esa-sceva/satcom-chunk-collection huggingface dataset:
# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 2 Methods  
### Crack Segmentation Model for Plunge Pool Corridors Based on the Improved U-Net  
U-Net, proposed by [PERSON] in 2015, is a general-purpose semantic segmentation model with a typical symmetric encoder-decoder network architecture, which has shown outstanding performance in the field of semantic segmentation for medical images. The U-Net model performs feature extraction of target objects through four down-sampling operations and then restores the spatial resolution of the image through four up-sampling operations and skip connections, ultimately achieving the semantic segmentation task. The original U-Net model is shown in Figure 1.  
The crack images in plunge pool corridors have complex backgrounds and diverse target pixel shapes. The original U-Net model uses traditional convolution, where the fixed kernel shape and size result in poor performance when processing different receptive field sizes. Additionally, using the same convolutional kernel for the entire image limits the feature extraction capability in different regions, which affects the segmentation results. The up-sampling and down-sampling structure makes the network insensitive to the precise location of the target, leading to positional deviations. To address these issues, this paper proposes the integration of morphological irregular dynamic convolution (MIDConv) to capture the morphological features of cracks and extract edge feature information. Furthermore, a cross-dimensional interaction attention module (CDIAM) is incorporated to enhance the model's ability to extract spatial semantic information of cracks. The improved model is shown in Figure 2.
## 3 Results
### Mixing Layer Growth Process  
To quantitatively evaluate the effect of WJISW on the mixing characteristics of the mixing layer, a velocity thickness index \(\delta_{b}\) is adopted, and it can be defined as the transverse distance between two positions with normalized mean velocity of 0.1 and 0.9,  
\[\delta_{b}=y_{\overline{n}=0.9}-y_{\overline{n}=0.1} \tag{36}\]  
where the normalized time-averaged velocity is defined as  
\[\overline{u}=\frac{U(y)-U_{1}}{U_{2}-U_  
thickness as Case 2, and the final velocity thickness at x = 200 mm in Case 3 has increased by 62% compared to Case 1, which greatly improves the mixing level.  
Because the interaction between shock wave and the mixing layer will cause the total pressure loss of the flow field, and the total pressure loss coefficient is an important index to evaluate the actual operation of the engine, we perform a detailed analysis of the total pressure loss coefficient, which is defined as,  
\[\zeta=1-\frac{\int\mathrm{P_{x}}\mathrm{pudA}}{\int\mathrm{P_{0}}\mathrm{pudA}} \tag{38}\]  
where \(\mathrm{P_{x}}\) is the time-averaged total pressure of a certain streamwise position and \(\mathrm{P_{0}}\) is the time-averaged total pressure at the inlet position; \(\mathrm{\rho}\) and \(\mathrm{u}\) represent the time-averaged density and velocity, respectively. The total pressure loss distribution along the streamwise x direction for three cases is shown in Figure 11. It can be seen from the figure that when jet shock interacts with the mixing layer, although the total pressure loss coefficient increases to a large extent at the jet ejection position, the growth rate of the total pressure coefficient obviously slows down at x = 75 mm. Eventually, at x = 200 mm, the total pressure loss of Case 3 is larger than Case 2, and the total pressure loss of Case 2 is larger than Case 1. Notably, due to the introduction of shock waves, many techniques [45; 46] for mixing enhancement cause a large loss in total pressure. In comparison, the flow loss of our scheme is acceptable. Besides, the total pressure loss of Case 3 is only slightly larger than that of Case 2, whereas Case 3 can achieve a higher mixing enhancement than Case 2. Therefore, in terms of comprehensive measurement of total pressure loss and mixing enhancement capacity, WJISW in a low frequency condition is a better choice.  
Figure 10: Comparison of velocity thickness of three cases.
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
## 3 Results
### Morphology
#### 3.1.2 Hot Cutting Analysis  
Figure 11 shows the sealed ends of the different PCFs. Three temperatures for hot cutting were tested (RT, 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C). When analyzing Figure 11, for the cuts made at RT, larger chips and imperfections are noted since the temperature for cutting and sealing was low and insufficient to seal the ends of the PCFs. At a temperature of 50 \({}^{\circ}\)C, a smaller amount of chips was observed, with the PCFs produced with average ejection velocities for PEG (Figure 11c,d) having better appearances. Finally, for the PCFs cut and sealed at 100 \({}^{\circ}\)C, chips were again observed for the lowest and highest PEG ejection velocity (Figure 11a,d), but with a better appearance than those cut at RT.  
