esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis
### Compound Systems Using Geometrical Optics  
Let us consider a concrete example: two lenses, separated by a distance (\(d\)). The generalization towards more complex systems, including various types of optical components, is straightforward.  
Two steps are needed to determine the position of the image formed by a two-lenses system. We determine the position of the image that is formed by the first lens. Then, we take this first image as an object for the second lens. We then obtain the image position for the second lens, which is also the position of the final image of the entire system.  
It is worth noting that \"paraxial ray propagator matrix\" could be applied, which is more general than geometric optics [26]. Two lenses, separated by a distance (\(d\)), are combined (Figure 4). Given that the thickness of both thin lenses does not enter into play, the matrixes of lenses 1 and 2 becomes:  
\[A_{1}=\left[\begin{array}{cc}1&1/f_{1}\\ 0&1\end{array}\right]\text{ and }A_{2}=\left[\begin{array}{cc}1&1/f_{2}\\ 0&1\end{array}\right] \tag{5}\]  
The transfer matrix from the first lens towards the second lens becomes:  
\[\text{T}_{21}=\left[\begin{array}{cc}1&0\\ -d&1\end{array}\right] \tag{6}\]  
We then obtain the matrix of the two lens-based compound system:  
\[\text{A}= A_{2}T_{21}A_{1}=\left[\begin{array}{cc}A_{11}&A_{12}\\ A_{21}&A_{22}\end{array}\right]=\left[\begin{array}{cc}1-d/f_{2}&1/f_{1}+1/ f_{2}-d/(f_{1}f_{2})\\ -d&1-d/f_{1}\end{array}\right] \tag{7}\]  
Figure 3: Additional ways to optical implement the two transforms of Figure 2: (**a**) Fresnel transform using three spherical lenses, i.e., two identical divergent lenses with focal length \(-z\) and one convergent lens with focal length \(z\). (**b**) FT using only one convergent lens with focal length \(z\).  
The resulting anterior (front) and posterior (back) focal lengths,\(f\) and\(f^{\prime}\), of the compound system become:  
\[\left\{\begin{array}{c}\frac{1}{f^{\prime}}=A_{12}=\frac{1}{f_{1}}+\frac{1}{f_ {2}}-\frac{d}{f_{1}f_{2}}\\ f=-f^{\prime}\end{array}\right. \tag{8}\]  
The principal planes are located as follows:  
\[h=\frac{1-A_{11}}{A_{12}}=d\frac{f^{\prime}}{f_{2}}\text{ and }h^{\prime}=\frac{A_ {22}-1}{A_{12}}=d\frac{f}{f_{1}} \tag{9}\]  
The following Gaussian formula gives us an image, described by the field \(g_{2}(x,z_{3})\):  
\[z_{3}=\frac{A_{21}-A_{22}z_{1}}{A_{11}-A_{12}z_{1}} \tag{10}\]  
We then obtain the linear magnification or transverse magnification (\(m\)):  
\[m=\frac{A_{22}-z_{3}A_{12}}{A_{11}A_{22}-A_{12}A_{21}}=\frac{1}{A_{11}-A_{12}z _{1}}=\frac{f_{1}f_{2}}{f_{1}(f_{2}-d)-z_{1}(f_{2}+f_{1}-d)} \tag{11}\]  
The transverse magnification is defined as the image size, divided by the object size.
# Effect of the Metro Train on the Smoke Back-Layering Length under Different Tunnel Cross-Sections
## 1 Introduction  
As part of city infrastructure, a rail transit system play a pivotal role in transportation [1]. The development of the metro has greatly facilitated the travel of people, but it has also brought many safety problems. Due to the large flow of people and the metro tunnel's special elongated building structure, tunnel fires are attracting widespread research attention as a serious threat to the safety of metro tunnels [2]. In the event of a tunnel fire, the longitudinal ventilation system is often used to create a smoke-free and safe environment upstream for evacuation and firefighting. If the ventilation rate is low, the smoke generated during a fire can propagate upstream against the direction of the ventilation air; the opposite direction of flow is called \"back-layering\" [3]. The length of the back layer is defined as the length of the reverse smoke flow upstream of the fire [4].  
