esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS-Based Monitoring Methods for Mining Headframes
## 3 Monitoring Data Processing and Analysis
### 3D Deformation Spatiotemporal Analysis  
#### 3.2.1 Analysis of Deformation Size and Spatial Variation  
For visualizing and analyzing the deformation (\(\Delta\)X, \(\Delta\)Y, \(\Delta\)H) of the eight GNSS monitoring stations for headframe monitoring, preliminary modeling of their 3D deformation data is vital. With the preprocessed data, the Origin2024 software is used to plot the 3D coordinate deformation of the upper and lower sheave wheels and the platform monitoring stations and to assess the displacement and settlement conditions on the basis of their overall deformation fluctuation images. Figure 5 shows the 3D deformation surface plot of some monitoring stations.  
The above deformation surface and statistical data demonstrate that for the four monitoring points, 90% of the deformation quantities in (\(\Delta\)X, \(\Delta\)Y, \(\Delta\)H) positions do not exceed 15 mm, with most concentrations between \(\pm\)8 mm, and are considered minor deformations. Closer examination indicates that the deformation surfaces of the monitoring points around the headframe, JC01 and JC03, are relatively flat, with small and uniform deformations. By contrast, the deformation surfaces at the sheave wheel positions, STL-L and XTL-L, exhibit larger fluctuations and concentrated peaks, indicating a slightly more intense degree of deformation. The main reason is that the monitoring stations closer to the sheave wheels are more severely affected by the obstruction of the headframe, thereby reducing the monitoring accuracy. To address this issue, the project team extended the receiver at the XTL-L point on the lower sheave platform by welding steel bars, thereby avoiding signal obstruction at the source.  
Figure 4: Deformation curve of partially cleaned data points.
###### Abstract  
This study proposes an image data preprocessing method to improve the efficiency of transmitting and processing images for object detection in distributed IoT systems such as digital CCTV. The proposed method prepares a background image using Gaussian Mixture-based modeling with a series of continuous images in the video. Then it is used as the reference to be compared with a target image to extract the ROIs by our DSSIM-based area filtering algorithm. The background areas beside the ROIs in the image are filled with a single color--either black or white to reduce data size, or a highly saturated color to improve object detection performance. Our implementation results confirm that the proposed method can considerably reduce the network overhead and the processing time at the server side. From additional experiments, we found that the model's inference time and accuracy for object detection can be significantly improved when our two new ideas are applied: expanding ROI areas to improve the objectness of each object in the image and filling the background with a highly saturated color.  
Keywords:intelligent traffic monitoring system; object detection; interest point detection; streaming media
## 3 Results and Discussion
### Effect of EGR on the Dual-Fuel Engine  
As discussed in the previous section, the addition of DMG significantly enhanced the power performance and fuel economy of the dual-fuel engine. Nevertheless, there were also increases in peak in-cylinder pressure, maximum pressure rise rate, and NOx emissions as the DMG substitution ratio increased. At a substitution ratio of 20%, the peak in-cylinder pressure reached approximately 20 MPa. Considering the overall performance as well as the combustion and emission characteristics, a DMG substitution ratio of 15% can be an optimal choice.  
