esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 2 Related Work
### Single-User Experiences
To this end, a virtual environment was created in which the user interacted with a virtual representation of a Fanuc injection molding machine. To evaluate QoE, a user study with a between-subjects design was performed in which the participants had to carry out a basic, beginner-level operation task on the Fanuc in a virtual reality environment under both subjective and objective evaluation. The results suggested that although the participants experienced a mild drop in QoE as a result of network delay, they tolerated delays up to 3000 ms with no significant deterioration in the perceived usability of the virtual environment. Finally, [PERSON] et al. [22] evaluated the effects of RTT and packet loss on user QoE in a streamed VR game. To this end, the game Serious Sam VR: The Last Hope was chosen for use in a user study. A total of 28 different network conditions with varying values of RTT and packet loss were evaluated. The subjective results suggested that RTTs of 75 ms and below yielded a good user MOS, while RTTs up to 175 ms gave an acceptable MOS of 3. A packet loss of 6% and below gave a good user MOS, while 12% and above resulted in unacceptable MOS degradation. [PERSON] et al. [23] investigated whether latency tolerances for modern, off-the-shelf VR systems were similar to those for more outdated hardware and software. In addition, they researched the effect of increasing and decreasing the latency on such tolerances. Tothis end, participants were positioned in a virtual environment consisting of a sparsely furnished model of the lab space where the experiment took place. In it, the participants sat in a swiveling desk chair and were asked to rotate themselves side-to-side in time with the tone of a digital metronome set to 50 BPM. Five levels of latency were investigated, ranging from 0 to 4 frames (0-44 ms). Users were asked to indicate whether or not they believed artificial latency was introduced in the scene. The results showed that the relative difficulty of detecting latency increased up until a certain point (18.81 ms) only to decrease again when the artificially induced latency was pushed higher.
## 6 Performance Evaluation
### Simulation Model
#### 6.1.2 Energy Model  
Our simulation used LinkQuest Inc.'s UWM1000 model for our underwater acoustic modem energy model. In this model, the payload data rate is 7 kbps, the transmission power 1 Watt, and the reception power 0.75 Watts [32]. Since the energy consumption for sleeping mode and data processing is much less compared to transmission/reception, we do not take these factors into account. We assumed that a node can consume up to 100,000 Joules of energy before it dies out.
## 1 Introduction
### Description of the Global Network of SatNOGS Ground Stations  
As part of the AztechSat-1 project, a ground station was established to fulfill the secondary objective of promoting activities among radio amateurs. In addition, this study encompasses an analysis of a full stack of open technologies based on open standards. The research uses data that was provided by the Satellite Networked Open Ground Station (SatNOGS) project, which provides access to open-source data contributed by radio amateurs.  
Traditionally, when an individual or organization builds a ground station, especially for non-commercial purposes, the system remains inactive for extended periods of time. To maximize the utilization of these resources, projects have been developed to enable their use by multiple users. The SatNOGS project facilitates the connection to a network of open-source ground stations, designed with modularity in mind. This network allows users to access satellite information uploaded by different individuals through a user-friendly web interface. Real-time information from numerous registered ground stations can be obtained through this network. To be compatible with the network, a ground station must have client software installed on an embedded computer. Additionally, a software-defined radio (SDR) should be connected to the computer and properly configured to work with SatNOGS [18].
# A DNN Architecture Generation Method for DDoS Detection via Genetic Algorithm
## 3 Methodology
### Model Generation
#### 3.3.3 Fitness Function  
The performance of the model is evaluated from two aspects: one is the detection rate of the model for DDoS traffic and the other is the running time of the model. The accuracy of the model after the test and the average time of the model processing a single sample on the test set are therefore selected to calculate its fitness [39]. The specific calculation formulas are as follows:  
\[ACC=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[FITNESS=\alpha_{1}\times\frac{ACC}{1.01-ACC}+\frac{\alpha_{2}}{\overline{T}} \tag{2}\]  
Here, ACC is the accuracy, while \(\overline{T}\) refers to the average time (ms) for processing a piece of data in the model classification stage. \(\alpha_{1}\) and \(\alpha_{2}\) represent the weight and are taken as 0.8 and 0.001, respectively, in this paper. By setting the weight of accuracy and running time in this way, the impact of accuracy and running time on the fitness calculation can be the same order of magnitude, and the final generated model has better performance in accuracy and running.  
