esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality Applications in Industry 4.0 Environment
## 5 Discussion  
AR application development involves a very complex approach. Developers need to be familiar with the task for which they are developing an AR application for and the same goes for users. It is very important that the user is shown the right amount of information at the appropriate time.  
AR provides easier and more natural interaction compared to other systems with traditional audio or video communication [54]. Almost all participants in the assessment process [28] of those who performed assembly tasks find it easy to understand the instructions. Most of them noticed increased efficiency with the use of AR. The application of AR can reduce the time required to locate objects and reduce head and neck movements during their performance [46]. The time savings depend on the complexity of the tasks and the quality of the developed AR application. In some cases, assembly tasks can save almost 30% of time [68]. A large number of users expressed confidence in their assessment of the selection and installation of parts during assembly compared to standard user manuals [62]. Furthermore, the number of errors are halved. With the help of AR, operators worked noticeably faster than in the case of printed instructions and about 4% faster in selecting tasks [115]. Using an AR application can also provide a reduction in time and required maintenance costs [52]. According to [116], maintenance time was reduced by an average of 43%. In the case of robotic tasks, after only a few minutes, users felt good with respect to the human robot collaboration using AR. What is very important is that users do not feel limited by the AR system [91]. A significant improvement in precision was observed during manual precision welding using AR compared to non-AR assistance [93].
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Regulation of Nucleic Acid Metabolism by Light Intensity and Spectrum
#### 4.5.2 Control of Nucleic Acid Metabolism by Light Spectrum  
The influence of light spectrum on nucleotide metabolism was demonstrated in the case of thymidine kinase (TK), which transfers a phosphate group to a thymidine molecule to form TMP in the nucleotide salvage pathway [202]. Namely, BL downregulated the expression of the _TK_ gene compared to RL [144]. In addition, nucleoside diphosphate kinases (NDPKs) showed specific phosphorylation after exposition to RL in pea (_Pisum sativum_) [208]. During the study of NDPK involvement in the light spectrum-dependent signalling, NDPK 2 was reported to act downstream of PhyA and PhyB in _Arabidopsis_ leaves [209]. NDPKs also have a role in ROS response and redox signalling, as indicated by the interaction of NDPK1 with CAT in _Arabidopsis_[210]. Furthermore, NDPK1-overexpressing plants exhibited paraquat tolerance and enhanced elimination of exogenous H\({}_{2}\)O\({}_{2}\)[210].  
The control of RNA synthesis by light spectrum is indicated through the influence of phytochromes on the expression of multiple nuclear-encoded, chloroplast-targeted sigma factors, SIG1-6 [211]. The expression of _SIG2_ and _SIG6_ was highly upregulated under FRL or BL, compared to WL or RL. In _Arabidopsis_, SIG1-4 and SIG6 were strongly downregulated in _phy_ mutants, whereas SIG5 showed less downregulation. However, SIG5 was shown to be mainly regulated by BL [211].  
UV-induced DNA damage can be repaired via light and dark repair processes, of which BL is involved in light repair [212]. When damage is non-extensive, usually light-dependent damage repair is facilitated by photolyases, which absorb BL and revert DNA to its normal configuration. Dark repair, by contrast, is light-independent and tends to repair specific types of DNA damage. The repair of nucleic acids may also depend on the light dependency of the metabolism of their precursors, which was shown in rice and _Arabidopsis_[213; 214].
