esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Characteristics, Availability, and Preprocessing  
The data inputs described below were provided by Eurocontrol, the Czech Hydrome-teorological Institute (CHMU), Prague International Airport (LKPR), and Air Navigation Services of the Czech Republic (ANS CR). These data inputs were chosen based on their availability and, therefore, some of the elements potentially contributing to delay predictability might be missing. Figure 1 presents a dataset obtained for the purpose of this study.  
Trajectory data provided by Eurocontrol contain multiple fields for each single flight record along the FTMF and CTFM profiles. A dataset combining LKPR and ANS CR information presents mainly information linked to a slot coordination, as well as additional flight progress timestamps and movement data. Finally, the meteorological data constituting an essential element of this study include forecasting information for the respective region represented by the longitude, latitude, and corresponding prediction of velocity elements. These parameters are further assigned to a corresponding altitude.  
Figure 1: Structure of data from respective sources.  
All datasets were thoroughly cleaned to ensure the removal of duplicate records and the standardization of formats. This preprocessing step was essential, as the subsequent activities required merging all available data into a single consolidated dataset. The initial step in this merging process involved integrating slot coordination data with FTFM profiles (a component of the trajectory dataset) as well as timestamps and movement data, primarily using the departure time as the key criterion. Notably, these three datasets did not share a common unique flight identifier. The trajectory dataset provided a time-dependent flight description, where each flight was associated with multiple waypoints, defined by latitude, longitude, and flight-over times, among other attributes (detailed in Figure 1). By combining slot coordination and timestamp/movement data with FTFM profiles, variables with consistent values for a given flight were generated. For example, SIBT or AIBT remained constant across all waypoints for a single flight. This discussion focuses specifically on merging FTFM profiles with other datasets, as the CTFM profile may differ in length from the FTFM profile for certain flights.
## 1 Introduction
The government aims to boost regional economic growth and increase productivity while promoting regional green technology innovation. Therefore, this article introduces the collaborative agglomeration of manufacturing and producer services industries into an analysis for the first time and deeply analyzes the relationships between high-speed rail, industrial collaborative agglomeration, and green technology innovation. The contributions and highlights of this article are mainly reflected in three aspects: firstly, this research proves that high-speed rail is a critical factor for promoting green technology innovation. This article not only analyzes the internal connection between the expansion of high-speed rail and regional green technology innovation in theory but also empirically tests this relationship by using the data of 284 Chinese cities between 2004 and 2013. Secondly, this paper establishes a theoretical framework that incorporates the collaborative agglomeration of manufacturing and producer services industries, high-speed rail, and green technology innovation for the first time. The intermediary role of industrial collaborative agglomeration is verified by the intermediary effect model. Thirdly, through the heterogeneity tests, it is found that the effects of green technology innovation brought about by high-speed rail are varied in different types of cities. Cities located in the western and central regions or that comparatively lack government support and are relatively backward in public cultural infrastructure are more able to obtain the benefits from the supply of high-speed rail services. The remainder of the study is arranged as follows: Section 2 presents a review and summary of past related literature; Section 3 presents the theoretical analysis of high-speed rail, industrial collaborative agglomeration, and green technology innovation; Section 4 presents an empirical test of the relationship between the high-speed railway and green technology innovation and verifies that the industrial collaborative agglomeration is an intermediary variable between high-speed rail and green technology innovation; Section 5 reveals the empirical results and analysis; finally, Section 6 summarizes and contributes some helpful hints on policy.
###### Abstract  
In recent years, with the development of deep learning technologies, Vision Transformers combined with Convolutional Neural Networks (CNNs) have made significant progress in the field of single-image super-resolution (SISR). However, existing methods still face issues such as incomplete high-frequency information reconstruction, training instability caused by residual connections, and insufficient cross-window information exchange. To address these problems and better leverage both local and global information, this paper proposes a super-resolution reconstruction network based on the Parallel Connection of Convolution and Swin Transformer Block (PCCSTB) to model the local and global features of an image. Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. Additionally, this paper designs a fusion module to integrate the global and local information extracted by CNNs. The experimental results show that the proposed network effectively balances SR performance and network complexity, achieving good results in the lightweight SR domain. For instance, in the 4\(\times\) super-resolution experiment on the Urban100 dataset, the network achieves an inference speed of 55 frames per second under the same device conditions, which is more than seven times as fast as the state-of-the-art network Shifted Window-based Image Restoration (SwinIR). Moreover, the network's Peak Signal-to-Noise Ratio (PSNR) outperforms SwinIR by 0.29 dB at a 4\(\times\) scale on the Set5 dataset, indicating that the network efficiently performs high-resolution image reconstruction.  
