esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering
### Interaction between Rock Structures and Soil  
In tunnel engineering, the interactions between tunnel rock, soil, and retaining wall structures are crucial for ensuring structural stability, safety, and cost-effectiveness. This interaction affects key factors such as load distribution, ground response, and deformation characteristics. The precise modeling of these interactions is vital for anticipating and addressing potential challenges, which in turn supports the design of effective retaining walls and appropriate reinforcement measures. Additionally, a deep understanding of these dynamics aids in developing optimal design strategies, improving risk management, and ensuring the long-term performance and maintenance of the infrastructure. Therefore,  
Figure 7: Results of surface profile and velocity field for water and elastic plate using the SPH method.  
a comprehensive grasp of these interactions is essential for the successful implementation and economic efficiency of tunneling projects.  
[PERSON] et al. [21] presented a simulation incorporating a novel spring-buffer contact model alongside the Mohr-Coulomb failure criterion. Upon verification, the results obtained from this approach exhibited enhanced accuracy, making it suitable for studying the impact of backfill on masonry arch bridges. Figure 8 presents the evolution of the horizontal displacement at the top of the retaining wall, along with the displacement profile at 3.0 s. The displacements calculated using the coupled DDA-SPH method are compared with those from other numerical methods, specifically the FEM and improved SPH62. The results show strong agreement in terms of deformation time history and maximum horizontal displacement. For instance, the maximum horizontal displacement is approximately 0.770 m, closely aligning with the 0.769 m predicted by the FEM and the 0.745 m predicted by improved SPH. This indicates that the coupled DDA-SPH method is effective for simulating interaction problems between granular soil and flexible structures.
###### Abstract  
Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.  
Keywords:landslide susceptibility prediction; multilayer perceptron; BP neural network; information theory +
Footnote †: journal: Applied Sciences
# Effective RNA Regulation by Combination of Multiple Programmable RNA-Binding Proteins
## 2 Materials and Methods
### Electrophoretic Mobility Shift Assay (EMSA)  
RNA oligonucleotides, labeled with 6-FAM at the 5'-end, were obtained from FASMAC. Labeled-RNA (10 nM) and three-fold serially diluted concentrations of purified PUF proteins (0-300 nM) were mixed in the reaction buffer containing 10 mM HEPES, 50 mM KCl, 0.5 mM EDTA, 2 mM DTT, 0.01% Tween20, 0.1 mg/mL BSA, 0.02 U/uL RNasin Plus RNase Inhibitor (Promega), and 2.5% Ficoll. The mixture was incubated at 25 \({}^{\circ}\)C for 30 min. Free RNA and bound RNA were separated via gel electrophoresis using 8% nondenaturing polyacrylamide gels in 0.5\(\times\) TBE at 4 \({}^{\circ}\)C. After electrophoresis, fluorescently labeled RNAs were detected using the Typhoon scanner RGB system (GE Healthcare). The peak intensity for bound and free labeled-RNA was measured using the ImageQuant TL software (GE Healthcare). The fractions of bound RNA were plotted against the protein concentrations. For calculating equilibrium dissociation constants (\(K_{\text{d}}\)), the plotted data were fit to the 1:1 binding equation using the Kaleida Graph software (Hulinks) as described before [23].
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Experimental Validation of Simulation Results
When both bandwidth and RTT are low, MPTCP coupled CC is too aggressive and outperforms TCP, in some cases by a large margin. Despite being restricted through using a couple _cvond_, MPTCP is able to grow its window more aggressively than TCP and attain a higher share of the bandwidth. When looking at higher bandwidth configurations in combination with lower RTT values, the coupled CC algorithms do a reasonable job in attaining good bandwidth sharing with the TCP flow. However, as the BDP increases, all of the MPTCP coupled CC algorithms start deteriorating in performance, even when path characteristics are the same. Compounding this is that as the path RTT difference increases, the coupled CC algorithms are unable to keep up with the aggressiveness of TCP and give up a significant proportion of their bandwidth to the TCP flow. The results indicate that MPTCP coupled CC algorithms are only able to attain fair bandwidth sharing under certain network configurations. A change in bandwidth or RTT can lead to either a loss in throughput to MPTCP or high levels of aggressiveness in utilising the available bandwidth. This demonstrates that even when operating in the environment which matches the assumptions made by all MPTCP coupled CC algorithms (i.e., all bottlenecks are shared), that the CC algorithms accede too much bandwidth to TCP and therefore do not perform as designed. Figure 21: LIA, OLIA and BALIA Throughput Share-Shared Paths with RTT1 = 50 ms and RTT2 = 100 ms.
