esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Variational Inference Model for Non-Linear Total Variability Embedding  
In the proposed algorithm, it is assumed that the ideal GMM supervector corresponding to a speech utterance \(\mathbf{X}\) is obtained through a non-linear mapping of a hidden variable onto the total variability space. Based on this assumption, the ideal GMM supervector is generated from a latent variable \(\mathbf{z}\) as follows:  
\[\mathbf{m}(\mathbf{X})=\mathbf{u}+\mathbf{g}(\mathbf{z}(\mathbf{X})), \tag{12}\]  
where \(\mathbf{g}\) is a non-linear function which transforms the hidden variable \(\mathbf{z}(\mathbf{X})\) to the adaptation factor representing the variability of the ideal GMM supervector \(\mathbf{m}(\mathbf{X})\). In order to find the optimal function \(\mathbf{g}\) and the hidden variable \(\mathbf{z}(\mathbf{X})\), we apply a VAE model consisting of an encoder and a decoder network as shown in Figure 1. In the proposed VAE architecture, the encoder network outputs an estimate of the hidden variable and the decoder network serves as the non-linear mapping function \(\mathbf{g}\).  
Analogous to the i-vector adaptation framework, the main task of the proposed VAE architecture is to generate a GMM so as to maximize the likelihood given the Baum-Welch statistics of the utterance. The encoder of the proposed system serves as a non-linear variability factor extraction model. Similar to the i-vector extractor, the encoder network takes the \(0^{\text{th}}\) and \(1^{\text{st}}\)-order Baum-Welch statistics of a given utterance \(\mathbf{X}\) as input and generates the parametric distribution of the latent variable. The latent variable \(\mathbf{z}\) is assumed to be a random variable following a Gaussian distribution, and each component of \(\mathbf{z}\) is assumed to be uncorrelated with each other. In order to infer the distribution of the latent variable \(\mathbf{z}(\mathbf{X})\), it is sufficient for the encoder to generate the mean and the variance of \(\mathbf{z}(\mathbf{X})\). The decoder of the proposed system acts as the GMM adaptation model, generating the GMM supervector from the given latent variable according to the maximum likelihood criterion.
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 3 Application Models for Cloud-based IoT
### GraphQL Framework and its Feasibility  
GraphQL employs message exchange similar to any classical request-response system, but it provides a single API endpoint on server for communication. GraphQL query language is used for building client applications and it aims to combine all the data requests into one, resulting in reduced number of requests for data retrieval/delivery. This approach can result in improvement of IoT network performance by reducing the number of round trips for data exchange. It is also efficient due to the reduction in size of data transactions, as clients can state their requests for particular fields of needed data for their working.  
GraphQL, developed in 2012, was internally used for three years by Facebook, but it was open-sourced with a specification release in October 2015 [11]. Its latest release was released on 10 June 2018, and the most recent working draft on 20 June 2019. It is increasingly adopted since its release and each day it handles hundreds of billions of API calls by Facebook applications' data fetching.  
Figure 3: Smart car example scenario with multiple requests from IoT device to POST data to multiple end-points using REST architecture.  
Implementation of its modules in popular programming languages and tools for its interaction with existing back-end technologies are available.  
GraphQL enables flexible queries on linked (No SQL graph-based) data and its single endpoint approach is backed by a structured hierarchical system for combining data of various sources. It does the representation of data as set of nodes which is able to have nesting and relations with other nodes. Its query is a set of hierarchical data fields requested and only those which are required are returned to the client, resulting in reducing the size of data. Data requests are to be sent to a unique endpoint for the unification, which means it requires a single URI to present all available data via its single API. Figure 4 presents same example IoT scenario of a smart car with two sensors to illustrate the working of the GraphQL-based client-server system. We can observe that, unlike REST architecture, data from multiple sensors can be posted to the server in a single transaction. This way its reduction in the number of round trips makes efficient use of available resources of the IoT network.  
