esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Metadata Systems for Name Disambiguation
### Others  
Numerous other systems are emerging; some of these show the potential for success, others do not. Another thing that remains to be determined is whether competition among different systems will help or hinder the emergence of metadata for name disambiguation and collocation. This will be determined by several factors, including the degree to which competing schemes collaborate and share data with each other, whether only a handful of initiatives emerge as dominant and make the smaller ones obsolete, and what model becomes the most popular and interoperable with authors, publishers, and search engines.  
Among the others, one that deserves a special mention is Wikipedia. Though not a database of names per se, it does have many articles about persons and corporate bodies, including many for names that are not unique. Wikipedia pays special attention to name disambiguation and has special disambiguation pages that list subjects of articles with the same name, differentiated by what they are known for, such as [PERSON] (American writer) and [PERSON] (Engineer). Table 2 lists some additional name metadata databases.  
\begin{table}
\begin{tabular}{|l|c|c|} \hline \multicolumn{2}{|c|}{**Database Name**} & \multicolumn{1}{c|}{**URL**} & \multicolumn{1}{c|}{**Comments**} \\ \hline academia.edu & [[http://www.academia.edu](http://www.academia.edu)]([http://www.academia.edu](http://www.academia.edu)) & Requires signup. \\ \hline arXiv’s author identifier system & [[http://arxiv.org/help/author](http://arxiv.org/help/author)]([http://arxiv.org/help/author](http://arxiv.org/help/author))\_identifiers & \\ \hline FRIDA (Norwegian National Research Database & [[http://frida.usit.uio.no/](http://frida.usit.uio.no/)]([http://frida.usit.uio.no/](http://frida.usit.uio.no/)) & \\ \hline GEPRIS (the German Research Society’s research information system) & [[http://gepris.dfg.de/gepris](http://gepris.dfg.de/gepris)]([http://gepris.dfg.de/gepris](http://gepris.dfg.de/gepris)) & Limited to academics and researchers \\ \hline International Registry for Authors: Links to Identify Scientists & [[http://www.iralis.org/](http://www.iralis.org/)]([http://www.iralis.org/](http://www.iralis.org/)) & Also Called IRALIS. Based in Spain. \\ \hline Names Project & [[http://names.mimas.ac.uk/](http://names.mimas.ac.uk/)]([http://names.mimas.ac.uk/](http://names.mimas.ac.uk/)) & Still in development. \\ \hline People Australia & [[http://www.nla.gov.au/initiatives/peopleAustralia/index](http://www.nla.gov.au/initiatives/peopleAustralia/index)]([http://www.nla.gov.au/initiatives/peopleAustralia/index](http://www.nla.gov.au/initiatives/peopleAustralia/index)) & Still in development. \\ \hline RePEc Author Service & [[http://authors.repec.org/about](http://authors.repec.org/about)]([http://authors.repec.org/about](http://authors.repec.org/about)) & \\ \hline \end{tabular}
\end{table}
Table 2: Other systems and databases that help with name disambiguation.
## 2 Methodology
### IPSO-LSSVM Model
#### 2.2.3 IPSO Algorithm Principle
During the later search period, the value of \(C_{1}\) should be small and the value of \(C_{2}\) should be large to make particles find the global optimum quickly. Therefore, the dynamic adjustment strategy of learning constants is introduced:  
\[\left\{ \begin{array}{l}C_{1}=(C_{\min}-C_{\max})\frac{k}{k_{\max}}+C_{\max} \\ C_{2}=(C_{\max}-C_{\min})\frac{k}{k_{\max}}+C_{\min}\end{array} \right. \tag{17}\]  
The basic idea of the IPSO algorithm is as follows: Under the condition of ensuring the uniform distribution of the initial swarm, the PSO algorithm is first used in the optimization process until the particles are judged to fall into the premature state, and then the particles are redistributed in the solution space to help the particles to jump out of the local optimum quickly and accelerate the convergence. The pseudo-code of the IPSO algorithm is detailed in Algorithm 1. ```
1: Define \(m\), \(\tau_{\max}\), \(\tau_{\min}\), \(C_{\max}\), \(C_{\min}\), \(k_{\max}\)
2: Define the boundaries of the optimization problem
3: Initialize particle swarm
4: Compute \(f\), \(\overline{f}\), and \(\overline{P}_{d}\); record \(P_{id}\) and \(P_{gd}\)
5: for \(k=1\) to \(k_{\max}\) do
6: Compute \(D\) (\(t\)) and \(\sigma^{2}\) of swarm using Equations (13) and (14)
7: if \(D(t)<\alpha\) and \(\psi^{2}<\delta\) then
8: Initialize particle swarm
9: re-record \(P_{id}\) and \(P_{gd}\)
10: end
11: for \(i=1\) to \(m\) do
12: Compute each particle's velocity using Equation (11)
13: Update each particle's position using Equation (12)
14: end
15: Update \(P_{id}\) and \(P_{gd}\)
16: Compute \(f\), \(\overline{f}\), and \(\overline{P}_{d}\)
17: end
```  
**Algorithm 1**Pseudo-code of the IPSO algorithm
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Speaker Identification Process  
#### 3.3.1 Speaker Identification Model  
The employed methodology first involves model training by GMM, followed by feature extraction through MFCC. Figure 4 provides a visual explanation of this methodology. It is calculated using 20 MFCCs and 20 Delta-MFCCs. There are thus 40 features available in total. Under the feature extraction module, a specially defined function calculated the delta MFCC. One crucial feature extraction method for speaker identification is the MFCC. Finding a set of utterance characteristics that are acoustically correlated to the speech signal, i.e., parameters that can be calculated or roughly estimated is the aim of feature extraction. These variables are referred to as features [28].  