image super-resolution; CNN; feature extract; feature fusion; Swin Transformer
## Results and Discussion  
### Initial Microstructure  
As shown in Figure 1a, the initial as-cast microstructure of 13 CrMoNbV steel consists of martensite, ferrite, and non-metallic particles. The chemical composition of ferrite and martensite significantly differs only in the concentration of Cr and Mo (Table 2). The low level of Nb in both ferrite and martensite phases may confirm that Nb forms boridcarbides of niobium Nb\({}_{2}\)B\({}_{4}\)C\({}_{4}\) during crystallization, such as in 10 CrMoWNb ferritic/martensitic steel [18]. The annealing at 1275 \({}^{\circ}\)C for 5 min before deformation increases the size of the ferrite and austenite grains and leads to the dissolution of small Nb\({}_{7}\)B\({}_{4}\)C\({}_{4}\) particles (Figure 1b). The redistribution of Cr and Mo between ferrite and martensite proceeds during annealing with the increase in its concentrations in the bcc-Fe phase (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**C** & **Mn** & **Si** & **S** & **P** & **Cr** & **Ni** & **Mo** & **V** & **N2** & **Nb** & **B** \\ \hline
0.12 & 0.77 & 0.42 & 0.002 & 0.017 & 13.6 & 0.17 & 1.51 & 0.21 & 0.07 & 0.25 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition (wt. %) (balance Fe).
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
## 3 Sierpinski Resonators
### Design and Test of 26 GHz Switched Triangular Resonators  
The nominal 26 GHz switched resonator configuration is shown in the layout of Figure 10. In this case, a C3 configuration represents the left branch of the filter, while the C2C3 structure is on the right side.  
The expected response for the 26 GHz filtering is shown in Figure 11, while the measured performance is given in Figure 12.  
Figure 10: C3-C2C3 switched-resonator for the 26 GHz operation. The same criterion is used with respect to the 20 GHz resonators, with the SPDT driving the output signal through the C3 (left side) or the C2C3 (right side) structure, depending on the actuated branch.  
Figure 9: Experimental response for the 20 GHz switched filter with the C0 and C1 triangles.  
Looking at the experimental results, like in the case of the switched 20 GHz triangles, it is worth noting the necessity of improving the I/O coupling for engineering purposes. On the other hand, a dynamic ON/OFF response of the primary mode was obtained with approximately 15 dB of rejection. Nevertheless, a frequency shift of the main mode up to approx. 27 GHz had to be accounted for. In detail, the expected resonances for the entire structure were 24.14 GHz for the C3 output and 24.25 GHz for the C2C3 output, while the experimental responses were 27.13 GHz and 27.53 GHz for the C3 and C2C3 outputs, respectively. It must be stressed that the design was strongly affected by a shift of the main mode up to approx. 27 GHz.  
Figure 11: Simulation of the C2C3 structure for the 26 GHz filtering.  
Figure 12: Measure of the 26 GHz filter performance.  
with respect to the chosen resonance frequency, and this was particularly evident when the wanted resonance frequency was higher, beginning to be closer to the millimeter wave range. Such an effect was probably due to the necessity of an improved simulation effort on the entire structure, involving more geometrical details and a specific characterization of the employed materials, which could contribute to the equivalent circuit modifying the spectrum in a non-negligible way. While the preliminarily studied single resonators exhibited resonance frequencies closer to the expected, the entire structure, including the SPDT and the feeding lines and discontinuities (such as the bending on the corners), showed significant differences compared to the original design goals. In this case, smaller resonators helped to induce a decreased interaction, even if, also for this configuration, a shift in the position of the resonators could be helpful for more effective isolation between the two states.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 2 Techniques and Methods
### The TGA  
The TGA simulates the theory of evolution in biology, and its main parameters include population size, crossover operator, mutation operator, selection operator, etc. Among them, the crossover operator represents the reproduction phenomenon, the mutation operator represents the mutation phenomenon with a small probability, and the selection operator represents the phenomenon of survival of the fittest. As the iteration continues, the population will become more and more \"adapted to the environment\", so as to obtain an optimal or near-optimal solution. The flow of the TGA is shown in Figure 3. The TGA can be used for problems with multiple minima/maxima, multidimensional problems, discrete problems, etc., and has inherent parallelism, which makes it very suitable for solving TSPs. However, its convergence speed is slow, and it is easy for it to converge to a local optimal solution [18]. Therefore, the TGA needs to be improved to better solve the TSP.  