By analysing its efficiency features suitable for IoT sensing applications, our proposal for integration of this web framework with IoT protocols and cloud services appears to be a viable solution.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 3 Results and Discussion  
### Pre-Earthquake Stress Conditions  
Figure 17 shows the stress state just before the start of the earthquake. In the reclaimed region behind the steel pipe piles, the initial horizontal stress and initial shear stress ratios are mostly around 0.5 (Jaky's lateral earth pressure coefficient \(K_{0}=1-\sin\ \phi^{\prime}\approx 0.5\) for sand [41]) and 0.0, respectively. Near the quay wall, \(K_{0}\) is in the range of 0.2-0.4, and \(\alpha\) ranges from 0.05 to approximately 0.1. Although there are higher values of \(K_{0}\approx 1.0\) and \(\alpha\approx 0.15\) at other locations, such as at the base of the quay wall (i.e., riprap), soils in these areas are considered to be non-liquefiable materials. Thus, their contributions to liquefaction are minimal. The predictions for the static state are reasonable for the realistic conditions of the Youngil-man port before the earthquake occurred.  
Figure 16: Modulus reduction and damping ratio curves for (**a**) silty sand, (**b**) sediment, (**c**) weather rock, and (**d**) gravel obtained from PROSHAKE analysis.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 5 Hansel Mobile Applications
### Collaborative Positioning Application (CPA)  
CPS main functionality is exercised through the API by the Collaborative Positioning Application (CPA), a scientific research-oriented Android(tm) application including the core algorithm for a collaborative DGNSS-based CP.  
The app is conceived to perform the following set of actions:  
1. Negotiate the registration of smart devices to the service;
2. Upload and download the GNSS raw measurements and positioning data shared by registered agents. The measurements exchange exploits User Datagram Protocol (UDP) protocol to limit the transmission delay through the possible network nodes;
3. Perform a rough GNSS-only PVT solution to synchronise asynchronous measurements downloaded by means of the CPS;
4. Compute the inter-agent distance by means of Double Differentiation of the pseudorange measurements retrieved w.r.t. to a set of common satellites;
5. Perform the post-processing of GNSS and collaborative measurements through Bayesian EKF-based tight-integration, thus providing both GNSS-only and hybrid positioning solutions for a consistent performance self-assessment;
6. Internally log the reference position estimation provided by the Google(tm) Fused Location Provider (GFLP);
7. Store locally and upload to the _testbed_ the output files generated during its operational timespan.  
The CPA is conceived as a mobile application running on GNSS-raw-measurements-ready Android(tm) mobile devices and interacting with other agents through the CPS. It is designed as a Proof Of Concept (PoC) of the GNSS-based collaborative positioning. Multiple instances of the CPA (installed in a set of independent smartphones) are expected to interact with the CPS by means of the CPS API to take advantage on the aforementioned paradigm. The app is natively implemented in Java, using the Android(tm) Studio environment. The CPA requires at least API level 27 (Android(tm) 8, or more recent) to provide full support to the public class GNSSMeasurements. CPA also implement CAC functionality, thus being able to receive real-time feedbacks about pre-defined test trajectories.  
The _testbed_ architecture was proven to be a valuable central-node for real-time constrained cooperative positioning, being a buffer and data dispatcher for networked smartphones operating CPA instances.
# Percolation Threshold of Red-Bed Soft Rock during Damage and Destruction
## 3 Results and Discussion
### Analysis of the Influencing Factors on Percolation Threshold  
#### 3.4.1 Confining Pressure  
According to the theory outlined in Sections 2.2 and 2.3.2 and the numerical simulation data in Section 3.3, the percolation threshold of red-bed soft rock under the conventional triaxial compression test can be calculated, as shown in Table 4. This table shows that when the confining pressure is 0, the percolation threshold is 0.32; when confining pressure rises to 1 MPa, the percolation threshold rises to 0.464 (an increase of 0.144); when the confining pressure rises to 3 MPa, the percolation threshold continues to rise to 0.504  
Figure 11: Damage characteristics of red-bed soft rock under the water-stress coupled triaxial compression numerical test (confining pressure in **(a1,b1)** is 0, in **(a2,b2)** is 1 MPa, in **(a3,b3)** is 3 MPa).  
