esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 1 Introduction
The comparison of the two methods in terms of the prediction accuracy of fatigue life, the complexity of parameter calibration and the relationship between fatigue and fracture lives is unclear. To determine the better method, we used the two methods to predict the damage of ULCF. Firstly, the material parameters of CDM and CVGM were calibrated on the basis of uniaxial tensile and strain cyclic loading tests. The finite element models suitable for CDM and CVGM were established on the basis of the calibration parameters. ULCF damage was predicted for the base metal and weld specimens. Compared with the CVGM model, the CDM model can predict the fatigue life and the relationships among fatigue and fracture lives, the post-fracture path and the number of cycles to initial damage. In addition, the parameter calibration is simple. CDM is superior to CVGM in predicting the ULCF damage of steel and its weld joints.
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 1 Introduction  
We are living in a world where geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates [1]. The efficient and innovative use of these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic influence has become an important challenge, which offers a wide range of research opportunities at the same time [2, 3]. This is particularly urgent for the hydrological research community at large who has relied on both spatially distributed and lumped hydrological models for hydrological simulations/predictions over the last several decades [4]. The demand for increasingly accurate water information spatially distributed at high resolution requires a deeper understanding of the underlying processes and more skillful predictions, at resolutions that do not commensurate with the traditional hydrological data. This is an important challenge to the conventional hydrological modelling.  
To address these challenges, efforts need to be made to innovatively integrate various remote sensing techniques into hydrological simulations and predictions, which is more critical in a changing world. This Special Issue of Remote Sensing contributes towards this aim through investigations on how a better and smarter use of high-to-moderate-resolution remote sensing datasets can improve hydrological simulations and predictions. Ten peer-reviewed papers are published in this Issue, which are grouped into four categories using remote sensing techniques:* Evapotranspiration estimation;
* Rainfall monitoring and prediction;
* Flood simulations and predictions; and
* Monitoring ecohydrological processes.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 4 Results
### Simulation of Wetting Front Retardation by Fracture-Matrix Transfer  
All results obtained from model TUFF2D show wetting-front velocities lower than those estimated by the hand calculations. The velocity differences mark the significant retardation of the front by fracture-matrix transfer. The front velocity diagrams Figure 19 reveals two distinct behaviours. Early time behaviour is marked by steep slopes, indicating rapid penetration of water into the fracture system. Subsequent front advance in the fracture system is up to three times slower. A similar behaviour is seen in the experiments conducted by [PERSON] and [PERSON] (2018), and referred to by these authors as early- and late-time response. For low fluid supply rates, capillary diffusion dominates the system, leading to extensive fracture-matrix transfer and front retardation. For this scenario, the aperture-related difference in wetting front velocity in model TUFF2D is small (Figure 19a, Table 4). Thus, during late-time behaviour, the delayed velocity of the wetting front is only one-tenth of the value obtained by the hand calculation.  
By contrast, in the high rainfall intensity scenario, gravity-driven fracture flow dominates the response of the system. Capillary-driven fracture-matrix transfer is relatively small. Consequently, front retardation is small and front velocity reaches about one third of the hand- calculation value (Figure 19b, Table 5). Also, the curves for the different fractures diverge more.  
As the last case that was considered for model TUFF2D, an elevated initial saturation of the rock matrix eliminates front retardation.  
Figure 18: Flow patterns of water and air near the wetting front in an initially dry version of model TUFF2D after 10 min of intense rainfall (Case B). Co- and counter-current flow at the wetting front are shown in the inserts on the left. Phase velocity is displayed in blue for water and green for air. Only phase velocities greater than \(10^{-6}\) m/s are displayed.
