esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Defect System Characterization
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Sample** \\ **Group** \\ \end{tabular} } & \multicolumn{4}{c}{**Coarse Aggregate**} & \multicolumn{4}{c}{**Void in Mortar**} & \multicolumn{4}{c}{**Crack in Mortar**} \\ \cline{2-10}  & \multirow{2}{*}{**Area Rate**} & \begin{tabular}{c} **Average** \\ **Area** \\ \end{tabular} & **Circularity** & **Area Rate** & \begin{tabular}{c} **Average** \\ **Area** \\ \end{tabular} & **Circularity** & **Area rate** & \begin{tabular}{c} **Average** \\ **Area** \\ \end{tabular} \\ \cline{2-10}  & & **[\%]** & **[mm\({}^{2}\)]** & **[-]** & **[\%]** & **[mm\({}^{2}\)]** & **[-]** & **[\%]** & **[mm\({}^{2}\)]** \\ \hline \multirow{4}{*}{\begin{tabular}{c} (No. 1–No. 12) \\ \end{tabular} } & Average & 44.15 & 111.10 & 0.72 & 0.78 & 1.53 & 0.97 & 1.55 & 17.94 \\  & Max & 50.89 & 153.39 & 0.76 & 1.09 & 2.05 & 0.98 & 2.42 & 29.67 \\  & Min & 35.23 & 91.04 & 0.68 & 0.44 & 1.21 & 0.96 & 0.31 & 7.35 \\  & SD & 4.43 & 18.86 & 0.02 & 0.19 & 0.24 & 0.00 & 0.57 & 5.39 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Selected \\ \end{tabular} } & No. 5 & 50.89 & 137.27 & 0.71 & 0.44 & 1.24 & 0.98 & 0.31 & 7.35 \\  & No. 8 & 41.92 & 105.07 & 0.73 & 0.78 & 1.40 & 0.98 & 1.49 & 15.76 \\  & No. 9 & 46.28 & 103.25 & 0.74 & 0.79 & 1.60 & 0.97 & 2.42 & 14.73 \\  & No. 11 & 45.27 & 109.69 & 0.74 & 0.73 & 1.39 & 0.97 & 1.48 & 19.60 \\  & No. 12 & 40.01 & 94.90 & 0.70 & 0.95 & 1.59 & 0.97 & 1.58 & 11.51 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Selected \\ \end{tabular} } & Average & 44.87 & 110.03 & 0.72 & 0.74 & 1.44 & 0.98 & 1.45 & 13.79 \\  & Max & 50.89 & 137.27 & 0.74 & 0.95 & 1.60 & 0.98 & 2.42 & 19.60 \\ \cline{1-1}  & Min & 40.01 & 94.90 & 0.70 & 0.44 & 1.24 & 0.97 & 0.31 & 7.35 \\ \cline{1-1}  & SD & 3.76 & 14.43 & 0.02 & 0.17 & 0.13 & 0.00 & 0.67 & 4.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Geometric properties.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 4 User Finite State Machine for Proposed Scheme  
In this section, we describe the novel user finite state machine (FSM) that operates with beam switching and handover. It is obtained using the FSM mathematical model as  
\[\text{FSM}=(\mathcal{C},\mathcal{S},\mathcal{S},\mathcal{S},\mathcal{S}), \tag{23}\]  
where \(\mathcal{C}=\{c1,c2,\cdots,c15\}\) is a set that represents state transition rules. \(\mathcal{S}=\{\mathcal{S}0,\mathcal{S}1,\cdots,\mathcal{S}11,F1,F2,\cdots,F7\}\) is a set of states \(S\) and final states \(F\), and \(\mathcal{S}0\) is an initial state of the FSM  
Figure 2: Beam switching process in the proposed scheme.  
