esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Characterization of Complex Systems--An Information Theoretic Approach
## 5 Case Study--Entropy Metric Formulation and Analysis of Simulated Complex Networks
### Simulation Models Used to Apply the Metrics
#### 5.3.1 Preferential Attachment Networks
The expected number of nodes it will pass the information  
Figure 4: Transfer entropy values between the nodes of the preferential attachment network. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Node** & **Destination Node** & **Transfer Entropy (S-D)** & **Transfer Entropy (D-S)** \\ \hline
0 & 1 & 0.000 & 0.117 \\
1 & 2 & 0.155 & 0.077 \\
2 & 3 & 0.058 & 0.032 \\
3 & 4 & 0.143 & 0.035 \\
4 & 5 & 0.185 & 0.104 \\
5 & 6 & 0.166 & 0.084 \\
6 & 7 & 0.114 & 0.054 \\
7 & 8 & 0.193 & 0.064 \\
8 & 9 & 0 to will be \(r\times(k-1)\), by excluding the nodes it previously communicated the information to. Taking a weighted average over all the nodes we obtain:  
\[R_{0}=r\times\frac{\sum_{i=1}^{n}K_{i}(K_{i}-1)}{\sum_{i=1}^{n}K_{i}} \tag{11}\]  
If the value of \(R_{0}\) is greater than 1, it implies that the number of nodes receiving the information grows exponentially; if \(R_{0}\) is less than 1 it implies that the information will dissipate rapidly over a network. Table 4 and Figure 5 portray that, as the network evolves, entropy increases, implying that in a preferential attachment network with more nodes evolving over a period of time, the network as a whole tries to reach a disordered/more diverse state from an ordered state initially. As entropy (diversity) increases, the reproduction number initially increases and then decreases. This implies that when the network starts to evolve, there is a greater chance of information spread, and when the network starts evolving to more than four nodes at time-step 3, the information spread slowly decreases.
## 3 Performance Evaluation
### Maximum Piconet Size  
We next investigate the maximum piconet size, _i.e._, the maximum number of slaves that a master can handle. In BLE, each connection between a master and a slave is identified by a 32-bit access address. Beyond this fact, the Bluetooth 4.0 specification does not impose further limits on the number of slaves that can be connected to a master. However, there exist practical limits on that number, depending on the type of communication between master and slave, on the _connInterval_ parameter setting and the BER that can be assumed. The maximum piconet size is independent of the _connSlaveLatency_ parameter, because the inactive connection events due to slave latency cannot be used for connections with other slaves.  
Figure 8 depicts the theoretical maximum number of slaves that a master can handle for various configurations. This number is evaluated for the one-way and round-trip ATT interactions considered in the latency study. An upper bound on the maximum number of slaves per master is obtained by considering ideal communications (_i.e._, BER = 0). In addition, a safe scheduling scheme has been  
Figure 7: Latency measurement of a one-way ATT exchange, performed on the basis of a CC2540 slave current consumption plot. The marker m1 is placed at the start of the reception of the poll packet from the master. The marker m2 is placed at the end of the notification transmission.  
evaluated, whereby the communications between a master and two different slaves cannot overlap even when bit errors lead to retransmissions.  
As shown in Figure 8, the number of slaves that a master can handle varies significantly depending on the setting of the _connInterval_ parameter. The most energy-demanding configurations (_i.e._, those that use the lowest _connInterval_ values) yield a limited piconet size. For example, for _connInterval_ equal to 7.5 ms, the number of slaves per master is between 2 and 11, depending on the scheme considered. On the other hand, the maximum number of slaves a master can obtain data from is 5917, in the one-way ATT communication and for _connInterval_ equal to 4 s.  
Nevertheless, the maximum piconet size in a real scenario may be smaller than the theoretical one, due to limitations in terms of memory or antenna availability in the master. It is expected that a computer or a smartphone acting as a master will not suffer significant memory limitations. However, in some current smartphone models, the antenna is used by the classic Bluetooth and the WiFi stacks in addition to the BLE one, thus reducing the total time available for BLE and the number of possible simultaneous BLE connections.
