esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 3 Fuzzy Clustering and Neuro-Fuzzy Modeling
### Neuro-Fuzzy Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Selected Fuzzy Membership Functions**} & \multicolumn{4}{c}{**Success Rate (\%)**} \\ \hline \(q_{t}\) (MPa) & \(R_{f}\) & \(B_{q}\) & **Clay** & **Silt** & **Sand** & **Average** \\ \hline Triangular & Triangular & 98.93 & 53.13 & 63.41 & 71.82 \\ \hline Triangular & Gaussian & 99.60 & 55.00 & 72.76 & 75.79 \\ \hline Triangular & Bell & 99.50 & 53.13 & 73.58 & 75.40 \\ \hline Triangular & Sigmoidal & 99.43 & 57.19 & 69.51 & 75.38 \\ \hline Gaussian & Triangular & Bad & Bad & Bad & Bad \\ \hline Gaussian & Gaussian & 99.73 & 57.50 & 77.64 & 78.29 \\ \hline Gaussian & Bell & 99.58 & 55.31 & 78.46 & 77.78 \\ \hline Gaussian & Sigmoidal & 99.45 & 55.31 & 77.24 & 77.33 \\ \hline Bell & Triangular & 99.80 & 54.06 & 80.89 & 78.25 \\ \hline Bell & Gaussian & 99.60 & 55.94 & 75.61 & 77.05 \\ \hline Bell & Bell & 99.50 & 54.69 & 75.61 & 76.60 \\ \hline Bell & Sigmoidal & 99.48 & 54.38 & 76.42 & 76.76 \\ \hline Sigmoidal & Triangular & 98.98 & 55.63 & 65.04 & 73.22 \\ \hline Sigmoidal & Gaussian & 99.58 & 54.06 & 76.83 & 76.82 \\ \hline Sigmoidal & Bell & 99.45 & 50.31 & 71.14 & 73.63 \\ \hline Sigmoidal & Sigmoidal & 99.48 & 54.69 & 72.36 & 75.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Success rate when using a bell-shaped membership function for \(q_{t}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Selected Fuzzy Membership Functions**} & \multicolumn{4}{c}{**Success Rate (\%)**} \\ \hline \(q_{t}\) (MPa) & \(F_{R}\) & \(B_{q}\) & **Clay** & **Silt** & **Sand** & **Average** \\ \hline Triangular & Triangular & 99.50 & 59.38 & 74.39 & 77.76 \\ \hline Triangular & Gaussian & 99.63 & 52.81 & 73.17 & 75.20 \\ \hline Triangular & Bell & 99.58 & 47.81 & 75.20 & 74.20 \\ \hline Triangular & Sigmoidal & 99.53 & 53.75 & 75.20 & 76.16 \\ \hline Gaussian & Triangular & 99.50 & 60.31 & 76.42 & 78.74 \\ \hline Gaussian & Gaussian & 99.60 & 53.44 & 74.80 & 75.95 \\ \hline Gaussian & Bell & 99.58 & 51.88 & 76.42 & 75.96 \\ \hline Gaussian & Sigmoidal & 99.55 & 52.81 & 76.83 & 76.40 \\ \hline Bell & Triangular & 99.53 & 59.06 & 78.46 & 79.02 \\ \hline Bell & Gaussian & 99.55 & 53.44 & 74.39 & 75.79 \\ \hline Bell & Bell & 99.58 & 52.50 & 74.80 & 75.63 \\ \hline Bell & Sigmoidal & 99.55 & 52.81 & 75.61 & 75.99 \\ \hline Sigmoidal & Triangular & 99.55 & 59.06 & 78.05 & 78.89 \\ \hline Sigmoidal & Gaussian & 99.60 & 52.50 & 77.24 & 76.45 \\ \hline Sigmoidal & Bell & 99.55 & 53.75 & 74.80 & 76.03 \\ \hline Sigmoidal & Sigmoidal & 99.55 & 52.81 & 73.98 & 75.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Success rate when using a sigmoidal membership function for \(q_{t}\).