As an effective emission reduction technology, EGR has been widely applied to diesel engines. Since EGR can significantly influenced the in-cylinder composition and temperature, consequently impacting an engine's operational process, it is valuable to investigate the effect of EGR on a diesel-DMG dual-fuel engine. In this study, the EGR gas is considered to be a mixture of CO\({}_{2}\), H\({}_{2}\)O, N\({}_{2}\), and O\({}_{2}\). Simulations were conducted at full load, an engine speed of 1500 rpm, a DMG substitution ratio of 15%, an intake temperature of 400 K, an intake pressure of 0.29 MPa, and varying EGR ratios from 0% to 20%. The initial intra-cylinder compositions were calculated for different EGR rates and are listed in Table 1 of Appendix A.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach
### Cloud-Based Data Management  
Scalable cloud-based data storage and processing facilitate system management and long-term data maintenance. Python Flask, a RESTful API framework developed in Python,provides a simple yet robust web service architecture that adheres to the HTTP protocol [39]. This cloud system uses Flask to handle data submission and retrieval via POST and GET methods. In addition, tags such as bridge location, bridge name, and sensor ID are managed in batches for improved monitoring scalability. These data points are identified by Uniform Resource Identifiers (URIs). When images are uploaded to the Flask-based web framework, they are stored in PNG format on an Amazon EC2 server. A watchdog script then automatically extracts and stores markers and three-dimensional displacement data from the images. Table 2 outlines the Flask-based web services implemented for managing sensor data and bridge information in the cloud-based bridge bearing monitoring system. InfluxDB, a NoSQL database, was chosen for its efficiency in data aggregation and query processing [40]. The database schema consists of two components: tags and fields. Tags, such as region and sensor ID, enable efficient indexing, with the \"status\" tag indicating battery conditions (normal or low voltage). Fields store metrics like RTC, temperature, spatial coordinates, orientation, and power status, supporting fast and accurate data retrieval.  
Figure 5 provides a comprehensive visual representation of this schema, illustrating the relationship between server objects and the corresponding InfluxDB column families. This detailed schema visualization aids in understanding how data are organized and stored within the system, highlighting the efficient handling of complex data sets.
## 1 Introduction
Hence, for systems with dead zones, the most effective method to obtain better control performance is to synthesize a robust controller using RBFNNs and a non-linear control technique. Based on the existing literature, this paper further studied the trajectory tracking control problem of FFSGRs with actuator dead zone non-linearity. Herein, an adaptive robust sliding mode control algorithm was proposed for the considered system, and RBFNN-based machine learning technology was also introduced for the estimation and mitigation of an actuator's dead zone. The parameters of the RBFNNs were restricted by the Lyapunov functions for system stability. The main contributions of this paper are: (1) a machine learning method based on RBFNNs was applied to the actuator non-linear control of FFSGRs; (2) in addition to the actuator dead zone, the model uncertainty, external disturbance, and joint friction in the grinding process were also taken into account in the design of the system control; (3) with the presented control method, dead zone non-linearity was not required to be totally symmetrical, as the dead-band could be a non-linear function. A simulation study was provided to further demonstrate the effectiveness of the developed control scheme. The experimental results of the FFSGR showed the superiority of the control scheme. The rest of this paper is organized as follows: The system model, dead zone non-linearity, RBFNNs, and necessary assumptions are introduced in Section 2. Section 3 illustrates a robust adaptive control method based on the dead zone compensation scheme. Section 4 reveals the validity and performance of the proposed method via simulation and experimental results. The conclusions are presented in Section 5.
# Improvements to Supercomputing Service Availability Based on Data Analysis
###### Abstract  
As the demand for high-performance computing (HPC) resources has increased in the field of computational science, an inevitable consideration is service availability in large cluster systems such as supercomputers. In particular, the factor that most affects availability in supercomputing services is the job scheduler utilized for allocating resources. Consequent to submitting user data through the job scheduler for data analysis, 25.6% of jobs failed because of program errors, scheduler errors, or I/O errors. Based on this analysis, we propose a K-hook method for scheduling to increase the success rate of job submissions and improve the availability of supercomputing services. By applying this method, the job-submission success rate was improved by 15% without negatively affecting users' waiting time. We also achieved a mean time between interrupts (MTBI) of 24.3 days and maintained average system availability at 97%. As this research was verified on the Nurion supercomputer in a real service environment, the value of the research is expected to be found in significant service improvements.  
high-performance computing; supercomputing service; data analysis; service availability; resource scheduler; resource utilization +
Footnote †: journal:on the causes of job failures by application category, and (2) to improve service availability under conditions that do not increase user job waiting time.  
In this study, we analyzed in more detail the success and failure rates of jobs using collected user-submitted job data and examined the causes of job failure. To provide high service availability while maintaining system stability, we applied a K-hook with scheduling to increase the job-submission success rate. We describe our operational technique; present our system's MTBI, which is an indicator of system stability [6; 7; 8]; and analyze our system availability statistics.  