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
## 3 Results and Discussion
### Risk Assessment Using an Agroinfiltrated Plant
(**a**) The recombinant Vip3 Aa protein immersion artificial diet and (**b**) agroinfiltrated plant leaf were used for insect bioassay as feed for _S. litura_. The experiment was repeated five times. (**c,d**) Impact of Vip3 Aa on the survival of _S. litura_ on the artificial diet (**c**) and agroinfiltrated leaf-feeding (**d**) for 6 and 12 d. Number, feeding time (day); dead, dead insect; Con, mock; Buffer 1, PBS buffer; LC, Vip3 Aa protein (40 ng/mg); HC, Vip3 Aa protein (100 ng/mg); Buffer 2, infiltration buffer; Buffer 3, vector; leaf: Vip3 Aa-agorin infiltrated leaf. Values are expressed as the means \(\pm\) SEM. ***, \(p<0.0001\)compared with the control. we confirmed that the agroinfiltration method as a risk assessment tool was more efficient than using a recombinant protein-based artificial diet. Although the agrobacterium penetration method is used in various fields to identify protein functions in plants, studies on the risk assessment of species have not been tried. The main objective of this study was to show the usefulness of a new risk assessment tool for evaluating the putative risks of the natural ecosystem on LM proteins. The agrobacterium infiltration model was used as a substitute for LM crops that take a long time to construct LM plants and elucidate the insect risk assessment of LM proteins quickly. Our results suggest that the new approach may be used more efficiently in terms of time and cost than artificial feed bioassays in laboratory trials with recombinant protein for LMO risk assessment. Transiently expressed LM protein in plants can satisfy laboratory trials, including toxicity tests with high-dose recombinant protein and direct environmental exposure assay using the LM crop. Future work based on this study will establish guidelines for LMO risk assessment for Lepidoptera in the potential receiving environments of South Korea. Therefore, we are improving this tool to assess the valued overall risk of Lepidoptera. Our findings could serve as a valuable reference for future studies seeking to develop appropriate assessment techniques to determine risk factors affecting the environmental ecosystem in South Korea.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 3 Multi-Strategy Hybrid Particle Swarm Optimization Algorithm (MSPSO)
### Multi-Strategy Hybrid Particle Swarm Optimization Algorithm
#### 3.2.2 Adaptive Inertia Weights and Learning Factor Strategies  
The inertia weight \(\omega\) is the most crucial parameter in the PSO algorithm, which balances the local search capability of the algorithm with the global search capability. The value of \(\omega\) needs to be kept large in the early iterations to obtain a strong global search capability and search velocity. In contrast, a smaller value of \(\omega\) is necessary for the later iterations to give the algorithm a higher local search capability and accuracy [27; 28]. Therefore, we adopt a strategy where the inertia weights change adaptively with the iteration of the algorithm.  
The basic idea of adaptive inertia weight is to adjust the inertia weights based on one or more feedback parameters, which can fully use the valuable information provided by the algorithm compared to the traditional constant inertia weight strategy and linear inertia weight strategy. We adaptively adjust the inertia weights using changes in the particle fitness values. Define the relatively change rate of particle fitness as follows:  
\[k_{i}(t)=\begin{cases}\frac{f(x_{i}^{t})-f(x_{i}^{t-1})}{f(x_{i}^{t-1})}\;\;t \geq 2\\ \;\;\;\;0\;\;\;t=1\end{cases} \tag{11}\]  
When \(k_{i}(t)\) is large, it means that the particle is far away from the optimal solution, and the inertia weight should be increased to bring it closer to the optimal solution; while when \(k_{i}(t)\) is small, it means that the particle is poorly updated or already attached to the optimal solution, and the inertia weight should be reduced to increase the particle local search ability. Based on this, the following inertia weight adjustment strategy is proposed:  
\[\omega_{i}(t)=\frac{1}{1+\exp(-k_{i}(t))} \tag{12}\]  
In the PSO algorithm, the learning factors \(c_{1}\) and \(c_{2}\) determine the influence of the particle's experience and the population's experience on the particle motion trajectory, and setting larger or smaller values of \(c_{1}\), \(c_{2}\) is detrimental to the algorithm [29]. We let the learning factor vary with the inertia weight to obtain the appropriate \(c_{1}\) and \(c_{2}\). \(c_{1}\) takes a larger value when the inertia weight is larger, and \(c_{2}\) takes a smaller value, allowing the particles to lean more towards the individual optimum; while the opposite is true for small inertial weight, enabling the particles to move more toward the optimum population. The relationship among \(c_{1}\), \(c_{2}\), and \(\omega\) is as follows:  
\[\begin{cases}\;\;\;\;c_{1}=\frac{1.7\cos[\omega_{i}(t)]}{\omega_{i}(t)}-0.4\\ \;\;\;\;c_{2}=3.3\omega_{i}(t)\sin[\omega_{i}(t)]-0.3\end{cases} \tag{13}\]  
Figure 6 shows how \(c_{1}\), \(c_{2}\) varies with \(\omega\).  
