esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### First Experimental: Synergic, Antagonistic and Additive Effects
#### 2.5.4 VOCs Analysis  
VOCs from three leaves per treatment and time of exposure were profiled by headspace-solid phase microextraction (HS/SPME) method. One leaf was sealed in a 20 mL hermetic vial with butyl lid and allowed to incubate for 20 min at room temperature. The fiber (50/30 um DVB/CAR/PDMS) (Supelco(r), Bellefonte, PA, USA), previously conditioned according to the supplier's instructions, was inserted into the headspace of the vial containing the sample and allowed to adsorb leaf volatiles for 20 min. The volatiles were then desorbed by placing the fiber for 6 min into the injection port of the gas chromatography-mass spectrometry (GC-MS) system. All the SPME sampling and desorption conditions were identical for all the samples. Blanks were performed before first SPME extraction and randomly repeated during each series of measurements.  
GC-MS analysis of VOCs were performed with a Thermo Fisher TRACE 1300 (Trace 1300, Thermo Fisher Scientific, Waltham, MA, USA) gas chromatograph equipped with a DB-5 capillary column (30 m \(\times\) 0.25 mm; coating thickness = 0.25 um, with 10 m of pre-column) coupled to a Thermo Fisher ISQ LT ion trap mass detector (ISQ LT, Thermo Fisher Scientific, Waltham, MA, USA) (emission current: 10 microamps; count threshold: 1 count; multiplier offset: 0 volts; scan time: 1.00 second; prescan ionization time: 100 microseconds; scan mass range: 30-300 \(m\)/z; ionization mode: EI).  
GC-MS data were obtained under the following analytical conditions: carrier gas Helium (He 99.99%); flow rate 1 mL/min; spiltless. The initial oven temperature was 60 \({}^{\circ}\)C for 3 min, after which it was raised to 240 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and finally isothermal for 3 min. The injection port, transfer line, and ion source were kept at 250 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, and 260 \({}^{\circ}\)C, respectively.  
Qualitative identification of VOCs was performed using GC-MS reference libraries (NIST x.0). Linear retention indices (LRI) were determined from the retention times of a series of n-alkane mixture (C8-C20, Sigma Aldrich, Milan, Italy) analyzed under the same conditions reported above [37]. Percentages of the studied compounds were calculated from the peak areas in the total ion chromatograms. The relative abundance of each volatile with respect to the total amount of released compounds was estimated from its peak area against the total ions chromatogram, and expressed as a percentage, after subtracting possible contaminants.
## 3 Establishment of Hydro-Mechanical Properties Creep Damage Constitutive Model  
### Plastic Yield Surface Equation  
An improved Drucker-Prager quadratic function criterion is used as the plastic yield and failure surface function [2,28]. By introducing coupling variable \(\xi\) to achieve the hydro-mechanical properties characteristics, and considering the total damage \(\omega\) introduced into the plastic yield surface equation, the plastic yield surface equation of the hydro-mechanical properties creep damage constitutive equation is obtained as Equation (13).  
\[f=q^{2}+A_{0}(1-\alpha\xi)(1-\omega)h(\theta)\alpha_{p}(p-C_{0})p_{0}=0, \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pore Pressure**} & \multicolumn{6}{c}{**Parameters of the Test Data**} \\ \cline{2-9}  & \(\mathbf{a_{1}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{c_{1}}\) & \(\mathbf{L}\) & \(\mathbf{a_{2}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{c_{2}}\) \\ \hline
1 MPa & 3.471 & 1.011 & 0.301 & 0.894 & 0.144 & 0.557 & 1.145 \\
2 MPa & 7.213 & 1.164 & \(-\)0.062 & 1.771 & 0.857 & 0.383 & 1.075 \\
3 MPa & 7.020 & 2.005 & 2.543 & 3.403 & 0.231 & 0.661 & 3.422 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test parameters based on the creep test results of [PERSON] et al. [27].  
The parameters \(C_{0}\) and \(A_{0}\) represent the initial cohesion and internal friction angle of the material failure surface, respectively, while \(p\) and \(q\) represent the average stress and deviator stress values; \(p_{0}\) is a normalized parameter, with a value of 1 MPa. The expression \(h(\theta)\) is used to consider the effect of [PERSON]'s angle \(\theta\) on the plastic yield surface under complex stress conditions, and the expression \(h(\theta)\) generally needs to be determined through experiments. In order to simplify the numerical calculation process, \(h(\theta)=1\) is taken in this model. \(\alpha\), \(\alpha_{1}\) and \(\alpha_{2}\) are the model coefficients and all the values are 1. \(\alpha_{p}\) is the plastic hardening function, which is related to the internal friction angle of the material and is a function of plastic deformation.
