esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies
### Dams with a Low Groundwater Level  
The Fukang pumped storage power station under construction is located in Fukang City, Xinjiang, China. Its lower reservoir is located in the main stream of Baiyang River, with one main dam and one embankment dam for sediment storage, forming an independent lower reservoir between the main dam and the embankment dam. The embankment dam is a composite geomembrane slope-wall rock-fill dam, with a crest elevation of 1815 m, a crest width of 8 m, and upstream and downstream comprehensive slope ratios of about 1:3 and 1:2, respectively. The ground surface elevation of the dam is 1795.8 m. As an anti-seepage material, the slope wall and blanket adopt a compound geomembrane made of two fabrics with a membrane. (500 g/1 mm/500 g).  
Two calculation conditions according to the dam design information provided by the Design Institute [42; 43] are listed as follows. The design flood level is 1807.43 m, and the corresponding water level downstream is 1775 m; the check flood level is 1812.5 m, and the corresponding water level downstream is 1775 m. A typical cross section and dam axis section of the embankment dam are shown in Figure 3, and the permeability coefficients of the materials are listed in Table 1.  
Figure 3: Sections of the embankment dam for sediment storage: (**A**) a typical cross section; (**B**) the dam axis section.
## 2 Materials and Methods
### Data Acquisition and Reference Point Cloud
#### 2.3.1 Study Areas and Objects  
The study area used for the single strip measurements is located at the Campus Klein-Altendorf near Bonn and is shown in Figure 3a with additional objects that were arranged over the entire area with a length of about 300 m.  
The location was chosen based on an existing network of high accuracy control points [1], which are shown in Figure 3a and used to georeference the TLS point cloud. The measurement setup was designed in a way that flight planning will be similar to the  
Figure 3: Study area for the analysis of single strip measurement with included objects (**a**) and the two flight directions of the individual measurements (**b**), \(\copyright\) [[https://www.google.de/maps/](https://www.google.de/maps/)]([https://www.google.de/maps/](https://www.google.de/maps/)) (accessed on 14 May 2021).  
corridor mapping application with a fixed flight direction, in this case parallel to the road. A measurement with the UAV system, shown in Figure 3b, includes the path along the road in the north-east direction (flight direction 1) and back along the same path (flight direction 2), which means that each object is always measured twice for one data set. The objects within the area used for the quality analysis are six tables (1.2 m \(\times\) 0.8 m) and four targets (0.3 m \(\times\) 0.3 m), shown in Figure 4b,c.  
The tables have a white and plain surface with high reflective properties and were placed directly below the trajectory. In addition, the tables were aligned horizontally using a spirit level so that each position on the table had the same height.  
The second study area used for the evaluation of the multiple strip measurements is a typical urban environment with buildings, streets and vegetation. The principle approach of analyzing the UAV-based laser scanning system is similar by the use of an accurately measured TLS reference point cloud and the following assessment of the UAV point clouds using point-based and parameter-based evaluation techniques. Compared to the data set before, a cross-flight pattern, which is typical for UAV-based laser scanning, is performed.  
The study area used for the multiple strip measurements is shown in Figure 5a, where additional targets distributed over the whole area as indicated. The 12 targets are in the same TLS design as before but with a size of 0.8 m \(\times\) 0.8 m (Example shown in Figure 5b) that targets center could be estimated also with a flight height of 25 m.
