esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
###### Abstract  
Introduction: Accurately detecting cracks is crucial for assessing the health of materials. Manual detection methods are time-consuming, leading to the development of automatic detection techniques based on image processing and machine learning. These methods utilize morphological image processing and material deformation analysis through Digital Image or Volume Correlation techniques (DIC/DVC) to identify cracks. The strain field derived from DIC/DVC tends to be noisy. Traditional denoising methods sacrifice spatial resolution, limiting their effectiveness in capturing abrupt structural deformations such as fractures. **Method:** In this study, a novel DVC regularization method is proposed to obtain a sharper and less noisy strain field. The method minimizes the total variation of spatial strain field components based on the assumption of approximate strain constancy within material phases. **Results:** The proposed methodology is validated using simulated data and actual 4D \(\mu\)-CT experimental data. Compared to classical denoising methods, the proposed DVC regularization method provides a more reliable crack detection with fewer false positives. **Conclusions:** These results highlight the possibility of estimating a low-noise strain field without relying on the spatial smoothness assumption, thereby improving accuracy and reliability in crack detection.  
c 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
crack detection; DVC; image registration; strain regularization; total variation; abrupt deformations; CT
# Stability Improvement Method for Embankment Dam with Respect to Conduit Cracks
## 3 Results and Discussion
### Seepage Line and Hydraulic Gradient: Numerical Analysis  
#### 3.3.1 Analysis Conditions  
Seepage analysis in embankment dams provides useful information for estimating the influence of seepage behavior according to the shapes of substructures inside the embankment and the physical properties of their materials, water level conditions, and soil-water characteristic curve. In this study, three-dimensional transient analysis was conducted to identify the effects of conduit cracks and reinforced conduits on the embankment. Transient analysis provides information supporting the validity of experimental results by evaluating the erosion potential of the seepage line and the hydraulic gradient. The analysis was performed using the elastic model (Geotextile) and Mohr-Coulomb model (Clay, Embankment, Sand) of GTS NX 3D with the finite element method applied [20]. The dimensions, analysis parameters (Table 2), water level (H: 35 cm), and time boundary (1 day = 86,400 s) of the cross-section of the embankment dam were the same as those of the experimental model in this study. Additionally, the foundation of an impermeable layer was configured to allow the leak to flow only in the embankment zone due to cracks in the conduit. The mesh used Delaunay triangulation. The mesh size was about 0.03 m,  
Figure 7: Changes in pore water pressure due to conduit cracking: (**a**) current model (Case 1—Crack), (**b**) improvement model (Case 2—Crack).  
and 357,027 elements were generated. Tables 3 and 4 show the input parameters used for 3D transient analysis. The soil-water characteristic curves were applied to silty clay (clay layer), sandy clay (embankment zone), and sand (sand layer) based on the van Genuchten data [21]. At the present stage, only the cavitation potential in the experiment is presented, and cavitation modeling was not included in this analysis.
## 1 Introduction
Additionally, forecasting solar radiation is a very complex phenomenon which can be influenced by many different factors and forecasting models tend to be more accurate. In this context, optimization algorithms are integrated to forecasting models to reduce error and improve its accuracy, so, it is necessary to present a review of prediction techniques with the use of search optimization algorithms (SOA) to improve prediction on different horizons. The objective of this article is to perform an analysis of solar radiation prediction techniques based on hybrid SVM and SOA models. The performance of SVM models is compared with conventional supervised learning models, such as artificial neural networks. SVM-SOA hybrid models are also analyzed to evaluate the accuracy of the predicted solar radiation data seen in the literature. It also presents the most suitable Kernel functions for hybrid models and finally, a general process flowchart of hybrid prediction techniques, according to the literature, is shown.
