esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
## 3 Wireless Propagation in On-Chip Multilayer Structure with TE and TM Polarizations
The wavelength is \(\lambda=1.55\;\upmu\)m.  
TM polarization. This is due to an almost guiding effect that occurs in the overlayer, where the multipath composition is, in general, constructive. The increase in the transmittance and its dependence on the cladding thickness are more pronounced for the TM case because of the different Fresnel coefficients in the multipath propagation. Another degree of freedom in the analysis of on-chip wireless communication is the cladding refractive index. Figure 14a,b show for the TE and the TM polarizations, respectively, the transmittance calculated as a function of the link distance for different cladding refractive indices. As can be seen in Figure 14, the variation in the cladding refractive index does not significantly change the transmittance for the TE polarization (Figure 14a). For the TM polarization, the transmittance at the receiving waveguide is almost the same for n\({}_{\text{clad}}\) = 1.492 and for n\({}_{\text{clad}}\) = 1.592, whereas it is lower when the cladding refractive index is equal to that of the SiO\({}_{2}\) substrate (red dashed curve of Figure 14b). The proposed [PERSON] antenna is a non-resonant structure, and it can allow broadband communication between two nodes in an optical network on chip. As an example, Figure 15 shows the transmittance calculated at the receiving antenna as a function of the wavelength, for the TE (solid curve) and the TM (dashed curves) polarizations. In this case, the link distance is d\({}_{\text{link}}\) = 100 \(\upmu\)m, and the PMMA is considered to be a cladding polymer (thickness d = 3 \(\upmu\)m) in the multilayer structure. As Figure 15 shows, the transmittance is almost constant with the wavelength (variation in less than 0.6 dB for both the polarizations), covering the entire C-band. Given the broadband behavior, the proposed antenna is suitable for the implementation of WDM schemes in on-chip communication. Given the higher transmittance for the TM polarization with respect to the TE one, the overall performance on the point-to-point link can be significantly improved by selecting the TM mode in input to the transmitting antenna. This opens new possibilities for the design of on-chip optical wireless interconnection networks, provided that the polarization selection is implemented at the input of the waveguide feeding the antenna (e.g., through TM grating couplers or polarization rotators). Figure 14: Transmittance in dB, which is calculated at the receiver waveguide as a function of the link distance for different values of the cladding refractive index: (**a**) TE and (**b**) TM polarizations. The black curves denote the transmittance calculated by the Friis transmission equation for a point-to-point link in a homogeneous medium (with refractive index n\({}_{\text{SiO2}}\) = 1.445). The cladding thickness is d = 3 \(\upmu\)m, and the wavelength is \(\lambda\) = 1.55 \(\upmu\)m.
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 2 Materials and Methods
### Methodology  
The main tool used in this study is AVAC-S software developed by EUMETSAT, which is related to CREW activity [8]. Optimal Cloud Analysis (OCA), Cloud Analysis (CLA) from EUMETSAT, and Climate Monitoring Satellite Application Facility (CMSAF) cloud products are included within the validation targets of AVAC-S. This information is intended to support scientists in evaluating processing schemes used to derive physical parameters from MSG-SEVIRI measurements in comparison with the relevant parameters from polar orbiting A-Train sensors, which offer high-accuracy, independent information. The main function of AVAC-S is the mapping of MSG-SEVIRI data and A-Train data, including CPR, CALIOP, MODIS, and Polarization and Directionality of the Earth's Reflectances (POLDER) products, on a common grid with several utilities for analysis and visualisation. AVAC-S software was set up in the UK Met Office; the ingestion routine for the SEVIRI parameters was added for this study.
# RF Exposure Assessment by Drone-Based Technology
## 2 Materials and Methods
### Aerial Vehicle  
Aerial measurements were performed by attaching the previously described PEM to a DJI S1000 drone using a plastic clamp (see Figure 2) (DJI, Shenzhen, China). The drone is a professional octocopter with a remote controller operating in the 2.4 GHz frequency band. It has a 15 min autonomy, a maximum takeoff weight of 11 kg, and includes the option of returning to its starting point in case the signal is lost. Additionally, we developed a system that allows for the remote pressing of the PEM button to mark points of interest for geo-referencing in the data file [13]. The drone's structure and operation produce alterations in the electric-field measurements captured by the PEM, which were analyzed and quantified in a previous study [13]. The electric-field intensity readings were affected, and these distortions were corrected through correction factors. Additionally, wireless communication between the controller and the vehicle caused interference in the 2.4 GHz Wi-Fi band, but did not affect readings in the telephone bands.
