esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrical Monitoring under Transient Conditions:
## 2 Foundations of the Transient Analysis of Electrical Quantities  
The underlying idea of the new diagnosis approach based on transient analysis relies on the fact that the fault harmonics, which fall at specific frequency values at steady-state (provided that the slip remains constant), change in frequency and time under transient operation. The most illustrative example of this fact is the _Lower Sideband Harmonic_ (LSH), which is typically associated with rotor damages and the frequency of which is given by Equation (1), where \(s\) is the slip of the machine (\(s=\frac{n_{\mathrm{s}}-n}{n_{\mathrm{s}}}\), with \(n_{\mathrm{s}}=\) synchronous speed and \(n=\) motor speed) and \(f\) is the supply frequency.  
\[f_{LSH}\;=\;f\cdot(1-2\cdot s) \tag{1}\]  
At steady state, the frequency of the LSH is constant, and both \(s\) and \(f\) remain stable always. However, under any transient regime provoking a variation of either \(s\) or \(f\), the frequency of this harmonic will change in a characteristic manner, in coherence with the variation of these parameters. A typical example is the direct on-line starting of an induction motor: under that transient, the slip decreases from \(s=1\) (when the motor is connected) to near zero (when the steady-state regime is reached). Consequently, the frequency of the LSH (\(f_{LSH}\)) will evolve accordingly, namely: it will drop from \(f_{LSH}=f\) (when \(s=1\), at the beginning of the starting) to \(f_{LSH}=0\) and it will later increase again to \(f_{LSH}\approx f\) (when \(s\approx 0\) at steady-state). Therefore, \(f_{LSH}\) will follow a very characteristic evolution during the starting that resembles a V-pattern. The detection and quantification of this evolution, which has been well characterized in many previous works [8, 10], is a reliable evidence of the presence of the harmonic and therefore of the existence of rotor damage.  
In order to detect this type of time-frequency evolution of fault-related harmonics (such as the LSH), suitable signal processing tools must be applied to the considered transient quantities. These tools, detailed in the next section, are known as time-frequency transforms and extract the time-frequency content of the analyzed signal, so that they enable visualizing, not only which frequencies are present in that signal, but also how they evolve over time.  
The following subsections describe the way in which the explained transient-based methodology is applied to diverse motor quantities, such as currents and stray-fluxes, which are captured during the transient operation of the motor.
## 2 Data  
To enhance the analysis, over 1000 sets of liquefaction investigation data based on \(V_{s}\) were collected and organized from publicly accessible sources [15; 22; 30; 31; 32; 33; 34]. These data were divided into two databases--Database A and Database B--categorized by their historical applications.  
Database A was utilized to evaluate the performance of existing formulae. It included 225 sets of data derived from 26 earthquakes and 70 site investigations summarized by [PERSON] et al. [30], comprising 105 liquefaction and 120 non-liquefaction cases. These data served as the foundation for developing \(V_{s}\)-based liquefaction assessment methods and were crucial for evaluating the optimal performance of the current formulae. Figure 1 depicts the sample distribution in Database A.  
Database B was employed for the development and validation of the new formulae. To enhance the accuracy and applicability, this database encompassed more extensive and representative \(V_{s}\) data related to earthquake liquefaction. It included all the data from Database A, supplemented by an additional 933 sets from the databases of [PERSON] et al. [15], [PERSON] et al. [22], [PERSON] et al. [31], [PERSON] et al. [32], [PERSON] et al. [33], and [PERSON] et al. [34], among others. This comprehensive dataset, including 417 liquefaction, 507 non-liquefaction, and 9 critical cases, supported the construction and refinement of the new formulae by incorporating key parameters such as the fines content at test sites. Figure 2 illustrates the sample distribution in Database B.
## 4 Experimental Evaluation
### Experimental Process  
In this study, the model was trained and assessed on the HCIE dataset with specific training parameters. The number of epochs is set at 100, the batch size is set at 20, and the SGD optimizer is utilized with an initial learning rate of 0.02. Selecting suitable training parameters is essential before initiating model training. After thorough adjustments, the finalized model training parameters are outlined in Table 1.  
The experimental setup includes an AMD Ryzen7 5800H CPU (AMD, Santa Clara, CA, USA) and NVIDIA GeForce RTX 3060 GPU (Nvidia, Santa Clara, CA, USA) running on Windows 11. The method and experiments in this article are implemented using a PyTorch 2.2.1-based deep learning framework. The development software comprises PyCharm 2023.3.4 and Python 3.11.
