esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 5 Conclusions  
On the basis of the numerical simulations using the new model established in this paper, suggestions are drawn from the numerical simulations that can provide safety guidance for the management of annular pressure:  
1. [label=(0)]
2. Cement hydration heat is the root reason for annular pressure. The cement hydration process is obviously affected by the environmental temperature in the wellbore. A higher temperature in the well bottom accelerates the cement hydration process. Thus, the waiting time for cement thickening mainly depends on the hydration process at the wellhead. 3. The temperature in the wellbore shows an obvious increase as a result of the cement hydration heat. The thermal expansion caused by the temperature increase is the main reason for annular pressure. The cement temperature development follows the same rules as the cement hydration degree. In general, the cement temperature increases at first, and then decreases to the environmental temperature slowly. 4. The annular pressure shows a rapid increase and then decreases, which is similar to that of the temperature. As a result of the cement hydration process, the annular pressure increases with the geothermal gradient, the cement hydration heat, and the wellbore diameter. A sensitive analysis can provide safety guidance for the management of annular pressure. Figure 14: Annular pressure under different wellbore diameters. **Author Contributions:** Conceptualization, X.W. and X.P. ; methodology, X.W. and Z.W. ; software, X.W. ; validation, M.X., Y.R. and Y.F. ; writing--original draft preparation, X.W. and Z.Z. ; writing--review and editing, B.S. and X.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation for Youth (52004315), the Natural Science Foundation of Shandong Province (ZR2020 QE113, ZR2021 ME001), and the National Natural Science Foundation (U21B2069, 51974352). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 3 Testing Results
### Strain Incremental Rate Analysis  
This section discusses the impact of RBL on rock failure by analyzing the incremental rates of axial and volumetric strain. Typically, strain rate serves as a metric to quantify the rate of damage incurred by a rock during deformation. The consensus exists that irrespective of whether a rock experiences creep, static, or cyclic loading [27; 28; 29], the strain  
Figure 6: Evolution of damping ratio with cycle number at different loading stages. (**a–d**) Marble samples containing stepped fissures with rock bridge lengths of 10, 20, 30, and 40 mm, respectively.  
Figure 7: The evolution of dynamic elastic modulus (E\({}_{\mathrm{d}}\)) with cycle number at different loading stages for marble samples containing stepped fissures with rock bridge lengths of 10 mm (**a**), 20 mm (**b**), 30 mm (**c**), and 40 mm (**d**).  
rate consistently correlates positively with the damage ratio. In this context, the strain incremental rate refers to the incremental change in strain (axial, radial, or volumetric) at the upper stress point of each cyclic loading stage (CLS), as depicted in Figure 8. This rate is calculated by dividing the difference in strain values between the different cycles by the number of cycles elapsed: \((\varepsilon_{\text{an}}-\varepsilon_{\text{a1}})/(\text{N}-1)\), where \(\varepsilon_{\text{an}}\) and \(\varepsilon_{\text{a1}}\) represent the axial strain of the Nth and 1 st cycles, respectively, at the upper stress level. The cycle number (N) in each CLS is 30 in this work. This study focuses on analyzing the axial strain rate (\(\varepsilon_{\text{ar}}\)) and volumetric strain rate (\(\varepsilon_{\text{vr}}\)), while the circumferential LVDT device records local deformation as radial strain. The calculation of incremental axial and volumetric strain rates is detailed in Tables 2 and 3. The gradual failure of marble is observed to be influenced by RBL, and sudden increases in axial and volumetric strain are attributed to the formation of rock bridges. Regions highlighted in green, yellow, and red denote varying levels of risk. It is noted that the risk levels categorized by axial and volumetric strain rates exhibit distinct patterns. As the volumetric strain rate is a comprehensive reflection of axial and radial deformation, the early warning using the volumetric rate is favorable for instability prediction. From Table 3, it is shown that warning predictions are found at the 5 th, 8 th, 10 th and 11 th levels; these are earlier than the warnings using axial strain rate (i.e., the 7 th, 11 th, 11 th, and 13 th levels; Table 2).  
Figure 8: Definition of axial, radial, and volumetric strain incremental rates. (**a–d**) Marble samples containing stepped fissures with RBLs of 10, 20, 30, and 40 mm, respectively.
# Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt
###### Abstract  
Stone Mastic Asphalt (SMA) is a tough, stable, rut-resistant mixture that takes advantage of the stone-to-stone contact to provide strength and durability for the material. Besides, the warm mix asphalt (WMA) technology allows reducing emissions and energy consumption by reducing the production temperature by 30-50 \({}^{\circ}\)C, compared to conventional hot mix asphalt technology (HMA). The dynamic modulus \(|\)E*\(|\) has been acknowledged as a vital material property in the mechanistic-empirical design and analysis and further reflects the strains and displacements of such layered pavement structures. The objective of this study is twofold, aiming at favoring the potential use of SMA with WMA technique. To this aim, first, laboratory tests were conducted to compare the performance of SMA and HMA through the dynamic modulus. Second, an advanced hybrid artificial intelligence technique to accurately predict the dynamic modulus of asphalt mixtures was developed. This hybrid model (ANN-TLBO) was based on an Artificial Neural Network (ANN) algorithm and Teaching Learning Based Optimization (TLBO) technique. A database containing the as-obtained experimental tests (96 data) was used for the development and assessment of the ANN-TLBO model. The experimental results showed that SMA mixtures exhibited higher values of the dynamic modulus \(|\)E*\(|\) than HMA, and the WMA technology increased the dynamic modulus values compared with the hot technology. Furthermore, the proposed hybrid algorithm could successfully predict the dynamic modulus with remarkable values of R\({}^{2}\) of 0.989 and 0.985 for the training and testing datasets, respectively. Lastly, the effects of temperature and frequency on the dynamic modulus were evaluated and discussed.  
stone mastic asphalt; warm mix asphalt; hot mix asphalt; dynamic modulus; artificial neural network; teaching-learning-based optimization Article
## 4 Conclusions  
In this study, low-density polyethylene (LDPE)/natural rubber (NR) compounds were prepared by both melt blending and dry blending to produce parts via rotational molding. In particular, the effect of rubber regeneration, ground tire rubber (GTR) content, oven temperature, heating time, rotational speed, and particle size for both the matrix and filler was evaluated. On the samples produced, complete morphological (optical and scanning electron microscopy) and mechanical characterization (tension, flexion, and Charpy impact) was performed, with physical properties of interest (density and hardness) for thermoplastic elastomers (TPE) also evaluated.  
According to the results obtained, several conclusions can be made.  
Firstly, it was observed that at low GTR content (0-20 wt %), a homogeneous distribution of the rubber phase in LDPE can be achieved. However, at higher GTR content (up to 50 wt %), a higher number of bubbles and defects was generated. In our case, it was not possible to use GTR content above 50 wt %, especially for dry-blended samples.  
Figure 13: Impact strength as a function of GTR concentration. (**a**) dry blending and (**b**) melt blending.  
Secondly, the mechanical properties of rotomolded samples decreased with increasing GTR content due to poor adhesion at the interface between GTR and LDPE and the low pressure inside the mold leading to the creation of voids inside the parts. For example, the GTR content not only decreased both the flexural and tensile moduli, but also decreased the ultimate strength, strain at break, and impact strength in samples from both the dry and melt blending techniques. This is also related to the lower rigidity of GTR particles compared to the LDPE matrix.  
Thirdly, the properties of dry-blended parts were usually slightly lower than those of melt-blended ones due to limited shear and high pressure in the dry blending technique leading to poor adhesion between LDPE and GTR. This also produced a higher number of bubbles and defects compared to melt-blended samples.  
Finally, most of the compounds can be considered as good TPE since their elongations at break were above 100% with up to 20 wt % GTR. Nevertheless, more work should be done to improve on these results, especially by performing GTR surface treatment before its introduction into the LDPE matrix to improve interfacial interactions. This work is currently under investigation using different techniques.  
Conceptualization, D.R.; methodology, D.R. and R.S.; validation, D.R.; formal analysis, R.S.; investigation, R.S.; resources, D.R.; data curation, D.R.; writing--original draft preparation, R.S.; writing--review and editing, D.R.; supervision, D.R.; project administration, D.R.; funding acquisition, D.R. This research received no external funding.  
The authors acknowledge the technical support of the Research Center on Advanced Materials (CERMA). The materials used for this work were kindly supplied by Phoenix Innovation Technologies (Montreal, Canada). Finally, the technical help of [PERSON] was highly appreciated.  
The authors declare no conflict of interest.
## 3 Results
### Descriptive Statistics and Group Effects  
Descriptive statistics for all functional and asymmetry-related variables are summarized in Table 1. At baseline, no significant differences were observed between the experimental group (EG) and the control group (CG) across any variable. Following the 4-week intervention, the EG demonstrated statistically significant improvements in key performance indicators.  
Jump Height increased from 28.97 \(\pm\) 8.79 cm to 31.21 \(\pm\) 7.86 cm (\(p\) = 0.030), RSI improved from 2.17 \(\pm\) 0.64 to 2.75 \(\pm\) 0.67 (\(p\) = 0.012), and Peak Power rose from 7493.24 \(\pm\) 2245.16 W to 7973.06 \(\pm\) 3360.97 W (\(p\) = 0.028). Although Active Stiffness increased by over 30% (from 26,224.18 \(\pm\) 15,426.95 N/m to 36,234.00 \(\pm\) 17,153.25 N/m), the change did not reach significance (\(p\) = 0.066).  
