esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Enabled Edge Server Selection for Multi-User Composite Services
## 5 Simulated Experiments and Analysis
### Experiment Results and Analysis
#### 5.3.1 The Impact of Resources  
According to Definition 5, because of the limited resources of each edge server, the resources required to invoke a service must be less than the selected edge server's remaining resources. Therefore, we first studied the impact of different edge server's resources on the total waiting time. We set the number of users to 14; the number of services invoked by each user was randomly generated from 4-10, and the number of edge server's resources was varied from 2-6. As shown in Figure 4, we can see that the total waiting time generated by MESP and traditional method decreased with the increasing of the edge server's resources. This was because that each edge server could host more services with the increase of the edge server's resources. When users connect to the remote servers, the round-trip should be taken into consideration. With the increasing of edge server's resources, more users can obtain services from edge servers instead of remote cloud servers. The round-trip latency was reduced, which means the total waiting time was also reduced. For the random algorithm, it selected edge servers randomly. Therefore, with the increasing of edge server's resources, the total waiting time generated by random algorithm may not decrease steadily. From Figure 4, we know that under the same conditions, the MESP algorithm outperformed the baseline approaches.
# Efficient False Positive Control Algorithms in Big Data Mining
## 2 Related Concepts and Techniques
### Concepts Related to False Positives  
#### 2.1.1 Hypothesis Testing  
In statistics, hypothesis testing is a method of inferring the total from the sample based on certain hypotheses. Hypothesis testing begins with the formulation of the hypothesis to be tested based on the idea of the counterfactual method and the calculation of the probability that the hypothesis holds using appropriate statistical methods, applying the principle of small probability. The specific implementation steps of hypothesis testing are as follows, first, establishing the null hypothesis \(H_{0}\) and the alternative hypothesis \(H_{1}\). The null hypothesis is usually set as the hypothesis that is opposite to the conclusion the researcher wants to draw, and the null hypothesis is the hypothesis to be tested. The alternative hypothesis is usually the conclusion that the researcher wants to reach. Next, the appropriate method is chosen to calculate the statistic for the test. Next, the magnitude of the probability, p, that the null hypothesis is true is calculated based on the magnitude of the statistic. If \(p>\alpha\), then the null hypothesis \(H_{0}\) is not rejected. Otherwise, the null hypothesis \(H_{0}\) is rejected and the alternative hypothesis \(H_{1}\) is accepted, where \(\alpha\) is referred to as the significant level. Researchers usually set the significance level to 0.05 in a one-tailed hypothesis test.  
Hypothesis testing is a statistical judgment based on \"small probability events\". The occurrence or non-occurrence of a particular type of event depends on the sample of events selected and the level of significance chosen. Since the sample is random and the selected significance level \(\alpha\) is different, the results of the test may differ from the real situation, so the hypothesis test may be incorrect. Errors that occur in hypothesis testing are generally classified into two categories [14; 15] and Type I errors [16] are those that reject the null hypothesis \(H_{0}\) when the null hypothesis \(H_{0}\) is correct and then commit the error of rejecting the true null hypothesis. The second type of error is accepting the false null hypothesis \(H_{0}\) when the null hypothesis \(H_{0}\) is false. Hopefully, the probability of both of these errors occurring during hypothesis testing is relatively small, but when determining the sample size, it is not possible to reduce the probability of both of these errors at the same time. That is, if the probability of one error decreases, then the probability of the other error increases. To solve this problem, the only way to reduce the probability of both types of error is to increase the number of data to be tested. Therefore, for a given amount of data to be tested, the probability of only one type of error can be controlled.
## 4 Experiments
### Visualization  
To understand the impact of our spatial encoder on the confidence distribution of match sampling, we conduct visualization experiments. We explore the confidence distribution of matches in different spatial regions as shown in Figure 10. We select the Brandenburg Gate, Buckingham Palace, and the Taj Mahal for our visualization experiments. We find that, in RANSAC and NG-RANSAC, the lack of constraints on the spatial distance of matches can lead to sampling pairs with similar spatial distances, which degrades the results of the relative pose estimation. SA-Net sampling maintains a relatively sparse distribution in space and achieves better results in relative pose estimation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Settings/Metric** & **F1 Score (\%)** & **Med. Epi. Error (px)** & **Run-Time (ms)** \\ \hline \(L=1\) & 44.29 & 2.61 & **21** \\ \(L=2\) & 47.50 & 1.83 & 25 \\ \(L=4\) & **49.31** & **1.07** & 34 \\ \(L=8\) & 43.77 & 4.12 & 46 \\ \(L=16\) & 34.81 & 13.23 & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of fixing or varying the number of parallel layers \(L\) in the spatial distribution encoder. We report the F1 score, mean epipolar error, and run-time on the fundamental matrix estimation from PhotoTourism [35]. Best results are in **bold**, and the second best are underlined.  