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Association Rule Mining Results
#### 3.2.3 Evaluation of Association Rules  
The FP-Growth algorithm can efficiently mine the association rules of driving behavior and traffic conflict, but not all rules have practical significance and application value. To select the key rules with strong correlations and high risk, it is necessary to evaluate the quality of the rules from statistical and professional perspectives. In this study, the Lift index is used to measure the effectiveness of association rules. The formula is as follows:  
\[Lift(X\Rightarrow Y)=\frac{Conf(X\Rightarrow Y)}{Sup(Y)}=\frac{|X\cup Y|\cdot N }{|X|\cdot|Y|} \tag{11}\]  
The degree of enhancement describes the multiple of enhancement of the probability of the occurrence of the rule after the given condition. If the degree of enhancement is greater than 1, this indicates that the occurrence of the former increases the occurrence probability of the latter, and the two are positively correlated; if the lifting degree is less than 1, this indicates that the appearance of the former reduces the occurrence probability of the latter, and the two are negatively correlated. The lifting degree is equal to 1 (the red dashed line in Figure 11), indicating that the former and the latter are independent of each other. Obviously, the higher the lifting degree, the stronger the explanatory power of driving behavior with respect to induction of traffic conflict. Figure 11 shows the promotion degree distribution of some association rules. It can be seen that the combination modes of driving behaviors, such as \"TS\(\uparrow+\text{LK}\text{\(\downarrow\)}+\text{LC}\text{\(\uparrow\)}\)\" and \"TS\(\uparrow+\text{LK}\text{\(\uparrow\)}+\text{HS}\)\", generally have a high degree of promotion, which is a high-risk incentive for traffic conflict. However, the promotion degree of single behaviors such as \"LK\(\downarrow\)\" and \"LC\(\uparrow\)\" is relatively low, indicating that the interactive superposition of driving behaviors will further magnify the risk of traffic conflict.  
Figure 11: Enhancement degree distribution of association rules.  
To further investigate the statistical significance of the rules, the association rules were tested for confidence difference. For each rule, the confidence interval, [pL, pU], of the probability, \(p\), of traffic conflict was calculated by a binomial distribution and compared with the confidence, p0, of the rule: if p0 > pU, the rule was considered significant at the 95% confidence level. Table 9 shows the key association rules that partially passed the significance test. It can be seen that these rules not only have a high lift (lift > 2) but also have a significantly higher confidence level than the upper limit of random occurrence probability, indicating that there is a stable and reliable internal correlation between these driving behavior patterns and traffic conflicts, and they are the key objects for prediction and intervention.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 3 Results and Discussion
### Membrane Swelling Property  
It is crucial to minimize membrane swelling in the vanadium electrolyte in order to achieve good mechanical stability and provide a long VRFB lifetime, even though water uptake is required for ionic transporting through the membrane. On the other side, a high swelling ratio could result in high vanadium ion crossover. Table 5 and Table S2 show the dimensional changes and mass changes of the investigated membranes in V (IV) electrolyte solution (1.6 M VOSO4 solution in 2 M H2 SO4) tested at 21 \({}^{\circ}\)C and RH 40%. In general, the dimensional and mass changes in VOSO4 solution for all the membranes are negligible (less than 10%) excluding FAP-450 and VAN membranes. The dimensional changes of VAN are minor; however, the mass change is very high (101%). It is understandable that the mass change is high due to the absorption of the electrolyte in the micro pores since VAN consists of a microporous layer and a dense Nafion layer. However, it is noticed that both the dimensional change and mass change for FAP-450 are high, specifically, \(\sim\)40% and 60%, respectively. For a membrane material in VRFB applications, dimensional change is a more important property that should be taken into consideration, rather than mass change, because severe three-dimensional swelling could result in not only a decrease in mechanical strength, but also an escalated stress for the membrane during VRFB operation. For the dimensional change of the membranes investigated in this report, the order is CMV \(\approx\) AMV \(\approx\) FS-930 \(\approx\) VAN \(\leq\) DP\(\leq\) AHA\(\leq\) N212 \(<\) FAP-450.
# Stochastic Generator of Earthquakes for Mainland France
## 2 Methodology
### Main Shock Generation
#### 2.1.1 Main Shock Generation in Time and Magnitude  
Synthetic main shocks are first generated through time and magnitude. The time step chosen is the year, while the magnitude step is set to 0.1. We first set the number of years \(N_{y}\) through which the synthetic catalogue will span. Then, the generator calls for a stochastic Frequency-Magnitude Distribution (FMD) of main shocks, which corresponds to a probabilistic distribution of annual number of main shocks for each magnitude step. The annual density \(\lambda_{y}(M)\) of main shocks is defined year after year \(y\) for each magnitude step \(M\) according to this stochastic FMD of main shocks.  
