esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Compared with Community Detection Algorithm  
The community identification algorithm of complex weighted networks is often used to divide complex product modules, and researchers who adopt this method use the pre-modular method to reduce the difficulty of complex product modeling. The complex weighted network model of an electric bicycle is shown in Figure 11, and the modularization scheme is obtained through the modified GN algorithm [42]. To facilitate this comparative study, the modular scheme with ten modules was selected as the optimal one, as shown in Table 6.  
Among the ten modules, the structure of the steering system module, front wheel module, pedal system module, back wheel module, back seat module, and bicycle basket module is the same in the two modular schemes. The differences between the two modular schemes are shown in Figure 12, and the rationality of the modules is analyzed as follows:  
1. The community recognition algorithm divides the support leg module from the frame of the bicycle module, and the support leg module only contains two parts and is closely connected with the frame of the bicycle module. Therefore, it is unreasonable for the support leg to be a module alone. Figure 10: Correlation matrix between non-core parts and modules. \begin{table}
\begin{tabular}{c c c} \hline
**No** & **Module** & **Module Parts** \\ \hline
1 & Motor & [1 33 34 45] \\
2 & Battery & [2 37 52 57 67 82 83] \\
3 & Control system & [3 13 32 42 44 54 62 70 77 81] \\
4 & Steering system & [4 9 25 30 31 43 53 56 86 88] \\
5 & Front wheel & [5 8 12 17 23 36 58 61 64 73 74 85 93] \\
6 & Back wheel & [6 7 16 22 35 55 59 60 65 72 75 84 92] \\
7 & Back seat & [10 15 51 63 6876 79 80 87 89] \\
8 & Frame of bicycle & [11 20 24 27 29 46 47 50 66 69 71 90] \\
9 & Pedal system & [14 18 19 21 38 39 40 41 48 49] \\
10 & Bicycle basket & [26 28 78 91] \\ \hline \end{tabular}
\end{table} 
Table 5: The ultimate modular solution for electric bicycles. The community recognition algorithm reconstructs the motor module, battery module, and control system into two modules: the display system and the control and power system. However, with electric bicycle manufacturers, the motor and battery are usually purchased parts and have different types of customer requirements. It is conducive to product configuration and new product development to use them as separate modules. 3. In the pre-modularization stage, the community identification algorithm categorizes the left guard plate and the right guard plate into the frame module, while the non-core parts association analysis method in this paper categorizes the left guard plate and the right guard plate into the battery module. The guard plate is close to the frame module in structure and has a strong functional correlation with the battery module, but it is a plastic product and is not compatible with the frame module in terms of material.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 10 Discussion and Conclusions  
Dispersive media can have a destructive effect on ultrashort pulses even at relatively short distances.  
This paper shows that almost any pulse shape in most practical scenarios can be formulated in a pulse form, whose propagation in dispersive medium has an exact analytical expression.  
Therefore, there is no need to restrict the analysis of pulse propagation in a dispersive medium to Gaussian pulses or to numerical analysis. Thus, almost any shape and form of pulses can be analyzed analytically. This is the main result and main conclusion of this paper.  
The experimentalist can choose from the multiple examples presented in this paper to predict the distortions that the relevant pulse would experience.  
In particular, if the pulses were generated by a mode-locked laser, then a Gaussian pulse is a good approximation. However, if there is an asymmetry between the pulse's rise and fall times (as in Q-switch lasers), then the experimentalist can use the smooth exponential pulses (Section 6.6) to predict the pulse's distortions.  
Similarly, in modern optical communications channels, the data signals consist of multiple pulses which mostly belong to two categories: rectangular and Nyquist pulses. Both types of pulses are presented in this paper; however, these pulses are both discontinuous: The former is discontinuous in the time domain while the latter is discontinuous in the frequency domain. In practical cases, the signals are always continuous (in both time and frequency domains). Therefore, the experimentalist or the engineer can measure the pulse's rise time and apply the smooth rectangular pulses (Section 6.2) to the relevant system to predict the pulse's distortion in the fiber. Similarly, Nyquist-Sinc pulses with a smooth spectrum (Section 7.2) can be used to simulate real systems, which are based on Nyquist-Sinc  
Figure 31: FWHM Comparison between four different pulses: Gaussian (dashed curve), Rectangular (dotted curve), Exponential (solid curve), and Sinc (dot-dash).  
pulses. The exact dispersion's distortion can be evaluated without the need for approximations or for numerical analysis.  
