esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ROI Selection  
To obtain mean QSM values in deep gray matter, seven regions of interest (ROIs), including the CN, PUT, GP, SN pars compacta (SNc), SN pars reticulate (SNr), RN, and DN, were manually drawn on two to three adjacent slices of each reconstructed QSM image (Figure 1). We selected these seven ROIs because they are correlated with the pathogenesis and progression of PD [6]. The thalamus was excluded because it had a wider area than the other regions and was challenging to delineate uniformly on the images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Patients with Early PD \({}^{\text{a}}\) (\(n=24\))** & **HCs \({}^{\text{b}}\) (\(n=27\))** & \(p\)**-Value** \\ \hline Mean age, years (range) & 68.8 (56–79) & 65.3 (53–82) & 0.139 \({}^{\text{c}}\) \\ Sex (male:female) & 8:16 & 7:20 & 0.759 \({}^{\text{d}}\) \\ Mean disease duration, years & & & \\ (range) \({}^{\text{d}}\) & 0.8 (0–3) & - & - \\ Mean NMSS \({}^{\text{c}}\) score (range) & 18 (4–47) & - & - \\ Mean Hoehn and Yahr stage & 1.6 (1–2.5) & - & - \\ Mean MDS-UPDRS \({}^{\text{d}}\) (part III & & & \\ sum) (range) & 17.8 (4–40.5) & - & - \\ \hline \hline \end{tabular} \({}^{\text{*}}\)\(p\) value from Student \(t\)-test. \({}^{\text{d}}\)\(p\) value from chi-square test. \({}^{\text{d}}\) Disease duration defined as duration from the onset of motor symptoms. \({}^{\text{a}}\), PD = [PERSON]’s disease, \({}^{\text{b}}\), HC = healthy control, \({}^{\text{c}}\), NMSS = Non-motor Symptoms Scale, \({}^{\text{d}}\), MDS-UPDRS = Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Ratings Scale.  
\end{table}
Table 1: Demographic and clinical characteristics of the study cohort.
## 2 Methodology
The fractions of the refracted waves(with the transmitted angle \(\theta_{\mathrm{t}}\)) around a steel bar can be simplified, as shown in Figure 3b. By applying the Pythagorean theorem and trigonometric formula to the simplified schema of the refracted waves in Figure 3b, the following equation is obtained:  
\[\sin\theta_{\mathrm{t}}=\frac{\Delta_{\mathrm{i}}}{\sqrt{\Delta_{\mathrm{i}}^{ 2}+\mathrm{e}^{2}}} \tag{2}\]  
By combining Equations (1) and (2), we get the equation:  
\[\frac{\left(\sin\theta_{\mathrm{i}}\right)^{2}}{\varepsilon_{2}}=\frac{\Delta _{\mathrm{i}}^{2}}{\Delta_{\mathrm{i}}^{2}+\mathrm{e}^{2}} \tag{3}\]  
Supposing that \(\mathrm{X}=\left(\sin\theta_{\mathrm{i}}\right)^{2}\) and \(\mathrm{Y}=\Delta_{\mathrm{i}}^{2}\), Equation (3) becomes:  
\[\mathrm{X}\times\mathrm{Y}+\mathrm{e}^{2}\times\mathrm{X}=\varepsilon_{2} \times\mathrm{Y} \tag{4}\]  
Figure 3: Schemas for geometrical relation between e, \(\Delta_{\mathrm{i}}\), and \(\theta_{\mathrm{t}}\): (**a**) Effect of the interference on the thermogram in transmission approach (\(\alpha=15^{\circ}\)) [35]; (**b**) Simplified schema of refracted waves around a steel bar. Equation (4) is a non-linear relation in which the concrete cover (e) as a constant parameter is to be found. By dividing this non-linear equation by X, another form of the equation is obtained:  
\[\mathrm{Y}=\varepsilon_{2}\times\left(\frac{\mathrm{Y}}{\mathrm{X}}\right)- \mathrm{e}^{2} \tag{5}\]  
In the proposed methodology, the non-linear equation (of the X and Y variables) is to be converted to a linear equation (in step 6). For this purpose, we suppose that \(\mathrm{X}=\frac{\mathrm{Y}}{\mathrm{X}}\), and \(\mathrm{y}=\mathrm{Y}\). Then, Equation (5) becomes:  
Figure 1:0  
which is a linear function with \(\varepsilon_{2}\) as the gradient constant, and a squared value of the concrete cover (\(\mathrm{e}^{2}\)) is to be found. Thus, the concrete cover thickness (e) can be deduced (at the final step of the methodology) from the line represented by linear Equation (6). It is worth noticing that two essential elements allowing us to detect the concrete cover (e) with the proposed methodology are: the angles of incident waves (\(\mathrm{\theta_{i}}\)) and the shift values (\(\Delta_{\mathrm{i}}\)).
