esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
Figure 11 shows the excited antenna measurement equipment in the backlight of the vehicle. The radiation beam pattern attributed to an elevated FOV varies only slightly, and the radiation power density is high for a low value of radiation power and azimuth radiation. Further, the radiation power concentrates near the edges and gaps in a large FOV. The proposed array antenna is mounted on the roof of a vehicle. High-frequency structure measurement system was analyzed around the vicinity of the patch antenna and the near-field components were determined. The geometry of the vehicle body and the excitation of the array antenna on the roof were combined for the high-frequency measurements. The measurement of the far-field radiation pattern of the proposed antenna takes about few hours in an anechoic chamber. The results of this measurement must be validated with those obtained using the antenna measurement equipment at Korea Automotive Technology Institute (KATECH), Cheonan-si, Korea. Figure 11 shows the electromagnetic anechoic chamber in KATECH. Figure 1:0 Simulation result showing the side view of the radiation beam pattern. Figure 1:1 Simulation result showing the top view of the radiation beam pattern. The maximum communication range for the intelligent transportation systems (ITS) operation is determined on the basis of the spacing between roadside beacons [12]. A spacing of 1 km implies a maximum operating range of 500 m to the nearest beacon; A link budget with 10 dB margin over 500 m range. If a high gain antenna with an elliptical directivity of 7 dBi was used for the vehicular antenna, to possibly enhance coverage, then the beacon antenna would require a directivity \(>40\) dBi. The antenna must also enable communication with nearby vehicles, by possibly forming a local area network for the transfer of data. This requirement for all-round coverage, which is free of radiation pattern nulls, conflicts with the requirement for a focused beam communicating with the access beacon. The antenna pattern must also be shaped such that the antenna gain is adjusted on the basis of the distance between the vehicle and the beacon. It is important to validate the abovementioned results against the results of the ground test. The high speed proving ground for the WAVE communication test is shown in Figure 1:2  
Figure 1:3 Anechoic chamber in KATECH for antenna measurements. Figure 1:4 Proving ground for vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communication measurement with the proposed antenna.
## 4 Simulation Results
### The Localization Error of ACFL and PCFL under Different Parameters
#### 4.1.2 The Number of Samples  
Filtering out more samples for ACFL (PCFL) in the process of simulation experiments can improve the localization error, but consumes more energy. Considering this situation, the number of samples which are deployed to localize mobile nodes should be reasonable to reach a compromise between energy savings and localization accuracy. In Figure 6 the threshold is set to be 0.0142 and the density of anchors is set to be 4.  
Figure 5: The threshold.  
Figure 6: The number of samples.  
When the number of the deployed samples which are filtered out to localize mobile nodes is much enough, samples are concentrated in the vicinity of the mobile node and the localization error reduces obviously. We can find that the localization error for both PCFL and ACFL is 0.91 m and 4.44 m when the number of the deployed samples is 500, respectively.
## 5 Conclusions  
In this article, we proposed a novel harbor extraction algorithm for high spatial resolution remote-sensing images, which is based on LEPA and EC-SIFT algorithms. First, in order to reduce the detail information of complex background for high spatial resolution remote-sensing images, we proposed a LEPA algorithm, which effectively reduces the interference of redundant detail information in the extraction algorithm and also retains useful feature information. Then we used the EC-SIFT algorithm to limit the extraction of keypoints to the edge regions, which makes our calculations in the process of keypoints matching and target extraction more accurate and less computational complexity. Finally, we used GOM-SIFT algorithm and QT clustering algorithm to extract the harbor candidate regions, and the SVM was used to identify the region containing the harbor target. In the experiment, we used 3000 test images and classical evaluation functions to evaluate the proposed harbor extraction algorithm. The results show that our algorithm has a significant effect in improving extraction accuracy and reducing computational complexity. It also outperforms other traditional harbor extraction algorithms. The proposed algorithm can be extended to other man-made target extraction in high spatial resolution remote-sensing images, such as airports and military bases, which requires additional research.  
