esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 1 Introduction  
The substation earth grid is one of many key role players that ensure a continuous reliable supply of a distribution substation. The primary function of the earth grid is to provide a low resistive path to earth for fault currents that pose a danger to the substation equipment, its reliability, and people in the vicinity of the substation. Various design elements such as soil resistivity, fault level, available surface area, surface material, etc., are taken into consideration when designing an earth grid to ensure that it complies with the design standards as set out by IEEE Std 80-2013. However, in some cases, these design elements are constrained, and it is not possible to meet the design requirements without refining the earth grid using the predetermined refinements methods proposed in IEEE Std 80-2013.  
The design of substation earth grids under such unfavorable design and environmental conditions can prove to be difficult. This is also true for maintaining existing earth grids to remain within the tolerable safety limits as required by IEEE Std 80. Often, the design input parameters, such as soil resistivity, available surface area and system fault level, as well as construction budget restrictions, are the main causes of the limitations that lead to flawed designs that endanger people and equipment.  
In this paper, the IEEE Std 80 earth grid design refinement methods that can be used to improve an earth grid design were modeled, simulated, and analyzed for a 20 MVA, 11/6.6 kV substation using ETAP. An 80 m \(\times\) 70 m earth grid made of 120 mm\({}^{2}\) annealed copper conductors with 12 in the \(x\)-axis direction and 10 in the \(y\)-axis direction was used. It also has 12 earth electrodes installed on the perimeter of the earth grid. This earth grid was superimposed on the power system model to accurately import system data and efficiently conduct simulations.
## 4 Experiments
### Training and Evaluate Model Accuracy  
The Mask R-CNN NN model for segmentation was trained with the following modified parameters:  
1. Number of iterations--6000;
2. Number of classes--1;
3. Learning rate--0.00025.  
A dropout layer with a value of 0.2 was also added to avoid overfitting. This value was chosen empirically. The size of the training and test samples was 800 and 200 images, respectively.  
A large number of metrics can be used to compare the predicted segments to the original masks. In this study, it was decided to use metrics that are the same as the considered analogs. DICE [46] and IoU [47] were used as metrics for accuracy estimation. The time taken to process one image by the model was also calculated. For this purpose, a smartphone with the same processor as the one used to estimate MobileSeg speed was used. The authors of the UPD model did not test their model on mobile devices. Formulas for the DICE (7) and IoU (8) metrics are presented below.  
\[DICE =\frac{1}{M}*\sum_{i}^{M}\frac{2 TP_{i}}{FP_{i}+2 TP_{i}+FN_{i}} \tag{7}\] \[IoU =\frac{1}{M}*\sum_{i}^{M}\frac{TP_{i}}{FP_{i}+TP_{i}+FN_{i}} \tag{8}\]  
where \(M\) is the number of images, \(TP\) is the true positive pixels, \(FN\) is the false negative pixels, and \(FP\) is the false positive pixels. To determine the clustering accuracy, 200 photos were used that had not been used before (Table 2).  
For the resulting set of estimates, a one-sided Student's \(t\)-test was calculated with a degree of freedom level \(\alpha\) = 0.01. The value of the criterion with a degree of freedom of 199 and a t-score of 2.613 was between 0.01 and 0.005, which made it possible to conclude that the accuracy of our model was statistically significant in comparison with analogs.  
It can be observed from the tables above that the Mask R-CNN model was found to have the best accuracy performance for the DICE and IoU metrics. Additionally, the value of the t-criterion confirmed the significance of our model.  
Figure 7 shows an example of a segmented human foot using the trained Mask R-CNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **DICE (\%)** & **IoU (\%)** & **Inference Time (ms)** \\ \hline
**Mask R-CNN** & **97.88** & **98.27** & **471** \\ UPD [48] & 95.35 & 91.11 & - \\ MobileSeg [29] & 97.64 & 95.52 & 552 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of NN models for foot segmentation.