\begin{table}
\begin{tabular}{c c c c|c c c} \hline \hline \multirow{2}{*}{**CDE**} & \multirow{2}{*}{**CGM**} & \multirow{2}{*}{**FAD**} & \multirow{2}{*}{**CL**} & \multicolumn{3}{c}{**AUC**} \\ \cline{5-8}  & & & & @5\({}^{\circ}\) & @10\({}^{\circ}\) & @20\({}^{\circ}\) \\ \hline ✓ & & & & 0.378 & 0.421 & 0.483 \\ ✓ & ✓ & & & 0.412 & 0.428 & 0.513 \\ ✓ & & ✓ & ✓ & 0.401 & 0.434 & 0.517 \\ ✓ & ✓ & ✓ & ✓ & **0.436** & **0.442** & **0.524** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of SA-Net on essential matrix estimation using PhotoTourism dataset. Best results are in **bold**, and the second best are underlined.  
Figure 10: Visualization of relative pose estimation. The green line represents the sampled matches. Three different scenarios are chosen to visualize our sampling and relative pose estimation results. Error_R represents the error of the rotation matrix, and Error_t represents the error of the translation vector. From top to bottom: Brandenburg Gate, Buckingham Palace, and Taj Mahal.
# Effect of Dune Sand on Drying Shrinkage Cracking of Fly Ash Concrete
## 2 Experimental Plan and Method  
### Experimental Plan  
Concrete properties and limiting values to resist corrosion induced by chlorides from sea water and chemical attacks are recommended in BS 8500-1 [17]. The minimum compressive strength, maximum water-to-binder (W/B) ratio, and minimum cement content are also suggested for various types of cement and combinations. In this study, a combination of OPC and fly ash was used as a binder for concrete. In order to satisfy the condition of sulfate-resisting cement, the fly ash replacement is required to be in the range of 25-35% according to BS 8500-1. Therefore, the replacement ratio of fly ash was set to 30% in this study. In addition, BS 8500-1 recommends W/B ratios from 0.35 to 0.55 in increments of 0.05. In this study, the water/cement ratio of 0.4 was set. The target slump of concrete was 180 \(\pm\) 25 mm. Regarding the mixture variables, the unit water content was set to 170 and 160 kg/m\({}^{3}\), and the DS/FA ratio was set to 10%, 20%, 40%, and 60%.
## 2 Materials and Methods
### Virtual Inertia Boundary Evaluation Based on RBF Neural Network
#### 2.1.2 Analysis of Inertial Time Constant Boundary Limitations in PV/ESS  
In virtual synchronous machine technology, the dynamic allocation of virtual inertia represents a critical mechanism for achieving power system frequency stability. However, its practical implementation faces multifaceted constraints. Firstly, the physical limitations of the equipment represent fundamental constraints: the maximum power output of the photovoltaic unit is constrained by real-time irradiance, while the inertia support capability of the energy storage system exhibits strong coupling with its SOC [22]. Should the system frequency disturbance exceed the power regulation margin available under the equipment's current operating state, the predetermined virtual inertia parameters become physically unrealizable. Existing research concerning PV and ESS virtual inertia constraints typically employs fixed upper and lower bounds for the inertia time constant. These static limits largely overlook the time-varying boundaries of the inertia time constant due to the diverse environmental conditions and system variables encountered in practice. Thus, this section utilizes RBFNN to determine the precise time-varying boundaries of the inertia time constant under varying operating conditions.
