esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mode Analysis of Beat Wave Pattern  
### Guided Wave and Leaky Wave  
In this section, we further discuss the beat wave propagation shown in Sections 2.1 and 2.2 and apply mode matching theory to solve the propagation constants of the excited modes.  
The beat wave discussed in this paper obviously consists of at least two modes so that the periodic field distribution can occur. It can be observed from Figures 2 and 3 that local minimums of field strength locate periodically along the \(z\) axis. As the local minimumshave small field strength relative to that of the local maximums as shown in Figure 5, it can be deduced that all the main modes in a beat wave have very close amplitudes.  
It can also be observed from Figure 5 that the minimums have a constant periodicity. The curve is approximately close to a sine curve. If the beat wave consists of more than two modes, some other local minimums will be found between two adjacent minimums. Therefore, the number of modes in the beat wave can only be two.  
It was discussed in Section 2.2 that the guided mode is not the only mode excited by the incident plane-wave. Either the leaky mode or evanescent mode exists so that the boundary condition can be satisfied. It has been shown in Figures 2 and 3 that the beat wave can propagate quite a long distance relative to the wavelength. The evanescent mode cannot maintain such a long distance. Therefore, the evanescent wave will not be considered in this paper. As a result, the beat wave mainly consists of a leaky wave (representing radiation modes that have a continuous spectrum) and a guided wave. If they have close amplitudes to each other, the minimums of the field strength on y axis will be close to zero.  
Leaky modes are typically considered as an approximation of the radiation modes that together with the guided modes constitute a complete set. Radiation modes have a continuous spectrum and are hard to solve theoretically. However, leaky modes provide convenience for the analysis of the beat wave. In our problem, there is only one leaky mode in consideration.
## 3 Fire Classification Based on FL with Image Clustering  
### Overall Architecture  
Figure 5 is a proposed architecture of a fire classification model that applies clustering techniques to fire, smoke, and general images and works as an FL structure. The model uses clustering techniques to process data and works by having multiple clients and servers cooperate to improve model performance. Algorithm 1 is a pseudocode representing clustering-based associative learning using ViT. First, the server trains on the server dataset using the ViT model. The ViT model loads the pretrained vit-base-patch16-224 model and trains on the dataset containing the fire, smoke, and normal labels. When training is complete, it saves the model's weights in a.pth file, which is later passed to multiple clients. The server extracts feature vectors from the trained ViT model, which it uses to perform clustering using the bisecting K-means algorithm. During this process, the global centroid for each class is calculated, and based on this, the global distribution is also calculated. This information is stored in the server's GCI, which is then delivered to the client.  
The client trains the local dataset using the GCI and.pth file delivered from the server. The client extracts feature vectors from the local dataset using the ViT model and performs clustering using the K-means algorithm (Algorithm 1). The client uses regular K-means clustering instead of the bisecting K-means used by the server. The client compares the global center points and local data received from the GCI to estimate the center point and local data distribution. Next, the client computes the Kullback-Leibler (KL) divergence. The KL divergence is an important metric that measures the difference in data distribution between the client and the server, allowing the client to understand how similar its data are to the server's global model. Based on this KL divergence, an alpha value and a style vector are calculated. The alpha value indicates how similar the client's data are to the server's global model, and the style vector indicates the difference between the client's data and the server's data, reflecting the characteristics of the client's data. The client sends the calculated alpha value, style vector, local cluster centroids, and density information to the server. The client does not send local data directly to the server but only these four pieces of information to maintain the privacy principle of associative learning.  
The server aggregates the alpha values, style vectors, local cluster centroids, and density information received from each client to update the global centroid and weights.  
Figure 5: Proposed architecture.  
The style vectors and alpha values sent by clients play an important role in helping the server update the weights in the model. Based on this updated information, the server generates a new GCI, which it passes back to the client for the next round of training. This process is repeated over multiple rounds, and the client and server work together to continuously improve the model's performance.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 3 Dual and Quad-Element Antenna  
The designed antenna is duplicated along the \(y\)-axis with an inter-element spacing of 3 mm to make a two-port antenna. To connect the ground, a rectangular stub is connected from edge to edge as shown in Figure 4. Figure 4b displays the dual-port antenna's S-parameters. The antenna gives an isolation of 20 dB as shown in the figure.  