## 1 Introduction
[33; 34] utilized THz-TDS to detect TBCs' porosity, employing effective medium theory and a time-domain broadening ratio for porosity evaluation. Additionally, methods based on machine learning and image processing have been applied to assess the quality of thermal barrier coatings. [PERSON] et al. [35; 36] conducted analysis and processing of terahertz time-domain data using data-driven models and machine learning algorithms, facilitating feature extraction and classification for the evaluation of TBCs. This represented a major leap forward in combining machine learning with signal processing. The potential of THz-TDS combined with machine learning algorithms was emphasized to improve the accuracy of detecting TBCs, thus promoting the development of the field of NDT. In summary, the current methodologies employed for assessing TBCs' porosity using THz-TDS present opportunities for improvement. The porosity of such coatings is influenced by multiple factors, demanding the comprehensive consideration of various key feature parameters. To achieve a thorough evaluation and prediction of porosity, this research conducted a detailed analysis of terahertz time-domain data, extracting vital information regarding the porosity structure. Section 2 elaborates on the extraction procedures for multiple feature parameters from terahertz time-domain data, encompassing correlation and significance analyses. Additionally, dung-beetle-optimization-algorithm-optimized random forest (DBO-RF) prediction models and cross-validation techniques are presented. In Section 3, an exploration of the visual representation of the correlation between multiple feature parameters and porosity is undertaken, integrating these fused feature parameters as inputs for the machine learning model to predict porosity. The model's superior performance is evidenced through evaluation indicators. The prediction accuracy is over 90%. The primary focus of this study is to develop an effective approach for the nondestructive assessment and prediction of TBCs' porosity. By combining multiple feature parameters obtained from terahertz data and employing advanced machine learning algorithms, this research aims to establish a practical method for the quality control and evaluation of TBCs, particularly in the application of coating materials on aero-engine blades. The proposed approach holds significant potential for enhancing the understanding of TBCs' porosity and ensuring the performance and reliability of these coatings in critical engineering applications. Through this interdisciplinary investigation encompassing materials science and information sciences, we aim to contribute to the advancement of nondestructive evaluation methods for TBCs, enhancing their performance and reliability in critical aerospace applications.
## 1 Introduction
* The scheme proposed in this paper considers the total wiring distance and utilizes a two-hop availability checking algorithm to find the void nodes and improve the data transmission efficiency of the UWSNs. * We evaluated the performance of the proposed scheme with HH-VBF [5], GEDAR [6], and TADR-EAODV [7] in network simulation experiments. The simulation experiments demonstrate the superiority of the proposed scheme over existing protocols.
## 4 Results and Discussion
### Nickel Distribution and Enrichment  
Analysis of the chemical and mineralogical composition data from the boreholes in the central Halmahera area (an additional 37 locations) confirms the conclusions drawn from the analysis of the trench samples. A clear division exists between the upper level of weathering, i.e., limonite characterized by a dominance of iron oxides, mainly in the form of goethite and to a lesser extent magnetite and maghemite, and the deeper saprolite containing mainly weathered silicates and magnesium aluminosilicates.  
In the weathered profiles, Ni enrichment was found to be residual within the limonite horizon, and there was even a depletion in relation to Fe, although the absolute concentration is almost always higher than in the bedrock. A pronounced supergene enrichment in nickel only occurs in the transition zone and in some saprolite zones. Supergene enrichment in Co occurs, especially in the lower part of the limonite horizon, decreases in the transition zone, and in the saprolite horizon its value is much less than 1, indicating depletion (Figure 7). The residual enrichment in this element, however, occurs in the upper part of the limonite corresponding approximately to the disrupted limonite. The different behavior of the two elements is due to the fact that Ni, unlike Co, enters the structure of many minerals present in the deposit at different levels, i.e., especially goethite, serpentine, talc group minerals, smectites and manganese minerals. On the other hand, Co is bound for the most part by manganese minerals occurring abundantly especially in the limonite level with a partially preserved structure, i.e., in the lower part of the oxidized zone.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Few-Shot/Zero-Shot Semantic Segmentation
First, due to the limited or absent annotated data, the accuracy and robustness of these methods may be reduced. Second, these methods require modeling of the semantic information and properties of the classes, leading to computational complexity and increased time cost for model learning and inference. Lastly, the generalization ability of these methods needs further improvement. Future research can focus on several areas. Few-shot and zero-shot semantic segmentation models can be improved to enhance their accuracy and robustness in scenarios involving scarce annotated data. Moreover, other efficient methods of learning semantic information and properties can be explored to reduce the computational complexity and time cost of models. The research on the generalization ability of few-shot and zero-shot semantic segmentation methods can also be deepened to explore effective generalization methods and strategies.