Moreover, this paper demonstrates that any singular pulse can be replaced with a smooth pulse whose evolution in dispersive medium can be formulated in an exact analytical form.  
Besides this generic recipe, the main new findings are the exact analytical derivations of the following pulses: (A) chirped rectangular pulses, which converge to extremely narrow pulses; (B) excellent analytical approximation of super-Gaussian pulses; (C) Nyquist-Sinc Pulses with a smooth spectrum; and (D) a physical realization of attenuation-compensating Airy pulses.  
This research received no external funding.  
The author declares no conflict of interest.
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
###### Abstract  
In general, studies of the seismic reliability of water supply systems through hydraulic analysis models fall into three categories: (1) model development through hydraulic analysis, (2) establishment of post-earthquake recovery strategies, and (3) robust design in preparation for earthquakes and the development of improvement techniques. In this study, with regard to the design of earthquake resistance techniques, which is the third category, a seismic reliability assessment was conducted that considers the water supply system, and a plan to maximize the seismic reliability was determined as a result of the analysis. The degree of improvement in seismic reliability was quantified for water supply systems with various sources and water supply routes via modeling of seismic protection for each water supply route. Korea's A industrial water system, which has a history of large-scale earthquakes, was divided into nine paths, and the reliability of the water supply system and the cost of earthquake resistance were calculated and compared between the different paths. A water supply path consists of a combination of pipes of various sizes and lengths, and the amount of water to be supplied differs between paths. Quantifying the effect of the water supply system and the seismic reliability of a route is intended to provide a basic methodology for identifying the critical path that should be the target of seismic protection when money and resources available for construction are limited. This methodology was used to confirm that the reliability of the water supply, the demand for water, and the cost of earthquake resistance should be considered together to establish an earthquake resistance strategy for the critical path in a water supply system. We expect that the results of this study will be used to establish detailed strategies for preliminary reinforcement against earthquake damage based on critical paths rather than all or individual pipelines in a water supply network.  
water supply systems; seismic; reliability assessment; critical paths +
Footnote †: journal: Applied Sciences
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 4 Results
### Results of the Flooded Vegetation Mapping Method Implemented in AUTOWADE  
A full example of the results of the flooded vegetation algorithm for the Vercelli test case (Table 4) is shown in Figure 13. Many objects belong to the cluster of high \(\Delta\sigma_{V}^{0}\), most of them because of the increase of soil moisture due to rain; hence the need to focus on objects adjacent to open water having \(\overline{NDVI}>0.2\) to limit confusion between backscatter increases due to soil moisture variations and the presence of a double bounce structure formed by the horizontal water surface and vertical stems. The AUTOWADE-derived floodwater map (panel e) underestimates floodwater mapped using S2 (red line in panel a). This is partially due to the water receding, considering that S2 data were acquired in the morning of 3 October 2020, i.e., few hours after the peak of the Vercelli event; conversely, S1 data were acquired in the afternoon (about 7 h later than S2 data). Nevertheless, including flooded vegetation in the AUTOWADE-derived classification, the omission error reduces from 45% to about 40%, with a negligible increase of the commission error (around 5%).
# Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
## 4 Discussion  
Next, we explore the transmission rate in our conducted D-band transmission system as a function of transmission frequency, thickness and type of blocking material. The theoretical penetration rate is calculated according to the above-modified theoretical model and compared with it, as shown in Figure 9. The experimental measurements are basically consistent with the theoretical values.  
Figure 9: Theoretical and actual transmittance of D-band millimeter-wave signal passing through the obstruction: (**a**) the relationship between the actual transmittance and the amount of vegetation at different frequencies; (**b**) the relationship between the theoretical and actual transmittance and the thickness of board at different frequencies; (**c**) the relationship between the theoretical and actual transmittance and the thickness of glass at different frequencies; (**d**) the relationship between the theoretical and actual transmittance and the thickness of slate at different frequencies.  