The two-port antenna is further copied along the \(y\)-axis and duplicated with a spacing of 3 mm to form a quad-port antenna. As seen in Figure 5a, the antenna's ground plane is connected through a stub. The antenna's scattering parameters are depicted in Figure 5b,c. Over its operational range, the antenna displays a minimum average isolation of 20 dB.  
Figure 4: Dual-element antenna: (**a**) configuration; (**b**) corresponding S-parameters.  
Figure 3: Optimization of the antenna parameters: (**a**) R2; (**b**) \(f\); (**c**) e.
## 3 Magnetic Field Intensity Measurement
### Measurement of Deviations from Normal Directions to the Sensor Coil  
By measuring the induced EMV in the three sensor coils, the magnitude projections of the EMF intensity vector \(H\) in the directions normal to them were obtained. The \(H\) measurements in Cartesian coordinates could only be used directly if the normal directions of the coil surfaces corresponded to the Cartesian coordinate directions, which is rare in practice because of sensor construction technology. For this purpose, the ultimate solution was to place each coil in an EMF with a known direction so that this direction successively corresponded (independently for each coil) to the two directions contained in the coil plane (and approximately perpendicular).  
If a minimum of the two induced EMVs was obtained through this location in space, then the normal line to the coil surface would be perpendicular to these two directions.  
Figure 2: Block structure of the signal measuring path.  
The measurement procedure, presented in Figure 3, consisted in the rotation of the emitting coil around an axis parallel to an axis of the Cartesian coordinate system, keeping the sensor coil in the center of the emitting coil. The rotation of the emitting coil aimed to minimize the induced EMV in the sensor coil. The minimum value was not zero due to the noise introduced in the measurement process.  
At the \(\delta\) angle, for which the minimum voltage was obtained, the normal axis of the emitting coil was thus perpendicular to the normal axis of the sensor coil (assuming that in the center of the emitting coil the EMF was perpendicular to the coil surface).  
It followed that the axis (normal to the coil surface) deviation of the sensor coil corresponding to the Z axis of the coordinate system in the Y direction was \(\varepsilon_{\text{xy}}=\delta\). The deviation was considered positive if it was in the positive direction of the Y axis and negative in the opposite direction. The deviation from the Z axis in the X direction, \(\varepsilon_{\text{zx}}\), could be obtained in a similar way. For the X and Y sensor coils, the deviations \(\varepsilon_{\text{xy}}\) and \(\varepsilon_{\text{xz}}\) and \(\varepsilon_{\text{yz}}\), respectively, were similarly obtained.  
The deviations were measured using this procedure. The following values were obtained:  
* \(\varepsilon_{\text{xy}}=-4.5^{\circ}\); \(\varepsilon_{\text{zx}}=-3.5^{\circ}\).
* \(\varepsilon_{\text{yx}}=2.7^{\circ}\); \(\varepsilon_{\text{yz}}=1.3^{\circ}\).
* \(\varepsilon_{\text{zx}}=-3.3^{\circ}\); \(\varepsilon_{\text{zy}}=3.3^{\circ}\).