## 3 Molecular Imprinting by Electropolymerization
The most significant results with MIP-based electrodes prepared by electropolymerization are provided in Table 2.  
For instance, [PERSON] and her co-workers [72] prepared an MIP-based electrochemical sensor for acetaminophen detection. Acetaminophen is an analgesic that can be harmful if used in excess. The synthesis of the film was conducted using a three-electrode system consisting of a gold (Au) electrode (working electrode), Ag/AgCl (reference electrode) and platinum electrode (counter electrode). In the first step, the gold electrode surface was modified with AuNPs. After that, the MIP film was deposited onto the modified electrode surface by electropolymerization. The MIP synthesis was performed by cyclic  
Figure 4: The principle of MIP deposition by electropolymerization. voltammetry, at a potential ranging from 0 to 1.3 V, at a scan rate of 100 mV/s for 30 scan cycles. The prepared electrodes were analyzed by CV and EIS. The performance of the MIP sensor was also studied by square wave voltammetry (SWV), in a concentration range between 4.5 \(\times 10^{-5}\) and 5 \(\times\)\(10^{-7}\) M, resulting in a limit of detection of 2.3 \(\times\)\(10^{-9}\) M [72]. Another concerning problem is the side effect related to the use of dimetriazole beyond the permitted limits. Recently, [PERSON] et al. [73] depicted the preparation of a polyarginine MIP based sensor that can be used for electrochemical detection of dimetriazole. In this study, the MIP film synthesis was carried out by CV, using a three-electrode system (glassy carbon electrode GCE-working electrode, Ag/AgCl electrode-reference electrode and platinum wire electrode-counter electrode) and a solution consisting of L-arginine, dimetriazole dissolved in PBS. The electropolymerization conditions consisted of a potential range between \(-2\) and 2.2 V, a scan rate of 100 mV/s and 12 scan cycles. The recognition experiments were performed by differential pulse voltammetry (DPV), using solutions with different concentration (10\({}^{-10}\) to 10\({}^{-5}\) M). The limit of detection for such sensors was 0.1 nM, which represents a promising future for dimetriazole detection [73]. In the past few years, both the production and consumption of antibiotics increased, which has led to a rise in environmental pollution. In recent years, sensors for antibiotics have been studied in order to improve the quality of life. For example, [PERSON] and co-workers [74] developed a sensor based on MIP for erythromycinic detection, using a screen-printed electrode with Au working electrode. The electropolymerization was performed under potentiostatic condition, meaning a constant potential of 0.63 V, using _m_-phenylenediamine as monomer. The recognition and the rebinding capacity of the sensors were studied by DPV, pointing to a limit of detection of 0.1 nM and also a good selectivity. The results presented in this study may represent a solution for erythromycinic
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 1 Introduction
\begin{table}
\begin{tabular}{c l l} \hline \hline  & **Tortuosity Metrics** & **Symbols** \\ \hline
1 & \
\begin{tabular}{l} **Tortuosity Density** [7] \\ \end{tabular} &
\begin{tabular}{l} TD \\ \end{tabular} \\
2 & \
\begin{tabular}{l} Absolute Curvature [14] \\ \end{tabular} &
\begin{tabular}{l} AC \\ \end{tabular} \\
3 & \
\begin{tabular}{l} Squared Curvature [14] \\ \end{tabular} &
\begin{tabular}{l} SQC \\ \end{tabular} \\
4 & \
\begin{tabular}{l} Weighted Absolute Curvature [15] \\ \end{tabular} &
\begin{tabular}{l} WAC \\ AAC \\ \end{tabular} \\
5 & \
\begin{tabular}{l} Absolute Direction Angle Change [16] \\ \end{tabular} &
\begin{tabular}{l} AAC \\ \end{tabular} \\
6 & \
\begin{tabular}{l} Arc-Chord Length Ratio [17] \\ \end{tabular} &
\begin{tabular}{l} ACR \\ \end{tabular} \\
7 & \
\begin{tabular}{l} Chord Length [18] \\ \end{tabular} &
\begin{tabular}{l} CHD \\ \end{tabular} \\
8 & \
\begin{tabular}{l} Curve Length [18] \\ \end{tabular} &
\begin{tabular}{l} CUR \\ \end{tabular} \\
9 & \
\begin{tabular}{l} Directional Change of a Line [19] \\ \end{tabular} &
