esa-sceva/satcom-chunk-collection huggingface dataset:
# Sharp Interface Capturing in Compressible Multi-Material Flows with a Diffuse Interface Method
## 6 Numerical Experiments: Two-Material Test Cases
### Two-Dimensional (2D) Triple Point Case: Two Materials and Three Phases  
In this section we show a 2-material and 3-phase triple point test case. This is a standard test case corresponding to a 2D Riemann problem in a rectangular computational domain, which together with the dimensions and initial conditions is displayed in Figure 10. We also consider that the two materials are governed by an ideal gas EOS given by Equation (49). We choose a coarse Cartesian mesh of size \(140\times 60\). The boundary conditions for this case are Homogeneous Neumann for scalars (\(\Pi\)), and slip for velocities.  
Our analysis for this test case primarily focuses on the comparison between the numerical results produced by using two different numerical recipes for the advection of scalars \(\Pi(\alpha_{\phi},\rho_{\phi},\epsilon_{\phi})\):  
Figure 9: 2D numerical results for an air-helium shock-bubble interaction test case. Qualitative comparison of the cell volume fraction (\(\alpha\)) of Helium bubble between the modeling (Figures (**a**,**c**,**e**)) and experimental results (Figures (**b**,**d**,**f**)) [47]. The scale of (\(\alpha\)) is shown in Figure (**g**).  
1. MUSCL+MLP-UB for \(\alpha_{\phi}\), and MINMOD (MUSCL+MLP-UB with \(\beta=1;\theta=0.5\)) for \(\rho_{\phi},e\phi\).
2. MINMOD for all \(\Pi(\alpha_{\phi},\rho_{\phi},e_{\phi})\).  
Figure 11: _Cont._  
Figure 10: 2D computational domain for the 2-material, 3-phase triple point test case. The units of \(\rho_{\phi}\), \(p\), \(u\), \(v\) and dimensions are in S.I. unit system.  
Firstly, we can observe in Figure 11 that two shocks propagate with different speeds due to the initial density difference between the left, top-right and top-bottom domains (see Figure 11). This creates the shear along the initial phase interfaces, and results in the formation of a vorticity. Regarding the coarse mesh used to compute this vorticity, we observe in Figure 11b,f,j that the numerical solution is diffused due to the use of MINMOD limiter in the numerical advection step of scalars \(\Pi(\alpha_{\phi},\rho_{\phi},\epsilon_{\phi})\). However, if we focus on Figure 11a,e,i; a significantly better capture of this vorticity can be seen. Numerical results also show an accurate capture of the shocks and multi-phase interfaces. Again, like the previous test case, we observe that the values of cell volume fraction (\(\alpha\)) for all the phases are in the range \([0,1]\).
## 3 Results
### Evaluation Metrics  
This study utilizes a confusion matrix to evaluate the proposed model. Accuracy (_Acc_), specificity (_Spec_), sensitivity (_Sens_), and F1-score (_F1_) are chosen to assess the model performance. In the present work, sensitivity and specificity are emphasized as they together provide balanced evaluations. High sensitivity ensures fracture cases are detected correctly, and high specificity reveals that non-fractured cases are correctly classified. The formulae for the performance metrics are given below:  
Figure 10: Feature activation map analysis for base models using normal and noisy images. The first column (**a**) represents the sample CT image. The subsequent columns are the heatmaps obtained from (**b**) WRN-40, (**c**) DenseNet-121, and (**d**) EfficientNet-B7.  