Conceptualization, J.H. and Y.N.; Methodology, J.H.; Software, J.H.; Validation, J.H. and Y.G.; Formal Analysis, J.H. and H.Y.; Investigation, J.H. and S.S.; Resources, Y.G.; Data Curation, J.H.; Writing-Original Draft Preparation, J.H.; Writing-Review & Editing, J.H.; Visualization, J.H. and Z.Z.; Supervision, Z.Z., H.Y., Y.N. and S.S.; Project Administration, Y.G.  
This research was funded by the National Key R&D Program of China (Grants nos. 2016 YFB0500100).  
The authors declare no conflict of interest.  
Figure 13: (**a**) The values of Acc corresponding to the values of different parameter C. (**b**) The values of Acc corresponding to the values of different parameter S.
## 7 Future Development Trends and Research Prospects
### Theoretical Research Directions
Although existing theoretical research has achieved breakthroughs in certain aspects, it remains overall in an exploratory stage, lacking a universally applicable theoretical framework. Future theoretical research should emphasize fundamentality, foresight, and systematicity, addressing current practical problems while also reserving space for future technological development. Simultaneously, theory-practice integration should be strengthened, verifying and perfecting theoretical models through empirical research and forming a virtuous interaction between theoretical innovation and application innovation.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### Temperature Load Analysis  
The mirror component works in the track process; the mirror component will be affected by the temperature change of the environment, thus in the process of component design, through simulation to analyze the degree of its impact, corresponding countermeasures are made [17]. In this paper, according to the temperature range given by the thermal control analysis, and the loading temperature conditions on the mirror assembly, with the mirror component loading temperature load for 16-24 \({}^{\circ}\)C, the mirror component axial and radial affected by the temperature will show different characteristics; therefore, we need to load the mirror component axial, radial 4 \({}^{\circ}\)C temperature gradient load. The coordinate values of all the points on the mirror surface are obtained by simulation and analysis software, which are brought into MATLAB 2014 calculation to simulate the mirror surface shape. As shown in Table 7, it is the root-mean-square value of the surface shape of the mirror component after loading the temperature load by simulation analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Condition** & **X Gravity Load (IG)** & **Y Gravity Load (IG)** \\ \hline Surface error & & \\ RMS/\(\lambda\) (\(\lambda\) = 632.8 nm) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMS of mirror shape error under Gravity Load of 1G.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Frequency (Hz)** & **Vibration Modes Description** \\ \hline
1 & 179.6 & Secondary mirror rotates around Z-axis \\
2 & 188.1 & Secondary mirror translational movement towards X-axis \\
3 & 215.4 & Secondary mirror translational movement towards Y-axis \\
4 & 303.7 & Secondary mirror rotates around Y-axis \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of four modes analysis of the mirror component.
## 2 Materials and Methods
The main advantage of an antenna array would be a precise and known distance between the antennas, as these are embedded in the array, and the possibility to work with several receivers at once. On the other side, a single antenna gives more flexibility in the positioning, but it also requires precise knowledge of the position where the recording was produced. Only the \(z\) component of the electric field was used to reconstruct the confocal map through the Delay and Sum (DAS) algorithm [27]. Although several reconstruction algorithms exist, DAS is the simplest and still among the best performing ones [28]. In depth description of the DAS can be found elsewhere [16,22,28] and only a short description is presented here. The DAS is composed of three parts. In the first part, the waveforms acquired by all the antennas are calibrated by subtracting their average. In fact, the averaged signal contains the reflection from the main tissues or objects which are in common to all the antennas, in this application the back-scattered signal deriving from the interaction between the air and the lung medium. The calibrated signals are then time aligned and integrated. This converts the back-scattered signals which were not removed by the calibration into bell-like peak shapes appearing at particular time delays. Radial attenuation was compensated by scaling the signals by the square of the distance. Every point of the half-space is then taken as synthetic focal point \(\vec{r}\) and the intensity \(I\) of the confocal map was reconstructed by [16; 28]:  
\[I(\vec{r})=\left[\sum_{n=1}^{N}B_{n}(\tau_{n}(\vec{r}))\right]^{2}, \tag{8}\]  
where \(B_{n}\) is the calibrated waveform acquired at the antenna \(n\) and \(\tau_{n}(\vec{r})\) is the return time from the point \(\vec{r}\) to the antenna \(n\). The return time was mapped into position considering the velocity of the electromagnetic wave in the medium. In this case the wave velocity was taken from the dielectric permittivity of the lung tissue at the central frequency of the electromagnetic excitation. Finally, the pixel intensity of the confocal maps was normalized to unity to facilitate the comparison.
