esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to analyze the cryoprotective effect of a ovalbumin (OVA) and \(\beta\)-cyclodextrinetrin (\(\beta\)CD) mixture (3:1, OVA/\(\beta\)CD) on the structure, rheology and gelling properties of myofibrillar proteins (MPs) during 90 days of frozen storage. A mixture of OVA/\(\beta\)CD at different concentrations (0, 2, 4, and 6%) was added to MPs and stored at \(-18\)\({}^{\circ}\)C for 90 days. The addition of OVA/\(\beta\)CD significantly decreased the sulfhydryl contents while it increased the surface hydrophobicity, which was closely connected with tertiary structural changes. Circular dichroism analysis showed that the addition of OVA/\(\beta\)CD enhanced the stability of the secondary structure by inhibiting the decline in the \(\alpha\)-helix. Rheological properties analysis indicated that 6% OVA/\(\beta\)CD treatment showed better storage modulus (G') and loss modulus (G\"). In addition, treatment of OVA/\(\beta\)CD showed better gel forming properties than the control group (0%), helping to form a homogeneous and denser gel network. The results proved that 6% OVA/\(\beta\)CD could be act as a promising cryoprotectant, which can improve the structure and gel behavior of _Culter alburnus_ MPs during frozen storage. Moreover, OVA/\(\beta\)CD could be a potential alternative to conventional cryoprotectants at the industrial level to increase the economic and commercial values of seafood products.  
myofibrillar proteins; ovalbumin; \(\beta\)-cyclodextrinrin; cryoprotectant; frozen storage 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/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
Sprinkler selection influences the water distribution uniformity of center pivot irrigation systems. The sprinkling uniformity of the center pivot is crucial for the yield and quality of crops on a large scale. Rotating and fixed spray plate sprinklers (RSPSs and FSPSs) are the two most popular types. However, sprinkler selection is mainly based on price, not on differences in performance between them. Under low-wind field conditions, the water distributions of individual RSPSs and FSPSs with different nozzles (2.78, 4.76, and 6.75 mm in diameter) were measured using a catch can method. Cubic spline interpolation was used for data conversion for FSPS measurements; the nozzle configuration model was used to simulate the water distribution of the same-nozzle-sprinkler pipe section and full circular irrigated areas in a simulated center pivot under three sprinkler intervals of 1.5, 3.0, and 4.5 m respectively. Results showed that (1) individual RSPSs distributed the most water around the sprinkler, whereas individual FSPSs distributed the most water over a ring-shaped region at the periphery of the sprinkler, and the wetted radii for RSPSs and FSPSs ranged from 4.88 to 7.05 m and from 5.02 to 6.85 m, respectively; (2) same-nozzle-sprinkler pipe sections of RSPSs distributed the most water around the central axes of the pipe sections, and their sprinkling uniformities were 44.7%-51.0%, whereas FSPSs distributed the most water over both sides of the axes symmetrically, and less water around the axes, with sprinkling uniformities of 40.3%-58.0%; and (3) the sprinkling uniformities of the full circular irrigated areas were 85.8%-91.7% and 85.8%-86.2% when using RSPSs and FSPSs, respectively, under different sprinkler intervals, and the uniformities were 3.1% and 6.2% higher using RSPSs than FSPSs with sprinkler intervals of 3.0 and 4.5 m, respectively. RSPS accommodated larger sprinkler intervals (>3.0 m) and maintained superior sprinkling performance when compared with FSPS.  
rotating spray plate sprinkler; fixed spray plate sprinkler; water distribution; center pivot irrigation system; sprinkling uniformity; nozzle configuration; cubic spline interpolation
## 5 Discussion and Conclusions
### Conclusions  
This study compares two super typhoons (Typhoon Surigae 2021 and Typhoon Nepar-tak 2016) using four simulation scenarios: (1) FNL-driven WRF model; (2) ERA5-driven WRF model; (3) Holland empirical typhoon model; and (4) Jelesnianski empirical typhoon model. Simulated wind fields were compared with SMAP satellite data, and the models' accuracy, particularly at high wind speeds, was evaluated. The main findings are:  
1. The Holland and Jelesnianski models simulate extreme wind speeds (above 50 m/s) better than the WRF model driven by FNL and ERA5 data.