## 3 Image-Servo Control and Pattern Recognition of the Positioning Mark
### Image Recognition Using COG and GHT
We have  
\[\overset{\rightarrow}{q}_{1}=R\cdot\overset{\rightarrow}{q}_{1}^{\prime} \tag{10}\] \[\overset{\rightarrow}{q}_{2}=R\cdot\overset{\rightarrow}{q}_{2}^ {\prime}\]  
where  
\[R=\left[ \begin{array}{cc}\cos\delta_{\theta}&-\sin\delta_{\theta}\\ \sin\delta_{\theta}&\cos\delta_{\theta}\end{array} \right] \tag{11}\]  
Figure 13: Kinematic relationship of XXY stage for image servo alignment. Therefore, from Equations (9)-(11), the following equation can be obtained. \[\overset{\rightarrow}{t}=\overset{\rightarrow}{q}_{1}^{\prime}+\overset{ \rightarrow}{c}_{1}-R\cdot\overset{\rightarrow}{q}_{1}^{\prime}=\overset{ \rightarrow}{q}_{2}^{\prime}+\overset{\rightarrow}{c}_{2}-R\cdot\overset{ \rightarrow}{q}_{2}^{\prime} \tag{12}\]  
From Equation (12), the angular displacement \(\delta_{\theta}\) can be obtained according to the following equation. \[\left[ \begin{array}{cc}1-\cos\delta_{\theta}&\sin\delta_{\theta}\\ -\sin\delta_{\theta}&1-\cos\delta_{\theta}\end{array} \right]\left(\overset{ \rightarrow}{q}_{1}^{\prime}-\overset{\rightarrow}{q}_{2}^{\prime}\right)= \left(\overset{\rightarrow}{c}_{2}-\overset{\rightarrow}{c}_{1}\right) \tag{13}\] \[\Rightarrow(I-R)(\overset{\rightarrow}{q}_{1}^{\prime}- \overset{\rightarrow}{q}_{2}^{\prime})=\overset{\rightarrow}{c}_{2}- \overset{\rightarrow}{c}_{1}\]  
After knowing the angular displacement \(\delta_{\theta}\), the translation displacement \(\overset{\rightarrow}{t}=\left[ \begin{array}{cc}\delta_{x}&\delta_{y}\end{array}  \right]^{T}\) can be obtained from Equation (12). Substituting \(\left[ \begin{array}{cc}\delta_{x}&\delta_{y}&\delta_{\theta}\end{array}  \right]^{T}\) into the proposed ANN method can obtain the motors' command. The alignment compensation steps are described in Figure 14. To test the robustness of the proposed method, Case 2 with the partial circle mark in Figure 12b is used to perform the alignment tests for these two methods (COG and GHT). The experimental results of the translation and rotational errors for 30 random alignment tasks and the comparisons between two methods are shown in Figures 15 and 16. From the experimental results, the proposed method using GHT has much better alignment precision than the traditional COG method. To achieve the better precision for the image-servo alignment tasks, there are two alignments performed in each testing. Table 8 summaries the experimental results of the root-mean-square error (RMSE) and the maximal alignment error for using COG and GHT. The experimental results also validate that the proposed method using GHT is better than the COG method. The computation time of the GHT method is a little longer than the COG method. Figure 14: Procedures of the proposed alignment compensation.