Figure 4: Algorithm flow chart GMM speaker model.  
Using a specific feature vector that was extracted from each speaker, the modelling technique aims to produce models for each speaker. By simulating the distributions of the feature vectors, it reduces the amount of feature data. The dependent and independent speakers constitute the two parts of the speaker reorganisation.  
The computer should ignore the speaker-specific characteristics of the speech signal when using the speech reorganisation technique in speaker independent mode, and instead, it extracts the desired message. Alternatively, if the speech reorganisation machine is operating in speaker-dependent mode, it should extract speaker characteristics from the acoustic signal.  
The primary goal of speaker identification is speech comparison. During the model training phase, the log-likelihood for each gmm model of each speaker was calculated. It was saved in a separate folder as a database. This data dictionary is used to match the gmm file of a 1: N speaker. The speaker with the highest score is chosen and identified.
## 5 Results and Discussion
### Numerical Results  
#### 5.1.1 Numerical Results of Velocity Field  
Figure 7a-i depicts numerical results of cases 1-3 for TTFHS, PTFHS, and MFLHS with LED under an inlet velocity of 6.13 m s\({}^{-1}\); cases 4-6 for the heat sinks with LED under an inlet velocity of 2.71 m s\({}^{-1}\); and cases 7-9 for the heat sinks with LED and housing under an inlet velocity of 6.13 m s\({}^{-1}\), respectively. In Figure 7a-c, the left plot shows velocity fields (represented by the streamline using an isometric view), whereas the right plot depicts the temperature field (represented by a front-view slice from cylindrical domain). Under different inlet velocities, all heat sinks with various thermophysical characteristics can be described using the arrow vector plots and streamline profiles. The flow patterns of the three heat sinks under different inlet velocity are considerably different as indicated by the isometric view for cases 1-9. In the isometric views presented in Figure 7a-c, the arrows indicate  
Figure 7: Numerical results of (**a**) case 1, (**b**) case 2, (**c**) case 3, (**d**) case 4, (**e**) case 5, (**f**) case 6, (**g**) case 7, (**h**) case 8, (**i**) case 9.  
flow direction, which is rotating along the z-axis. A displacement of the fan in the negative direction produces a rotation of velocity field in the positive direction.  
The arrow patterns, for the case studies 3, 6, and 9 in Figure 7c,f,i, respectively, represent the velocity vector rotating toward the center uniformly, because the pattern derived from MFLHS is a symmetric structure along the x-z and y-z planes. However, in Figure 7a,b,d,e,g,h, the velocity vector is rotating along the z-axis. This occurs because the pattern derived from TTFHS or PTFHS is a symmetric structure along the x-z plane.  
Streamline visualization plays a critical role in understanding the complex flow mechanism. As shown in Figure 7a-i, the streamline plots indicate that the flow passes through fan channels with high speed jet impinging onto heat sink surfaces. Comparing the jet impingement cooling performance among the investigation schemes (Table 1), the PTFHS exhibited the highest cooling performance followed by MFLHS and TTFHS because in the plots derived from TTFHS the streamline only pass through the x-z plane, whereas the PTFHS streamline not only passes through the x-z plane but also the y-z plane in the plots. The performance of MFLHS was less than that of PTFHS because even though the streamline passed through the x-z and y-z planes, it passed through fewer streamlines. Thus, the heat sink with horizontal perforated holes possessed higher heat transfer capability.  