# A New Orthogonal Least Squares Identification Method for a Class of Fractional Hammerstein Models
## 2 Model and Problem Formulation
### MISO Fractional Hammerstein System  
Consider a MISO fractional Hammerstein CAR model described by  
\[\begin{cases}\bar{y}(t)=\sum_{i=1}^{r}\frac{B_{i}(s)}{A(s)}\bar{a}_{i}(t),\\ y(t)=\bar{y}(t)+\frac{1}{A(s)}v(t),\end{cases} \tag{8}\]  
where \(\{y(t)\}\) is the measured data of the unobserved output \(\{\bar{q}(t)\}\), \(\{v(t)\}\) is the noise with zero mean and variance \(\sigma^{2}\), \(A(s)=1+\sum_{i=0}^{n_{a}}a_{i}{}^{\text{is}}\), and \(B_{i}=\sum_{d=0}^{n_{b_{i}}}b_{i,d}{}^{\text{s}}\delta{}^{\text{s}}\). \(\{\bar{a}_{i}(t)\}\) represents the output of the memoryless nonlinear function with known basis, which serves as the input to the linear part. It can be expressed as  
\[\bar{u}_{i}(t)=f_{i}(u_{i}(t))=\sum_{q=1}^{m_{i}}c_{i,q}f_{i,q}(u_{i}(t)), \tag{9}\]  
where \(\{u_{i}(t)\}\) denotes the \(r\) inputs at the \(t\)-th sampling instant, \(m_{i}\) is the nonlinear order, \(c_{i,q}\) is the parameter to be identified, and \(f_{i,q}(.)\) is a known basis function, respectively. According to Equation (3), Model (8) can be rewritten as  
\[\begin{split} y(t)=&\frac{1}{1+\sum_{i=1}^{n_{a}}a_{ i}/h^{\text{i}a}}\Bigg{[}\sum_{i=1}^{n_{b_{i}}}\sum_{q=1}^{m_{i}}\frac{b_{i,d}}{h^{ \text{s}}}c_{i,q}\sum_{j=1}^{\lfloor t/h\rfloor}w_{j}  
**Assumption 1**.: _Only \(n_{a}\) is known, while the parameters \(a_{1},\,\cdots,\,a_{n_{a}},\,b_{i,1},\cdots,\,b_{i,n_{b}},\,c_{i,2},\cdots,\) and \(c_{i,m_{i}}\), as well as the orders \(m_{i}\) and \(n_{b_{i}}\), are to be identified._  
**Assumption 2**.: _From Equations (8)-(14), it is observed that for any nonzero constant \(\zeta\), the system would yield an identical output with \(\{c_{i,\boldsymbol{\theta}}/\zeta,\,\mathcal{\zeta}\boldsymbol{R}_{i}^{ \mathrm{T}}\}\). To ensure parameter uniqueness, it is assumed that \(c_{i,1}=1\)[12]._  
Let \(Y=[y(1),y(2),\cdots,y(m)]^{\mathrm{T}}\in\mathbb{R}^{m}\) and \(\boldsymbol{\Phi}=[\boldsymbol{\varphi}(1),\boldsymbol{\varphi}(2),\cdots, \boldsymbol{\varphi}(m)]^{\mathrm{T}}\in\mathbb{R}^{m\times n}\). The goal is to estimate the sparse parameter vector \(\boldsymbol{\theta}\) and then to extract the system parameters \(a_{i}\), \(b_{i,\boldsymbol{\theta}}\) and \(c_{i,\boldsymbol{\theta}}\) from the sparse structure of \(\boldsymbol{\theta}\). Though minimizing a least squares criterion \(J(\boldsymbol{\theta})=\|\boldsymbol{Y}-\boldsymbol{\Phi}\boldsymbol{\theta} \|^{2}\) leads to a solution \(\hat{\boldsymbol{\theta}}_{\mathrm{LS}}=[\boldsymbol{\Phi}^{\mathrm{T}} \boldsymbol{\Phi}]^{-1}\boldsymbol{\Phi}^{\mathrm{T}}Y\), it requires a large amount of sampled data, since the dimension of \(\boldsymbol{\theta}\) is high. Furthermore, the least squares method does not inherently produce sparse solutions, which poses another challenge in the identification process.