The rest of this paper is organized as follows: Section 2 describes the system configuration, which includes the hardware and software structures of the Nurion system, and Section 3 provides the main problem statements. Section 4 introduces the proposed method, which increases the service availability. Section 5 analyzes the results of applying our method. Finally, the conclusions are presented in Section 6.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Human Health Risk Assessment  
Human health risks from exposure to PTEs in atmospheric deposition, as identified by the United States Environmental Protection Agency (USEPA), are categorized into non-carcinogenic and carcinogenic risks [18]. These risk assessments are conducive to quantifying exposure to PTEs [14; 20]. Exposure routes include unintentional oral ingestion and inhalation. We conducted a human health risk assessment of PTEs in atmospheric deposition from IAREEs for two demographic groups (adults and children). The primary exposure routes to PTEs in atmospheric deposition are ingestion and inhalation [18]. This assessment evaluates both carcinogenic and non-carcinogenic risks from atmospheric deposition of PTEs at mine sites. Carcinogenicity assessments were conducted for ingestion (As, Cr, Pb) and inhalation (As, Cd, Cr, Ni) pathways due to the unavailability of carcinogenic slope factors for other PTEs [19; 20].  
The non-carcinogenic risk (HQ) of PTEs to the human via the ingestion and inhalation routes was calculated using the following Equations (5) and (6) [30]:  
\[HQ_{\text{ingestion}} =C_{i}\cdot\frac{IR\cdot RBA\cdot EF\cdot ED}{BW\cdot AT\cdot RfD_ {0}\cdot 10^{6}} \tag{5}\] \[HQ_{\text{inhalation}} =C_{i}\cdot\frac{InhR\cdot EF\cdot ED\cdot PM_{10}}{AT\cdot RfC} \tag{6}\]  
The carcinogenic risk (CR) of PTEs to the population via the ingestion and inhalation routes was calculated using the following Equations (7) and (8) [30]:  
\[CR_{\text{ingestion}} =C_{i}\cdot\frac{IR\cdot RBA\cdot EF\cdot ED\cdot CSF}{BW\cdot AT \cdot 10^{6}} \tag{7}\] \[CR_{\text{inhalation}} =C_{i}\frac{InhR\cdot EF\cdot ED\cdot IUR\cdot PM_{10}\cdot 1000}{AT} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Date** & **Path/Raw** & **Resolution (m)** \\ \hline Landsat OLI & 22 October 2019 & 121/41 & 30 \\ Landsat OLI & 22 October 2019 & 121/42 & 30 \\ Landsat OLI & 20 September 2019 & 121/43 & 30 \\ Landsat OLI & 14 November 2019 & 122/42 & 30 \\ Landsat OLI & 18 February 2020 & 122/43 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The information of Landsat 8 remote sensing images.  
The total non-carcinogenic risk (HI) of PTEs to the population was calculated [30] using the following Equation (9):  
\[HI=HQ_{\text{ingestion}}\ +HQ_{\text{inhalation}} \tag{9}\]  
The total carcinogenic risk (TCR) of PTEs to the population was calculated [30] using the following Equation (10):  
\[TCR=CR_{ingestion}\ +CR_{inhalation} \tag{10}\]  
The relevant calculation parameters for human health risk assessment are shown in Tables 3 and 4.  
If the values of HQ and HI are below 1, it is assumed that no non-carcinogenic risk will occur. If the values are higher than 1, there is a potential health risk to human health [20; 29]. Besides, no greater carcinogenic risk is expected if the carcinogenic risk value does not exceed \(10^{-4}\)[20; 29].