## 3 Results and Analysis
### Test Results and Analysis of Improved Loess with Single Index
#### 3.1.3 Influence of Grain Size Characteristics on Permeability Coefficient  
In this paper, PL2, PL7, PL9, PL10, PL13, and PL15 specimens did not have admixture, and the dry density varied from 1.82 g/cm\({}^{3}\) to 1.86 g/cm\({}^{3}\). The range of dry density variation is small, and it can be assumed that the dry density does not affect the permeability coefficient.  
Unlike sandy soils, which are composed of sand grains and have a bulk soil structure, loess is mainly an agglomerate (loess-like) or condensed structure based on gravitational force. Therefore, the improved loess material's permeability coefficient did not correlate with the effective grain size d\({}_{10}\) as with the results of the sandy soil study.  
The average grain size d\({}_{50}\) as a whole reflects the grain size of the soil. With a certain void ratio and particle gradation, the total pore volume of the soil is decided, and its average pore size increases with the increase in the average grain size. The correlation between d\({}_{50}\) and the permeability coefficient was studied in the experiment by mixing different kinds of soils to change it. As can be seen from Figure 8, the permeability coefficient is nearly linearly and positively correlated with d\({}_{50}\), with a goodness of fit R\({}^{2}\) = 0.8561. The increase in the average grain size makes the inter-particle pore diameter larger. The increase in the pore cross-sectional area makes the water head loss of flow through the pore smaller and the water velocity greater. According to \(v\) = \(ki\) (\(v\) is the seepage velocity and \(i\) is the hydraulic gradient), the permeability coefficient \(k\) becomes larger when \(v\) becomes larger and \(i\) becomes smaller.  
Figure 7: Effect of dry density on permeability coefficient.  
The grain size between \(\mathrm{d}_{10}\) and \(\mathrm{d}_{60}\) is decisive in constituting the soil skeleton action. As can be seen from Figure 9, the permeability coefficient and the restricted grain size \(\mathrm{d}_{60}\) are close to a linear positive correlation; the goodness of fit \(\mathrm{R}^{2}\) = 0.7915. Its maximum difference in the change in permeability coefficient is a factor of 6.65.
###### Abstract  
Electro-optical frequency mapping (EOFM) technology can detect node signals from the backside of integrated circuits (ICs). In the past, the detected signal only qualitatively represented the electrical activity strength inside the device. In this paper, the electro-optical signal generation mechanism of the device is systematically studied, and a concise physical model of laser beam modulation based on the optical transmission matrix is proposed. Firstly, the influence of the bandwidth of the laser and each structure covered by the laser spot on the electro-optical signal of the device is well described by the model. Secondly, the model quantifies the electro-optical signal strength, and shows it is positively correlated with the laser wavelength and the reverse bias voltage. Finally, the model is used to quantitatively calculate the accurate voltage level of the internal node inside the device using the detected signal, and the calculation results match well with the experimental results. The model provides theoretical guidance for the efficient and accurate extraction of internal voltage values of devices by EOFM technology.  
EOFM; electro-optical signal; optical transmission matrix; semiconductor devices Article  
Preliminary Study on Detecting the Internal Voltage Values of Integrated Circuits Based on Electro-Optical Frequency Mapping. _Appl. Sci._**2022**, _12_, 1188. [[https://doi.org/10.3390/app1203188](https://doi.org/10.3390/app1203188)]([https://doi.org/10.3390/app1203188](https://doi.org/10.3390/app1203188))  
Academic Editor: [PERSON]  
Received: 10 January 2022  
Accepted: 21 January 2022  
Published: 24 January 2022  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Super-Wideband Monopole Printed Antenna with Half-Elliptical-Shaped Patch
## 5 Conclusions  
A super-wideband monopole antenna with a half-elliptical-shaped patch was designed for various applications. The proposed antenna was developed using a Taconic TLY-5 substrate with a microstrip feedline and a half-elliptical-shaped patch for which the dimensions of the elliptical shape were derived from a rectangular microstrip patch formula with an optimization to achieve the desired lower frequency. The proposed antenna exhibits a bandwidth of 23 GHz (from 0.5 GHz to 23.5 GHz) with a bandwidth ratio of 47:1.  