As a result, under higher inlet velocity, all heat sinks exhibited higher velocity magnitude than that under lower inlet velocity. This implied that higher Reynolds number increased the heat transfer rate.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 3 Our Proposed Methods
### Decision Fusion  
Considering the significant effects of fusion of multiple networks on the improvement of classification performance, a weighted algorithm is introduced to fuse the output results of the two sub-networks.  
Details of the algorithm are shown as follows,  
\[\mathbf{l}_{i}=\sigma(\mathbf{x}_{i})=\frac{1}{1+e^{-\mathbf{x}_{i}}},i\in[1,2], \tag{7}\]  
first, the \(\sigma\) function is used to normalize each network output values. Where \(\mathbf{x}_{i}\) indicates the output vector of the \(i\)-th sub-network.  
The normalized probability distribution is expressed as  
\[\mathbf{P}_{i}=\frac{I_{im}}{\sum_{j=1}^{n}I_{ij}},\left\{\begin{array}{l}m\in[ 1,n]\\ i\in[1,2]\end{array}\right., \tag{8}\]  
where \(\mathbf{P}_{i}\) is the probability vector output by the network, \(I_{im}\) is the value of the \(m\)-th category, and \(n\) indicates the number of cloud image categories.  
The output probability matrix of the transformed combined network is expressed as  
\[\mathbf{P}=\begin{bmatrix}\mathbf{P}_{1}\\ \mathbf{P}_{2}\end{bmatrix}, \tag{9}\]  
Subsequently, the information entropy \(H_{i}\) of the probability distribution of each network output is  
\[H_{i}=-\sum_{j=1}^{n}\mathbf{P}_{ij}\log_{2}\mathbf{P}_{ij},i\in[1,2], \tag{10}\]  
Figure 6: Improved ECA. (**a**) GAP share weights with GMP (ECA-WS); (**b**) GAP and GMP weight independent (ECA-WI).  
The greater information entropy, the greater uncertainty of the network prediction results and the higher probability of being misdiagnosed. Therefore, a lower weight should be assigned. The weight \(w_{hi}\) of sub-network is given as follows  
\[w_{hi}=\frac{e^{-H_{i}}}{\sum_{k=1}^{n}e^{-H_{k}}},i\in[1,2], \tag{11}\]  
The weight \(w_{cm}\) of each category according to the number of samples of different categories is defined as follows  
\[w_{cm}=\frac{1-\frac{N_{m}}{N_{total}}}{\sum_{j=1}^{n}(1-\frac{N_{j}}{N_{total} })},\left\{\begin{array}{l}m\in[1,n]\\ i\in[1,2]\end{array}\right., \tag{12}\]  
where \(N_{m}\) represents the number of samples in the \(m\)-th category and \(N_{total}\) represents the total number of samples.  
Multiply the sub-network weight \(w_{ci}\) and category weight \(w_{ci}\) with the original output vector \(\mathbf{x}\). The weighted matrix \(\mathbf{W}\) of the combined network is written as  
\[\mathbf{W}=\begin{bmatrix}w_{h1}w_{c1}\mathbf{x}_{11}&w_{h1}w_{c2}\mathbf{x}_{12}\cdots w _{h1}w_{cn}\mathbf{x}_{1n}\\ w_{h2}w_{c1}\mathbf{x}_{21}&w_{h2}w_{c2}\mathbf{x}_{22}\cdots w_{h2}w_{cn}\mathbf{x}_{2n} \end{bmatrix}, \tag{13}\]  
Add \(\mathbf{W}\) in rows,  
\[\mathbf{W}^{\prime}=\left[\sum_{i=1}^{2}w_{hi}w_{c1}\mathbf{x}_{i1},\sum_{i=1}^{2}w_{hi }w_{c2}\mathbf{x}_{i2},\cdots,\sum_{i=1}^{2}w_{hi}w_{cn}\mathbf{x}_{in}\right], \tag{14}\]  
Take the maximum index of the column to be the final classification decision result,  
\[\textit{lable}=\textit{arg}\;\textit{max}(\mathbf{W}^{\prime}). \tag{15}\]
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 3 Smart Mobility in Smart Cities
### Challenges  
Some of the main challenges that are presented to smart mobility nowadays include: Infrastructure: Implementing smart mobility solutions in a smart city system has high infrastructural demands to overcome the pressure on the suboptimal transportation systems in most parts of the world. The increasing popularity of self-driving and electric vehicles requires network connectivity, high bandwidth and electric charging stations. To fully utilize the potential of smart mobility, there is a need to develop the infrastructure that can realize the concepts surrounding smart systems.  