Main shocks are simulated yearly following a homogeneous Poisson point process of annual density \(\lambda_{y}(M)\):  
\[n_{y}(M)\sim Poisson(\lambda_{y}(M))\,, \tag{1}\]  
with \(n_{y}(M)\) being the number of main shocks of magnitude \(M\in[M_{min},M_{max}]\) generated at the year \(y\in[1,N_{y}]\).  
The annual number of main shocks of magnitude equal to \(M\) at the year \(y\) (\(\lambda_{y}(M)\) Equation (1)) is drawn in the stochastic FMD of main shocks. However, these FMDs are defined for main shocks with magnitude greater than or equal to \(M\) (\(\lambda_{y}(\geq\)\(M\))). The annual density obtained from this kind of FMD is not \(\lambda_{y}(M)\) but \(\lambda_{y}(\geq\)\(M\)). Thus, \(\lambda_{y}(M)\) is defined as:  
Figure 1: General workflow of earthquake generation. Blue: generator. Green: generator’s inputs. Orange: data. FMD: Frequency–Magnitude Distribution. PMD: Proportion–Magnitude Distribution.  
\[\forall\,y\in[1,N_{y}],\,\forall\,M\in[M_{min},M_{max}],\] \[\lambda_{y}(M)=\left\{\begin{array}{ll}\lambda_{y}(\geq M)&\text{ if }M=M_{max}\\ \lambda_{y}(\geq M)-\lambda_{y}(\geq M+dM)&\text{ otherwise}\end{array}\right.. \tag{2}\]  
In some cases, \(\lambda_{y}(\geq M+dM)\) can be higher than \(\lambda_{y}(\geq M)\), and so \(\lambda_{y}(M)\) is set to 0.  
This step is referred as 1A in Figure 1.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.6 Measured Strain Along the Sensor Evaluation
\[\varepsilon_{d}=\varepsilon_{11}d_{1}^{2}+\varepsilon_{22}d_{2}^{2}+ \varepsilon_{33}d_{3}^{2}+2\varepsilon_{12}d_{1}d_{2}+2\varepsilon_{13}d_{1} d_{3}+2\varepsilon_{23}d_{2}d_{3} \tag{20}\]  
In addition to strain along the sensor's detection direction, some strain sensors (like FBG) may also be sensitive to strain components perpendicular to this direction. Two orthogonal unit vectors, \(\mathbf{m}=[m_{1},\ m_{2},\ m_{3}]\) and \(\mathbf{p}=[p_{1},\ p_{2},\ p_{3}]\), define an orthonormal basis together with \(\mathbf{d}.\) The strain components in these directions, denoted as \(\varepsilon_{m}\) and \(\varepsilon_{p}\), are given by the following:  
\[\varepsilon_{m}=\mathbf{m}^{\text{T}}\varepsilon\mathbf{m}\varepsilon_{p}= \mathbf{p}^{\text{T}}\varepsilon\mathbf{p} \tag{21}\]  
Expanding these, we obtain  
\[ \begin{array}{c}\varepsilon_{m}=\varepsilon_{11}m_{1}^{2}+\varepsilon_{22}m_ {2}^{2}+\varepsilon_{33}m_{3}^{2}+2\varepsilon_{12}m_{1}m_{2}+2\varepsilon_{ 13}m_{1}m_{3}+2\varepsilon_{23}m_{2}m_{3}\\ \varepsilon_{p}=\varepsilon_{11}p_{1}^{2}+\varepsilon_{22}p_{2}^{2}+ \varepsilon_{33}p_{3}^{2}+2\varepsilon_{12}p_{1}p_{2}+2\varepsilon_{13}p_{1} p_{3}+2\varepsilon_{23}p_{2}p_{3}\end{array}  \tag{22}\]  
To model the overall strain reading \(S\) of a sensor, factors \(\alpha\) and \(\beta\) are introduced to consider the sensitivity of the sensor towards perpendicular to sensor strain componentsalong \(\mathbf{m}\) and \(\mathbf{p}\), respectively. The total strain reading \(S\) is then a weighted sum of strain components in directions \(\mathbf{d}\), \(\mathbf{m}\), and \(\mathbf{p}\):  
