esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Adaptive Mesh Refinement (AMR)  
AMR dynamically enhances the mesh around the crack tip to precisely capture stress intensity factors. This method aids in upholding computational efficiency while ensuring precise results near the crack front. By concentrating mesh refinement solely around the advancing crack, the adaptive approach notably diminishes the total number of model degrees of freedom. Numerous studies have indicated that the computational expenses are markedly lowered by the Adaptive Mesh Refinement strategy without sacrificing the accuracy of numerical predictions [52; 137]. This algorithm refines the mesh based on error estimators, ensuring that the mesh is denser where higher accuracy is needed, such as near the crack tip [138; 139; 140; 141]. AMR techniques have been widely used in conjunction with adaptive Phase Field Modeling approaches to simulate fatigue crack growth. This coupling allows for more accurate predictions of crack behavior while also significantly reducing the computational time required. By dynamically refining the mesh around the crack tip, the AMR approach can capture the critical stress intensity factors with high fidelity without needing to use a globally refined mesh. Multiple studies have demonstrated that this AMR-enhanced phase field framework for fatigue crack modeling provides highly accurate results at a much lower computational cost compared to uniform mesh refinement schemes [137; 142; 143; 144]. The flexibility of the adaptive meshing approach enables modeling of fatigue crack propagation in components with complex geometries, such as parts with holes, notches, or multiple cracks. The local refinement can adapt to capture the evolving crack paths in these challenging configurations.
## 3 Results and Discussion
### DFT Study
#### 3.5.5 Antioxidant Mechanisms of the Target Molecules **1**-**14**
Based on the calculated values, compounds **5**, **9**, and **11** are found to have good antioxidant properties, however, **9** appeared to have the lowest PDE + AIP value of 391.52 kcal.mol\({}^{-1}\) in accordance with Table 5.  
As a result, regardless of whether the reaction followed the HAT or SETPT mechanism, this molecule might be the superior antioxidant. Comparison with the calculated BDE and AIP values of **9** (75.61 and 164.045 kcal.mol\({}^{-1}\) at B3 LYP/6-311+G (d,p), respectively) was made, and concluded that **9** slightly matched with in vitro biological experiments (FRAP and reducing power ability). PA and ETE are essential parameters to describe the SPLET mechanism. A lower PA value indicates stronger antioxidant capacity. The PA values of the compounds were initially analyzed by the B3 LYP/6-311\({}^{++}\)G (d,p), as stated above (Table 5). The PA values of O-H bonds are lower than those of N-H bonds, as seen in Table 5 The lowest PA values for O-H bonds in the gas phase in **6** and **9** are 305.49 and 313.97 kcal.mol\({}^{-1}\), respectively. While for N-H bonds in the gas phase, the lowest PA values in **2** and **3** are 343.04 and 330.71 kcal.mol\({}^{-1}\), respectively (Table 5). The final step in the SPLET mechanism is determined by the ETE. Table 5 showed that the PAs are significantly greater than the ETE values of the gas. The single electron transfer process from the neutral form is therefore less favorable than the procedure from the anionic form in this case. This conclusion is consistent with reported studies [54; 55]. In fact, ETE values in the examined solvent (methanol) are significantly higher than those found in the gas phase. In all of the examined environments, **9** showed significant antioxidant property based on the computed PA and ETE values of both steps in the SPLET mechanism, with a total PA + ETE value of roughly 391.53 kcal.mol\({}^{-1}\). This is consistent with our findings in the HAT and SETPT mechanisms. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Comp.** & **BDE** & **IP** & **PDE** & **PA** & **ETE** \\ \hline
**5** & 78.8146275 & 169.60321 & 225.12943 & 300.0272025 & 94.7054375 \\
**9** & 78.2611725 & 135.4063425 & 258.7728425 & 283.7410675 & 110.4381175 \\
**11** & 83.79384 & 136.969445 & 262.7424075 & 297.975905 & 101.7359475 \\ \hline \end{tabular}
\end{table} 
Table 6: The O–H BDE, IP, PDE, PA, and ETE at the B3 LYP/6-311++g (2d,2p) level of theory in methanol solvent.