\[\text{(i)}\quad Acc=\frac{TP+TN}{TP+FP+TN+FN}\] \[\text{(ii)}\quad Spec=\frac{TN}{TN+FP}\] \[\text{(iii)}\quad Sens=\frac{TP}{TP+FN}\] \[\text{(iv)}\quad F1=\frac{2\times Precision\times Sens}{Precision+ Sens}\]  
Here, True Positive (\(TP\)), True Negative (\(TN\)), False Positive (\(FP\)), and False Negative (\(FN\)) are the entries of the confusion matrix. However, precision may not be a suitable evaluation measure in our context, since high precision may sometimes fail to detect the fracture cases. But, as it is required to calculate the F1-score, the following formula is used to calculate the precision:  
\[\text{(v)}\quad Precision=\frac{TP}{TP+FP}\]
## 4 Results
### Quality Assessment of GPM  
The satellite rainfall observation dataset for the Jianxi watershed is constructed by collating GPM data at a 0.1\({}^{\circ}\) scale and 0.5 h interval from 2006 to 2018 into a precipitation dataset with a 1 h time scale. Based on this dataset, the eight evaluation indicators are applied to assess GPM at the grid points of the 15 rainfall stations, and the results are presented in Figure 5.  
According to the quantitative evaluation results, on the whole, GPM overestimates rainfall events by approximately twice as much as the ground observations. The _BIAS_ between GPM and gauge observation is greater than 1, and the _RATIO_ is between 2 and 3. The _RSME_ value does not vary significantly among gauges--mostly between 2 and 2.2 mm. In contrast, the _CC_ varies widely among different gauges, with values ranging from 0.1 to 0.45. Qilijie station has the lowest CC value of 0.117, while Cao Dun station has the highest _CC_ value of 0.422.  
Regarding the categorical evaluation index results, the POD of GPM varies from 0.55 to 0.73 at each rainfall gauge, with an average value of 0.661. This indicates that approximately 66% of actual rainfall events can be captured and reflected by GPM. The _FAR_ of GPM is around 0.7, which means that about 70% of the rainfall events observed by GPM are false alarms, i.e., no actual rainfall occurs. The _MAR_ of GPM varies between 0.25 and 0.45 at each station, suggesting that approximately 30% of actual rainfall events correspond to GPM data of 0. It is important to note that individual classification index evaluation can be one-sided, and high _POD_ may coincide with high _FAR_. _TS_ integrates the evaluation of GPM on both hit and missed rainfall events, with an average value of 0.247 across all stations.  
Overall, GPM data can partially reflect the actual rainfall patterns. However, there is an overall tendency to overestimate rainfall events, as well as omissions and the misreporting of some events, which indicates that the GPM data lacks stability.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 3 Proposed Work
### Workflow of the Proposed XML Querying Scheme  
1. Partition of the XML data: The proposed method for improving XML querying over wireless broadcast channels begins by dividing the XML data into segments. The data are divided into partitions.
2. Assign partitions to channels: Each partition is then assigned to a specific channel for broadcasting. Each partition is broadcast over a specific channel.
3. Query routing: When a query is received, it is routed via the Query Router. Queries are routed via the Query Router. * Query Decomposition: If necessary, the query is split into sub-queries and routed to the relevant partitions. * Query Caching: The system also includes query caching, which checks to see if the results are already cached; if not, the query is executed, and the results are cached for later use. * Query Broadcasting: Queries can be broadcast to all channels if required.
4. Aggregate results: The results from the partitions or cache are aggregated to form the final result.
5. Return query results: The aggregated results are returned to the user.
## 4 The DIK Architecture
### The Architecture of the DIK Three-Layer Knowledge Graph
#### 4.2.3 Knowledge Layer  
The KG\({}_{\text{DIK}}\) is a knowledge layer graph obtained by refining the links between the Information\({}_{\text{DIK}}\) in IG\({}_{\text{DIK}}\) and integrating and summarizing the Information\({}_{\text{DIK}}\) node in IG\({}_{\text{DIK}}\), integrating and summarizing the Information\({}_{\text{DIK}}\) to transform it into the Knowledge\({}_{\text{DIK}}\) node. The KG\({}_{\text{DIK}}\) layer contains various semantic relations that perform information reasoning and entity linking. Information reasoning requires the support of relevant relational rules, which are constructed manually and often take a lot of time and effort. The path ordering algorithm used each different relational path as a one-dimensional feature to extract relations by constructing many relational paths in KG\({}_{\text{DIK}}\) to build feature vectors for relational classification and relational classifiers. In this paper, Formulas (7) and (8) were used to measure the accuracy of the extracted relations and the importance of the fully evaluated nodes, respectively, to improve the medical knowledge graph representation. P (E\({}_{1}\rightarrow\) E\({}_{2}\)) denotes a path from E\({}_{1}\) to E\({}_{2}\). Q denotes all complete paths between two entities, \(\pi\) denotes a path. \(\theta(\pi)\) denotes the weight of the path \(\pi\), and this relation is considered to be established when the Cr exceeds a preset threshold, and the weight of the path as well as the Cr can be obtained by training. Final_Import denotes the importance of the fully evaluated node on KG\({}_{\text{DIK}}\), \(\lambda\) is the weight of the relation Rel, and \(n\) is the number of relation types.  