\[S=\varepsilon_{d}+\alpha\varepsilon_{m}+\beta\varepsilon_{p} \tag{23}\]  
Substituting expanded expressions for \(\varepsilon_{d}\), \(\varepsilon_{m}\), and \(\varepsilon_{p}\) we obtain  
\[S=\varepsilon_{11}\big{(}d_{1}^{2}+\alpha m_{1}^{2}+\beta p_{1}^ {2}\big{)}+\varepsilon_{22}\big{(}d_{2}^{2}+\alpha m_{2}^{2}+\beta p_{2}^{2} \big{)}+\varepsilon_{33}\big{(}d_{3}^{2}+\alpha m_{3}^{2}+\beta p_{3}^{2}\big{)}\] \[+2\varepsilon_{12}(d_{1}d_{2}+\alpha m_{1}m_{2}+\beta p_{1}p_{2}) +2\varepsilon_{13}(d_{1}d_{3}+\alpha m_{1}m_{3}+\beta p_{1}p_{3}) \tag{24}\] \[+2\varepsilon_{23}(d_{2}d_{3}+\alpha m_{2}m_{3}+\beta p_{2}p_{3})\]  
For each load case, strain detection by sensor is calculated as the integral of strain along its length, using the interpolated values of the strain tensor. The average of these strain values provides the representative strain measurement along the line, which corresponds to the detected strain \((\varepsilon_{det})\). Considering that \(\varepsilon_{q}=S\) is strain at point \(q\) along a sensor, Equation (9) for the strain takes the following form:  
\[\varepsilon_{det_{i}}=\frac{1}{l}\int_{\mathbf{p_{1}}}^{\mathbf{P_{2}}} \varepsilon_{q}\mathrm{d}q\approx\frac{1}{v}\sum_{g=1}^{v}\varepsilon_{g} \tag{25}\]
###### Abstract  
The purpose of this study was to determine the mineral-associated organic carbon (MOC) and its relationship to day minerals under different temperatures and precipitation. We selected three typical grassland transects in China: Titanium Plate (TP), Mongolian Plate (MP), and Loess Plate (LP) with natural temperature gradients. Along the transect, there is a gradient in the precipitation between the various types of grasslands. The surface soil (0-10 cm) was sampled to determine the MOC. Clay minerals were characterised by X-ray diffraction (XRD). According to the findings, the MOC content increased with decreasing temperature (5.41-14.89 g/kg). MOC had a positive correlation (r = 0.67) with the amount of clay mineral content. In the large-scale study of transects, precipitation masks the effect of temperature change on the MOC to a certain extent. It indirectly affected the MOC content by affecting the mixed-layer illite/smectite (I/Sme) content, and this effect was strongest at the lowest temperature TP (r = \(-\)0.73). Except for precipitation, CaO in the soil can affect soil organic carbon (SOC) stability by influencing the pH and I/Sme. The amount of bacteria increased as a result of I/Sme, and the influence of bacteria on the MOC was surpassed only by the soil pH. Climate and clay mineral composition characteristics affected the MOC to a certain extent. Among them, the effect of precipitation change on the MOC is higher than temperature, but it has little effect on soil with a higher weathering degree and CaO content.  