Figure 6: Evolution of \(c_{1}\), \(c_{2}\) as a function of \(\omega\).
## 3 Results
### Latency Sensibility Analysis  
The AR application has been developed specifically for a holographic table, which is an Augmented Reality device that utilizes 2D elements for 3D visualization. The stereoscopic principle is employed to create a sense of depth perception for users. This is achieved by providing a pair of slightly different 2D images to the user, which the brain processes into a single image with the perception of depth.  
During the exploration of the application, motion sickness symptoms [24; 25; 26] have been observed in some users. This subsection of the paper aims to analyze the causes and symptoms of motion sickness in the context of the AR application, as well as provide a quantitative analysis of the impact of model sizes on the overall latency of the application.  
The analysis will include quantitative data analysis of the impact of different model sizes on the overall latency of the application, as latency can potentially contribute to motion sickness symptoms. This analysis will provide insights into the factors contributing to motion sickness in the AR application and propose potential strategies or workarounds for reducing motion sickness proneness in users. The findings from this analysis can contribute to the overall understanding of the user experience with the AR application and inform potential improvements to mitigate motion sickness and enhance user comfort during exploration.  
This paper focuses mainly on FPS analysis in order to evaluate the user experience comfort. It is worth noting that user comfort during the immersive experience depends on a multitude of factors; however, the FPS has been chosen as the main focus for this study given the performance-related task of obtaining 3D models. Size-increase performance requirements often worsen the application performance after a critical point of FPS rendering. This analysis will evaluate the impact on the application of the newly obtained 3D model regarding FPS amount.  
The obtained models can then be optimized using techniques such as retopology and normal maps in order to further improve the application performance.
## 2 System and Channel Models  
A dual-hop mixed RF-FSO communication system is depicted in Figure 1, which has an RF transmitter (S), an AF relay (R), and an FSO receiver (D). The S and D are equippedwith a single antenna or aperture. The relay R converts the radio waves into optical waves with the AF relaying protocol.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Connection Probability Using Dynamic Communication Ranges
#### 3.5.1 Case 1: Static Communication Range  
The probability of connectivity for static communication ranges can be generally mapped as;  
\[P_{\text{c}}^{\text{Stat}}=\frac{\gamma\Big{(}2-\frac{1}{\
u},\Big{(}\frac{ \rho R}{\Gamma\big{(}2-\frac{1}{\
u}\big{)}}\Big{)}^{\
u}\Big{)}}{\Gamma(2- \frac{1}{\
u})}, \tag{31}\]where \(R\) is range in (2), \(\gamma(.,.)\) is the lower incomplete gamma function.  
Proof.: Two vehicle nodes communicate when their inter-vehicle distance is less than the communication range. Thus the probability of connectivity is found as;  
\[\begin{split}\mathrm{P}_{\mathrm{c}}^{\mathrm{Stat}}& \stackrel{{\triangle}}{{=}}\ \mathrm{Pr}(D\leq R)=\int_{0}^{R}f_{D}(x)dx\\ &=\int_{0}^{R}\frac{\
u\rho^{2\beta+1}x^{2\beta}e^{-a\rho^{ \
u}x^{\
u}}}{\left(\Gamma(\frac{2\beta+1}{v})\right)^{2\
u}}dx\\ &\stackrel{{ a}}{{=}}B\int_{0}^{R}x^{2\beta}e^{-Ax^{ \
u}}dx\\ &=\frac{B\gamma\big{(}\frac{1+2\beta}{v},AR^{\
u}\big{)}}{\
u A^ {\
u}}=\frac{\gamma\bigg{(}2-\frac{1}{v},\left(\frac{\rho R}{\Gamma\left(2- \frac{1}{v}\right)}\right)^{v}\bigg{)}}{\Gamma(2-\frac{1}{v})}.\end{split}\]  
In \(\mathrm{P}_{\mathrm{c}}^{\mathrm{stat}}(a)\) above, we let \(A=a\rho^{v}\) and \(B=\frac{\
u\rho^{2\beta+1}}{\left(\Gamma\left(\frac{2\beta+1}{v}\right)\right) ^{\infty}}\); then using procedure in [31] in \(\mathrm{P}_{\mathrm{c}}^{\mathrm{stat}}(b)\) and substituting \(A\) and \(B\) gives (31).  