## 2 Operating Principle of Our OV Characterizer
We consider the scenario of an arbitrary polarized OV with a TC of \(\ell\) incident on the setup for our characterizer (Figure 1). Initially, an arbitrary polarized OV can be treated as a superposition of LCP and RCP OV components with a TC of \(\ell\). These two OV components are first incident on the SPG and diffracted in the direction of the \(+1\)st and \(-1\)st order modes with the handedness of their polarization flipped. RCP and LCP OVs are incident on the \(\text{PPG}_{(+)}\) and \(\text{PPG}_{(-)}\), respectively. The \(\text{PPG}_{(+)}\) and \(\text{PPG}_{(-)}\) diffract incident OVs, the wavevectors of which are aligned parallel to each other. Furthermore, both PPGs introduce a parabolic phase \(\exp[-i\phi(y)]\), so that cylindrical focusing occurs at the focal length positions of the \(\text{PPG}_{(+)}\) and \(\text{PPG}_{(-)}\). When OVs are cylindrically focused by the cylindrical lens, a bright bar-like spot appears in the focal plane [14, 15, 16]. The number of dark lines in this bar-like spot yields the magnitude of the TC; its sign is determined from the angle of the spot, being either \(+45^{\circ}\) or \(-45^{\circ}\). Therefore, using an imaging camera, the TC of any incident OV can be determined from its beam profile. Our system can determine the TC of the LCP and RCP components of incident light without any optical loss because the SPG and PPGs have a diffraction efficiency of 100%. In addition, because SoPs can be treated as superpositions of LCP and RCP components, our system can be used for characterizing OVs with arbitrary SoPs. This function is realized by assembling a combination of a SPG and PPGs. The \(\text{PPG}_{(+)}/\text{PPG}_{(-)}\) can only determine the TC value for only RCP/LCP light. From Equations (6) and (9), we find that the LCP/RCP light passing through the \(\text{PPG}_{(+)}/\text{PPG}_{(-)}\) is cylindrically spread. In this case, a bright bar-like spot is not obtained and therefore we cannot determine the TC of the incident OV. To solve this problem, we introduce a single SPG to individually convert the LCP and RCP components of incident OV into a bright bar-like spot using the PPGs. As a result, our system can determine the TC of incident OV regardless of its SoP. Figure 1: Schematic of the OV characterizer assembled with SPG and two PPGs. We note that our system also can determine the TC of a polarization vortex (PV) such as a radially polarized beam [22]. The Jones vector of a PV is expressible as  
\[|PV\rangle= \begin{pmatrix}\cos(p\theta)\\ \sin(p\theta)\end{pmatrix} =\frac{1}{\sqrt{2}}e^{ip\theta}|R\rangle+\frac{1}{ \sqrt{2}}e^{-ip\theta}|L\rangle, \tag{10}\]  
where \(p\) denotes the TC of the PV, which corresponds to the rotational symmetry of the polarization pattern around the optical axis. From Equation (10), we find that the PV is a superposition of RCP and LCP OVs with \(\ell=p\) and \(\ell=-p\). Therefore, when the PV is incident on the system (Figure 1), the patterns obtained in the focal plane are different bar-like spots. From their patterns, we can also determine the TC of each PV.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 5 Landing Buffering and Resetting Mechanisms in Animals and Robots  
The main purpose of jumping in animals is escaping from dangers. It is quite normal to see some animals loose stability after take-off and land on their back, especially in animals that adopt intermittent jumping strategy, while kangaroos or galagoes are able to land softly and jump again thanks to their compliant body structure and flexible leg movements. These animals can absorb shock without damaging themselves during landing and be ready for the next jump or other locomotion modes.  
