esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.4 Page-Blocks Data Set
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameter Set**} & \multicolumn{5}{c}{**Evaluation Index**} \\ \cline{3-8}  & & & **Accuracy** & **Macro-F1** & \
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} & **CBA** & **Cost** \\ \hline CS-BT-MTS &
\begin{tabular}{c} synergistic optimization model \\ \end{tabular} & 0.9662 & 0.8156 & 0.8145 & 0.7368 & 813.4696 \\ BT-MTS & Use SNR\({}_{\text{R,g}}\) gain value to select feature variables, PTM & 0.9251 & 0.4635 & 0.4939 & 0.3837 & 4.801.1563 \\ RF & Ntree = 100, NumPredictorsToSample = 3 & 0.9744 & 0.8375 & 0.8681 & 0.8070 & 560.3284 \\ FCBF-SVM & BoxConstraint = 2.0625, KernelScale = 1.0625 & 0.9269 & 0.5044 & 0.5423 & 0.4537 & 2.470.5484 \\ SMOTE-KNN & K = 6 & 0.9579 & 0.9757 & 0.9699 & 0.9679 & 1.1898.887 \\ BPNN & Hiddensize = 3 & 0.9534 & 0.7702 & 0.7362 & 0.6915 & 1325.6219 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of the classification results of the Page-blocks data set. Figure 7: The DRR of MTS classifiers of the Page-blocks data set. higher, its misclassification leads to a larger loss, which will lead to serious consequences in real life.
# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
## 1 Introduction
In this context, this research aims to exploit a combination of independent, well-established satellite-based observational datasets with large coverage to assess the fre  
Figure 1: Distribution of **(a)** terrain elevation, **(b)** Mexican regions [33], **(c)** annual mean precipitation rate (mm) (2008–2017) and **(d)** land-use and vegetation (see Table in Appendix A) over the study area. In **(b)**, the sub-areas of the transition zone, Trans-Mexican Volcanic Belt (TMVB), Southern Sierra Madre (SSM), Eastern Sierra Madre (ESM) and Western Sierra Madre (WSM) are indicated with the following fill patterns: black squares, white squares, white dots and black dots, respectively. In **(a)**, the red points indicate the locations of reported tornadoes (2008–2017) [32]. quency occurrence and natural variability of DCCs in the TMVB region. Thus, 10 years of MODIS Collection-6 cloud data are combined with the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data. The substantial sampling of the satellite observations provides the opportunity for the first estimation of the spatio-temporal distributions of deep convective cloud systems and associated severe weather in the form of extreme precipitation. For future model-based studies, the observed spatio-temporal characteristics can provide valuable information about the quality of physical parameterizations, which would be relevant for convective cloud development and associated severe weather. The paper is organized as follows. Section 2 introduces the study area, while Section 3 describes the datasets and methods used. Section 4 presents the results and corresponding discussions. First, the MODIS-based spatio-temporal distributions of cloud fraction and deep convective clouds in the study area are characterized. Then, spatio-temporal relationships between observations of deep convection and extreme precipitation events, as well as the relation to geographical variations in the form of terrain height, are assessed. Then, a sub-region analysis allows a first examination of the possible influence of varying local and large-scale conditions and accompanying physical processes on the satellite-observed deep convection distributions. Finally, conclusions are given.
# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 3 Methods
### Boundary-Inner Disentanglement Enhanced Learning  
According to the measurement results from Section 3.1, a lightweight Boundary-Inner Disentanglement Enhanced Learning (BIDEL) framework for improving the segmentation accuracy of boundary points is proposed. Specifically, BIDEL maximizes the boundary-inner feature distinction through the boundary loss function \(L_{BIDEL}\):  
\[L_{BIDEL}=-\frac{1}{|B_{l}|}\sum\
olimits_{\chi_{i}\in B_{l}}log\left(P_{i}^{1}+ P_{i}^{3}\right), \tag{6}\]  
Figure 2: From left to right, (**a**) the entanglement between the boundary point and its four neighboring collections in the control group, and (**b**) the entanglement between the boundary point and its four neighboring collections in the experimental group. Each point in the figure represents a boundary point selected from a batch of input.  