Last Mile Connectivity: One of the major issues in public transportation systems is the low-cost last mile connectivity. For efficient smart mobility solutions, there is a need for door-to-door connectivity irrespective of the mode of transportation.  
Security and Privacy: The need for connected devices and the rapid generation of rich personal data pose serious privacy concerns related to data sharing amongst devices and users. Moreover, due to the connected devices, the entire network is vulnerable to outside attack and breaches.  
Governance: The concept of smart mobility has extended the scope of conventional transportation systems to include other stakeholders such as tech companies and service providers. The inclusion of these new actors requires modified policies and rules governing the smart mobility. The regulations governing smart mobility systems are still lagging behind. With no standards governing the use of smart mobility solutions, the mass adoption of these smart solutions is still far from reality. There is a need to seamlessly integrate the existing traffic laws to meet the demands of smart mobility solutions.  
Initial Adoption: One of the major issues with smart transportation is creating awareness amongst the potential users for its adoption. The majority of solutions are in the early stages of development, which does not provide significant proof of the exact goals of the mobility solutions.  
Dynamic Routing and Transportation Mobility: The efficient solutions require dynamic routing systems to estimate the travel demands of the users and optimize the available resources to provide the solutions. This process requires sophisticated software and technological solutions.  
Network Management and Monitoring: The smart ecosystems comprise millions of connected entities, which makes the network management and monitoring complex and costly.  
Data Acquisition and Integration: The data are collected from heterogeneous sources with different security and privacy protocols. Handling the large volumes of real time data generated from the connected devices is a complex task.  
Legal Challenges: The involvement of multiple stakeholders such as payment companies, governments, city administrations, public-private transportation, users, etc., requires a well-defined legal system and support policies for smart mobility.
###### Abstract  
Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture's spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R\({}^{2}\)) of 0.948 and 0.908 and a root mean square error (RMSE) of 2.651 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R\({}^{2}\) and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.  
soil texture; GF5; hyperspectral; machine learning; inversion model +
Footnote †: journal: Medical Imaging
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 2 Theoretical Framework for Seismic Bearing Capacity  
### Upper Bound Theorem of Limit Analysis  
Limit analysis is used to assess different types of geotechnical stability problems due to its solid theoretical foundation and ease of use [17]. Limit analysis consists of an upper bound method and a lower bound method, in which the first method refers to the bearing capacity that is greater than the actual damage load based on the assumed reasonable failure mechanism and energy consumption balance, while the second method refers to the bearing capacity that is not greater than the actual damage load based on the stress balance and yield condition. In this paper, the upper bound theorem is adopted, which can be expressed as Equation (1). The specific calculation process is as follows: firstly, a velocity field conforming to kinematics is assumed, and then based on this velocity field, the upper bound solution of the bearing capacity can be found when the external power is equal to the internal energy dissipation. In addition, it is to be noted that seismic forces are considered as external forces.  
\[\int_{S}T_{i}v_{i}\mathrm{d}S+\int_{V}F_{i}v_{i}\mathrm{d}V=\int_{V}\sigma_{ ij}\epsilon_{ij}\mathrm{d}V \tag{1}\]  
Figure 1: Process of shear band propagation with different setback distances of the footing. (a) d = 0 cm; (b) d = 3.5 cm; (c) d = 7 cm [12].  
In order to use the Upper bound theorem of limit analysis, the following assumptions need to be met:  
1. Soil material is an ideal elastic-plastic material;
2. The soil satisfies the assumption of small deformation;
3. Drucker postulate.
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 2 A New Mapping Scheme of Rmap
### Applied Usage Case of Rmap for Coverage  
The Rmap also can be used for coverage application with slight modifications in the unknown environment. The coverage refers to the algorithm of visiting all of the areas at least once. The cleaning robot is a representative example of coverage application.  
