esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 7 Case Study and Results
### Results and Discussion
#### 7.4.1 Fault-Free Environment  
In the fault-free scenario, both EDD and PFAS operate under identical scheduling principles, as neither method needs to consider fault avoidance. The scheduling decisions are based on the following scheduling criteria:  
* Due Date: Determines the deadline for job completion
* Higher priority jobs may be scheduled earlier even with later due dates
- Critical for real-time decision making when conflicts occur
* Task precedence constraints (e.g., Task A must precede Task B)  
As shown in Figure 5, both methods achieve identical schedules with optimal resource utilization and timely completion of all jobs. The schedule demonstrates efficient resource allocation, with Machine Group A maintaining 100% utilization through continuous task processing. Tasks are effectively distributed across parallel machines, minimizing idle time while respecting precedence constraints. For instance, Job_05, Job_08, and Job_03 start simultaneously on different machines in Group A, followed by their respective Task B operations in Group B, showcasing the scheduler's ability to parallelize operations efficiently. This parallel processing strategy, combined with priority-based task allocation, ensures all jobs complete within their due dates while maximizing resource utilization across all machine groups.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Job ID** & **Set of Tasks \({}^{\dagger}\) (Duration in Time Units)** & **Due Date** & **Priority** \\ \hline Job\_01 & Task A (3 units), Task B (2 units) & 8 & 6 \\ Job\_02 & Task A (3 units), Task B (2 units), Task C (3 units) & 12 & 4 \\ Job\_03 & Task A (3 units), Task B (2 units) & 7 & 7 \\ Job\_04 & Task A (3 units), Task B (2 units) & 11 & 5 \\ Job\_05 & Task A (3 units), Task B (2 units) & 7 & 9 \\ Job\_06 & Task C (3 units) & 8 & 3 \\ Job\_07 & Task A (3 units), Task B (2 units) & 13 & 5 \\ Job\_08 & Task A (3 units), Task B (2 units) & 7 & 8 \\ Job\_09 & Task C (3 units) & 8 & 2 \\ Job\_10 & Task A (3 units), Task B (2 units), Task C (3 units) & 12 & 7 \\ Job\_11 & Task A (3 units), Task B (2 units) & 8 & 6 \\ Job\_12 & Task C (3 units) & 11 & 1 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Task A must precede Task B when both tasks are present in a job.  
\end{table}
Table 5: Job definitions and details.
## 4 Discussion  
### Simulation Results and Spatiotemporal Variations in ET, Et, Ei, and Es  
In this research, the PT-JPL model was applied to simulate ET, Et, Ei, and Es in the HLS from 2000 to 2018. The validation results indicate that the PT-JPL model effectively simulates the ET process in the HLS, demonstrating a strong correlation with MODIS products and high accuracy. The simulation results revealed that ET, Es, and Ei exhibited an increasing trend during the study period, while Et displayed a decreasing trend. In a study in the same direction, ref. [15] used the generalized complementary relationship to quantitatively analyze the impact of climate change and vegetation restoration on the increase in ET in the Loess Plateau by establishing the relationship between AC and climatic vegetation factors in 14 watersheds of the Loess Plateau from 1981 to 2014. The results indicated that the average annual ET across the 14 watersheds of the Loess Plateau ranged from 300 to 600 mm, with a consistent upward trend observed each year. Work [13] used the ETWatch model to estimate the actual evapotranspiration (ET) in China's Loess Plateau from 2000 to 2020 and employed the generalized additive model to quantitatively assess the contributions of climate factors and human activities to ET changes. The results showed that the ET increased from 386.67 mm in 2000 to 544.73 mm in 2020, and the impact of LUCC on ET in the Loess Plateau was greater than that of climate change on ET. [PERSON] et al. [42] simulated the actual ET of the Yanhe River Basin in the typical hilly and gully region of the Loess Plateau from 2000 to 2016 by using the VIP model based on remote sensing. The actual ET was 324-529 mm, and the ET continued to increase over the 17 years. These results are consistent with our simulation results, indicating that our PT-JPL model is reliable and applicable in capturing the dynamic changes in evapotranspiration in the HLS.
# Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation
## 2 Materials and Methods  
### Stereo Vision  
Arrangements that consist of two image sensors (cameras) separated by a known distance are known as stereo systems. The principle of stereoscopy is based on the ability of the human brain to estimate the depth of objects present in the images captured by eyes [24]. In the stereoscopic configuration, two cameras are placed close to each other with parallel optical axes. Both cameras, with centers \(\mathrm{C_{L}}\) and \(\mathrm{C_{R}}\) separated by a distance B, called the baseline, have the same focal length f so that the left and right images are in parallel planes. A point in the three-dimensional space P will be projected in different positions, \(\mathrm{P_{L}}\) and \(\mathrm{P_{R}}\) with coordinates (\(\mathrm{x_{L}}\), \(\mathrm{y_{L}}\)) and (\(\mathrm{x_{R}}\), \(\mathrm{y_{R}}\)), respectively, of the planes of the images because it is seen from slightly different angles. This difference in position is known as disparity and is mathematically described as disparity = \(\mathrm{x_{L}}-\mathrm{x_{R}}\) and is used to calculate the z distance in (1) through the geometric relationship [25], as shown in Figure 1.  
\[\mathrm{z}=\frac{\mathrm{B\ \times f}}{\mathrm{disparity}} \tag{1}\]
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### OpenPose 2D Library  
As previously mentioned, the application base is the OpenPose library [37], which allows 2D human pose estimation to be performed. In [38], an online and real-time multiperson 2D pose estimator and tracker is proposed. Figure 2 shows the network structure, where the part affinity fields (PAFs) and confidence maps are predicted iteratively. The PAFs predicted in the first stage are represented by Lt. The predictions in the second stage are confidence maps, represented by St. To achieve this, OpenPose uses a two-step process (see Figure 2).  
First, it extracts features from the image using a VGG-19 model and passes those features through a pair of convolutional neural networks (CNNs) running in parallel. One of the pair's CNNs calculates confidence maps (S) to detect body parts. The other calculates a set of 2D vector fields of PAFs called part affinity fields (L) and combines the parts to form the skeleton of the individual person poses. These parallel branches can be repeated many times to refine PAF and confidence map predictions. Then, part confidence maps (S) and part affinity fields (L) are:  
\[S=\big{(}S_{1},S_{2},\ldots,S_{j}\big{)}\text{ where }S_{j}\in\ R^{w\times h },\ j\in 1\ldots J \tag{1}\]  
\[L=\big{(}L_{1},L_{2},\ldots,L_{C}\big{)}\text{ where }L_{C}\in\ R^{w\times h \times 2},\ c\in 1\ldots C \tag{2}\]
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
###### Abstract  
To provide further insight into humid air condensation on hybrid surfaces, an experiment was conducted to visually investigate the condensation process on wettability-interval grooved surfaces, which had hydrophobic ridges and hydrophilic grooves. The droplet dynamic behavior and heat transfer performance of condensation on a wettability-interval grooved surface were explored and compared with four other functional surfaces, including the plain hydrophilic surface, plain hydrophobic surface, hydrophilic grooved surface, and hydrophobic grooved surface. The presence of hydrophobic ridges perpendicular to the groove direction and hydrophilic grooves allowed for the exclusion and easy spreading of droplets, respectively. Compared with the other four functional surfaces, the coupling phenomena during condensation, i.e., the spontaneous suction and directional drainage via hydrophilic grooves, were only found on the wettability-interval grooved surface. These could not only remove condensate quickly but also suppress the formation of the flooded liquid film, which was beneficial to the enhancement of heat transfer performance. It was proven by the experimental results that at subcooling 12 K, the condensation heat flux on the wettability-interval grooved surface reached 1280 W/m\({}^{2}\), which was 1.25 times that of the plain hydrophobic surface (1030 W/m\({}^{2}\)), and 15% higher than that of the hydrophobic grooved surface (1110 W/m\({}^{2}\)). This indicated that the wettability-interval microgrooves could effectively enhance humid air condensation heat transfer performance.  
