esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 2 FT-Based Beamforming Methodology for Reflecting PMS
The situation becomes more complex for multi-beam radiation patterns, in which the direction shift and the sidelobe levels may become even larger. For example, the project pattern and the reconstructed electric field pattern created by the same PMS operating at 10 GHz with two main beams directed at \((u_{0,1},v_{0,1})=(0.51,0.51)\) and \((u_{0,2},v_{0,2})=(-0.51,-0.51)\) are shown in Figure 4c,d. These figures demonstrate that the two main beams in the reconstructed pattern merge under these conditions. This is even better seen in Figure 5a,b, which depict the patterns as surfaces in the real three-dimensional (3D) space \((x,y,z)\). In this representation, which is limited to the visible range only, the far-field magnitude in a given direction in the 3D space is proportional to the distance from the origin to the corresponding point on the plot surface. Figure 4: _Cont._  
Consequently, we may conclude that using the FT-based beamforming alone may lead to large inaccuracies in the reproduced radiation patterns. Therefore, applying an optimization to the Fourier transform-based initial phases is necessary in order to suppress unwanted sidelobes and achieve a reasonable accuracy. However, we have to note here that involving optimization algorithms is time-consuming, which in many cases forbids real-time beamforming, especially on inexpensive hardware. Therefore, in Section 5 we will discuss how the optimized phase data can be used to train an ANN and how the trained ANN can be utilized to achieve beamforming in real time.
## 3 Methodology  
### SAR Polarization Selection  
Environmental conditions such as water level, wind speed and wind direction during image acquisition strongly affect the contrast between water and land in HH polarized RADARSAT-2 images, making it difficult to separate these features within HH polarized images. In order to accurately evaluate the shoreline, an analysis was conducted to select the best polarization for the study. The contrast between land and water using the backscatter coefficient and its standard deviation at different polarization was evaluated.  
Under a CSA DUAP project, CHS is evaluating RADARSAT Constellation Mission (RCM) data as a replacement for RADARSAT-2 data for its existing SAR applications. Since RCM images are still not available, simulated RCM products are derived using the RCM simulator developed at the Canada Centre for Mapping and Earth Observation (CCMEO). RADARSAT-2 imagery is used as input in this toolbox to generate simulated data using RCM specifications including: spatial resolution, polarization, and the Noise Equivalent Sigma Zero (NESZ). In CHS simulations, FQ15 and FQ25 images were re-processed using medium resolution of 16 m and the expected nominal NESZ for this beam mode (-25 dB). The generated RH (right circular transmit, horizontal receive) and RR (right circular transmit, right circular receive) polarization intensities are compared to RADARSAT-2 classical HH and HV polarizations. It is worth noting that as the RCM constellation will be replacing RADARSAT-2 in the near future, it is very important for CHS to ensure data and project continuity and, therefore, to test the capacity of RCM simulated backscatter in different polarizations. Ideally, RCM will be able to provide information similar or close to RADARSAT-2 data.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 2 The Proposed Methodology  
The proposed SP-SSA method includes three stages as described in Figure 1. In stage 1, superpixel segmentation is applied on the input HSI to obtain the segmented HSI. In stage 2, each segmented region is reconstructed using 2D-SSA to obtain the reconstructed HSI. In the final stage, an SVM classifier is applied on the reconstructed HSI to build the final classification map. A detailed description of each of these stages is presented in the subsections below.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### Trust Based Secure and QoS Routing Scheme
#### 3.3.3 Trust Based Secure and QoS Routing Strategy  
Figure 4 shows the flowchart for isolating a malicious node. All the nodes including the source, the intermediates, and the destination are cooperating during the route discovery process. The process of sending data towards the destination is as follows:  
Before data transmission begins, the source node finds the entry of the destination node in its routing table. If such an entry exists, the data is sent to the destination through a trusted hop. Otherwise, the source node starts a route discovery process by flooding route request (RREQ) packets into the network to discover a route to the destination node.  