## 2 Materials and Methods  
### Geopolymer Precursor Material  
Metakaolin (MK) is the primary precursor material used in synthesizing the geopolymer matrix in this study. The MK was made by thermal treatment of kaolin clay mined from Kankara, Katsina State, Nigeria. Previous research by [PERSON] et al. [14] confirmed its suitability for use as a matrix in the geopolymer composite. Before heat treatment, pulverization and wet beneficiation of the raw kaolin clay were carried out following the procedures reported by [PERSON] et al. [47] and [PERSON] et al. [48]. The raw kaolin was calcined into metakaolin in a Nabertherm electric furnace chamber at 700 \({}^{\circ}\)C for 2 h. The previous work of [PERSON] et al. [14] has shown these treatment conditions to be suitable for processing MK. The chemical composition of the kaolin and MK powder was investigated by X-ray florescence analysis (Olympus VANTA XRF Analyzer). The result (Table 1) showed silica (60.50%) and alumina (34.30%) to be the major constituents of the metakaolin. The MK powder's Si/Al ratio and loss on ignition (LOI) were 1.76 and 2.15%, respectively. The metakaolin's bulk density and specific gravity were computed and found to be 650 kg/m\({}^{3}\) and 2.86, respectively. Metakaolin was sieved with a 150 \(\upmu\)m sieve before use, so the metakaolin particle size was \(<\)150 \(\upmu\)m. The raw Kankara kaolin clay was cream-like before heat treatment and turned pink after the calcination process (Figure 1a,b). Figure 1c shows the XRD diffractogram of the kaolin and metakaolin as obtained using a D-5000 PSC-8 X-ray diffractometer. The major minerals in the XRD diffractograms are kaolinite, quartz, and illite [14]. The main characteristic peaks of kaolinite in the raw kaolin were exhibited at 12.35\({}^{\circ}\) and 24.88\({}^{\circ}\) two-theta (20). The intense peak observed at 26.63\({}^{\circ}\) 20 corresponded to quartz. Comparing the diffractogram of raw kaolin and metakaolin, changes in the intensity of the peaks of kaolinite and quartz were observed. The color change and changes in chemical composition and diffractogram of kaolin indicated the transformation from kaolin to metakaolin material.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 3 Blocking Performance Analysis for Tree-Type Data Center Networks
### Link Load Independence-Based Blocking Probability Performance Analysis  
The blocking probability analysis methods based on link load independence assumption mainly include [PERSON]'s method [11] and the EFPA method [14]. In [PERSON]'s method, links in different stages of the crossbar network are assumed to be independent. The loads of adjacent links can be approximated as independently distributed, if most of the cumulative traffic on different links is aggregated from different connections. Thus, the blocking probability of connection \(C_{i}\) can be directly approximated by  
\[P_{c,i}\approx 1-\prod_{ls\in C_{i}}{(1-P_{ls})}, \tag{3}\]  
where \(ls\in C_{i}\) represents the link \(ls\) which belongs to the path of connection \(C_{i}\), and \(P_{ls}\) is obtained according to the cumulative load on link \(ls\). [PERSON]'s method did not consider that the effective load of one link is not simply equal to the sum load from connections going through this link.  
The classical EFPA method was founded on the flow conservation principle, where the effective flow in link \(ls\) is equal to the sum of effective flows from all connections going through link \(ls\), denoted by  
\[\rho_{ls}(1-P_{ls})=\sum_{ls\in C_{i}}{\rho_{c,i}(1-P_{c,i})}, \tag{4}\]  
where \(\rho_{ls}\) is the effective load on link \(ls\), and \(P_{ls}\) is the blocking probability of link \(ls\). Generally \(\rho_{c,i}\) is known, while \(\rho_{ls}\) is deduced from \(\rho_{c,i}\). The EFPA method also assumes that the effective loads on all links are independent and, therefore, the blocking probability of \(C_{i}\) can be also approximated by (3). Thus, \(\rho_{ls}\), \(P_{ls}\), and \(P_{c,i}\) can be modified iteratively until convergence, and the final network blocking probability can be obtained.
###### Abstract  
This study presents the development of a novel biodegradable, self-expanding stent designed to facilitate arteriovenous fistula (AVF) maturation. The stent, made of polylactic acid (PLA), is engineered to be crimped into a standard 6 Fr (2 mm) catheter for delivery and to self-expand, increasing the vein diameter beyond 4.2 mm with the aid of pre-strained elastic lines, thereby enhancing maturation rates. A validated finite element model was utilized to design the stent, ensuring it meets functional requirements with less than 3% strain in both crimped and fully expanded states. The stent prototype was fabricated using a modified fused deposition modeling (FDM) 3D printer, and the Taguchi method was employed to optimize manufacturing parameters, achieving strut width and thickness variations of less than 5%. Experimental validation demonstrated that the PLA stent could be crimped to 2 mm, self-expand to 6.4 mm, and deliver a radial force of 0.08 N/mm, meeting the performance requirements of AVF stents. Additionally, the stent exhibits excellent elasticity post-implantation, minimizing the risk of damage from external forces, and fully degrades after AVF maturation, reducing the risk of long-term vascular obstruction and related complications. This novel stent design offers a promising biodegradable solution for enhancing AVF maturation and improving patient outcomes.  