Since vegetation is not comparable the other three typical building materials, parameters such as relative permittivity can be queried, so the theoretical transmittance of vegetation is not listed here. In Figure 9a, only the transmittance of vegetation measured experimentally is plotted.  
As shown in Figure 9b,c, for both the experimental value and the theoretical value, with the increase in the thickness of the board and glass, the penetration rate will decrease and the loss will increase. However, the experimentally measured transmittance is greater than the theoretical value. This is caused by multipath propagation effects. This phenomenon is related to factors such as the location of the obstacle and the surrounding environment. Since this experiment was conducted in an indoor environment, the received interference included reflections from walls and objects around the experimental site, diffraction from occluders, and scattering from vegetation. Moreover, although the amplitude of the signal that reaches the receiving antenna is small under the occlusion of obstacles, the system error is still relatively large in the case of small signal reception. This error can be improved by reducing the measurement error by increasing the measurement accuracy of the system.  
In Figure 9d, for both the experimental measurement and the theoretical analysis, the calculated transmittance of slate is extremely low, and it can be observed that the D-band millimeter wave can hardly penetrate 5 cm-thick slate. According to the general theory, the shielding effect of the material includes the following two parts: reflection shielding and absorption shielding, and the penetration loss effect of slate combines these two factors. Reflective shielding is caused by the impedance mismatch of propagating waves, and absorbing shielding is caused by heat loss from hydrates inside the concrete and steel mesh.
# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Classification and Performance of Deep Learning Models
This division varied between studies. In refs. [12; 29], 70% of the dataset was allocated for training, and the remainder was allocated for validation and testing. Some studies used a higher percentage. For example, refs. [39; 41; 43] allocated 80% for training, while study [40] allocated 90%. Recent studies have utilized ANN, MLP, and CNN models for dyslexia prediction and most have reported high accuracy in most studies. Figure 5 organizes the reviewed studies according to the DL algorithms adopted. The ANN model suggested by the authors in [30] achieved an accuracy of 75% after ten-fold cross-validation when used with a dataset on pupils' performance acquired from a structured questionnaire. In addition, the same model achieved the highest accuracy among other models in two studies (89.7% and 94.87%) [29; 33; 34]. [PERSON] et al. were the first to use a handwriting dataset for the prediction of dyslexia disorder. They proposed a CNN  
Figure 5: The reviewed studies organized according to the DL algorithms that each model adopted. model to automatically recognize dyslexia children based on their handwriting [25]. They achieved an accuracy of 55.7 \(\pm\) 1.4% by employing five-fold CV. Then, they developed their model further [26], reaching an accuracy of 77.6%. [PERSON] et al. [28] utilized handwriting images from children's notebooks as a dataset to build a CNN model, achieving good accuracy of 86.14 \(\pm\) 1.02%. Two consecutive studies [4,29] used the same datasets from NIST 2019 to build a prediction model. The first used different CNN models, such as CNN-1, CNN-2, CNN-3, and LeNet-5, to compare the prediction performance. All models showed an accuracy of more than 87%, with CNN-1 providing the highest accuracy. The second study utilized a LeNet-5 model and achieved an outstanding accuracy of 95%, which is the highest accuracy for predicting dyslexia using a handwriting dataset. Regarding the performance of DL models for predicting dyslexia using MRI and fMRI, [PERSON] et al. [37] presented a 3D CNN model that achieved an accuracy of 72.7%, a sensitivity of 75%, an F1-score of 67%, a precision of 60%, and a specificity of 71.4%. [PERSON] and [PERSON] [12] proposed a two-path cascading CNN model and achieved an accuracy of 84.6%, a specificity of 78.2%, and a sensitivity of 76.5%. In addition, [PERSON] et al. [39] developed a CNN model and obtained the highest accuracy of 94%. [PERSON] et al. utilized an MLP classifier to monitor accuracy with eyes opened and closed, reporting accuracies of 86% and 84.95% for eyes opened and closed, respectively. Eye-tracking datasets have been used to build a CNN model for dyslexia prediction, which showed good results [40,41,43]. [PERSON] et al. [40] achieved the highest accuracy of 96.6%, while [PERSON] et al. [43] achieved an accuracy of 87%. Moreover, [PERSON] and [PERSON] [41] achieved an accuracy of 82% for scan path images and 87% for fixation images. Table 3 summarizes the performance of the predictive model of each study in this review.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### Ifma Procedure  
The purpose of BDS-3 IfMEA is to identify all kinds of abnormal factors that affect the system's integrity and analyze the abnormal characteristics. Through continuous summary and accumulation, effective information is provided for system design, upgrading and maintenance, and reducing the system's integrity risk probability.  
