esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Frequency Bands  
LoRa can operate with the frequencies 915 MHz, 868 MHz and 433 MHz, however the frequency 868 MHz and 433 MHz is a band that operates in a regulated manner only in Europe and North America. In this case, in Brazil the frequency that can operate in a regulated way is 915 MHz [38].
## 6 Performance Analysis
### Numerical Results
#### 6.4.2 Loading and Miss Rate Analysis  
All three of our proposed models effectively reduce the number of simultaneous attempts. It is also observed that the RL-based model (purple) performs better in all scenarios (device number). We observed that the BFD model, which targets strict task completion, keeps the number of devices per group at around 10; this is because of the time constraint. The MILP-based model (R-BFD) offers a more relaxed strategy where more devices can join the groups. It is reflected in Figure 6 that, as the \(\gamma\) factor increases, almost 30 devices joined the group instead of 15 when using the BFD model. Although  
Figure 5: Success and failure probabilities vs. number of preambles (k).  
this surely impacts the reduction in the number of groups, it may also increase the miss rate ratio. Even if the goal is to have fewer possible groups, it is worth mentioning that having overly crowded groups is not ideal, as each device in a group will have to wait for its turn to be active and transmit. This creates a trade-off between the traditional RACH procedure, where all attempts are simultaneous, causing massive collisions, and having crowded groups by reducing simultaneous attempts; the consequence of the latter will be the transmission latency of the devices, which will indeed cause some devices to miss their transmission timeline.  
To set a balance in this trade-off, we consider the heterogeneity of MTC devices, as each category has a specific time constraint (delay-tolerant vs. delay-non-tolerant). With this in mind, we can afford a small percentage of miss rate, especially on non-urgent reports (e.g., smart meters).  
It is shown in Figure 6 that the RL-based model outperforms the other models with a less than 4% miss rate in the worst-case scenario, where we have fewer groups and more \"in-group\" devices. Both MILP and BFD outperform the feature-based and location-based models. As the paper targets a massive MTC scenario, the RL-based model shows that scalability is needed to group devices. The primary goal is to reduce the number of groups created and guarantee punctual transmission for devices with a miss rate margin of 4%, which is affordable for delay-tolerant devices.
# The Influence of Track Irregularity in Front of the Turnout on the Dynamic Performance of Vehicles
## 4 Result Analysis  
### Simulation of Track Irregularity  
Generally, compared with the vehicle passing straight through the turnout, the dynamic behavior and safety of the vehicle passing through the diverging track of the turnout are worse. However, it is challenging to analyze the influence of track irregularity on driving behavior and safety when the curves are considered at the same time [8]. Therefore, attention is focused on how the preceding track irregularities of the turnout affect the dynamic performance of vehicles in the main direction in this paper.  
Track irregularities come in a variety of forms, but they can all be thought of as a single harmonic excitation or a complex wave generated by the superposition of several single harmonic waves. The single harmonic wave can be expressed simply as a cosine function [12; 37], which is:  
\[Z_{0}(t)=\frac{1}{2}A\bigg{(}1-cos\frac{2\pi vt}{L}\bigg{)},0\leq t\leq\frac{ L}{v} \tag{5}\]  
where \(v\) is the vehicle speed; \(t\) is the running time of the vehicle; \(L\) is the wavelength of track irregularity, and \(A\) is the amplitude of track irregularity.  
Figure 9: Calculation process based on the stochastic method.  
The displacement function is used as the system excitation input for the track irregularity applied in front of the turnout, and its end position is at the position of the starting point of the switch panel, as shown in Figure 10a. The alignment irregularity (AL), the irregularity of the longitudinal level (LL), and the irregularity of the cross level (CL) are considered, as shown in Figure 10b-d. It is worth mentioning that the CL is applied on the switch rail side.  
According to Reference [42], in the case where harmonic irregularities and random irregularities are applied together, it is usually the influence of harmonic irregularities that covers the influence of random irregularities on dynamic responses of vehicles. Therefore, to better reflect the impact of irregularity in front of the turnout, random irregularities are not considered in the model.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 6 Conclusions  
Given our primary goal of not constraining BSM use contextually and geographically, in this paper, we introduced a new philosophy that aims at conserving collected BSMs and adopting the In-Vehicle Computing paradigm in order to create a reliable and useful transport data source. We then proposed our new BSM reuse model based on three-stage process. In its first stage, our proposed model captures generated and acquired BSMs. Then, in the second stage, it would perform a series of procedures on the raw BSMs to be storable according to the proposed model. In the third stage, our model aims at opening up new possibilities for the endless reuse of stored BSMs.  
