esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The growing demand for high-speed and high-capacity wireless communication has intensified the need for compact, wideband, and efficient MIMO antenna systems, particularly for 5G mid-band and UWB applications. This article presents a miniaturized dual and quad port MIMO antenna design optimized for 5G mid-band (n7/7/n78/n79/n96/n102) and Ultra-Wideband (UWB) applications without employing any decoupling structures between the radiating elements. The 2-port configuration features two closely spaced symmetric monopole elements (spacing \(<\)\(\lambda_{\max}/2\)), promoting efficient use of space without degrading performance. An FR4 substrate (\(\varepsilon\)r = 4.4) is used for fabrication with a compact size of 30 \(\times\) 41 \(\times\) 1.6 mm\({}^{3}\). This layout is extended orthogonally and symmetrically to form a compact quad-port variant with dimensions of 60 \(\times\) 41 \(\times\) 1.6 mm\({}^{3}\). Both designs offer a broad operational bandwidth from 2.6 GHz to 10.8 GHz (8.2 GHz), retaining return loss (S\({}_{\mathrm{XX}}\)) below \(-\)10 dB and strong isolation (S\({}_{\mathrm{XY}}<-\)20 dB at high frequencies, \(<\)\(-\)15 dB at low frequencies). The proposed MIMO antennas demonstrate strong performance and excellent diversity characteristics. The two-port antenna achieves an average envelope correlation coefficient (ECC) of 0.00204, diversity gain (DG) of 9.98 dB, and a mean effective gain difference (MEG\({}_{\mathrm{ij}}\)) of 0.3 dB, with a total active reflection coefficient (TARC) below \(-\)10 dB and signal delay variation under 0.25 ns, ensuring minimal pulse distortion. Similarly, the four-port design reports an average ECC of 0.01432, DG of 9.65 dB, MEG\({}_{\mathrm{ij}}\) difference below 0.3 dB, and TARC below \(-\)10 dB, confirming robust diversity and MIMO performance across both configurations.  
MIMO antenna; 2-port; 4-port; 5G mid-band; UWB; ECC; TARC; MEG +
Footnote †: journal:
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 6 Discussion  
In this article, we investigated the integration for IoT solutions with large-scale, low-cost and secure end devices. We focused on the following three aspects. Firstly, featured by Azure Sphere device and Azure cloud services, a novel integration of hardware, software and services was designed, deployed and tested. Secondly, the hardware prototypes including MT3620 direct connect device and MT3620 gateway device were designed and implemented. Thirdly, the programs for hardware devices,  
Figure 16: Designed reports on different platforms: (**a**) Windows Desktop; (**b**) Android device.  
Figure 15: The layout of windows-form based native application.  
the configurations for cloud services as well as the applications for Windows devices were designed and tested.  
According to the research in [12; 13; 14; 15; 16; 17], a lot of end devices in industry, healthcare and other general user scenarios mostly consist of sensors and actuators, making them susceptible to threats and attacks due to the missing of security and privacy design for those low-cost IoT devices. Furthermore, most of the current solutions have considered the security threats and strategies in one specific layer or multiple layers. However, very few of them have treated security all the way from the device to the cloud. To address these issues, Azure Sphere certified MT3620 is used to serve as direct connected device and gateway device with low cost and high security in the proposed solution. Meanwhile, the security policies and rules are designed to work together in a harmonious whole from device, connection and cloud perspectives. Furthermore, profiting from Azure IoT Hub, Azure Device Provisioning Service and Azure Sphere Security Service, the lager-scale device provision, configuration and management can be achieved in a secure and efficient way. In summary, this article is concentrated on the integration of hardware, software and services that is based on Azure Sphere device and Azure cloud services to achieve security all the way from every low-cost device to the cloud simultaneously. As such, one limitation of the work is that the integration of the available devices and services for large-scale scenarios with limited budgets, rather than the creation of security policies and rules is concentrated upon. Another limitation of current work is that the system response to the real security threats is not considered yet. However, according to the literature [8; 49], with the Azure IoT platform, which is built with high security and privacy, along with Security development lifecycle (SDL) [50] and Operational Security Assurance (OSA) [51] for secure development and operation, the overall security can be achieved.