2. WRF simulations driven by FNL data perform better in simulating high wind speeds than those driven by ERA5. Additionally, WRF simulates the wind field in the outer regions of the typhoon more accurately than the empirical models.
3. The maximum wind radius range simulated by the Holland empirical parameterized typhoon model tends to be overestimated, resulting in higher wind speed and significant wave height simulations at locations closer to the typhoon's central path. Therefore, parameters such as the maximum wind radius require careful consideration and refinement.  
Future research will focus on combining empirical parameterized typhoon models with WRF simulations to achieve a balance between accurately reproducing the typhoon's core wind speeds and considering the interactions between the typhoon and its surrounding environment. For instance: integrating four-dimensional data assimilation (FDDA) with Observation Nudging into the WRF model to improve extreme wind speed simulations, using unstructured grids in the SWAN model for higher resolution in nearshore areas, and optimizing the maximum wind speed radius in the empirical models to enhance their accuracy. Additionally, attempts to use stochastic methods to simulate typhoon activities around Taiwan and its surrounding areas. By integrating satellite observation data, try to summarize the characteristics of extreme wind speeds associated with typhoons in the Taiwan region.  
Conceptualization, S.S.; Methodology, H.F.; Validation, Y.W.; Investigation, Y.W., C.L. and Z.H.; Data curation, H.F., C.L. and G.W.; Writing--original draft, H.F.; Writing--review & editing, Y.W. and Y.X.; Visualization, H.F.; Supervision, Y.X., S.S., Z.H. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China. (Grant 2017 YFC1404800).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods
### Related Work
#### 2.1.3 Cost Aggregation  
Cost aggregation is an essential module in stereo matching and is used to solve the issue where pixel matching is affected by noise; the result is that the cost value after aggregation can more accurately reflect the correlation between pixels.  
The feature extraction of GC-Net is only completed in some stacked convolution layers [29], and the ill-posed region is not taken into account when adjusting the network matching accuracy in the cost aggregation part. As a result, it is difficult to form an understanding of the weak texture, deformation, and object edge.  
PSM-Net introduces the pyramid pooling module to incorporate global context information into image features, significantly improving the cost aggregation compared with GC-Net. PSM-Net provides a stacked hourglass 3D CNN to adjust the matching cost volume from fine to coarse and then from coarse to fine; this is performed to expand the regional support of the context information in the cost volume stage [14] so that the stereo matching process achieves high accuracy. However, this method still has the problem of mismatching in the low texture, deformation, and object edge regions.  
The features in the cost aggregation stage need to contain spatial detailed information. In particular, more information needs to be sampled in the ill-posed area. Because the image feature resolutions obtained at different scales are different, inspired by [21; 26], we constructed multi-scale cost volumes by performing feature correlation operations on feature maps at different resolutions and fused the cost volumes at different scales. In this way, each representation, from high resolution to low resolution, repeatedly receives information from other representations to obtain rich high-resolution representations, which can have high matching accuracy in the weak texture, deformation, and object edge regions.
# Numerical Investigation of Excitation of Various Lamb Waves Modes in Thin Plastic Films
## 4 Conclusions and Discussion  
There are some tasks in which applications of a few different guided waves enable us to obtain more information about structures under a test. For example, the measurement of phase velocities of symmetrical \(S_{0}\) and antisymmetrical \(A_{0}\) guided wave modes allowed us to determine the thickness and Young's modulus of thin PVC films [21]. The main problem is how to excite such modes in thin films especially using air-coupled methods.  