# The Preparation of Active Support-Based Sealing Material and Sealing Effect Analysis
## 2 Sealing Material Preparation Based on Orthogonal Test
### Raw Materials and Test Method  
#### 2.2.1 Selection of Raw Materials  
To prepare a new sealing material with high expansibility, early strength and fluidity, Portland cement, quicklime, an accelerating agent, expanding agent, and fly ash are used to design the mix ratio of sealing materials, as is shown in Figure 2.  
1. Ordinary Portland Cement (OPC)  
OPC of grade PO42.5 is used as the binder material for the sealing material.
2. Lime  
The CaO in lime reacts with the hydration product of cement, namely sulphoaluminate calcium, to produce ettrinigite. The more ettrinigite is produced, the higher the strength of the material. Additionally, the heat released during lime hydration accelerates the reaction of the material.
3. Fly Ash  
Fly ash mainly comes from the combustion of coal in thermal power plants. It has a small particle size, low density, and acts as a suspending agent that can reduce the water seepage rate of the sealing material. It also partially replaces some cement, reducing material costs.
4. Expanding Agent  
Cement-based materials tend to shrink due to water seepage, which can cause air-leakage channels to form between the sealing material and the borehole wall. Adding  
Figure 1: The schematic diagram of active support sealing.  
expanding agents to the material can compensate for the reduction in volume caused by water seepage, reducing the development of borehole fractures and providing additional support resistance. The expansion agent used in this experiment is a calcium sulfoaluminate expansion agent (CSA) produced by Wuhan Sanyuan Special Building Materials Co., LTD. (Wuhan, China), which is prepared by calcium sulfoaluminate (CAS), calcium aluminate (CA) and an appropriate amount of natural anhydrite and activator.  
The hydration reaction of cement is slower. Therefore, adding a set accelerator to the material can shorten the initial setting time. The set accelerator adopted is the PCS-3 rapid-setting and early-strength agent.
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 5 Example: Analyzing the 10 Cohort Studies in Table 1
### Meta-Analysis for r  
The following R functions were used to conduct a meta-analysis of the correlation coefficients.  
fixed_effect_meta_r<-function(v1,v2,cov12,sample_size){ r<-cov12/sqrt(v1*v2) #correlationcoefficient z<-0.5*log((1+r)/(1-r)) #Fisherztransformation v<-1/(sample_size-3) #varianceforz W<-1/v #weight z_overall<-sum(W*z)/sum(W) #overallrandomeffectofz r_overall<-(exp(2*z_overall)-1)/(exp(2*z_overall)+1)#transformbacktor return(r_overall) } random_effect_meta_r<-function(v1,v2,cov12,sample_size){ r<-cov12/sqrt(v1*v2) #correlationcoefficient z<-0.5*log((1+r)/(1-r)) #Fisherztransformation v<-1/(sample_size-3) #varianceforz W<-1/v #weight Q<-sum(W*z^2)-(sum(W*z))^2/sum(W) #totalvarianceorQstatistics c<-sum(W)-sum(W^2)/sum(W) #cisthescalingfactor df=length(v1)-1 #degreeoffreedom tau_square<-ifelse(Q>df,(Q-df)/c,0) #betweenstudyvariance v_star=v+tau_square W_star=1/v_star #newweight z_star<-sum(W_star*z)/sum(W_star) #overallrandomeffectofz r_overall<-(exp(2*z_star)-1)/(exp(2*z_star)+1)#transformbacktor return(r_overall) } v1<-studies$var_b1 v2<-studies$var_b2 v12<-studies$cov_b1b2 sample_size<-studies$sample_size r<-v12/sqrt(v1*v2) fixed_meta_r<-fixed_effect_meta_r(v1,v2,v12,sample_size)  
Figure 3: Forest plot for \(\dot{\beta}_{2}\).  
