esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Crack Length Measurement with the DC-Potential Drop Method
### Calibration of the Crack Length Measurement  
The Johnson formula (Equation (1)) as an analytical solution of [PERSON]'s differential equation contains, apart from the potential ratio U/U\({}_{0}\), only geometric quantities and should provide exact crack lengths when using the exact specimen dimensions. In crack propagation experiments, however, there are clear discrepancies between the crack lengths determined on the fracture surface and the potential drop measurement. For this reason, a calibration of the DCPDM, i.e., an adaptation of the Johnson equation, is necessary. Aside from a calibration using finite element method (FEM) calculations which is suitable for special crack geometries [12; 26], the calibration can be undertaken by simulating the crack propagation via defined saw cuts and correlating the measured potential probe data with the \"artificial\" crack lengths generated this way [27; 28]. Another possibility is to mark the crack extension on the fracture surface by overloads or by load cycle blocks with a different stress ratio, so-called marker loads [27; 28; 22; 29]. Using saw cuts has the advantage that the \"crack front\" runs almost perpendicular to the direction of crack propagation. In case of saw cuts, the measurement of the \"crack length\" on the surface of the specimen is sufficient, while for fatigue cracks, the crack length is underestimated due to a curvature of the crack front.  
For the calibration of the DCPDM, the marking of the crack length by means of overloads as well as marker loads has proven itself. A suitable method to equalize the curvature of the crack front is the measurement of the crack area. The mean crack length is obtained by dividing the measured crack area by the specimen width. For a SEN-specimen made of AA/745, the crack fronts marked by overloads are shown in Figure 2a as red lines and the resulting mean crack lengths perpendicular to the propagation direction are shown as yellow dashed lines. Regarding this, a linear dependency of potential and crack length is assumed, whereby the error in the case of curved crack fronts can only be minimized, but not eliminated [30; 31]. The calibration is undertaken by drawing the optical crack length a divided by the specimen width (w) against the measured potential drop U/U\({}_{0}\) and adapting the Johnson equation with a least square fit by using the distance of the potential grips \(y_{0}\) as a free parameter. As a result, a grip spacing of \(y_{0}\) = 2.53 mm was obtained. The curve for the geometrical grip spacing of \(y_{0}\) = 2 mm added for comparison in Figure 2b shows clearly that the actual crack length is underestimated when using the real geometric spacing of the potential grips.
## 2 Materials and Methods
### Manufacturing Process
#### 2.1.3 Contour Profile Milling  
The CNC contour profile milling process was executed on a Haas VF1 three-axis vertical machining center, following the tool path outlined in Figure 5. The workpiece was securely affixed to the milling fixture, maintaining a 30 mm overhang distance from the milling vice.  
The milling conditions were carefully monitored and controlled throughout the machining process to maintain stability and minimize variations to obtain accurate and reliable results. The conditions considered were the coolant flow, tool wear, vibration, cutting speed, and feed rate. The sequential machining operations executed are detailed below:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Operation** & **S (Rev/min)** & **F (mm/min)** \\ \hline \multirow{3}{*}{ASI 1018} & Face milling & 596 & 60 \\  & Contour roughing & 795 & 79 \\  & Contour finishing & 1000 & 120 \\ \hline \multirow{3}{*}{Aluminum} & Face milling & 1200 & 300 \\  & Contour roughing & 1600 & 350 \\  & Contour finishing & 2000 & 240 \\ \hline \multirow{3}{*}{Nylon 60} & Face milling & 1400 & 500 \\  & Contour roughing & 1900 & 700 \\  & Contour finishing & 2400 & 300 \\ \hline \multirow{3}{*}{Delrin} & Face milling & 1800 & 400 \\  & Contour roughing & 2400 & 500 \\ \cline{1-1}  & Contour finishing & 3000 & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feeds and speeds for different materials.  
Figure 5: Calibration piece milling profile.  
1. The face milling operation was performed using a 16 mm coated solid carbide end mill, removing a depth of 1.5 mm from the workpiece with direct coolant flow.