(an increase of 0.04). When the confining pressure increases from 0 to 1 mpa, the threshold increases by 45%. When the confining pressure increases from 1 MPa to 3 MPa, the threshold increases by only 8.6%. It can be seen that the percolation threshold increasing speed decreases with the confining pressure increase, as shown in Figure 12. When there is no confining pressure, the soft rock as a whole will be destroyed when one-third of the cement inside is damaged. However, when there is confining pressure and when one-third of the cement inside the soft rock is damaged, the strength from the confining pressure means that the soft rock is not destroyed but continues to be damaged. On the other hand, with increase in confining pressure, the strain at which soft rock failure occurs also increases, as shown in Table 4. This pattern indicates that the ductility of the soft rock improves; that is, the degree of damage can be greater and the percolation threshold increased.
## 5 Parametric Analysis on the Assessment Results
### Effect of Crack Size  
By analyzing the data of the circumferential weld anomalies detected in each pipe section obtained through high-definition magnetic flux leakage test results [26], it was found that most of the circumferential weld anomalies are distributed at a height of 30% of the wall thickness and the length of 200 mm. Only a small number of defects are found at heights greater than 40% the wall thickness and length greater than 250 mm. All of the detected abnormalities in girth welds did not exceed 50% of the wall thickness. To cover most of the crack size ranges, different crack depth ratios (\(a\)/\(B\) = 0.1, 0.2, 0.3) and crack length-to-cycle ratios (\(2c\)/\(\pi D\) = 0.01, 0.03, 0.05, 0.07) were selected to determine and analyze  
Figure 6: Axial misalignment at girth welds in pipes.  
the effect of crack size on the applicability evaluation results of \(\mathcal{O}813\times 17.5\) mm pipeline girth welded joints in the Mo-Da pipeline.  
By comparing the evaluation results obtained for different crack sizes (Figure 7), it can be deduced that, the larger the crack size, the closer the pipeline to the critical failure state, i.e., the higher the risk of pipeline operation. Moreover, based on the effect trends of changing the size parameters on the evaluation results, it can be seen that the effect of the crack depth on pipeline safety is far more significant than that of the crack length.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 3 The Novel Iterative QR-Based SFSIC Detection and Decoder Algorithm
### Iterative QR-Based SFSIC Detector (Inner Decoder)  
The iterative QRSFSIC, based on a progressive partition of the transmitted symbols into the reliable/residual symbol segment according to a posterior LLR, gives rise to inner iterations to evaluate the MMSE estimate of residual symbols which resort to soft feedback and interference cancellation of the reliable segment. Its crucial idea is to save computation by QR decomposition, which results in a simple successive detection algorithm with reduced complexity. When it is applied to detect multi-transmit antenna signals, its novelties can be stated as follows.
Estimation of Equivalent Transmitted Power of Sparse Array Based on Unmanned Aerial Vehicles in Collaborative Jamming  
[PERSON]  
1 Unmanned Aerial Vehicles (UAVs) are widely used in defense applications. Multiple UAVs equipped with jamming sources can form a sparse array. The sparse array can be quickly deployed and achieve an extensive range of effective jamming. At present, the jamming power in the target area is mainly calculated through the superposition of node waveforms. The algorithm needs to sequentially calculate the angle of the target position relative to each node to obtain the corresponding gain and then calculate the path loss by the transmission model to obtain the total jamming power, which has high algorithmic complexity and needs to recount the power every time after adjusting the position of the node. An equivalent transmitted power estimation algorithm based on the pattern multiplication theorem is proposed in this paper, which regards the sparse array as a whole directional jammer. The power in the target area can be estimated according to the jammer gain, the equivalent transmitted power, and the jamming distance. In the calculation of jamming power, the proposed array-based algorithm reduces the complexity by 50% compared with the waveform superposition algorithm, and the estimation variance of the jamming power is less than 1.4%.
## 3 Numerical Methodology and Experimental Verification
### Mesh Validation  
A reasonable number of grids for numerical simulation should be selected to save time and computing resources. Therefore, five groups of grids were used in the grid independence test. The total number of grids in each computing domain ranged from 3,072,474 to 9,283,628. Table 2 shows the number of cells in each computing domain. The boost equation is defined as:  
\[P_{\text{b}}=P_{\text{out}}-P_{\text{in}}, \tag{1}\]  
where \(P_{\text{out}}\) represents the outlet pressure of the mixed-transport pump and \(P_{\text{in}}\) represents the inlet pressure.  