## 2 Materials and Methods
### Quantification of Extracted DNA by qPCR-HRM  
The diluted NaOH extracts or paramagnetic-bead-purified DNAs were quantified for total bacterial DNA using qPCR for 16S rDNA using primers 8F and 936R [27]. Reactions were run in triplicates of 20 uL in 1\(\times\) Taq Buffer (50 mM Tris pH 8.3, 1.25 mM MgCl2, 300 ng/\(\mu\)L BSA), 0.2 mM dNTPs, 0.25 uM primers, 1\(\times\) EvaGreen(r) Dye (Biotinum, Fremont, CA, USA), 2 uL of extracted DNA, and 4 U Taq Polymerase in 96-well plates in a CFX96 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Cycle parameters were as follows: 90 \({}^{\circ}\)C for 45 s, 5 cycles of 90 \({}^{\circ}\)C for 15 s, 71.5 \({}^{\circ}\)C for 15 s, and 72 \({}^{\circ}\)C for 60 s, followed by 35 cycles with the same parameters with a plate read after each cycle. The products were then subjected to high-resolution melt (HRM) analysis (to verify that any amplification signal was not artifactual) consisting of 72\({}^{\circ}\)C for 180 s, 90 \({}^{\circ}\)C for 60 s, 70 \({}^{\circ}\)C for 120 s, followed by a 70 \({}^{\circ}\)C to 90 \({}^{\circ}\)C melt with steps of 0.1 \({}^{\circ}\)C for 5 s and a plate read. The amplification and melt curves were analyzed using CFX Manager v 3.1 (Bio-Rad Laboratories).
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 5 Conclusions  
The primary goal of this study was to create a quality control system by integrating smart glasses with a server-based image recognition system and assess its effectiveness in a controlled environment. Additionally, this study aimed to evaluate the system's performance and feasibility for larger-scale implementation in the manufacturing industry, providing insights to facilitate the adoption of smart glasses in industrial quality control.  
In achieving these objectives, this study demonstrated notable success. The system effectively improved the quality control process in controlled conditions, showcasing the benefits of real-time feedback, reduced human error, and enhanced observation capabilities facilitated by smart glasses.  
The case study underscored the efficacy of combining YOLO and OpenCV for constructing robust real-time object detection systems. Such integrated systems have the potential to automate tasks, enhance accuracy, and streamline operations, leading to significant efficiency gains.  
Through prototype testing in real-world conditions, this study identified potential challenges and areas for improvement. This valuable insight is crucial for refining the mixed reality (MR) system for broader operational scales, ensuring its robustness and reliability in the manufacturing industry. The successful prototype testing confirmed the theoretical promise and practical applicability of implementing this MR system on a larger scale.  
Furthermore, the study provides a practical roadmap for adopting smart glasses in industrial quality control. The detailed analysis of the development, implementation, and evaluation process serves as a valuable resource for organizations considering the integration of MR technology into their quality control processes. As a result, this study contributes to the advancement of MR technology and its applications in industrial settings.  
The advantages presented by smart glasses in quality control, such as real-time feedback, increased accuracy, reduced human error, and improved overall efficiency, make the development of an MR-based quality control system an attractive proposition for industries seeking to innovate and optimize their production processes.
## 1 Introduction  
The increased amount of anthropogenic CO\({}_{2}\) emissions since the beginning of the industrial era (starting around 1750) has significantly affected the natural biogeochemical carbon cycle. An analysis of the components of the anthropogenic \"CO\({}_{2}\) budget\", as defined in Reports released by the Intergovernmental Panel on Climate Change (IPCC) [1] and the Global Carbon Budget Project [2] (GCB Project, Table S1), show that, since 1750, global CO\({}_{2}\)-emissions amounted to about 700 GtC compared to 400 GtC fixed by CO\({}_{2}\)-natural sinks (Table S1). Therefore, approximately 285 GtC has accumulated in the atmosphere causing an increase to the actual value of 407 ppm.  