Figure 10: Micrographs of the morphology of PCFs incorporated with PEG 2000 obtained by Bright-field microscopy. (**a**) PCF_50/50_120; (**b**) PCF_50/50_130; (**c**) PCF_50/50_140; (**d**) PCF_50/50_150.  
As mentioned previously, the only variable parameter between the four PCFs was the PEG ejection velocity, keeping the CA molecular weight the same to verify the reactions in hot cuts at different temperatures. CA has a glass transition temperature (Tg) between 95-130 \({}^{\circ}\)C [24]. Furthermore, the sealing temperature of the PCFs must be maintained below 230 \({}^{\circ}\)C, as thermal degradation of the CA may occur above this temperature [12; 24]. Therefore, the sealing temperature must be kept slightly below this limit. Regarding the material incorporated into the PCF core, PEG 2000 has a phase change of around 53-55 \({}^{\circ}\)C [12]; when it is incorporated into PCFs, it can reach temperatures slightly above its phase change, thus affecting the properties of PCFs and the integrity of cutting and sealing.  
An important point that must be taken into consideration in this and future analyses is that when PEG is ejected into the core of the PCFs, part of the material can bind to the protective sheath, and when cuts are made at temperatures above its temperature change, phase, this may harm the integrity of the cutting and sealing.  
In summary, the ideal cutting and sealing temperature needs to be around 50 \({}^{\circ}\)C, that is, slightly below the PEG phase change temperature and heated enough to cut and seal the CA sheath, which is in accordance with the literature [15]. Therefore, sealing temperatures must be below the CA thermal degradation point. The procedure must be  
Figure 11: Micrographs of the hot cutting zones (RT, 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C) of PCFs incorporated into PEG 2000 obtained by bright-field microscopy. (**a**) PCF_50/50_120; (**b**) PCF_50/50_130; (**c**) PCF_50/50_140; (**d**) PCF_50/50_150.  
carried out uniformly across the entire thickness of the material to avoid imperfections or chips. However, it is important to highlight that the ideal temperature for cutting and sealing PCFs probably depends on the initial production formulation and thickness.
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 4 Results
### Results of the Impact of Each Replacement Scheme on the Superstructure
#### 4.2.3 Arch Rib Deformation  
The influence of tie cable replacement on the displacement of the main arch crown is shown in Figure 9.  
Figure 8: Change in the arch rib bending moment.  
Since the upstream side was selected for the unilateral replacement scheme, the displacement of the upstream side of schemes 1, 5, and 6 is greater than that of the downstream side. It can be seen from Figure 9 that  
1. The displacement caused by scheme 1 and scheme 3 using the non-substitution method is larger, and the maximum displacement is 34.5 mm in scheme 3. The arch rib deformation caused by the temporary substitution method is smaller, and the maximum is 15.2 mm in scheme 8.
2. The displacement of the main arch caused by scheme 5 is the smallest, followed by scheme 7. This indicates that the temporary substitution method of replacing one by one (scheme 5) and one by one symmetrically on both the upstream and downstream sides (scheme 7) has the least influence on the displacement of the main arch crown.
3. In the temporary substitution schemes, the deformation of one side arch rib is linearly related to the number of cables replaced on this side. For example, the deformation of the arch rib caused by scheme 8 is about twice that of scheme 7.
# Scale-Model Experiment on the Delayed Failure Mechanism of Deep Tunnels in Brittle Rock
## 6 Conclusions  
To investigate the mechanism of the delayed failure in brittle rock tunnel, this paper performed scale-model tests and numerical simulations based upon the Jinping II headrace tunnelling project. The following conclusions have been obtained:  
1. The scale-model test results show that the failure shape under isotropic geostress has a smooth circle boundary; the delayed failure time is 56 h. While under anisotropic geostress, the failure shape has a dog-eared breakdown; the delayed failure time is 72 h.