2. A 12 mm coated solid carbide end mill performed a contour profile roughing operation, penetrating 1.5 mm each cut depth along the Zh axis until the total machined level was reached, leaving a 0.5 mm wall stock for finishing with direct coolant flow.
3. Contour profile finishing was performed utilizing a 9.52 mm coated solid carbide end mill to remove burrs and surface flaws. The cutting depth followed the same direction as the previous machining but with an increased cutting speed and a decreased feed rate with direct coolant flow.
## 5 Optimization of the Rain Visor on the Reduction of the Window Buffeting Noise  
### Design Variables and Constraints  
In this research, the front rain visor of a sedan is selected as the optimization object, and the optimization parameters are shown in Figure 8.  
Figure 8: Optimization parameters of the visor.  
Figure 6: Vorticity on the horizontal plane through the driver’s ear. (**a**) Without the visor; (**b**) With the visor.  
Figure 7: Fluctuation pressure and sound pressure level at the driver’s left ear with the fully opened left front window. (**a**) Fluctuation pressure; (**b**) Sound pressure level.  
According to the modelling features of the rain visor, the following parameters were selected as design variables without affecting the driver's field of vision:  
1. The design variable \(L_{1}\): Take the midpoint of the upper and lower sides of the front window and make the midline on the window's curved surface. Make the intersection midline of the two endpoints of the rain visor on the window's curve at one point, then the curve will be regarded as the edge line of the rain visor, and the intersection point is the radiant point of the rain visor's edge. Considering the rain shielding effect of the rain visor and the range of visual field of the driver, two points, A and B, are taken as the limit position of the intersection, and the curve between them is \(L_{1}\). Make the position of point B zero, and then the value range of \(L_{1}\) is [0-39 mm].
2. The design variable \(W\): \(W\) is the distance between the lower edge of the visor and the window's curved surface. The slope of the noise reduction visor's surface can be controlled by changing the value of W. When the slope of the visor's surface is greater than 45\({}^{\circ}\), the rain shielding effect will decrease sharply; set the minimum value of \(W\) as 20 mm, so the value range of \(W\) is [20-36 mm].
3. The design variable \(L_{2}\): Set the length of the long side of the visor, i.e., the length of the rear side of the B pillar, as \(L_{2}\). According to the requirements of the radian of the window, the value range of \(L_{2}\) is set as [50-65 mm].
4. The design variable \(R\): The radius of the rounded angle of the visor is set as \(R\). Restricted by the short edge of the visor, the value range of \(R\) is set as [0-15 mm].  
According to the previous analysis, the front visor can change the trajectory of the vortex in the shear layer separated from the A-pillar. Therefore, when the left front window is fully open and the inlet velocity is 30 m/s, the peak value of the sound pressure level at the driver's left ear is taken as the objective function, \(P\), and the objective function value cannot exceed 116 dB.
## 4 Analysis Results and Discussion
### Digital Image Correlation Analysis
As the pressure continued to increase, shear cracks gradually appeared and eventually led to overall instability. The digital image correlation analysis results indicate that for tensile cracks, the deformation and strain perpendicular to the crack direction are very sensitive, while the deformation and strain parallel to the crack direction are not obvious. Conversely, for shear cracks, the deformation and strain parallel to the crack direction are very sensitive, while the deformation and strain perpendicular to the crack direction are not obvious. This phenomenon can serve as a characteristic indicator for identifying tensile and shear cracks. For inclined cracks, the crack type can be analyzed and determined based on their distribution in the coordinate system and the changes in strain and displacement in different directions.
# Particle Swarm Optimization Based on a Novel Evaluation of Diversity
## 3 Pso-Ed
### Normal and Disturbance Update Modes  
Note that _Gbest_ and _Pbest_ are more likely to be closer to the real global optimal solution in some dimensions. On the other hand, a poor particle's current position is far away from the optimal solution, and it requires more cost (e.g., FEs) to move to a promising region.  