## 1 Introduction
However, the study focused solely on ANNs, without exploring other potential models. Additionally, [PERSON] et al. [13] conducted numerical simulations of the thermohydraulic performance of supercritical carbon dioxide in serrated channels. Their findings indicate that reducing the serrated bending angle enhances heat transfer performance but compromises hydraulic performance. Moreover, higher heat transfer efficiency correlates with better convective heat transfer, while a very low heat capacity ratio may lead to a slight reduction in heat transfer efficiency. The study determined that the optimal range for the bending angle is between 110\({}^{\circ}\) and 130\({}^{\circ}\)[13]. [PERSON] performed pioneering research on the thermohydraulic performance of a novel PCHE with straight channels inserted into zigzag flow paths. This research elucidated the impact of the inserted straight channels on heat transfer and pressure drop as functions of mass flow rate. It found that the heat transfer characteristics of 0.5 mm and 1 mm straight channels were very similar to those of Z-shaped channels but superior to those of wavy channels [14]. [PERSON] et al. [15] were the first to investigate the effects of different rib structures in straight channel configurations on the thermohydraulic performance of supercritical carbon [PERSON] during flow. They utilized the field synergy method to analyze heat transfer enhancement and identified the optimal rib structure [15]. [PERSON] proposed empirical correlations for cross-flow, parallel-flow, and counter-flow arrangements in PCHEs with straight channels and varying flow directions, examining the influence of flow channel dimensions, spacing, and overall size on heat transfer performance [16]. In summary, although there is a substantial body of experimental and numerical research on PCHEs, the [PERSON] of robust parameter prediction models and structural generalization remains a significant challenge. Consequently, accurately calculating the thermohydraulic performance within PCHE channels, particularly the heat transfer coefficient used to evaluate the heat transfer characteristics, remains highly challenging. The aim of this study is to investigate the heat transfer coefficient of methane in PCHE channels with different flow path geometries using machine learning methods. First, this study introduces a novel approach for dataset acquisition (the distance division method), enabling the collection of more detailed samples. Second, for the first time, we comprehensively evaluate the potential of various deep learning models (CNN, LSTM, GRU, and Transformer) as well as the integration of specific attention mechanisms (SA, SE, and LA) into deep learning frameworks for predicting PCHE heat exchange performance. Finally, to the best of our knowledge, we conduct a more in-depth interpretability analysis to further examine the contributions of different features to model predictions. This study not only provides a new methodology for predicting PCHE performance parameters but also establishes a standardized workflow, offering valuable insights for the future design of heat exchangers.
## 4 Target Identification Method between Star Trackers
### Identification of Targets Observed in the Same Orbital Arc  
The effectiveness analysis of information obtained from a single star tracker shows that the magnitude brightness and velocity components of the debris are different with the observation position when the debris is observed with respect to different star trackers, making it impossible to be used as the debris identification characteristic between the star trackers. The debris identifies the feature value. However, the geocentric observation vector of the debris can be transformed by the observation vector of the star tracker and the rotation matrix between the geocentric coordinate system and the coordinate system of the star tracker based on the invariance of the geocentric observation of the debris, and the calculation of the rotation matrix can be calculated by star image recognition and attitude of the star tracker. Thus, the identification of the debris image trajectory observed by the star tracker can be transformed into the similarity of the geocentric observation vector. The debris observed by different star trackers is identified by comparing the number of similar points of the motion trajectories of the debris observed by the star tracker.  
The vector C is defined as follows:  
\[b\ =\ \frac{1}{\sqrt{\alpha^{2}+\delta^{2}}}\left[\begin{array}{c}\alpha\\ \delta\end{array}\right] \tag{16}\]  
\(\alpha\), \(\delta\) are the rectascension and declination of the debris, respectively.  
The similarity criterion uses the Bhattacharyya coefficient, and Bhattacharyya is defined as follows:  
\[\hat{\rho}(b^{\prime})\equiv\rho\big{[}b,b^{\prime}\big{]}\ =\ \sum_{m=\ 1}^{n} \sqrt{b(m)b^{\prime}(m)} \tag{17}\]  
Figure 9: Target detection method based on time information.  
It can be seen from the definition of the Bhattacharyya coefficient that the closer the two modes are, the larger the value \(\hat{\rho}(b^{\prime})\) is, and the above formula can be transformed into the following:  
\[\xi(b^{\prime})\ =\ 1-\hat{\rho}(b^{\prime}) \tag{18}\]  
The more similar the measurement mode structure to the standard mode structure is, the smaller the value \(\xi(b^{\prime})\) is. When \(\xi(b^{\prime})<\varepsilon\), it can be judged as a point in a similar trajectory.  