There may be many poor performance solutions in the current population. Taking accuracy as the evaluation standard will make the probability of selecting excellent individuals very low. Therefore, fitness will be dynamically amplified to improve the probability of selecting an ideal solution. From the fitness calculation formula, the closer the accuracy is to 1.0, the faster the fitness increases, which can better highlight the probability of selecting an excellent solution.
## 3 Results and Discussion
### Mean Velocity Profiles
Table 4 shows the relative error of mean velocity. It is calculated according to the experimental data of [PERSON] et al. [27]. It can be seen that the relative error values predicted by all six models at x/L = 0.07 are higher than those at x/L = 0.27 and 0.47. The minimum average relative error is 22.65% predicted by the LS model, while the maximum average relative error is 132.80% predicted by the LB model. This result is the same as the previously mentioned conclusion.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 2 Materials and Methods
### State-of-the-Art Description
Contrary to the cited works that use the classification after detection approach, we propose a single-step method thanks to the you only look once version 3 (YOLOv3) neural network [8]. The YOLOv3 network finds the objects and then classifies them in a single pass. There is no need for extra image preparation. Additionally, this approach allows \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Title** & **Domain** & **Method** & **Metrics** & **Results** \\ \hline
[3] & Classification of breast cancer cytological specimen using convolutional neural network & classification & CNN & accuracy & 83\% \\ \hline
[4] & Nasal cytology with deep learning techniques & classification & CNN & accuracy & 94\% \\ \hline
[5] & Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks & classification & CNN & accuracy & 71\% \\ \hline
[6] & Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides & classification & CNN & accuracy & 71\% \\ \hline
[7] & Automated Pap smear Cervical Cancer Screening Using Deep Learning & detection & ResNet ResNet & mAP & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The summary of state-of-the-art in machine learning for cytological image analysis us to count the actual number of cells in the given image. For additional description of YOLOv3 please see Appendix A.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 3 Methods
### Coupled Model of Electricity Consumption and Total Night-Light Value  
Denoised night-light images only contain electrical illumination, e.g., from streetlights. Using Cambodia's electricity consumption from 2012 to 2019 as the independent variable and the total night-light value of annual night-light images data as the dependent variable, several one-dimensional regression models were established based on linear, power, and exponential functions. The regression models for this study were obtained via curve fitting using the _ordinary least squares_ (OLS) and _maximum likelihood estimation_ (MLE) methods [39]. Linear optimization problems like linear regression models are obtained by OLS, and non-linear optimization problems like exponential models are obtained by MLE. The equations for these three regression models are shown in Table 3.  
In the mathematical equation, \(x\) represents electricity consumption, \(y\) represents the total night-light value, \(e\) is a natural constant, \(a\) and \(b\) are the derived coefficients after performing the OLS regression.  
The models were evaluated in terms of the adjusted coefficient of determination (adj-\(R^{2}\)) and its significance (\(p\)). In regression analysis, the coefficient of determination (\(R^{2}\)) is used to determine the explanatory power of a model. This process measures the proportion of variation in the dependent variable that can be explained by the independent variable. The adj-\(R^{2}\) can eliminate the effect of variation in the number of independent variables and is more appropriate for judging the merits of a regression model than \(R^{2}\); higher adj-\(R^{2}\) values indicate a better model. Significant differences in the regression analysis are an evaluation of the variability of the data. Significant differences are usually expressed as \(p>0.05\) for non-significant differences, \(0.01<p<0.05\) for significant differences, and \(p<0.01\) for very significant differences. In regression analysis, a regression model is often considered valid when \(p<0.05\). The smaller the \(p\)-value, the higher the correlation between the two sets of data and the better the regression model.
# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
#### 4.1.4 Weaknesses and Anti-Forensic Use Cases of RAM  
Other sections may be somewhat open-ended; this one, however, is very specific, as focuses only on the RAM of a computer system. Researchers such as [PERSON] et al. [4] explain how to hide data in RAM, and others, such as [PERSON] et al. [158], deal with anti-forensic techniques and tools applied to RAM.  
The study by [PERSON] [159] examines anti-forensic techniques, tests commercial and free memory acquisition tools, and presents a novel technique based on direct page table manipulation and PCI hardware introspection, which does not depend on operating system facilities, being more difficult to subvert.  