model. In addition, \(\delta\) is a state transition that can be shown as \(\delta:\mathcal{S}\times\mathcal{C}\rightarrow\mathcal{S}\), and \(\mathcal{F}=\{F1,F2,\cdots,F7\}\) is a set of final states \(F\), which is a subset of \(\mathcal{S}\). A diagram of the novel user FSM model is shown in Figure 3.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 6 Conclusions and Future Directions  
In this paper, we developed a novel FF-PCA-LDA system for crop leaf disease detection based on feature fusion, reduction by PCA, and followed by LDA classification. Deep features are extracted using a modified ResNet50 and combined with handcrafted hybrid image features. A considerable improvement of 6% in the average accuracy has been observed over the handcrafted-hybrid-features-based models. The developed approach offers average accuracy, sensitivity, specificity, F-Score, and AUC values of 98.21%, 98.79%, 92.67%, 95.33%, and 98.46%, respectively. It outperformed in comparison to the MLBPNN, KNN, and PNN classifiers on the same dataset. These improvements are due to feature fusion and dimension reduction followed by LDA classification. Performance indicators show that the developed approach can be used for crop leaf disease detection. It can be extended for other crop disease identification. Moreover, it might be used as SaaS for the development of mobile or web apps and available around the clock for better crop health monitoring and management.  
[PERSON] contributed to the model development, data analysis and interpretation, and manuscript writeup. [PERSON] designed the study, idea development, conducted experiments, and manuscript writeup. [PERSON] performed data analysis and study design validation. [PERSON], [PERSON] and [PERSON] helped in the discussion of the manuscript and data analysis. The manuscript was revised by all authors. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the BK21 FOUR Program (Fostering Outstanding Universities for Research, 5199991714138) funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF).  
Not applicable.  
The data and code are available for further study. Please contact the corresponding authors for the computer code and data.  
The authors declare that they have no conflict of interest. No funding agency has been involved to conduct the experiments.
## 4 Simulation  
To compare with the experimental results and potentially optimize the design in the future, a simulation of the 3D qubit-resonator system was implemented using Ansys. The model consists of the resonant cavity with the transmon qubit structure on top of the silicon substrate at its center. The properties of the single parts were modeled as follows: silicon substrate as dielectric with relative dielectric constant \(e_{s}=11.8\), transmon pads as 2D structure with boundary condition set to superconductor, the Josephson junction as a lumped LC, \(L_{j}=10\) nH and \(C_{j}=0.8\) fF, and the cavity as 3D rectangular structure with perfect conductivity as the boundary conditions and vacuum inside. The simulated design in Ansys HFSS is shown in Figure 7.  
Several characteristic parameters of the system can be calculated by the simulation. These include the capacitance of the transmon \(C\), the cavity-transmon dipole coupling strength \(g_{01}\), and the lifetime \(T_{1}\) (Most calculations could be performed directly in Ansys using the fields calculator tool [29]). The values are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Values** \\ \hline \(P_{tot}\) & \(4.4\times 10^{-4}\) \\ \(T_{parcell}\) [us] & 156 \\ \(T_{int}\) [us] & 57 \\ \(T_{1}\) [us] & 42 \\ \(C_{tot}\) [fF] & 56 \\ \(g_{01}/2\pi\) [MHz] & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated qubit-resonator parameters  
Figure 6: (**Left**): Ground state population (\(P_{g}\)) as a function of the delay time between excitation pulse and qubit readout. \(v_{excitation}=6.4194\) GHZ, \(P_{excitation}=-93\) dbm. (**Right**): Ramsey spectroscopy, 600 KHz detuned. \(v_{excitation}=6.42\) GHZ, \(P_{excitation}=-93\) dbm.
###### Abstract
Figure 2 and Table 1 suggest that the spatial distribution of CMLs is not, and cannot be homogenous. However, our study is based on the assumption that any given region can be partitioned into sub-regions, each homogeneous in the sense of CMLs density, meaning that all the CMLs in a sub-region have their positions drawn from the same uniform distribution. Here and throughout this paper, when referring to a CML's position, it is the position of its midpoint that is considered. Based on [12], we suggest describing CML distribution in any homogeneous region by three characteristics:  
1. Spatial density of the CMLs;
2. Orientations of the CMLs;
3. Lengths of the CMLs. The rest of the paper is organized as follows: in Sections 2 and 3 we study the CMLs' statistical characteristics as listed above and suggest statistical models. Section 2 addresses the CMLs' spatial density, and Section 3 addresses their orientations and lengths. Section 4 suggests a mathematical model for the relationship between CMLs' lengths and their spatial density. Section 5 then combines the statistical derivations from previous sections to suggest a novel computational method for simulating sets of synthetic CMLs. Section 6 then concludes and discusses the results. The geographical regions analyzed here are described in the appendix. Note that as this paper is an extension of [13], its novelty is presented in Sections 4 and 5.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 5 Discussion  
### Efficiency of the Parallel Adaptive Laplacian Mesh Smoothing  
#### 5.1.1 Efficiency Analysis of Data Layouts  
Figure 9a,b show the running time of the different data layouts when using Form A and Form B, respectively. It can be clearly seen that: (1) for Form A, when the number of nodes is smaller than 5k, the running time of the SoA version is shorter than that of the AoS version; and when the number of nodes is larger than 5k, AoS version shows better performance than SoA version with a max speedup of 1.44; (2) for Form B, when the number of nodes is smaller than 59k, SoA version shows a better performance; and when the number of nodes is larger than 59k, AoS version shows a better performance with a max speedup of 1.23.  