where \(|\cdot|\) represents the number of points. \(L_{BIDEL}\) maximizes the sum of \(P_{i}^{1}\) and \(P_{i}^{3}\), as a result of which the sum of \(P_{i}^{2}\) and \(P_{i}^{4}\) is minimized, and the boundary-inner disentanglement is enhanced. As shown in Figure 3, BIDEL pushes neighboring collections \(C_{i}^{2}\) (points in orange) and \(C_{i}^{4}\) (points in yellow) apart, thus preserving the boundary by preventing it from being contaminated by the features of inner points, which improves the segmentation accuracy of boundary points in particular.  
Notably, the boundary information generated from the ground truth is only used for network training; therefore, an additional boundary prediction module is not required.  
\(L_{BIDEL}\) is added to the final loss function as a regularizer, through which the model can achieve two training objectives: (1) minimize the overall segmentation cross-entropy loss; (2) minimize boundary-inner entanglement. The final loss function is  
\[L=L_{cross\;entropy}+\lambda L_{BIDEL}, \tag{7}\]  
where \(\lambda\) is the loss weight of \(L_{BIDEL}\).
## 4 Results and Discussion
The phenomena suggest that the length of the filament generated by the BG beams is positively related to the TC value. However, as stated previously, the filament length also affects the degree of spectral broadening. Therefore, it seems that our conclusion is contradictory. In effect, it can be seen from Equation (1) that, apart from the filament length, other factors such as plasma density and laser intensity also affect the degree of spectral broadening. Since the incident pulse energy is the same for all the TC values (Figures 7 and 8), with the increase of TC value, though the filament length is larger, the plasma density and laser intensity are lower, whose contribution to the degree of spectral broadening becomes lower. Comprehensively considering these factors, the degree of spectral broadening is hardly affected by the TC value of BG beams. It should be noted that temporal coherence is one key feature of SC generation, which is not explored in this paper. In future work, we will conduct systematic research on the temporal coherence of the SC generated by different vortex beams, which can provide a deeper understanding of the unique characteristics of SC generated by vortex beams.
## 2 Literature Review  
Comparative analysis plays a crucial role in understanding the impact of observing systems, offering nuanced insights into the effectiveness of GNSS-RO data across temporal epochs [10]. A recent scholarly exploration has delved into the comparative analysis of Binhu and COSMIC-2 RO data, highlighting the avant-garde exploration of Earth's atmosphere facilitated by innovative GNSS-RO technology [11]. This research sheds light on the unique contributions and capabilities of these observational systems.  
[PERSON] et al. [10] conducted a comprehensive exploration of Earth's atmosphere through the lens of RO measurements emanating from the Global Positioning System (GPS). This pioneering research laid the foundational cornerstone for subsequent studies that leveraged advanced NWP models to meticulously validate and refine RO profiles. The seamless integration of cutting-edge NWP models facilitates a holistic evaluation of the congruence between observed RO profiles and those prognosticated by sophisticated numerical simulations.  
[PERSON] et al. [12] conducted a comprehensive study to evaluate the accuracy and uncertainty of the GNSS RO data processing system developed by the Center for Satellite Applications and Research (STAR). They focused on quantifying the impact of observation uncertainties stemming from clock error and geometry determination on bending angle and refractivity profiles. The research team utilized data from the COSMIC-2 mission and the Formosa Satellite Mission 7, both integral to NOAA's observation infrastructure. Specifically, they established NOAA's dedicated GNSS RO Satellite Processing and Science Application Center (RO-SAAC) within STAR to facilitate this analysis.  