During the routing process, if an intermediate node identifies a distrusted (marked as malicious during the trust update process) next hop in its routing table to the destination node, then the entry of  
Figure 4: Trust based secure Quality of Service (QoS) routing strategy.  
that particular node is discarded. The route discovery process is initiated by the intermediate node to discover an alternative trusted next hop.  
After the destination node is found, the route reply (RREP) is sent back to the sender node through trusted hops. If more than one RREP are delivered to the source node than the route with the highest destination sequence number is chosen and a trusted route is created for the destination node and saved in the routing table for routing.  
Finally, data is sent to the destination. If no routes are found, the whole process will repeat.
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
## 4 Field Experiments and Data Analysis
### Data Analysis  
The compensation parameters were solved according to the process shown in Figure 11. The geomagnetic gradient was considered in the process of solving the compensation coefficients. The raw data were sampled at 160 Hz, then filtered by a lowpass filter with a cut-off frequency of 20 Hz. The geomagnetic field column \(H_{\epsilon}\) was computed with Equation (16), and the information matrix \(A\) was constituted by Equation (6). Finally, the compensation parameters were solved by Equation (9). After the calibration flight, the effectiveness of the proposed method was examined based on the test flight data.  
The compensation coefficients could be obtained without taking into consideration the geomagnetic gradient (i.e., the traditional method), as shown in Table 5. Table 6 shows the compensation coefficients of  
The three-axis magnetometer data in the calibration flight are shown in Figure 12. It is obvious that there were four periods of time for the compensation maneuvers. At each stage, the pitch, yaw, and roll were executed in five cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline c1 & 2.697 & c2 & 1.580 \\ c3 & \(-0.428\) & c4 & \(-4.884\times 10^{7}\) \\ c5 & \(-3.632\times 10^{6}\) & c6 & \(-8.316\times 10^{6}\) \\ c7 & \(-6.011\times 10^{6}\) & c8 & \(-1.781\times 10^{5}\) \\ c9 & \(1.270\times 10^{4}\) & c10 & \(8.491\times 10^{4}\) \\ c11 & \(-1.534\times 10^{6}\) & c12 & \(3.432\times 10^{6}\) \\ c13 & \(-3.585\times 10^{6}\) & c14 & \(8.434\times 10^{4}\) \\ c15 & \(2.242\times 10^{5}\) & c16 & \(1.355\times 10^{6}\) \\ c17 & \(-1.104\times 10^{6}\) & c18 & \(7.972\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Compensation coefficients (proposed method).  
Figure 11: The process for solving compensation parameters.  
Figure 12: The three-axis magnetometer data in the calibration flight (after a bandpass filter, offset for clarity).  
Based on the five sets of data shown in Table 7, it was concluded that the performance of the proposed method was better than that of the traditional method and the Ge method. For the four groups of calibrated flight data, compared to the traditional method, the improvement in IR after considering the geomagnetic gradient was between 152% and 224%, which is quite significant. The improvement in IR for the level flight data reached a significant 329%. As shown in Figure 15, the peak-to-peak value of the residual noise after compensation was 19.4 pT, which proves that the proposed method can well compensate the interference field of the aircraft and improve the ability of the system to detect weak magnetic anomalies.
# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 3 NextDet
### Bounding Box Regression  
The complex task of object detection can be divided into following two sub-tasks for simplicity: object classification and object localization. The task of object localization depends on Bounding Box Regression (BBR) to locate an object of interest within an image and draw a predicted rectangular bounding box around it by overlapping the area within the predicted bounding box and the ground truth bounding box. This overlap area is called as Intersection over Union (IoU) loss. IoU, also known as Jaccard Index, is a popular metric used to measure diversity and similarity of two arbitrary shapes. It can be defined as the ratio of the intersection and union of the predicted bounding box (\(A\)) and the ground-truth bounding box (\(B\)), and can be mathematically defined as follows:  
\[IoU(A,\ B)=\frac{A\ \cap\ B}{A\ \cup\ B} \tag{7}\]  
IoU loss is a popular BBR approach, but it does not work when predicted bounding box and the ground truth bounding box do not overlap i.e., when \(IoU(A,\ B)=0\). To overcome this disjoint condition of \(A\) and \(B\) in IoU, Generalized IoU (GIoU) [8] loss has been implemented into the design of the proposed NextDet object detector. GIoU solves IoU's disjoint problem by increasing the overlap area size between the predicted bounding box and the ground truth bounding box, and slowly moving the predicted bounding box towards the target ground truth box. GIoU loss can be mathematically represented by Equation (8) as follows:  
\[\mathcal{L}_{GIoU}=1-IoU+\frac{|C-A\ \cup\ B|}{|C|} \tag{8}\]  
Here, \(C\) is the smallest box covering \(A\) and \(B\). Experiments in [8] demonstrate that GIoU achieves an improved performance over Mean Squared Error (MSE) and IoU losses. Furthermore, the experiments demonstrate that GIoU is effective in addressing vanishing gradients in non-overlapping cases.
## 2 Research Background
### Program Slicing and Decomposition  
Program slicing comprises two techniques, static slicing and dynamic slicing [24; 25; 26]. The static slicing proposed by [PERSON] refers to selecting a specific variable V and separating and testing the program statements that make it functional on the premise that it does not affect the overall program behavior. It is often used in maintenance and verification testing.  
Taking Figure 2 as an example, the results of the static slicing of X, Z, and TOTAL are shown in Figure 3.  
1 BEGIN  
2 READ(X, Y)  
3 TOTAL := 0.0  
4 SUM := 0.0  
5 IFX<=I  
6 THENSUM := Y  
7 ELSEBEGIN  
8 READ(Z)  
9 TOTAL := X*Y  
10 END  
11 WRITE(TOTAL, SUM)  
12 END.  
Figure 2: Static slicing sample code.  
[PERSON] et al. presented the idea of dynamic slicing [27], which involves selecting and slicing sentences that affect or change the content of variable V in the program, with the aim of achieving slicing analysis more efficiently than with static slicing.  
In Figure 4, the dynamic slicing path when variable N = 2 is {1, 2, 3, 4, 51, 61, 71, 81, 91, 62, 72, 82, 92, 63, 10}. Program decomposition [28] occurs because of the limited effectiveness of program analysis, and the algorithms used by developers usually have fixed signs to follow. Therefore, this algorithm is designed to statically decompose the program into multiple tokens. The program is orderly decomposed into various components, by which the designed module program disassembles, analyzes, and checks the relationships between each semantic fragment.
## 4 Analysis of Influence Factors
### Wheel-Rail Friction Coefficient  
The alignment irregularities in the tangent before the switch cause abnormal wheel-rail, aggravating the wheel-rail dynamic effect, leading to the severe wear of rail surface, which results in a large variation in the wheel-rail friction coefficient. Subsequently, the increase in friction will also influence the wheel-rail contact state, affecting the safety of the train passing switch. The magnitudes of simulated results under the wheel-rail friction coefficient 0.10, 0.175, 0.25 (basic friction), 0.325 and 0.40 are shown in Figure 12. It should be noted that in this paper, the wheel-rail friction coefficient refers to the friction coefficient of both the rail top and gauge angle of the stock rail and the blade rail.  