Based on this consideration, we use different update modes for particles with excellent fitness and poor fitness to speed up the search process. We take the worst \(N_{w}\) particles in each iteration as the particles with poor fitness. As shown in Equation (11), \(N_{w}\) is proportional to \(E_{d}\). The larger the value of \(E_{d}\) is, the more dispersed the particles are. In this way, we force more particles to jump to a promising region to while reducing the waste of the FEs on poor particles.  
\[N_{w}=N_{s}\times E_{d}/2 \tag{11}\]  
1. For particles with better fitness, the update mode follows the usual manner shown in Equations (2 and 10). Note that this update mode is slightly different from the traditional update due to the _Tbest_ term in the velocity update formula.
2. For particles with poor fitness, we use a disturbance update mode as follows.  
Step 1: Select the position of _Pbest_ or _Gbest_ as the seed (denoted as \(P\)) for generating new positions. Refer to Equation (12), the choice of _Pbest_ position (denoted as \(P_{i}\)) or _Gbest_ position (denoted as \(P_{g}\)) depends on the value of \(E_{d}\). When \(E_{d}\) is large, we tend to choose _Pbest_ (i.e., self-cognition) to avoid fast convergence to some local optimum; when \(E_{d}\) is small, we choose _Gbest_ (social-cognition) to achieve a finer result.  
\[P=\begin{cases}P_{i}&r<E_{d}\\ P_{g}&otherwise\end{cases} \tag{12}\]  
where r is a random number uniformly distributed in \([0,1]\).  
Step 2: Since \(P\) is very close to the optimal solution in some dimensions, we can randomly select a dimension d of \(P\) and perturb it into a new position, and keep the other dimensions remain the same thus to maintain the property of \(P\) as much as possible.  
\[x_{i}=\begin{cases}P_{i}+r_{n}\times(x_{ub}-x_{lb})&i=d\\ P_{i}&otherwise\end{cases} \tag{13}\]  
where \(r_{n}\) is a random number and \(r_{n}\sim N\big{(}0,\ E_{d}^{2}\big{)}\).  
Step 3: Replace the position of poor particles with the new position generated by the disturbance update mode.
# Evaluation of Rock Brittleness Index under Dynamic Load
## 4 Discussion  
### Narrow Brittleness Definition and Generalized Brittleness Definition  
The definition of brittleness was initially aimed at isotropic homogeneous materials such as metals and ceramics. It is generally believed that metals have strong toughness, while ceramics and glass have strong brittleness. However, rock materials have obvious anisotropy and inhomogeneity, so the definition of brittleness that is directly applied to metal and ceramic is controversial. Some scholars [38; 39; 40] believe that brittleness is an inherent attribute of rocks which is only related to mineral composition and structural composition, and the influence of environmental and stress state changes on rock brittleness should not be considered. Another group of scholars [41; 42; 43; 44] believes that different external factors (such as water content, temperature, confining pressure, etc.) will inevitably lead to changes in the brittleness states and failure modes of rock materials. When evaluating  
Figure 8: Dynamic stress–strain curves of sandstone at different strain rates.  
Figure 9: Changes in \(B_{8}\) at different strain rates.  
brittleness changes in rock, internal and external conditions such as temperature, stress state, and microstructure should be comprehensively considered.  
It can be seen from the tests carried out in this paper that, under different loading modes and different strain rates, the brittleness values calculated by all of the brittleness indices underwent great changes. At the same time, various characteristics considered to reflect the brittleness of rock, such as compressive strength and elastic modulus, also underwent significant changes, indicating that the brittleness of rock will change with different loading rates. However, all changes were based on an initial state, so this paper posits that rock brittleness can be defined in two ways. One is the narrow sense of rock brittleness, and the other is the broad sense of rock brittleness. The narrow brittleness definition classifies it as an inherent property of rock materials, i.e., a mechanical behavior characteristic of rock under natural conditions. The generalized brittleness definition consists of two parts: one is the brittleness of rock foundations, and the other is the increase or decrease in the brittleness of rocks. Rock foundation brittleness is equivalent to the narrow rock brittleness definition. The increase or decrease in the brittleness of rocks is the change in rock brittleness under different influencing factors (loading rate, confining pressure, temperature, water content, etc.).