\[\text{Cr}(\text{E}_{1},\text{R},\text{E}_{2})=\frac{\sum_{\pi\in\text{Q}}\text {P}(\text{E}_{1}\rightarrow\text{E}_{2})\theta(\pi)}{|\text{Q}|} \tag{7}\]  
\[\text{Final\_Import}=\text{Import}\times\gamma\frac{\sum_{i=1}^{n}\lambda_{i} \times\text{Rel}_{i}}{n} \tag{8}\]  
Final_Import integrated the characteristics of the DIK three-layer graph considering the data frequency DFreq on DG\({}_{\text{DIK}}\), the interaction degree Import between entity nodes on IG\({}_{\text{DIK}}\), and the semantic relationship type on KG\({}_{\text{DIK}}\). By combing the importance of nodes on the three-layer graph comprehensively and evaluating the importance of nodes, we avoided losing some nodes with low frequency but containing important relational interactions, thus improving the data integrality. Finally, KG\({}_{\text{DIK}}\) was defined as a directed graph, where the relationships between nodes were directed and differentiated, which could satisfy more different kinds of semantic relationships.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 5 Feature Extraction
### Spectral Information  
The spectral information describes how the power of EEG signal varies in some specific frequency bands [39]. Frequency bands define the cerebral activity measured through the electrodes in the EEG study. These frequency bands are beta, alpha, theta, delta, and gamma. They work in below 3.5 Hz to above 30 Hz [40]. These power bands are obtained from the information that each channel record of the scalp surface. Usually, these bands allow one distinguish the energy that is related to a concentrate or sleep state.  
* Average band power/absolute band power. One single number summarizes the given frequency band's contribution to the signal's overall power, which is useful in a machine-learning approach.
* Relative band power. It is the percentage of the total of the band's frequency in the signal's total. It is calculated on the mean of the band power.
* Power spectral density (PSD). PSD is defined by oscillatory activity and can be associated with the synchronous activity of motor units. This feature estimates how the total power of the signal is distributed over its frequency. It is important because many other features are obtained by applying a mathematical analysis of PSD [38].
* [PERSON]'s method. In [41], it is described as an averaged periodogram of a signal. The method divides the signal into segments and overlaps these segments according to a window. Then, the discrete Fourier transform of each segment is calculated, and the average of the periodograms of it. The Welch method is yet another approach for spectral density estimation. For this method, one can use fast Fourier transformation  
Figure 7: Feature extraction techniques.  
or a periodogram. Despite the technique employed, two important facts must be considered: smaller data chunks of time series and their averaged PSD estimates.
## 5 Capacity Trends Analysis
### Number of Nodes  
It is obvious that larger networks (with more nodes and links) provide greater opportunities for data transmission. However, the network capacity does not scale linearly with the size of the network. For example, if there are multiple sensors communicating with each single satellite, satellite \(m\) can communicate only with \(s_{m}\) sensors at a given time instant. In this simulation, each satellite can only support 20 sensors with \(e_{\mathrm{min}}=10^{\circ}\). Seventy satellites are launched into 500 to 1500 km altitude random orbits. Additionally, the visibility duration is calculated relative to the increasing number of sensors that is randomly distributed in the area between 40\({}^{\circ}\) S and 40\({}^{\circ}\) N on the Earth. The following figures are the results with 1000 Monte Carlo simulations.  
