esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Design of the Proposed Antenna  
### The Design of the Proposed Antenna with a Wide-Beamwidth Pattern  
Figure 1 shows the geometry of the proposed dielectric resonator (DR) and the feeding line. The antenna comprised a fixed alumina DR (\(\epsilon_{r}=9.6\)), a ground etched with a rectangular slot, a substrate, and a feeding line integrated with a microstrip LPF.  
The DR in Figure 1a comprised a fixed cylinder shape divided by a \(13\times 13\) grid. There are 137 bars in the grid, each with a size of \(L_{pixel}\ \times\ W_{pixel}=3.3\times 3.3\) mm\({}^{2}\). The height of the bar was optimized with the GLPSO algorithm to induce a high-order mode with wide HPBW characteristics, and the length of each bar has a value in the range of 5-60 mm. To shorten the optimization time, the grid was divided into quadrants that were symmetrical to the original (first) quadrant. The chromosomes correspond to the height of the rod. The GLPSO included 1000 iterations and 40 populations and was set at a mutation ratio of 0.01. The cost function was set to form a wide beamwidth with a far-field radiation pattern with a constant gain at \(\theta=-90^{\circ}\)\(\sim\)+90\({}^{\circ}\).  
Taconic RF-35 (\(\epsilon_{r}=3.5\)), with a thickness of 1.52 mm and a size of \(W_{sub}\ \times\ L_{sub}=60\ \times\ 60\) mm\({}^{2}\), was used as a substrate. A slot etched on the ground surface (\(W_{slot}\ \times\ l_{slot}=40\times 7\) mm\({}^{2}\)) enabled the coupling of the feeding line and the DR (Table 1).  
Figure 1: The configuration of the proposed antenna: (**a**) overall view, (**b**) substrate view, and (**c**) back side-cut view of the substrate (the \(x\)–\(y\) plane). DR = dielectric resonator. LPF = low-pass filter.  
A seventh-order Chebyshev LPF, which was integrated with the feeding line and etched onto the back of the substrate, was applied to suppress higher modes, as shown in Figure 1c. To overcome the specificity of the space on the back of the narrow substrate, a structure was used that modified the step-impedance filter. The detailed parameters are shown in Table 2.
## 3 Results and Discussion
### Optimizing the Headway of Vehicles Using the QGIS Software and the PostGIS/PostgreSQL Database  
Optimizing the headway of vehicles is based on public transport information (stops and schedule), using a standardized GTFS format, and from the direct observation of the phenomenon that often occurs when two or more terms simultaneously reach the same station: the agglomeration of platforms with unwanted consequences for the transfer time, the loss of the desired line, and safety issues because of crowding, leading to a considerable decrease in the attractiveness of public transport.  
The integration and correlation of data were performed for all the tramlines by using open-source software (QGIS and PostGIS), resulting a database with more than 125,000 records, consisting of all the tramline schedules and stations (Figure 9).  
By running the model, the practical observations were confirmed: several vehicles arrived at the same stations at the same times.  
The SQL query and its result are presented in Figure 10, where it can be observed that, in 5400 cases, two vehicles arrived at the same station at the same time.  
The same algorithm was applied to see if three or four vehicles could reach the same station at the same time, resulting in 348 and 14 cases, respectively (Figure 11). Applying the algorithm for five vehicles returned one case (Figure 12). Having more than three or four tram vehicles at the same station at the same time might affect the operation, when considering the access and egress of vehicles. Additionally, it could affect the image as well as the safety of the passengers on the platform because of disorganized movement, especially when passengers want to switch line.  
Figure 9: Extract from database with all tramlines in Bucharest.  
The proposed model allows the visualization of the vehicles' positions on the network by making use of the QGIS Temporal controller feature. By running this feature, we can obtain an animation with the positions of vehicles on the digital map and observe the  
Figure 14: No. of vehicles arriving at stop_id 122 at the same time after changes of schedule.  
Figure 12: Number of cases of 5 trans reaching the same station at the same time.  