## 2 Related Works
### MAC Based on CSMA Mechanism  
Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) stands as one of the foundational medium access control (MAC) protocols for ad hoc networks. Its CSMA-based variants have been widely adopted in unmanned aerial vehicle (UAV) networks due to their adaptability and efficiency. Nonetheless, as the network scales and the number of nodes increases, the likelihood of collisions escalates, posing significant challenges to maintaining optimal performance. In pursuit of maximizing network throughput, ref. [30] introduces an optimized distributed CSMA/CA strategy, enhanced by opportunistic Reconfigurable Intelligent Surface (RIS) assistance, grounded in statistical optimization principles. Addressing the issue of collision probability, ref. [31] proposes a Non-Orthogonal Multiple Access (NOMA)-based CSMA/CA (NCSMA) protocol. This innovative NCSMA protocol leverages Zadoff-Chu (ZC) sequences alongside a NOMA Power Coefficient Allocation (NPCA) frame to meticulously select NOMA nodes and allocate power levels, thereby circumventing packet collisions typically induced by random selection processes. The NCSMA protocol demonstrates a marked improvement in normalized saturation throughput and a significant reduction in average packet delay.  
Despite these advancements, the integration of either RIS assistance or NOMA technology into existing UAV systems remains a complex endeavor, necessitating a substantial developmental timeline under current technological and operational constraints.
A high concentration of particulate matter with mean levels of 20.2 \(\pm\) 7.3 ug/m\({}^{3}\) (ranging from 1.4 ug/m\({}_{3}\) to 278.5 ug/m\({}^{3}\)) for PM2.5 and 21.3 \(\pm\) 7.2 ug/m\({}^{3}\) (ranging from 2.9 ug/m\({}^{3}\) to 279.0 ug/m\({}^{3}\)) for PM10 were found in 13 grocery stores in the Lisbon District (Portugal) [32]. The same range of concentrations was found for nine shopping malls in Hong Kong, with a maximum PM2.5 concentration of 217 ug/m\({}^{3}\)[33]. Indoor particulate matter can negatively affect human health, particularly for individuals with respiratory or cardiovascular problems. Long-term exposure to indoor particulate matter has been linked to various health problems, including lung cancer, asthma, and heart disease [34; 35]. Prolonged exposure to indoor air pollutants can determine the development of \"sick building syndrome\" (SBS). This syndrome was described especially in association with office buildings and refers to a higher-than-normal prevalence of various symptoms concerning the eyes, the skin, the respiratory tract, or the head [36]. These symptoms can affect 20-30% of office workers [37], while nasal symptoms (25.3%) and skin-related symptoms (19%) are most frequently reported [38]. Few studies investigated the presence of SBS in general stores [39]. In Romania, in 2021, no exceedances of the annual limit value for particulate matter were recorded. From TVOCs, only benzene is measured, and the average concentration is about 2 ug/m\({}^{3}\)[40]. For the Romania National Air Quality Network's local station CS-1, the one nearest to our experimental locations, the annual mean value of PM10 in 2021 was 12.21 ug/m\({}^{3}\)[41]. A recent study conducted in Bucharest, Romania, showed that the particulate matter concentration in indoor air is 60 ug/m\({}^{3}\) for PM10 and 40 ug/m\({}^{3}\) for PM2.5 [42]. The western part of Romania has diverse stores, especially in large cities. Resita (an industrial town with around 60,000 inhabitants) has a mix of local businesses, grocery stores, clothing boutiques, and some specialty shops. In any event, there are few general shops with different departments. To our knowledge, there are no studies regarding indoor air quality inside the general shops in Romania. Our study aims to assess the concentration of formaldehyde, total volatile organic compounds, monoterpenes, BTEX, lactic acid, and particulate matter in three different shop departments and outside to understand which compounds affect people and store workers in general stores while shopping or working. A secondary objective of the research was to explore the frequency of typical symptoms of SBS among the store's employees.
## 3 Materials and Methods
### Data Collection  
Testing and gathering the data with the application were conducted at the University of Pannonia and at the University of Debrecen. This happened in September 2019. At the University of Pannonia, the Gear VR was used, and the tests were carried out by 61 students. At the University of Debrecen, 240 students used an LG 20M37A (19.5\") desktop display [21] for the tests. The VR testers consisted of Information Technology (IT) and non-IT students, while the students who used the desktop display were either Mechanical Engineering (ME) or Architectural Engineering (AE) students, mostly in their first year.  