In previous studies, less attention has been paid to the phenomenon of back layering caused by the same train blocked in tunnels with different sections. Since soil conditions vary from soft clay to hard rock, tunnel construction methods are different [5]. Thus, even within the same metro line, the tunnel cross-section is not always the same, but the same type of metro train has the same cross-sectional shape, which will lead to different blocking ratios. Most of the previous studies on the effect of metro trains on the smoke back-layering length only considered one blocking ratio. When studying the influence of different blocking ratios on the flow of smoke, the focus is mainly aimed at the situation of road tunnel fires, when different vehicles are blocked. Compared with the blockage of carsin road tunnels, metro trains have a larger cross-sectional area and longer length, and the metro trains have a greater impact on the flow of fire smoke. Therefore, this article aims to study the influence of the same metro train on the control of tunnel fire smoke under different tunnel cross-sections, in order to provide theoretical guidance for the control of metro tunnel fire smoke.  
In this work, the effect of the metro train on the smoke back-layering in metro tunnel fires with different cross-sections was investigated. Following the dimensional analysis and FDS simulation results, the length of smoke back-layering was analyzed and compared with previous studies. To further investigate the influence of metro trains on smoke back-layering length in metro tunnel fires with different tunnel cross-sections, the headroom ratio \(\varepsilon\), defined as the ratio of the metro train blocking the upper space height to the tunnel height was proposed. Then, the influence of the metro train on the smoke back-layering was discussed in detail, and a new prediction model based on the simulation results was developed. Further, Figure 1 shows the research framework of the paper.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 3 Design and Performance Analysis of Sparse SAR Imaging Mode
### System Performance Analysis of Sliding Spotlight Mode
In addition, it is found that AASR increases by about 15.2 dB in sparse Sliding Spotlight mode, resulting in serious azimuth ambiguity. But the mixed-norm regularization-based sparse SAR imaging method can solve this problem by introducing the azimuth ambiguity terms into the model, which will be presented in Section 3.4. Figure 6 illustrates the change in AASR with the steering angle. The power loss is normalized to the boresight case. It is seen that when the steering angle is above \(1^{\circ}\), AASR deteriorates by about 8.6 dB in traditional Sliding Spotlight mode. But if steering angle \(=1.2^{\circ}\), the change in AASR is less than 2 dB in sparse mode. Thus, the sparse Sliding Spotlight mode effectively reduces the effect on AASR from a steering angle (antenna beam azimuth scanning), which means that it can achieve higher-quality images compared to the traditional mode. **Range Ambiguity Analysis**. Figure 7 shows the variation in RASR with PRF. From Figure 7, it is seen that the sparse Sliding Spotlight mode can reduce RASR by about 10.7 dB. This indicates that it can obtain a high-quality SAR image. In summary, compared with the traditional Sliding Spotlight mode, the designed sparse mode has a wider swath coverage at lower PRF. The system performance analysis results indicate that the sparse Sliding Spotlight mode can mitigate the deterioration of AASR with increasing steering angle and significantly reduce RASR. Figure 4: Element and total AAP of Sliding Spotlight mode. Steering angle is equal to \(1^{\circ}\). Figure 5: AASR versus PRF under different steering angles in Sliding Spotlight. Figure 6: AASR deterioration versus steering angle in Sliding Spotlight. The power loss is normalized to the boresight case. Figure 7: The relationship between RASR and PRF in Sliding Spotlight.
# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
###### Abstract  
The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately describe the health status of critical components. Therefore, it is widely used in the field of rotating equipment health state prediction. However, there are two major problems in predicting the health status of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and if only one feature in the time or frequency domain is selected for fault analysis, it will be susceptible to harsh operating environments and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time series data, which are difficult to accurately convert into the health status of key components. In order to solve the above problems, this paper proposes a combined prediction model combining a bidirectional long- and short-term memory network (BiLSTM), a self-organizing neural network (SOM) and particle swarm optimization (PSO). Firstly, the SOM is utilized to fuse the fault characteristics of multiple vibration signals of key components to obtain an indicator (HI) that can reflect the health status of rotating equipment and to also compensate for the vulnerability of single signal characteristics in the time or frequency domain to environmental influences. Secondly, the K-means clustering method is employed to cluster the health indicators and determine the health state, which solves the problem of determining the health of a component from unsupervised vibration signal data which is quite difficult. Finally, the particle swarm optimized BiLSTM model is used to predict the health state of key components and the bearing dataset from the IEEE PHM 2012 Data Challenge verifies the method's effectiveness and validity.  