2. The failure mode obtained in the scale-model tests indicates that the loop stress of the tunnel surrounding rock is the main factor for the delayed failure. Under the isotropic geostress condition, the loop stress increases shortly after excavation, and then abruptly drops down. For the anisotropic geostress condition, the loop stress only has a decreasing tend. The surrounding rocks turn from elastic to rapidly plastic after excavation, the turnaround time for this status is shorter for anisotropic geostress.
3. In the scale-model tests the radial strain increases more slowly during the failure process; the loop strain shows an abrupt change. These results explain the phenomenon of the rupture interfaces mainly following loop direction.
4. The implementation of the rock delayed failure on an exponential relation was used in the FEM code. The failure time and failure mode results are obtained by numerical simulations. The predicted failure mode obtained by the numerical simulation matches the scale-model testing results, both on the failure boundary and delay failure  
Figure 12: Time-to-failure values at different distance to the tunnel boundary.  
time. The delayed failure mechanism of the deep brittle rock tunnel was verified by laboratory tests and numerical simulations.  
Conceptualization, N.Z.; methodology, N.Z.; software, N.Z.; validation, N.Z.; formal analysis, N.Z. and Z.C.; investigation, N.Z. and Z.C.; data curation, N.Z.; writing--original draft preparation, N.Z.; writing--review and editing, Z.C.; visualization, N.Z.; supervision, N.Z.; project administration, N.Z.; funding acquisition, N.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51709268).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
We are grateful to the High Performance Computing Center of Nanjing Tech University for supporting the computational resources.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Rapid Detection of Flavour  
SJ, SW and DL samples were easily distinguished from each other by E-nose (Figure 5A). Fermentation and distillation significantly increased the response values of SW and DL (Figure 5B), indicating gradual enrichment of VOCs. The result is consistent with the findings of GC-MS. Correlation analysis was performed between the changes in E-nose values and each category of VOCs in the different groups (Figure 5C). The results indicate that seven sensors, W5S, W6S, W1S, W1W, W2S, W2W and W3S, were positively correlated with increased alcohol and ester contents. SBJLp (lactic acid bacteria)-fermented SJ also represented high response values on W1W, W1S, W5S and W3S, similar to this experiment result [17]. W1C, W3C and W5C were negatively correlated with alcohol and ester contents, and positively correlated with acid contents. W3C and W5C reflected the increasing trends of carbonyl compounds, alkanes and terpenoids. In comparison with SJ, the response values of W3S, W2W, W2S, W1W and W1S for SW and DL were significantly increased, indicating that the alcohols and esters in the samples increased significantly after fermentation and distillation (especially the former). This result is consistent with the trends detected by GC-MS. The above findings show that E-nose could be used for rapid detection and evaluation of the changing trends of the main flavour compounds observed during the production of SW and DL.  
E-nose is not commonly used in flavour detection of sea buckthorn wine. [PERSON] et al. [50] used E-nose to study the effects of different ultra-high-pressure treatments on the flavour and aging of SW, and found that treatment at 400 MPa significantly enhanced the aroma of SW. Some researchers have begun to establish correlations between GC-MS and E-nose data. For example, [PERSON] et al. [51] reported that S2, S6, S7 and S9 were important E-nose sensors for distinguishing between different cultivars of _Alpinia officinarum_ based on the results of correlation analysis.
## 3 Method: Massive MIMO Network Design
### Problem Description  
In this study, a realistic suburban area of 6.85 km\({}^{2}\) in Ghent, Belgium (Figure 2) where a Belgian mobile operator has deployed its 4G network, has been used for the design of the massive MIMO network. The operator's network consists of 75 possible base stations locations to cover the entire area and provide voice and data services to the users. Two types of optimization problems are considered here, during the design of massive MIMO-LTE networks: maximum area coverage problem and maximum user coverage problem, while simultaneously minimizing the power the consumption, the downlink exposure due to the BSs and the uplink exposure resulting from the transmissions of the users. Here, we use the electric field (in V/m) and the specific absorption rate (SAR in W/kg) as metrics for the downlink and uplink exposure, respectively. These two metrics are standardized by the ICNIRP [29] that defines exposure guidelines for the general public to 61 V/m (electric field) and 0.08 W/kg (whole-body SAR). Hence, the level of the EMF exposure due to the deployed massive MIMO network should be lower than these limitations to comply with the ICNIRP standards. The details of the problems are presented below.  