Figure 1: **(a)** The MRT test with an orthographic camera, 7:1 contrast ratio, shadows turned on and no extra rotation; **(b)** The MCT test with a perspective camera, a 60\({}^{\circ}\) field of view, 3:1 contrast ratio, shadows turned on and no extra rotation; **(c)** The PSVT test with a perspective camera, a 45\({}^{\circ}\) field of view, 1.5:1 contrast ratio, shadows turned off and no extra rotation.  
The VR tests were three weeks long and the students tested in a sequential order as only one Gear VR device was available at the University of Pannonia. Different number of students tested each day. The smallest number of testers was two and the largest number was eight. As more desktop display devices were available at the University of Debrecen, testing there was different: The tests were done in a computer laboratory. Due to the laboratory being small, students were grouped into twenty groups, each group consisting of twenty students. Each test type had to be done three times: First, the students had to do the MRT test type once, then the MCT once, then the PSVT once. After that, they started from the beginning with different display parameters.  
The reason the testers had to repeat the test types three times is that each test option had different display parameters. However, testing three times was not enough to test all parameters for influences and interactions. Therefore, to test all parameters, the authors used the randomization technique. Due to this, each test randomized two or three, but different, parameters. The authors believe that when testing in large numbers, sufficiently large numbers of results regarding each parameter were obtained. The following information was saved into a.csv file by the application in real-time:  
* The technical information about the display parameters in each test: The virtual camera type, its field of view, its rotation, the contrast ratio in the scene and whether the shadows are turned off.
* The user-related information: Their gender, age, primary hand, number of years at a university and what their major is. This category is not focused on in this manuscript.
* The test type, its completion time and the number of correct and incorrect answers.
## 4 Experimental Results
### Environment Setup and Parameter Setting  
Firstly, the spoofing scenario constructed is introduced. Figure 11 shows a spoofing scenario consisting of two spoofing sources, a single rotating antenna, and a piece of intermediate frequency signal-acquisition equipment. Two spoofing sources simulate multi-agent spoofing devices sending spoofing signals, and the intermediate frequency signal collection equipment is connected to a single rotating antenna to store the navigation signals received by the single rotating antenna. Two spoofers are connected to the same terminal. The terminal controls two spoofers to send a total of six spoofing signals to achieve time spoofing. Subsequently, through the FGI-GSRx software receiver (the codes can be downloaded from [[https://github.com/nlsfi/FGI-GSRx](https://github.com/nlsfi/FGI-GSRx)]([https://github.com/nlsfi/FGI-GSRx](https://github.com/nlsfi/FGI-GSRx)), accessed on 5 September 2023), we processed the intermediate frequency signal in the dataset and validated the spoofing detection algorithm on the processed data.  
The configuration and parameter settings of the experiment are shown in Table 2. The single rotating antenna was placed at the position of (0,0,0), and spoofers A and B  
Figure 11: Spoofing scenario.  
Figure 10: Relationship between \(\omega\) and ROC.  
were located at (0,2,0.5) and (0,\(-\)2,0.5) in the local coordinate system, with the coordinates in meters.  
Figure 12 shows the navigation solution throughout the entire spoofing process. The position information in the navigation solution remained stable. The time information in the navigation solution is consistent with the spoofing strategy.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Optimal Estimation Algorithm
By combining the strengths of the SRON-MAP and RemoTeC algorithms, RemoTAP supports versatile applications: aerosol retrieval from MAP-only measurements, trace gas retrieval from spectrometer-only measurements, and simultaneous retrieval of both aerosols and trace gases using data from both MAP and spectrometer [81]. The Institute of Atmospheric Physics at the Chinese Academy of Sciences has developed the IAPCAS satellite remote sensing inversion algorithm [82; 83]. This \"full-physics\" algorithm employs a nonlinear optimal estimation method to simulate solar radiation transmission in the atmosphere with high precision. Initially, it used LBLRTM for gas absorption coefficient calculations in GOSAT observations, optimizing aerosol optical properties and cirrus cloud absorption to enhance inversion accuracy. The updated IAPCAS v2.0 improves efficiency and accuracy by replacing LBLRTM with a multi-dimensional gas absorption coefficient lookup table and refining line strength, position, and profile [84]. Validation against TCCON measurements demonstrated a 1% (4 ppm) error in XCO\({}_{2}\) retrieval [85], indicating that the algorithm's accuracy is now at a leading international standard.