deep learning; health status; rolling element bearing; multi-feature fusion +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Virtual Reality (VR) has emerged as a transformative tool in neuropsychological assessment, enabling the creation of immersive, three-dimensional environments that simulate real-world scenarios with high fidelity. Initially developed to enhance gaming experiences, VR has, over the past two decades, been increasingly adopted in research, particularly for replicating environment-specific stimuli within controlled laboratory contexts that were previously difficult to access [1; 2; 3; 4]. This growing interest in VR-based methodologies has been particularly notable in the assessment and enhancement of cognitive [5] and motor functions [6] through both immersive and non-immersive technologies. Its utility in psychological research is underscored by its naturalistic interaction capabilities, which allow for the presentation of complex, real-world tasks in a controlled, repeatable manner. This has proven invaluable for applications in rehabilitation, cognitive training, and neuropsychological assessment [7; 8].  
Extensive research, including our own, has demonstrated the efficacy of VR in neuropsychological assessments across various populations, such as children with attention deficit hyperactivity disorder (ADHD) and adults with autism spectrum disorder (ASD). These studies suggest that VR provides a flexible, adaptable environment that can be customized to minimize or incorporate external distractions, creating a safe, engaging setting conducive to assessment [9; 10]. Additionally, VR-based tools offer a platform not only for diverse behavioral and cognitive tests but also for enhancing and measuring critical cognitive functions such as working memory, executive functioning, and behavior [11; 12; 13]. Moreover, these tools are positioned to be both cost-effective and time-efficient, potentially revolutionizing the landscape of cognitive assessment, diagnosis, and rehabilitation by making these processes more accessible and scalable.
## 4 Results
### Measurement Model (Reliability and Validity)  
The assessment of the measurement model was undertaken to estimate the validity and internal consistency of the construct. The construct validity was examined based on the Average Variance Extracted (AVE) and composite reliability. Table 3 shows the reliability and convergent validity measures where item loading is extracted from confirmatory factor analysis, the Cronbach alpha measuring construct reliability calculated using SPSS software 25. The composite reliability and AVE are the validity measure extracted from the MS-excel spreadsheet formula. All the constructs (Table 3) have AVE values higher than 0.5, implying convergent validity [98,101]. Table 4 represents the discriminant validity tested with the popular Fornel Larker method that used correlations assessment via SPSS software. The results of the analysis (Table 4) indicate discriminant validity, as the value of the AVE's square root in the diagonal exceeds other constructs off-diagonal [101]. The value of CR and Cronbach's Alpha-which was greater than 0.7-indicated a good model and was considered highly acceptable for the early stages of the research [102] (Table 3). The constructs of this study are deemed to be statistically satisfactory, as the CR and alpha values exceed the cut-off values earlier stated. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aspects** & **Classification** & **F** & **\%** & **Aspects** & **Classification** & **F** & **\%** \\ \hline \multirow{2}{*}{Gender} & Male & 194 & 53.2 & & Up to 5235 & 16 & 4.5 \\ \cline{2-6}  & Female & 171 & 46.8 & & \$235–$470 & 73 & 20.0 \\ \hline \multirow{4}{*}{Age} & \textless{}20 & 38 & 10.4 & \multirow{2}{*}{(\$1 equals Tx. 85)} & \multirow{2}{*}{(\$1 equals Tx. 85)} & \multirow{2}{*}{(\$1 equals Tx. 85)} & \multirow{2}{*}{(\$1 equals Tx. 85)} & \multirow{2}{*}{(\$1 equals Tx. 85)} \\ \cline{2-6}  & 20–30 & 55 & 15.0 & & & \\ \cline{2-6}  & 30–40 & 163 & 44.6 & & & \\ \cline{2-6}  & 40–50 & 92 & 25.3 & & & \\ \cline{2-6}  & \(>\)50 & 17 & 4.7 & & & \\ \hline \multirow{4}{*}{Educational level} & No formal education & 39 & 10.6 & \multirow{2}{*}{Profession} & \multirow{2}{*}{Student} & \multirow{2}{*}{41} & \multirow{2}{*}{11.1} \\ \cline{2-6}  & Higher Secondary or below & 114 & 31.3 & & & & \\ \cline{2-6}  & Graduate & 174 & 47.7 & & & \\ \cline{2-6}  & Postgraduate and above & 38 & 10.4 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Respondents’ Profile. For robustness, this study also measured the HTMT value due to its supremacy over Cornell-Larcker in various situations [103]. Table 5 indicates the measurement of HTMT ratio calculated with the help of individual item's correlation and using HTMT formula
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 6 Performance Evaluation  
The performance of the proposed framework is evaluated based on two criteria: the average time required to process the user's requests on the cloud and the time required to access the users' data.  