Figure 3: Flow chart of the TGA.
###### Abstract  
We propose a Multispectral Earth Polarization Imager (MEPI), which is located at the Earth-Moon system's Lagrange point L1. The imager can be used to measure the sunlight reflected by the Earth and the Moon. The measured sunlight has specific polarization information and spectral information, which can provide strong support for a comprehensive understanding of the Earth system and the construction of a perfect Earth-Moon system model. The MEPI provides multispectral images with wavelengths of 400-885 nm, and uses four sub-aperture systems to share a main system. The imager can capture the two-dimensional shape and polarization spectral information of the entire Earth at a spatial resolution of 10 km, and all spectral images can be simultaneously acquired on a single detector. The optical system of the instrument was designed and simulated. The simulation and analysis results showed that the camera can obtain high-quality images of the Earth disc with a 2.5\({}^{\circ}\) field of view (FOV). The novel MEPI provides a new way to generate climate-related knowledge from the perspective of global Earth observation. The imager can also be used for lunar observation to obtain spectral polarization information on the lunar surface. In addition, it also shows great potential in other applications of space remote sensing spectral imaging.  
spatial observation; spectral polarization; polarization imaging; optical design Article
## 4 MPC Scheme Design  
### Sideslip Constraints  
Generally, the sideslip first starts on the rear tire; thus, the sideslip constraint is enforced as bounds on the vehicle's rear tire sideslip angle (\(\alpha_{r}\)) and yaw rate (\(\omega_{z}\)). The range of \(\alpha_{r}\) is defined from the linear tire model used in (7) and converted into a bound on vehicle states with (5):  
\[\big{|}\big{(}v_{y}-l_{r}\omega_{z}\big{)}/v_{x}\big{|}\leq\alpha_{r,lim} \tag{11}\]  
To determine an appropriate bound on yaw rate (\(\omega_{z}\)), a steady-state analysis is used based on (1b). This ensures that the vehicle does not exceed the linear region of the brush tire model and yields a maximum sustained yaw rate as:  
\[\bigg{|}\omega_{z}+\frac{g}{v_{x}}\phi_{l}\bigg{|}\leq\frac{\bar{C}_{\alpha r} \alpha_{r,lim}\left(1+l_{r}\Big{/}l_{f}\right)}{mv_{x}} \tag{12}\]  
Assuming real-time estimation of \(\bar{C}_{\alpha r}\) is available, the vehicle envelope described is easily calculated in real time. Let \(\mathbf{S}_{sh}^{(k)}=\left[\alpha_{r,slack},\omega_{z,slack}\right]^{T}\) represent the slacks to the sideslip constraint, then (11) and (12) can be compactly represented as the following linear inequality for each time step \(k\) into the prediction horizon:  
\[-\mathbf{G}_{\text{sh}}-\mathbf{S}_{\text{sh}}^{(k)}\leq\mathbf{H}_{\text{sh}}\xi^{(k)}+ \mathbf{P}_{\text{sh}}u_{2}^{(k)}\leq\mathbf{G}_{\text{sh}}+\mathbf{S}_{\text{sh}}^{(k)} \tag{13}\]  
with:  
\[\mathbf{H}_{\text{sh}}=\left[\begin{array}{ccccc}1/v_{x}&-l_{r}/v_{x}&0&0&0&0\\ 0&1&0&0&0&0\end{array}\right],\mathbf{P}_{\text{sh}}=\left[\begin{array}{cc}0&0 \\ g/v_{x}&0\end{array}\right],\mathbf{G}_{\text{sh}}=\left[\begin{array}{c}\alpha _{r,lim}\\ \bar{C}_{\alpha r}\alpha_{r,lim}\left(1+l_{r}\Big{/}l_{f}\right)\Big{/}mv_{x} \end{array}\right]\]  
where \(\bar{\sigma}^{(k)}_{\text{s}}\) indicates the vehicle state at the \(k\)-th time step into the prediction horizon.  