Figure 10 shows the simulating orbital trajectory of space debris in 100 different motion velocities. The 100 sets of debris are identified by similarity criteria, and the error of 20 s of arc is added to 100 sets of data to form another set of observation data of the star tracker. After 100 measurements, the recognition rate is above 85%.
# Design of a Flat-Panel Metasurface Reflectarray C-Band Antenna
###### Abstract  
This paper presents the design of a flat-panel metasurface reflectarray antenna fed by a circular horn antenna for satellite applications. A metasurface-based reflectarray antenna is similar to a flat-panel reflector and is characterized by a reflection angle adjustment that is free from the well-known Snell's law. This was done by compensating the angle of the incident wave using the structure of each unit cell. A unit cell of the designed metasurface is composed of a dual-ring resonator. Many satellites use a reflectarray antenna due to its flat-panel structure and the capability of steering the reflection angle of the incident wave. This paper presents the detailed design procedure using a commercial 3D EM simulator and the operation principle of the flat-panel metasurface reflectarray antenna, including the simulation setup, design environment and automation. The proposed design method is scalable to any EM solvers for numerical analysis. A reflectarray composed of a 16 \(\times\) 16-unit cell array at 5.8 GHz (C-band) was designed and validated by measurement as a proof of concept. It is excited by a low-cost linearly polarized circular hornantenna. The measured antenna gain and radiation patterns show good agreement with the simulation. The measured antenna gain of the reflectarray was 22.4 dBi (cross-pol suppression level: 36 dB), and the reflection angle was 15\({}^{\circ}\) at normal incidence.  
Keywords:metasurface reflectarray; high gain antenna; array antenna; C-band +
Footnote †: journal: _electronics_
## 5 Conclusions  
Fundamental to remote sensing crop mapping is extracting and learning inherent time-series features that can distinguish crop types from incomplete satellite observation sequences. This study developed a contrastive-learning-based framework for time-series feature representation to improve crop classification using incomplete Sentinel-2 image sequences. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with multi-temporal Sentinel-2 images. The classification results, with significant improvements greater than 3% in their overall accuracy and 0.04 in F1 scores over comparison methods, revealed the effectiveness of the proposed method in learning time-series features for parcel-based crop classification using incomplete Sentinel-2 image sequences.  
In addition, evaluations of accuracy and comparisons were performed on parcel-based classification results to discuss the number of training samples, the benefit of type-wise contrastive learning, the sensitivity of dimensions in feature representation, and assistance from time-series composition and vegetation indices. We concluded that (1) the combination of feature representation and traditional machine-learning-based classifications could improve parcel-based crop mapping with limited labeled samples. (2) Type-wise contrastive learning is more effective than instance-wise in time-series classification tasks. (3) Preprocessing time-series composition and vegetation indices is not necessary for contrastive-learning-based feature representation.  
These experiments and their conclusion can provide insights and ideas for time-series classification in agricultural remote sensing applications. In addition, the proposed method is adaptable to other satellite images and applications in future works.  
Conceptualization, Y.Z. and N.Y.; methodology, Y.Z., N.Y. and L.F.; software, Y.W., L.F. and T.W.; validation, Y.W. and T.W.; formal analysis, Y.Z., Y.W. and L.F.; investigation, Y.W., N.Y. and Y.C.; resources, N.Y. and J.G.; data curation, Y.W., Y.C. and J.G.; writing--original draft preparation, Y.Z. and W.Z.; writing--review and editing, Y.Z. and N.Y.; visualization, Y.W., L.F. and X.Z.; supervision, Y.Z. and X.Z.; project administration, N.Y. and W.Z.; funding acquisition, Y.Z. and W.Z. All authors have read and agreed to the published version of the manuscript.  
Please add: This research was funded by the Third Xinjiang Scientific Expedition Program, grant number 20213 jkk1305 and 2022 ajkk0402, the National Key Research and Development Program of China, grant number 2019 YFC1804301, and the National Natural Science Foundation of China, grant number 42071316 and 41901292.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We thank the anonymous reviewers for their insights and constructive comments to help improve the paper.  
The authors declare no conflict of interest.
## 4 Result and Discussion
### Result in Two Machines Test  
The performances of the total number of orders in the schedule and the total setup time in minutes were evaluated with the use of two machines test, and the results are presented in Figures 8 and 9, respectively. Table 5 shows the overall performance of the total number of orders in the schedule and the total setup time used in the schedule.  