When designing super-wideband (SWB) antennas for the millimeter-wave (mm-wave) regime, it is crucial to carefully consider the connector model within the simulation en  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Dimension (\(\lambda_{0}\))** & **Substrate Material** & \
\begin{tabular}{c} **Operating** \\ **Frequency (GHz)** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth** \\ **Ratio** \\ \end{tabular} & **Result Type** \\ \hline
[25] & 1.46 \(\times\) 1.4 & FR4 (h = 1.6 mm) & 1.7-20 & 1:11.8 & Simulation \\ \hline
[26] & 1.01 \(\times\) 1.24 & FR4 (h = 1.6 mm) & 1.44–18.8 & 1:13 & Measurement \\  
vironment. Accurately modeling the connector can ensure the simulation results closely match the measurements.  
Conceptualization, N.N. and F.Y.Z.; Methodology, F.Y.Z.; Software, A.I.W. and A.Z.; Validation, N.N., E.S. and F.Y.Z.; Formal analysis, A.Z.; Investigation, A.I.W. and F.Y.Z.; Resources, N.N.; Data curation, A.Z. and A.I.W.; Writing--original draft preparation, A.Z.; Writing--review and editing, N.N., E.S., A.I.W. and F.Y.Z.; Visualization, A.Z.; Supervision, F.Y.Z.; Project administration, F.Y.Z.; Funding acquisition, N.N. and E.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Riset Kolaborasi Indonesia (RKI) Universitas Indonesia, grant number: NKB-1064/UN2.RST/HKP.05.00/2023.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Settings, Results and Discussion
### Experimental Settings Setup  
#### 4.1.1 Datasets and Backbone  
The paper uses the standard VQA evaluation metric [1] to evaluate the performance of the model on the VQA-CP v2 [67], VQA-CP v1 [67] and VQA v2 [7] datasets. For fair comparisons, all the methods are based on the UpDn model, and their best-recorded performance is compared. The experiment trains and tests the models on two Titan Xp GPUs.  
Currently, for the VQA language bias issue, researchers evaluate the performance of the proposed models on the VQA-CP v2 dataset and conduct auxiliary verification on the VQA v2 dataset. Most findings test the models on VQA-CP v2 and VQA v2 and calculate the gap index [36] as an auxiliary index to verify the robustness of the model.  
**VQA-CP v2.** The researchers propose the VQA-CP v2 dataset, which is derived from the re-classification of the samples in the VQA v2 dataset, to measure language bias. The VQA-CP v2 and VQA-CP v1 datasets are the only open-source datasets for language bias evaluation. The questions and answers in the training and testing sets are distributed in considerably different ways. In other words, for the same type of questions, the answers in the training set and testing set are distributed very differently. Therefore, the VQA-CP v2 dataset is suitable for measuring the language bias of the models. The training set consists of 121 K images, 438 K questions and 4.4 million answers, and the testing set consists of 98 K images, 220 K questions and 2.2 million answers.  
**VQA-CP v1.** The VQA-CP v1 dataset, the first version of the VQA-CP dataset, is the first-ever dataset for language bias evaluation. It is derived from the re-classification of the VQA v1 [1] dataset. The VQA-CP v1 training set consists of 118 K images, 245 K questions  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**VQA-CP v2**[67]} & \multicolumn{3}{c}{**VQA-CP v1**[67]} & \multicolumn{3}{c}{**VQA v2 [7]} \\ \cline{2-10}  & **Train** & **Test** & **Total** & **Train** & **Test** & **Total** & **Train** & **Test** & **Total** \\ \hline Images & 121 K & 98 K & 219 K & 118 K & 87 K & 205 K & 440 K & 214 K & 654 K \\ Questions & 438 K & 220 K & 658 K & 245 K & 125 K & 370 K & 83 K & 41 K & 124 K \\ Answers & 4.4 M & 2.2 M & 6.6 M & 2.5 M & 1.3 M & 3.8 M & 4.4 M & 2.1 M & 6.5 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of VQA-CP v2, VQA v2 and VQA-CP v1.  
and 2.5 million answers, and the VQA-CP v1 testing set consists of 87 K images, 125 K questions and 1.3 million answers.  
**VQA v2.** The VQA v2 dataset is the second version of the VQA dataset. The training set consists of 82,783 images, 443,757 questions and 4,437,570 answers. The testing set consists of 40,504 images, 214,354 questions and 2,143,540 answers. The VQA v2 dataset is double the VQA v1 dataset in size.