## 5 Discussion
### Open Challenges  
We discuss the main challenges the SatComs industry will face to implement ML techniques in RRM.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Analytical Vs. Experimental Validation  
A handful of research works on the STL of sandwich and composite structures have been carried out using analytical solutions and these have been validated through experimental measurement [142; 143]. Most authors validated their analytical predictions by either performing experiment using the same parametric conditions or by comparing them with the measured data of previous works. [PERSON] and [PERSON] [46] performed experiments to validate their analytical solution of the STL of cylindrical shells with absorbing materials. The SEA method was used to obtain exact solution while two experimental techniques were used to obtain the measured data. The experimental methods utilized were the transition suite and sound intensity methods. The results of their investigation showed that the analytical solution used for predicting the STL gave improved results especially in the low frequency region due to the application of correction factors at this frequency range. Moreover, a good correlation of the STL at the ring and critical frequencies were observed for both the exact and measured solutions.
# Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection
## 4 Study Design
### Methodology of the Evaluation  
In our research, results of the WNNSA method were compared to the results of stratified matching (SM), nearest neighbour matching (NNM) [35], Mahalanobis metric matching (MMM) [36], two types of the PSM method and the WNNEM method.  
In practical studies, the PSM method is generally applied with a restrictive condition. This constraint is controlled by setting the 'caliper size' parameter. Generally, the caliper size is set to 0.2 of the standard deviation of the logit of the propensity scores. This means that the control individuals can only be selected from a reduced environment of the treated elements. In the followings, this type of the PSM method is denoted as _PSM_02_. However, using this constraint, the control group selection method may also result in a control group that contains fewer individuals than the treated group. In case of the second version of the PSM method, for a fair evaluation, propensity score matching was executed with dynamic caliper size. It means that the size of the neighbourhood (aka the caliper size) of the treated individuals was determined dynamically such that in each case, an appropriately sized control group could be selected. In the following, this type of PSM method is denoted as _PSM_DYN_.  
In the case of the WNNSA algorithm, the minimal size of the reduced \(k\)-size environment (\(k_{min}\)) was calculated in accordance with Algorithm 2. To increase the search space and the freedom of the algorithm, the value of \(k\) was set to \(k=\left\lfloor k_{min}*1.15\right\rfloor\) in all scenarios.  
As mentioned before, the effectiveness of the proposed methods was evaluated through Monte Carlo simulations. In each scenario, 100 independent datasets were generated with the given parameters. As WNNEM, SM, NNM and MMM are deterministic, they were only executed once on each generated dataset. In contrast, as PSM_02, PSM_DYN and WNNSA methods are not deterministic, they were executed ten times on each dataset. For these methods, the best result from the ten runs was considered for evaluation.  
The quality of the selected control groups was evaluated from several perspectives. For distribution-based evaluation, SMD, \(t\)-test, chi-squared test, Hansen-Bowers test and Distribution Dissimilarity Index have been used. The pairwise similarities of the paired elements were evaluated by the Nearest Neighbour Index and the Global Dissimilarity Index.
# An Application of Manifold Learning in Global Shape Descriptors
## 1 Introduction
To the best of our knowledge, the application of manifold learning, an active research topic in statistics and machine learning, has not been investigated in the computer graphics community for extracting global shape descriptors. This motivates the primary aim of this research, which is to explore the effectiveness of a manifold learning method, more specifically _Laplacian Eigenmap_[27], in representing a 3D model with a low-dimensional vector. Our work introduces a novel Laplacian Eigenmap-based global shape descriptor and provides a straightforward normalization method that significantly outperforms existing spectral approaches. In our **first contribution**, inspired by the idea of Laplacian Eigenmaps [27], we learned the manifold of a 3D model and then, analogous to the approach taken by Shape-DNA, used the spectrum of the embedded manifold to build the global shape descriptor. This approach has two main advantages. First, it relies on the adjacency of the nodes, disregarding the fine details of the mesh structure. Therefore, it can be used for degenerate or non-uniform sampled meshes. Second, as manifold learning does not rely on the mesh structure and is not limited to a specific type of meshes, e.g., triangulated meshes, it can be applied easily to any other mesh types such as quadrilateral meshes. In our **second contribution**, we presented a simple and straightforward normalization technique (motivated by the work in [20; 28; 29]) to obtain a scale-invariant global shape descriptor that is more robust to noise. To this end, we propose to subtract the first non-zero eigenvalue from the shape descriptor after taking the logarithm of the spectrum. One advantage of our approach over the idea of [PERSON] et al. [28] is that we avoid taking the direct derivative; this advantage is significant since the differential operator amplifies the noise. Taking the logarithm additionally helps to suppress the effect of the noise that is present in higher order elements of the spectrum. The remainder of this paper is organized as follows. In Section 2, we briefly overview spectral shape analysis and manifold learning. Then, in Section 3, we introduce the proposed shape descriptor along with some technical background. In Section 4, the performance of the proposed method, as well as the robustness of the algorithm are examined and compared with multiple well-known shape descriptors by performing several qualitative and quantitative experiments using widely used 3D model datasets. Section 5 discusses the results in more detail and draws conclusions.