climate; mineral-associated organic carbon; clay mineralogy; spatial variation; grassland transects +
Footnote †: journal: Applied Sciences
## 4 Results
### Preparatory and Priming Layers
In particular, the signal at 2920 cm\({}^{-1}\) is assigned to CH\({}_{2}\) stretch due to the combination of CH\({}_{2}\) stretch in waxy material (2918 cm\({}^{-1}\)) and CH\({}_{2}\) stretch in lipidic compounds (2926-2929 cm\({}^{-1}\)), while the weak signals at 1318 cm\({}^{-1}\), 1650 cm\({}^{-1}\) and 1545 cm\({}^{-1}\) are better evidenced in the unsmoothed spectra. On the other hand, the spectrum of GSM4 shows peaks assigned to waxes (CH\({}_{2}\) stretch at 2918 and 2850 cm\({}^{-1}\), C=O stretch at 1735 cm\({}^{-1}\), CH\({}_{2}\) scissoring at 1470 and 1460 cm\({}^{-1}\), CH\({}_{2}\) rocking vibration at 729 and 720 cm\({}^{-1}\)) [37]. In contrast, the spectrum of GSM4 shows peaks assigned to waxes and small amounts of calcium oxalates. Instead, GSM6, as already reported, contains signals due to lipidic and carboxylate products. For the sake of clarity, it should be noted that the identification of oils and waxes in complex mixtures by FTIR is not easy due to both the low signal intensity (due to the low concentration of the organic materials) and the co-presence of peaks of other compounds in the spectrum. In this work, the range 3000-2800 cm\({}^{-1}\) has been considered as a diagnostic region for the identification of oils and waxes. Indeed, oils and waxes have strong peaks in this region, contrary to the other compounds frequently found in paintings. A detail of the 3000-2800 cm\({}^{-1}\) region for samples GSM1, GSM4, GSS1a, GSS1b and GSM6 is shown in Figure 2b. According to stratigraphic investigations and FTIR, the ground layer of _[PERSON] and the Archaeigned Raphael_ canvas is made of gypsum with quartz and aluminosilicates impurities and has a maximum thickness of 150 \(\upmu\)m (Tables S1 and S4). The FTIR spectrum of GSS1a (light brown ground) shows signals assigned to gypsum, proteinaceous material, silicates, calcium oxalates and partially hydrolyzed esters of carboxylic acids and their carboxylates (Figure 2a). Meanwhile, the FTIR spectrum of GSS1b (dark brown ground) shows peaks due to partially hydrolyzed esters of carboxylic acids and their carboxylates, silicates, sulphates (e.g., magnesium or iron sulphate) and traces of oxalates. PLM and ESEM-EDX observations of the flesh (sample SF1, Table S3) from the _Visitation_ altarpiece detected a priming layer (5 \(\upmu\)m) composed of lead white and Fe-based pigments [38]. Lead white, Fe-based pigments and a few orpiment particles were revealed in the off-white priming of the Virgin's blue mantle (sample SF6: Figure S4a-c, Table S3).
## 3 Probabilistic Hesitant Fuzzy Evidence Theory
### Generation Methods of Probabilistic Hesitant Fuzzy Basic Probability Assignment
#### 3.2.2 Multi-Classifier-Based Generation Method  
Sometimes, obtaining accurate occurrence probabilities for uncertain elements in PHFE through the subjective judgment of experts can be challenging. To address this, we designed a method to generate PHFBPs based on multiple classifiers. The specific steps are as follows:  
* Step 1: Divide the original data set into a training set and test set.
* Step 2: Construct multiple classifiers. Utilize the training data to create \(n\) distinct classifiers for each piece of evidence. These classifiers are denoted as classifier \(1,2,\cdots,j,\cdots,n\). The output of each classifier should consist of sets of real numbers in \([0,1]\), representing the degree to which a sample belongs to different classes. The classifiers should be capable of providing the probability of each class to which a sample belongs, denoted as \(y=\{y(\theta_{1}),\cdots,y(\theta_{i})\}\). The accuracy of a classifier, represented as \(Acc\), measures the proportion of correctly classified samples. It can be calculated using the following formula: \[Acc=\frac{1}{N}\sum_{i=1}^{N}\frac{TP_{i}+TN_{i}}{TP_{i}+TN_{i}+FP_{i}+FN_{i}},\] (33) where \(i\) represents class index and \(N\) is the total number of classes. \(TP\), \(TN\), \(FP\), and \(FN\) denote True Positives, True Negatives, False Positives, and False Negatives, respectively.