The bounds (11) and (12) define a linear time-varying closed envelope, as shown in Figure 5. (11) serves as the basis for upper and lower bounds, and (12) defines the left and right bounds. Vehicle stability is guaranteed for all states in this envelope. That is, at any point within this envelope, a steering command exists such that the vehicle can safely remain inside. However, exceeding these bounds does not necessarily result in instability, but for states outside the envelope, there is no guarantee that a control input exists to move the system closer to the boundary in the next time step. Regions of the state space beyond the sideslip bounds entail nonlinearity of the rear tire force, which can be difficult to control. Regions directly past the yaw rate bounds, however, do not entail tire saturation and can lead back to the handling envelope without a spin. Often, vehicle states first exceed the yaw rate bound on the way to exceeding the side slip bound, which motivates using the yaw rate bound. However, small violations of the yaw rate bound by itself can occur without ensuing sideslip violations, and this behavior is considered acceptable in motion control.
## 3 Methods
### Overview
```
1:Raw point cloud \(\mathcal{P}=\left\{\mathbf{p}_{i}\right\}_{i=1}^{N}\in\mathbb{R}^{N\times 3}\)
2:Smoothed boundary point cloud \(\mathcal{B}\)
3:/* Step 1: Feature Extraction and Outlier Removal */
4:for all\(\mathbf{p}\in\mathcal{P}\)do
5: Find neighbors \(\left\{\mathbf{p}_{i}\right\}\) within radius \(R\)
6: Calculate LRF \(\mathbf{v}=\left\{\mathbf{x},\mathbf{y},\mathbf{z}\right\}\) and disambiguate direction using Equations (4)-(7)
7:endfor
8: Concatenate \(\mathcal{P}\) with LRF \(\rightarrow\mathbb{R}^{N\times 12}\)
9: Extract features \(\mathcal{F}\) via SA + PT + FP (see Figure 3)
10: Predict outlier labels \(\mathcal{L}_{0}\in\mathbb{R}^{N\times 1}\)
11: Remove outliers using Equation (1): \(\mathcal{P}_{C}\leftarrow\) replace outliers with first non-outlier /* Step 2: Boundary Detection */
12: Project \(\mathcal{P}_{C}\) onto XY-plane: \(\mathcal{P}_{2D}\in\mathbb{R}^{N\times 2}\)
13: Extract 2D feature \(\mathcal{F}_{2D}\) using Equation (8)
14: Perform cross-attention: \(\mathcal{F}_{B}\leftarrow\text{CrossTransformer}(\mathcal{F},\mathcal{F}_{2D})\)
15: Predict boundary labels \(\mathcal{L}_{B}\in\mathbb{R}^{N\times 1}\)
16: Extract boundary points \(\mathcal{P}_{B}\) using Equation (2) /* Step 3: Smoothing */
17: Project \(\mathcal{P}_{B}\) to 2D: \(\mathcal{P}_{B2D}\)
18: Find kNN of \(\mathcal{P}_{B2D}\) in \(\mathcal{P}_{2D}\); gather \(\mathcal{F},\mathcal{F}_{2D},\mathcal{P}_{C}\)
19: Fuse features via PAConv + reshape + PT
20: Predict displacement vector \(\mathcal{V}_{B}\in\mathbb{R}^{N\times 3}\) via MLP
21: Compute final smoothed boundary: \(\mathcal{B}=\mathcal{P}_{B}+\mathcal{V}_{B}\) (Equation (3))
22:return\(\mathcal{B}\)
```  
**Algorithm 1** Overall Pipeline of PcBD Processing Flow
## 1 Introduction  
Phased array systems are a key technology in wireless communication, utilizing beamforming and beam-shaping techniques. These systems consist of multiple antenna elements that can independently adjust the phase and amplitude of signals, enabling precise control over the direction of transmission or reception [1]. In communication applications, phased arrays enable high-speed wireless data transmission and multi-user access, offering enhanced network capacity and improved link reliability. Phased arrays also play a critical role in high-resolution target detection and tracking, providing accurate spatial localization. Moreover, they have broad applications in fields such as radio antennas, aerospace communications, and millimeter-wave imaging systems [2].  