Later in our study case, we built an embedded data platform accrediting the Model-View-Controller design commonly used for developing user interfaces. This new platform has accomplished several purposes of data reduction, delivery, and visualization. We were able to perform lossless data compression and considerably reduce the data size; the thing that has a positive impact on bandwidth and storage requirements that have been reduced by about 80%. We have also achieved different data delivery according to the Pull and Push mechanisms to cater to the different data consumer use case requirements. Adopting the ANN paradigm, we obtained an accuracy of 0.9988 in carrying out traffic volume prediction. We attained the visualization of some data elements to enhance analytics and support decisions-making for transportation.  
Our work bears certain limitations that should be recognized. First, the proposed model was partially developed using a pre-collected BSMs, forthcoming works should focus on the data capture stage. Second, our in-vehicle platform has been restricted to an isolated vehicle far from real-world traffic, so the number of vehicles, as well as real-world traffic conditions, should be considered in future work. Third, our in-vehicle platform was tested in a private wide area network by limited users, further works are required to correct this deficiency by making our platform publicly available.  
[PERSON] was involved in all parts of the study, including conceptualization, methodology, software, investigation, visualization, original draft preparation, reviewing, and editing. [PERSON] was mainly involved in conceptualization, interpretation, and discussion of results, reviewing, and editing. [PERSON] was mainly involved in supervision and validation. All authors have read and agreed to the published version of the manuscript  
This research was funded by the University of MEDEA with the LESIA laboratory of the University of Biskra.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results
## 2 Analysis of Antenna Pattern Errors
### Antenna Pattern Measurement Errors  
When antenna patterns are measured in a grounded anechoic chamber, measurement errors arise due to the limitations of the testing system's accuracy. For the FPASR operating in orbit, achieving the requisite high precision necessitates a larger array size. The existing anechoic chamber struggles to meet the requirements for the antenna pattern measurement of large-size arrays. The array needs to be segmented and grouped for measurement [23]. However, this segmentation alters the boundary conditions of the antennas compared to those of the complete array, leading to errors in the measured antenna patterns. After the system is in orbit, the antenna pattern will change with the in-orbit environment. Therefore, inconsistencies likely exist between the actual antenna patterns in orbit and the ground measurement results. The antenna pattern with measurement errors is expressed as follows:  
\[F_{n}^{cr}(\xi,\eta)=\left|F_{n}(\xi,\eta)\right|(1+\widetilde{F}_{n}(\xi,\eta ))e^{i\left(\varphi_{F}(\xi,\eta)+\widetilde{\varphi}_{F}(\xi,\eta)\right)} \tag{11}\]  
where \(F_{n}^{cr}\) represents the antenna pattern with measurement errors. \(\left|F_{n}(\xi,\eta)\right|\) and \(\varphi_{F}(\xi,\eta)\) are the actual amplitude and phase of the in-orbit antenna pattern. \(\widetilde{F}_{n}(\xi,\eta)\) is the amplitude error of the pattern caused by ground measurement and in-orbit change. \(\widetilde{\varphi}_{F}(\xi,\eta)\) is the phase error of the pattern. \((1+\widetilde{F}_{n}(\xi,\eta))e^{i\widetilde{\varphi}_{F}(\xi,\eta)}\) is the difference between the actual antenna patterns in orbit and those characterized on the ground. The antenna pattern is an important component of the G-matrix. Thus, any measurement error in the antenna pattern will propagate as an error within the G-matrix, subsequently affecting the reconstructed BT image.
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 1 Introduction  
While the resolution of images has been rapidly increasing in recent years with the development of high-performance cameras, advanced image compression, and display panels, the demands to generate high resolution images from pre-existing low-resolution images are also increasing for rendering on high resolution displays. In the field of computer vision, single image super-resolution (SISR) methods aim at recovering a high-resolution image from a single low-resolution image. Since the low-resolution images cannot represent the high-frequency information properly, most super-resolution (SR) methods have focused on restoring high-frequency components. For this reason, SR methods are used to restore the high-frequency components from quantized images at the image and video post-processing stage [1, 2, 3].  