If we let \(\beta=0,1\) for exponential and GUE (synchronised) inter-vehicle distance distributions respectively,  
\[\begin{split}\mathrm{P}_{\mathrm{c}}^{\mathrm{Stat}}=\begin{cases}1 -e^{-\rho R},\ \beta=0\\ \Phi\Big{(}\frac{2\rho R}{\sqrt{\pi}}\Big{)}-\frac{4\rho R}{\pi}\exp\Big{\{}- \frac{4\rho^{2}R}{\pi}\Big{\}},\ \beta=1.\end{cases}\end{split} \tag{32}\]  
\(\Phi(.)\) is the error function. Equation (26) can be directly evaluated for individual distributions as well.
# A Cell Counting Framework Based on Random Forest and Density Map
## 4 Experiments and Results
### Results and Discussion
#### 4.3.2 MBM Cells  
The greatest challenge of the MBM cells is the pink tissue and the fake particles. With the dilated extracted features, the patch in feature space is enlarged with the dimension reduced. The first two compared methods are based on CNNs and both of them count cells by predicting a density map. The comparison results listed in Table 3 indicate that CCF has the lowest MAE, which means it is more suitable to handle the counting problem without enough training data. [PERSON] et al. [31] designed a network that can be adaptive to various visual domains and it only outputs the number of objects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **N = 8** & **N = 16** & **N = 32** & **N = 50** \\ \hline [PERSON] et al. [1] & \(4.9\pm 0.7\) & \(3.8\pm 0.2\) & \(3.5\pm 0.2\) & N/A 1  \\ [PERSON] et al. [2] & \(3.4\pm 0.1\) & N/A & \(3.2\pm 0.1\) & N/A \\ [PERSON] et al. [30] & \(4.5\pm 0.6\) & \(3.8\pm 0.3\) & \(3.5\pm 0.1\) & N/A \\ FCRN-A [5] & \(3.9\pm 0.5\) & \(3.4\pm 0.2\) & \(2.9\pm 0.2\) & \(2.9\pm 0.2\)2  \\ SAU-Net [21] & N/A & N/A & N/A & \(2.6\pm 0.4\)2  \\ Count-ception [6] & \(3.9\pm 0.4\) & \(2.9\pm 0.5\) & \(2.4\pm 0.4\) & \(2.3\pm 0.4\) \\ CCF & \(2.9\pm 0.2\) & \(2.8\pm 0.1\) & \(2.6\pm 0.1\) & \(2.6\pm 0.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of mean absolute error (MAE) on the VGG cells. The testing set is fixed and 10 times training with different training data and different network initializations are used to calculate the mean and standard deviation.  
Figure 11: A test image of VGG cells. The two boxes highlighted in the input image contains high density overlapped cells that are difficult for human to count correctly. But the proposed CCF could predict the number of cells with acceptable errors. The second row displays the ground truth of the density map and the predicted density map.  
Similar to CCF, Cell-Net [29] also counted cells by locating the centroids of cells. However, Cell-Net has many parameters to be learned and it is not suitable for the small datasets. In Figure 12, the predictions of two local patches show that there is no noise in the background and CCF can detect the separated cells well. In the full-size density maps, it can be observed that there are several uncertain locations which are not approved by all RFs. It is exactly the function that averaging all detection maps plays. The number of ground truth is 174 cells and the prediction is 168.2 cells.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 4 Summary and Conclusions  
Through static-loading tests and analysis, the mechanical properties of square thin-walled CFST columns with steel-bar stiffeners were studied. The effect of different structural measures was compared. The following conclusions were drawn:  
(1) When steel-bar stiffeners are placed inside or outside the steel tube of square thin-walled CFST columns, they can effectively delay local buckling in thin steel tubes. Higher local-buckling loads and ultimate bearing capacity of the columns can be achieved as well. However, the ductility of the columns decreased.  