The above results illustrate that the AoS is more efficient than SoA for mesh models with a large number of nodes. This is consistent with our previous studies [23; 28; 29]; the GPU version with AoS data layout has better performance is due to the aligned global memory accesses. Therefore, in complex application scenarios, we recommend using AoS data layout when accelerating the Laplacian mesh smoothing algorithm on the GPU.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{4}{c}{**Speedup**} \\ \cline{2-6}  & \multicolumn{2}{c}{**GPU-AoS**} & \multicolumn{2}{c}{**GPU-SoA**} & \multirow{2}{*}{**Max Speedup**} \\ \cline{2-6} \cline{5-6}  & **Single Block** & & & **Multiple Blocks** & \\ \hline
5k & 8.716 & 9.503 & 11.331 & 15.344 & 15.344 \\
10k & 6.906 & 12.319 & 13.049 & 9.627 & 13.049 \\
59k & 11.075 & 16.833 & 11.639 & 12.920 & 16.833 \\
77k & 13.366 & 22.447 & 12.530 & 13.411 & 22.447 \\
109k & 10.261 & 22.620 & 8.389 & 16.409 & 22.620 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Speedup of the proposed algorithm over serial algorithm when using Form B.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{4}{c}{**Speedup**} \\ \cline{2-6}  & \multicolumn{2}{c}{**GPU-AoS**} & \multicolumn{2}{c}{**GPU-SoA**} & \multirow{2}{*}{**Max Speedup**} \\ \cline{2-6} \cline{5-6}  & **Single Block** & & & **Multiple Blocks** & **Single Block** & **Multiple Blocks** \\ \hline
5k & 3.273 & 3.154 & 9.755 & 10.597 & 10.597 \\
10k & 6.974 & 7.004 & 4.832 & 11.310 & 11.31 \\
59k & 10.576 & 8.857 & 8.570 & 6.740 & 10.576 \\
77k & 4.866 & 9.448 & 4.515 & 7.093 & 9.448 \\
109k & 6.297 & 7.520 & 5.447 & 8.839 & 8.839 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speedup of the proposed algorithm over serial algorithm when using Form A.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 3 System Expected Capabilities
### Minimum Detectable Target Size
One example is the emission of different waveforms with low cross correlation between them,such as Orthogonal Frequency Division Multiplexing (OFDM) signals, in order to suppress range ambiguity [35].
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 2 Materials and Methods  
### Materials  
Ground granulated blast furnace slag (GGBFS) with a density of 2.9 g/cm\({}^{3}\) and Blaine fineness of 435 m\({}^{2}\)/kg was collected from Viet Nam. The chemical properties of GGBFS were analyzed by using an X-ray fluorescence (Thermo Scientific, ARL ADVANT'X, Waltham, MA, USA) instrument and are listed in Table 1. Sodium hydroxide (NaOH) solution and water glass (8.2% Na\({}_{2}\)O, 26.4% SiO\({}_{2}\), and 65.4% H\({}_{2}\)O by weight) were mixed together to make an activator with a silicate modulus of 1 (M\({}_{8}\) = 1). River sand from a local supplier was used as a fine aggregate, with a size of less than 5 mm. Acetone (99%), n-hexane (99.7%), and hydrochloric acid (HCl, 37%) were purchased from Daejung (Daejeon, Republic of Korea). Sodium azide (NaN\({}_{3}\), 98%) was supplied by Junsei (Tokyo, Japan). Phenanthrene (PHE), pyrene (PYR), naphthalene (NAP), diisobutyl phthalate (DiBP), and dibutyl phthalate (DBP) were purchased from Sigma-Aldrich (St. Louis, MO, USA). A polyethylene (PE) sheet of 51 \(\upmu\)m thick for use as a passive sampler was obtained from Husky Plastics (Teaneck, NJ, USA). The real water samples were collected from Andong Lake, the Nakdong River, industrial wastewater, Busan groundwater, and seawater in the Republic of Korea.
# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 1 Introduction
* Building a scratch model combining CNN and LSTM to classify the five sports classes from the TBSV dataset. * Exploring the transfer learning approach in this task through training some popular and recent pretrained models: ResNet, Inception v3, Xception, VGG, incorporating with LSTM, and comparing their performance. * Fine tuning the two best performing pretrained models, VGG16 and VGG19, integrated with some custom layers of LSTM and dense layer. * Assessing the proposed model, fine-tuned last eight layers of VGG19 incorporated with LSTM, over four challenging datasets, KTH, UCF-11, UCF-101, and UCF Sports, and comparing performance with related recent works.
# Hardware Acceleration of Tsunami Wave Propagation Modeling in the Southern Part of Japan
## 1 Introduction
Therefore, we do not compute the wave at small depths (below 10 m) and we suggest reflection-type boundary conditions at such a depth to estimate the wave height in the near-shore area and to account for reflected waves. At the parts of the boundary that separate our computational domain from the ocean, we use free passage of the wave out of the domain. According to [8; 9; 10], wherein numerical dispersive models for long waves were studied, the influence of dispersion is valuable only in the case when the depth is of the same order as the wavelength. We are not studying short waves, where dispersion occurs in many cases. So, neglecting the dispersive effects and studying long wave propagation at large enough depths, we use the shallow water approximation as the governing equation. This is common in tsunami simulation, even if more precise models (like the Boussinesq system, for example) are used in a number of research papers. Inundation mapping (or any other fine analysis of the processes in the vicinity of the coast, like breaking waves or low/high tide effects) is not discussed in the paper, even though such matters are very important for studying possible flooding areas. We speed up computer code execution using the advantages of GPUs and FPGAs. The paper consists of the following parts. The shallow water system is introduced, and the finite difference version of this system according to the MacCormack numerical scheme is also given. In a few words we discuss the advantages of an FPGA-based calculation platform. Briefly we mention the realistic digital bathymetry, based on Japanese bathymetric data produced by the Japan Oceanographic Data Center. Numerical tests show the dependence of the wave height maxima distribution on the exact position of the tsunami source of the same shape. The paper concludes with a discussion.
## 1 Introduction
It is more appropriate to use a Z-weighting filter to better evaluate the effects of LFN on health and the effects of acoustic energy not only on the human organ of hearing but also regarding the non-hearing effects of LFN. A-weighting is applied to the sound levels measured by the instrument to consider the relative volume perceived by the human ear, as the ear is less sensitive to low sound frequencies. A-weighting is the most-used group of curves defined in the international standard IEC 61672-2 [22] concerning the measurement of sound pressure levels. Permissible noise exposure levels are usually based on dB(A) level measurements, and noise protection is exclusively associated with hearing protection devices, which is an essential requirement of Directive 2003/10/EC [6]. Frequency \"Z\" weighting represents the frequency response without a microphone response, which is a zero-weight or flat-frequency response. A number of different methods in individual countries [23; 24; 25; 26; 27] have been suggested for the assessment of low-frequency noise. The procedures used in different countries to enforce the criteria for low-frequency noise are very different. Regarding the Danish method [23], a recommended measurement method is specified. Noise is measured at several locations in the interior and analyzed in 1/3-octave bands. Nominal A-weighted corrections are added to the spectra, and the weighted spectrum is summed to produce an A-weighted noise floor in the frequency range 10-160 Hz. For the German method [24], low-frequency noise is defined as noise where the C-weighted noise level is at least 20 dB higher than the A-weighted level, either based on equivalent levels or maximum levels. If the noise is evaluated as \"low frequency\", a 1/3-octave frequency analysis is performed. This method considers the frequency range of 10-80 Hz, but in special situations, the 8 Hz and/or 100 Hz band may also be included. For Swedish method [25], the recommendations contain a curve of criteria for recommended maximum levels of low-frequency noise. The curve covers the frequency range og 31.5-200 Hz and is valid for an equivalent noise level. The Polish method also uses a threshold curve [26]. This is defined over a frequency range of 10-250 Hz and corresponds to 1/3-octave levels, each giving an A-weighted level of 10 dB. Finally, the Dutch method [27] is defined in the frequency range of 10-200 Hz and uses a criterion curve. In the upper part of the frequency range, the criterion curve is identical to the Swedish criterion curve. At the lowest frequencies, it corresponds to the hearing threshold specified in the German method.