condensation; wettability; microgrooves; spontaneous suction; directional drainage +
Footnote †: journal: Applied
###### Abstract  
The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT).  
deep-learning; internet of things (IoT); software defined networks (SDN); artificial intelligence (AI); autonomous vehicle; quality of service (QoS); drowsy detection; 5G slicing; V2X; MEC  
10.390/app13020875
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 4 Discussion  
In this paper, we conduct a comparative study of loop closure detection based on traditional methods and neural network methods. As traditional methods, such as visual Bag-of-Visual-Words and feature descriptor methods, are susceptible to environmental factors, deep learning-based methods are gradually coming into the focus of researchers. This paper focuses on the extraction of image features by convolutional neural networks and applies them to loop closure detection.  
The SCNN method proposed in this paper is compared and analyzed with another deep learning method (CNN) and two other artificially designed features (BOVW, GIST) in terms of accuracy and time performance. The experimental results show that, compared with the traditional feature extraction method, the neural network method has significantly higher efficiency and real-time performance. Moreover, the SCNN method greatly improves the efficiency and accuracy of image feature extraction.  
In addition, we make full use of the depth information and divide it into three fusion strategies according to the different fusion positions of RGB stream and depth stream in the neural network, which are called early fusion, mid-level fusion, and late fusion, and the experiments prove that the late fusion strategy has a higher accuracy and recall rate.  
In the study of loop closure detection using deep learning algorithms, although the accuracy and computational efficiency have been improved, how to combine the improved algorithm with the back-end optimization of visual SLAM requires further research.
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil
### Results
#### 3.1.2 Analysis of Basic Physical Index for Natural and Modified Soil  
Liquid-plastic limit tests of natural and modified soil were carried out according to [33]. The specimen was sieved through a 0.5 mm sieve, and was then tested after setting for 24 h. The water content corresponding to the sinking depth of 10 mm and 2 mm is the LL and PL. Thereafter, the plasticity index (PI) was calculated by taking the difference between LL and PL, and those indexes with varying percentages of quicklime/cement are illustrated in Table 9.  
Table 3 indicates that the WAS belongs to low-liquid-limit soil and silty soil with weak stability. After being modified, LL and PI decrease considerably with the addition of quicklime/cement. PL increased from 22.4% to 24.11% up to 2% addition of quicklime,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Samples** & **Quartz (\%)** & **Muscovite (\%)** & **Illite (\%)** & **Feldspar (\%)** & **Kaolinite (\%)** & **Siderite (\%)** & **Calcite (\%)** \\ \hline Natural soil & 47.54 & 36.42 & 4.47 & 1.57 & 3.59 & 5.68 & 2.87 \\ +3\% cement & 49.17 & 33.82 & 3.23 & 1.70 & 3.78 & 4.27 & 3.65 \\ +5\% cement & 55.90 & 26.85 & 2.82 & 1.93 & 3.87 & 3.54 & 3.79 \\ +6\% quicklime & 52.37 & 28.47 & 3.46 & 1.82 & 3.62 & 3.26 & 3.53 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Analysis of mineral components of modified Soil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Physical Index** & **Mixture** & \begin{tabular}{c} **Liquid Limit** \\ **LL (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Plastic Limit** \\ **PL (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Plasticity Index** \\ **PI (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Curing 1 day} & +4\% quicklime & 23.26 & 23.26 & 9.97 \\  & +6\% quicklime & 24.11 & 24.11 & 8.47 \\  & +8\% quicklime & 23.45 & 23.45 & 8.40 \\ \hline \multirow{3}{*}{Curing 3 days} & +4\% cement & 24.07 & 24.07 & 10.92 \\  & +5\% cement & 25.23 & 25.23 & 7.47 \\ \cline{1-1}  & +6\% cement & 24.16 & 24.16 & 7.07 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test results of combined determination of liquid-plastic limit.  