## 4 Torsional Shear Deformation Characteristics of Segment Joints
### Load-Bearing Performance of the Segment Joints
This indicates that while the bolts are more influential under lower axial force conditions, their overall effect diminishes as the system becomes increasingly dominated by other force mechanisms at higher axial forces. The third stage is the mortise and tenon force stage (\(\theta>0.09^{\circ}\)). When the rotational angle exceeds \(0.09^{\circ}\), contact between the mortise and tenon begins, thereby constraining the torsional shear deformation of the segment. The presence of the mortise and tenon greatly enhances torsional shear resistance, particularly at lower axial force levels. However, as the axial force increases, the relative effect of the mortise and tenon decreases. Compared to the conditions without the mortise and tenon, the maximum tangential forces under axial forces of 0 kN, 1000 kN, 2000 kN, 3000 kN, and 4000 kN increase by 70.22%, 22.17%, 14.27%, 8.49%, and 6.12%, respectively. This diminishing improvement suggests that higher axial forces tend to saturate the system's shear resistance, reducing the additional benefit provided by the mortise and tenon. The conclusions are as follows: The shear resistance increases significantly with higher normal forces in all stages. The presence of the mortise and tenon provides a substantial advantage in the third stage, although its relative impact diminishes at higher axial forces. The system transitions from being dominated by frictional forces to bolt engagement and, finally, to mortise and tenon contact, as the tangential displacement progresses. Figure 12: Load–displacement relationships of the segment joint. It found that the role of bolts and mortise and tenon in resisting torsional shear in segment joints is relatively small, while axial force is the primary factor influencing the torsional shear performance of segment joints. Therefore, improving the torsional shear resistance of segment joints can be achieved by enhancing the interface friction coefficient. The gap between the mortise and tenon directly affects the torsional shear resistance of the segment joint. An excessively large gap may prevent the mortise and tenon from functioning during rotational deformation. However, an overly large gap also increases the difficulty of segment assembly. Therefore, the design of the gap between the mortise and tenon should be considered comprehensively based on practical conditions. Appropriately reducing the gap can effectively control the twisting deformation of the segment.
## 9 Quickest Spectrum Sensing  
### Single Channel and Non-Bayesian Case  
In this subsection, we consider the case of monitoring a single frequency channel in which the detector has no _a priori_ information about the emergence time of primary user. For simplicity, we assume that, at the beginning, there is no primary user and the primary user could emerge at any time \(T\) (it is also possible that it never emerges, then \(T=\infty\)). We denote by \(X_{t}\) the \(t\)-th observation. Meanwhile, the probability density functions (we assume that they exist) of observation when primary user exists or not are denoted by \(p_{1}\) and \(p_{0}\), respectively.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Qualitative Analysis of Fault Tree  
As shown in Figure 3, the Boolean expression of its fault tree is listed, and its structural function expression is as follows:  
\[\begin{array}{rcl}T&=&(B_{1}+B_{2}+B_{3}+B_{4})\cdot B_{5}\\ &=&[(X_{1}+X_{2}+X_{3})+(X_{4}+X_{5}+X_{6}+X_{7}+X_{8}+X_{9}+X_{10})+(X_{11}+X_ {12}+X_{13})\\ &&\quad\quad+(X_{14}+X_{15}+X_{16})]\cdot(X_{17}+X_{18}+X_{19})\end{array} \tag{16}\]  
The fault tree is transformed into a dual success tree by changing the OR gates into AND gates, and the AND gates into OR gates, as well as turning the events into their dual event. Then, the structural function of the success tree is written and subsequently simplified to obtain the structural function of the success tree represented by the minimum cut set. Two minimum path sets can be obtained as follows:  
\[\begin{array}{rcl}P_{1}&=X_{1}+X_{2}+X_{3}+X_{4}+X_{5}+X_{6}+X_{7}+X_{8}+X_{ 9}+X_{10}+X_{11}\\ &&+X_{12}+X_{13}+X_{14}+X_{15}+X_{16}\end{array} \tag{17}\]  
\[P_{2}=X_{17}+X_{18}+X_{19} \tag{18}\]
## 2 Design of a Structural Health Monitoring System for a Jacket Offshore Platform
### Sensor Subsystem  
The first task in the construction of the sensor subsystem is to determine the selection and layout of the sensors. According to the structural characteristics of a jacket offshore platform, the types of sensors can be divided into those for: (a) offshore environmental information monitoring; (b) marine environmental information monitoring; (c) structure information monitoring; (d) platform component monitoring; and (e) vibration monitoring.