Figure 13: Example of 3 vehicles arriving at the stop_id 122 at the same time.  
situation of tram operation during a specific time range or at a specific time. Several timeframes are depicted in Figure 15.  
The capacity of the station and the tram schedule can be appropriately designed if the number of passengers accessing and egressing the vehicle is known.  
The model can be improved by adding the number of passengers that access/egress from vehicles or the population within the 400 m isochrone. Thus, the model allows the simulation of the vehicle movement in the existing urban public transportation tram network and can support decisions related to optimizing the tram schedule during peak hours. Furthermore, it allows an appropriate design of platforms or the allocation of different-capacity vehicles to adapt the fleet to demand.
## 7 Performance Evaluation
### Simulation Results
#### 7.2.3 Impact of Node Density  
To evaluate impact of node density on SCODE_plus_, we vary the number of nodes from 200 to 600. The number of sinks is 8. Each sink keeps moving with speed 10m/s as the default setting. The number of sources is 2. The sensor field size is not changed.  
Figure 14: Success ratio vs. sink speed.  
Figure 15: Energy consumption vs. sink speed.  
Figure 13: Average delay vs. sink speed.  
Figure 15 shows the energy consumption with different node densities. The figure demonstrates that SCODE_plus_ consumes less energy than SCODE, TTDD, and DD. As the number of nodes increases, the total energy consumed slightly increases while that of TTDD and DD significantly increases. This is mainly because SCODE_plus_ turns off radio most of the time. Therefore, energy is consumed mostly by the coordinators. Whereas, in TTDD and DD, nodes do not participate in communication still consume much energy in idling mode. Figure 16 shows that SCODE_plus_ almost delivers all packets successfully (above 99%).
# Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System
## 8 Conclusions  
This paper presents the analysis and design of a flat-top reflectarray antenna for an MEO satellite system for remote sensing at an X-band frequency of 8.5 GHz. The feeding antenna is a Yagi-Uda antenna. The amplitude and phase of the field distribution due to the feeding antenna at the aperture of the reflectarray antenna are obtained numerically without introducing any approximation. This field distribution is used to obtain the required reflection phase distribution to obtain the required flat-top radiation pattern. This phase distribution is obtained using genetic algorithm optimization. The initial phase distribution of the optimization process is taken to be the phase distribution of a broadside pencil beam, which is obtained analytically. The problem of the tilted pencil beam is also investigated using analytical calculations. These reflection phase distributions are implemented using periodic reflecting elements. The reflecting element is composed of stacked circular patches on a grounded double-layered dielectric substrate. The reflection phases of these reflecting elements are adjusted by controlling the diameter of the stacked circular patches. These reflecting elements are arranged according to the required phase distribution for the cases of broadside pencil beam, tilted pencil beam, and flat-top beam. The complete reflectarray  
Figure 21: Measured and optimized radiation pattern of the proposed reflectarray antenna.  
Figure 22: Three-dimensional radiation pattern of the proposed reflectarray antenna.  
systems for the three cases are investigated. Details of simulation steps are discussed. The flat-top beam reflectarray was fabricated and measured for verification. Good agreements between the obtained results and the simulated results were obtained. The results of the reflection coefficient of the designed and fabricated feeding antenna showed a good agreement. Moreover, the obtained radiation pattern of the complete reflectarray antenna was found to satisfy the required radiation mask to a good extent.  
S.A.M.S., E.M.E. and A.M.A. have contributed to the conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing, review, editing and visualizations. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Expanded Scenarios for Validation  
Several scenarios with different user locations and dynamic user motions have been included in the assessment in order to better evaluate the suggested framework. These further examples show how the recommendation system may be used with mobile agents and geographic data while being resilient and flexible.