The performed numerical modelling by 2D and 3D finite element methods showed that for such purpose, a contactless linear phased array might be successfully exploited. It was shown that the most serious problem is the excitation of the symmetrical \(S_{0}\) mode, as in this mode the biggest challenge is the in-plane displacement component, which is almost impossible to excite by a single ultrasonic transducer. For a solution of this problem, we proposed using a linear phased array where the elements of which are excited by electric signals with properly selected delays. The delays of the excitation instants between adjacent elements are equal to the propagation time of the \(S_{0}\) mode.  
For the excitation of the \(A_{0}\) mode we proposed using the same phased array, except the delays between adjacent elements of the array in this case were equal to the propagation time of the antisymmetrical \(A_{0}\) mode. In our case, at the frequency 150 kHz this delay was \(\Delta\tau\) = 22.8 us, e.g., 6.9 times longer than the delay for the excitation of the \(S_{0}\) mode.  
The obtained results clearly demonstrate feasibility to excite efficiently different guided wave modes with very different phase velocities using the proposed method based on an electric readjustment of the same phased array.  
[PERSON] supervised the research, wrote and edited the paper; [PERSON] carried out the simulations, analyzed the data, and wrote parts of the paper; [PERSON] analyzed the data of simulations and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Social Fund under the No 09.3.3-LMT-K-712 \"Development of Competences of Scientists, other Researchers and Students through Practical Research Activities\" measure.  
Not applicable.  
Figure 12: Ultrasonic pulses of the off-plane \(y\) component of the particle velocity at three different distances from the excitation zone: (**a**) 75 mm, (**b**) 110 mm, (**c**) 125 mm.
## 6 Conclusions  
This paper proposes an analytical method to predict the deformation of the existing pipeline parallel to and above the new shield tunnel. The pipeline's deformation is induced by different force patterns considering the shield tunnel construction process. The findings are as follows:  
1. Compared with the final deformation in previous research, our analyses focus on the pipeline's deformation during the tunnel construction and grouting reinforcement. The importance of deformation control during tunnel construction is emphasised.
2. The influences of different force patterns on the spatiotemporal deformation during the construction phase is considered in more detail. The force patterns consist of the ground volume loss, support pressure at tunnel face, frictional force on shield shell, and torsional force of cutter head.
3. The spatiotemporal deformation of the existing pipeline is studied considering the shield tunnel advance. The calculated results agree well with the monitoring data of the existing pipeline in Subway Line 3 of Nanchang. Our proposed method provides a feasible estimation of the pipeline's deformation with the tunnel advance.
4. The pipeline's deformation increases with the increased ground volume loss, support pressure, frictional force, and torsional force. Although most of the deformation is induced by the ground volume loss, the effects of other force patterns on the deformation should not be ignored.
5. We provide the longitudinal deformation curve (LDC), deformation development curve (DDC), and grouting reinforcement curve (GRC) to represent the spatiotemporal characteristics of the existing pipeline's deformation. The deformation at any point and any moment during the tunnel advance can be roughly extrapolated from the monitored data of any two points.
6. The curves combined with LDC, DDC, and GRC are provided to represent the spatiotemporal characteristics. It can be applied to suggest an appropriate construction and grouting scheme during the shield tunnel construction.  
Conceptualisation, X.L., A.J. and Q.F.; methodology, X.L.; software, X.L.; validation, X.L., J.L. and X.G.; formal analysis, X.L.; investigation, Y.W.; resources, Y.W.; data curation, Y.W. and X.G.; writing--original draft preparation, X.L.; writing--review and editing, Q.F.; visualisation, J.L.; supervision, Q.F.; project administration, A.J.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chinese National Natural Science Foundation, grant number 52108363 and 52078093, China Postdoctoral Science Foundation, grant number 2021M700654, Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China, grant number U1934210, and LiaoNing Revitalization Talents Program, grant number XLYC1905015.  
Not applicable.