After take-off, locusts adopt flapping or gliding to move longer and maneuver their body posture to be ready for landing. During landing, the locomotion of their legs can absorb most part of landing shock [110]. In some animals, the peak contact force during landing is three times higher than the peak force during take-off [278; 279]. The elastic tendons in legs can deform to absorb the landing shock, and the energy stored in deformed tendons can be reused for the next jump. The tendons work as power amplifiers and achieve metabolic energy conservation [280].  
Animals which execute continuous jumping do not need to reset themselves after landing, while intermittent jumping animals landing on their back have to reset themselves in order to prepare for next jump. When humans fall down, they reset themselves very easily with the support of hands. Similar strategies are also found in insects: when they fall down on one side of their body, they can use the coordination of their legs to reset themselves [107]. Similar mechanisms are used by turtles: they coordinate the movement of their head and legs to reset themselves when they are turned upside down [281].  
The ability to reset after falling and the ability to land softly are the most important abilities in intermittent jumping robots and continuous jumping robots. In existing jumping robots, the adopted strategies can be classified into four types: (i) Active resetting using support legs; (ii) Passive resetting assisted by flexible frame; (iii) Gliding wings that help land softly; (iv) Elastic buffering legs that help absorb impact.
## 5 Data Analysis and Method Implementation
### Second Stage: Model Evaluation  
ML classifier is designed to predict the unseen data. ML models can perform the various required actions; it is essential to feed them the training subsets, followed by the validation subsets, ensuring that the model interprets the correct data. A significant and relevant dataset can help the ML model to learn faster and better. As mentioned in the earlier section, there are five candidate algorithms with which the models are trained and fit, namely SVM, KNN, Bagging, RF, and ET. Using the RepeatedStratifiedKFold class from the scikit learn library, the models are trained to be not too optimistically biased and catch the selected model's variance.  
Model performance can be influenced by two configuration factors: parameter and hyperparameter. Parameters are the internal configuration variable and are often included as a part of the learning model. They help the model in making predictions and can characterize the model's skill for the given problem. In general, parameters can not be set manually; instead, they are saved as a part of historical learned models.  
Hyperparameters can be configured externally (often set heuristically) and are tuned based on the predictive model user has selected for solving the problem. Hyperparameters help estimate the model parameters, and the user often searches for the best hyperparameter by tuning the values using methods like grid search or random search available in scikit learn library.  
For the study, all the models are created using the default hyperparameters for their respective algorithms. While examining the models, the mean and standard deviation values of predictive accuracy are observed at each iteration. Every algorithm's efficiencyis analyzed based on the highest achieved predictive accuracy for the unseen test data. Figure 9 represents the candidate algorithms' predictive accuracy for each of the respective datasets. The observed results suggest that ET performs best on the given datasets while classifying the unseen test data; the overall accuracy is 64%, 63%, 72%, and 67% for Ecuador, Haiti, Nepal, and Pohang, respectively. ET and RF have performed better at predicting test data in comparison to the rest of the ML techniques.
## 4 Discussion  
Through this publication, we develop a classification based on mechanical characteristics: recommended powers, electrical resistance, wire surface and air resistance of an atomiser. We explain how these two groups of devices are linked to the inhalation behaviours. In reality, this illustrates how manufacturers probably developed devices: regarding the inhalation behaviour. Therefore, this new classification is not based on \"what this device looks like?\" but for \"who these devices are intended for?\".  
Additionally, results show that inhaling with a DL behaviour through a device with a high air resistance (first group) is physically impossible. In the other side, a user has to restrict significantly his inhalation in order to practice MTL with atomisers having low air resistance (second group). This would lead to discomfort during vaping. This also illustrates how the respiratory limits influence the design of the devices. Furthermore, we highlight an average heat flux (0.43 \(\pm\) 0.17 W mm\({}^{-2}\)) that looks to be a constant of devices (excepted for Veco plus). In a recent publication [4], authors carried boiling experiments using wire immersed in propylene-glycol and glycerol. They found a critical heat flux of 0.34 W mm\({}^{-2}\) for PG and 0.87 W mm\({}^{-2}\) for VG. These fluxes are consistent with the average one observed through the devices. These fluxes also appear to be a boiling regimen limit between a low and a high aldehyde production regimen. Consequently, the power ranges look to be empirically required in a range reducing the risk for the consumers.  