#[1] -0.9600286 random_meta_r<-random_effect_meta_r(v1,v2,v12,sample_size) random_meta_r
#[1] -0.9493409  
The overall \(r\) for fixed-effect meta-analysis is \(-0.9600\), while the random-effects meta-analysis gives \(-0.9493\).  
Similarly, meta-analysis on the correlation coefficient can be performed using the'meta' package.  
r<-v12/sqrt(v1*v2) r.meta <- metacor(cor = r, n = sample_size, studlab = LETTERS[1:10], data =, fixed = TRUE, random = TRUE, method.tau = \"DL\", hakh = FALSE, title = \"Use R meta package\") summary(r.meta) # # # Review: Use R meta package # # COR 95%-CI %W(common) %W(random) # A -0.8654 [-0.8722; -0.8583] 8.1 10.0 # B -0.9680 [-0.9687; -0.9673] 48.4 10.1 # C -0.9686 [-0.9698; -0.9674] 16.1 10.0 # D -0.9416 [-0.9455; -0.9374] 4.8 10.0 # E -0.9217 [-0.9281; -0.9149] 3.2 10.0 # F -0.9637 [-0.9656; -0.9616] 8.1 10.0 # G -0.9233 [-0.9319; -0.9136] 1.6 9.9 # H -0.9486 [-0.9528; -0.9441] 3.2 10.0 # I -0.9685 [-0.9725; -0.9639] 1.3 9.9 # J -0.9613 [-0.9639; -0.9586] 5.2 10.0 # # Number of studies: k = 10 # Number of observations: o = 62000
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 3 Proposed Methods
### Local Detail Enhancement  
Pyramid-based fusion methods can retain most details in normal areas but cannot preserve details in brighter and darker areas. The larger number of pyramid layers, the more obvious this weakness is. In this paper, we first use the K-means algorithm to divide the input image sequence into over-exposed images (OEI) and low-exposure images (LEI) according to the luminance histogram features of each image. According to \(\hat{l}\), the brightness the region(BoE) and the darkness the region(DoE) can be determined, then, the fast local Laplacian filter (FLLF) proposed by [PERSON] et al. [24] can be used to enhance the BoE area of the LEI and the DoE area of the OEI. The frame diagram is shown in Figure 7.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques
#### 5.2.4 Hybrid Approaches  
Hybrid task scheduling algorithms in fog computing refer to approaches that combine two or more approaches to achieve better performance. These algorithms leverage the strengths of multiple scheduling techniques, such as heuristics, meta-heuristics, or optimization methods. The hybrid nature allows these algorithms to exploit complementary features of the different methods to improve scheduling decisions [69].
## 5 Signal Acquisition Approaches
### Cup Electrodes  
Comprising small cups made of silver or gold plating, these electrodes are positioned on the scalp to record electrical activity from specific or multiple areas [99]. The choice of materials, such as silver or gold, is crucial for their high conductivity and biocompatibility. Setting up these electrodes involves using conductive paste or gel, and their placement is critical for targeted brain region monitoring. While they offer high signal quality, the setup can be cumbersome, and there might be discomfort involved for the subject, especially in long-term recordings.