articipuous fistula; stent; polylactic acid; 3D printing Article
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 4 Discussion  
In this paper, we propose a self-supervised machine learning blind deconvolution method based on joint inversion of seismic wavelet and reflectivity. Through the estimation of the initial zero-phase wavelet and the sparsity-promoted process on predicted reflectivity, we mitigate the ill-posedness of the blind deconvolution under a more concise parameter selection. Though the SSL-BD method achieves impressive deconvolution performance, there are several aspects that can be further explored and improved in the future.  
Firstly, for the joint inversion of reflectivity and wavelet, we utilize a residual neural network and a fully connected convolutional neural network. However, many efficient network structures and components have been proposed and achieve superior experimental results. Therefore, different network components can be explored to enhance the deconvolution performance. Secondly, the proposed SSL-BD method derives reflectivity and seismic wavelet solely from seismic data, and does not make full use of well-logging data. In this regard, adding stratigraphic information from well-logging data to our framework will be a fruitful topic. Thirdly, for each new seismic survey, the proposed SSL-BD method needs to retrain networks so as to obtain the corresponding reflectivity and wavelet. This method does not take full advantage of the generalization properties of neural networks. We consider taking strategies like transfer learning and pre-training into our framework to improve the computational efficiency in the future. Fourthly, due to the limited computational resources, the numerical examples presented in this paper are in small-scale or middle-scale. However, due to the scalability of neural networks, our method should be able to deal with large-scale seismic datasets with better computational resources. Fifthly, the wavelet is assumed to be stationary in this work. For future work, the proposed method has potential to be generalized to the nonstationary seismic blind deconvolution by combining with a quality-factor-based nonstationary convolution model or segmentation-based nonstationary convolution model.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 3 Results
### Prediction Results of the Network Model  
#### 3.1.1 Blood Vessels with Idealized Geometry  
Figure 5 shows the prediction results of the drug concentration distribution at four different instants, 0.11 (t/T), 0.12 (t/T), 2.11 (t/T), 2.13 (t/T), which correspond to the times when the injection starts, stabilizes and ends and after the injection. The drug is injected from the central axis of the blood vessels. The first, second and third lines show the network prediction, the numerical simulation and the relative error between the two, respectively. As a reminder, we scale the length and height of the blood vessel, as well as the concentration field to [0,1]. As presented, the liquid drug flows along the central axis and the relative errors at the four different times are all less than 7%. After training, the accuracy of the proposed model, which is measured by the average relative error, could achieve 97.1%--which is satisfactory to meet the requirements of physician-assisted procedures in real time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Batch Size** & \(\lambda\) & \(\beta_{1}\) & \(\beta_{2}\) & \(\varepsilon\) & \(\alpha\) \\ \hline Bifurcated blood vessels & 12 & 0.00008 & 0.99 & 0.999 & 0.0001 & 0.0006 \\ Hepatic Artery(HA) & 32 & 0.001 & 0.99 & 0.999 & 0.0001 & 0.00005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of the optimization algorithm: \(\lambda\) is the regularization coefficient. \(\beta_{1}\), \(\beta_{2}\) and \(\varepsilon\) are the hyperparameters of the ADAM algorithm, \(\alpha\) denotes the learning rate.  
Figure 5: Drug concentration field and error distribution at four different times for the straight blood vessel with a drug injection on the central axis.  