# Ammonia-Oxidizing Bacterial Communities in Tilapia Pond Systems and the Influencing Factors
## 3 Results
### Ordination Analysis and the Environmental Interpretations  
In order to investigate the variations of belt diversity of ammonia-oxidizing bacterial communities, the nonmetric multidimensional scaling (NMDS) method based on the Bray-Curtis similarity was used. The NMDS diagrams of the 36 water samples and 36 sediment samples are listed in Figure 4A,B, respectively. The results show that both the water samples and the sediment samples were separated into three groups, and the ordination basis of the water samples (Figure 4A) and sediment samples (Figure 4B) were months and ponds, respectively. This may suggest that the ammonia-oxidizing bacterial communities in water were more sensitive to monthly changes than to pond differentiations, while the ammonia-oxidizing bacterial communities in sediments were more sensitive to pond differentiations than monthly changes.  
Figure 3: Richness (the Ace index) and diversity (the Shannon index) of ammonia-oxidizing bacterial communities in water (**A**,**B**) and surface sediment (**C**,**D**) of tilapia ponds. Higher values of Ace index and Shannon index represent higher richness and diversity, respectively. The different letters above the columns mean that there are significant differences between groups at 95% level, “bc” represents that this group has no significant differences with either the group of “b” or “c”.  
Moreover, in order to understand whether the ordinations were related to the changes in the physico-chemical parameters, we performed an interpretation of the NMDS ordinations of the ammonia-oxidizing bacterial communities with the environmental variables mentioned above (Table 2). The results showed that TP, TN, ammonia nitrite, sulphate, nitrate, TOC, DO, SD, and ORP in water were all significantly related to the NMDS ordinations (\(p<0.05\)), and TP, TN, and OM in sediments were all significantly related to the NMDS ordinations (\(p<0.05\)). Afterwards, we overlaid the significantly related variables (\(p<0.05\)) in water and surface sediments onto their NMDS ordination diagrams, respectively. The results are shown in Figure 4A,B. From Figure 4A (in water), we observed that nitrate was more related to the ammonia-oxidizing bacterial community ordinations in May, TP and TN were more related to such ordinations in July, and sulphate and ammonia in September and October. Further, from Figure 4B, we can observe that TP, TN, and OM were all more related to the ammonia-oxidizing bacterial community ordinations in pond 1.
## 3 Tapped Delay Line Model  
Modeling the propagation channel is one of the most challenging tasks in the design of wireless communication systems, especially for A2G scenarios, where special requirements on channel measurement requirements are addressed. Let us first consider a typical orthogonal frequency division multiplexing (OFDM) symbol duration (i.e., 66.67 \(\upmu\)s) in Fifth Generation (5G) NR, and a typical UAV flight speed of 100 km/h. In compliance with the 5G NR standard, \(B_{e}=20\) MHz bandwidth was used for subsequent analysis and modeling, i.e., the delay bin is \(\frac{1}{B_{e}}\). Within on data symbol duration, the maximum movement of antenna is 0.002 m, which has negligible impact on large scale parameters of the channel. Therefore, we present the A2G TDL channel model for communication applications based on the WSSUS assumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mark** & **Flight Phase** & **Height** & **Speed** & **Distance** \\ \hline FS1 & Vertical landing & 0–40 m & 1.2 m/s & 35–60 m \\ FS2 & Low approach & 40–100 m & 1.2–27 m/s & 70–300 m \\ FS3 & Hover preparation & 100–300 m & 27 m/s & 400–600 m \\ FS4 & Steady flight & 300 m & 27 m/s & 1690–2160 m \\ FS5 & Steady flight & 500 m & 27 m/s & 1740–2170 m \\ FS6 & Steady flight & 800 m & 27 m/s & 1750–2170 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed measurement parameters of different flight stages.  
Figure 2: Typical flight phases of VTOL UAV communications.  
Based on the WSSUS assumption, the channel can be simplified such that the complexity of the model is low where the CIR is easily to simulate and the important stochastic characteristics of the channel are kept. However, the WSSUS assumption is often invalid in realistic environments, depending on different application scenarios. For instance, positioning applications require accurate time evolution of the channel path parameters in order to ensure an accurate simulation environment for positioning algorithm tests. Therefore, the WSSUS assumption is not valid anymore. A more popular approach, i.e., a geometry-based stochastic channel model (GSCM), has attracted more attention with more accurate simulated CIR, but also with higher complexity in both modeling and parameter extraction. Nevertheless, in this paper, the tap delay line channel model is sufficient for communication purposes.