## 5 Limitations  
The proposed multi-stage uncertainty quantification framework demonstrates significant advantages in reconstructing complex terrain data; however, several limitations remain in both its design and application. Although the activation-driven adaptive dropout mechanism dynamically adjusts dropout rates via local activation levels, it introduces additional computational complexity during real-time activation computation and may, if threshold settings are suboptimal, affect the preservation of critical features. Moreover, the stability of the learnable gating coefficients in the dynamic gating network under extreme conditions has not been fully validated, which could lead to suboptimal reconstruction in certain regions. Additionally, the overall model still exhibits limitations in local detail recovery and high-frequency information capture--especially in areas with high noise or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sampling Counts (N)** & **R 1 ECE (\%)** & **R 2 ECE (\%)** & **Inference Time (s)** & **R 1 PSNR** & **R 2 PSNR** \\ \hline
10 & 12.34 & 7.45 & 0.35 & 44.85 & 50.58 \\
20 & 8.12 & 5.23 & 0.70 & 44.91 & 50.61 \\
50 & 7.91 & 4.08 & 1.25 & 44.96 & 50.66 \\
100 & 5.92 & 3.56 & 2.50 & 44.95 & 50.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of MC-Dropout Sampling counts on uncertainty calibration and computational efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **SNR (dB)** & **Region** & **RMSE (m) \(\downarrow\)** & **PSNR (dB) \(\uparrow\)** & **SSIM \(\uparrow\)** & **Error Variance (m\({}^{2}\))** & **ECE (\%) \(\downarrow\)** \\ \hline TfaSR & 10 & 1 & 41.27 & 38.52 & 0.931 & 2154.39 & 22.17 \\ E & 10 & 1 & 33.15 & 42.86 & 0.962 & 1276.55 & 14.05 \\ TfaSR & 20 & 1 & 32.16 & 43.88 & 0.975 & 1480.27 & 15.34 \\ E & 20 & 1 & 28.89 & 46.21 & 0.981 & 832.74 & 8.93 \\ TfaSR & 30 & 1 & 28.78 & 45.10 & 0.984 & 1353.91 & 12.45 \\ E & 30 & 1 & 28.63 & 44.96 & 0.984 & 793.43 & 7.91 \\ TfaSR & 10 & 2 & 29.45 & 40.27 & 0.945 & 1783.22 & 19.83 \\ E & 10 & 2 & 23.18 & 45.12 & 0.971 & 1042.17 & 11.24 \\ TfaSR & 20 & 2 & 19.54 & 48.02 & 4.08 & 921.45 & 10.55 \\ E & 20 & 2 & 17.32 & 50.15 & 0.993 & 489.33 & 5.89 \\ TfaSR & 30 & 2 & 17.41 & 49.51 & 0.997 & 477.34 & 8.92 \\ E & 30 & 2 & 16.99 & 50.66 & 0.997 & 242.20 & 4.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Noise robustness analysis: Experiment E vs. TfaSR.  
complex features--where prediction uncertainty does not fully reflect the actual conditions, leaving room for further improvement in local detail recovery.
## 3 Technological Advancements in Bee-Based Biosensors/Biomonitors  
### Bee and Hive Monitoring  
The first marriage of electronic sensors and bees was a frequency analyzer called the Apidictor (Figure 3), developed, patented, and sold during the 1960s by a UK beekeeper and sound engineer, [PERSON] [41]. The Apidictor was a frequency band-pass filter for detecting sound changes which occur in beehives up to two-to-three weeks before a bee colony swarmed. Work on a modern Apidictor has re-emerged in Europe, and [PERSON]'s schematics are available on the Internet. There also is video of a 2012 iPhone-based Apidictor. In 2011, [PERSON] _et al._[42] described their swarm detector using accelerometers to analyze hive vibrations.  
The original Apidictor was followed by two inventions at Oak Ridge National Laboratory in the USA: (1) research-only, prototype tags for marking and tracking bees [43,44]; and (2) an acoustic device for discriminating races of bees [45] based on wing-beat frequency. Simple hive entrance-mounted bee activity/movement counters were mentioned and discussed in the 1980s. The first commercial bee-counters were marketed by a Belgian electronics company in the mid-1990s [46]. In 1995, we deployed the first electronic SmartHives(r) (UM, Missoula, MT, USA), equipped with scales, bee-counters, temperature and relative humidity sensors, digital weather stations, and telephone communications for remote monitoring via the Internet [47, 48, 49].  
Figure 2: Solar-powered, micro-processor-controlled bee-conditioner system with scent and reward (proprietary syrup solution) dispensing systems. Reward intervals can be controlled automatically by the device, re-programmed as needed, or remotely controlled by wireless communications.  