## 4 Experiments
### Visual Comparison  
To provide a direct visual comparison of the denoised images, Figure 8 shows the ground truth of a grey image, its noisy image (\(\sigma=25\)), and denoised images generated using different methods: BM3D [1], WNNM [3], DnCNN [4], DRUNet [27], DIP [23], FFDNet [5], SwinIR [11], N2V [16], and the proposed method. As shown in Figure 8, our proposed method achieves the best PSNR value on the \"Couple\" image. By observing the enlarged areas of the recovered images generated by different competing methods, we can see that the denoised images generated by FFDNet, DRUNet, and SwinIR lost some edge information and over-smoothed the image, whereas our proposed method preserved more image details. Visual images of denoising results can be found at [[https://github.com/chenxiaojun0101/Learning-from-multiple-instances-for-Image-denoising](https://github.com/chenxiaojun0101/Learning-from-multiple-instances-for-Image-denoising)]([https://github.com/chenxiaojun0101/Learning-from-multiple-instances-for-Image-denoising](https://github.com/chenxiaojun0101/Learning-from-multiple-instances-for-Image-denoising)) (accessed on 10 September 2022).  
Figure 9 shows the color image denoising results of different methods with a noise level \(\sigma=25\). Compared with grayscale images, color images contain more complex texture details, which is conducive to testing the robustness of denoising methods. As shown in Figure 9, the structural information and texture details in the ground truth are well preserved in the denoised image generated by the proposed method. The PSNR result of our proposed method is significantly higher than that of DIP and N2V and is very close to that of the newly proposed supervised SwinIR method. This indicates that our proposed method can effectively recover the noisy image while preserving the image details to the extent possible.  
Figure 8: Visual comparison of our proposed method against the competing methods with the quantitative PSNR results listed behind the noisy and denoised images.  
Figure 9: Visual comparison of the denoised results on the color image dataset. The quantitative PSNR results are listed behind the noisy and denoised images.
# Lightning Interferometry with the Long Wavelength Array
## 3 Results  
In order to investigate the dependency of VHF lightning maps on the number of antennas used, the processing methodology above was applied to the example flash using increasing numbers of antennas. To allow the multiple processing runs to complete in a reasonable amount of time, the imaging window was incremented by 5000 samples (24.4 us), with the start of each integration period being identical between runs. This resulted in 9665 windows being imaged and processed during the 235 ms duration of the lightning flash. Imaging for these surveys was done using a single beam steering at the approximate geometric center of the flash (coordinates 0.66, \(-\)0.38). Before imaging, the outrigger antenna and the six antennas identified as faulty during calibration were removed from the array, leaving 249 antennas to use for imaging. The antennas were ordered such  
Figure 4: Figure showing the effects of delay calibration on source location by examining how the locations of 46 sources vary as antennas are removed from the solution. Panel (**a**) (red) shows the variation before calibration, and panel (**b**) (green) after. In both, the solid line shows the mean, the shaded region the standard deviation, and the dashed line the maximum deviation above and below for the 46 solutions. Source variation is measured on the image plane; a variation of 0.0025 corresponds to 0.20\({}^{\circ}\) at 45\({}^{\circ}\) elevation.  
that imaging with the first N antennas would always result in a reasonable, but perhaps not ideal, antenna geometry. This was done by choosing the antennas sequentially such that the next antenna chosen always maximized the distance between itself and all antennas already selected. The resulting antenna geometries were somewhat heavily weighted towards long baseline lengths but were comparable to the sparse arrays used in previous lightning interferometer studies [11].
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 3 Proposed Algorithm for Content Protection in JPEG Images
### Generating the Visually Transformed JPEG Images
#### 3.1.3 Modifying the DC Coefficients  
Differential pulse code modulation (DPCM) and Huffman coding are used to encode DC coefficients by exploiting the correlations between adjacent DC coefficients. To enhance the security while reducing the JPEG file size, all DC coefficients in selected regions are modified to similar values. For a DC coefficient \(d\), the modification is conducted by  
\[d^{\prime}=d-delta^{\prime}, \tag{4}\]  
Figure 1: A flowchart of visual transformation for content and privacy protection in JPEG images.  
