triaxial accelerometer installed in the building.  
Figure 5: Exemplary acceleration records: (**a**) ambient vibration, (**b**) a seismic event, and (**c**) adjacent construction.  
studies, such as those by [PERSON] et al. [6] and [PERSON] et al. [37], which demonstrate that limited sensor configurations, when strategically placed, can provide valuable insights into the dynamic behavior of structures.  
Nevertheless, it is important to acknowledge that using a reduced number of sensors may introduce a margin of error in the characterization of the building's dynamic response. Factors such as unmeasured local effects or missed modal contributions could influence the results. However, this potential limitation was mitigated by complementing the accelerometer data with modal frequencies obtained from the numerical model. This combination of experimental and numerical data not only enhanced the accuracy of the dynamic characterization but also helped confirm key frequencies identified through the monitoring campaign, thus increasing the reliability of the findings.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 1 Introduction
This motivates us to conduct an analysis on the prediction performance of the Lasso model with CNC sparse regularization, thereby substantiating that CNC sparse regularization outperforms \(\ell_{1}\) regularization. In this paper, we consider the following Lasso model with CNC sparse regularization  
\[\hat{\beta}_{\lambda}^{CNC}\in\arg\min_{\beta}\frac{1}{2n}\|y-X\beta\|_{2}^{2 }+\lambda\Psi_{B}^{CNC}(\beta), \tag{3}\]  
where the non-convex regularization term \(\Psi_{B}^{CNC}(\beta)\) is parameterized by a matrix \(B\), and the global convexity of the objective function in (3) can also be guaranteed by adjusting \(B\). Through rigorous theoretical analysis and comprehensive experimental evaluations, we demonstrate that the utilization of non-convex regularization significantly enhances the prediction performance of the Lasso model, enabling accurate estimation of unknown variables of interest. Our contributions can be summarized as follows  
* Theoretically, for the Lasso model with a specific CNC sparse regularization, we establish the conditions necessary to ensure global convexity of the objective function. Subsequently, by leveraging oracle inequality, we derive an improved upper bound on prediction performance compared to that of the Lasso model with \(\ell_{1}\) regularization. * Algorithmically, we derive an ADMM algorithm that ensures convergence to a critical point for the proposed Lasso model with CNC sparse regularization. * Empirically, we demonstrate that the proposed Lasso model with generalized minimax concave (GMC) regularization outperforms \(\ell_{1}\) regularization in both synthetic data and MRI reconstruction experiments, owing to its utilization of non-convex regularization. The subsequent sections of this paper are arranged as follows. Section 2 presents some preliminary and related work concerning CNC sparse regularization, predictive performance results, and convex optimization. In Section 3, we give theoretical analysis on the prediction performance of the Lasso model with CNC sparse regularization and further propose an effective ADMM algorithm to solve it. In Section 4, we verified the superiority of the proposed model through synthetic datasets and real datasets. Finally, the conclusions are summarized in Section 5.
## 3 Results and Discussion
### Analysis and Discussion
#### 3.2.3 CS Analysis  
The results of CS (R\({}_{2}\)) analysis of variance are shown in the second row of Table 7. R-squared and Adj. R-squared are both close to 1, and Adeq. Precision is greater than 4, which indicates that the fitted model is significant. According to the variance results in Table 8, X\({}_{1}\), X\({}_{2}\), X\({}_{3}\), and (X\({}_{2}\))\({}^{2}\) are the significant factors of the CS quadratic model. After excluding insignificant factors, the least squares method is used to fit the CS second-order polynomial equation:  
\[\text{CS}=33.09-2.98X_{1}+2.38X_{2}-4.31X_{3}-3.08(X_{2})^{2} \tag{3}\]  
The diagnostic data points of the statistical model in Figure 3 are approximately linear sets, indicating that the model has high significance.  
Figure 3: Normal plot of residuals for CS.  
Figure 2: 3D response surface plot between SP and factors: (**a**) Factors: BFC and WBR at RC = 20%; (**b**) Factors: RC and WBR at BFC = 5 kg/m\({}^{3}\); (**c**) Factors: RC and BFC at WBR = 0.44.  
Figure 4 is a three-dimensional response surface diagram of CS, revealing the relationship between WBR, BFC, RC and CS, and the interaction between WBR, BFC, and RC.  
As shown in Figure 4, the CS of concrete showed a significant decreasing trend with the increase of WBR and RC. When the WBR increased, the water content in the concrete would increase. When the water required for concrete mixing is saturated, the excess water is released from the concrete and adsorbed on the surface of the concrete structure. When the strength of concrete increases, the excess water will be absorbed by the concrete itself or evaporate to the air. Bubbles of water droplets inside the original concrete or adsorbed on the surface of the formwork will naturally form, which would increase the internal porosity of concrete and reduce the CS of RBFC. In addition, the CS of RBFC showed a trend of first increasing and then decreasing with the increase of BFC. When the basalt fiber content was low, the basalt fiber could be evenly distributed in concrete to form a network structure, which can effectively prevent the extension of cracks when the concrete is compressed. However, when the amount of basalt fiber was large, the fiber was not easy to disperse during the mixing process, and agglomeration was prone to occur, which increased the defects in concrete and adversely affected  
Figure 4: 3D response surface plot between CS and factors: (**a**) Factors: BFC and WBR at RC = 20%; (**b**) Factors: RC and WBR at BFC = 5 kg/m\({}^{3}\); (**c**) Factors: RC and BFC at WBR = 0.44.  
the compressive strength of concrete. From the comparative analysis in Figure 4 and the results of variance analysis in Table 7, it can be seen that RC had the most significant effects on the CS of RBFC.