(2) When the increase of the cross-section area ratio of steel to column is 1.11%, steel-bar stiffeners can work better when placed inside the steel tube and obtain higher bearing capacity of columns, because of the constraint effect of core concrete. When the increase of the cross-section area ratio of steel to column is 1.95%, steel-bar stiffeners should be placed outside the steel tube to enlarge the cross-section area of the column and obtain higher bearing capacity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Column Number** & \(N_{u}\) **(kN)** & \(N_{q}\) **(kN)** & \(N_{q}\)/\(N_{u}\) & \(N_{qi}\)/\(N_{q2}\) \\ \hline C1 & 145.5 & 120.0 & 0.825 & 0.194 \\ C2 & 802.0 & 620.0 & 0.773 & 1.000 \\ C3 & 1154.5 & 700.0 & 0.606 & 1.129 \\ C4 & 1200.0 & 820.0 & 0.683 & 1.323 \\ C5 & 1100.0 & 760.0 & 0.691 & 1.226 \\ C6 & 1353.0 & 880.0 & 0.650 & 1.419 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of local-buckling load of columns.  
(3) A higher cross-section area ratio of steel to column can lead to lower deformation capacity for the column, while a lower steel-section area ratio can result in higher deformation capacity.  
(4) The structural measures of using steel bars as stiffeners and using spot welding to fix the steel bars onto the tube to strengthen square thin-walled CFST columns are effective and feasible, which can delay local buckling in the tube, increase the bearing capacity of the column, and save construction costs.  
[PERSON] and [PERSON] designed the experiment procedures. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the original draft of this paper. [PERSON] gave some suggestions to polish the paper.  
The work was financially supported by the National Natural Science Foundation of China (Project code: 51678457), the Scientific Research Key Project Fund of Ministry of Education of Hubei Province (Project code: D20171106), and the Research Foundation of Wuhan Construction Committee (Project code: 201820). Their support is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Frequency Variations in Tuning-Fork Oscillators  
Nowadays, many electronic devices are controlled by periodical signals. These signals are produced by electronic components, known as oscillators. They are used in a great range of devices, e.g., digital clocks, smartphones, electrical appliances, computers, vehicles, airplanes, _etc_.  
The utilization of oscillators involves synchronization problems that have to be solved in order to optimize the performance of electronic devices. The oscillation frequency may vary due to different reasons. This fact produces a variation between the real oscillation frequency and the nominal frequency (which is the frequency given by the manufacturer). The above mentioned variation is usually defined as the _clock skew_ or _clock drift_.  
In nature, all the elements have a resonance frequency that allows their atoms to have an oscillatory movement. Several elements can be found in nature for creating a periodical signal, but among all of them, quartz crystals are the most used ones because of their resonance capability with a fixed frequency. The oscillation frequency is mainly due to the cut of the crystal, the temperature and the voltage applied.  
A quartz crystal can be cut at different angles. However, the most commonly cut is the right-angled one. Tuning fork oscillators are an example of a right-angled cut. This kind of oscillator is commonly used in most WSN motes, because of its good relation between accuracy and low cost.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 2 Related Works
Most of them rely on consensus algorithms to coordinate independent clocks in the network. For example, the main idea of GTSP and Average TimeSynch (ATS) is to average local information repeatedly until all nodes eventually have a common clock. However, one drawback of this approach is its slow convergence speed [18]. To increase the convergence speed, the maximum time synchronization (MTS) [18] protocol was proposed. Essentially, MTS forces all nodes to follow the fastest clock in the network. Therefore, it can achieve faster convergence. However, clocks synchronized under MTS are faster than the desired clock. Moreover, if a malicious node with an abnormally high clock value enters the network, the synchronization may break down. Besides the message passing-based time synchronization protocols, recent studies have leveraged existing infrastructures, such as Wi-Fi beacons [35] and electromagnetic energy radiation [36], to globally synchronize all nodes in the network. The advantage of these solutions is that the energy consumption of sensor nodes is reduced. However, the synchronization error of these protocols is higher than that of state-of-the-art solutions in WSNs (e.g., the mean synchronization error of [36] was 121 us). In summary, numerous time synchronization protocols in WSNs have been proposed in recent decades. However, there has not been a quantitative performance study on the diverse aspects affecting the performance of time synchronization in WSNs. The aim of this study is to provide comprehensive insights into the operations, problems, and possible improvements in time synchronization protocols through a quantitative analysis, which considers a diverse range of factors that affect performance, such as message delay jitter, time synchronization period, network topology, and packet loss ratio of a wireless link.