Efficiency \(\eta\) is defined as the ratio of the actual pump head \(H_{\text{a}}\) and the theoretical calculation head \(H_{\text{t}}\). The expression is given as follows:  
\[\eta=H_{\text{a}}/H_{\text{t}}, \tag{2}\]  
where the actual pump head \(H_{\text{a}}\) is defined as:  
\[H_{\text{a}}=(P_{\text{out}}-P_{\text{in}})/\rho_{\text{mix}}\text{g}, \tag{3}\]  
\[\rho_{\text{mix}}=IGVF\times\rho_{\text{gas}}+(1-IGVF)\times\rho_{\text{ water}}, \tag{4}\]  
where \(\rho_{\text{mix}}\), \(\rho_{\text{gas}}\), and \(\rho_{\text{water}}\) are the gas-liquid mixed density, the gas-phase density, and the liquid-phase density, respectively. g represents acceleration due to gravity.  
Moreover, the theoretical head \(H_{\text{t}}\) is defined as follows:  
\[H_{\text{t}}=(M\times\omega)/\rho_{\text{mix}}\times Q\times\text{g}, \tag{5}\]  
where \(M\) represents the torque, \(\omega\) represents the angular velocity, and \(Q\) represents the volume flow rate.  
It can be obtained from the calculation results shown in Figure 4 for the mesh listed in Table 2 that after grid IV, the diversity in the pressure rise and the efficiency of the mixed-transportation pump was small. \(\Delta Pd/Pd\leq 0.01\) and \(\Delta\eta/\eta\leq 0.001\), where \(Pd1\) is the boost pressure under mesh I, \(\Delta Pd\) is the difference between the boost pressure calculated using each group of mesh I and the boost pressure corresponding to mesh I. \(\eta_{c}\) is the efficiency value calculated via simulation under mesh I, \(\Delta\eta\) is the difference between the efficiency corresponding to each group of mesh I and the efficiency corresponding to mesh II. Finally, in the following simulation, mesh IV with a total grid number of 7,734,156 was selected as the final grid for numerical calculations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Items** & **Mesh I** & **Mesh II** & **Mesh III** & **Mesh IV** & **Mesh V** \\ \hline Total grid number & 3,072,474 & 5,378,384 & 6,604,412 & 7,734,156 & 9,283,628 \\ Pressure rise (kPa) & 776.12 & 784.52 & 788.61 & 789.20 & 789.10 \\ Efficiency (\%) & 78.25 & 80.45 & 80.51 & 80.57 & 80.59 \\ Relative pressure rise \(Pr/Pd1\) & 1 & 1.0108 & 1.0161 & 1.0168 & 1.0167 \\ Relative efficiency \(\eta/\eta\kappa\) & 1 & 1.0025 & 1.0032 & 1.0040 & 1.0041 \\ \hline \end{tabular}
\end{table}
Table 2: Supercharging and efficiency in relation to mesh elements.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 2 Elastic Global and Local Shear Buckling Stress of CSWs
### Elastic Local Shear Buckling Stress of CSWs  
Similar to straight girders, the local shear buckling of CSWs for curved girders can also be solved by analyzing a single flat panel constrained by adjacent panels and girder flanges under shear force. For straight and curved girder the same formulas apply for calculating the elastic local shear buckling stress of CSWs which are given by the classical plate buckling theory [3] as Equation (22):  
\[\tau_{l}^{e}=k_{l}\frac{\pi^{2}E}{12(1-v^{2})}\left(\frac{t}{p}\right)^{2} \tag{22}\]  
where \(k_{l}\) is the elastic local shear buckling coefficient of CSWs; \(p\) is the maximum sub-panel width (maximum of flat panel width \(a\) and inclined panel width \(c\)).  
The elastic local shear buckling coefficient \(k_{l}\) can be expressed as Equations (23)-(25).  