Figure 6 displays the prediction results of the concentration field in the bifurcated blood vessel for four different injection positions at t = 2.1 (t/T) in the test dataset. The distances between the injection position and the central axis are 0.8 R, 0.7 R, 0.55 R and \(-\)0.14 R, respectively. The figure clearly shows that all of the liquid drug flows into the branch with the 0.8 R injection position, while all the drug remains in the parent artery with the \(-\)0.14 R injection position. In the other two cases, the drug is divided into two parts at the bifurcation. The numerical simulation and prediction results are in good agreement, which confirms that the proposed model has the ability to capture minor features, such as drug shunting and reflux. The average accuracy of the prediction results achieves 99.4%, the maximum error is less than 6% mostly in the region with greatest drug concentration.
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 4 The Filtering Multi-Type Graph Neural Networks
### The Alignment Module  
After the embedding module, the entity embeddings of the heterogeneous graphs have a uniform vector space. The alignment module realizes the alignment task by calculating  
Figure 5: A schematic diagram of the jointly embedding phase.  
the similarity metric between the embedding features. Our approach utilizes the \(L_{2}\) norm to calculate the distance between pairs of candidate entities, which is depicted in Equation (12):  
\[D\big{(}e_{i},e_{j}\big{)}=\big{\|}h(e_{i})-h\big{(}e_{j}\big{)}\big{\|}_{2} \tag{12}\]  
The variables \(e_{i}\) and \(e_{j}\), respectively, represent pairs of candidate entities in two heterogeneous graphs, while \(D\big{(}e_{i},e_{j}\big{)}\) signifies the distance of the candidate entities between \(e_{i}\) and \(e_{j}\). Respectively, \(h(e_{i})\) and \(h\big{(}e_{j}\big{)}\) represent the eigenvectors of the embedded entities, \(e_{i}\) and \(e_{j}\).  
Our approach falls under the category of an unsupervised entity alignment method, so we introduce an unsupervised alignment consistency loss for the approach. Firstly, we extract the set of all neighboring entities, \(e_{j}\), connected to \(e_{i}\) in \(KG_{1}\), as \(N_{i}\). Similarly, we identify the corresponding entity \(e_{i}^{\prime}\) in \(KG_{2}\), and collect its neighbouring entities \(e_{k}\), to form \(N_{i}^{\prime}\). Subsequently, by evaluating the difference term for each entity pair \((e_{i},e_{i}^{\prime})\) using Equation (13) and aggregating the results, we obtain the alignment discrepancy.  
\[\mathcal{L}=\sum_{e_{i}\in KG_{1}}\left\|\frac{1}{|N_{i}|}\sum_{e_{j}\in N_{i}} D\big{(}e_{i},e_{j}\big{)}-\frac{1}{|N_{i}^{\prime}|}\sum_{e_{i}\in N_{i}^{ \prime}}D\big{(}e_{i}^{\prime},e_{k}\big{)}\right\| \tag{13}\]  
According to Equation (13), it encourages similar entities to possess similar neighboring entities in both knowledge graphs while maintaining consistency in their alignments. Ultimately, by minimizing the consistency loss, our approach can learn a consistent alignment pattern and facilitate the alignment of entity pairs.
## 4 Proposed Algorithm EEWBP
### Network Operation
#### 4.3.2 Data Transmission Phase  
Inter and intra-cluster communication in the second stage is known as data transmission. The active nodes use a one-hop mechanism for intra-cluster communication to transmit their detected data straight to their respective CH. In contrast, in inter-cluster communication, CHs use a multi-hop approach to convey selective information from one cluster to another [48]. In this case, the communication path is chosen using the shortest path technique as shown in Figure 3. As a result, EEWBP selects the most optimal CHs and active nodes to ensure long-term coverage. A data compression method merges all the acquired data from all the CH's. A critical XOR operation is now performed on the data detected by neighbor nodes to ensure that the CH does not send a copy of this information to the BS [38; 39; 40]. Bit scrambling is accomplished using the XOR algorithm. A \"True\" result is deemed to have two copies of the data, whereas a \"False\" result is considered to have just one copy. The duplicate-gathered data would not be transmitted if the data sent out were identical bit for a bit [16]. As a result, duplicate data can be avoided and the amount of data sent to BS has decreased automatically. Due to this, the entire system is going to utilize less energy [46].