## 3 Numerical Study
### Material Model and Input Parameters  
#### 3.2.1 Soil Constitutive Model and Parameters  
This study is concerned with the deformation of the surrounding soil caused by the excavation of the foundation pit and the effect on the existing tunnel. The HSS model is a mature advanced model with complete correspondence between the model parameters and common engineering test parameters. The HSS model not only describes the mechanical behavior of soil under loading, unloading, and consolidation but also introduces small-strain properties for more accurate soil deformation prediction [21]. In the conventional numerical analysis of geotechnical engineering, the soil deformation results calculated by the HSS model are most consistent with the actual engineering. It is significantly better than other soil ontological models, such as the commonly used the Mohr-Coulomb model, modified Cam-Clay model, and Duncan-Chang model [22]. Therefore, in this study, the HSS model is used as the constitutive model of soil and it has 10 main parameters, including heaviness \(\gamma\), strength parameters \(c\) and \(\varphi\), and seven stiffness-related parameters. Among them, \(E_{50}\)\({}^{\text{ref}}\) is the triaxial drainage cutline stiffness, \(E_{\text{o}\text{o}\text{o}\text{o}\text{o}\text{o}\text{o}\text{o}\text{o} \text{o}\text{o}\text{o}\text{o}\text{o}\text{o}\text{o  
Nevertheless, only the \(E_{\text{s(1-2)}}\) of the plain fill and silty clay are provided in the geological investigation report, the \(E_{\text{s(1-2)}}\) of the other soils need to be obtained from the SPT and HDPT data. Empirical formulas for calculating the friction angle \(\varphi\) and cohesion \(c\) of the soil using the modified blow count \(N_{60}\) of the SPT are given in Equations (5) and (6), in which the \(I_{\text{p}}\) and \(p\) are plasticity index and fines content of soil [23]. The literature [24] gives the relationship between \(N_{60}\) and \(E_{\text{s(1-2)}}\) in Equation (7).  
\[c=\ 0.00054\ \times\ I_{\text{p}}+0.005\times N_{60}+0.09 \tag{5}\]  
\[\varphi=0.24\times N_{60}+0.0061\times I_{\text{p}}-0.313\times p+43.42 \tag{6}\]  
\[E_{\text{s(1-2)}}=4.6+0.21N_{60}\ \ \text{(for c$\
eg$\varphi$ soil, and $3\leq N_{60}\leq 15$)} \tag{7}\]  
Figure 7 illustrates 77 sets of data of the HDPT blow count \(N_{63.5}\) for the highly weathered muddy sandstone. The average of \(N_{63.5}\) is 15.1, and the coefficient of variation is 0.16. Although the range of the data is large, most of the data are concentrated around the average value of \(N_{63.5}\) which can reflect the soil strength and stiffness when the number of tests is large enough. Treating the strongly weathered rock as cohesionless soil according to the weathering products, the deformation modulus \(E_{0}\) and internal friction angle \(\varphi\) of this soil layer can be obtained using Tables 2 and 3[25], and then \(E_{0}\) is converted to compression modulus \(E_{\text{s(1-2)}}\) using [PERSON]'s rate \(\mu\) and the theoretical conversion equation is Equation (8) [26].  
\[E_{s}=E_{0}\frac{1-\mu}{(1+\mu)(1-2\mu)} \tag{8}\]
# Anthocyanin Delivery Systems: A Critical Review of Recent Research Findings
## 4 Comparison of Formulation Techniques  
Several research studies have explored ACN-based delivery systems and their potential applications in the food industry. The variations observed in the properties of the ACN-based delivery systems can be attributed to the choice of wall material and preparation techniques, making ACNs highly versatile additives for food and non-food applications. Despite their potential for industrial applications, ACN-based delivery systems suffer from certain drawbacks, restricting their application. The primary limiting factor in this regard is the inherent instability of ACNs which has remained a major concern in terms of their use in the processing of different food products. More recent research has indicated that ACNs exhibited optimal stability and functionality at low storage temperatures and pH values under 5 [125, 165]. The stabilization of ACNs can be achieved by, among other things, various microencapsulation techniques, which can be categorized into chemical, physical, and physicochemical methods. Spray drying has become a promising method for the encapsulation of ANCs, as it is rapid, produces a high yield of encapsulated ACNs, and is easy to scale up if process parameters are optimized [166]. During surface drying, the optimization of process parameters including time and temperature is critical, as it may manifest in the production of unstable, non-uniform, or degraded ACNs, ultimately culminating in the loss of yield [167]. Freeze-drying is also regarded as an effective technique for the stabilization of the ACNs, provided that low temperature and pressure are maintained, as variations in temperature and pressure render ACNs more prone to oxidation, and therefore, instability [168]. However, long processing times, energy expenditure, instability, susceptibility to oxidation, and high process costs are major drawbacks of freeze-drying [167]. The use of electrohydrodynamic (EHD) processes including electrospinning and electrospraying (EHD atomization) has demonstrated multiple advantages over other encapsulation techniques in the context of encapsulation of food ingredients, such as low-cost (one-step production process), operation under mild conditions, the possibility of use of the majority of food-grade materials, high encapsulation efficiencies, and the feasibility of tailoring the size and morphology of the resulting encapsulated structures by altering the processing conditions [169]. One of the major considerations regarding this technique is the negative effect of high voltage on the quality of bioactives such as ACNs and biopolymer materials [170]. However, the research encompassing the effect of high voltage on the release rate of the ACNs is insufficient [171]. Further research into EHD processes, therefore, can improve the outcomes for encapsulated ACNs, and their application in food fortification and food packaging operations.