In contrast, the control group exhibited no significant pre-post differences in any performance outcome (all \(p\) > 0.05), confirming that the observed improvements were attributable to the intervention protocol and supporting Hypothesis H4.  
Between-group post-test comparisons (Table 2) further confirmed these results: significant differences were observed in Jump Height (\(p\) = 0.001, d = 0.74) and RSI (\(p\) = 0.001, d = 0.90), favoring the EG. However, differences in Peak Power (\(p\) = 0.186), Active Stiffness (\(p\) = 0.776), and asymmetry indices remained statistically non-significant. Notably, FER was significantly lower in EG post-intervention (\(p\) = 0.031, d = -0.46), suggesting greater force production efficiency.
## 3 Results and Discussion
### Characterization of the Catalyst  
XRF analysis revealed the following composition of the catalyst: Co\({}_{3}\)O\({}_{4}\)--13.7 wt%; ZnO--2.65 wt%; K\({}_{2}\)O--0.22 wt%; SO\({}_{3}\)--0.70 wt%; and Al\({}_{2}\)O\({}_{3}\)--82.73 wt%. The analysis revealed the presence of sulfur trioxide on the catalyst. The composition of the catalyst after operation in the reactor did not change and is consistent with the original specifications. The only observed disparity is the presence of sulfates in the catalyst sample.  
Surface composition analysis by XPS revealed the presence of sulfur (2.5%) in the sample after operation in the reactor (Figure 3B). The position of the maximum for S at a Binding Energy (BE) around 170 eV indicates the presence of sulfur in the form of sulfates [36]. Analysis of the catalyst sample that was not placed in reactor (Figure 3A) did not show the presence of sulfates.  
Porograms of the carrier (\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)), the fresh catalyst (K-CoZn/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)_fresh), and the catalyst after trials (K-CoZn/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)_used) are presented on Figure 4. After operation, the morphology of the catalyst shifts to bimodal distribution with the majority of pores in the same range as the carrier and fresh catalyst and the second peak shifted towards greater pore diameters (-900 nm). The smaller pore volume is a result of long-term exposure (sintering) at high temperature, but it does not have a negative impact on the catalyst's activity [15].  
Figure 4: Porograms of the carrier and catalyst before and after operation.  
Figure 3: (**A**) XPS sulfur spectra for a fresh catalyst sample. (**B**) Sample after laboratory and pilot-scale trials.  
Table 4 summarizes the textural properties of the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\), the fresh catalyst K-CoZn/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)_fresh, and K-CoZn/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)_used.
## 2 Methodology
### Data Preprocessing
#### 2.2.3 Missing Data Imputation  
Data completeness is an important prerequisite for ensuring analytical accuracy. This study conducted comprehensive missing value checks on the processed trajectory data. Fortunately, no missing values were found in the data after the first two processing steps, possibly due to the well-designed data collection system. Therefore, this study did not perform missing data imputation operations, but retained this module in the methodological framework to address potential missing value issues in other datasets.
## 2 Data and Methodologies
### ERA5 Data  
The production of the ERA5 dataset was based on the operational use of the Integrated Forecasting System (IFS) Cycle 41r2, with the advanced four-dimensional data assimilation technique and modelling, which provides estimates of atmospheric parameters on an hourly basis from 1950 onwards. More information regarding the ERA5 configuration, how it was produced, and how it fits into the framework of other reanalysis activities in the ECMWF can be found in the study by [PERSON] et al. (2014).  
With the hourly temperature \(T\) (in Kelvin), specific humidity \(q\) (in kg kg\({}^{-1}\)), and pressure \(P\) (in hPa) at 37 pressure levels provided by ERA5, the \(ZTD\) (in meters) was calculated with Equation (1) ([PERSON] et al., 2014).  
\[ZTD=10^{-6}k_{1}R_{d}\int_{0}^{P_{ant}}\ \frac{1}{g(z)}\cdot dP+10^{-6}R_{w}\ \int_{0}^{P_{ant}}\ q\frac{1}{g(z)}\left(k_{2}^{\prime}+\frac{k_{3}}{T} \right)\,dP \tag{1}\]  
where \(R_{d}=287.053\) J\(\cdot\)K\({}^{-1}\cdot\)Kg\({}^{-1}\) and \(R_{w}=461.5\) J\(\cdot\)K\({}^{-1}\cdot\)Kg\({}^{-1}\) are the gas constants for dry air and water vapour, respectively; k\({}_{1}=77.60\) KhPa\({}^{-1}\) and k\({}_{2}^{\prime}=22.1\) KhPa\({}^{-1}\)(K  
\[\left\{\begin{split} S_{s}\approx 9.80620\cdot\left(1-0.0026442\cdot cos (2\varphi)+5.8\cdot 10^{-6}\cdot cos^{2}(2\varphi)\right)\\ R_{s}=6378.137/\left(1.006803-0.006706\cdot\sin^{2}(\varphi) \right)\end{split}\right. \tag{3}\]  
The PWV (in meters) can be calculated using:  
\[\mathrm{PWV}=\frac{1}{\rho_{w}}\int_{0}^{P_{ant}}\frac{q}{g(z)}dP \tag{4}\]  
where \(\rho_{w}=1000\) kgm\({}^{-3}\) is the density of water vapour, \(P\) is the pressure (in Pa), and \(q\) is the specific humidity.  