# Hybrid CMP Slurry Supply System Using Ionization and Atomization
## 2 Materials and Methods
### Experimental Setup
#### 2.3.2 Step Height Reduction and Dishing during the CMP of Patterned Wafers  
The 300-mm Cu-patterned wafer, SKW 6-3.18C (SKW Associates, Inc., USA), was designed as a damascene structure with an electroplated Cu layer. We investigated the reduction in step height and dishing based on the slurry supply systems. Figure 3 depicts the considered pattern lines with different pitches and the cross-sectional view of the PW used in this experiment. The step height of the film deposited on the patterns is considered as the vertical height of the surface asperity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \multicolumn{2}{c}{**Conditions**} \\ \hline Machine & POLI-762 (G\&P Technology Inc., Korea) \\ \hline Velocity & Carrier: 93 rpm; Platen: 87 rpm \\ \hline Wafer & 300 mm 1.5 \(\upmu\)m Cu & 300 mm 2 \(\upmu\)m SiO\({}_{2}\) \\ \hline \multirow{3}{*}{Pressure} & Wafer: 20 kPa & Wafer: 30 kPa \\ \cline{2-3}  & Retainer: 30 kPa & Retainer: 40 kPa \\ \hline Slurry type & Colloidal silica slurry & Ceria slurry \\ \hline Gas type & O\({}_{2}\) & N\({}_{2}\) \\ \hline Gas pressure & 0.05 MPa & \\ \hline Slurry nozzle & Full-cone spray nozzle (twin-fluid) \\ \hline Power supply & AC \\ \hline Voltage & 30 V \\ \hline Slurry flow rate & 160 mL/min \\ \hline Conditioning & In situ \\ \hline Process time & 1 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions based on the wafer type for global planarization.  
Figure 3: Information on the Cu-patterned wafer. (**a**) Layout of the Cu patterns and the patterns considered in the experiment and (**b**) the cross-sectional view of the patterned wafer.  
The patterns were measured before and after the completion of the CMP process using a non-contact 3D surface profiler ([PERSON], Nanosystems, Korea). Cu dishing is the difference in height between the center and the edge of the Cu line. As dishing primarily occurs in the wide trenches, 200-\(\upmu\)m (100 \(\upmu\)m /100 \(\upmu\)m) and 100-\(\upmu\)m (50 \(\upmu\)m /50 \(\upmu\)m) pitches of equal density (50%) were selected (Figure 4). The patterns were measured at 30, 60, 90, and 120 s during the process. Based on the process time when the Cu film was nearly removed, we performed over-polishing from 0% to 30%. The specific experimental conditions were identical to the experiment described in Section 2.3.1 (Table 1).
## 5 The Experiment and Analysis
### Fault Data from QPZZ-T  
To further prove the effectiveness of the KMVMD-PGMCKD, the fault signal from QPZZ-II rolling bearing is selected here. The bearing is N205, the pitch diameter is 39 mm, rolling elements with a diameter of 7.5 mm are 13, the speed is 1500 r/min, and the sampling frequency is 12 kHz. The signal length is 2048. Therefore, the fault characteristic frequency is 125.2 Hz, the inner race fault characteristic frequency is 194.2 Hz and the rotating frequency \(f_{r}\) is 25 Hz.  
The time waveform and power spectrum are given in Figure 7.  
Figure 6: The obtained results of rolling element fault signal under variable working conditions.  
From Figure 6(a), the signal contains strong noise interference, and faults are not obvious. In Figure 6(b), the rotating frequency and its second harmonic are only obviously obtained, and the amplitude of other components is relatively weak, so it is difficult to find any useful features for fault diagnosis. The KMVMD-PGMCKD is used to further process in order to extract fault features.
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 3 Results
The most important variables to explain the spatial distribution of SOC, Ca, N, P and K contents were remote sensing data, while the terrain covariates were responsible for pH and Mg prediction. For example, S2A B12 band, NDVI and aspect were responsible for the SOC variation. Since there was a close positive correlation between the SOC content and N (r = 0.86), the key predictors of these parameters were practically the same. Terrain attributes were less responsible for agrochemical property variations, which can be explained by the relatively flat topography of the plots, low pedologic diversity and insufficient spatial resolution of the DEM. The variation in pH levels was entirely controlled by the slope, aspect and elevation covariates. The slope variable was also the leading predictor of Mg content. The spatial distribution of the remaining elements was controlled by both types of variables. The generated digital maps of soil properties in the upper horizon (0-10 cm) using the RF method are shown in Figure 3. The spatial distribution of the elements was not homogeneous. The elevated concentrations of pH, Ca, P and K were found mainly in the northern part, while Mg content was found in the southern part of the plot.