We assume that we have a massive MIMO based mobile network that consists of \(K\) BSs and \(N\) users. The sets of BSs and users are \(\mathcal{K}~{}=~{}\{1,2,\cdots,K\}\) and \(\mathcal{N}=\{1,2,\cdots,N\}\), respectively. Moreover, we define the set of power values of each BS. This is denoted with \(\mathcal{P}~{}=~{}\{p_{1},p_{2},\cdots,p_{K}\}\). We consider the binary variable, \(x_{kn}\) that describes the association of the \(k\)-th BS with the \(n\)-th user and can be formulated as  
\[x_{kn}=\left\{\begin{array}{ll}1,&\text{if $n$-th user is associated with the $k$-th BS}\\ 0,&\text{otherwise.}\end{array}\right. \tag{4}\]  
Moreover, we define the binary variable \(y_{k}\) that describes the operation or not of the \(k\)-th BS and can be formulated as  
\[y_{k}=\left\{\begin{array}{ll}1,&\text{if BS k is turned on}\\ 0,&\text{otherwise.}\end{array}\right. \tag{5}\]  
Additionally, we can define the discrete variable, \(p_{k}\) that describes the transmission power value of the \(k\)-th BS as \(p_{k}\in\{0,1,2,\cdots,p_{t}\},\forall k\in\mathcal{K}\). where \(p_{t}\) is the maximum allowable transmission power for a BS according to 3 GPP recommendations (\(p_{t}=43\) dBm in this work). Thus, we define the solution vector to an integer vector that contains the active or not BSs, the operating power in dBm and the users associated.
## 2 Materials and Methods
### Numerical Simulation Experiment
#### 2.3.2 Model Construction and Calculation Results  
To study the impact of various parameters on stope stability, the model was appropriately simplified. The rock mechanics parameters used in the numerical simulation in this article are those listed in Table 1. Assuming an average ore body thickness of 65 m and a surface dip angle of 65\({}^{\circ}\), and based on [PERSON]' s principle, the excavation range was set to 3-5 times the model boundary, with dimensions of 90 m in length, 400 m in width, and 350 m in height. The finite element type is three-dimensional hexahedral, with the number of nodes per element being 3, the number of degrees of freedom per node being 2, and a total of 38,912 elements and 7959 nodes. The mesh type is triangular, with a mesh size of 4. The computer system used for numerical simulation is a PC, and the numerical simulation method employed is the finite element method, as shown in Figure 2. For stope spans of 15-20 m, pillar widths of 10-14 m, and roof thicknesses of 10-14 m, 15 test points were designed for numerical simulation analysis according to the central composite design principle. The stope parameters, as well as the horizontal and numerical calculation results, are shown in Table 6.  
Figure 2: Finite element analysis model diagrams.  
The numerical analysis results indicate that after excavation of the stope, the maximum principal stress is tensile, while the minimum principal stress is compressive, mainly concentrated at the center of the roof and the pillar. Among the analyzed schemes, the maximum tensile stress is 0.388 MPa and the maximum compressive stress is 5.768 MPa, both of which are within the limits of the rock mass tensile strength (0.341 MPa) and compressive strength (5.973 MPa). The maximum vertical displacement of the stope is 5.249 mm. Thus, the stress and displacement of the stope in the above schemes are within reasonable ranges, and no damage has occurred to the stope.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 3 Applications
### Environmental Applications  
The growing number of nanoparticle applications for industrial and domestic products can lead to their leakage into the environment [96]. Such applications lead to the direct release of harmful materials in the groundwater and soil, which are common environmental hazards to test ground pollution. Environmental risk of NPs is assessed by identifying their exposure pathways, persistence, reactivity, ecotoxicity and mobility. Nanotechnology advancements have opened new possibilities for improved environmental remediation.  
Nanoparticles can help satisfy the demand for accessible, potable water by allowing for the fast and low-cost assessment and management of impurities in water. For example, Cu NPs in paper filters for water purification resulted in high bacteria reduction of _E. coli_ and low levels of copper released in drinking water (1 ppm) [97].  