Beginning in 1995, significant funding from the US Army and DARPA, concurrent with advances in sensor and electronics technology, resulted in a proliferation of bee and pollinator-related sentinel, monitoring, and chemical signal detection methods and technologies. Together, these technologies are posed to radically change bee management, agricultural practices, and research on chemical signals between organisms. As such, a summary history and overview of current and emerging technologies, especially those involving electronics and distance communications of data, is warranted.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 3: Estimation of the Fault Current  
The estimation of the fault current depends on the location of the DER relative to the line section analyzed. The two possible conditions are illustrated in Figure 8.  
Figure 8: Analysis conditions to calculate the fault current: (**a**) analysis of a fault upstream of the DER and (**b**) analysis of a fault downstream of the DER.  
Figure 7: Equivalent circuit in phase components during current limiting operation.  
A fault between the substation and the DER is represented in Figure 8a. In this case the DER effect is considered in the current feeding the fault from the downstream circuit. For this condition, the strategies of Sections 3.5.1 and 3.5.2 are used. For the second condition, the DER effect is considered in the current feeding the fault from the upstream circuit. Therefore, the fault current is estimated on the basis of the fault distance \(x\); as in the original formulation proposed previously [11]. Therefore, the fault current is given by Equation (21).  
\[\mathbf{I}_{F}=\mathbf{I}_{k,F}^{f}+\mathbf{I}_{F,k+1}^{f} \tag{21}\]  
Given that the fault current is a function of \(x\), it is necessary to perform an iterative process to determine a precise value for the current \(\mathbf{I}_{F,k+1}^{f}\), as shown in Figure 3. The initial value adopted for \(\mathbf{I}_{F,k+1}^{f}\) is given by Equation (22).  
\[\mathbf{I}_{F,k+1}^{f}=\mathbf{I}_{k,F}^{p} \tag{22}\]
## 5 Discussion
### Effects of Atmospheric Correction on SIF Retrieval  
The SIFSpec system is generally mounted on the flux towers at a height ranging from 5 m to 50 m depending on the specific ecosystem at a given site. As [PERSON] et al. [29] pointed out, the 3 FLD method widely used in ground-based SIF retrieval will underestimate the SIF if the atmospheric correction effects at oxygen absorption bands are not compensated for. Recent studies [40; 42] also quantitatively demonstrated the effects of atmospheric correction on the SIF retrieval for tower-based observations. According to the results of field experiments conducted on tower-based measurements [39], afteratmospheric correction, the root-mean-square error (RMSE) decreased from 0.221 to 0.078 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). Thus, it is necessary to use the atmospheric correction method based on an LUT used for long-term observations by the SIFSpec system for more accurate retrieval of SIF from tower-based observations.
## 5 Experimental Results  
### Test Dataset of DNN  
In the test dataset used to evaluate the performance of the DNN, test samples were generated for all cases within the maximum number of agents and jobs of 10, and the total number of samples was approximately 67,430. Table 3 lists the ground cost values for the test dataset. Table 3a represents the maximum (worst) and minimum (best) average travel times of the agent when all jobs are assigned to one agent. The left column of Table 3b is the sum of the average travel times of all agents when jobs are assigned to multiple agents. The right column of Table 3b shows the bottleneck of the average travel time of the agent, which was the longest when all agents started at the same time and completed their assigned jobs. Table 3c shows the average travel time reduction ratio when several agents divide and perform multiple jobs versus when one agent executes all jobs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Agents (j) and** \\ **Jobs (k)** \\ \end{tabular} } & \multicolumn{6}{c}{**Cost (Average Travel Time [s])**} \\ \cline{2-7}  & \multicolumn{3}{c}{**An Agent**} & \multicolumn{3}{c}{**Multi-Agent**} & \multicolumn{3}{c}{**Average Travel Time**} \\  & \multicolumn{3}{c}{**Does All the Work**} & \multicolumn{3}{c}{**Does Their Work**} & \multicolumn{3}{c}{**Reduction Ratio**} \\  & \multicolumn{3}{c}{**(a)**} & \multicolumn{3}{c}{**(b)**} & \multicolumn{3}{c}{**(c)**} \\ \hline \(j\) & \(k\) & _Worst_ & _Best_ & _Sum_ & _Bottleneck_ & _S/Best_ & _B/Best_ \\ \hline