[PERSON] et al. [160] focus on the difficulty of acquiring volatile data (temporary information in memory) due to the limitations of current forensic tools and the rise of anti-forensic techniques. Their paper raises the need to develop new methods to counter these techniques.  
Certain papers explore the challenges of digital memory forensics due to the anti-forensic techniques used by malware to hide evidence [4; 158; 159]. Various technical solutions are proposed to detect and mitigate these attacks, including new anti-forensicmethods [4], memory acquisition techniques resistant to these techniques [158; 159], and discussion of the limitations of current forensic tools [160].  
As novel contributions, paper [4] presents three novel anti-forensic methods for hiding information in RAM, while paper [158] proposes an innovative technique for acquiring memory in a way that is resistant to simple anti-forensic techniques.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 3 Experimental Scheme  
The permeability of gas-containing coal under combined static and dynamic loading is closely related to factors such as static load, dynamic load, and the amount of gas adsorption. The experimental variables chosen for this test were the dynamic loading impact energy, the static loading stress stages, and the gas adsorption quantity. Dynamic impact energy is primarily achieved by adjusting the frequency and magnitude of the dynamic load. The static loading stress phase is divided into the compaction stage, the elastic stage, and the yield stage. The gas adsorption amount is adjusted through different adsorptive gasses at the same adsorption pressure, and all the gasses are in a gaseous form. The dynamic load along the trough of a mining face is mainly caused by the excavation process, and the source energy level in our study was about 100 J. Based on the magnitude of the energy and the compressive strength of the coal samples, it was determined that the dynamic load amplitude should not exceed 6.5 MPa. The dynamic load frequency band was mainly at 3-6 Hz, and the main deformation stage of coal and rock occurred within the first 300 cycles [33]. According to the storage conditions of the gas pressure in coal under the actual working conditions, the gas pressure was selected to be 1 MPa. At the same time, in order to create a triaxial stress environment and mitigate the differences in permeability arising from variations in effective stress, the confining pressure was maintained at a constant value of 1.5 MPa. The ambient temperature throughout the experiment was maintained at approximately 25 \({}^{\circ}\)C, with the adsorption time of the adsorptive gasses uniformly regulated to around 48 h. The detailed experimental plan is presented in Table 1, where \(\sigma_{c}\) is the triaxial compressive strength.  
In order to ensure the comparability of the experimental data, a testing loading path was designed, as illustrated in Figure 2. The sample was initially subjected to a predeterminable confining pressure, after which it was infused with a predefined pressure of gas. After the gas adsorption had reached an equilibrium, a static load was applied to the sample until it reached the designated value. On this basis, a cyclic dynamic load was applied with the predetermined amplitude and frequency. After the dynamic loading disturbance ended, it was returned to the uniform predetermined stress state of 2 MPa, and we conducted the permeability test.  
Figure 1: Experimental apparatus.
## 1 Introduction
Secondly, the dispersion may induce power loss and signal broadening, which results in poor signal detection to recover the original transmitted data. Thirdly, high power injected into the system may also cause different phase imbalances, which have a great influence on the performance of the mm-wave signal and directly affect the system performance. Therefore, it is imperative to maximize the mm-wave output and study the modulation index and dispersion as well as the phase imbalance in this type of system. This study is arranged as follows. The following section describes the system design of the DSB-OCS technique. Section 3 models the system at various modulation indexes. Section 4 presents the effects of phase imbalance on the generated optical mm-wave in RoF systems. Finally, the effect of dispersion on different fiber lengths is explained in Section 5.
## 5 CFD Modeling
### Computational Mesh  
An unstructured tetrahedral computational mesh was generated in the computational domain to achieve a model shape with high accuracy using the finite volume method (Figure 9). Four layers of prism elements with a thickness variation rate of 1.1 were inserted on wall boundaries to capture the effect of walls of the fluid with high numerical accuracy [41]. Computational zones with highly skewed, low-aspect ratio elements were refined to minimize numerical error. In addition, high gradient zones and narrow passages, such as the scrubber inlet and outlet, were also refined. The y+ value (normalized wall distance for a wall-bounded flow) at all walls was maintained between 30 and 300 to permit correct application of logarithmic law wall function, except some extremely low-velocity zones, between the simulated coal face and shearer body. The quality of the mesh generated in the computational domain was good, with average prism layer insertion percentage and h-ratio of the tetrahedral mesh at 99.85% and 0.27, respectively.  