Nanoparticles have also been developed to clean up oil spills. Fine carbon particles with engineered surface chemistry have been shown to stabilise oil-in-water emulsions [98]. Functionalised carbon black (CB) NPs showed non-toxic effects in brine shrimp, and also were shown to adsorb benzene. One research found that magnetic NPs coated with green hydrophobic biocomponents isolated from _Anthemis pseudocotula_ improved their performance of heavy crude oil collection [99].  
Heavy metals such as mercury, lead, thallium, cadmium and arsenic are dangerous to the environment and humans as they are environmentally undesirable and have harmful effects; it is vital to remove such metals to avoid harms. Carbon nanotubes, nano-zeolites and metal oxides have been used to environmental remediation. The most promising agent is nanoscale zerovalent iron (nZVI), as it is non-toxic, economical and easily produced [100]. Another alternative is the use of bimetallic nanoparticles, consisting of elemental iron or other metals in conjunction with metal catalysts such as silver, gold, nickel and palladium, to improve the rate of reduction.
## 3 Results
### The Spatial Distribution of MODEI
The grids were mainly distributed in the first quadrant and the third quadrant, indicating that the distribution of the MODEL had an obvious spatial positive correlation, showing a clear trend of high-high (HH) and low-low (LL) aggregation, and the clustering effect of grids and adjacent grids was significant. The degree of autocorrelation in 2018 was relatively weak compared with that in 2008, implying that HH aggregation or LL aggregation in the MODEI was slightly weakened in 2018, while the random distribution trend was slightly enhanced. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pasture Type**} & \multicolumn{3}{c}{**2008**} & \multicolumn{3}{c}{**2018**} \\ \cline{2-7}  & **Minimum** & **Maximum** & **Mean** & **Minimum** & **Maximum** & **Mean** \\ \hline Spring and & 0.03 & 0.95 & 0.18 \(\pm\) 0.17 & 0.01 & 0.96 & 0.19 \(\pm\) 0.19 \\ autumn pasture & 0 & 0.76 & 0.09 \(\pm\) 0.05 & 0 & 0.89 & 0.09 \(\pm\) 0.08 \\ Summer pasture & 0.2 & 1 & 0.59 \(\pm\) 0.16 & 0.19 & 1 & 0.62 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MODEI of different seasonal pastures. Figure 2: The spatial distribution of MODEL level in 2008 and 2018. The point clustering in the first quadrant indicated that the MODEL values in the areas with higher ecological environment quality had few differences, and the same situation happened in the areas with lower ecological environment quality, as shown by the point clustering in the third quadrant. In addition, the Monte Carlo simulation method was used to test the significance of Moran's I in Geoda095i, and the \(p\) values of both phases were equal to 0.001, suggesting that the spatial auto-correlation was significant at a 99.9% confidence level. The spatial distribution features of the MODEL were visualized by the LISA clustering map and the LISA significance level map (Figures 4 and 5). As shown in Figure 4, the HH area was mainly distributed in summer pastures, while the LL area was located primarily in spring and autumn pastures and winter pastures. The LH region was scattered in the HH region of the summer pastures. The HL region was mainly distributed along the Irtysh River and the Ulungur River. Compared with 2008, the LH area and HL area expanded in 2018. A percentage of 84.74 and 81.41 of the region reached the significance level of 0.01 in 2008 and 2018, respectively. The region of the significance level of 0.05 was mainly distributed at the edge of the 0.01 significance area and the edge of the two rivers (Figure 5).
## 2 Material and Methods
### Satellite Data  
The LAI data set used in this study (LAI3g) was generated using an Artificial Neural Network (ANN) from the latest version (third generation) of GIMMS AVHRR NDVI data for the period July 1981 to December 2010 at 15-day frequency [86]. The ANN was trained with best-quality Collection 5 MODIS LAI product and corresponding GIMMS NDVI data for an overlapping period of 5 years (2000 to 2004) and then tested for its predictive capability over another five year period (2005 to 2009). The accuracy of the MODIS LAI product is estimated to be 0.66 LAI units [86] and the data is for 1-sided LAI. Further details on the LAI3g and the comparison with other satellite products are provided in [86, 87].