The BDS-3 IfMEA process is shown in Figure 2. First, it is necessary to clarify the system's integrity index requirements. According to the system's design requirements and expert experience, the system anomaly modes can be systematically sorted. In the system operation process, all kinds of abnormal data from the space segment and ground control segment are collected comprehensively, and abnormal events affecting system integrity are identified according to the system integrity definition. The abnormal characteristic table is designed to record the root cause of abnormalities, magnitude and characteristics of the influence on signal-in-space accuracy, abnormal response time and occurrence frequency. The new abnormal events are recorded in the abnormal mode database. If it is the first occurrence, the database is updated; otherwise, only the number of occurrences increases. An abnormal mode database can be updated routinely and needs to be maintained continuously in the long term. According to the IfMEA results, possible system improvement recommendations are provided to reduce the system's integrity risk probability.  
Additionally, the statistical results of abnormal occurrence and response time contribute to upgrading the system's integrity index, improving the system's operation ability and guiding the subsequent upgrade design of the system.
## 3 The Proposed Approach
### Optimizing the Parameters of FNN with IPSO  
By the use of the improved evolution algorithm, the three kinds of FNN parameters (\(c\), \(\sigma\), and \(\omega\)) can be determined. In the process of searching optimal parameters, the vector described as Equation (4), is represented by a particle. The objective of training FNN is to improve the prediction performance of FNN model and the fitness function has been formulated as Equation (5). The process of optimizing the FNN parameters by IPSO can be described as follows.  
1. Initialize the particle swarm size \(M\), the maximum of generations \(t_{\max}\), the maximum inertial weight \(\alpha_{\max}\), the minimum inertial weight \(\alpha_{\min}\), \(c_{1}\) and \(c_{2}\), \(r_{1}\) and \(r_{2}\), [\(V_{\min}\), \(V_{\max}\)], the number of input variables \(k\), the number of neurons in the hidden layer \(n\), \(H\), and set \(D=2 kn+n\), \(t=1\);
2. Update the velocity \(V_{i}\) and position \(X_{i}\) of each particle according to Equations (6) and (7), respectively. Synchronously, update the inertial weight \(\alpha\) according to Equation (8);
3. The individual best \(P_{i}\) is compared with each particle; if \(P_{i}\) is worse than the fitness value of each particle, then \(P_{i}\) is updated as current position;
4. The global best \(P_{g}\) is compared with individual best \(P_{i}\) of each particle; if \(P_{g}\) is worse than \(P_{i}\), then \(P_{g}\) is updated as current position;
5. If the convergence criteria or one of the stopping criteria (generally, a sufficiently good fitness or maximum iteration is met) is satisfied, go to step 7;
6. The group fitness variance \(\delta^{2}\) is calculated through Equations (9) and (10). If \(\delta^{2}<H\), the velocity and position of the premature particles are updated according to Equations (11) and (7), and the inertial weight \(\alpha\) is updated according to Equation (8), go back to Step 3; otherwise, go back to Step 2. Let \(t=t+1\); and
7. The optimal parameters \(c\), \(\sigma\), and \(\omega\) of the FNN model can be obtained.  
According to the above description about the FNN coupled with IPSO, the proposed approach is an iterative algorithm and can be coded on the computer. The flowchart is summarized, as shown in Figure 2.
## 4 Use Cases and Application Scenarios of Internet of Emergency Services
### Medical Emergency
At the hospital, the healthcare providers have access to the user/citizen's medical history and real-time data collected by the IoT devices and ambulance equipment, allowing them to make informed decisions about treatment. The hospital staff uses Geographic Information Systems (GIS) to analyze the user/citizen's location and identify the closest available hospital with the necessary resources and capacity to provide the appropriate level of care. 10. The hospital staff uses Data Analytics to analyze the data collected from the IoT devices and ambulance equipment to identify patterns and trends in heart attack cases, which can inform future treatment protocols and improve patient outcomes. The Medical Emergency scenario in the IoES holds a tremendous potential for improving the emergency medical response and ultimately saving lives. By leveraging advanced technologies such as wearables and AI, medical professionals can gain real-time insights into patients' conditions and provide more targeted and effective treatments. As these technologies continue to advance and become more widely available, we can expect to observe even more innovations in the emergency medical response in the years to come.