# Antivirus Evasion Methods in Modern Operating Systems
## 5 Research Environment and Toolset
### Basic Tools  
Kali Linux [35] is a Linux distribution that is free of charge and is based on Debian. It was launched in 2013 and is designed for advanced penetration testing and security auditing purposes. Kali provides a plethora of tools for tasks such as penetration testing, security research, reverse engineering, and computer forensics [36]. The operating system contains over 600 penetration tools, and it is important to mention that it is open-source software, which enables users to modify the software provided according to their requirements.  
Metasploit is a widely-used framework by penetration testers and hackers that provides a vast array of tools for testing system vulnerabilities and breaking security [37]. With approximately 600 payloads, 2200 exploits, and 45 encoders, Metasploit is a powerful tool that is built into Kali Linux by default, making installation unnecessary. Once the user logs in to Kali Linux and enters \"msfconsoole\" in the terminal, the tool is ready for use, and the user can listen for connections on the port generated by the payload. More details about the tool and its functionalities will be discussed in a subsequent section.  
Msfvenom [38] is a payload generation tool provided by the Metasploit framework. It was utilized to generate payloads that were used in subsequent tests. The program can be invoked through the terminal by entering the Msfvenom command with the required arguments. The tool provides a range of options, including the generation of payloads, encoding, selecting the output file format, and adjusting the number of load coding iterations.  
Shikata Ga Nai (SNG) [39] is a polymorphic XOR encoder with additive feedback that is available in the Metasploit framework. This tool has been around for some time but is still in use today. It employs code obfuscation techniques such as altering the order of code instructions, randomly changing registers, and adding junk code to evade signature recognition every time the code is compiled. Additionally, the additive feedback allows the output data to be incorrect in subsequent iteration stages when incorrect input data are received. This is due to the algorithm performing XOR operations on subsequent instructions, using a randomly-generated key, and then adding the current instruction to the key. Code decoding involves performing all steps in reverse order [40]. SNG can be used by adding an extra argument to the previously described example of payload generation, where the argument preceded by the -e flag selects the SNG encoder.  
Example usage is as follows:  
msfvenom -p windows/meterpreter/reverse_tcp LHOST=192.168.224.134 LPORT=8080 -e x86/shikata_ga_nai -f exec > defaultMsfvenom.exe
###### Abstract  
Prolonging the network lifetime is one of the fundamental requirements in wireless sensor networks (WSNs). Sensor node clustering is a very popular energy conservation strategy in WSNs, allowing to achieve energy efficiency, low latency, and scalability. According to this strategy, sensor nodes are grouped into several clusters, and one sensor node in each cluster is assigned to be a cluster head (CH). The responsibility of each CH is to aggregate data from the other sensor nodes within its cluster and send these data to the sink. However, the distribution of sensor nodes in the sensing region is often non-uniform, which may lead to an unbalanced number of sensor nodes between clusters and thus unbalanced energy consumption between CHs. This, in turn, may result in a reduced network lifetime. Furthermore, a different number of clusters lead to a different quality of service of a WSN system. To address the problems of unbalanced number of sensor nodes between clusters and selecting an optimal number of clusters, this study proposes an energy-balanced cluster-routing protocol (EBCRP) based on particle swarm optimization (PSO) with five mutation operators for WSNs. The five mutation operators are specially proposed to improve the performance of PSO in optimizing sensor node clustering. A rotation CH selection scheme based on the highest residual energy is used to dynamically select a CH for each cluster in each round. Simulation results show that the proposed EBCRP method performs well in balancing energy consumption and prolonging the network lifetime.  
wireless sensor networks; cluster; energy balance; particle swarm optimization; mutation operators +
Footnote †: journal: Sensors
## 4 Experimental Results and Analysis
### Comparative Experiment of Different Detection Models
In addition, to evaluate the performance of the model in different scenarios, we conducted actual test experiments on each model for three scenarios: crowd, outdoor, and smoking area, and we pointed out the problems of false detection and missed detection in each model. The specific results are shown in Figure 8. As can be seen from the figure, in the first column, when the target object is small in the crowd, YOLOv5n, YOLOv8n, and YOLOv10n cannot detect the small target cigarette on the left, while LSOD-YOLOv8n successfully identifies all targets. In the second column of outdoor situations, only YOLOv5n misdetects the elderly's fingers as cigarettes, and the other models correctly identify the targets, but the LSOD-YOLOv8 model is the best in detection accuracy. In the third column, the most complex smoking area, the false detection and missed detection of each model increased significantly, but our model still maintained a high accuracy and anti-interference ability. This shows that when facing cigarette detection in complex environments, our proposed model can also reliably identify targets and determine their locations.