1 & 1 & 14.7 & 14.7 & 14.7 & 14.7 & 1.00 & 1.00 \\
1 & 10 & 131.6 & 131.6 & 61.2 & 61.2 & 0.47 & 0.47 \\ \hline
3 & 3 & 49.2 & 30.0 & 36.7 & 10.9 & 1.22 & 0.36 \\
3 & 10 & 103.9 & 61.3 & 78.4 & 16.0 & 1.28 & 0.26 \\ \hline
5 & 5 & 79.8 & 39.4 & 53.9 & 8.3 & 1.37 & 0.21 \\
5 & 10 & 107.2 & 58.7 & 86.2 & 8.9 & 1.47 & 0.15 \\ \hline
8 & 5 & 80.8 & 38.3 & 55.7 & 7.5 & 1.45 & 0.20 \\
8 & 8 & 109.4 & 51.7 & 81.1 & 6.6 & 1.57 & 0.13 \\
8 & 10 & 110.4 & 58.1 & 93.1 & 6.3 & 1.60 & 0.11 \\ \hline
9 & 5 & 82.3 & 38.6 & 56.7 & 7.5 & 1.47 & 0.19 \\
9 & 10 & 111.6 & 58.5 & 94.2 & 5.9 & 1.61 & 0.10 \\ \hline
10 & 3 & 52.6 & 27.6 & 36.8 & 8.6 & 1.33 & 0.31 \\
10 & 5 & 83.4 & 38.5 & 56.0 & 6.9 & 1.45 & 0.18 \\
10 & 10 & 111.1 & 57.3 & 94.4 & 5.5 & 1.65 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ground cost value of the test dataset.  
Figure 6: Loss and accuracy of training and validation for the deep neural network.
## 5 Conclusions  
This study developed a machine learning technique for synergistic use of three optical satellite sensors to increase spatio-temporal coverage of NDVI imagery with cloud contaminated pixels in coastal wetlands. A virtual constellation using the random forest algorithm blended Landsat 8 OLI, Sentinel-2A MSI, and ASTER to produce enhanced NDVI imagery over the study area in Apalachicola, FL (USA) with improved spatial coverage. The enhanced NDVI imagery product was designed to correspond with the spatial and spectral properties of Landsat 8 NDVI. The salient benefit of using compatible sensor data is the retention of spatial patterns in the newly reconstructed NDVI imagery which is important for long-term evolution and change detection in monthly coastal wetland modeling.  
While we see no major barriers to scaling this technique for operational use as all the included sensors develop to maturity and others are brought online, some future work including further tuning the random forest hyper parameters, accounting for catastrophic events that substantially alter the NDVI between complementary sensor acquisitions in the same calendar month, and addressing open water pixels in tidally influenced systems would be beneficial.  
The idea of spatial information recovery using machine learning and multi-sensor fusion has been demonstrated to be a viable approach to mitigate cloud, shadow and other anomalous contamination with enough accuracy to support long-term coastal wetland studies. These applications would be strengthened by a deeper collaboration between the engineering and application communities. Stated another way, end-users would benefit from not only the new observation capabilities of proposed sensors, but also the redundancy and continuity they could provide to existing higher level data products.  
S.T.: Conceptualization, Methodology, Software, Formal Analysis, Data Curation, Writing--Original. S.C.M.: Validation, Writing--Review and Editing, Visualization, Supervision, Project Administration, Funding Acquisition. A.S.: Writing--Review and Editing, Supervision, Project Administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part under award NA16 NOS4780208 from the National Oceanic and Atmospheric Administration (NOAA) Ecological Effects of Sea Level Rise (EESLR) Program. A.S. acknowledges the partial support from the Donors of the American Chemical Society.  
S.C.M. would like to thank [PERSON] for her assistance in preparing Figures 2 and 4. The authors are grateful for the efforts of the editorial staff and the anonymous peer reviewers for their consideration and constructive suggestions.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.