Therefore, we illustrate the link between the inhalation behaviour and the mechanical characteristics of the device. By coupling these observations and our previously published ones [5], we highlight the needed to use a vaping regimen adapted to the group of devices tested. Otherwise, results would not be consistent with the real condition of use. Without considering this link, this is the functioning itself of a device that is impaired. Carrying this kind of experiments would mainly provide conclusions showing a malfunctioning (low energy efficiency, short power range in optimal regimen of vaporization) while in reality, they reflect a misuse.  
To avoid this, we propose considering the defined limits between the two groups as criteria to select the vaping regimen that should be applied. By default, a device is considered as a one of the first group. Then, if a device has one characteristic of the second one (commonly called sub-ohm), even air resistance, it should be used with a direct lung vaping regimen like the one previously proposed. Finally, Figure 14 summarizes the classification developed in this publication and provides a graphical illustration of the discussions.  
Figure 13: Recommended powers of the twenty-six tested devices regarding their air resistances (extracted from the pressured drop measurements).
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Som  
Self-organizing maps (SOMs, [PERSON] (2000)) are a type of artificial neural network used for unsupervised learning. SOMs are particularly useful for dimensionality reduction and clustering, enabling the visualization of high-dimensional data in a low-dimensional (typically two-dimensional) space. The primary objective of a SOM is to produce a low-dimensional, discretized representation of the input space, preserving the topological properties of the data.  
A SOM consists of a grid of neurons, each associated with a weight vector of the same dimensionality as the input data. During the training process, input vectors are presented to the network, and the neurons compete to respond to the input. The neuron whose weight vector is closest to the input vector (according to a specified distance metric, typically the Euclidean distance) is declared the winner. This neuron, along with its topological neighbors, adjusts its weight vector to become more similar to the input vector. This process, repeated over many iterations, causes the map to self-organize and produce a meaningful clustering of the input data.  
Let \(\mathbf{x}\in\mathbb{R}^{n}\) be an input vector, and let \(\mathbf{w}_{i,j}\in\mathbb{R}^{n}\) be the weight vector associated with the neuron located at position \((i,j)\) on the grid. The training process of a SOM involves, as per Algorithm 1, the different steps.  
```
Input vectors \(\{\mathbf{x}\}\), learning rate \(\eta(t)\), neighborhood function \(h_{i,j;\mu_{i},j^{*}}(t)\), number of iterations \(T\)  Trained weight vectors \(\mathbf{w}_{i,j}\) Initialization: Initialize the weight vectors \(\mathbf{w}_{i,j}\) to small random values. for iteration \(t=1\) to \(T\)do for each input vector \(\mathbf{x}\)do Competition: Find the Best Matching Unit (BMU) \[(i^{*},j^{*})=\arg\min_{i,j}\|\mathbf{x}-\mathbf{w}_{i,j}\|\] Adaptation: Update the weight vectors of the BMU and its neighbors \[\mathbf{w}_{i,j}(t+1)=\mathbf{w}_{i,j}(t)+\eta(t)\cdot h_{i,j;\mu_{i},j^{*}}(t )\cdot(\mathbf{x}(t)-\mathbf{w}_{i,j}(t))\] where \[h_{i,j;\mu_{i},j^{*}}(t)=\exp\left(-\frac{\|\mathbf{r}_{i,j}-\mathbf{r}_{i^{* },j^{*}}\|^{2}}{2\sigma(t)^{2}}\right)\] endfor  Gradually reduce the learning rate \(\eta(t)\) and the neighborhood width \(\sigma(t)\). endfor
```  
**Algorithm 1** Self-Organizing Map (SOM) Training  
where the Best Matching Unit (BMU) is the neuron whose weight vector is closest to the input vector (this is calculated using the Euclidean distance).  
The result of the training process is a topologically ordered map where similar input vectors are mapped to nearby neurons, enabling the visualization of the high-dimensional input space in a low-dimensional grid. This characteristic makes SOMs a powerful tool for exploratory data analysis and visualization.