However, estimates of carbon fluxes between different compartments of terrestrial ecosystems (Figure S1, Table S2, Supplementary Materials) evidence that, during the last six decades, oceans and land have adapted to such \"CO\({}_{2}\) pressure\", increasing CO\({}_{2}\) natural sink [2].There is a consensus that terrestrial carbon sink (S\({}_{\text{LAND}}\), see Section S1) has constantly increased in the Northern Hemisphere largely due to the \"CO\({}_{2}\)-fertilization effect\". This effect accounts for higher rates of photosynthesis and, consequently, for increase of plant Gross Primary Production (GPP) due to rising atmospheric CO\({}_{2}\) concentration [3,4] strengthened by concomitant increased N-deposition [5].  
At the biochemical level Rubisco is the key enzyme in the Calvin cycle (see Section S2), responsible for photosynthetic CO\({}_{2}\)-fixation, and is considered the most abundant protein of the biosphere [6]. Considering the extensive literature dealing with the rate of photosynthetic carbon assimilation [7,8], plant growth and biomass production [9], the main focus of this review is to provide as complete a framework as possible of the mechanism of the Rubisco carboxylation reaction. Throughout the paper, particular attention is placed on the structure and functioning of the active site as well on the functioning of the protein \"molecular machine\" including its physical molecular properties underlying CO\({}_{2}\) vs. O\({}_{2}\) selection. An overview of stepwise progresses achieved in elucidation of the carboxylase reaction mechanism since 2008 is presented, including a very recent model proposed by the Gready's Group in 2020. Finally, recent advances revealing the existence of strong biochemical links between the C- and N-fixation processes by plants will be reported.  
The review includes a short summary of the literature dealing with the Rubisco protein structure, with the aim of providing information needed for the subsequent description of the reaction mechanism.
# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 3 DM Evaluation
### Analysis of the Selection of Regression Data Mining Technique
#### 3.2.3 Boosted Trees (BT)  
The aforementioned random forests are a combination of tree predictors. Therefore, each tree depends on the value of a random sampled vector. The generalization error for random forests converges to the limit, as the number of trees can reach a very large value. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using random feature selection to split each node gives error rates that are more favorable using the Adaboost algorithm [48] and are more reliable with respect to noise. The boosted tree algorithm evolved from the use of methods of boosting to regression trees. The main idea is to create a sequence of (very) simple trees, where each successive one is built to predict the residuals generated by the previous one. It can be shown that such a procedure of \"additive weighted expansion\" of trees will result in a perfect fit between predicted and observed values, even if the very nature of the relationship between the predictors and the dependent variable is very complex (e.g., non-linear). Thus, the gradient enhancement method--fitting weighted additive expansion of simple trees--is a very general and powerful machine learning algorithm. The decision tree is commonly used for classification and regression tasks because it offers many advantages, such as high efficiency, simplicity, and interpretability [49]. The boosting strategy was originally developed for classification problems, but demonstrates successful application to regression tasks. Random tree boosting is a technique that builds each regression tree step by step, using a predefined loss function to measure the error at each step and correct it with each subsequent step. Therefore, a predictive model is actually a collection of weaker predictive models [50]. Gradient amplification takes into account additive models, which are taught in stages of the form [51] (4):  
\[\text{F}_{\text{m}}=\text{F}_{\text{m}-1}(\text{x})+\text{h}_{\text{m}}(\text {x}) \tag{4}\]  
where: \(\text{h}_{\text{m}}(\text{x})\)--weak learning functions. When applying the gradient boosting strategy to regression trees, the small regression trees are the basis functions, and the entire regression model using the boosted tree method is the sum of them.  