## 2 Background and Literature Review
[PERSON] et al. [17] proposed a Gradient Boosted Regression Trees (GBRT) model. They compared their proposed model with various neural network models. Their model outperformed others on window-based time-series data. [PERSON] and [PERSON] [18] showed a random forest model for forecasting volatility. [PERSON] et al. [19] proposed a deep learning stacking ensemble method on three different datasets of electricity load demand. They have also applied Support Vector Regression (SVR), FeedForward Neural Network (FNN), Deep Belief Network (DBN) and Ensemble FeedForward Neural Network (ENN) on the datasets to compare the result with their proposed stacking ensemble model. Their proposed model was built with 20 DBM models and an SVR model. They evaluated the models' performance using RMSE and some other statistical evaluation metrics. Their proposed model's RMSE value showed slightly improved results compared to the existing single models. [PERSON] et al. [9] showed a deep learning ensemble method using a set of Stacked Denoising AutoEncoders (SDAE) for the base model. The prediction values from the SDAE models were averaged together to form the final prediction value. They compared their proposed model with a Random Walk (RW), Markov Regime-Switching model (MRS), Feedforward Neural Network (FNN), and SVR ensembled model. Their proposed result outperformed all the models. [PERSON] et al. [20] proposed a multilayer stacking ensemble method, where they preprocessed the time series data into images and used the images as input for their proposed model. Their proposed model consists of two layers. Layer-1 builds with hundreds of CNN models, and Layer-2 builds with the reinforcement learning process for the meta-learner. Their proposed model gave the highest accuracy of 0.56 for classifying the trading day's decision. [PERSON] et al. [21] proposed three types of ensemble methods such as averaging, bagging and stacking. The ensemble methods use LSTM and BiLSTM models for the base learner and LR, SVR, KNN, and DTR models for meta-learner. They applied the models on cryptocurrency time-series data and found that the stacking ensemble method provides the highest accuracy compared to the averaging and bagging ensemble methods. [PERSON] et al. [22] showed a similar stacking technique using three convolutional layers. They extended the stacking technique concept using 3, 5 and 7 layers of CNN models for the base models with decreasing filter size in each layer. [PERSON] et al. [23] proposed a machine learning ensemble method called Extreme Gradient Boosting (XGBoost) model. The ensemble method outperformed SVM and ANN with an accuracy of 99 percent for predicting the stock market's direction. Though the aforementioned papers have shown all techniques on time series data, the risk factor of frontier markets has not been discussed and investigated in detail so far. Thus, the main target of this paper is to investigate the risk factor of the frontier markets.
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
## 1 Introduction
In assessing landslide mobility, the mechanical deformation from failure to post-failure may affect the shear zone formation, which depends on the potential for grain crushing to occur. Thus, in a traditional laboratory test condition using a smooth surface for a given loading, the underestimation of shear stress appears to be inevitable. The objective of this study was to examine the effect of surface roughness on the shear strength of gravels, as a granular matter, in the ring shear test. First, the characteristics of shear stress are examined as a function of the drainage and shear velocity. Second, the surface roughness effect under drained and undrained conditions is compared for different shear velocities. Third, the determined shear strength and shear velocity relationships are discussed. Finally, the grain crushing effect is highlighted using a grain size distribution analysis of gravels before and after the tests. However, the roughness of the grain itself, cyclic behavior, and the strain rate effect based on the grain size were considered to be beyond the scope of this study.
# Vibration Responses of a Coaxial Dual-Rotor System with Supporting Misalignment
## 4 Experimental Study of Bearing Supporting Misalignment
### Supporting Misalignment at Bearing 1  
The orbits and frequency spectra of the rotor system are shown in Figures 18-21 when Bearing 1 is intentionally elevated. Observations of these figures indicates the following:  
(1) Both the oscillogram and the spectrogram show that the vibration in the vertical direction (misalignment direction) is more complicated than in the horizontal direction. The frequency components in the vertical direction are dominated by 1\(\times\), 1.6\(\times\), and 2\(\times\). The orbits at the three bearings are 8-shaped. These are consistent with the simulation results.  
(2) Although there are 3\(\times\) and 4\(\times\) in the spectrograms, the amplitudes are relatively small. This is inconsistent with the simulation results of the equal weight coefficients model. It confirms that the non-uniform weight coefficient model proposed in this study is more in line with the actual situation.  
(3) All bearings have no obvious natural frequency. This suggests that although the damping of the rolling bearing is small, it still cannot be ignored in the simulation process.  
Figure 17: Experimental setup of a dual-rotor system.  
Figure 18: The vibration displacement and orbit of Bearing 1.