## 5 Discussion
The FRFCM method achieved the best expert and PROMETHEE-IFS ranks (Table 8) since morphological reconstruction (MR) effectively suppressed noise while maintaining precise object contours. The result validation in Table 8 showed that the proposed PROMETHEE-IFS correlates with a subjective evaluation with an accuracy of less than one rank, confirming the effectiveness of the proposed method. At the same time, the rank sensitivity analysis inTable 9 shows rank differences between alternatives within one rank, validating the robustness of the methodology. For example, despite minor differences in evaluations across all evaluator groups, the RSSFCA method consistently ranked last due to its visually worst segmentation results. Subjective evaluation methods can be adapted to different types of content. They can be used to evaluate different aspects of image content or quality, such as image sharpness, color fidelity, or contrast. In the context of our research, this adaptability could be particularly valuable when describing the feature-rich content of satellite images. The user is mostly interested in a situation where the image segmentation methods return visually distinct results (i.e., the clear winner is not apparent). In such cases, human judgment can provide insight into the quality and appropriateness of the segmentation result that objective metrics may not always capture. In cases where the segmentation method always gives a poor result (especially with complex images), there is no need to use our methodology since such a method will always be in the last place. Alternatively, if all segmentation methods return visually similar outcomes (especially with less complex images), there is no necessity to determine the optimal method. Subjective content evaluation methods can provide insights into the overall perceptual aspects of the image content. While most visual image features may be relatively independent of others, there is typically some level of interdependence. Combining objective and subjective evaluation criteria can achieve a more comprehensive evaluation of image content. However, our task is to combine the features of the image content with the evaluation of algorithms, which was achieved during step 3 of the proposed PROMETHEE-IFS method.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 4 Approaches for Preventing Sticking of MXenes  
### Surface Functionalization and Passivation  
Recent study has examined the effective functionalization of acid-treated carbon fiber (CF) using Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\), leading to a 186% improvement in interfacial shear strength [72]. Additionally, the modified CF's surface topography changed, enhancing the mechanical engagement effect during debonding. To incorporate short CF-functionalized MXene molecules into an epoxy resin (ER) matrix, the CF was immersed in a Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) dispersion. After coating, they were mixed with the ER by stirring under sonication. In this case, composites with a 2 wt% load exhibited the best behavior, with tensile strength 100% higher than ER and 13% higher than a composite with non-coated CF. The MXene-coated CF formed a stronger interfacial interaction with the epoxy matrix by physically interlocking and forming strong hydrogen bonds.  
Applying amino functionalities to carbon fibers (CF) through aminopropyl triethoxysilane (APTES) grafting following acid treatment is a standard method to enhance the interphase and prevent self-sticking. The resulting CF-NH\({}_{2}\)/ER composite showed increased tensile strength and flexural strength by 40.8% and 45.9%, respectively, when compared to CF-NH\({}_{2}\)/ER without MXene. The inclusion of Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) nanosheets improved the interface connection between the fiber and ER, reducing interlaminar stress concentration, as these nanosheets exhibit superior mechanical properties compared to the ER (Figure 16). An alternative approach was explored by [PERSON] et al. [73], involving grafting APTES to Ti\({}_{2}\)C nanosheets, followed by their covalent bonding to the carboxyl groups of acid-treated CF through amide bonds. The remaining amine groups of the MXene also participate in a chemical linkage with the ER, playing a pivotal role in impeding agglomeration. This mechanism also effectively prevents crack propagation along the interface when the fiber breaks into shorter units.
## 4 Discussion
### Image Layers and Thresholding  
The contribution of NDVI and texture to class differentiation on RE is similar to what previous studies have found with just the addition of NDVI on RE [54]. The decrease in accuracy on WV with these added layers leads to additional questions and could have been caused by the SVM overfitting the training samples, differences in training and validation samples, or too much noise in the additional layers. A growing body of work suggests dimensionality reduction may be as effective as adding data dimensions in some applications [55; 56; 57]. Our results from analyzing the contribution of these layers promote the claim that one must consider the specific platform, and its spectral and spatial characteristics, to decide whether additional indices or texture features will add to the prediction power of a multispectral image. As deep learning classifiers find their way into the mainstream, where handcrafted layers are not typically necessary, these added dimensions may fall out of favor.  