while further addition of quicklime caused a slight reduction. The same changing rule was applied for cement-improved WAS. Hence, the resulting PI shows considerable reduction, reaching up to 10%. The amount of admixture has an effect on the plasticity of soil; the comparative study indicates that the reduction in the PI of mountain soil is relatively higher in the case of cement-treated soil when compared to lime-treated soil. The results have good agreements with other studies on soil-lime and soil-cement mixtures [41; 42; 43; 44]. This results in a reduction in plasticity, which thereby enhances 'workability'. The increase in the consistency limits of soils upon the addition of lime can be attributed to the increase in affinity of clay particles to water. However, expansive clays are rather inert to the action of hydroxide.
## 3 Inherent Optical Properties (IOPs)
### Atmosphere IOPs
#### 3.4.1 Gases in the Earth's Atmosphere  
The total number of air molecules in a 1 m\({}^{2}\) wide vertical column extending from sea level to the top of the atmosphere is about 2.15 \(\times\) 10\({}^{29}\). In comparison, the total amount of ozone (a trace gas) in the same vertical column is about 1.0 \(\times\) 10\({}^{23}\). [PERSON] et al. [25] compiled six model atmospheres including, (i) the US Standard atmosphere 1976, (ii) tropical, (iii) midlatitude summer, (iv) midlatitude winter, (v) subarctic summer, and (vi) subarctic winter (see Appendix U of [14] for a numerical tabulation of these models). These atmospheric models contain profiles of temperature, pressure, and the concentrations of the main atmospheric constituents, molecular nitrogen (N\({}_{2}\)) and molecular oxygen (O\({}_{2}\)). In addition, they contain profiles of the concentrations of several trace gases including water vapor (H\({}_{2}\)O), ozone O\({}_{3}\), carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), nitrous oxide (N\({}_{2}\)O), and the chlorofluormethanes (CFCs) in the Earth's atmosphere.  
The clear atmosphere (no clouds or aerosols) molecular (Rayleigh) scattering coefficient can be expressed as  
\[\sigma_{\rm Ray}(\lambda,z)\equiv\sigma_{\rm Ray,n}n(z)=\frac{32\pi^{3}(m_{ \rm r}-1)^{2}}{3\lambda^{4}n(z)}\qquad[{\rm m}^{-1}] \tag{65}\]  
where \(n(z)\) is the bulk air concentration (see Equation (64)), \(m_{\rm r}\) is the real part of the refractive index, and \(\sigma_{\rm Ray,n}\) is the Rayleigh scattering cross section. Please note that since \(m_{\rm r}\) depends on wavelength, the Rayleigh scattering coefficient does not have an exact \(\lambda^{-4}\) dependence. For air, a convenient numerical formula for the Rayleigh scattering cross section (accurate to 0.3%) is given by [14]  
\[\sigma_{\rm Ray,n}=\lambda^{-4}\sum_{i=0}^{3}a_{i}\lambda^{-2i}\times 10^{-28} \quad[{\rm cm}^{2}]\quad(0.205<\lambda<1.05\ \mu{\rm m})\]  
where the coefficients are \(a_{0}=3.9729066\), \(a_{1}=4.6547659\times 10^{-2}\), \(a_{2}=4.5055995\times 10^{-4}\), and \(a_{3}=2.3229848\times 10^{-5}\). The scattering phase function is given by Equation (9).  
Computer codes like MODTRAN [26; 27] have been developed to provide atmospheric transmittance and thereby absorption coefficients for all important atmospheric trace gases for a large variety of atmospheric conditions. In AccuRT we use a band model based on MODTRAN [28] to generate absorption coefficients and optical depths due to atmospheric gases including O\({}_{2}\), H\({}_{2}\)O, CO\({}_{2}\), O\({}_{3}\), CH\({}_{4}\), and NO\({}_{2}\). An example of how to use this approach to deal with gaseous absorption in shortwave near infrared bands for an atmosphere overlying a snow surface is provided in [29].