## 2 Materials and Methods
### Model Construction Method  
In this study, partial least squares (PLS) and support vector machines (SVM) were utilized to develop prediction models for jujube fruit SSC and MC [33; 34; 35; 36]. The influence of various detection azimuth angles on the models' prediction results was examined. In addition, we compared the prediction results based on reflectance acquisition at multiple azimuth angles with the results based on reflectance acquisition at a single angle.
## 3 Results
### XTT Assay  
The enzyme activity--considering an exposure dose equivalent to approximately 0.2 m\({}^{3}\) of sampled air and expressed in terms of absorbance--ranged from 1.11 to 3.27, with a mean value of 2.35 \(\pm\) 0.52. These values were compared with the viability measured in control cells, and the percentage of dehydrogenase activity inhibition following exposure (\(\Delta\) dehydrogenase activity/0.2 m\({}^{3}\)) was calculated.  
Figure 2: Concentrations of PM ((**A**), log scale y axis) and endotoxins ((**B**), linear scale y axis) recorded in each typology of treatment plant. Outliers are indicated by \({}^{\circ}\), while \({}^{\star}\) denotes extreme values.  
Overall, as illustrated in Figure 3, most samples induced an enzyme activity decrease ranging from 0% to 20%, which is consistent with subsequent gene expression analyses. In addition to this decrease, an increase in mitochondrial activity (up to 27.5%) was observed in eight PM\({}_{0.49}\) samples.  
Considering samples from different types of plants (Figure 4), the delta of bioreductase activity varies significantly. Among these, composting plants exhibited a significantly higher cytotoxic particulate activity compared to agro-zootechnical matrix and lignocellulosic matrix plants (ANOVA, \(p<0.05\)). However, when comparing sampling areas within the plants (grouped into groups 1 and 2), no significant difference in the delta of dehydrogenase activity was detected.  
Analyzing the two PM sub-fractions separately, a marked but non-significant difference was observed for PM\({}_{0.49}\). Specifically, the dehydrogenase activity reduction was 15% in samples collected from biomass entry and treatment areas, compared to only 3% in samples from product exit areas. This non-significant difference might be influenced by the lower inhibition of dehydrogenase activity due to most of the PM\({}_{0.49}\) samples having been collected from areas corresponding to product exit points.  
Figure 4: Cytotoxicity related to PM sampled from the four different types of plants. Extreme data are marked with an asterisk (*) and outliers with a circle. Significant model (ANOVA, \(p<0.05\); \(\clubsuit\)).  
Figure 3: Cytotoxicity analysis data, expressed as the percentage of change with respect to the negative control, for a PM concentration equivalent to approximately 0.2 m\({}^{3}\) of sampled air (PM 1%) with respect to the control. Each point in the graph represents a single tested sample.  
No significant variations in bio reductase activity were observed based on season or sampling time (_t_-test, \(p\) > 0.05). However, a significant direct correlation was found between PM levels (\(\upmu\)g/m\({}^{3}\)) and the delta of dehydrogenase activity (Spearman's rho = 0.260; \(p\) < 0.05), indicating that cytotoxicity per unit volume of air increases with rising PM concentrations.
## 1 Introduction
In another study, human chondrocytes were subjected to a static magnetic field (SMF) of 0.6 T and PEMF (21.2 MHz cycle per 15 ms, continuous signal duration per 2 ms, amplification of 250 W, 3 dBm (0.1 V), and maximum output) for 72 h. When cultured with continuous application, cells treated with SMF showed a significantly higher rate of increased viability compared to control cells, whereas the increase in viability between cells treated with PEMF and non-treated control cells was not significant. This could be due to frequency-dependent effects on human cells [15]. In another study, a human chondrocyte cell culture exposed to PEMF showed a significantly higher rate of mitosis (almost two-fold) compared to chondrocytes in untreated cultures [16]. When PEMF of 10-15 Gauss and 10-30 Hz was applied to human chondrocytes and the cells were cultured for 5 days, in the case of control group cells without PEMF treatment, the rate of increase in cell numbers through mitosis reached 130%. In the case of the cells treated with PEMF, the rate of increase in the number of cells through mitosis was significantly increased to 192%. In addition, after 9 days, the control group showed a 207% increase in cell number, but in the PEMF group, the cell number increased significantly to 393%. Thus, this study aimed to evaluate the effects of PEMF treatment on the growth rate of growth plates in an animal model. Using a device with a frequency of 28 Hz and a magnetic field strength of 20 Gauss, a PEMF was applied to the knee tibia of rats to evaluate changes in the size of the growth plate and the level of growth hormone. In addition, to determine the side effects of exposure to pulsed magnetic field, changes in weight and dietary efficiency, kidney and liver function levels, and lactate dehydrogenase (LDH) levels were evaluated.