In the experiment for testing, the following calculation principles were used:  
* Number of additive terms(trees): 300;
* Random test data proportion: 0.3;
* Subsample proportion: 0.5;
* Minimum n in child node: 1;
* Maximum n of nodes: 3;
* Minimum n of cases: 5;
* Maximum n of levels: 10.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 5 Discussion  
With the new potential applications discussed in Section 4, the competitiveness of small satellite constellations is assessed. Table 10 shows the cost breakdown of small-satellite SAR missions. Both the cost per kilogram (1 st column) and the satellite cost for each mission (3 rd column) include the launch cost, whose proportion is given by percentage (4 th column). The cost per satellite is similar between ICEYE and Capella X-SAR, so it can be regarded that the cost of further reducing satellite sizes seems to counterbalance the benefit of lower launch costs due to the reduced mass; in both ICEYE and Capella X-SAR missions, the second generation had a larger volume and mass than the first generation, which implies the limitations of over-downsizing. When it comes to the launch cost, it may be reduced by making use of rideshares, mass deployment, and/or reusable launch vehicles. Albeit its higher degree of freedom in launch options, small rockets are not economically competitive at the moment; Electron costs 5 million USD (MUSD) to launch a 150-225 kg payload to a 500 km Sun-synchronous orbit whilst SpaceX's rideshare program takes 1 MUSD under similar conditions [149; 150]. The operational cost may also be reduced by sharing the established ground station network such as KSAT or Amazon Web Service (AWS), the latter of which has open-price policies throughout the globe in the range of 10-20 USD per minute [151]. After all, cost reduction, which has motivated the transition from large SAR satellites to small ones, will continue to happen in multiple cost components.  
The performance and the performance per cost is not the scope of this paper, and it would suffice for now to point out that optical data and SAR data cannot be compared in parallel; in SAR data, the most valuable information lies in its phase, which does not exist in optical data at all [152]. This is because SAR data is produced from the complex signal domain in InSAR, polarimetric SAR, and tomographic SAR. Another unique feature is that SAR data may contain underground information, possessing capabilities to look through the ground surface as well as clouds [153]. New automated methods for data analysis, mostly based on artificial intelligence (AI), should take these SAR-specific characteristics into account such that we can take full advantage of such systems. Having relevant datasets is quintessential during the training of AI-based methods, which may be learning from scratch or transfer learning [154]. Once the developed algorithms are powered by computing infrastructures, near-real-time monitoring of global biosphere and change detection thereof would be possible with the existing ground station services that have shortened the lead time from data acquisition to customer deliveries on the order of 15 min to 30 min [155; 156; 157; 158].
## 4 Non-Coaxial Image Motion Compensation Method
### Precise Image Motion Compensation Method  
The constraint Equations (13)-(15) form a nonlinear system; however, since there are only two variables, \(\kappa\) and \(\theta\), the system can be simplified into a single-variable equation by successively eliminating variables. From Equation (13), the following relation can be obtained:  
\[\cos(2\kappa)=\frac{\sin\theta_{start}}{\sin\theta} \tag{16}\]  
Substituting this into Equations (14) and (15) yields the following relationship:  
\[\begin{split}\sin(2\kappa)&=\frac{\cos\theta_{start }\sin\phi_{start}-\sin(\phi_{start}+\Delta\phi)\cos\frac{\sin\theta_{start}}{ \sin\theta}}{\cos(\phi_{start}+\Delta\phi)}\\ \sin(2\kappa)&=\frac{-\cos\theta_{start}\cos\phi_{ start}+\cos(\phi_{start}+\Delta\phi)\cos\frac{\sin\theta_{start}}{\sin \theta}}{\sin(\phi_{start}+\Delta\phi)}\\ \end{split} \tag{17}\]  
Combining the two equations of Equation (17) and eliminating sin\(2\kappa\), an equation containing only the variable \(\theta\) can be obtained. The final solution is as follows:  
\[\begin{split}\theta&=\arctan\left(\frac{\tan \theta_{start}}{\cos(\Delta\phi)}\right)\\ 2\kappa&=\arccos\frac{\sin\theta_{start}}{\sin \left(\arctan\left(\frac{\tan\theta_{start}}{\cos(\Delta\phi)}\right)\right) }\end{split} \tag{18}\]  
As seen from Equation (18), the exact analytical solutions for \(\theta\) and \(\kappa\) depend only on \(\theta_{start}\) and \(\Delta\phi\), and are independent of the initial roll angle \(\phi_{start}\). Although this result provides an exact solution, the compensation angle \(\kappa\) involves nested trigonometric functions, making the computation complex. Therefore, further simplification of this method is required.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.1 Ontological Structure  
According to numerous authors (e.g., [26, 27, 28, 29]) the usability and reusability of an ontology can be improved by different levels of abstraction, while the intelligibility and adaptability may benefit from modularization. The common abstraction is the subdivision in upper ontology, domain ontology, task ontology and application ontology. Due the very vague definition how to really implement a task ontology we did not utilize it in our model, while the other three retained. Figure 1 shows the structure of ontologies, which act as the semantic core models for the semantic service description and context-based orchestration. Each level itself is represented by a collection of smaller sub-ontologies, which are modeled using OWL and Protege 4.2.