###### Abstract  
Two hypotheses divide experts on determining the effective properties of composite materials using multi-scale homogenization methods. The first hypothesis states that multi-scale homogenization methods can ensure the direct determination of effective properties, at the macro level, of composite materials from a single representation of the medium at the lowest possible scale that allows for a good representation of all heterogeneities. The second hypothesis states that the determination cannot be ensured directly from a single scale but rather through multistep homogenization where each step represents the medium at a different scale from the lowest to the macroscale. To answer this question, a rigorous study is carried out; it includes calculating the two effective elastic properties, bulk, and shear moduli of three phases of a multi-layered sphere composite model by studying three phases. A multistep homogenization method is used to determine the effective properties of the composite and the obtained results are compared with those of the direct homogenization. Two different studies are considered: the first is based on an analytical model and the second on the numerical homogenization based on finite element calculation. To consider the effect of some influential parameters, several situations are treated by the combination of the variation of the volume fractions of the three phases and their property contrasts. The analytical calculations are performed using the Python 3.10 commercial software. It could be concluded that the effective elastic properties obtained either by the multistep or by the direct homogenization show no significant difference.  
heterogeneous materials; effective elastic properties; direct homogenization; multistep homogenization; finite element homogenization +
Footnote †: journal: Applied Sciences  
0000-0001-6507-9999][PERSON]  
1 Laboratoire de Mecanique des Structures et Materiaux, Batna 2 University, Batna 05078, Algeria; [EMAIL_ADDRESS] (N.K.); [EMAIL_ADDRESS] (M.M.J.); [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (W.K.)
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 6 Simulation Results
### Impact of LF, MF, and HF on PDM-SAC-OCDMA-Enabled FSO System  
As seen in Table 1, different levels of fog cause different attenuation, varying from low attenuation caused by LF to high attenuation caused by HF. Accordingly, the effects of this different attenuation on the performance of the proposed model in terms of Log(BER) and Q-factor versus different FSO ranges are given in Figures 6 and 7, respectively. A shorter range provides a high performance than a longer range, while the best performance is achieved under LF conditions. The log(BER) and Q-factor for User 1 at 1.6 km are \(-\)9.26 and 6.09 under LF, respectively, while under HF, this range is decreased by 0.85 km at approximately the same log(BER) and Q-factor values.  
Figure 5: Eye diagrams for PDM-SAC-OCDMA-enabled FSO transmission at 10 km under clear weather conditions transmitted on (**a**) \(X_{polar}\) and (**b**) \(Y_{polar}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Users** & **Log(BER)** & **Q-Factor** \\ \hline
1 on \(X_{polar}\) & \(-\)9.24 & 6.09 \\
2 on \(X_{polar}\) & \(-\)11.22 & 6.77 \\
3 on \(X_{polar}\) & \(-\)11.79 & 6.96 \\
4 on \(Y_{polar}\) & \(-\)10.09 & 6.39 \\
5 on \(Y_{polar}\) & \(-\)11.19 & 6.77 \\
6 on \(Y_{polar}\) & \(-\)12.55 & 7.21 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance results for clear weather conditions.  
Table 6 summarizes the performance values for six users of PDM-SAC-OCDMA-enabled FSO transmission under fog conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Users**} & \multicolumn{2}{c}{**Log(BER)**} & \multicolumn{3}{c}{**Q-Factor (dB)**} \\  & **LF (1.6 km)** & **MF (1 km)** & **HF (0.75 km)** & **LF (1.6 km)** & **MF (1 km)** & **HF (0.75 km)** \\ \hline
1 on \(X_{polar}\) & \(-9.26\) & \(-10.15\) & \(-10.22\) & 6.09 & 6.41 & 6.44 \\
2 on \(X_{polar}\) & \(-10.69\) & \(-11.30\) & \(-11.11\) & 6.60 & 6.80 & 6.74 \\
3 on \(X_{polar}\) & \(-11.78\) & \(-14.54\) & \(-13.56\) & 6.96 & 7.81 & 7.52 \\
4 on \(Y_{polar}\) & \(-9.18\) & \(-10.63\) & \(-9.83\) & 6.06 & 6.58 & 6.30 \\
5 on \(Y_{polar}\) & \(-11.03\) & \(-12\) & \(-11.83\) & 6.71 & 7.03 & 6.99 \\
6 on \(Y_{polar}\) & \(-12.07\) & \(-14.43\) & \(-12.40\) & 7.05 & 7.77 & 7.16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance results for fog conditions.  