For a four-edge simple support:  
\[k_{l,s}=5.34+4(p/h)^{2} \tag{23}\]  
For a four-edge fixed support:  
\[k_{l,f}=8.98+5.6(p/h)^{2} \tag{24}\]  
For the two edges constrained by flanges fixed and the other two edges simply supported:  
\[k_{l,fs}=5.34+2.31(p/h)-3.44(p/h)^{2}+8.39(p/h)^{3} \tag{25}\]
# Influences of High-Speed Train Speed on Tunnel Aerodynamic Pressures
## 5 Conclusions  
To study the influences of high-speed train speed on tunnel aerodynamic pressures, we carry out a series of three-dimensional numerical simulations. The input parameters of our simulations are verified by field monitoring data. The process of a single high-speed train travelling through a railway tunnel is divided into three stages according to the spatial relationship between train and tunnel. The influences of high-speed train speed on tunnel aerodynamic pressures of these three stages are systematically studied. The main results are as follows:  
(1) The peak pressure values at different train speeds are much lower than the fracture strength of tunnel lining, and the total number of peak pressures during the entire life cycle of tunnel in Stage I is much higher than \(10^{6}\). Thus, the aerodynamic fatigue damage of tunnel lining of Stage I can be calculated by the method of high-cycle fatigue.  
(2) The characteristics of maximum peak pressure values acting on the tunnel vault along the tunnel's longitudinal axis at different train speeds are similar in Stage I. The maximum peak pressure value decreases with increasing distance from the entrance and increases with increasing train speed.  
(3) Three types of maximum peak pressure values (positive peak, negative peak, and peak-to-peak pressures) increase with increasing train speed in Stage II. There is an approximately linear relationship between each of these three types of maximum peak pressure values and high-speed train speed raised to the power from 1.12 to 2.33.  
(4) The three types of maximum peak pressure values of the points near tunnel portals increase with increasing train speed in Stage III. These three types of maximum peak pressure of the tunnel middle section at different train speeds are more complex than those near the tunnel portals, and there is one or more turning point of the curves due to the superimposed effects of different pressure waves.  
J.D.: Conceptualization, formal analysis, software. Q.F.: Funding acquisition, Supervision, Validation. J.W.: Investigation, Writing-review & editing, Visualization. G.W.: Formal analysis, Data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Beijing, China grant number 8202037, and the Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China grant number U1934210.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors thank the reviewers for their great help on the article during its review progress.  
The authors declared that there is no conflict of interest.
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 1 Introduction
### Related Work  
[PERSON] and [PERSON]'s seminal paper [2] requires much sophisticated machinery: a multilevel approach [3; 13] using recursive preconditioning, preconditioners based on low-stretch spanning trees [14] and spectral graph sparsifiers [7; 15]. Later papers extended and improved upon this approach, both by making it simpler and by reducing the exponents of the polylogarithmic time factors, e.g., [16]. [PERSON] provides a comprehensive overview of later work online [17]. We focus on an algorithm by [PERSON] et al. [12] that reinterprets the problem of solving an SDD linear system as finding an electrical flow in a graph. It only needs low-stretch spanning trees and achieves in its best variant \(\mathcal{O}\big{(}m\log^{2}\!n\log\log n\log(1/\epsilon)\big{)}\) time. Another interesting nearly-linear time SDD solver is the recursive sparsification approach by [PERSON] and [PERSON] [18]. With a parallel sparsification algorithm (e.g., [19]), it yields nearly-linear work in parallel.  
[PERSON] and [PERSON]'s algorithm crucially uses the low-stretch spanning trees first introduced by [PERSON] et al. [20] (for a definition of stretch, see Section 2). [PERSON] et al. [21] provide an algorithm for computing spanning trees with polynomial stretch in nearly-linear time. Specifically, they get a spanning tree with \(\mathcal{O}(m\log^{2}\!n\log\log n)\) stretch in \(O(m\log^{2}\!n)\) time. [PERSON] et al. [22; 23] later showed how to get rid of some of the logarithmic factors in both stretch and time. [PERSON] [24] tested these algorithms in practice and showed that they do not usually result in spanning trees with lower stretch than a simple minimum-weight spanning tree computed with [PERSON]'s algorithm and that the original algorithm of [PERSON] et al. [21] achieves the best results among the provably good approaches. We use these low-stretch spanning trees in our implementation of the algorithm of [PERSON] et al. [12] and compare their effectiveness for the solver.  
It should also be noted that there are a few methods available for the problem with fast empirical running times; but with no equivalent guarantee on the theoretical worst-case running time: combinatorial multigrid (CMG) [25] and lean algebraic multigrid (LAMG) [26] are, as the names suggest, multigrid schemes; the work by [PERSON] et al. [27; 28] has evolved into a multi-iterative scheme combining multigrid and graph-based preconditioners (among others using spanning trees).