To ensure that the results obtained through CFD modeling do not depend on the mesh resolution and to explain the physics with utmost accuracy, a grid independence study was carried out. The grid independence study started with a coarse mesh that was gradually refined until the variations in the results were deemed acceptable. Four different cases with a computational domain element growth factor of approximately 1.5 were tested for the scenario in which the scrubber fan was operating at full capacity. The total number of elements in the computational domain for the four cases were 3.8 million, 5.7 million, 8.4 million, and 12.3 million, in that order.  
The magnitude of average air velocities was measured and compared on the ten points shown in Figures 7 and 10,b show the grid independence study results for the average velocity at the points on the first and second lines, respectively, marked in Figure 7. The grid independence study indicated that results became independent of the grid size at 5.7 million elements. A comparison of results, separately at each point, indicated a maximum percentage difference of 1.7% between 5.7 million and 8.4 million mesh resolutions. Based on the results obtained from the grid independence study, the CFD validation was performed at 5.7 million elements in the computational domain.  
Figure 10: Comparison of average velocity along lines 1 and 2 for the four cases of grid independence study.  
Figure 9: Mesh in the computational domain (not to scale) showing tetrahedral volume mesh and prism boundary layers.
## 4 Conclusions
### Nomenclature  
\begin{tabular}{l l} _COP_ & Coefficient of performance \((-)\) \\ \(C_{p}\) & Specific heat \(\left(\mathrm{J\!\cdot\!kg^{-1}\!\cdot\!K^{-1}}\right)\) \\ \(D_{s}\) & Surface diffusivity \(\left(\mathrm{m^{2}\!\cdot\!s^{-1}}\right)\) \\ \(D_{so}\) & Pre-exponent constant of surface diffusivity \(\left(\mathrm{m^{2}\!\cdot\!s^{-1}}\right)\) \\ _SDIP_ & Specific Daily ice production \(\left(\mathrm{kg_{ice}\!\cdot\!kg_{ads}\,\mathrm{day^{-1}}}\right)\) \\ \(E_{a}\) & The activation energy of surface diffusion \(\left(\mathrm{J\!\cdot\!mol^{-1}}\right)\) \\ \(k\) & Thermal conductivity \(\left(\mathrm{W\!\cdot\!m^{-1}\!\cdot\!K^{-1}}\right)\) \\ \(k_{T}\) & Thermal turbulent conductivity \(\left(\mathrm{W\!\cdot\!m^{-1}\!\cdot\!K^{-1}}\right)\) \\ \(K_{LDF}\) & Mass transfer coefficient \(\left(\mathrm{s^{-1}}\right)\) \\ \(LH\) & Latent heat \(\left(\mathrm{J\!\cdot\!kg^{-1}}\right)\) \\ \(M\) & Mass \(\left(\mathrm{kg}\right)\) \\ \(n\) & Completed cycles per day \\ \(p\) & Pressure \(\left(\mathrm{Pa}\right)\) \\ \(Q_{m}\) & Source term in conservation of mass equation \(\left(\mathrm{m^{2}}\right)\) \\ \(Q_{ads}\) & Heat of adsorption \(\left(\mathrm{J\!\cdot\!kg^{-1}}\right)\) \\ \(\mathcal{R}\) & Specific gas constant \(\left(\mathrm{J\!\cdot\!kg^{-1}\!\cdot\!k^{-1}}\right)\) \\ \(R\) & Radius \(\left(\mathrm{m}\right)\) \\ \(T\) & Temperature \(\left(\mathrm{K}\right)\) \\ \(t\) & Time \(\left(\mathrm{s}\right)\) \\ \(UA\) & Heat transfer conductance \(\left(\mathrm{W\!\cdot\!K^{-1}}\right)\) \\ \(u\) & Fluid velocity \(\left(\mathrm{m\!\cdot\!s^{-1}}\right)\) \\ \(X\) & Uptake \(\left(\mathrm{kg_{a}\!\cdot\!kg_{ads}^{-1}}\right)\) \\ \(X_{eq}\) & Equilibrium adsorption uptake \(\left(\mathrm{kg_{a}\!\cdot\!kg_{ads}^{-1}}\right)\) \\ Greek Symbols: & \\ \(\rho\) & Density \(\left(\mathrm{kg\!\cdot\!m^{-3}}\right)\) \\ \(\mu\) & Dynamic viscosity \(\left(\mathrm{Pa\!\cdot\!s}\right)\) \\ \(\mu^{T}\) & Turbulent dynamic viscosity \(\left(\mathrm{Pa\!