\begin{tabular}{l} DCI \\ \end{tabular} \\
10 & \
\begin{tabular}{l} Inflection Count Metric [20] \\ \end{tabular} &
\begin{tabular}{l} ICM \\ \end{tabular} \\
11 & \
\begin{tabular}{l} Mean Direction Angle Change [21] \\ \end{tabular} &
\begin{tabular}{l} MAC \\ \end{tabular} \\
12 & \
\begin{tabular}{l} Slope Chain Coding [22] \\ \end{tabular} &
\begin{tabular}{l} SCC \\ \end{tabular} \\
13 & \
\begin{tabular}{l} Tortuosity Coefficient [23] \\ \end{tabular} &
\begin{tabular}{l} TC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of tortuosity metrics. robustness of the method. Most of the tortuosity metrics are evaluated quantitatively on the dataset. The effectiveness of four enhancement methods on the tortuosity assessment is also evaluated on the internal and public segmentation method, respectively. The contributions of this paper are described as follows:  
* We propose an automatic curvilinear tortuosity measurement method with the exponential curvature estimation, which measures the tortuosity value from the original image directly with few complex pre-processing steps of the traditional methods. * Our proposed method is robust, which has been validated quantitatively using one retinal blood vessel tortuosity dataset and two corneal nerve tortuosity datasets. As a complementary output, we have made all the tortuosity datasets available online.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 2 Study Area and Flood Detection Datasets
### Flood Detection Datasets
#### 2.2.2 S1 GFloods Dataset  
The benchmarking data of our model consists of the data in Reference [16], which used the Sentinel-1 SAR database satellite images. This radar imaging system, designed by the European Space Agency (ESA), can create high-resolution images regardless of weather, time of day, or atmosphere opacity. The dataset covers common and high-impact causes of flooding, such as heavy rainfall, riverine flooding, dams and levee failure, tropical storms, and hurricanes. Its geographic variation ensures the adaptability of the flood monitoring method to a wide range of environmental scenarios, such as rural, mountainous, urban communities, vegetated zones, rivers, ponds, lakes, and reservoirs.  
The dataset contains 4830 image sets consisting of pre-flood, post-flood, and change-labeled images. Each image is of size 256 \(\times\) 256 pixels and has 3 channels that represent the Red (R), Green (G), and Blue (B) in RGB images. The labels are provided as a single channel; each pixel is assigned as flood (0) or non-flood (1). In order to better train the flood detection model, the dataset is divided into a training set, which has 4300 image sets (90% in total), and a test set, which consists of 530 image sets (10% in total). Training sets are further divided into two smaller training subsets and a validation subset to train the model with the right parameters. A model that has this division allows a good generalization that reduces the chance of overfitting and enhances learning.
# Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information
## 4 Experiment  
### Experiment Setup  
#### 4.1.1 Dataset  
The proposed model BEMCI is evaluated using six public datasets: MSRA [29], WEIBO [30], RESUME [13], AISHELL [31], CLUENER [32], and BOSON. MSRA, WEIBO, and RESUME, which are widely used in Chinese NER.  
* MSRA is derived from news, which has a large-scale corpus, long sentences, and lacks a dedicated test set. Therefore, the validation set is used as the test set.
* WEIBO and RESUME are collected from social media platforms, and they have smaller data sizes. The WEIBO dataset can be further divided into three subsets: NE, NM, and ALL. The label annotations in the WEIBO dataset are relatively sparse, leading to sub-optimal performance of existing methods compared with other datasets. RESUME is a self-built dataset that focuses on electronic resumes, with dense entity labels and consistent data formatting rules.
* AISHELL is based on the open-source Mandarin speech corpus AISHELL-1, which includes over 170 h of Mandarin speech data from various domains.
* CLUENER is based on the THUCTC dataset, which consists of ten different entity types.