# Performances of the Synergy of Silica Fume and Waste Glass Powder in Ternary Blended Concrete
## 3 Discussion of Results
### Effect of Silica Fume and Glass Powder Combination on the Fresh Density of the Concrete  
The specific gravity of SF and WGP were lower than that of the OPC, therefore the combination of glass and silica fume generated a lighter concrete compared to the OPC concrete. Besides, the higher the proportion of the glass in the mixture, the denser the fresh density of the ternary blended concrete (C\({}_{90}\)G\({}_{\infty}\)S\({}_{10-x}\)), but lower than the density of the OPC (C\({}_{100}\)G\({}_{0}\)S\({}_{0}\)) concrete. The highest density was observed in C\({}_{90}\)G\({}_{0}\)S\({}_{0}\) and then followed by C\({}_{90}\)G\({}_{10}\)S\({}_{0}\), while the least was in C\({}_{90}\)G\({}_{0}\)S\({}_{10}\). The fresh density of the OPC concrete was 2390 kg/m\({}^{3}\), as shown in Figure 7, and then reduced by 1%, 1.8%, 1.5%, 0.4% and 0.1% as the glass-to-silica-fume proportion increased from 0 to 10 wt%. and as SF reduced from 10 to 0 wt.%, at an interval of 2.5 wt.%, respectively.  
From Figure 8, the inclusion of WGP and SF in ternary blended concrete reduced the early strength development due to the dilution effect of the nucleation site. This can be observed in the 3- and 7-day strength in C\({}_{90}\)G\({}_{0}\)S\({}_{10}\) and C\({}_{90}\)G\({}_{10}\)S\({}_{0}\) with a reduction of 8.4% and 18.8%, respectively. There was a further reduction to 17% upon the inclusion of 7.5% WGP in synergy with 2.5% silica fume (C\({}_{90}\)G\({}_{2.5}\)S\({}_{7.5}\)). However, the interaction of glass and silica fume reduced the dilution effect, as can be seen in C\({}_{90}\)G\({}_{5}\)S\({}_{5}\). This implies that the presence of silica fume and glass powder in concrete could reduce the dilution effect, which could affect the early strength in blended concrete. The presence of glass powder reduced the rate of the pozzolanic reaction, thereby reducing the later strength development, as noticed at 28 days. The rate of strength development was significant over 28 days in glass-free, blended silica-fume concrete (C\({}_{90}\)G\({}_{0}\)S\({}_{10}\)) in comparison with the blended samples (C\({}_{90}\)G\({}_{2.5}\),S\({}_{7.5}\). C\({}_{90}\)G\({}_{5}\)S\({}_{5}\), C\({}_{90}\)G\({}_{7.5}\)S\({}_{2.5}\), C\({}_{90}\)G\({}_{10}\)S\({}_{0}\)). Additionally, comparing C\({}_{90}\)G\({}_{7.5}\)S\({}_{2.5}\) with C\({}_{90}\)G\({}_{2.5}\)S\({}_{7.5}\) implies that SiO\({}_{2}\) in silica fume could be said to be more reactive compared to the available glass powder. Figure 2 corroborates this fact as the diffractive halo in the silica fume X-ray diffractogram is more pronounced than that of WGP.  
Figure 6: Morphology of silica fume (binary) blended paste (left) in comparison with the silica fume/glass (ternary) paste (right). (**a**) Silica-fume blended paste. (**b**) Silica-fume/glass-powder (ternary) paste.  
Figure 8: Concrete compressive strength and synergistic effect of silica fume and glass powder.  
Figure 7: Density of the fresh blended silica-fume/glass concrete.