The average time required to evaluate several access user requests in the cloud was calculated (Figure 8). Compared to the approach without an authentication mechanism, our proposed system, which encompasses access control on smart contracts, takes longer to execute service requests. The time spent on identity management and authorization confirmation was added to this cost. However, the increased cost was approximately 5 s in the worst-case scenario--50 queries. Thus, it is considered to be minimal and appropriate for real-world situations. This result demonstrates the compact architecture of the proposed model for user authentication and authorization management.  
In addition, we demonstrate the capability of our EHR distribution system through the computational complexity of acquiring EHRs for the IPFS blockchain cloud storage (Figure 9). We analyzed the amount of time it took to complete the EHR access process, from the time of demanding data access to the time of obtaining information on a smartphone. In addition, we investigated the time efficiency of EHRs using a blockchain-based repository, IPFS, versus a central server supported by a simple storage service (S3). The findings of the evaluation, considering various large EHR files, reveal that our distributed storage solution on the blockchain is faster (takes less time) than the centralized storage strategy to access data.  
Figure 8: Average Time Overhead Based on the Number of User Requests.  
Compared with traditional cloud designs, the network latency measurement shows that our design achieves a compact EHR distribution with low computational latency. The second conceptual aim is explained in the performance section.
# Application Research of Negative Pressure Wave Signal Denoising Method Based on VMD
## 3 Results and Discussion  
### The Laboratory Experiments  
#### 3.1.1 Experimental Environment  
To verify the NPW denoising effect of various methods, a leakage simulation experimental system is built, as shown in Figure 2. The pipe material is carbon steel, and the diameter of the pipe is 0.08 m. We simulate the burst leakage by rapidly opening a valve. The high frequency dynamic pressure sensors are powered by the battery. The pressure signals are transmitted to the computer by an acquisition card. The sampling frequency is 2000 Hz.  
The design of experimental parameters is shown in Table 1. The distance between sensors is 23.34 m. Three simulated leak points are set on the pipeline. The distance between the leak point and sensor A are 17.34 m, 14.47 m, and 2.47 m, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(L\) **(m)** & **Leak Point** & \(X_{\textbf{A}}\) **(m)** \\ \hline  & 1 & 17.34 \\
23.34 & 2 & 14.47 \\  & 3 & 2.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design of experimental parameters.  
Figure 2: Test schematic diagram.  
Table 2 lists the physical parameters of the test pipeline, according to which the negative pressure wave velocity can be calculated.  
The wave velocity \(v\) of the NPW is 452.67 m/s in this paper.
## 3 Proposed Detection Mechanism
### Packets Categorization  
The captured packets and the extracted features' data are further categorized. The main categories are normal traffic and abnormal traffic. These categories are designed by taking some threshold values that are calculated by considering the dual median of the records. While designing an adaptive and intelligent system, it is obvious that the threshold value should not be a constant number. For this purpose, during the data capture, time  
Figure 2: The overall structure of the proposed model.  
slots, each having a minute, are made. Each time slot is further divided into twelve equal intervals. The median number of received packets in each interval is computed, as shown in Equation (1), where \(n\) is the number of packets in a particular time interval.  
\[median_{interval}=\frac{[n+1]}{2}th\ \ item \tag{1}\]  
There are twelve intervals; therefore, twelve medians are obtained for each time slot of one minute. The same equation is applied again to calculate the median of medians. The value obtained from the median of the median is considered as the threshold value \(TH\), as shown in Equation (2). In this case, mean and mode values cannot be considered. Because these values are highly susceptible when the data values have abnormal variations, however, in the case of normal data distribution, mean, mode, and median may give similar results and may be used interchangeably.  
\[TH=median_{slot}[median_{interval}] \tag{2}\]  
In any time slot, if the number of packets is greater than the TH value, then it is considered suspicious traffic.