## 2 Materials and Methods
### Enhanced Feature Extraction from Chemically Etched Metallographic Specimens
#### 2.3.2. Ferrite/Perlite Analysis  
Once carbide quantification is complete, the workflow proceeds to ferrite-perlite analysis. Phase discrimination relies on their distinct optical signatures, which differ in brightness, texture, and etching response [72].  
* Appearance: Light or bright areas in OM or SEM images. Low contrast compared to other phases. Morphology, smooth, featureless regions with no specific structure.
* Identification: Ferrite is the softest phase in iron and appears as the brightest area in etched samples because it is less affected by the etchant. It often forms the matrix in low-carbon irons.  
* Appearance: Intermediate gray tones in optical microscopy. Higher contrast than ferrite but lower than carbides. Morphology, lamellar (layered) structure consisting of alternating layers of ferrite and cementie (\(Fe_{3}\)C).
Identification: Perlite appears as striped or banded regions due to its alternating ferrite-cementite layers. The spacing between the lamellae depends on the cooling rate. Finer lamellae indicate faster cooling.  
In the current work, phase fractions are obtained by measuring dark (perlite) and bright (ferrite) areas in the etched micrograph. As graphite nodules and perlite share the same low gray level after etching, thresholding operations alone cannot separate them. We therefore subtract the graphite fraction (previously quantified in the corresponding non-etched image) from the total dark area. The remainder represents cementite, from which the perlite content is inferred.
## 3 Recent Advances in Different Configurations of STSCs  
The STSCs can be classified into the following five distinct types as illustrated in Figure 5: (i) intrinsic, (ii) semiconductor metals, (iii) double-cermet, (iv) multilayered stacks, and (v) textured surface STSCs, according to their design principles and absorptance mechanisms [30]. Among these, multilayered stacks and cermet STSCs are the most commonly utilized [36]. The multilayered stacks utilize light interference to absorb energy near the peak of solar irradiation, rather than in intrinsic bands, aligning with the high reflective properties of the substrate [37]. Each multilayered stack can consist of dielectrics, metals, or composite materials made of dielectrics [37]. However, they require stringent manufacturing processes and high precision. The latter type, cermet STSCs, are composites with metal particles dispersed in a dielectric matrix [38]. Through banding-to-banding transitions, these coatings achieve high spectral selectivity [38]. The best coatings are often designed by integrating multiple mechanisms. Typically, a standard double-cermet STSC comprises a metal reflective layer, followed by cermet solar absorption layers with high and low metal volume fractions [39]. Common optimizations for double-cermet coatings focus on layer thickness, the number of layers, each layer structure, and the metallic gradation from the substrate to the anti-reflective layer (AR) [39]. The cermet layers consist of nanoscale metal particles embedded in either ceramic matrix or transited dielectric, whereas the metal layer functions as an infrared reflector. Solar intensity is scattered by the interfaces between the dielectric and metallic phases. As the optical path length of the irradiation increases, absorption also increases, and low refractive dielectric indices effectively minimize surface reflection and enhance the absorptance of coatings within the sunny spectrum.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 2 Related Work
### Particle Swarm Optimization  
Particle swarm optimization (PSO) was first proposed by two American scientists based on the process of birds foraging to find the best foraging area [27]. As an intelligent algorithm, PSO simulates the best decision-making process. Bird foraging is similar to the human decision-making process. Think about whether you will be affected by your own experience (local optimum) and the experience of people around you before you make a choice (global optimum). In the same way, in the process of foraging, the initial position of each bird is in a random state. Of course, we do not know where the best foraging point is, and the flight direction of each bird is also random. It can be considered that during the early stage of foraging, the trajectory of birds is disordered. Over time, birds in a random position learn from each other and share foraging information within the group. In each foraging process, each bird estimates the value of finding food by combining its own experience and information transmitted by peers. Based on this way of research, particle swarm optimization (PSO) emerges as the times require.  