Water habitats within this estuarine environment are highly complex; for example, some areas exhibit clear deep water and low reflectance values, while other water pixels have high NDVI, turbidity, and textured bottom types (oyster shells, debris, etc.). This drives a high amount of spectral variation in the subtidal haline class, causing confusion for classification algorithms. This thresholding step eliminated that confusion by successfully delineating water from other classes and has the potential for applicability across environments.
## 3 Result and Discussion
### Statistics of Behavior in Mechanical Ventilation Operation
#### 3.1.4 The Long-Term IAQ of Households with Mechanical Ventilation Systems  
Figure 8 shows the boxplots of the percentage of time that the hourly average indoor CO\({}_{2}\) concentration exceeded 1000 ppm and when the indoor PM\({}_{2,5}\) concentration exceeded 35 \(\upmu\)g/m\({}^{3}\). Not considering outliers, for households that run the system nearly all day, the hourly average CO\({}_{2}\) concentration is above 1000 ppm for 0-14% of a year, and for households that run the system intermittently, it is 0-23%. Regarding indoor PM\({}_{2,5}\), correspondingly, the percentages of a year with concentration exceeding 35 \(\upmu\)g/m\({}^{3}\) is 0-49% in the former households and 7-76% in the latter households. Overall, the indoor air quality of the households using the system almost all day was better than that of intermittent  
Figure 7: Boxplots of the average ventilation volumes of the two type systems in different seasons: the split system and the centralized system.  
use. However, different from the simulation results [15], even with the system operating throughout the day, IAQ cannot be good (with CO\({}_{2}\) concentration below 1000 ppm and PM\({}_{2.5}\) concentration below 35 \(\upmu\)g/m\({}^{3}\)) at any time in reality.  
In addition, several outliers on the boxplot are observed. For example, the proportion of indoor CO\({}_{2}\) concentration exceeding the standard is greater than 0.4, and the indoor PM\({}_{2.5}\) concentration exceeding the standard proportion is close to 0.8. This may be because the system has been running for a long time without cleaning and maintenance, and the dust of the filter element has accumulated substantially, increasing wind resistance, resulting in a reduction in the actual air supply volume, and secondary pollution will also occur, which affects purification effects. Therefore, it is necessary to replace the filter regularly for the households who often use the mechanical ventilation system.
# Precise Measurement of Hyperfine Structure of Cesium 7S\({}_{1/2}\) Excited State
###### Abstract  
We present a precise measurement of the hyperfine structure of cesium 7S\({}_{1/2}\) excited state by employing electromagnetically induced spectroscopy (EIS) with a cesium three-level cascade (\(6S_{1/2}-6B_{3/2}-7S_{1/2}\)) atom in a room temperature vapor cell. A probe laser, \(\lambda_{p}\) = 852 nm, was coupled to a transition \(|6S_{1/2}\rangle\rightarrow|6P_{3/2}\rangle\), related frequency locked to the resonance hyperfine transition of \(|6S_{1/2}\rangle\rightarrow|6P_{3/2}\rangle\) with a Fabry-Perot (FP) cavity and an electro-optic modulator (EOM). A coupling laser, \(\lambda_{c}\) = 1470 nm, drove the \(|6P_{3/2}\rangle\rightarrow|7S_{1/2}\rangle\) transition with the frequency scanned over the \(|6P_{3/2}\rangle\rightarrow|7S_{1/2}\rangle\) transition line. The hyperfine level interval was extracted to be 2183.61 \(\pm\) 0.50 MHz by analyzing EIS spectroscopy. The optical-optical double-resonance (OODR) spectroscopy is also presented for comparison, with the corresponding value of the hyperfine level interval being 2183.48 MHz \(\pm\) 0.04 MHz, and the measured hyperfine splitting of excited 7S\({}_{1/2}\) state is shown to be in excellent agreement with the previous work.  
hyperfine structure; electromagnetically induced spectroscopy; cesium 7S\({}_{1/2}\) excited state +
Footnote †: journal: Applied Physics Letters