Figure 4 shows the simulation results when the number of sensor nodes that are injected into the IWSSN increase; the utilization of the satellite increases until a saturation point is reached, where additional sensor nodes can no longer be supported. As increasing numbers of sensor nodes are injected into the network, the saturation point of the satellite network in the sensors distribution area will be reached. It is 87.21% of the whole space network resource according to the statistical results, and the other 12.79% space network resource cannot cover the sensor distribution area dominated by the satellite trajectories. When the saturation point is reached, additional sensor nodes can no longer be supported, and the capacity of the network will not increase. The network capacity when considering the establishment of the path will be less than or equal to the summed total visibility durations.  
Figure 5 shows failed access time, defined as the time when sensors cannot access satellites due to the capacity constraints of the satellite network. The growth of failed access time is exponential with the growing ratio between ground sensor nodes and satellite nodes, which is nearly or already at capacity, rapidly reducing the overall satellite network utilization. Hence, from Figures 4 and 5, it is obvious that realistic scheduling constraints must be considered in network scheduling algorithm \(\chi\in\mathrm{X}\). Intelligent node deployment methods and network scheduling algorithms are critical to maximize the network capacity of the growing IWSSN.
# Experimental Study of the Dynamic Characteristics of PVA-Amended Expansive Soils
## 1 Introduction
[18] used _Bacillus subtilis_ isolated from nonexpanding soils and were able to reduce the free swelling of expanding soils by a maximum of 21.7%. In summary, many studies have been performed to improve expansive soils, and polyvinyl alcohol (polyvinyl alcohol, PVA), a kind of organic compound that is easily soluble in water, not only has the advantages of easy synthesis, safety, and low toxicity, but is also inexpensive and easy to use, has acid and alkali resistance characteristics, and is difficult to dissolve in organic solvents. In fact, China's PVA production capacity reached 124.6 t/a in 2016, making it the largest PVA-producing country in the world [19], and studies on its application in soil improvement have been conducted. As a new type of soil structure improvement agent, PVA has obvious effects on adjusting soil structure and improving soil physicochemical properties. [PERSON] et al. [20] investigated the effect of polyvinyl alcohol on soil physicochemical properties and reported that PVA can significantly reduce the amount of soil water loss. [PERSON] et al. [21] reported that PVA can increase the optimum water content, liquid limit, and plastic limit of soft soil but has no significant effect on the maximum dry density. Moreover, these researchers have determined that PVA can also significantly increase the unconfined compressive strength, dynamic strength, and damping ratio of soft soil. However, studies of the dynamic properties of PVA-amended expansive soils have not yet been reported. The dynamic stress-strain relationship, dynamic elastic modulus, and damping characteristics of modified expansive soils are particularly important, and these dynamic characteristics are directly related to the safety of roads and buildings under traffic loads and earthquakes. In this study, we used polyvinyl alcohol (PVA) to improve expansive soils and determined the optimum dosage of PVA through ordinary triaxial compression tests. Based on the optimum dosage, we used a GDS dynamic triaxial instrument to study the dynamic characteristics of the improved soil, which provides a reference basis for engineering practice.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Performance Evaluation
#### 6.2.3 Analysis in Dynamic Conditions
Furthermore, our buffer-based approach ensures the throughput increases more quickly than a solution without buffer, as in the former case, no packet is lost.