In particle swarm optimization, the search for the objective function is solved by using the motion of each particle. The motion direction of particles includes two parts: the random part and definite part. That is to say, the possible motion direction of each particle is not only towards the current global optimum and the optimum of the particle itself but also random motion. Compared with genetic algorithm, it does not use an evolutionary operator, but treats each individual as a particle in the optimization space and has a certain speed. Particle motion is dynamically adjusted by individual trajectory and group historical trajectory. The following is the algorithm process:  
1. Determine the specific function equation to be optimized f(x);
2. Define initial value: velocity \(v\) and position \(x\) of N particles;
3. Start to search the global optimum and the fitness value of each individual was calculated;
4. The fitness value of each particle's current position is compared with the global best and its own historical best to update the particle's historical best and global best;
5. The updating formula of velocity and position is used to change the \(v\) and \(x\);
6. Repeat steps 2-5 until the stop rule is met.  
Update formula of \(v\) and \(x\):  
\[v_{i}^{d}=wv_{i}^{d}+c_{1}r_{1}(p_{i}^{d}-x_{i}^{d})+c_{2}r_{2}(p_{g}^{d}-x_{ i}^{d}) \tag{4}\]  
\[x_{i}^{d}=x_{i}^{d}+\alpha v_{i}^{d} \tag{5}\]  
where \(i\) = 1, 2,..., \(m\) denotes the \(i\)th particle (bird); \(d\) = 1, 2,..., \(D\) denotes the \(d\) dimension; \(w\) is a non-negative number, which is called the inertia factor; \(r_{1}\) and \(r_{2}\) are random numbers from 0 to 1; \(\alpha\) is called constraint factor, which is to control the weight of speed.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## Appendix A  
The formulas of the time-domain features typically extracted from condition monitoring data and also used in this paper are provided in Table A1.  
Pearson correlation analysis is a filter-based method that considers variables with a high linear relationship containing redundant information [59]. The level of correlation between two features is usually provided by a fixed threshold, which has been set equal to the absolute value of 0.9 in this paper. Then, the Pearson correlation coefficient is computed among all features through Equation (A1) to build the correlation matrix.  
\[r_{XY}=\frac{Cov(X,Y)}{\sqrt{Var(X)Var(Y)}}\] (A1)  
where \(X\) and \(Y\) are two random variables, \(Cov(X,Y)\) is the covariance of the two variables, and \(Var(X)\) and \(Var(Y)\) are the variance of the variable \(X\) and \(Y\), respectively. The Pearson correlation coefficient, \(r_{XY}\), is always included in the range \([-1,1]\), where \(-1\) indicates that the two variables are negatively correlated, \(1\) indicates that the variables are positively correlated, and \(0\) that the variables do not correlate. After the correlation matrix construction, an iterative process for feature elimination is conducted. Hence, for each column, and for each row, if \(r_{XY}\leq-0.9\) or \(r_{XY}\geq 0.9\), the column is eliminated.  
The principal component analysis (PCA) [60] is a feature learning method. It is a statistical analysis approach to mapping multiple characteristic parameters to a few comprehensive features. These PCA-based comprehensive features are not related to each other and can represent original fault features effectively [61]. Given a dataset \(X\), of dimension \(m\), PCA aims to find a set of orthonormal basis vectors of dimension \(p<m\), which are called principal components (PCs), that maximize the variance over the dataset when it is projected onto the subspace spanned by these PCs. Basically, if we have data points in a two-dimensional space and we want to project them in one-dimensional space, what PCA does is to find the direction of the vector and the position of the points on that vector, which is expressed by coefficients, such that the reconstruction or projection error is minimized. To this aim, the covariance matrix of the dataset is first computed, and the eigenvalues and eigenvectors are extracted. The eigenvectors correspond to the PCs, whilethe corresponding eigenvalues represent the variance associated with that PC. The PCs are selected so that the cumulative variance described by them is higher than a certain percentage (usually from 90 to 99%).