## 3 Experimental Results and Analysis
### Experiment Setup
#### 3.1.3 Competing Methods and Quantitative Metrics  
This method is compared with seven leading methods, including traditional and deep-learning-based approaches. In terms of traditional methods, the low-rank method LLRT is considered. In terms of deep-learning-based methods, it is compared with MAFNET [38], HSD-CNN [33], QRNN3D [34] and SQAD [37]. To objectively evaluate the denoising performance of the algorithm, three complementary quantitative evaluation metrics are used: Peak Signal-to-Noise Ratio (PSNR) [44] and Structural Similarity Index (SSIM) [45] based on the spatial domain, and Spectral Angle Mapper (SAM) [46] in the spectral domain. Specifically, PSNR evaluates pixel-level reconstruction accuracy by calculating the mean square error between the denoised image and the ground truth image. Higher values indicate better denoising performance. SSIM measures the structural similarity of the image in three dimensions: luminance, contrast, and structure. The value is in the [0, 1] range, with values closer to 1 indicating higher structural fidelity. SAM evaluates the preservation of spectral features by calculating the angle between the original spectral vector and the denoised spectral vector. A smaller value (with an ideal value of 0) indicates less spectral distortion.
## 5 Simulation Results
### Correlation Results  
This section contains the correlation results among the dissimilarity metrics obtained using the GP and the Euclidean distance. Table 1 contains the correlation results achieved by the best dissimilarity metrics obtained by the GP for the three depths considered. The GP parameter settings that lead to the best results are shown in Table 1. Traditional dissimilarity metrics have been included for the sake of comparison.  
Figure 4 shows the best solution obtained. According to Table 1, it correlates with the Euclidean distance better than classical dissimilarity metrics in 8.9%. More details about other solutions obtained can be accessed at [32]. In regard to the expression for the best achieved metric is:  
\[S_{new}=\frac{\sqrt[4]{a_{k}}+a_{ik}}{\left[a_{ik}\sqrt[4]{a_{ik}}+2(a_{i}+a_ {k})+\sqrt[4]{a_{k}}\right]^{2}} \tag{21}\]  
It is interesting the appearance of the term \(2(a_{i}+a_{k})\) in the divisor. It also appears in Sokal-Sneath expression (see Section 3), which achieves the best results among the classical dissimilarity metrics (see Table 1). Therefore, it plays an important role in the correlation with the Euclidean distance. Figure 5 shows the Euclidean distance versus the metric obtained for 120 vehicles. Figure 5 shows that the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **Correlation** \\ \hline Jaccard & 0.661357 \\ Dice & 0.622318 \\ [PERSON] & 0.616629 \\ Fowlkes-Mallows & 0.620337 \\ Sokal-Sneath & 0.620337 \\ GP Metric (Depth = 4, \(p_{c}\) = 0.7, \(p_{m}\) = 0.1) & 0.738470 \\ GP Metric (Depth = 5, \(p_{c}\) = 0.8, \(p_{m}\) = 0.15) & 0.741575 \\ GP Metric (Depth = 6, \(p_{c}\) = 0.8, \(p_{m}\) = 0.2) & 0.740777 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation results.  
obtained metric is a similarity metric since it decreases with an increase in the Euclidean distance. Therefore, \(DM_{new}=\mathbf{1}-S_{new}\). Thus, \(DM_{new}\) will be used to replace the Euclidean distance in the next section.
## 2 Materials and Methods
### The Additional Testsuite Framework: An Overview  
The additional testsuite framework (ATF) [18] is novel approach for the management of code testsuites, providing relevant structures and instrumentation. ATF supports a multitude of features, including management of tests for multi-version applications, test-driven development, dynamic/selective program builds, feature-based builds, testing in different environments, and source code analysis. ATF utilizes annotations to associate tests with specific application characteristics, and dynamically matches these characteristics against build specifications and/or deployment environment attributes to (a) retrieve from the source code repositories the relevant sources to be used for the specific build, (b) create the executable image of the tailored software component according to the build specifications, and (c) deploy the executable image to the designated deployment environment.  
The benefits stemming from the introduction and use of ATF include the following:  
Figure 1: Proposed framework architecture.  