In the validation of COSMIC RO data, [PERSON]. [13] compared COSMIC RO refractivity profiles with observations from 38 radiosonde stations of the Australian Bureau of Meteorology over a one-year period. Their study focused on establishing optimal criteria for collocating COSMIC and radiosonde observations, highlighting the importance of factors such as time difference and radial distance. This investigation provided crucial insights into the accuracy and reliability of COSMIC RO data, particularly in the troposphere and lower stratosphere.  
[PERSON] et al. [14] assessed the performance of FS7/C2 GNSS RO data, revealing its enhanced capabilities in providing deeper profiles, particularly at lower latitudes, compared to FS3/C. Validation against chartered mission data confirms the reliability of FS7/C2 RO data, with minimal deviations from ground truth observations.  
Furthermore, studies have elucidated differences between GPS RO and radiosonde atmosphere profiles, providing valuable insights into atmospheric variabilities. Particularly, research in regions like Egypt, led by [15], contributes to our understanding of atmospheric dynamics in specific geographical contexts. These comparative analyses collectively contribute to advancing our comprehension of atmospheric phenomena and optimizing the utilization of GNSS-RO data in various scientific applications.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
###### Abstract  
In this paper, a high-efficiency GaN Doherty power amplifier (DPA) for 5G applications in the N78 sub-6 GHz band is introduced. The theoretical analysis of the matching networks for the peak and carrier transistors is presented, with a focus on the impact of unequal power splitting for both transistors and the recommendation of a post-harmonic suppression network. The proposed design features an unequal Wilkinson power divider at the input and a post-harmonic suppression network at the output, both of which are crucial for achieving high efficiency. The Doherty power amplifier comprises two GaN 10 W HEMTs, measured across the 3.3 GHz to 3.8 GHz band (the N78 band), and the results reveal significant improvements in gain, output power, drain efficiency, and power-added efficiency. Specifically, the proposed design achieved a power gain of over 12 dB and 42 dBm saturated output power. It also achieved a drain efficiency of 80% at saturation and a power-added efficiency of 75.2%. Furthermore, the proposed harmonic suppression network effectively attenuated the harmonics at the output of the amplifier from the second to the fourth order to more than \(-\)50 dB, thus enhancing the device's linearity.  
Doherty power amplifier (DPA); GaN HEMT sub-6 GHz for 5G; power divider/combiner; high efficiency; power combiners; harmonic suppression; unequal Wilkinson power divider +
Footnote †: journal: Electronics Letters  
0203
## 8 Conclusions  
Dynamic systems with relatively large delays with respect to the dynamics of the system are tackled in this work. A control proposal based on a delay predictor (observer) scheme is presented. A fundamental part of this work focuses on achieving the convergence of the observer-predictor scheme. The approaches used in the study allow the obtaining of necessary and sufficient conditions for the convergence of the scheme presented. Using this scheme, it is possible to control systems with longer delays than those treated in previous works [11; 24].  
For example, in the [11] the maximum limit on the size of the delay is \(\tau<\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^{2}}}\) for a second order delayed system. In the present work, it is possible to exceed the aforementioned delay limit, reaching up to \(\tau<2\bigg{(}\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^ {2}}}\bigg{)}\): as can be seen, the allowable size in the present proposal surpasses the previous condition by a factor of two. A similar improvement is achieved for high-order delayed plants. Finally, some results concerning tracking the reference in the steady state and the rejection of disturbances, both of the step types, are presented and validated via numerical simulations.  
Conceptualization, J.F.M.-R., L.A.B.-B. and A.U.-C.; methodology, J.F.M.-R., L.A.B.-B. and A.U.-C.; formal analysis, J.F.M.-R. and B.D.M.-C.; validation, R.J.V.-G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Diagnostics and Oven  
We have implemented a diagnostic paddle that is located on top of the oven on the same manipulator (see Figure 2), allowing for precise positioning inside the interaction region of the MBES spectrometer. The diagnostic paddle hosts a number of tools used for spatial as well as temporal characterization.  