## 3 On Orbit Operations and Anomaly Analysis
Contact was lost with the spacecraft between 15 February and 2 April, during which ground stations that had previously been able to receive IDEASSat beacon packets were unable to do so, indicating that the cause of the anomaly was on the spacecraft end. Detailed analysis of this critical anomaly will be presented in more detail in Section 3.3. The spacecraft briefly recovered between 2 April and 4 April, during which commands were successfully uplinked to the spacecraft commanding the replay of saved flight data. Contact was again lost with the spacecraft following this period, with no recovery detected in the following months. The spacecraft was decommissioned on 2 September 2021, following the conclusion of the previously defined six months mission lifetime. Key events during the on-orbit lifetime of the spacecraft will be described in the following sections.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Wavelet-Based Damping Extraction
#### 3.2.2 Wavelet-Based Energy Estimation  
Based on [PERSON]'s theorem, the energy of a signal is formulated as  
\[E_{s}=\int_{-\infty}^{\infty}\left|x(t)\right|^{2}dt=\frac{1}{2\pi}\int_{-\infty }^{\infty}\left|X(\omega)\right|^{2}d\omega, \tag{27}\]  
where \(X(\omega)\) is the continuous Fourier transform of \(x(t)\).  
\[X(\omega)=\int_{-\infty}^{\infty}x(t)e^{-i\omega t}dt. \tag{28}\]  
Equation (27), which is a part of density spectral density, describes how the energy of a signal in the time domain is distributed in the frequency domain. The main reason that Fourier transformation preserves energy is that multiplying a signal by complex sinusoids does not add or reduce the energy within the signal since \(\int_{-\infty}^{+\infty}\left|e^{-i\omega t}\right|^{2}dt=1\). This reason can be extended to wavelet transform. Based on Equations (23) and (24), one is able to conclude  
\[\int_{-\infty}^{+\infty}\left|\frac{1}{\sqrt{s}}\Psi(\frac{t-\tau}{s})\right| ^{2}dt=1. \tag{29}\]  
Hence, similar to Fourier, wavelet transformation preserves energy. As documented in [42]:  
\[E_{s}=\frac{1}{C_{\Psi}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\frac{1 }{s^{2}}|\mathcal{W}_{\Psi,x}(s,\tau)|^{2}d\tau ds, \tag{30}\]  
where \(C_{\Psi}=\int_{-\infty}^{\infty}\frac{\left|\Psi(\omega)\right|^{2}}{\left| \omega\right|}d\omega<\infty\) and \(\hat{\Psi}(\omega)\) is the Fourier transform of the mother wavelet \(\Psi(t)\).  
Wavelet-based energy is the tool used to align impact responses as the time of impact (\(\tau_{0}^{k}\)) can be estimated by the time of maximum energy. The alignment is necessary for damping extraction with the wavelet method, especially for the averaging step, which will be discussed further. It should be noted that, in this article, we are not interested in finding the exact moment of impact. The main interest is to locate a reference point that enables us to align impact responses. The energy distribution in the wavelet spectrum is the primary resource to find this reference point.  
Theoretically, the energy of the signal represented in Equation (4) is zero before the impact, and it is expected to increase significantly after the impact, and due to the damping nature of the signal, the energy dissipates over time. Therefore, the maximum energy happens just after the impact. This maximum energy point is potentially one of the best reference points to align impact response recordings.