1. Tests are written once, and can be flexibly associated with any number of software programs and versions, limiting the effort and complexity needed for the maintenance of test cases.
2. It supports dynamic/selective program builds that include only the portions of the software that match some designated functionality.
3. For software applications that are developed or organized according to the featured-based development paradigm [37], builds can be tailored to create executables that only support a subset of the available features.
4. It can underpin the localization of bugs introduced during software evolution, including regression bugs, through the comparison of code in versions producing erroneous results against the code in versions yielding correct results.
5. It can facilitate documentation compilation, since functionality-oriented and feature-based tests can be included in documentation on the functionality/feature they pertain to, serving as examples of the specific functionality/feature as well as providing examples of usage.  
For more information regarding the capabilities and functionality of the ATF, the interested reader is referred to [18]. In the next subsection, we describe the feature management functionality of ATF, which are utilized in the context of the proposed software vulnerability management framework.
## 3 Results and Discussion
### Further Discussion
The evolution patterns of volatile compounds expressed as ng/berry in our study revealed that, although water-loss concentration played a considerable role during non-destructive dehydration, biosynthesis and biodegradationalso occurred during this process, which eventually modified some volatiles significantly, especially some powerful odorants such as hexanal, _trans_-2-hexenal, 2-phenethyl acetate, \(\beta\)-myrcene, linalool, geraniol, _cis_-rose oxide, and \(\beta\)-damascenone. The results of this paper also suggest that the metabolic pattern of volatiles during on-vine dehydration is likely to fundamentally differ from that during post-harvest off-vine dehydration. [PERSON] et al. (2006) [20] have proposed that, during post-harvest off-vine dehydration, the grape cells shift from aerobic to anaerobic metabolism, which was marked by an increase in alcohol dehydrogenase (ADH) activity and the significant accumulation of ethanol. This rapid increase in ethanol has also observed in icewise grapes at the final stage of freeze-thaw cycling by other authors [19]. However, no significant accumulation of ethanol (ng/berry) was observed in our study (Table S2), suggesting that anaerobic metabolism of grape berries did not occur during on-vine non-destructive dehydration. Unlike destructive or off-vine dehydration, the structure of the grape berry during on-vine dehydration was not destroyed and metabolite exchange between the grape berry and vine was not cut off, such that the metabolic pattern might completely differ. Of course, the gene expression of key odorants during on-vine dehydration and the influence of dehydration degree on wine quality--especially regarding the balance between grape yield and wine quality--needs to be further investigated.
# Development of Automatic Electric Drive Drilling System for Core Drilling
## 1 Introduction
However, the large-scale equipment used at the petroleum and gas industry application level is not suitable for laboratory experiments. In terms of electric drive drilling system, [PERSON] developed a miniature drilling device with a drilling parameter acquisition module, but the device did not realize closed-loop control in the drilling process [16]. [PERSON] set up an experimental device to study the stick-slip phenomenon in drilling with DC top drive. However, the WOB and torque provided by the device are limited [8]. [PERSON] et al. built a drilling experiment device to explore the stick-slip characteristics of drilling tools in the process of drilling with electric jacks. However, in the actual process of drilling, the motor speed oscillated significantly and could not provide a stable rotation speed [9]. [PERSON] et al. developed an automatic drilling system, which is powered by a small top drive hollow shaft DC motor [7]. It is equipped with a measuring-while-drilling module to realize automatic drilling control, but the motor's power is not enough to complete drilling experiments at real drilling level. In summary, most of the current laboratory drilling systems are driven by hydraulic hybrid drive or a pure hydraulic system, and the development of electric drive is lagging. Moreover, laboratory drilling systems with pure electric drive mostly provide very limited WOB and torque, which are only used for specific experiments. In addition, most of the control of drilling parameters is based on the feedback control between people and sensors, which does not realize the real sense of automatic control. Most laboratory drilling systems with well-designed control systems are not capable of drilling at real size, especially for core drilling, which is unable to restore actual drilling. The main objective of this study is to develop an electric driven laboratory core drill rig, and to develop a supporting automatic drilling control system to meet the requirements of simulating actual core drilling engineering. The system can perform simple drilling experiments with four drilling modes, record drilling data, and optimize the ROP for energy consumption. In this paper, the basic theory of drilling is introduced. Then, the drilling system design, including the hardware and software system, is presented. Subsequently, four control modes are introduced and verified by experiments, and the rate of penetration is optimized by power optimization. Finally, the results and discussion are presented to demonstrate the performance of the proposed automation drilling system.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Self-Supervised Loss Functions
#### 3.5.3. Peakiness Loss (\(L_{peak}\))  
To encourage sharp, well-localized keypoint heatmaps on the templates with concentrated activations, we utilize the peakiness loss. Its objective is to promote \"peaky\" heatmaps, where the probability mass is concentrated at a small number of locations, leading to more precise keypoint localization. The mechanism calculates the negative spatial entropy of each heatmap. Minimizing this negative entropy (equivalent to maximizing the peak value and concentrating the distribution) encourages the desired sharpness as follows:  
\[L_{peak}=-\frac{1}{B\cdot K}\sum_{b=1}^{B}\sum_{k=1}^{K}\log\left(\max_{h,w}(H_ {pred,b,k}(h,w))+\epsilon\right) \tag{6}\]  
where \(\max_{h,w}\) denotes the maximum value over the spatial dimensions of the heatmap for batch item \(b\) and keypoint \(k\), and \(\epsilon\) is a low constant (e.g., \(10^{-6}\)) for numerical stability, preventing issues with the logarithm of zero. This loss is used in both Stage 1 and Stage 2.