The spatial diagnostics serve to spatially overlap the UV and X-ray beams in the interaction region, as well as estimating the beam size. We use a 0.2 mm thick, 25 mm diameter YAG screen, possessing a matte, sandblasted surface. The screen is externally viewed through a vacuum window with the aid of a long working distance lens.  
For timing diagnostics, we use a fast AXUV-type diode on a subminiature version A (SMA) connector that is not directly illuminated, but by a highly-attenuated beam created using solid filters which are moved in and out. The beam hits the mounting rim of the diode and only scattered light hits the active area in our case. We illuminate it separately with the optical and X-ray pulses. These signals are viewed on a 13 GHz bandwidth oscilloscope. The X-ray induced trace is saved as reference on the scope and the delay of the optical pulse is manipulated by a delay stage such that the rising edges of the two signals overlap. This strategy allows us to temporally overlap the optical and X-ray pulses with a sub 100 ps accuracy. Temporal overlap on the femtosecond scale is achieved using photo or Auger electrons of atoms or molecules shown later in this paper.  
We use a resistively-heated capillary oven that we developed some years ago and successfully used before as a sample source at the linac coherent light source (LCLS) [16; 18; 21; 25; 26] as well as with high harmonic vacuum ultraviolet sources [27; 28]. The oven consists of a body, a cap that is screwed on after filling the main body with a solid molecular sample, and a capillary tip. All components are made from aluminum. The three parts are separately heated using thin-film sheet heaters. The temperatures of the three sections are monitored using thermocouples and we control the heating current using a proportional-integral-derivative PID feedback circuit implemented on the control computer. The sample density has been once tested with the nucleobase thymine and can reach up to 10\({}^{12}\) molecules/cm\({}^{3}\)[21]. The capillary reduces the angular width of the beam compared to usual effusive sources and we have measured the full width at half maximum (FWHM) of 10 degrees when operating in the 10\({}^{11}\) molecules/cm\({}^{3}\) density regime [21].
## 3 Evaluation of the Model Precision  
To assess the precision of the models and compare the models with each other, five statistical indexes were used: the regression coefficient (\(R^{2}\)), root mean square error (RMSE), coefficient of determination (CoD), sum of squares error (SSE), and mean errors (AVG). These indexes can be specified via the following relations:  
\[R^{2}=\left[\frac{1}{N}\frac{\sum_{i=1}^{N}\bigl{(}T_{i}-\overline{T}\bigr{)} \bigl{(}O_{i}-\overline{O}\bigr{)}}{\sigma_{T}\ \sigma_{O}}\right]^{2} \tag{12}\]  
Figure 1: EPR flowchart [59].  
\[CoD=1-\frac{\sum_{i=1}^{N}\sum_{N_{i}}\left(O_{i}-T_{i}\right)^{2}}{\sum_{i=1}^{N }\left(T_{i}-\overline{T}\right)^{2}} \tag{13}\]  
\[SSE=\frac{1}{N}\sum_{i=1}^{N}(T_{i}-O_{i})^{2} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}\cdot\sum_{i=1}^{N}\left(T_{i}-O_{i}\right)^{2}} \tag{15}\]  
\[AVG=\frac{\sum_{i=1}^{N}\ \sqrt{\left(1-T_{i}/O_{i}\right)^{2}}}{N} \tag{16}\]  
where \(N\) is the number of data points, \(T_{i}\) is the vector of the measured values, \(O_{i}\) is the vector of the predicted values, \(\overline{T}\) is the mean of the measured values, \(\overline{O}\) is the mean of the predicted values, \(\sigma_{T}\) is the SD of the measured values, and \(\sigma_{O}\) is the SD of the predicted values.
## 3 Numerical Modelling
### Loading and Boundary Conditions  
#### 3.3.1 Loading  
The loading used two conditions. The first condition was zero period acceleration, which was used for the quasi-static analysis. The second condition consisted of the time  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Modulus of** & **Poisson’s** & **Yield** & **Ultimate** \\  & **(kg/m3)** & **Elasticity** & **Ratio** & **Strength(MPa)** & **Strength (MPa)** \\ \hline Concrete & 2300 & 30,000 & 0.18 & - & 24 \\ PDF Panel & 980 & 1100 & 0.42 & 25 & 33 \\ Carbon Steel & 7850 & 200,000 & 0.30 & 250 & 460 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties.  