Deep learning schemes such as convolutional neural network (CNN) and multi-layer perceptron (MLP) are a branch of machine learning which aims to learn the correlations between input and output data. In general, the output in the process of the convolution operations is one pixel, which is a weighted sum between an input image block and a filter, so an output image represents the spatial correlation of input image corresponding to the filters used. As CNN-based deep learning technologies have recently shown impressive results in the area of SISR, various CNN-based SR methods have been developed thatsurpass the conventional SR methods, such as image statistical methods and patch- based methods [4; 5]. In order to improve the quality of low-resolution images, CNN-based SR networks tend to deploy more complicated schemes, which have deeper and denser CNN structures and cause increases in the computational complexity like the required memory to store network parameters, the number of convolution operations, and the inference speed. We propose two SR-based lightweight neural networks (LNNs) with hybrid residual and dense networks, which are the \"inter-layered SR-LNN\" and \"simplified SR-LNN\", respectively, which we denote in this paper as \"SR-ILLNN\" and \"SR-SLNN\", respectively. The proposed methods were designed to produce similar image quality while reducing the number of networks parameters, compared to previous methods. Those SR technologies can be applied to the pre-processing stages of face and gesture recognition [6; 7; 8].  
The remainder of this paper is organized as follows: In Section 2, we review previous studies related to CNN-based SISR methods. In Section 3, we describe the frameworks of the proposed two SR-LNNs for SISR. Finally, experimental results and conclusions are given in Sections 4 and 5, respectively.
# Development of Multi-Item Air Quality Monitoring System Based on Real-Time Data
## 4 Conclusions  
The developed multi-item air quality monitoring system based on real time consists of 16 items (PM\({}_{10}\), PM\({}_{4.0}\), PM\({}_{2.5}\), PM\({}_{1.0}\), CO\({}_{2}\), CH\({}_{4}\), temperature, humidity, VOCs, O\({}_{2}\), H\({}_{2}\)S, NH\({}_{3}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO). This system was developed by dividing it into hardware (measuring device) and software (smartphone application). To measure sixteen items in the measuring device, ten sensors in the main device and two sensors in the probe rod are installed. All sensors were installed in consideration of the minimum area in the main device. A smartphone application was developed with a home screen, measurement screen, and management screen in consideration of the user's ease of use and clear information delivery. Bluetooth and Wi-Fi are installed for efficient communication between the two devices. Comparative test, temperature-humidity accuracy test, and operating temperature test were conducted to test the availability and reliability of the developed device. It was found to be PM\({}_{10}\)\(\pm\) 5%, PM\({}_{2.5}\)\(\pm\) 6%, O\({}_{3}\)\(\pm\) 4%, NO\({}_{2}\)\(\pm\) 5%, CO \(\pm\) 7%, and SO\({}_{2}\)\(\pm\) 7%. The temperature and humidity accuracy test result showed high reliability at \(\pm\)1% and humidity \(\pm\) 2%. The result of the operating temperature test showed that there was no problem in normal operation. However, the test conducted was a comparative test, all installed sensors were not tested, the test time and frequency were not sufficient, and the closed chamber test was not performed. It is not enough to prove the reliability of the developed device. Additional work including a long-term test for more clear reliability of the device and closed chamber accuracy test for all sixteen item sensors, data acquisition rate, and data transmit rate are necessary for commercializing the device.  
This development device will enable real-time environmental monitoring in the near future, and the developed device will be used as a device to help create a better environment for humans.  
Conceptualization, B.P. and H.P.; methodology, B.P. and S.K.; investigation B.P. and M.K.; software B.P. and S.P.; validation B.P. and T.Y.K.; writing--review and editing, B.P. and H.P.; supervision, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Environmental Industry & Technology Institute in South Korea, grant number L120-00005-0001-0.  
Not applicable.  
All authors declare no conflict of interest.