## 2 Data Acquisition
### Data Preparation
Figure 5: The computation of knee joint angles from proximal and distal inertial sensors. Figure 6: Dataset preparation using a sampling window. For deeper analysis, three varieties of input datasets were created. One dataset has only inertial data of one of the four sensor positions on either leg, which is shaped into a 100 \(\times\) 6 array. Another dataset consists of inertial data of both feet (bFID) and pelvis inertial data (PID). The resultant dataset was then structured into a 100 \(\times\) 18 array. The 18 columns are the 6-axis inertial data of the pelvis and both feet. This set was created to examine the estimation performance improvement by combining the inertial data of the pelvis and both feet. The last one adds the subjects' biometric information to the PID. Each person has a distinctive gait, step size, walking speed, and range of motion. Age, gender, weight, and height are among the factors that could affect these variations. Hence, adding this information to the training process could improve the estimation accuracy. Except for gender, the other quantities are expressed numerically. Hence, gender was represented with a binary quantity that 1 indicates male participants while 0 is for female participants. As a result, the last dataset will have two separate inputs: a 100 \(\times\) 6 PID and 4 \(\times\) 1 biometric information data (BID). A total of 50,973 datasets were prepared for deep learning. First, it was divided into three categories as follows: 84.5% of the datasets for training, 14% of the datasets for validation, and the rest 1.5% of the datasets for testing. The testing dataset was collected from a separate subject whose data are not included in the training. The testing data from the 16 th subject, which is less than 10 min data, is a new and unencountered dataset for the trained model.
###### Abstract  
Steel-fiber-reinforced concrete (SFRC) is a composite material made by randomly distributing short steel fibers in normal concrete (NC). In this study, central pull-out tests of 32 specimens were performed to investigate the bond behavior between high-strength rebar and SFRC under the influence of the fraction of steel fiber by volume (\(V_{f}\) = 0%, 0.5%, 1.0% and 1.5%) and temperature (\(T\) = 20, 200, 400 and 600 \({}^{\circ}\)C). The results show that in NC specimens, splitting failure occurs below 400 \({}^{\circ}\)C, while split-pullout failure occurs above 600 \({}^{\circ}\)C. Split-pullout failure occurs in all SFRC specimens at each tested temperature. The bond strength between rebar and SFRC was found to decay significantly between 400 and 600 \({}^{\circ}\)C. The effect of \(V_{f}\) on the improvement in bond strength was more obvious between 400 and 600 \({}^{\circ}\)C than between 20 and 400 \({}^{\circ}\)C. The positive contribution of steel fibers to bond behavior is the construction of a rigid skeleton with coarse aggregates that can play a bridging role and effectively retard the expansion of concrete cracks. This improves the bond strength between rebar and SFRC at high temperatures. The bond-slip curve can be divided into five stages, namely the initial micro-slide phase, slip phase, splitting failure phase, stress drop phase and residual pull-out phase. A model of the bond-slip relationship between rebar and SFRC considering temperature and \(V_{f}\) was developed by modifying the existing model of the bond-slip relationship between rebar and NC. The model calculation results agree well with those of testing.  