## Independence Test and Model Validation  
For the numerical investigation, selection of the appropriate grid size and time step was important to improve numerical efficiency without decreasing accuracy. Tables 1 and 2, respectively, compare the effects of grid size \(\Delta i\) (ranging from 0.25 mm to 2.0 mm) and time step \(\Delta t\) (ranging from 0.05 \(\upmu\)s to 0.4 \(\upmu\)s) on the key parameters (propagating period of detonation wave \(t_{rot}\), average outlet total pressure \(\overline{P}_{t,out}\), and average outlet total temperature \(\overline{T}_{t,out}\)) of the CRD combustor. The tables show that the numerical results were independent, with a \(\Delta i\) of 1.0 mm and \(\Delta t\) of 0.2 \(\upmu\)s.  
On this basis, to evaluate the calculation accuracy of the present numerical method, Figure 2 qualitatively compared the experimental results of [PERSON] [36], the numerical results of [PERSON] [37], and the numerical results obtained in this study. The comparisons show that the present numerical results effectively describe the basic characteristics of CRD waves, including complex waves (such as detonation wave A and oblique shock wave B) and typical combustion field composition (such as mixing region C, expansion region D, discontinuity region E between fresh premixure and product [38], blocking region F, and fresh premixure region G) and are in good agreement with the published results. Figure 3 shows variation in static pressure over time, at the monitoring point shown in Figure 1b. It can be seen that when a CJ ignition kernel was initiated at time 0 \(\upmu\)s, pressure at the monitoring point increased quickly. After a short time of detonation wave self-adjusting process (about 1000 \(\upmu\)s), CRD waves propagated steadily and periodically. Under the stable propagation condition, the propagation period and speed of CRD waves was 448.0 \(\upmu\)s and 1785.7 m/s, respectively. According to the CJ theory, the theoretical propagation velocity of detonation waves should be 1808.0 m/s in this working condition. There was only an error of 1.2% between the theory value and the numerical data, which further verifies the validity of the present numerical method.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(\Delta t\)/\(\upmu\)s & \(t_{rot}\)/\(\upmu\)s & \(|\Delta t_{rot}|\)\% & \(\overline{P}_{t,out}\)/MPa & \(|\Delta\overline{P}_
# Numerical Evaluation of Residual Stress Influence on SIF in CT Specimen
## 4 Stress Intensity Factor Calculation  
### FE Model for the Stress Intensity Factor Calculation  
The FE model of the CT specimen with a straight crack front was created for the calculation of the SIF. Due to the symmetric geometry of the CT specimen, only half of the model was created (see Figure 9). The same FE CU20 as for the stainless-steel plate was used in this model. In order to obtain high calculation precision of the SIF calculation, the mesh of small elements around the crack tip was created. Using this CT specimen FE model, three cases were analyzed: residual stress analysis and two cases for SIF analysis with and without residual stress. The different load combinations were used for these cases:  
* previously find temperature distribution for residual stress calculation;
* displacement for stress intensity factor calculation;
* previously find temperature distribution and displacement for stress intensity factor calculation with residual stress.  
The displacement load of 40 \(\upmu\)m (or 80 \(\upmu\)m for full-scale model) along the coordinate Y was used. As displacement imitates the movement of the pin of the testing machine, it was applied on the red-colored line shown in Figure 9.  
The following boundary conditions of the model were used: displacement of point P1 was restrained in all three directions; displacement of point P2 was restrained along the \(Z\) axis; displacement of the yellow-colored surface was restrained along the \(Y\) axis which imitates the symmetric boundary condition.  
The green-colored line is the crack front and the line where the stress profile is shown.  
An elastic calculation was used for residual stress analysis and elastic--plastic with kinematic hardening calculations were used for other cases.  