where \(d^{\prime}\) represents the modified DC coefficient and _delta_' is obtained as follows. At first, the difference between \(d\) and the average (denoted by _avg_) of all DC coefficients in the selected blocks is calculated by  
\[delta=d-avg, \tag{5}\]  
where _delta_ is the original difference between \(d\) and _avg_. To reduce the data for representation, _delta_ is quantized to _delta_' with the method mentioned in [23] by  
\[delta^{\prime}=\left\{\begin{array}{ll}8\times round\Big{(}\frac{delta}{8} \Big{)}&,\ \ delta>0\\ 8\times floor\Big{(}\frac{delta}{8}\Big{)}+4&,\ \ delta<0\end{array}\right., \tag{6}\]  
where _delta_' is the multiple of 4 so that it can be represented with fewer bits. For recovery, the value of _delta_' is embedded into the AC coefficients in the same block by adopting an RDH algorithm proposed in [16]. Specifically, the binary value of \(\frac{|delta^{\prime}|}{4}\) is hidden in the AC coefficients and we can recover _delta_' from \(\frac{|delta^{\prime}|}{4}\) by multiplying it with 4. In addition, the sign of _delta_ can be inferred from whether \(\frac{|delta^{\prime}|}{4}\) is odd or even. As the experimental results conducted in [24] show, hiding data in low-frequency coefficients helps to achieve a smaller file size, and the 3 rd to 11 th AC coefficients of each block are chosen to carry the bit values. Since each DCT coefficient ranges from \(-1023\) to \(1023\), _delta_ is within \([-2046,\,2046]\), which indicates that \(\frac{|delta^{\prime}|}{4}\) can be expressed with 9 bits. For each AC coefficient \(a_{j}\) (\(j=3\), \(4,5,\ldots,11\)) and a bit value \(s\) (0 or 1), data embedding is performed by  
\[a_{j}\,^{\prime}=a_{j}\,*2+s. \tag{7}\]  
Now we take the example as shown in Figure 2 to explain the aforementioned process. Suppose there are four blocks in the selected region and the DC coefficients are 37, 3, 20, 20 with an average value of 20. For the first DC coefficient, the value of _delta_ is 17 and the value of _delta_ is modified to 16 according to Equation (6). After obtaining the quantified _delta_, the DC coefficient is modified to 21 by calculating the difference between the original one and _delta_'. Finally, \(\frac{|delta^{\prime}|}{4}\) is represented in nine bits, which are embedded into nine AC coefficients of the same block with Equation (7). The cases of modifying the other DC coefficients are also shown in Figure 2.
## 4 Stability Control Measures for Step Method Construction and Analysis of Design Parameters
### Analysis of the Pipe Roof Design Parameters  
For different pipe shed reinforcement lengths and different pipe shed reinforcement ranges, the deformation calculation results of the surrounding rock and supporting structure at the Y = 25 m section are shown in Table 3.  
Figure 5: Development law of surrounding rock deformation and failure during advanced pipe roof support.  
The following can be seen from Table 3:  
1. When the reinforcement range of the pipe roof increases from 90\({}^{\circ}\) to 180\({}^{\circ}\), the arch settlement, horizontal convergence, and tunnel face displacement are significantly reduced, and the decreases are \(-\)59.4%, \(-\)52.0%, and \(-\)25.6%, respectively. The maximum displacement position of the surrounding rock around the tunnel gradually shifts from the arch foot to the sidewall and, finally, to the wall foot. The deformation of the supporting structure is also significantly reduced, which indicates that the pipe roof also reduces the deformation of the supporting structure by reducing the advanced deformation. From the perspective of displacement reduction caused by unit angle, it is more economical to adopt a reinforcement range of 120\({}^{\circ}\) for controlling the arch settlement and the displacement of the tunnel face, and a reinforcement range of 150\({}^{\circ}\) for controlling the convergence displacement is more economical.
2. The length of the pipe shed increases from 15 m to 30 m. Compared with 15 m, when the length of the pipe shed is 20 m, the advanced deformation of the surrounding rock, the settlement of the arch of the supporting structure, and the displacement of the tunnel face are significantly reduced, and the reductions are \(-\)15.5%, \(-\)24.1%, and \(-\)32.3%, respectively. Compared with the pipe shed lengths of 30 m and 20 m, the displacement of the pipe shed is basically unchanged, with a decrease of approximately 1% to 6%.