As seen in Figure 12, similarly due to the alignment irregularities in the tangent before the switch, the wheel-rail contact forces in the tangent are greater than those in the switch. In terms of wheel-rail lateral force, the general trend is that the lateral forces of the motor and trailer wheel, in both the tangent and switch, increase with the increase in the friction  
Figure 12: Dynamic characteristics of the train passing the tangent and switch under different friction coefficients. (**a**) Wheel–rail lateral force; (**b**) wheel–rail vertical force; (**c**) derailment coefficient; (**d**) wheel load reduction; (—) magnitudes in the tangent; (–) magnitudes in the switch.  
coefficient. In particular, the lateral force of the trailer wheel on the tangent declines at the friction coefficient 0.25, and then rises again. Moreover, the lateral force of the trailer wheel in the switch zone increases rapidly with the friction coefficient increasing from 0.175 to 0.4, which suggests that the independently rotating wheelset is more sensitive to the variation of the wheel-rail friction in the switch zone than the integrated wheelset. As for the vertical force, it also tends to increase with the increase in the friction coefficient, and the vertical force of the trailer wheel also declines at the friction coefficient of 0.25 on the tangent. The vertical force of the motor wheel in the switch zone exceeds that in the tangent as the friction coefficient increases. The variation trends of the derailment coefficient and wheel load reduction are inconsistent with trends of the lateral force and vertical force, respectively. It can be concluded that the dynamic indexes of the integrated wheelset in the tangent with alignment irregularity increase with the increase in the friction coefficient, while the dynamic indexes of the independently rotating wheelset will increase both under the condition of smaller and larger friction coefficient, but tend to decrease under the intermediate friction coefficient. In the switch, the dynamic indexes of both wheelsets increase as the friction coefficient increases, and the lateral force and derailment coefficient of the independently rotating wheelset increases remarkably.
## 5 Results and Discussion
### Strain Force and Power Consumption  
For stirred tanks in industrial applications, power consumption is a significant cost consideration. At the same rotation speed, the larger the strain rate, the smaller the strain stress (that is, the smaller the fluid resistance received), and the lower the input power requirement for the shaft. Compared with Figure 19a, the modification had little effect on the strain rate near the blade tip, but the baffle cut enhanced the average strain rate. In the annular flow area between the impeller and the baffle, the strain area was enlarged (0.25 h), which was also more conducive to the homogenization distribution of particles. The modified baffle improved the uniformity of the strain rate distribution outside the impeller and was also beneficial to the average degree of the axial strain rate distribution. When \(A/W=0.333\) and \(\lambda/h=0.028\), the strain rate near the axis was obviously enhanced (0.6 h, 0.95 h), but the strain-rate distributions between different modified structures were considerably similar.  
As shown in Table 4, comparing the power of different tooth heights under \(\lambda/h=0.104\), with the increase of the sinusoidal sawtooth amplitude, the power consumption decreased more obviously, with a maximum reduction of 9.8%. When \(A/W=0.333\), the relative tooth width decreased, the power consumption tended to be lower, and the power consumption was reduced by 11.7% (Table 5). By analogy with the experimental results of [PERSON] et al. (2018), the result of the sawtooth baffle for reducing power consumption was reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative Tooth** & **Relative Tooth** & **Power (W)** &
\begin{tabular}{c} **Reduced Power** \\ **Consumption (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{0.104} & 0 & 21.4 & - \\  & 0.167 & 19.6 & 8.4 \\ \cline{1-1}  & 0.250 & 19.5 & 8.9 \\ \cline{1-1}  & 0.333 & 19.3 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of relative tooth height on power consumption.  