## 3 Methodology
### Quality of Service Parameters  
Three QoS parameters are used to assess the performance of data transmission in this study. The QoS parameters are the PDR, delay, and throughput. The PDR measures the ratio of the number of information packets that arrive at the destination compared to the number of packets sent by the source's network [31]. It is defined as follows:  
\[PDR=\frac{\sum P_{R}}{\sum P_{S}} \tag{5}\]  
Figure 4: UAV-GS simulation flowchart.  
where \(P_{R}\) and \(P_{S}\) are the number of packets received and sent, respectively. The simulator in MATLAB increases counters for both successfully delivered and transmitted packets at the end of each time step. The ratio of these two counters is used to calculate the final PDR at the conclusion of the simulation.  
Delay is the time taken for a packet to travel from the source UAV to the destination GS. It can be calculated as the time difference between the packet transmission and its successful reception. In the simulation, the delay is calculated based on the distance between the UAV and the GS and the speed of data transmission. It is defined as follows:  
\[\mathrm{Delay}=\frac{D}{P_{T}} \tag{6}\]  
where \(D\) and \(P_{T}\) are the distance and packet transmission speed. MATLAB calculates and accumulates the delay for each packet that is successfully delivered, then computes the average delay for all packets.  
Throughput represents the rate of successful packet delivery over time, measured in packets per second (pps). It can be calculated as the total number of packets successfully delivered divided by the simulation time. To dynamically track throughput throughout the run, MATLAB divides the number of delivered packets by the simulation time that has passed after each time step.
# A Novel Approach to Quantifying the Failure Modes of Concrete-Epoxy Interface
###### Abstract  
The failure or debonding of CEIs (Concrete-Epoxy Interfaces) in Fiber-Reinforced Polymer concrete (FRP) systems occurs in one or a combination of three modes: CC (Cohesive failure in Concrete), CE (Cohesive failure in Epoxy), and IF (Interfacial Failure). These failure modes are usually identified, and their relationships are established by human intuition, which is prone to subjectivity. This study proposes a novel method based on image processing techniques to analyze CEI fracture surfaces and evaluate their failure modes. The failure modes of CEI fracture surfaces of specimens from a 3 PB (Three-Point Bending) experiment were assessed using an HVS, CIE L*a*b*, YcbC\({}_{r}\), or RGB color space image segmentation-based image processing technique on the preprocessed images of the CEI failure sides. A manual approach was adopted to validate the accuracy of the proposed method. Comparing the failure mode (CE) obtained using the manual and the proposed methodology, an RMSE (Root Means Square Error) of 0.19, 0.10, 0.23, and 0.26 was obtained for HVS, CIE L*a*b*, YcbC\({}_{r}\), or RGB color space, respectively. The epoxy area selected with CIE L*a*b* color space produced the most accurate evaluation of the failure modes. This study provides an accurate method of quantifying the failure modes of CEI fracture surfaces. The methodology proposed in this study is recommended for forensic investigations to understand better the possible causes of failure in externally bounded fiber-reinforced polymers.  
Keywords:fiber-reinforced polymer; concrete-epoxy interface; fracture surface analysis; image processing; image segmentation; color space; RGB; HSV; YcbC\({}_{r}\); CIE L*a*b* +
Footnote †: journal: materials