## 4 Performance Evaluation
### Experimental Setup  
First, to train the model parameters for each of the five different SE methods and three different VAD methods, 4620 speech utterances were excerpted from the TIMIT training database [34]. Then, one of eight different noises (buccabber1, destroyerengine, destroyerops, factory1, hfchannel, leopard, m109, and machinegun) from the NOISEX-92 database [35] was artificially added to each speech utterance under four different SNR conditions in the range of \(-5\)-\(10\) dB in a 5 dB step. Table 1 compares the hyperparameters and model footprint of each SE method used in this experiment, where the model footprints were measured using the 32-bit floating point format. As shown in the table, speech and noise bases were only required to estimate speech signal from the SNMF-based SE method, which was enough to model the bases with 1 MB. On the other hand, the NN-based SE methods including MTU-Net required many model parameters. Among them, SEGAN required the largest model size due to using time-domain signals as its input features. The proposed MTU-Net had to be about 1.5 times larger than U-Net because the proposed method had one more decoding network to estimate the noise spectrogram than U-Net. Next, the performance of the SE and VAD methods was evaluated under two different noise conditions, matched and mismatched noise conditions. To this end, 200 speech utterances from 129 males and 71 females were excerpted from the TIMIT test database where all the sentences selected were not included in the training dataset. Then, the matched noise condition was simulated by mixing the same types of noises used for the training dataset for each of the 200 utterances; the result was referred to as the matched evaluation dataset. On the other hand, the mismatched evaluation dataset was simulated by mixing one of four different noises (babble, f16, buccabber2, and factory2) from the NOISEX-92 database, which was unseen noise in the training dataset; these noises were mixed to each of the 200 utterances.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments Conducted on UAV-Mounted miniSAR-Ku Strip Images  
#### 3.3.1 Experimental Data  
The Ku-band UAV SAR data set (with a wavelength of 0.0205 m) utilized in this study was developed by the Aerospace Information Research Institute of the Chinese Academy of Sciences. An octocopter UAV was employed as the payload platform for the sensor. The miniSAR-Ku system was used to acquire the experimental data set in June 2021 over Anyang, Henan Province, China. The UAV flew at a relative altitude of 350 m. Two strip images, miniKu-1 and miniKu-2, from two flight lines were selected for experiments. The azimuth resolution is 0.080 m and the slant range resolution is 0.125 m. Both images have a column width of 3536 pixels, while the miniKu-1 image has a row height of 28,800 pixels and the miniKu-2 image has a row height of 26,752 pixels. These sizes indicate that the two data sets include long-strip images. Both strip images have uniformly distributed reference points, with 28 in miniKu-1 and 38 in miniKu-2. The distribution of some reference points used as GCPs in the following experiments and parts of the two images are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GCP/CP Number** & **Classical Model** & \begin{tabular}{c} **One-Parameter** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Three-Parameter** \\ **Model** \\ \end{tabular} & \
\begin{tabular}{c} **Four-Parameter** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Six-Parameter** \\ **Model** \\ \end{tabular} \\ \hline
0/17 & 183.74 & 183.74 & 183.74 & 183.74 & 183.74 \\ \hline
1/16 & 0.49 & 0.49 & / & / & / \\ \hline
3/14 & 0.39 & 0.41 & 0.37 & / & / \\ \hline
6/11 & 0.34 & 0.40 & 0.33 & 0.35 & 0.37 \\ \hline
12/5 & 0.30 & 0.37 & 0.31 & 0.28 & 0.28 \\ \hline
16/17(LOOCV) & 0.23 & 0.38 & 0.24 & 0.22 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geolocation accuracy in the airSAR-X-2 image for the five models with different GCPs (unit: meters).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GCP/CP Number** & \begin{tabular}{c} **Classical Model** \\ \end{tabular} & \begin{tabular}{c} **One-Parameter** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Three-Parameter** \\ **Model** \\ \end{tabular} & \
\begin{tabular}{c} **Four-Parameter** \\ **Model** \\ \end{tabular} &
\begin{tabular}{c} **Six-Parameter** \\ **Model** \\ \end{tabular} \\ \hline
0/20 & 174.50 & 174.50 & 174.50 & 174.50 & 174.50 \\ \hline
1/19 & 0.65 & 0.59 & / & / & / \\ \hline
3/17 & 0.63 & 0.58 & 0.61 & / & / \\ \hline
6/6 & 0.42 & 0.44 & 0.40 & 0.43 & 0.45 \\ \hline
12/8 & 0.32 & 0.43 & 0.33 & 0.30 &