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 4 The Proposed Genetic Algorithm
### Genetic Algorithm Design for Scheduling Problem of Prefabricated Building
#### 4.2.2 Fitness Function  
In GA, the expression of fitness function determines the direction of population evolution, and the survival ability of an individual generally corresponds to its fitness value. The mathematical model established in this research is to minimize the project completion time under the limited renewable resources and the supply constraints of prefabricated components. Therefore, it is necessary to convert the target value of the construction period of the problem into a fitness value. The design of the fitness function in this study is shown in Formula (5).  
\[Fit(j)=\frac{Fit_{max}-Fit_{j}+w}{Fit_{max}+Fit_{min}+w} \tag{5}\]  
where, \(Fit(j)\) is the fitness function of individual \(j\), which can be used as a criterion to evaluate the individual's adaptability; \(Fit_{j}\) is the target value of individual \(j\), and the value is the corresponding project completion time; \(Fit_{max}\) and \(Fit_{min}\) are the maximum and minimum target values of individuals in the current population, respectively; \(w\) is a positive real number in the interval (0,1). On the one hand, \(w\) can ensure that Equation (5) will not be divided by zero. On the other hand, \(w\) can adjust the selection behavior from fitness proportional selection to pure random selection. In the next section of this study, the value of \(w\) is 0.5.
## 3 Results and Discussion
### Chloride Profile of the CNTs Nano-Modified Mortars  
#### 3.2.1 RCPT Method  
The resistance to chloride penetration was initially evaluated qualitatively using the RCPT method; results of current passing through nano-modified mortars with different CNT loadings after curing duration of 28 days are illustrated in Figure 5. It is therein observed that throughout the whole test duration, current was lower for plain specimens and increased with CNT concentration. The current passing through the sample with 0.8% wt. CNTs was approximately 20% higher than in the sample with no nano-reinforcement. The observed behavior is rationalized upon the anticipated increase in conductivity endowed by higher concentrations of the conductive filler and compares favorably to relevant recent findings [56; 57]  
The electric charge allowed to pass through the nano-modified mortars is illustrated in Figure 6 as a function of tube concentration. Therein, negligible charge variation is observed with CNT loading, while according to Table 1 classification, all samples appear to exhibit high chloride ion penetrability. Furthermore, it is shown that the charge passing through the plain specimens is almost identical to that passing through nano-modified mortars, a finding which suggests complete invariance of the particular behavior to carbon nanotube presence. In view of these findings, three factors in the RCPT method must not be neglected: (i) That the current passed is related to all ions in the pore solution, not just chloride ions, (ii) that measurements are taken before steady-state migration is achieved, and (iii) that the high voltage applied may lead to temperature increase which, in turn, may influence the measurement [18; 28].
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 3 Results  
### Assessment of Revisit Rate and Revisit Time  
The revisit rate, mean revisit time, and maximum revisit time were calculated from each result of the five GCPs. Based on this calculation, we found that the mean revisit time achieved was around 7 h with \(T\) = 8, 5 h with \(T\) = 12, 3 h with \(T\) = 20, 2.5 h with \(T\) = 24, 2 h with \(T\) = 28, 1.5 h with \(T\) = 40, and 1 h with \(T\) = 56. These results suggest a nearly linear correlation between the number of satellites and the revisit time. We speculated that this correlation has its roots in the constellation design process, wherein the RAAN and MA were arranged symmetrically at even intervals.  