## 1 Introduction
Bio-Inspired Optimization for Cost Efficiency: MRFCO-ZTS employs the Manta Ray Foraging Optimization (MRFO) algorithm--a bio-inspired technique--to optimize the ZTS cost function. This function considers the likelihood of attacks, potential risk impact, and computational overhead. By integrating a cost-optimized classification system, the model effectively distinguishes between legitimate and malicious nodes, enhancing secure data access in satellite communications. 3. Balanced Performance and Efficiency: The MRFCO-ZTS framework achieves an optimal balance between satellite network security, communication latency, and resource utilization. Its performance has been validated through extensive simulations, demonstrating improvements over traditional satellite authentication methods in terms of accuracy, speed, and efficiency [3; 4; 6]. The traditional metaheuristic security schemes were designed for one-time or static trust assumptions. In contrast, the proposed MRFCO-ZTS is implemented for fully dynamic zero-trust score updating. In addition, the traditional metaheuristic security schemes were developed for ground networks (IoT, WSN, and MANET) or generic cyber systems. However, the MRFCO-ZTS is proposed for inter-satellite environments. In traditional metaheuristic security, optimization focuses on generic search (e.g., PSO, GA). In contrast, the proposed MRFCO-ZTS used Manta Ray Foraging Optimization for dynamic orbital constraints. Traditional Metaheuristic Security Schemes support only limited feedback loops or static decision logic, whereas the proposed MRFCO-ZTS uses real-time continuous monitoring and adaptive trust updates. The rest of the paper is formulated as follows: Section 2 shows related works, Section 3 describes the LEO satellite network model, Section 4 provides the detailed processes of the developed MRFCO-ZTS approach for authentication and secure data access, Section 5 illustrates experimental results and features a comparison with conventional methods, and Section 6 concludes the paper.
## 4 Data Processing
### Mobile Mapping System (MMS) Point Cloud Processing  
The open-source software CloudCompare has been used to process the 3D point cloud by MMS, with the purpose of analysing the raw data acquired (Figure 8). As a default parameter, the number of points composing the sharpened MMS point cloud is 94.30 million.  
In order to obtain ground and non-ground points from MLS point cloud, the data filtering algorithm Cloth Simulation Filter (CSF) was used [33]. This method allows users to obtain the \"steep slope\" model setting some advanced parameters such as cloth resolution, maximum iterations and classification threshold (Table 5). The cloth resolution refers to the grid size of cloth composed by particles interconnected through virtual springs, which is used to cover the terrain. The positions of the particles in three-dimensional space determine the position and shape of the cloth. The grid size has the same unit of the point cloud. The lower the value of cloth resolution, the softer the resulting mesh from the filtered point cloud is. The number of iterations is linked to the maximum iteration  
Figure 7: Cortes de Pallás sector: (**a**) point cloud without filtering; (**b**) cleaned and filtered point cloud.
## 2 Materials and Methods
### Location Estimation Methods of Autonomous Driving Robot
#### 2.2.2 Location Estimation Methods  
Figure 3 shows the overall system architecture of the sensors and data processing equipment. Three types of orientation sensors (accelerometer, gyroscope, and magnetometer) measure the robot's attitude and heading. Each measured data point is fused through the Kalman filter and then converted into a Euler angle. The encoder sensor measures the travel distance by counting the robot wheels. The LiDAR sensor measures the distance difference between the left and right walls, and calculates the robot's heading through an autonomous driving algorithm.  
Figure 3: System architecture of the data processing procedure for the autonomous driving robot used in this study.  
This study compared three location estimation methods of autonomous driving robots. In all the methods, the encoder sensor was used to measure the travel distance of the robot. Additionally, the IMU and LiDAR sensors were also used to measure the robot's heading angle in the IMU + encoder and LiDAR + encoder methods, respectively. The IMU + LiDAR + encoder method measures the headings by switching the IMU and LiDAR sensors according to the threshold angle.  
GPS cannot be used in underground mine environments, and location estimation through a camera sensor is also impossible to use because of the lack of lighting. Therefore, in this study, a localization method was constructed by combining sensors that can measure the robot's travel distance and direction on their own.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Evaluation Criteria
#### 3.3.1 Ground Resistance  
Grounding resistance of a grounding device comprises three components: the resistance of the grounding body, the contact resistance between the grounding body's surface and the soil, and the soil resistance as the current flows from the grounding body into the ground. The total ground resistance is a critical measure of the performance of the grounding system. Lower resistance enhances fault current dissipation efficiency and safety. The goal is to minimize \(R_{ground}\) while ensuring the system meets all other design requirements. The efficiency of fault current dissipation is determined by the grounding system's ability to absorb and safely direct fault currents into the earth. This efficiency is assessed by comparing the fault current dissipated by the system to the total fault current applied. Table 3 presents the evaluation criteria for grounding system optimization.  
The dissipation efficiency \(\eta_{fault}\) can be calculated as follows:  
\[\eta_{fault}=\frac{I_{dissipated}}{I_{total}}\times 100 \tag{11}\]  
where \(I_{dissipated}\) is the current dissipated into the ground, and \(I_{total}\) is the total fault current.  
These evaluation criteria offer a comprehensive approach to evaluating the optimization of grounding systems, from computational modeling to real-world measurements and performance criteria.