# Full Three-Dimensional Inverse Design Method for S-Ducts Using a New Dimensionless Flow Parameter
## 2 Numerical Procedure  
### Flow Solver and Geometry Definition  
In iterative inverse design methods, a shape modification algorithm is incorporated into a flow solver, and the flow field is solved to obtain the wall pressure distribution at each shape-modification step. In the current work, the equations of the flow inside the duct geometry were numerically solved using an in-house 3-D Euler solver based on the flux difference-splitting scheme of the Roe method [42]. A higher order of spatial accuracy was obtained in all calculations using MUSL (monotone upstream centered scheme) for conservation laws with a third-order interpolation.  
Figure 1a shows the geometry and grid generation of a 3-D S-shaped diffuser. The white dots on the wall surface indicate the control points (balls), which can be displaced according to the inverse design algorithm. The grid size was optimized through a grid refinement study. In this respect, the computational domain was meshed by O-grid structured mesh with \(N_{x}\times N_{r}\times N_{\theta}=80\times 10\times 30,\ 100\times 20\times 30\), and \(100\times 30\times 40\) in which \(N_{x},\ N_{r}\), and \(N_{\theta}\) imply the number of nodes in the axial, radial, and angular directions, respectively. The wall pressure distributions along the upper and lower lines of the symmetry plane were obtained for various grid sizes and plotted in Figure 1b. It is apparent that for grid sizes finer than \(N_{x}\times N_{r}\times N_{\theta}=100\times 20\times 30\), the results are identical.  
Figure 1: Mesh and control points for the S-shaped diffuser (**a**). Grid-independency for wall pressure distribution along the upper and lower lines of the symmetry plane (**b**).
## 5 Conclusions  
This study numerically and experimentally investigated the dynamic responses of asphalt pavement in dry and saturated conditions under full-scale accelerated loading. Based on the analysis discussed above, the following conclusions could be obtained.  
1. Trends of modeling results were consistent with those of field measurements.
2. The increase in vehicle load enhanced dynamic responses. With the increase in vehicle speed, pore water pressure increased, but vertical stress gradually decreased, while transverse strain decreased in dry conditions but slowly increased in saturated conditions in the middle and lower layers. Vertical strain showed different increasing and decreasing trends in the asphalt pavement at different depths.
3. Dynamic responses decreased with pavement depths. Water made dynamic responses more complex, and pore water pressure significantly decreased with depths within the upper layer of saturated asphalt pavement.  
* Transverse distributions of indicators presented obvious compressive states in the regions in direct contact with the vehicle wheels, while tensile states were found in the range of the middle vehicle axle.
* The numerical results provided a basis for field measurements in future studies, especially for the exploration of factors of temperature and layer depth.  
Conceptualization, W.W.; methodology, W.W., G.Y. and K.Z.; software, W.W., G.Y. and K.Z.; resources, G.Y.; data curation, W.W. and K.Z.; writing--original draft preparation, W.W.; writing--review and editing, L.W.; supervision, L.W. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of the China Postdoctoral Science Foundation (Project No. 2021M690356).  
Not applicable.  
The authors declare no conflict of interest.
# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 2 Antenna Array Diagnosis Problem Formulation
### Signal-to-Noise Ratios (SNRs)  
Total variation (TV), mixed \(\ell_{1}/\ell_{2}\) norm, and minimization of \(\ell_{1}\) techniques are the leading compressive techniques, and they exhibit low efficiency and reliability in antenna diagnosis for low SNRs. This drawback fosters the need for a more robust diagnosis procedure in the presence of noisy data.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
###### Abstract  
Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including Wireless Sensor Networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new localization approaches that combine DV-Hop with Chicken Swarm Optimization (CSO). The primary objective is to improve the precision of DV-Hop-based approaches. In this paper, we compare the efficiency of the proposed localization algorithms with other existing approaches, including several algorithms based on Particle Swarm Optimization (PSO), while considering random network topologies. The simulation results validate the efficiency of our proposed algorithms. The proposed HW-DV-HopCSO algorithm achieves a considerable improvement in positioning accuracy compared to those of existing models.  