In phased array systems, the phase shifter is one of the key components, as it adjusts the phase differences between antenna elements according to the desired beam direction and shape. This adjustment allows for coherent signal addition, enabling beam steering and focusing. The resolution and phase accuracy of the phase shifter directly influence the beam resolution and width in beamforming. As a result, there are increasingly stringent requirements for phase shifters in terms of insertion loss, accuracy, bandwidth, and chip area for phased array system applications [3]. Phase shifters can generally be categorized into passive and active types. Passive phase shifters, such as switch-type phase shifters (STPSs) [4, 5, 6], reflection-type phase shifters (RITSs) [7, 8, 9], and hybrid passive implementations [10; 11; 12], offer excellent linearity and consume no static power. However, they are often constrained by limited phase resolution, a restricted phase tuning range, increased insertion loss, and a large chip area. In contrast, vector-modulated active phase shifters [13; 14; 15] demonstrate superior characteristics, including high resolution, low loss, and compact integration. Among the various technology platforms, silicon-germanium (SiGe) BiCMOS has emerged as a highly attractive solution for 5G transceiver design. Compared to CMOS and III-V technologies, SiGe offers a balanced trade-off among cost efficiency, integration density, noise performance, and output power capability. Recent developments in SiGe-based phased array architectures have validated the feasibility of implementing compact, multi-channel RF beamforming systems for 5G applications, as reported in several contemporary studies [16; 17].  
This paper presents an ultra-wideband active phase shifter operating in the K and Ka bands, tailored for phased array systems. The proposed phase shifter features an innovative variable tail current approach. By modifying the tail current of the variable-gain amplifier (VGA), the range of selectable phase points is extended, leading to improved phase shifting accuracy and reduced gain fluctuations.
## 2 Materials and Methods
### Analytical Methods
#### 2.4.6 Analysis of Varietal Thiols  
The thiols in the wines were extracted with Dowex resin according to previously published methods [46], with some slight modifications in terms of sample preparation, as described [45]. In brief, 50 mL of wine sample was spiked with internal standards, i.e., 4-methoxy-2-methyl-2-mercaptoethanol (Sigma Aldrich, Schnelldorf, Germany) for 4-mercapto-4-methylpentan-2-one (4 MMP) quantification, [2H2]-3-mercaptoethanol acetate (University of Auckland, Auckland, New Zealand) for 3 MHA quantification, and [2H2]-3-mercaptoethanol-1-ol (University of Auckland, New Zealand) for 3 MH quantification. The pH of the wine was adjusted to 7 and the wine was passed through a Dowex column before being eluted with cysteine buffer as described previously [45; 46]. The thiols were extracted from the cysteine buffer by liquid-liquid extraction with ethyl acetate and dichloromethane. The organic phase was collected, dried over anhydrous sodium sulphate, and concentrated to a final volume of 50 \(\upmu\)L. The samples were analysed using a gas chromatograph (GC) (Agilent Technologies 7890A, CA, USA) coupled to a mass spectrometer (Agilent Technologies 5975C, CA, USA). The chromatograph was equipped with a capillary column (Agilent J&W GC column: HP-INNOWAX, 60 m \(\times\) 0.25 mm; film thickness 0.25 \(\upmu\)m; CA, USA). Helium gas at a constant flow of 0.6 mL/min was used as a carrier. The injector temperature was set to 240 \({}^{\circ}\)C with an oven temperature gradient of 50 \({}^{\circ}\)C for 5 min, then from 50 \({}^{\circ}\)C to 115 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then from 115 \({}^{\circ}\)C to 150 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min, 3 min at 150 \({}^{\circ}\)C, then from 150 \({}^{\circ}\)C to 205 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, from 205 \({}^{\circ}\)C to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, 19.625 min at 250 \({}^{\circ}\)C, then back from 250 \({}^{\circ}\)C to 50 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min and 3 min at 50 \({}^{\circ}\)C. The ion source temperature was 230 \({}^{\circ}\)C, the auxiliary temperature was 250 \({}^{\circ}\)C and the quadrupole temperature was 150 \({}^{\circ}\)C [45]. One-point calibration was performed using calibration standards in an alcoholic solution with a final concentration of 65 ng/L 4 MMP, 650 ng/L 3 MHA and 1202 ng/L 3 MH, and injected after every ninth sample. The limit of quantification (LOQ) was 2, 4, and 60 ng/L for 4 MMP, 3 MHA, and 3 MH, respectively.