## 2 Theoretical Analysis of Reflection Error Correction for Ambient Radiation of Turbine Blade
### Simulation Results and Analysis of Dynamic Reflection Model
The energy reflected from the target blade to the adjacent blades account for more than 25% of the actual energy received by the radiation pyrometer, so the temperature measurement error exceeds 40 K. Moreover, the proportion of reflected radiation and the reflected temperature measurement error do not change significantly with the angle. This is because the temperature of the guide vanes is lower than that of the upper rotor blade in the adjacent blade, and the radiation propagating from the upper rotor blade to the target blade accounts for the main proportion of ambient radiation; the relative position between the rotor blades does not change with the rotation of the blades. We then set the relative position coordinates of point \(P_{0}\) to (0.5, 0.66), the target blade temperature range to 950-1250 K, at \(\varphi\) = 0 degrees, the guide vane temperature to 100 K lower than the target blade temperature, and the upper rotor blade temperature to the target blade temperature. The emissivity of the target blade is 0.58, and the wavelength of radiation temperature measurement is 1550 nm. The ratio of the reflected energy on the surface of the target blade to the total energy received by the radiation pyrometer and the temperature measurement error caused by reflection are calculated. The relationship between them and the temperature of the target blade is shown in Figure 9b. The proportion of reflected radiation and the reflected temperature measurement error increase as the target blade temperature increases. For aircraft turbine engines with a higher operating temperature, the temperature measurement error caused by reflection will be larger. Therefore, when performing radiation temperature measurement on turbine blades, target blade reflection must be corrected to reduce the resulting temperature measurement error. Figure 9: Simulation analysis of the reflection error of turbine blade radiation temperature measurement: (**a**) angular factor distribution between the target blade and blade 2; (**b**) target point reflection ratio and temperature measurement error at different temperatures.
## 3 Design and Implementation
### Design
\begin{table}
\begin{tabular}{l l c} \hline \hline \multicolumn{1}{c}{**Component**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Cost [EUR]**} \\ \hline USB-C Power Supply & 15 W 5.1 V USB-C power supply & 10 \\ \hline Raspberry Pi 4B 4 GB & Quad-core CPU compute SBC. All-round I/O options. & 55 \\ \hline AIY Voice Kit * & Microphone: SPH1642 HT5H-1 & \\ AIY Voice Bonnet v2 HAT & Supports far-field voice recognition and extends RPi GPIO I/O. & 60 * \\ Arcade Button & LED Button provides user-friendly interaction. & 60 * \\ Speaker & NH 40 hm 3 W 76 mm & \\  & Connects to the AIY Voice Bonnet. & \\ \hline Micro SD Card & Capacity: 32 GB & \\ Micro SD Card & UHS Speed Class: 3, Video Speed Class: V30 & 9 \\  & Application Speed Class: A1, Speed Class: Class 10 & \\ \hline
2x SG90 Servo & Basic flipper movement. & 16 \\  & Connected to the voice bonnet via GPIO pins. & 10 \\ \hline NeoPixel Ring & Features 16 individually addressable LEDs. & \\
16 \(\times\) 5050 RGB LED & Offers aesthetic illumination, visual indication, and mood lighting. & 10 \\  & Connected to RPi and controlled with PWM. & \\ \hline Touch Display & Nextion Enhanced NX3224T024 2.4” HMI 320 \(\times\) 240 Touch Display & \\  & Equipped with a microcontroller and internal memory. & 53 \\  & Enhances touch modality and displays menus. & \\  & Connects to RPi, controlled via UART. & \\ \hline Logic Level Shifter & 4-Channel, Bidirectional; translates voltage logic level signals: 5 V & 3 \\  & (peripherals) to 3.3 V (RPi). Allows RPi to communicate with peripherals. & \\ \hline
3D Printing Filament & White \& Blue PLA and White TPU & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bill of materials for the RSC. The RSC's design overall prioritises a cost-effective, portable solution at \(\sim\)11 cm high and a weight of \(\sim\)375 g. Our RSC prototype-development process (Figure 4) helped establish the size, shape, and peripheral placement. The Pi bracket houses bespoke slots that fit servos to actuate the flippers and features openings for both microphones (Figures 2 and 3). In addition to the arcade button, the lid houses an LED ring. We chose a blue-and-white colour palette to give the device a friendly appearance [76], using white and blue polylactic acid (PLA) 3D-printing filament. The base is printed out of white thermoplastic polyurethane (TPU) filament, which improves grip and stability on desktop surfaces. All CAD files for this design are available in the MechDesign repository in our replication package [78] with ready-to-print files and assembly instructions.