To assess the difference between the parameter settings of \(P\) and \(F\), the revisit rates, the mean revisit time, and the maximum revisit time as a function of \(P\) for each number of \(T\) were set as shown in Figure 3. Here, the optimal parameter setting of \(F\) was set for each \(P\), so the figure allows us to evaluate the parameter settings of \(T/P/F\) that yield the best performance in terms of revisit rates, mean revisit time, and maximum revisit time when the number of satellites (\(T\)) is given. As these results indicate, the revisit rates and mean revisit time remain almost unchanged with the variation of \(P\), while the maximum revisit time exhibits different behavior that roughly improves as \(P\) increases. Based on these results, Table 2 lists the best parameter settings of \(P\) and \(F\) for the revisit rates, the mean revisit time, and the maximum revisit time, which are for each \(T\) the largest one in Figure 3a, the shortest one in Figure 3b, and the shortest one in Figure 3c, respectively. As shown in Table 2, for revisit rates and mean revisit time, the product of these two values nearly equals to 24 h. It was also found that the optimal settings of \(P\) and \(F\) are mostly the same between these two values with an exception of \(T\) = 24. This point should be more deeply examined in the further analysis with a higher number of GCPs in broad areas.  
Figure 3: Results as a function of \(P\) for: (**a**) Revisit rates; (**b**) Mean revisit time; (**c**) Maximum revisit time.  
Based on the above results, we attempted to determine the parameter settings to be used in the subsequent virtual experiment. In terms of emergency observation during floods, the maximum revisit time is a critical factor in avoiding missing an observation timing as soon as possible. Therefore, we decided to adopt the optimal parameter setting for the maximum revisit time. However, a single setting for each \(T\) may not be sufficient for the evaluation of the virtual experiment, so we also took the optimal parameter setting for the revisit rates, because this was calculated using a different method than that used for the former.
## 3 Results  
### Inactivation of Inculated E. coli and S. cerevisiae by Continuous PEF Treatment  
The PEF inactivation of _E. coli_ and _S. cerevisiae_ inoculated into sterilized orange juice is shown in Figure 1. We selected _E. coli_ and _S. cerevisiae_ as test microorganisms for PEF inactivation. _E. coli_ (ATCC 11775) was selected since it is a typical commensal microorganism and hygienic indicator which can be contaminated during food preparation [24]. _S. cerevisiae_ (ATCC 4105) was selected because it is the most common spoilage microorganism in refrigerated citrus juice [2].  
The number of _E. coli_ was reduced as the specific energy input was increased. When the electrical field strength was 10 kV/cm, the _E. coli_ was reduced by 1.4, 3.0, and 6.4 decimal reductions with a specific energy input of 50, 100, and 150 kJ/L, respectively (Figure 1A). With an electric field strength of 15 kV/cm, slightly more inactivation of _E. coli_ was observed after the PEF treatments. _Escherichia coli_ was most rapidly inactivated with a field strength of 20 kV/cm, and its complete inactivation was observed in a PEF treatment of 20 kV/cm, with a specific energy input of 150 kJ/L. _Saccharomyces cerevisiae_ was more susceptible to PEF treatment than _E. coli_ (Figure 1B). Even at an electric field strength of 10 kV/cm, _S. cerevisiae_ was completely inactivated with specific energy inputs of 100 and 150 kJ/L. Its inactivation by 15 and 20 kV/cm fields exhibited characteristics similar to those of the 10 kV/cm inactivation. Inactivation was limited with a specific energy input of 50 kJ/L but increased rapidly at higher PEF energies.  
(4.7%), valencene (2.5%), and \(\alpha\)-pinene (0.6%). This combination of volatile components represented the unique aroma of orange juice. DI-limonene is a monoterpene hydrocarbon, which contributes to the refreshing aroma of sweet oranges. The area% of dl-limonene in PEF-treated juice was similar to that in untreated juice, but it was slightly reduced in heat-treated juice, particularly sterilized orange juice (Thermal-2). Valencene is a major volatile component of Valencia orange and can be biosynthesized from farnesyl pyrophosphate (FPP) by terpene cyclase enzyme [30]. The area% of valencene tended to be slightly higher after both the PEF and heat treatment.  