Figure 6: The performance of the number of orders for one machine.  
Figure 7: The performance of total setup time for one machine.  
Figure 8 shows the overall performance of the total number of orders in the schedule. Before the two machines applied the proposed algorithms, the number of orders arranged in the schedule was 42. The total number of orders in the schedule has increased to 75 after applying the proposed algorithms. There were 33 orders increased after applying the proposed algorithms.  
Figure 9 presents the overall performance of the setup time for two machines. The total amount of setup time before these two machines applied the proposed algorithms used about 570 min, which was 9 h and 30 min to change material. Given this, most of the time in the production line was used to change material. Hence, the material changing lead time was very high. However, the total time for the setup of two machines was decreased to 345 min which was 5 h 45 min, while the overall setup time was reduced by approximately 40% after applying the proposed algorithms. Table 6 shows the total number of orders in the schedule and the total setup time used in the schedule for each machine during the two machines' test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Total Number of Orders in Schedule** & **Total Setup Time (min)** \\ \hline Two machines without using algorithms & 42 & 570 \\ \hline Two machines using algorithms & 75 & 345 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The overall performance of the total number of orders and total setup time used in the schedule.  
Figure 8: The overall performance of the number of schedules for two machines.  
Figure 9: The overall performance of setup time for two machines.  
Figure 11 shows the performance of the total setup times for each machine. When the proposed algorithm was not applied to machine M1, the total setup time for M1 is 300 min. It was the same as machine 2, which had a total of 270 min for setup time. When the proposed algorithm was applied to these two machines, the setup time for machine M1 was reduced to 210 min and reduced to 135 min for M2. This is expected due to the fact that the factor of setup time imposed additional constraints on the scheduling problem. The reduced setup times had minimized the idle time of the machines and created more balanced schedules.
## 3 Results and Discussions
### Analysis of Flow Field in Interstage Pipelines of Compressors  
Figures 7 and 8 show the static pressure and total pressure distribution in the interstage pipe, respectively. As can be seen from Figure 7, there are obvious differences in the static pressure at different sections of each pipeline, and the static pressure distribution at the inlet and outlet of the bend is relatively uniform. Due to the stabilizing effect, the unsteady flow induced by the LPS is improved when the gas flows through the straight section. However, in the bend section, the obvious static pressure changes caused by the centrifugal force can be seen clearly. From Figure 8 it can be seen that the total pressure distribution of the three models is significantly different. The total pressure in the straight section of the EPC model is higher than that in the SPC and TPC models, but when the air flows from the bend to the HPS inlet, the total pressure amplitude gradually decreases at each section compared with the other two models, which indicates that the loss in the interstage pipeline of the EPC model mainly occurs in the bend section.  
Figure 6: Performance comparison between three models.  
Figure 7: Static pressure distribution in the interstage pipeline.  
The comparative analysis of the three models shows that the high total pressure area in the interstage pipeline is located above the bend section, while below the bend section, the vortex induced by centrifugal force results in a low-pressure area. Therefore, the flow loss in the interstage pipeline mainly occurs in the lower part of the bend section. The SPC model has the smallest range of the low-pressure zone, so the total pressure distortion at the inlet of the HPS impeller is also the smallest.  
Figure 9 shows the streamline distribution on the middle height of the interstage pipeline of the three models. It can be seen that after the airflow enters the bend section, the streamline is shifted to the outer wall of the pipe under the action of centrifugal force and wall viscosity. From the EPC model in Figure 9, it can be seen that the inside of the bend produces obvious low speed vortex, and the flow state at the outlet section of the interstage pipeline is relatively severe. While, form Figure 9 it is found that the streamline in the SPC model is more uniform, and the low speed vortex inside the bend section is smallest compared with the other two models.  
By comparing the velocities of the three models, it can be seen that the model with fast airflow acceleration in the bend section will induce large vortex and high strength total pressure distortion. On the contrary, the model with slow airflow acceleration has better total pressure distortion and internal flow field distribution. In the bend section, compared with SPC and TPC models, the EPC model produces stronger secondary flow and the distortion will inevitably affect the impeller.  
Figure 8: Total pressure distribution in the interstage pipeline.  
Figure 9: Flow field distribution of 50% section of interstage pipeline.