## 1 Introduction
The existing body of research has significantly advanced our understanding of the impact of post-grouting on pile-bearing capacity enhancement, as well as the improvement of tip and lateral resistance. However, several deficiencies remain: (1) most of the existing research primarily focuses on a single grouting form, neglecting to explore the differential effects and efficiency of various grouting forms. This limitation hinders the flexible selection of grouting methods in engineering applications. (2) There is a lack of systematic studies investigating the influence of grouting volume and pressure on the reinforcement range and effect. Consequently, accurate calculation and prediction of pile-bearing capacity are compromised, leading to an overreliance on increased safety factors in engineering design, which results in unnecessary economic waste. (3) Current model test studies often overlook the unloading effect of soil mass during the drilling process of cast-in-place piles. This oversight creates a gap between the analysis results and actual engineering scenarios [34]. Therefore, in order to address the aforementioned deficiencies and enhance the application efficiency of post-grouting techniques in pile foundations, the author aims to investigate the variations among various grouting methods and systematically study the influence of grout volume and pressure, as well as consider the soil unloading effect during the drilling process. In this manner, the accuracy and efficiency of pile reinforcement and bearing capacity prediction can be significantly improved. This, in turn, will contribute to more cost-effective and optimized engineering practices. Building upon the aforementioned analysis, this research proposes a novel model pile construction method capable of achieving both tip, side, and tip-side combined grouting. The study further conducts model tests to evaluate the vertical bearing capacity of individual piles, considering different grouting volumes and grouting pressures. Throughan analysis of the load-displacement relationship at the pile top, as well as the lateral friction resistance and tip resistance, the efficiency of various grouting forms in enhancing pile-bearing capacity and the reinforcement effects under different grouting parameters are thoroughly examined. Following the completion of the tests, the excavated piles are subjected to an analysis of the grouting body to investigate the diffusion range and diffusion mechanism of the grout. This comprehensive examination contributes valuable insights for both the calculation and construction of grouted piles, providing a reliable reference for future engineering applications.
## 3 Results
Figure 2: Receiver operating characteristic (ROC) curves of significant textural and dynamic features with AUC values. The best performance (ACC = 0.82; SENS = 077; SPEC = 0.88; PPV = 0.86; NPV = 0.79; AUC=0.83) using dynamic features was reached with all 81 metrics and an SVM trained with balanced data (with ADASYN function). However, the best results overall (ACC = 0.88; SENS = 086; SPEC = 0.89; PPV = 0.89; NPV = 0.86; AUC = 0.93) were obtained considering all the features at the same time with an SVM trained with balanced data (with ADASYN function) and a subset of 37 robust radiomic features. The subset of 37 robust radiomic features includes range, skewness, sum average, autocorrelation, GLN_GLRLM, GLV_GLRLM, LZHGE, GLV_GLSZM, convexity (ratio between the smallest volume with convex curvature that contains the lesion and its volume), irregularity (deviation of the lesion surface from the surface of a sphere), roughness (distance of each point of the center than the radial length average), gleaning, surface, MSD_KURTOISIS, MSD_SKEWNESS, MSD_IQR, ME_STD, ME_RANGE, AUC_MODE, AUC_KURTOIS, q2_MODE, q2_MEDIAN, q2_KURTOIS, q2_SKEWNESS, q3_SKEWNESS, WIN_MEDIAN, WIN_KURTOIS, WIN_SKEWNESS, WOUT_MODE, WOUT_KURTOISIS, WOUT_SKEWN ESS, WOUT_IQR, AUC_WIN_MODE, AUC_WIN_KURTOIS, AUC_WIN_STD, AUC_WOUT_MODE and AUC_WOUT_KURTOISIS. Figure 4 shows the ROC curves of the best classifiers. Figure 4: ROC curve of the best classifiers in (**a**) an LDA (linear discrimination analysis) using the all-balanced textural features set; in (**b**) a SVM using a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface); in (**c**) an SVM using the all-balanced dynamic features set; in (**d**) an SVM with a subset of 37 robust textural, morphological and dynamic features.