Figure 6: Log(BER) for PDM-SAC-OCDMA-enabled FSO transmission versus range under (**a**) LF, (**b**) MF, (**c**) HF weather.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Chemical Analysis
#### 3.1.3 Multivariate Statistical Analysis
The sampling points included in this cluster are those located at Pretty Bay and St. George's Bay, verifying a previously discussed outcome. The third cluster links samples from Rinella Bay depicting moderate to high concentrations for all the elements reviewed. The higher concentrations were mapped for Fe, Cu, and Mn for samples gathered from Rinella Bay, while the lowest concentration identified in this group is that for Sr content found at Marsalform Bay in Gozo. The fourth cluster identified contained a large number of samples and grouped mostly high concentrations of Fe, Mn, and Zn across different sampling points at Gnejna Bay, Golden Bay, Riviera Bay in Malta, and Ramla Bay in Gozo. The lowest concentrations were mapped for Pb content at Golden Bay. It is important to note that these mentioned bays are highly popular with locals and tourists and are of the largest sand beaches on the islands. This proves that mixed lithogenic and anthropogenic origin of these elements is highly possible, especially at these extremely visited beaches at particular locations highlighted by the respective sampling points along the transects taken on these beaches. Figure 2: **(a)** Biplot, **(b)** Principal Components Loading Plot, and **(c)** 3D Scatterplot showing Principal 1, Principal 2, and Principal 3. Figure 3: Cluster analysis including a dendrogram and a heat map. A—Armier Bay, C—Ballut Reserve Bay, G—Ghadira Bay, H—Gnejna Bay, I—Golden Bay, K—Little Armier Bay, L—Mistra Bay, M—Paradise Bay, N—Pretty Bay, O—Rinella Bay, P—Riviera Bay, Q—St. George’s Bay, S—Xemxija Bay, T—White Tower Bay, U—Hondoq Bay, W—Ramla Bay, Y—Xlendi Bay, Z—Massalforn Bay.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet: Training and Testing
#### 5.2.2 Training  
During the training process, the network learns to minimize the loss between the predicted symbols \(\mathbf{\hat{s}}\) and the true transmitted symbols \(s\). The loss function used is the mean squared error loss, given by  
\[\mathcal{L}=\frac{1}{N_{\text{Train}}}\sum_{i=1}^{N_{\text{Train}}}(\mathbf{ \hat{s}}_{i}-\mathbf{s}_{i})^{2} \tag{27}\]  
where \(N_{\text{Train}}\) is the number of training samples. The weights and biases of the network are updated using backpropagation and the Adam optimizer. The gradient of the loss function with respect to each parameter is calculated, and the weights are updated using the following update rule:  
\[\Psi_{i}=\Psi_{i}-\eta\cdot\frac{\partial\mathcal{L}}{\partial\Psi_{i}},\quad \beta_{i}=\beta_{i}-\eta\cdot\frac{\partial\mathcal{L}}{\partial\beta_{i}} \tag{28}\]  
where \(\eta\) is the learning rate.
## 2 Methodology
### Gross Error Detection  
Considering that the WGC is in the construction state during the monitoring period, the actual monitoring environment is complex, and the radar signal is simultaneously affected by various factors, such as construction vibration, wind load, temperature, and sunlight. Gross errors may be found in the deformation time series obtained based on the radar signal. Therefore, the gross error in the deformation time series of the monitoring target point should be detected. The deformation corresponding to this moment is removed when the gross error is detected in the deformation time series. The deformation at this moment is interpolated based on the deformation data before and after this moment. Gross error detection was performed on the deformation time series until no gross error was found in the time series.
# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 2 Data Source  
The data in this study were on-site liquefaction data collected and published by [PERSON] et al. and [PERSON] et al. [5; 8]. These sites were distributed across six countries and regions around the world, and the time span was from 1906 to 2011. The published data included the site characteristic parameters of liquefied and non-liquefied sites, as shown in Table 1. All data are in the horizontal field, which is also the hypothesis applied in this method.  
The seismic intensity in Table 1 was determined based on the maximum ground acceleration(\(a_{\text{max}}\)). The liquefied sites were divided based on the parameter \(a_{\text{max}}\) following the Chinese Code (GB18306-2015) [14], and the categories are shown in Table 2. In this study, intensities VI, VIII, and IX were considered.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Categories** & **Parameters** \\ \hline Liquefiable layer conditions & Compactness, gradation, fine grain content, in situ test results \\ Site conditions & Age, water table depth, layer depth, thickness, vertical stress \\ Ground motion condition & Mw, intensity, \(a_{\text{max}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Site characteristic parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(a_{\text{max}}\) & \(a_{\text{max}}\) **\textless{} 0.04 g** & **0.04 g \(\leq a_{\text{max}}\) \textless{} 0.09 g** & **0.09 g \(\leq a_{\text{max}}\) \textless{} 0.19 g** & **0.19 g \(\leq