## 5 Discussion and Conclusions
Thus, further research is necessary, and there are several research directions that can be taken to enhance model performance. Future research should focus on creating more complex cases, such as air voids, reinforcement bars, and materials with scatterers of a certain size (i.e., aggregates) could be included to improve the reconstruction, as well as the use of more complex morphologies of the embedded materials, along with more complex simulations including noise (to evaluate the robustness of the reconstruction). Our current model could be used as a tool for labeling new cases and training a more complex model to achieve further generalization. Additionally, a few real cases should be added in the training of the model. The use of GANs as well as a parametric optimization of the simulation model variables (material properties) could decrease the difference between simulated and real data and improve the results of the reconstruction model. Moreover, the inclination of the transducers to have an oblique incidence and look for the excitation of surface waves could help to find answers on the damaged front. Enhancing the measurement system and feature extraction (i.e., frequency-dependent attenuation or slowness), reducing noise (i.e., filtering), and incorporating advanced neural architectures such as convolutional networks, autoencoders (with multidimensional input sinograms), or convolutional autoencoder could further improve the reconstruction accuracy. Additionally, applying this methodology to practical cases, such as carbonation front detection in concrete, could provide valuable insights for structural health monitoring in civil engineering applications where the early detection of material degradation could prevent costly repairs and enhance infrastructure safety. Conceptualization, J.G., J.P. and A.F. ; methodology, S.V., S.M.-M. and J.P.; software, [PERSON] and C.G.-L.; validation, [PERSON], [PERSON] and J.G. ; formal analysis, [PERSON]-L.; investigation, [PERSON] and C.G.-L.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, C.G.-L.; writing--review and editing, [PERSON] and J.P.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript. This research was funded by MCIN/AEI/10.13039/501100011033 (grant number PID2020-12026 GB-I00). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. Figure 22: Example of real sinogram used for evaluating the accuracy of the model in a real case and a simulated sinogram used for training the model. **Acknowledgments:** The authors thank to ICITECH for the construction material and equipment and all the staff involved in the creation of the specimens. **Conflicts of Interest:** The authors declare no conflicts 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.
## 3 Results
### VOC Classes Percentage to Each Other (Pie Chart Analysis)  
To visualize the relative changes in VOC composition due to rootstock and infestation, we grouped the compounds by VOC class and calculated percentages of the total concentration (Figure 3). The compounds that were associated with each VOC class can be found in Table 1. We found that in the control trees the composition of Sugar Belle scion leaf volatiles was fairly consistent regardless of the rootstock, consisting of about 35% monoterpenes, 40% terpene alcohols, 18% fatty acids, and 3% sesquiterpenes, 1-2% aldehydes, and with the balance left to a few other compounds and unknowns (Figure 3). After infestation of the Sugar Belle trees with _D. citri_, large decreases in fatty acids were observed in the leaves from all three of the rootstocks and these losses were accompanied by relative increases in monoterpenes in scion leaves from US-897 and C-35 (Figure 3). The gain in monoterpens was modest in SO (Figure 3). The monoterpene class made up 35.2, 33.7, and 34.6% of the peak area of the control Sugar Belle scion leaf extracts compared to 47.9, 45.2, and 37.5% of extracts from the _D. citri_-infested leaves from US-897, C-35, and SO rootstocks respectively. The monoterpene levels were elevated in _D. citri_-infested samples compared to the controls, and US-897 had both the highest (473.63 \(\upmu\cdot\)g\({}^{-1}\) F.W.) and lowest (299.13 \(\upmu\cdot\)g\({}^{-1}\)) monoterpene levels in the infested and control samples, respectively. Among the monoterpenes, \(\gamma\)-terpinene, \(\alpha\)/\(\beta\)-pinene, \(\beta\)-myrorene, and p-cymene were the most abundant (abundancesof all of the compounds can be found in Table 2). Sesquiterpene concentrations increased from 4.6% to 7.49% (\(p\) < 0.0001) after infestation in scion leaves that were grown on SO compared to C-35 and US-897 (Figure 3).
## 3 Analysis and Results  
In this section, in order to verify whether the CBTN can characterize the information interaction between two time series, it was first tested on the unidirectional coupled Honen model and its performance was compared with the original CPE method. Next, the complex network constructed by the CBTN was applied to the realistic EEG recordings during either the resting state or the mental arithmetic task in order to evaluate the performance of the proposed method in detecting the changes in the cognitive load.