## 2 Materials and Methods
### Fisher-Based Distance Metric  
In the real application, EEG signals collected by some electrodes might serve mostly as noise which interferences with classification performance, reducing the accuracy rate of the authentication. Electrode selection, which picks those electrodes whose EEG signal could be used for feature extraction to identify the sample class, is therefore necessary. Fisher distance, which is often applied in classification research to represent the dissimilarity between classes, is used in this study. Fisher distance is proportional to the dissimilarity between classes. The bigger the dissimilarity degree, the larger the Fisher distance. The calculation of Fisher distance is as following [22]:  
\[F=\frac{\left(\mu_{1}-\mu_{2}\right)^{2}}{\sigma_{1}^{2}-\sigma_{2}^{2}} \tag{20}\]  
where \(F\) is Fisher distance, \(\mu\) and \(\sigma\) are the mean and variance, respectively, and the subscripts 1, 2 denote the classes. For each data point, the Fisher distance indicates the contribution to classification of a particular electrode's data points. A greater Fisher distance implies that the classification result is obvious. The Fisher distance is calculated with all data in the data set for a certain electrode at each time point.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 6 Simulation Result Analysis
### Influence of Different Grouting Pressures
#### 6.2.4 Stress Changes Before and After Grouting  
Under different grouting pressures, the stress increases in different directions are different. Analysis of data from Table 4, which compares stress changes in regions X1, X2, and X3 before and after grouting, reveals distinct patterns as grouting pressure varies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grouting** & \multicolumn{3}{c}{**Variations in the X Direction**} & \multicolumn{3}{c}{**Variations in the Z Direction**} \\ \cline{2-7}
**Pressure** & **Before** & **After** & **Increase** & **Before** & **After** & **Increase** \\
**(_MPa_)** & **Grouting (_Pa_)** & **Grouting (_Pa_)** & **Amplitude (\%)** & **Grouting (_Pa_)** & **Grouting (_Pa_)** & **Amplitude (\%)** \\ \hline
0.1 & \(-\)218,150 & \(-\)225,610 & 3.42 & \(-\)238,390 & \(-\)247,300 & 3.73 \\
0.3 & \(-\)218,150 & \(-\)231,020 & 5.90 & \(-\)238,390 & \(-\)254,340 & 6.69 \\
0.5 & \(-\)218,150 & \(-\)236,690 & 8.50 & \(-\)238,390 & \(-\)260,500 & 9.27 \\
0.7 & \(-\)218,150 & \(-\)241,020 & 10.48 & \(-\)238,390 & \(-\)264,340 & 10.89 \\
0.9 & \(-\)218,150 & \(-\)245,610 & 12.59 & \(-\)238,390 & \(-\)267,300 & 12.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stress changes in different directions after grouting under different grouting pressures.  
Figure 15: The changes of regional porosity under different grouting pressure.  
At 0.5 MPa, these values rise to 8.50% and 9.27%.
4. At 0.7 MPa, stress increases to 10.48% in the X direction and 10.89% in the Z direction.
5. Finally, at 0.9 MPa, the increases are 12.59% and 12.13%, respectively.  
From a grouting pressure of 0.1 MPa to 0.9 MPa, the stress increment in the X direction progressively increases from 3.42% to 12.59%. Similarly, in the Z direction, it rises from 3.73% to 12.13%. These trends suggest that stress in both the X and Z directions escalates with increasing grouting pressure, exhibiting an approximately linear relationship between stress increase and grouting pressure. Notably, while both directions show stress increases, there is a discernible difference between them. At lower pressures (e.g., 0.1 MPa and 0.3 MPa), the increase in the Z direction slightly exceeds that in the X direction, whereas at higher pressures (e.g., 0.9 MPa), the pattern reverses.  
In conclusion, the stress in both the X and Z directions consistently rises with increasing grouting pressure, maintaining an approximately linear relationship. Additionally, the magnitude of stress increase differs between the X and Z directions depending on the grouting pressure applied.