\cdot\!s}\right)\) \\ \(\varepsilon_{t}\) & Total porosity \(\left(-\right)\) \\ \(\varepsilon\) & Effectiveness \(\left(-\right)\) \\ Subscripts and Superscripts: & \\ _ads_ & Adsorbent \\ \(a\) & Adsorbate \\ \(b\) & Bed \\ _cw_ & Cooling water \\ _cond_ & Condenser \\ _comp_ & Composite \\ _Eth_, _Gly_ & Ethylene Glycol \\ _eva_ & Evaporator \\ _eq_ & Equilibrium \\ \(f\) & Fluid \\ _hw_ & Heating water \\ \(i\) & Inlet \\ _init_ & Initial \\ \(m\) & Metal \\ _out_ & Outlet \\ _rl_ & Refrigerant liquid \\ _rv_ & Refrigerant Vapor \\ \(s\) & Solid Adsorbent \\ _sat_ & Saturation \\ \(v\) & Vapor \\ \(w\) & water \\ \end{tabular}
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 4 Problem Formulation and Analysis  
### Energy Cost  
#### 4.1.1 Path Energy Cost  
For the energy model, since user task data may reach the satellite providing computing service in LEO Constellation edge cloud network through multi-hop path, the energy consumption of a single user's computing task should include path energy consumption in addition to CPU computing energy consumption of onboard MEC servers. Path energy consumption refers to the energy consumption generated when the task data sent from the ground terminal to the service satellite pass through multi-hop forwarding nodes and the energy consumption generated when the response data sent from the service satellite to the ground terminal passes through multi-hop forwarding nodes. We call the former the forward path energy consumption expressed as \(E_{n,m}^{fp}\) and the latter the reverse path energy consumption expressed as \(E_{n,m}^{bp}\). Therefore, the total path energy consumption of a computation task is expressed as  
\[E_{n,m}^{p}=E_{n,m}^{fp}+E_{n,m}^{bp} \tag{6}\]  
For simplicity, assume that the forward and reverse paths are symmetric and consistent, i.e., the forward and reverse paths pass through the same number of nodes and hops. Forward and backward path energy consumption can be further expressed as  
\[E_{n,m}^{fp}=E_{n,m}^{fp,1}+\sum_{j=2}^{hop}E_{n,m}^{fp,j} \tag{7}\]\[E_{n,m}^{bp}=\sum\limits_{i=1}^{hop-1}E_{n,m}^{bp,i}+E_{n,m}^{bp,hop} \tag{8}\]  
where _hop_ indicates the number of hops on the path, \(E_{n,m}^{fp,1}\) represents the energy consumption of the first hop transmission in the forward path, namely, the energy consumption of the satellite terminal satellite-ground uplink, \(E_{n,m}^{bp,hop}\) represents the energy consumption of the last hop of the reverse path, that is, the energy consumption of the satellite terminal satellite-ground downlink, \(\sum\limits_{i=2}^{hop}E_{n,m}^{fp,i}\), and \(\sum\limits_{i=1}^{hop-1}E_{n,m}^{bp,i}\) respectively represents the total energy consumption of inter-satellite multiple hops in the forward and reverse path. Considering that the amount of data after calculation is generally small, the transmission energy consumption of the reverse path can be ignored for simplicity. Therefore, the total energy consumption of the path can be modeled as  
\[E_{n,m}^{p}=E_{n,m}^{fp,1}+\sum\limits_{i=2}^{hop}E_{n,m}^{fp,i} \tag{9}\] \[E_{n,m}^{fp,1}=P_{n}^{ss}\frac{x_{n,m}D_{n}}{R_{n}^{ss}}\] (10) \[E_{n,m}^{fp,i}=P_{i}^{ss}\frac{x_{n,m}D_{n}}{R_{i}^{ss}} \tag{11}\]
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Swift-Heavy Ion Irradiation
#### 2.1.6 Gold Ion Irradiation  
[PERSON] et al. [114] bombarded MoS\({}_{2}\) (0001) bulk surfaces using Au ions with 13.4 MeV. Under ion doses of \(1.0\times 10^{13}\) ions/cm\({}^{2}\), 1.0-3.5 nm craters were generated on the surface.