SFRC; high temperature; bond behavior; fraction of steel fiber by volume; bond-slip relationship +
Footnote †: journal: Applied Sciences  
Article
## 4 Numerical Example
### Results of Dataset 1
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Model** & **MSE** & **PRR** & **PP** & **MAE** & \(\mathbf{R^{2}}\) & \(\mathbf{adj_{z}R^{2}}\) & **AIC** & **BIC** & **PRV** & **RMSPE** & **TS** & **PC** & **PIC** \\ \hline GO & 3017.775 & 2.426 & 14.218 & 47.533 & 0.991 & 0.991 & 636.406 & 640.420 & 52.680 & 54.392 & 4.720 & 213.381 & 159,944.1 \\ \hline DS & 539.960 & 6.214 & 1.137 & 20.579 & 0.998 & 0.998 & 532.665 & 536.679 & 22.463 & 23.011 & 1.996 & 167.781 & 28,619.9 \\ \hline IS & 587.384 & 1.031 & 3.516 & 20.749 & 0.998 & 0.998 & 544.631 & 550.653 & 23.501 & 23.778 & 2.062 & 167.425 & 30,547.1 \\ \hline YID1 & 3078.198 & 2.443 & 14.389 & 48.499 & 0.991 & 0.991 & 637.655 & 643.677 & 52.606 & 54.412 & 4.721 & 210.492 & 160,609.4 \\ \hline YID2 & 3699.782 & 2.339 & 13.480 & 53.137 & 0.989 & 0.989 & 667.306 & 673.328 & 59.058 & 59.677 & 5.176 & 215.274 & 192,391.8 \\ \hline YR & 2216.822 & 24.191 & 2.545 & 42.812 & 0.994 & 0.993 & 620.157 & 628.187 & 43.891 & 45.723 & 3.968 & 198.758 & 113,062.1 \\ \hline YE & 3136.404 & 2.427 & 14.230 & 49.395 & 0.991 & 0.990 & 640.449 & 648.478 & 52.622 & 54.394 & 4.720 & 207.606 & 159,960.8 \\ \hline PZ & 285.207 & 0.447 & 1.004 & 13.543 & 0.999 & 0.999 & 527.946 & 537.982 & 16.222 & 16.250 & 1.409 & 144.348 & 14,265.7 \\ \hline PNZ & 279.679 & 0.448 & 1.006 & 13.277 & 0.999 & 0.999 & 525.986 & 534.015 & 16.223 & 16.252 & 1.409 & 145.968 & 14,267.9 \\ \hline IFD & 1883.516 & 1.610 & 6.338 & 37.886 & 0.995 & 0.994 & 593.414 & 599.436 & 41.885 & 42.576 & 3.693 & 197.720 & 97,945.9 \\ \hline TC & 302.382 & 0.135 & 0.189 & 14.502 & 0.999 & 0.999 & 518.597 & 528.634 & 16.720 & 16.733 & 1.451 & 145.810 & 15,124.5 \\ \hline TP & 293.365 & 0.305 & 0.584 & 14.301 & 0.999 & 0.999 & 525.980 & 540.031 & 16.136 & 16.148 & 1.400 & 140.962 & 14,089.4 \\ \hline
3P & 281.614 & 0.297 & 0.563 & 13.698 & 0.999 & 0.999 & 521.957 & 531.994 & 16.137 & 16.148 & 1.400 & 144.031 & 14,086.1 \\ \hline Vtub & 287.974 & 0.443 & 1.208 & 14.127 & 0.999 & 0.999 & 524.983 & 535.020 & 16.313 & 16.329 & 1.416 & 144.589 & 14,404.1 \\ \hline NEW & 273.018 & 0.694 & 0.351 & 13.483 & 0.999 & 0.999 & 518.486 & 526.515 & 16.057 & 16.058 & 1.393 & 145.353 & 13,928.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of criteria from Dataset 1. Figure 2: Prediction of all models for dataset 1.
## 2 Materials and Methods
### Electromyogram (EMG) Signal Recording and Processing  
A Trigno Wireless EMG System (Delsys Inc., Natick, Massachusetts, USA) was used to collect the EMG signals during stair climbing. EMG is a non-invasive technique for the assessment of the myoelectric signal [22] and useful for understanding muscle activity [23]. The EMG sensors wereattached to the gastrocnemius lateralis (GL), gastrocnemius medialis (GM), soleus (SOL) and tibialis anterior (TA) of the participant's leg [24]. The raw EMG signal was resampled at 1000 Hz, rectified, and smoothed at a cut-off frequency of 6 Hz for Butterworth low-pass fourth-order filter. EMG amplitudes were normalized to the maximum peak of each trial.