## 2 Related Work
### Introduction to the Generic SAR SHIP Dataset  
Usually, to obtain better detection results through deep learning methods, a large number of training samples are needed. As mentioned above, the existing public datasets of SAR image ship data mainly include OpenSARShip [4; 5], SSDD/SSDD+ [6; 7; 8; 9; 10; 11; 12], AIR-SARShip2.0 [13], SAR-Ship-Dataset [14], and HRSID [15]. Each of these datasets had its own characteristics.  
Each image in the AIR-SARShip series of datasets covered a large area, including both distant sea scenes and docks, roads, and nearby buildings.  
The HRSID dataset consists of 5604 panoramic SAR images of 800 \(\times\) 800 pixels. The offshore scenes with ships distributed in the sea are the main component of HRSID. Ship detection in inshorne scenes is influenced by man-made facilities or buildings. The inshorne scenes are regarded as the interferential scene to maintain a certain amount in HRSID. While the challenging scenes, such as the adjacent ships, cluster-distributed small ships in the canal, and large size ships are added to HRSID. The HRSID dataset was similar to AIR-SARShip. The dataset had a larger image size, and larger width. The datasets were suitable for training detectors trained on large scenes with wide banners.  
OpenSAR Ship2.0 contained 11,346 image slices; each image contains only one ship target, and the slice image size ranged from 1 K-30 K. The SAR-Ship-Dataset was similar to OpenSAR Ship 2.0, and both contained images in dual polarization mode. It was suitable  
Figure 1: Public SAR ship images: (**a,b**) are from the public dataset SSDD; (**c**) is from the public dataset HRSID; (**d**) is from the public dataset OpenSAR. For convenience of display, all pictures were stretched.  
for analyzing the training models with different polarization modes. [PERSON] et al. [5] proposed that OpenSARShip 2.0 has special features, as shown in Table 1. This will have a certain impact when training the model later.
## 5 Experimental Study and Result Analysis
### Embedding Rate
\begin{table}
\begin{tabular}{l|c c c c c c c c} \hline \hline  & \(\mathbf{P^{7}}\) & \(\mathbf{P^{6}}\) & \(\mathbf{P^{5}}\) & \(\mathbf{P^{4}}\) & \(\mathbf{P^{3}}\) & \(\mathbf{P^{2}}\) & \(\mathbf{P^{1}}\) & \(\mathbf{P^{0}}\) \\ \hline
**Peppers** & 4.147 & 2.204 & 1.216 & 0.803 & 0.409 & 0.568 & 0.562 & 0.565 \\ \hline
**Baboon** & 1.192 & 0.800 & 0.712 & 0.812 & 0.871 & 0.566 & 0.565 & 0.565 \\ \hline
**Airplane** & 4.059 & 2.038 & 1.620 & 1.070 & 0.804 & 0.649 & 0.586 & 0.571 \\ \hline
**Boat** & 2.674 & 1.967 & 1.119 & 1.748 & 0.405 & 0.571 & 0.567 & 1.567 \\ \hline
**Medical image-1** & 5.981 & 3.914 & 2.639 & 1.610 & 1.226 & 0.791 & 0.715 & 0.669 \\ \hline
**Medical image-2** & 6.182 & 3.990 & 2.794 & 1.665 & 1.219 & 1.093 & 0.760 & 0.655 \\ \hline
**Medical image-3** & 5.000 & 3.633 & 3.113 & 2.198 & 1.824 & 1.426 & 1.415 & 0.967 \\ \hline
**Medical image-4** & 1.493 & 3.469 & 3.118 & 1.918 & 1.405 & 0.944 & 0.796 & 0.655 \\ \hline  & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Compression ratio obtained from sample images. Figure 8: Embedding rate obtained from natural image classes. Figure 9: Embedding rate of 100 medical images.