* The BOSON dataset is sourced from the BOSON platform and contains six entity types. Due to the lack of predefined splits in the BOSON dataset, we perform a 7:2:1 random split of the training set, the verification set, and the test set.  
Table 2 presents the detailed information about these commonly used public datasets in Chinese NER, including the number of sentences, characters, total entities, and respective domains.
## 2 Structure and Model
### Mathematical Model
According to the Hurwitz stability criterion, the necessary and sufficient condition for the solution of Equation (19) is that its coefficient determinant is zero, that is:  
\[\left| \begin{matrix}Z_{xx}-v^{2}M&Z_{xy}\\ Z_{yx}&Z_{yy}-v^{2}M\end{matrix} \right|=0 \tag{20}\]  
By solving Equation (20), the critical whirl ratio and the critical mass can be obtained:  
\[\text{Critical whirl ratio:}\quad\quad\quad\gamma_{cr}=\frac{v}{\omega}=\sqrt{ \frac{\left(K_{xx}-K_{0}\right)\left(K_{yy}-K_{0}\right)-K_{xy}K_{yx}}{D_{xx}D_{ yy}-D_{xy}D_{yx}}} \tag{21}\]  
\[\text{Critical mass:}\quad\quad\quad\quad m_{cr}=\frac{p_{a}r^{2}K_{0}}{c_{0} \omega^{2}\gamma_{cr}^{2}} \tag{22}\]  
where \(K_{0}\) represents the dimensionless critical stiffness coefficient:  
\[K_{0}=\frac{K_{xx}D_{yy}+K_{yy}D_{xx}-K_{xy}D_{yx}-K_{yx}D_{xy}}{D_{xx}+D_{yy}} \tag{23}\]The calculation flow chart for this method is shown in Figure 2. First, the calculation parameters are input. The Reynolds equation is solved using the linear perturbation method, and the static gas film pressure and thickness distribution are obtained. Then, the convergence for the gas film pressure is checked. After the pressure converges, the load capacity, deflection angle, friction moment and dynamic gas film force are computed. Next, the critical whirl ratio is calculated, and its convergence is checked. Afterwards, the critical mass is computed. Finally, the calculation results are output.
## 3 Test Results
### Test Series C1
During the initial three applied load cycles, the stiffness increased significantly more than the stiffness increase observed in the subsequent load cycles. Figure 11: Resulting stiffness values K\({}_{\text{ser}}\) of push-out tests C1.9, C1.11, and C1.12. Figure 10: Resulting stiffness values \(K_{\text{ser}}\) of push-out tests C1.3 and C1.8. The results for tests C1.13 to C1.16 are shown in Figure 12. For these tests, the stiffness resulted into similar values compared to tests C1.9 to C1.12. Specifically, the stiffness of the specimens with DC, regardless of the torque, was higher compared to specimens with PV. For DC, a significant increase was observed between the first load cycle and the following load cycles. However, for PV, the resulting stiffness values are similar for each load cycle and relatively close to the reference value. For tests C1.17 to C1.20, the number of load cycles was increased to one hundred. Figure 13 shows the resulting stiffness values for each load cycle up to ten cycles and, subsequently, for every tenth load cycle. Tests C1.17 and C1.18 show similar values compared to tests C1.9, C1.13, and C1.14. The results show an increase in stiffness during the first ten load cycles. For the subsequently conducted load cycles, the stiffness values remained almost constant. The two lower stiffness values shown for test C1.18 may be due to vibration or measurement errors during the test. Tests C1.19 and C1.20, conducted with PV, showed an increase in stiffness only during the first two load cycles, followed by a practically constant stiffness that was slightly higher than the reference value of 9.45 kN/mm. The tests showed that the increase in stiffness for the first twenty load cycles was approximately + 23% for tests C1.19 and C1.20, + 64% for test C1.17, and + 102% for test C1.18. Figure 12: Resulting stiffness values \(K_{\text{ser}}\) of push-out tests C1.13 to C1.16 (ten load cycles each). Figure 13: Resulting stiffness values \(K_{\text{ser}}\) of push-out tests C1.17 to C1.20 (one hundred load cycles each). The tests showed that the mechanical properties of the connection are influenced by the test conditions as well as by the type of contact surface and pressure between the timber and concrete. Two static tests were performed with direct contact and a torque level of 75 Nm. Figure 14 shows the load-slip curves obtained, and Table 5 shows the stiffness values \(K_{\text{ser}}\) together with the maximum load \(F_{\text{max}}\). In summary, the stiffness of the connection increased gradually (constantly) as the torque level applied to the screws increased for direct contact (DC) between the timber and concrete. However, with plastic foil (P) or plastic foil and Vaseline (PV) between the timber and concrete, there was no significant increase in the resulting stiffness values with higher pressure between the timber and concrete.