## 4 Failure Mechanisms
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Failure** & & \\
**Mechanisms** & & & \\ \hline \multirow{6}{*}{Deep-seated Rotational Failure} & \(\bullet\) & Presence of relict failure surfaces due to poor historic construction practices \\  & \(\bullet\) & Over-steepened cut slopes in plastic clays (26 degrees rather than stable 14 degrees) and embankment slopes \\  & \(\bullet\) & Reactivation of low-strength residual shear surfaces due to increase in pore water pressure (inadequacy of historic drainage) \\  & \(\bullet\) & Increase in destabilising forces including loading of the head of a slope, train loading on embankments, or unloading of the toe of the slope due to erosion and shallow failures \\  & \(\bullet\) & Formation of progressive failure due to ratcheting of the slope surface resulting from shrink-swell of the \\  & \(\bullet\) & Weathered surface \\ \hline \multirow{6}{*}{ Shallow Translational Failure} & \(\bullet\) & Formation of a weathered zone owing to seasonal shrink-swell and formation of micro-cracking and desiccation \\  & \(\bullet\) & Increase in permeability of the surficial weathered layer allowing penetration of water in the near surface, reducing negative pore pressures, resulting in downslope flow and formation of perched water tables \\  & \(\bullet\) & Intense summer rainfall and presence of desiccation cracks, possibly allowing for hydraulic forces to act \\  & \(\bullet\) & between cracks and thus initiate a shallow slump \\  & \(\bullet\) & Presence of vegetation which increases seasonal shrink-swell cycles, aiding in the deterioration of the \\  & \(\bullet\) & Wetathered front \\  & \(\bullet\) & Internal erosion and undercutting resulting from the increased permeability and outflow of water from the \\  & \(\bullet\) & Slope surface \\ \hline \multirow{2}{*}{Washout} & \(\bullet\) & Erosion resulting from over-ground flow during intense rainfall, usually resulting from the presence of \\  & & insufficient drainage \\ \hline \multirow{2}{*}{Debris Flow} & \(\bullet\) & Movement of vegetation and soil resulting from intense rainfall and surface flows initiated from washouts or \\  & & earth flows from a shallow failure \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Failure mechanism for both embankments and cutting formed in soils in the UK (adapted from Network Rail [9]). Figure 5: Embankment cross section showing the development of progressive failure mechanism, where (**a**) is the early development of a rupture surface at the toe of the slope, followed by (**b**) just before failure (modified after Network Rail [9]). Deep-seated failure mechanisms have also been attributed to historic construction practices. This generally results from over-steepened cut slopes and embankment gradients and the presence of relict failure surfaces, particularly in cuttings. This produces highly localised vulnerabilities, which exacerbate geotechnical risk, especially with respect to climatic changes.
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.2 SOS System Activate Salt Glands to Exclude Na from Cells  
The sodium overly sensitive system (SOS) plays a crucial role in excluding Na\({}^{+}\) from roots and loading to the xylem to regulate ion homeostasis. The SOS system is composed of SOS1, SOS2, and SOS3. Under salt stress, the increase in cytosolic Ca\({}^{+2}\) is perceived by the SOS3 component, which interacts and activates SOS2, which finally activates the SOS1 Na/H antiporter. SOS1 then mediates Na\({}^{+}\) efflux to the apoplast through active transport driven by the H gradient across the plasma membrane established by H-ATPase.  
Recretohalophytes show higher salt tolerance potential than other halophytes due to the presence of salt glands, which secrete excess salt crystals onto the leaf surface to maintain cellular ion homeostasis. The genera of recretohalophytes that show such mechanisms include _Limonium_ and _Tamarix_. Ions secreted through salt glands include cations i.e., Na\({}^{+1}\), K\({}^{+1}\), Ca\({}^{+2}\), Mg\({}^{+2}\), Mn\({}^{+2}\), and Fe\({}^{+2}\), and anions (Cl\({}^{-1}\), Br\({}^{-1}\), I\({}^{-1}\), SO\({}_{4}\)\({}^{-2}\), PO\({}_{4}\)\({}^{3-}\), and NO\({}_{3}\)\({}^{-1}\)). _Sueda salsa_ exhibited greater expression of the _S. salsa SOS1_ gene in roots, which was correlated with the increased exclusion of Na by roots [34].