The material properties presented in Section 2 were used to describe the material behavior of the CT specimen model.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 4 Research Methods
### Improved Wavelet Adaptive Denoising
#### 4.1.1 Adaptive Determination of Optimal Decomposition Level  
The modular square equation of calculating additive white Gaussian noise (AWGN) through wavelet decomposition is represented as [4]  
\[E\Big{(}|\omega|^{2}\Big{)}=\frac{\|\psi\|^{2}\sigma^{2}}{i}, \tag{1}\]  
where \(i\) represents the number of decomposition levels, \(\psi\) refers to the wavelet basis, \(\sigma\) is the standard deviation of the AWGN, and \(E\) is the mathematical expectation of the sub-band coefficients at each level, representing the randomness of the signal over time. The equation involves the decomposition scale and wavelet basis, indicating that a larger scale more effectively distinguishes noise from the signal, thus improving denoising performance. However, experiments show that too-large decomposition levels can distort coefficients, thereby increasing the error of the inverse wavelet transform. The analysis of the wavelet coefficients at each level reveals that the detail coefficient, such as noise, retains part of the AWGN sequence after wavelet decomposition [36]. Subsequently, we perform a whitening test on the autocorrelation coefficient of the detail coefficients, defining it as \(\text{Corr}_{i}^{2}\):  
\[\text{Corr}_{i}^{2}\leq\frac{1.38}{\sqrt{i}}. \tag{2}\]  
When Equation (2) is satisfied, further decomposition of the detail coefficient is required. Otherwise, the optimal decomposition level is obtained by reducing one. In practice, however, we observed that as noise intensity increases, the \(\text{Corr}_{i}^{2}\) value becomes too low to determine the number of decomposition levels accurately. Inspired by [PERSON] et al. and [PERSON] et al., we noted that as the decomposition scale increases, certain approximation coefficients are misassigned to detailed ones, distorting the reconstructed signal [13; 37]. Therefore, an autocorrelation coefficient restriction is introduced for approximation coefficients, which is defined as \(\text{Corr}_{i}^{1}\). It is generally believed that when \(\text{Corr}_{i}^{1}\in[0.8,1.0]\), the approximation coefficients are highly correlated, so the restriction is set as  
\[\text{Corr}_{i}^{1}\geq 0.8. \tag{3}\]  
When \(\text{Corr}_{i}^{1}\) fails to satisfy Equation (3), further decomposition distorts the signal, resulting in the loss of valuable information during reconstruction. At this point, the loop is terminated, and the subsequent decomposition is not performed.
## 5 Evaluation  
We evaluate our system to study the feasibility of our system to achieve high speed reception and the performance of the automated shutter protocol for multi channel visible light signal reception on a single photodiode receiver. All the experiments were conducted in a lab setting, indoors, under ceiling  
\begin{table}
\begin{tabular}{c c} \hline \hline
**LED Specifications** & \\ \hline Number of LEDs & 02 \\ LED Type and Size & Red, T-1 3/4 (5 mm) \\ Horizontal distance between LED’s [cm] & 14.88 cm \\ Viewing Angle of LED [deg.] & 30\({}^{\circ}\) \\ Luminous Intensity at each LED [med] & 3500 mcd \\ Optical Output Power at each LED [mW] & 125 mW \\ Wavelength of each LED [nm] & 650 nm \\ \hline
**Photodiode (PD) Specifications** & \\ \hline Physical Active Area of the PD [mm\({}^{2}\)] & 0.8 mm\({}^{2}\) \\ Wavelength Range of PD [nm] & 200 to 1100 nm \\ Bandwidth of PD [MHz] & 150 MHz \\ Peak Response of PD [A/W] & 0.44 A/W \\ \hline
**Lens Specifications** & \\ \hline Lens Type & Aspheric Condenser Lens \\ Outer Diameter [mm] & 80 mm \\ Back Focal [mm] & 37.5 mm \\ \hline
**LCD Shutter Specifications** & \\ \hline LCD Type & TN, Transmissive, Positive \\ Dimensions of LCD Pixel & 36 \(\times\) 36 mm \\ Driving Voltage of LCD [V] & 3–5 V \\ Maximum Opaqueness (\%) & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: LED, Photodiode, Lens and LCD specifications.  
Figure 4: USRP GNU Radio Block Diagram for (**a**) transmitter LED 1, (**b**) transmitter LED 2, a and (**c**) pixelated shutter receiver. Note that the vector source values shown are only example values. The setup uses 2 N210 USRPs with LFTX daughterboard for the transmitters and 0–30 MHz LFRX daughterboards for the receiver.  
white ambient lighting. Unless mentioned, the distance between the LED and photodiode in our experiments was set to 15.5 cm.
# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 4 Experiments
### Experimental Results
#### 4.3.1 Results of Feasibility Assessment  
With the EDU-DT dataset, the effectiveness of word embedding refinement on K-means clustering was firstly explored; refinement is a prerequisite for clustering in the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Statistics**} & \multicolumn{2}{c}{**EDU-DT**} & \multicolumn{2}{c}{**EDUTECH-DT**} \\ \cline{2-6} \multicolumn{2}{c}{} & _Training Set_ & _Test Set_ & _Training Set_ & _Test Set_ \\ \hline \multicolumn{2}{c}{**Number of concepts**} & 2000 & 1956 & 1832 & 1016 \\ \hline \multirow{4}{*}{**Concept length**} & _Max._ & 21 & 18 & 15 & 20 \\ \cline{2-6}  & _Min._ & 1 & 1 & 1 & 1 \\ \cline{2-6}  & _Average_ & 4.96 & 4.90 & 5.11 & 4.98 \\ \cline{2-6}  & _Standard deviation_ & 1.84 & 2.15 & 1.70 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics for our experimental datasets.  
WERECE method. Figure 3 depicts the clustering results for the K-means algorithm with or without a re-embedding vector; in this instance, the hyperparameter of the IsoMap algorithm \(n\_neighbors\) was set to 200. The CH score for clustering results upon integrating the K-means algorithm and the re-embedding method was consistently higher than for results obtained with the K-means algorithm alone. This finding suggests that using IsoMap to refine a pre-trained word embedding model can enhance the K-means algorithm's clustering performance. Furthermore, these CH scores declined sharply as the number of clusters increased. Scores later dropped consistently beyond 20 clusters. We also determined that the optimal number of clusters for K-means (with or without word embedding refinement) was about 20 as per the Elbow method.  
Next, a Mann-Whitney U test was performed to determine whether WERECE's capacity and validity were significantly different from the K-means algorithm in terms of extracting educational concepts. The input for this test included six lists for WERECE and K-means, each containing three sets of 30 scores (precision, recall, and F1). As Figure 4 indicates, WERECE's scores hovered around 0.8, revealing an improvement of approximately 0.1-0.2 versus K-means. WERECE appeared notably capable and valid given the statistically significant performance at the 99% confidence level (all \(p\) values were less than 0.01). In particular, the K-means method had a higher standard deviation for each evaluation metric, whereas WERECE scores were much lower. This trend could be attributed to WERECE's Box-Cox transformation; the step diminished the impact from the random selection of original data points on the uncertainty factor associated with clusters yielded by K-means.  
The aforementioned findings imply that both the word embedding refinement and discriminant function in the WERECE method are highly feasible and enhance educational concepts' clustering performance. Therefore, concepts were accurately extracted.  
Figure 3: Performance comparison of K-means with and without word embedding refinement on the EDU-DT dataset.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 3 Results and Discussion  
### Spectral Broadening of Light Coupling to Waveguide Modes  
Generally, the light coupling to waveguide modes in a photonic device with disordered gratings reveal a spectral broadening compared to the situation without disorder. This can be explained by (1) the broadening of the wave vector transfer and (2) the broadening of the waveguide modes. Both increase the total coupling of the incident light to waveguide modes. The former process leads to a certain range of wave vectors that is transferred to incident photons. Due to the dispersion relation of the waveguide mode, this range is translated to spectrally broader resonance energy. The latter process results in an internal optical damping of the waveguide mode, which is directly accompanied by broader resonance. This means that both possible processes lead to a broadening of the resonances in an optical experiment. Therefore, it is difficult to derive their contribution directly.  
In the following sections, we demonstrate that the dominating process of spectral broadening of light coupling to waveguide modes depends on the way in which disorder is introduced to the nanophotonic grating structure. For this work, a texture analysis based on fast Fourier transformation(FFT) is applied as well as an analysis of the local absorber layer thickness variation. Finally, the impact of the disordered grating textures on the spectral response of the solar cells is investigated.
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 2 Materials and Methods
### Training Network  
The developed architecture for segmentation was trained using Adam optimizers, with a batch size of sixteen images. The initial learning rate was set to 0.0008, and the learning rate scheduler was defined so that every ten epochs learning rate is 0.6 times lower. The number of maximum epochs for network training was set to 300, and the patience for early stopping was 5.  