Figure 4: Material definition of the target structure (highlighted in green colour). (**a**) Concrete pad; (**b**) PDF panel; (**c**) Steel beam frame.  
Figure 3: Drawing and geometry of the firefighting water tank. (**a**) Drawing in top view; (**b**) Drawing in side view; (**c**) Generated geometry model in ANSYS Workbench.  
histories of the earthquake, which were calculated from the design standard and used for the transient and one-way FSI analyses.  
In the quasi-static analysis, the zero period acceleration was determined based on the results of the response spectrum of the seismic load. The accelerations on the x-, y-, and z-axes at the zero period were 0.205\(\times\)\(g\), 0.236\(\times\)\(g\), and 0.101\(\times\)\(g\), respectively Therefore, the accelerations of the quasi-static analysis in the horizontal and vertical directions were 1.5 and 0.8 times the maximum values, respectively.  
The seismic loads in the transient and one-way FSI analyses are shown in Figure 5. Their response spectra were compared with the required level suggested by the Korea Building and Structure Code (2022). This revealed that the seismic loads were in accordance with the design criteria. The maximum acceleration occurred at 16.9 s.  
Figure 5: Calculated acceleration and its response spectrum of the seismic load. (**a**) Time histories of acceleration in _x-axis_; (**b**) Response spectrum of acceleration in _x-axis_; (**c**) Time histories of acceleration in _y-axis_; (**d**) Response spectrum of acceleration in _y-axis_; (**e**) Time histories of acceleration in _z-axis_; (**f**) Response spectrum of acceleration in _z-axis_.
Future Internet_**2015**, \(7\), 184-195; doi:10.3390/fi7020184  
_future internet_  
ISSN **1999-5903**  
www.mdpi.com/journal/futureinternet  
_Project Report_  
**Utilizing the ECHO Model in the Veterans Health Affairs System: Guidelines for Setup, Operations and Preliminary Findings**  
**[PERSON] * and [PERSON]**  
Veterans Health Administration, Primary Care Department, Greater Los Angeles Healthcare System, 11301 Wilshire Boulevard, Building 210, Room 210, Los Angeles, CA 90073, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-310-478-3711 (ext. 48568).  
Academic Editor: [PERSON]  
_Received: 21 January 2015 / Accepted: 21 May 2015 / Published: 8 June 2015_  
**Abstract:** Background: In 2011, the Veterans Health Administration (VHA) consulted with the Project ECHO (Extension for Community Healthcare Outcomes) team at the University of New Mexico, Albuquerque, to reproduce their successful model within the VHA. Methods: The VHA launched SCAN-ECHO (Specialty Care Access Network-Extension for Community Healthcare Outcomes), a multisite videoconferencing system to conduct live clinical consultations between specialists at a VHA Medical Center (hospital) and primary care providers stationed at satellite VHA CBOCs (Community-Based Outpatient Clinic). Results: Analysis of the first three years rendered a mean attendee satisfaction of 89.53% and a consultation satisfaction score of 88.10%. About half of the SCAN-ECHO consultations resulted in patients receiving their treatment from their local primary care providers; the remaining half were referred to the VHA Medical Center when the treatment involved equipment or services not available at the CBOCs (e.g., MRI, surgery). Conclusion: This paper details the setup, operation logistics and preliminary findings, suggesting that SCAN-ECHO is a viable model for providing quality specialty clinical consultation service, prompter access to care, reduced commutes and continuing education. Additionally, the use of a secured Internet-based videoconferencing system that supports connectivity to multiple (mobile) devices could expand the utilization of this service.  
**Keywords:** ECHO; videoconference; consultation; consult; remote; specialty; education; health; diagnostic