IoT; PSO; CSO; WSN; DV-Hop +
Footnote †: journal: Information
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.2 Land Use/Cover  
Identifying suitable land types (e.g., barren land or agricultural zones) helps avoid conflicts with agricultural, urban, or ecologically sensitive areas, ensuring sustainability and ease of project development. Therefore, land use is an essential criterion in the AHP for determining the suitability of locations for PV farms. Proper land use classification is crucial to avoid conflicts with existing land-based activities and ensures that solar installations are both technically feasible and legally permissible [17; 26]. In this context, suitability levels were evaluated using the CORINE Land Use/Land Cover (LULC) data [46]. The suitable areas were classified based on their suitability scores, including categories such as bare rock (3.3.2), sparsely vegetated areas (3.3.3), natural grassland (3.2.1), sclerophyllous vegetation (3.2.3), transitional woodland shrub (3.2.4), beaches, dunes, and sand plains (3.3.1), burnt areas (3.3.4), mineral extraction sites (1.3.1), construction sites (1.3.3), complex cultivation patterns (2.4.2), and land principally occupied by agriculture (2.4.3). Areas that fall outside these classes were classified as unsuitable for PV site selection. In Turkey, to obtain permission for establishing a solar power plant, the land must qualify as marginal agricultural land. To elaborate on the concept of marginal agricultural land, it refers to land that is not classified as absolute agricultural land, special cropland, or planted agricultural land [47]. Therefore, agricultural lands and other areas were selected as unsuitable for solar installations. In regions like Gaziantep, integrating land use data with other criteria, such as solar radiation and proximity to infrastructure, ensures a comprehensive and effective site selection process for solar energy projects.
## 2 Data  
In this study, optical images from Landsat and Sentinel-2 are used to compute monthly land-water masks and finally surface area time series of lakes and reservoirs. To validate our results, we use water level time series from in situ stations and satellite altimetry.
## 3 Preliminaries
### The Actuator Dynamic Model  
In this study, two brushed DC motors are used to drive the wheels of the DDR. The actuator model is needed to find the relationship between the control signal and the input of the mechanical system. The electrical circuit and the free body diagram of the rotor of a brushed DC motor are depicted in Figure 4[25].  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Parameters (Notation)** & **Unit** \\ \hline
01 & The linear velocity of the DDR (\(v\)) & m\(\cdot\)s\({}^{-1}\) \\
02 & The orientation of the DDR (\(\theta\)) & rad \\
03 & The angular velocity of the right wheel (\(\omega_{r}\)) & rad\(\cdot\)s\({}^{-1}\) \\
04 & The angular velocity of the left wheel (\(\omega_{l}\)) & rad\(\cdot\)s\({}^{-1}\) \\
05 & The linear velocity of the right wheel (\(v_{r}\)) & m\(\cdot\)s\({}^{-1}\) \\
06 & The linear velocity of the left wheel (\(v_{l}\)) & m\(\cdot\)s\({}^{-1}\) \\
07 & The radius of the right and the left wheels (\(r\)) & m \\
08 & The distance between the right and the left wheels (\(b\)) & m \\
09 & The center of the axis between the right and the left wheels (\(Q\)) & - \\
10 & The center of gravity of the DDR (\(G\)) & - \\
11 & The distance between Q and G (\(a\)) & m \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the differential drive robot.  
Figure 3: Geometry of a differential drive robot.  
An equation governing the mechanical dynamics of the DC motor is expressed in (5). The armature current that has its own dynamic equation is expressed in (6) as follows:  
\[J\ddot{\theta}_{m}+B\dot{\theta}_{m}=K_{t}i_{a} \tag{5}\]  
\[L_{a}\frac{\mathrm{d}i_{a}}{\mathrm{d}t}+R_{a}i_{a}=v_{a}-K_{e}\dot{\theta}_{m} \tag{6}\]  
where \(J\) is the moment of inertia, \(\theta_{m}\) is the angle of rotation of the output shaft, \(\tau_{m}=K_{t}i_{a}\) is the mechanical torque developed in the motor rotor, \(K_{t}\) is the torque constant, \(i_{a}\) is the armature current, \(B\) is the damping coefficient, \(v_{a}\) is the input voltage, \(K_{e}\) is the back emf constant, \(R_{a}\) stands for the armature resistance, \(L_{a}\) is the armature inductance, and \(v_{e}=K_{e}\dot{\theta}_{m}\) is the back emf [25].  
Referring to [25], the relationship between the angular velocity (\(\dot{\theta}_{m}\)) and the input voltage (\(v_{a}\)) of the motor is expected in the form of a transfer function by Equation (7).  
\[F(s)=\frac{\dot{\theta}_{m}(s)}{\dot{v}_{a}(s)}=\frac{K_{t}}{(R_{a}+sL_{a})(Js+ B)+K_{t}K_{e}} \tag{7}\]  
Thus, the block diagram of the brushed DC motor can be expressed by Figure 5.