## 5 Simulation and Experimental Results
### Experimental Model  
The elastic rotary attachment is a perfect test for modeling a dynamic relation to a robot or a spacecraft. The model emphasizes the effects on robot control systems of flexible link connections. Figure 8 shows a schematic diagram of the SLFM. Because of the flexible structure of the link, the end-point is shifted, as shown in the diagram, whenever it is actuated by an angle theta \((\theta)\) at the servomotor end. Illustrations of various parameters are given in Table 3. The link's flexibility is modeled as a linear torsional motion.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{2}{l}{**Set-Point (\(r\))**} & \multicolumn{2}{l}{**Error Indices**} & & \\  & **Output (\(y\))** & \begin{tabular}{l} **IAE** \\ **(10\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{l} **ISE** \\ **(10\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{l} **ITAE** \\ **(10\({}^{-2}\))** \\ \end{tabular} & \
\begin{tabular}{l} **ITSE** \\ **(10\({}^{-3}\))** \\ \end{tabular} &
\begin{tabular}{l} **Control Efforts (\(u\))** \\ **in ITAE** \\ \end{tabular} \\ \hline GESOBC & error = y \(-\) 0 & 712.9 & 67.52 & 569.8 & 456.7 & 246.5 \\ EGESOBC & error = y \(-\) 0 & 233.3 & 14.50 & 113.7 & 67.89 & 248.2 \\ LESOMC & error = y \(-\) 0 & 113.3 & 4.173 & 66.9 & 25.97 & 455.3 \\ Proposed scheme & error = y \(-\) 0 & 35.95 & 0.3193 & 19.60 & 1.314 & 238.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of performance indices for Example 2.  
Figure 8: Flexible link manipulator schematic diagram.  
Figure 7: Error dynamics of proposed controller with reaching phase elimination for Example 2.
## 6 Concluding Remarks  
This paper deals with dynamic management of the communication resources in WCSs. A cross-layer adaptive feedback scheduling scheme, which features co-design of real-time control and wireless communications, has been developed. With this scheme, the effects of noise interference and changes in link capacity on QoC can be addressed effectively, thus enabling wireless control in dynamic and uncertain environments. To avoid the difficulty of time-triggered invocation in making trade-offs between response speed and overhead in wireless environments, an event-triggered invocation mechanism has also been proposed, which improves the practical performance of feedback scheduling.  
The proposed scheme could be extended in several aspects. One possibility is generalizing the cross-layer design framework. For example, in order to take into account the effect of different MAC protocols, the MAC sub-layer may be included in the framework. In cases where the energy consumption of the nodes is a concern, physical-layer parameters such as the transmit power may be made available for other upper layers. Another possibility is improving the adaptive feedback scheduling algorithm. Given that the behaviour of the wireless network with respect to deadline miss  
\begin{table}
\begin{tabular}{l l l l l} \hline  & \multicolumn{2}{c}{Scenario I} & \multicolumn{2}{c}{Scenario II} \\ \cline{2-5}  & Time-Triggered & Event-Triggered & Time-Triggered & Event-Triggered \\ \hline \(\Sigma\)IAE & 1.131 & 1.127 & 1.295 & 1.293 \\ Times of Execution & 16 & 10 & 16 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of event-triggered and time-triggered invocations.  
ratio could be modelled with sufficient accuracy, for instance, it is possible to obtain analytically an optimized adaptive feedback scheduling algorithm using relevant control theory and techniques.  
Our future work in this direction includes development of an experiment system for WCSs over WLAN, which will be used to assess the performance of the proposed scheme with more extensive results. Another topic is to conduct theoretical stability analysis of WCSs that employ the proposed feedback scheduling scheme.