## 4 Tests and Results
### Parameters of the Bio-Inspired Model
#### 4.1.2 CNN Results  
Unlike the OCR system, the nature of the CNN's operation does not require prior binarization, unless its detection objective requires it. The trained CNN, in general, presents  
Figure 11: Example of response for a tilted picture. (**a**) Original picture. (**b**) Picture with bio-inspired retina model.  
Figure 12: Example of bio-inspired retina result for a picture with a rivet. (**a**) Original picture. (**b**) Picture with bio-inspired retina model.  
Figure 10: Bio-inspired retina response example. (**a**) Original picture. (**b**) Picture with bio-inspired retina model.  
Figure 13: Example of bio-inspired retina result for a picture with dirt. (**a**) Original picture. (**b**) Picture with bio-inspired retina model.  
better results than OCR, as suggested by the literature, and with an execution time of around 150 milliseconds for processing static images.  
In the case of Figure 10, where the license plate corresponds to \"BJZC72\", the CNN produces only a 16% error, equivalent to one incorrect character. After applying the bio-inspired retina model, the error decreases to 0%. For Figure 11 (\"JD GC 57\"), the CNN model fails to detect two characters, resulting in a 33% error, which is reduced to 0% after applying the bio-inspired retina model. In the case of Figure 12, corresponding to \"JF FP 14\", a 33% error is also obtained, but when applying the retina, it is only reduced to 16%. Finally, for the license plate \"CTCB99\", corresponding to Figure 13, although the CNN model achieves a 0% error, the error increases to 33% after applying the retina model.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 5 Conclusions  
In this paper, we built TYCOS, a specialized dataset for the detection of typical components of space satellites. In contrast to conventional work that treats a single target as a whole, as in existing datasets, we leveraged more refined structural components as the indicators for satellite object detection. We conducted simulations of numerous typical and challenging working conditions, with the aim of representing the space targets and scenes captured with on-orbit satellites. In consideration of the future application of the dataset, we also established a benchmark for detecting typical satellite components and performed qualitative and quantitative evaluations of state-of-the-art object detection algorithms. We believe that the proposed TYCOS dataset will provide valuable guidance for the development of space scene perception and satellite safety.  
In our future work, we plan to expand the number of categories and increase the size of the datasets. Additionally, we aim to design several novel satellite object detectors through a part-to-whole detection framework based on TYCOS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Type** & **Faster-R-CNN** & **YOLOv3** & **YOLOv5** & **YOLOv8** \\ \hline SPEED & Synthetic & **93.09**1  & **71.28**1  & **78.57**1  & **93.56**1  \\ SPEED+ & Synthetic and real & 92.20 & 68.37 & 75.05 & 93.12 \\ TYCOS & Real & 90.61 & 64.31 & 71.24 & 91.07 \\ \hline \end{tabular}
\end{table}
Table 7: The AP values for solar panel detection results using different methods on three datasets.  
**Author Contributions:** Conceptualization and methodology, H.B.; software and validation, Z.Z. and J.D.; formal analysis and investigation, Z.Z.; resources and data curation, J.D.; writing--original draft preparation, H.B.; writing--review and editing, C.L. and Z.Z.; visualization, J.D; supervision, J.C. and G.Z.; project administration and funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported financially by the Photon Plan in Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences (Grant No. S24-025-III).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The test subset of the proposed TYCOS dataset built in this work is available at: [[https://github.com/[PERSON]/TYCOS](https://github.com/[PERSON]/TYCOS)]([https://github.com/[PERSON]/TYCOS](https://github.com/[PERSON]/TYCOS)) (accessed on 27 April 2024). The entire dataset will be released in the near future.  
**Acknowledgments:** We thank the editor and anonymous reviewers for their suggestions and comments that ameliorated our work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.