The loss function used for training this network was based on the Serensen-Dice coefficient (_Dice_), Equation (1),  
\[loss=1-Dice. \tag{1}\]  
The _Dice_ coefficient compares the similarities between a segmented image and a mask with marked pixels [30]. The _Dice_ coefficient represents the ratio of cross-section and union between two images, Equation (2),  
\[Dice=2*\frac{|X\cap Y|}{|X|+|Y|}, \tag{2}\]  
wherewith \(|X|\) is the considerable sum of pixels of the segmented image, and with \(|Y|\) the sum of the mask's pixels.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 4 Analysis of Normal Section Capacity
### Existing Calculation Methods
#### 4.1.1 Basic Calculation Model  
Based on the equilibrium conditions, the basic calculation equations of the normal section capacity are as follows:  
\[\left\{\begin{array}{l}N_{\text{u}}=\alpha_{1}f_{\text{c}}bx+f_{\text{y}}A^{ \prime}{}_{\text{s}}-\sigma_{\text{s}}A_{\text{s}}\\ N_{\text{u}}e=\alpha_{1}f_{\text{c}}bx\Big{(}h_{0}-\frac{\pi}{2}\Big{)}+f_{\text {y}}A^{\prime}{}_{\text{s}}(h_{0}-a^{\prime}{}_{\text{s}})\\ e=e_{i}+\frac{h}{2}-a_{s}\\ e_{i}=e_{0}+e_{a}\\ e_{a}=\min\Big{(}20\text{ mm},\frac{h}{30}\Big{)}\\ \sigma_{\text{s}}=f_{\text{y}}\frac{0.8-\xi}{0.8-\xi_{\text{b}}}-f_{\text{y}} \leq\sigma_{\text{s}}\leq f_{\text{y}}\end{array}\right. \tag{1}\]  
where \(N_{\text{u}}\) is the ultimate load (kN), \(\alpha_{1}\) is the rectangular stress block factor, \(f_{\text{c}}\) is the concrete axial compressive strength (MPa), \(x\) is the height of the concrete compression zone (mm), \(f_{\text{y}}\) is the yield stress (MPa), and \(\sigma_{\text{s}}\) is the tensile reinforcement stress (MPa); \(\xi\) and \(\xi_{\text{b}}\) are the relative and limiting relative heights of the compression zone, respectively. When \(\sigma_{\text{s}}>f_{\text{y}}\) (i.e., \(\xi\leq\xi_{\text{b}}\)), \(\sigma_{\text{s}}=f_{\text{y}}\). When \(\sigma_{\text{s}}<f_{\text{y}}\) (i.e., \(\xi>1.6-\xi_{\text{b}}\)), \(\sigma_{\text{s}}=-f_{\text{y}}\). Parameter \(e\) is the distance between the axial force point and the resultant point of the tensile reinforcement (mm). Parameter \(e_{\text{i}}\) is the initial eccentricity (mm). Parameter \(e_{0}\) is the eccentricity (mm). Parameter \(e_{\text{a}}\) is the additional eccentricity (mm). Some other size parameters are depicted in Figure 9.
# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
## 7 Sensitivity of the Layout Schemes to the Severity of the Flat  
As previously stated, when the envelope spectrum analysis is carried out using acceleration signals, the amplitudes of the envelope spectrum obtained in a scenario with healthy wheels and in another with defective ones are significantly different. The amplitude of the envelope spectrum for a defective wheel is greater than for a healthy one. The present section aims to illustrate the sensitivity of the layout schemes to the severity of the flat. Hence, the influence of the flat geometry on the acceleration response is investigated in this section. In addition to the flat geometry used in the previous sections (\(L=150\) mm and \(D=1.5\) mm), other flat geometries are also considered in this section with different flat lengths (25 mm \(<L<80\) mm) and flat depths (0.1 mm \(<D<1\) mm). Moreover, signal contamination with 10% noise and the track irregularity profiles presented before are also considered in the analyses discussed in the present section.  
Figure 17 shows the envelope spectrum detection analysis for 12 acceleration responses obtained on the rail (position 1 to 12 shown in Figure 5) considering a healthy and a defective wheel of the Alfa Pendular with 10% noise incorporated in the signals. Again, the layout composed by accelerometers proved to be efficient since both the lag and the amplitude indicators are suitable for distinguishing a healthy from a defective wheel, regardless of the flat geometry properties. From the results presented in the above figure, it can be concluded that the system is effective for the range of small to severe flat properties, considering a combination of noise level and rail unevenness.  
Figure 16: Optimization of the sensor layout.