Decanal is the major aldehyde in oranges [31], and levels in excess of 0.72 ppm have a negative impact on the flavor of orange juice [32]. The area% of decanal was lower in PEF-treated orange juice (0.06-0.08%) than in untreated juice (0.12%) and was higher after the heat treatment (0.31-0.42%). Sterilized orange juice also showed an increase in the area% of additional unfavorable volatile compounds, such as terripelone, \(\alpha\)-phellandrene, \(\beta\)-fenchyl alcohol, and \(\beta\)-terripenol, resulting in spicy, chemical, camphor, and woody flavors. [PERSON], [PERSON], and [PERSON] [19] reported that heat treatment resulted in irreversible undesirable changes in the flavor of citrus juice due to accelerated chemical reactions during the heating process.
There are a variety of techniques for constructing metamodel such as response surface [15; 16], radial basis functions [17], Kriging [18], ANN, etc. Two strategies are mainly used in the literature to build the metamodel. The first one is based on an extremely coarse database initially and then updates dynamically according to the error level [9], and the second one is a one-shot strategy based on a very large database and does not update during the optimization process [2; 19]. It is well-known that the design procedure of a turbomachinery mainly includes two steps, one is the preliminary design, and the other is the 3-D design. The preliminary design is fundamental and it provides some main geometry parameters and aerodynamic performance parameters to the sequential 3-D design. In this study, an integrated design and optimization approach for radial inflow turbines was proposed, which consists of an automated preliminary design module and a flexible 3-D multidisciplinary optimization module. In Part I [20], an automated preliminary design approach for radial inflow turbines has been developed to reduce the dependency on human expertise by Genetic Algorithm (GA) driving exploration of the design space. The approach was verified by T-100 turbine [21] and the redesign work was conducted to demonstrate it. The results show that through the redesign, the total-to-static efficiency of T-100 turbine increases by 1.0% under design condition and the rotor weight decreases by 0.35 kg (26.7%), which also indicates the importance of the preliminary design. As Part II of this integrated design approach, a 3-D multidisciplinary optimization method is developed to maximize the total-to-static efficiency and minimize the rotor weight while keeping the mass flow rate constant and guaranteeing the mechanical integrity of the rotor. Based on the geometrical and aerodynamic parameters of T-100 turbine and T-100 re turbine obtained in Part I [20], the nozzle aerodynamic optimization and rotor multidisciplinary optimization for the two turbines are carried out in this paper, respectively.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### 5.4.7 Total Light Approach in TD-FS and TD-FT  
[PERSON] et al. proposed a concept of the total light to calculate light propagation of fluorescence emission [212]. The total light is defined as \(\phi_{t}=\phi_{x}+{\phi_{m}}^{*}/\gamma\), where \({\phi_{m}}^{*}\) is the fluence rate of the emission light when the fluorescence life time is zero, i.e., \(\phi_{m}(\mathbf{r},t)={\phi_{m}}^{*}(\mathbf{r},t)\otimes(1/\tau)\text{exp}( -t/\tau)\) with \(\otimes\) denoting the convolution operator. With additional assumption of \(\mu_{ax}=\mu_{am}=\mu_{a}\) and \(D_{x}=D_{m}=D\), the DE for \(\phi_{t}\) is given as,  
\[\left[\frac{1}{c}\frac{\partial}{\partial t}-\
abla\cdot D(\mathbf{r})\
abla+ \mu_{a}(\mathbf{r})\right]\phi_{t}(\mathbf{r},t)=q_{x}(\mathbf{r},t), \tag{57}\]  
which is almost the same form as Equation (55) except the absorption by the fluorophore. So, computation is simplified. The total light approach was applied to TD-FT for reconstructing \(\mu_{a}\) and \(\varepsilon N(\mathbf{r})\) in a 2D circular medium with a single and double fluorophore targets using \(<\!\!t>\) as a featured TD-data [213]. [PERSON] et al. applied the total light approach to estimate the life-time function, \((1/\tau)\text{exp}(-t/\tau)\), in heterogeneous scattering media with a more general scheme using the TD-RTE [214].