For applying the Rmap to coverage application, some part of the Rmap algorithm needs to be changed. The Rmap in coverage cases works the same until the R1 rectangle is selected. After getting the R1 rectangle in the Rmap algorithm, the robot has to follow the boundary line of the R1 rectangle to obtain more information about the unknown area. However, for coverage, the robot needs to visit the whole R1 rectangle area. For covering the R1 rectangle area, the Boustrophedon path algorithm [21] can be adopted. The Boustrophedon path algorithm is a well-known algorithm in the coverage field. The Boustrophedon path means a zigzag style movement, as shown in Figure 7. While covering the R1 rectangle using the Boustrophedon path algorithm, the modified exploration policy also obtains new information of the unknown area for a map update. After covering the R1 rectangle area, the rest of the processes are the same as the original Rmap scheme. Algorithm 2 shows the modified version of the Rmap for coverage application. As previously described, the algorithm has changed only for the exploration mechanism after updating the R1 rectangle. For the coverage case, the Boustrophedon path algorithm instead of the R1's borderline following is used for the exploration step.  
Figure 6: A representation of the creation method of the adjacent center point. (**a**) Horizontal overlapping case, (**b**) Vertical overlapping case.  
Figure 7: The coverage path planning for Rmap with the Boustrophedon algorithm. The blue box is MBR, the green box is R1, and the red line is the created path using the Boustrophedon.
## 2 Numerical Formulation of the Magneto-Quasi-Static Problem  
An electromagnetic problem in the so-called magneto-quasi-static problem (MQS) limit is studied here; namely, a low frequency eddy currents problem in non-magnetic conductors: these are typical conditions encountered in the applications analyzed in this paper, that are related to the plasma fusion machines, such as the fusion reactor ITER [23]. The mathematical model may be cast in integral form by introducing a vector potential, \(\mathbf{A}\), and a scalar one, \(\phi\), to define the electrical field, \(\mathbf{E}\), and the magnetic one, \(\mathbf{B}\), as follows:  
\[\mathbf{E}=-\frac{\partial\mathbf{A}}{\partial t}-\
abla\phi,\ \ \ \
abla\times \mathbf{A}=\mathbf{B},\ \ \text{(Coulomb gauge}\ \
abla\cdot\mathbf{A}=0) \tag{1}\]  
In this way, [PERSON]'s law is implicitly imposed. The vector potential is calculated from the current density, \(\mathbf{J}\), flowing inside the conductors' region, \(V_{C}\), and from the density, \(\mathbf{J}_{S}\), of the currents located outside that region, as follows:  
\[\mathbf{A}(\mathbf{r},t)=\frac{\mu_{0}}{4\pi}\int_{V_{C}}\frac{\mathbf{J}( \mathbf{r}^{\prime},t)}{|\mathbf{r}-\mathbf{r}^{\prime}|}dV^{\prime}+\mathbf{ A}_{s}(\mathbf{r},t),\ \ \mathbf{A}_{s}(\mathbf{r},t)=\frac{\mu_{0}}{4\pi}\int_{R^{3}-V_{C}}\frac{ \mathbf{J}(\mathbf{r}^{\prime},t)}{|\mathbf{r}-\mathbf{r}^{\prime}|}dV^{\prime}) \tag{2}\]  
The integral formulation of the problem comes from the imposition of the constitutive equation \(\mathbf{J}=\sigma\mathbf{E}\) in the ohmic conductors by means of the weighted residual approach, as follows:  
\[\int_{V_{C}}\mathbf{W}\cdot\Big{(}\sigma^{-1}\mathbf{J}-\mathbf{E}\Big{)}dV=0,\ \ \ \forall\mathbf{W}\in S \tag{3}\]  
The subspace \(S\) contains the functions solenoidal in \(V_{C}\), satisfying the interface conditions for the current at the boundary: \(S=\left\{\mathbf{J}\in\mathbf{L}_{div}^{2},\ \
abla\cdot\mathbf{J}=0\ in\ V_{C},\ \mathbf{J}\cdot\hat{n}=0\ on\ \partial V_{C}\right\}\). By replacing (1) and (2) in (3), the final weak form is obtained, as follows:  
\[\int_{V_{C}}\mathbf{W}\cdot\left\{\sigma^{-1}\mathbf{J}+\frac{\partial}{ \partial t}\left[\frac{\mu_{0}}{4\pi}\int_{V_{C}}\frac{\mathbf{J}(\mathbf{r}^{ \prime},t)}{|\mathbf{r}-\mathbf{r}^{\prime}|}dV^{\prime}+\frac{\partial \mathbf{A}_{s}}{\partial t}\right]\right\}dV=0,\ \ \ \forall\mathbf{W},\ \mathbf{J}\in S \tag{4}\]  
The above formulation is implemented in the code CARIDDI [21], that is here adopted to perform the numerical analysis of the considered case studies.