## 4 Experimental Settings and Results
### Dataset
#### 4.1.2 Real Dataset  
To evaluate the proposed model on the real image, which is not synthetic, the real images with their corresponding text were needed. However, books were written with different fonts, and most importantly, their corresponding text was not available. Nevertheless, Microsoft Office was used to type the context of different and random books, some of which included figures, and then the typed contests were printed. Afterward, we took an image of each printed paper with a cell phone Samsung A5 in an ordinary room with natural light. Since the images' original size was too large, for instance, 109/22*145/63, they were scaled to height 27/9, with different width sizes. Besides, the resolution of images is 300 dpi. Finally, the model was applied to the collected images to extract their text. Since collecting a dataset in this manner is time-consuming and costly, only 20 images were collected [51]. The utilized fonts were Times New Roman, Arial, Mitra, Nazanin, Calibri, and Tahoma, with size 14. The collected dataset was uploaded on Kaggle websites [51], and its link is available on the Data and Signal Processing (DSP) webpage [52] for student use.  
Figures 4-6 show examples of the dataset for three different fonts and sizes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Train Data of** & **Test Data of** & **Test Data of** \\  & **Persian-English** & **Persian** & **Persian-English** \\ \hline Number of Lines & 4,000,000 & 4000 & 4000 \\ Number of Images & 2,000,000 & 4600 & 4600 \\ Size & 12-14-18 & 12-14-18 & 12-14-18 \\ Fonts & 1—Arial. 2—Homa. 3—Calibri. 4—Lotus. 5—Mitra. 6—Nazanin. \\  & 7—Tahoma. 8—Time New Roman. 9—Traffic. 10—Yagut. 11—Zar. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the generated PECI dataset for the Persian OCR.  
Figure 4: An example of font Homa-size 12.
# Predicting Runoff from the Weigan River under Climate Change
## 2 Data Sources and Methods
### Research Methodology  
#### 2.3.1 Delta Change Method  
In this study, the delta change method was used for bias revision downscaling. The delta correction method assumes that model biases (absolute increase in temperature, relative rate of change in precipitation, etc.) do not change over time and applies them to point or regional historical observations to obtain point or regional future climate change scenarios. This method, as a part of the Bias Correction and Spatial Decomposition (BCSD) method, is mostly applied to correct data in the mean state, and has been widely used in research studies because of its relatively simple principle and high accuracy [24; 25]. The specific calculation formulae are as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Pattern Name** & **Research Institutions** & **Source Country** & **Original Resolution** \\ \hline
1 & ACCESS-ESM1-5 & Australian Community Climate and Earth & Australia & 1.88 \(\times\) 1.25 \\  & & System Simulator & & \\
2 & BCC-CSM2-MR & National Climate Centre & China & 2.88 \(\times\) 1.92 \\
3 & CanESM5 & Canadian Centre for Climate Modelling & & \\  & & and Analysis & & 2.8 \(\times\) 2.8 \\
4 & CMCC-ESM2 & Centro Euro-Mediterraneo sui & & \\  & & Cambianenti Climatici & Italy & 1.25 \(\times\) 1.0 \\
5 & CNRM-ESM2-1 & National Center for Meteorological Research & France & 1.41 \(\times\) 1.39 \\
6 & INM-CM4-8 & Marchuk Institute of Numerical Mathematics & Russia & 2.0 \(\times\) 1.5 \\
7 & MRI-ESM2-0 & Meteorological Research Institute & Japan & 1.1 \(\times\) 1.1 \\ \hline \end{tabular}
\end{table}
Table 1: Basic information on seven global climate models.  
Temperature:  
(1) Calculation Bias  
\[delta_{mon_{i}}=\overline{T}_{M,mon_{i}}-\overline{T}_{O,mon_{i}} \tag{1}\]  
(2) Monthly Revisions  
\[T_{Cor,M,mon_{i}}=T_{M,mon_{i}}-delta_{mon_{i}} \tag{2}\]  
Precipitation:  
(1) Calculation Bias  
\[delta_{mon_{i}}=\overline{P}_{O,mon_{i}}/\overline{P}_{M,mon_{i}} \tag{3}\]  
(2) Monthly Revisions  
\[P_{Cor,M,mon_{i}}=P_{M,mon_{i}}\times delta_{mon_{i}} \tag{4}\]  
where \(delta\) is the reconstructed future gridded model data series, i.e., the month-by-month deviation of the model data from the observed data within a historical period; \(\overline{T}_{mon_{i}}\) denotes the monthly average gridded temperature series; \(\overline{P}_{mon_{i}}\) represents the month-by-month mean gridded precipitation series; \(M\) and \(O\) represent model data and observation data, respectively; and \(Cor\) represents the corrected temperature series.