# Communication-Efficient Vertical Federated Learning
## 3 Proposed Method
### First Step
#### 3.1.1 Principal Component Analysis  
The goal of PCA is to explain the correlation structure of a set of features using a smaller set of linear combinations of these features [27]. These linear combinations are referred to as components. Suppose there are \(m\) features in a dataset, applying PCA to it  
Figure 2: Architecture of Proposed Method.  
would yield \(d\) number of linear combinations in such a way that \(d\) components explain most of the information in the dataset. Hence, the dimension of the dataset is changed from \(m\) to \(d\) where \(d<m\). The main steps for performing PCA on each of the client's local data are as follows:  
* Standardizing the local data;
* Computing the covariance matrix of the features of the local data;
* Calculating eigenvalues and eigenvectors [28] for the covariance matrix;
* Sorting the eigenvectors by the magnitude of their corresponding eigenvalues;
* Determining \(d\), the number of top principal components to select by methods such as imposing threshold on magnitude of eigenvalues or cumulative variance of the data.  
PCA is performed on the local data of each client to simplify it and bring it into a new dimension while also retaining trends and patterns. Thus, latent or compressed representations of the local data are generated. PCA, as a dimension reduction technique, has several advantages. Computations are simple because PCA is based on linear algebra and the PCA components are simply linear combinations of the original features. Correlated features do not contribute significantly to the decision-making process in ML algorithms. As a result, removing them has no negative impact on the final output. PCA can efficiently eliminate these correlated features. However, PCA assumes that the principal components are a linear combination of the original features. If this does not hold, PCA will not yield sensible results. PCA is also sensitive to the scale of the features, so it is important that the data be standardized beforehand.
# Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
## 3 Problem Definition  
Given a set '\(J\)' of '\(n\)' jobs, where, \(J=\{j_{1},j_{2},j_{3}\ldots j_{n}\}\) with different job size, and a set '\(VM\)' of '\(m\)' virtual machines, where \(VM=\{vm_{1},vm_{2},\)\(vm_{3}\ldots\)\(vm_{m}\}\), jobs in '\(J\)' are classified into small, medium, large, and extra-large based on the threshold value 50 MB, 100 MB, 150 MB, 200 MB, and above for the job size, respectively. Once classification is complete, it starts merging smaller jobs based on VM capacity. The profiler gives a set of requirements '\(P\)' to execute a job, where \(P=\{Memory,\)\(CPU,\)\(RAM,\)\(Bandwidth,\)\(Start\) time and \(End\) time}. Profiler information is submitted to the broker, and in turn, the broker interacts between the user and service provider. The broker assigns the tasks to virtual machines by satisfying all the requirements in '\(P\)'. The broker is the one who is responsible for creating and destroying the virtual machines.  
The objective of this work is to maximize resource utilization and minimize the brokerage in cloud computing by using multi-objective optimization.  
Assumption: The execution time of every job is assumed to be predicted based on the derived results of profiling, and virtual machine capacity is assumed to be 200 MB.