## 4 Simulation Results  
Conditions of the investigation done in Matlab/Simulink were as follows: the force and torque values were limited in order to avoid their excessive values at the beginning of the vehicle movement, i.e., \(|\tau_{u}|\leq 5\) N \(|\tau_{r}|\leq 5\) Nm, time of motion \(t=120\) s (for linear trajectory), \(t=300\) s (for sine trajectory), the time step \(\Delta t=0.03\), and using the method ODE3 Bogacki-Shampine. The simulation test was done based on software given in [66].  
Two underwater vehicles were intended for the test, namely ROPOS described in [67] and XX AUV model in [53; 68]. Their parameters are given in Table 1 and Table 2, respectively. For tracking, the desired trajectory position profiles are assumed, described as \(p_{d}=[x_{r},y_{r}]\):  
\[p_{d} =[0.7\,t,\ 0.7\,t]^{T}, \tag{48}\] \[p_{d} =[0.5\,t,\ 40\sin 0.02\,t]^{T} \tag{49}\]  
linear and sine trajectory, respectively. The start point was \(p_{0}=[-2,\ \ 6]^{T}\) (for ROPOS) and \(p_{0}=[-4,\ \ 8]^{T}\) (for XX AUV). The starting points were chosen according to the dynamic parameters of the vehicles (due to different dynamics of the tested vehicles), whereas the disturbances \(V_{x}=0.05\) m/s, \(V_{y}=-0.08\) m/s were taken from [56].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **ROPOS** & \\ \hline
**Symbol** & **Value** & **Unit** \\ \hline \(L\) & \(1.75\) & m \\ \(W\) & \(2.6\) & m \\ \(H\) & \(1.45\) & m \\ \(m\) & \(2268\) & kg \\ \(I\) & \(2457\) & kg\(\cdot\)m\({}^{2}\) \\ \(X_{ii}\) & \(-4380\) & kg \\ \(Y_{0}\) & \(-9518\) & kg \\ \(N_{r}\) & \(-5000\) & kg\(\cdot\)m\({}^{2}\) \\ \(m_{11}\) & \(6648\) & kg \\ \(m_{22}\) & \(11,786\) & kg \\ \(m_{23}\) & \(-1134\) & kg\(\cdot\)m \\ \(m_{33}\) & \(7457\) & kg\(\cdot\)m\({}^{2}\) \\ \(d_{11}\) & \(725\) & kg/s \\ \(d_{22}\) & \(1240\) & kg/s \\ \(d_{33}\) & \(1804\) & k\(\cdot\)m\({}^{2}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of ROPOS vehicle.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 3 System Model
### IRS Architecture and Working
The devices are furnished with active electronic components, including mixers, low-noise amplifiers for receiving, power amplifiers for transmission, and digital-to-analog converters (DACs and ADCs). Decoder-and-forward (DF) and amplify-and-forward (AF) relays usually need a number of electrical components. This means that relays to implement multiple-antenna systems in millimeter and submillimeter wave frequency bands may be expensive and energy-intensive [23]. On the other hand, IRSs are composite material layers composed of printed metallic or dielectric patches on a grounded dielectric substrate. Low-power, low-complexity electronic circuits (switches or varactors) guarantee their configurability [24]. Since discrete power amplifiers, mixers, and DACs/ADCs are typically not needed, IRSs are intended to be less complicated than relays, especially when IRSs are implemented in large quantities and with low-cost big-area electronics [25]. The additive noise that exists in relays and degrades the functionality of traditional relaying methods is caused by the active electrical components employed in relays [25]. However, additive noise does not affect IRSs that exhibit aberrant reflector behavior. Nevertheless, phase noises might interfere with them [26]. Furthermore, they are unable to amplify or renew the impulses if they are almost inactive [27]. The spectral efficiency of relay-assisted systems depends on the duplexing method. A half-duplex (HD) reduces the rate by half but can improve the signal-to-noise ratio. A full-duplex (FD) avoids this but introduces interference from simultaneous transmissions. IRSs bypass these issues, as they do not face HD constraints or self-interference and can enhance signal combinations by optimizing their reflection properties [25].