## 3 Results and Discussions
### Results and Discussions of Trajectory Prediction for Cruise Phase  
In this section, all the mentioned four approaches are applied to predict the flight trajectory. The following figures report the experiment results of predicting the trajectory for a cruising flight at 8400 m, including prediction accuracy, predicted NFT, and its CIs (Figure 6).
# Identification of Complicated Lithology with Machine Learning
## 2 Model Principles  
### Decision Tree and Random Forest Algorithm Principles  
The structure of a decision tree consists of several key elements, including the root node, which serves as the starting point of the tree; branches that represent the decision paths; and leaf nodes that indicate the final prediction outcomes. The root node, located at the beginning of the decision tree, serves as the starting point of the entire tree structure. Each branch represents the path leading to a new decision point or a leaf node. Each decision node reflects a specific objective or requirement, usually associated with the characteristics of the target instances. The leaf nodes represent the predicted values derived within a specific range. When traversing the decision tree, the process begins at the root node and moves downward step by step. At each node, a prediction is made, and based on the different prediction outcomes, the path continues along the corresponding branch until it reaches a leaf node. This process embodies the core of the decision tree algorithm, which is to use multiple variables to perform classification prediction or regression analysis on the target. Figure 1 illustrates a simple conceptual diagram of the decision tree principle.  
Random forest (RF) is an ensemble learning method that improves prediction accuracy and stability by constructing multiple decision trees and integrating their results. This method introduces two main types of randomness when building each decision tree: first, by using bootstrap sampling to generate multiple different training subsets from the original dataset, it ensures that each tree is trained on a different subset of data; second, at each split node, a random subset of features is selected as candidate features, and the best feature is chosen from this subset to perform the split. This further increases diversity among the trees [31]. The final prediction of the random forest is obtained by aggregating the results of multiple decision trees. For classification problems, the majority voting method is typically used; for regression problems, the average of all the trees' predicted results is calculated as the final output. The advantage of this method lies in its ability to provide high prediction accuracy while effectively resisting overfitting.
# Reliable Visual Exploration System with Fault Tolerance Structure
## 6 Experiments
### Experiments Set-Up  
For quantitative evaluation, two challenging datasets (UA-CSC1 and UA-CSC2) are collected, which contain the image sequence during exploration in our lab captured by a monocular camera, and ground truth from motion capture system. The configuration of experimental platform is illustrated as Figure 2, where the only input of our system is the image from monocular camera and the control command is generated for TurtleBot mobile robot (TurtleBot 2.0, Willow Garage, Palo Alto, CA, USA) with differential driving. The max linear speed of controller output is 0.3 m per second.  
Regarding the active controller, simulations are designed to evaluate the controller in an indoor environment. The simulation is set-up in the Gazebo simulation environment. In addition, the results of demonstration in the real world are shown, and the ability of obstacle avoidance is verified.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Interval** & **Distribution** & **Mean** & **Standard Deviation** \\ \hline Ideal & 0 & 1.813 \\
1 degree & 0 & 1.821 \\
10 degree & 0 & 1.889 \\
20 degree & 0 & 1.964 \\
30 degree & 0 & 2.039 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parametric distribution with interval sampling, where the ideal one is regarded as an evaluation standard for completeness calculation. A series of parametric results are shown with different sampling intervals, and they are applied in our implementation.  
Figure 8: Example of grid map in the distribution-based evaluation. Arrows in the left figure are the pose of collected keyframes and the value of grid in the right figure is determined by the exploration score.  
To demonstrate the fault tolerance in VLM, comparison is designed. As a popular VSLAM system, ORB-SLAM 2.0 (abbreviated as ORB-SLAM in this section) is selected for comparison.  
In the third part, evaluations of the proposed termination condition are shown. To show the fault prevention for localization by complete mapping, we conduct the experiments on the proposed completeness evaluation by the performance of re-localization with the built map. The successful percentage of re-localization is supposed to be improved by the termination condition.  
The system is run on a computer with the Intel Core i5 (2.30 GHZ) CPU (Santa Clara, CA, USA), 8 GB memory and Nvidia GeForce GTX 960 GPU (Taiwan).