* Step 3: Generate PHFBPs. The test set is inputted into the trained classifiers to obtain the output of each classifier. The corresponding probability based on the accuracy of each classifier is calculated as follows: \[p_{j}=\frac{Acc_{j}}{\sum_{j=1}^{n}Acc}.\] (34) Subsequently, the PHFBPs can be obtained as shown below: \[m_{P}(\theta(i))=\{y_{j}(\theta_{i})(p_{j})\mid j=1,2,\cdots,n\}.\] (35)
## 2 The BEAM Toolbox and the Land Surface Temperature Processor
### The LST Processor as a BEAM Plug-In  
The LST processor is a BEAM plug-in that allows the processing of MERIS/AATSR L1b pairs, and simulated OLCI/SLSTR surface directional reflectance. The final objective of this plug-in is to provide an LST product using the algorithms developed in the framework of the SEN4 LT project to be applied to MERIS/AATSR and potentially the forthcoming S3 OLCI/SLSTR instruments. Inputs needed for application of the SEN4 LT algorithms (e.g., the split-window algorithm given by Equation (1)) are surface directional reflectances (SDRs), which for MERIS/AATSR are retrieved in a foregoing processing step applying the MERIS/AATSR synergistic approach [8]. This method is based on an atmospheric correction scheme together with a cloud-screening procedure, both developed within the frame of ESA's MERIS/AATSR Synergy Toolbox project.
# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 2 Methodology
### Chromosome Detection by Neural Network  
The second stage consists of \(M\) deep convolutional neural networks, denoted as \(\mathcal{D}_{j}\), \(j\in\{1,...,M\}\), for object detection. The model \(\mathcal{D}_{j}\) is generated after a training process by using those images from \(\mathcal{T}\) obtained by the operator \(G_{j}\) and their associated ground-truth. A YOLOv8 model [28] has been used as the selected neural network architecture. The application of this model is not limited to the proposed approach and can be replaced with other object detection models if needed.  
Each neural network receives an image \(\mathbf{Z}\) as input and produces a set of detections (detected chromosomes) \(\mathcal{R}\) as output.  
\[\mathcal{R}_{j}=\mathcal{D}_{j}(\mathbf{Z}) \tag{4}\]  
Figure 1: Schema of the overall proposal’s operation. The input image is preprocessed using multiple operators. Each preprocessed image is supplied independently on a deep neural network (DNN) model. Predictions released by each model are finally merged by ensembling and evaluated with a consensus function, producing the final output prediction.  
Each \(k\)-th detection \(r_{j,k}\) contains an axis-aligned bounding box \(\mathbf{w}_{j,k}\), an object class label \(q_{j,k}\), and a confidence level \(v_{j,k}\):  
\[R_{j}=\Big{\{}r_{j,k}\mid k\in\{1,...,W\}\Big{\}} \tag{5}\]  
\[r_{j,k}=\Big{(}\mathbf{w}_{j,k},q_{j,k},v_{j,k}\Big{)}\,\ k\in\{1,...,W\} \tag{6}\]  
\[\mathbf{w}_{j,k}=\Big{(}a_{j,k},b_{j,k},c_{j,k},d_{j,k}\Big{)} \tag{7}\]  
where \(W\) is the number of detections, \(\Big{(}a_{j,k},b_{j,k}\Big{)}\in\mathbb{R}^{2}\) are the coordinates of the upper left corner of the \(k\)-th detection within the image \(\mathbf{X}\), \(\Big{(}c_{j,k},d_{j,k}\Big{)}\in\mathbb{R}^{2}\) are the coordinates of the lower right corner of the \(k\)-th detection within \(\mathbf{X}\), \(q_{j,k}\) is the class label of the \(k\)-th detection, and \(v_{j,k}\in\mathbb{R}\) is the confidence level of the \(k\)-th detection.  
A minimum confidence level \(v_{min}\) is defined so that detections below that threshold are ignored. As a result, the filtered set of detections is computed:  
\[R^{\prime}_{j}=\{r_{j,k}\in R_{j}\mid v_{j,k}\geq v_{min}\} \tag{8}\]