For GNSS stations located above or below the lowest pressure level of ERA5, an interpolation or extrapolation procedure must be performed to determine the pressure, humidity, and temperature at the GNSS station altitude. More details on the extrapolation or interpolation procedures can be found in our previous studies [49; 50]. The procedures for converting the GNSS altitude referring to an ellipsoid to an altitude relative to the mean sea level were also described in our previous study [49].
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Proposed Approach  
#### 3.1.1 Overall Network Structure  
The overall network structure of our proposed model is designed to leverage FastSAM as the encoder for feature extraction, taking advantage of its efficiency and strong generalization capabilities. The network adopts a Siamese structure, which processes the input bitemporal images in parallel, allowing shared and consistent feature extraction. Specifically, the extracted features are further enhanced through two specialized branches for global information enhancement and differential information enhancement, respectively. These enhanced features are then fused through a global and differential information fusion module, which integrates the complementary aspects of both types of information to generate a comprehensive representation of changes. Finally, the fused features are decoded through the decoder to produce the final change detection map.  
The overall structure is visualized in Figure 2, where the snowflake icons indicate frozen parameters of the FastSAM encoder, while the spark icons represent trainable parameters. This distinction highlights the effective adaptation of the pre-trained FastSAM model within the Siamese structure while focusing the training on task-specific modules, ensuring both computational efficiency and optimal performance.
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 3 Results and Discussion  
### Linear and Rotation Scan Images  
Line scan is a convenient way for imaging cylindrical object because an unwrapped image of the entire surface without distortion can be obtained by rotating the cylinder when scanning. Furthermore, line-scan allows the application to adjust the image pixel aspect ratio by controlling the scan line pitch. If the image pixel aspect ratio was greater than one when imaging a cylindrical object by rotation scan, the distance between scan lines would be greater than the width of scan line, and thus the surface between could not be interrogated. In this work, the rotation scan was set up to let the image of a cylinder with a diameter of 60 mm under rotation scan had a 1:1 image pixel aspect ratio. By using this setting, all of the image pixel aspect ratios of jujube specimens (diameter < 60 mm) under rotation scan were less than one. Therefore, the surface feature of specimens would not be missed.  
The pseudo color images of typical jujube specimens obtained by using linear and rotation scan are shown in Figure 4. Because the specimen was put on the rotation stage during scan, a part of the jujube surface was blocked by the supporting plate when scanning. The viewable surfaces of specimens using linear and rotation scan methods are about 49% and 95% of the whole fruit surface respectively. By using linear scan, the intensity distortion of the peripheral areas of fruit was severe due to the curved shape of fruit [14], and thus the pixels in peripheral areas were difficult to use for surface defect detection because the intensity of these pixels was too low. To cover the whole surface of fruit without serious distortion by linear scan, the images taken from the six views shown in Figure 4b were required. Figure 4c shows the rectangular images of specimen obtained using rotation scan. These images were similar to the two-dimensional flat surface of Earth's curved surface obtained by a cylindrical projection. With the experimental setup used for the rotation scan in this work, the distortion of image is minimal in equatorial regions and maximal at the poles. Because surface defect detection is a qualitative analysis, the shape distortion in the images and replicated sampling of some surface regions are not problems. In the fruit images obtained by using rotation scan, the areas with severe intensity distortion were only located near the upper and lower boundaries of fruit images. Thus the percentage of area with severe intensity distortion of images obtained by rotation scan was less than that of images obtained by linear scan. Furthermore, it can be seen from Figure 4c that the areas occupied by the rectangle and circles could cover the whole surface of specimen without severe distortion. Defect detection can be performed on the pixels in these areas only.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 4 Methodology
### Flexibility and Optimization Capabilities of the Proposed Healthcare Scheduling Model  
The proposed model allows for parameter variation and distinct outputs based on the optimization goals. Healthcare scheduling may be optimized by changing the model's parameters, including weights, costs, penalties, skill levels, demand, and constraints. This versatility allows the model to be tailored to specific demands, resource availability, patient requirements, and organizational decisions. Considering many aspects and constraints, the model optimizes staff, patients, resources, and time slots. The model's flexibility and optimization facilitate healthcare scheduling decision-making and process improvement.