Figure 19: The contour diagrams of strain rates at different heights before and after baffle modification (0.25 h, 0.6 h, and 0.95 h). (**a**) The standard baffle; (**b**) \(A/W=0.333\), \(\lambda/h=0.104\); and (**c**) \(A/W=0.333\), \(\lambda/h=0.028\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relative Tooth** & **Relative Tooth** & **Power (W)** &
\begin{tabular}{c} **Reduced Power** \\ **Consumption (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{0.333} & 0 & 21.4 & - \\  & 0.028 & 18.9 & 11.7 \\ \cline{1-1}  & 0.056 & 19.0 & 11.2 \\ \cline{1-1}  & 0.104 & 19.1 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The effect of relative tooth width on power consumption.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 4 Analysis of Vibration Isolation Structure
### Vibration Response  
#### 4.2.1 Modal Analysis  
The modal analysis of the common structure and the vibration isolation structure is carried out to obtain the values of the vertical natural vibration frequency and horizontal natural vibration frequency of the structure (Table 5). It can be seen from Table 5 that eight vibration isolators are arranged to change the vertical frequency of the overall structure, making the first vertical frequency smaller. The effects on the horizontal frequency can be negligible. For the vibration isolation structure, the first-order vertical frequency is significantly reduced after vibration isolation because the stiffness of the vibration isolator layer is smaller than the rigidity of the superstructure. The vibration mode appears as the overall motion of the vibration isolator superstructure. If vibration isolators are installed under all columns at the same time, the vertical natural vibration frequency of the structure will become smaller and further from the excellent frequency of metro vibration waves. Thus, the ring spring vibration isolator is able to extend the natural vibration period of the structure effectively and reduce the transmission of vertical vibration to the superstructure.
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 7 Experimental Results  
To evaluate the results of the proposed SOA algorithm, a set of experiments has been conducted. Such results have been compared to those produced by the Artificial Bee Colony (ABC) [9], the Covariance matrix adaptation evolution strategy (CMAES) [4], the Crow Search Algorithm (CSA) [8], the Differential Evolution (DE) [6], the Meth-flame optimization algorithm (MFO) [18] and the Particle Swarm Optimization (PSO) [10], which are considered the most popular metaheuristic schemes in many optimization studies [32].  
For the comparison, all methods have been set according to their reported guidelines. Such configurations are described as follows:  
* ABC: Onlooker Bees = 50, acceleration coefficient = 1 [9].
* DE: crossover probability = 0.2, Beta = 1 [6].
* CMAES: Lambda = 50, father number = 25, sigma = 60, csigma = 0.32586, dsigma = 1.32586 [4].
* CSA: Flock = 50, awareness probability = 0.1, flight length = 2 [8].
* MFO: search agents = 50, \"a\" linearly decreases from 2 to 0 [18].
* SOA: the experimental results give the best algorithm performance with the next parameter set par1 = 0.7, par2 = 0.3 and par3 = 0.05.  
In our analysis, the population size \(N\) has been set to 50 search agents. The maximum iteration number \((Maxgen)\) for all functions has been set to 1000. This stop criterion has been decided to keep compatibility with similar works published in the literature [33; 34]. To evaluate the results, three different indicators are considered: The Average Best-so-far (**AB**) solution, the Median Best-so-far (**MB**) solution and the Standard Deviation (**SD**) of the best-so-far solutions. In the analysis, each optimization problem is solved using every algorithm 30 times. From this operation, 30 results are produced. From all these values, the mean value of all best-found solutions represents the Average Best-so-far (**AB**) solution.  
Likewise, the median of all 30 results is computed to generate **MB** and the standard deviation of the 30 data is estimated to obtain **SD** of the best-so-far solutions. Indicators **AB** and **MB** correspond to the accuracy of the solutions, while **SD** their dispersion, and thus, the robustness of the algorithm.  
The experimental section is divided into five sub-sections. In the first Section 7.1, the performance of SOA is evaluated with regard to multimodal functions. In the second Section 7.2, the results of the OTSA method in comparison with other similar approaches are analyzed in terms of unimodal functions. In the third Section 7.3, a comparative study among the algorithms examining hybrid functions is accomplished. In the fourth Section 7.4, the ability of all algorithms to converge is analyzed. Finally, in the fifth Section 7.5, the performance of the SOA method to solve the CEC 2017 set of functions is also analyzed.
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 2 Design of Antenna  
After making modifications to the initial design and optimizing its performance through a series of four steps, we successfully developed an improved antenna that yields superior results, holding promise for delivering enhanced functionality and meeting specific requirements in various applications. The evolution of the antenna is discussed in four different steps given below.