esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Strategy Improved Honey Badger Algorithm for Engineering Design Problems
## 2 Algorithm Design
### Proposed Algorithm  
#### 2.2.1 Halton Sequence Initializes the Population  
In the original honey badger algorithm, a highly random population was obtained by randomly initializing the population using the rand function. However, this randomization does not necessarily ensure that individuals are uniformly distributed throughout the solution space, which may lead to a slow population search, as well as reduce the diversity of the algorithms. To solve this problem, this paper introduces Halton sequence to generate pseudo-random numbers to initialize the population [31]. The pseudo-randomness of the Halton sequence ensures that the individuals are more evenly distributed throughout the solution space, thus increasing the diversity of the initialization phase of the algorithm. This helps the individual to quickly discover the location of high-quality solutions, speeds up the convergence of the algorithm, and improves the convergence accuracy of the algorithm.  
The two-dimensional Halton sequence is realized by selecting two primes as the basis quantities and forming a series of uniformly distributed and non-repeating points by constant slicing of these two basis quantities. This slicing process can be described by a mathematical model as described in Equations (7)-(9).  
\[n=\sum_{i=0}^{m}b_{i}\times p^{i}=b_{m}\times p^{m}+L+b_{1}\times p^{1}+b_{0}, \tag{7}\]  
\[\theta(n)=b_{0}p^{-1}+b_{1}p^{-2}+L+b_{m}p^{-m-1}, \tag{8}\]  
\[H(n)=[q_{1}(n),q_{2}(n)], \tag{9}\]  
where \(n\in[1,N]\) is any integer, \(p\) is a prime number greater than or equal to 2, denoted by the underlying quantity of the Halton sequence, \(b_{i}\in\{0,1,2,\cdots,p-1\}\) is a constant, \(\theta(n)\) is a defined sequence function, and \(H(n)\) is the resulting Halton sequence. This sequence replaces Equation (1) of the original algorithm for population initialization. As shown in Figure 1, where Figure 1a shows random distribution map, Figure 1b shows Halton distribution map, and Figure 1c,d show the histograms of particle distributions corresponding to the two initializations, respectively. According to Figure 1, it can be clearly seen that the Halton initialization possesses a more uniform particle distribution than the random initialization.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods  
### System Architecture  
#### 3.1.1 Personal-Computer-Based  
Figure 1 is the overall system architecture of using a personal computer with the Windows operating system as the computing platform. The communication of peripheral equipment, calculation of kinematics, establishment of the virtual force sensor, and the motion control are calculated on the platform. Robotics are responsible for calculating the posture of the robotic arm. The trajectory planning algorithm can generate smooth movement trajectories based on target points. The dynamic model calculation is performed using Python for machine learning. The dynamic model can calculate the ideal torque for each axis in the current posture based on the feedback of the arm's position and velocity. The results calculated through the dynamic model can be differentiated from the feedback motor torque of each axis to obtain the external torque of each axis. The external torque value can be converted into a virtual force value on the operating surface of the arm's end through the virtual force sensor algorithm. Finally, applications related to force can be performed based on the calculation results. The communication component utilizes EtherCAT to transmit commands and data for each axis of the six-axis robotic arm. In this architecture, the communication cycle is set to 2 ms due to the limitations of the Windows system's underlying clock.  
Figure 1: Architecture of the personal-computer-based system.
# A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
## 1 Introduction
We propose a scheme, namely Constellation Constraints (CC) algorithm for determining the reliability of the initial vector. 2. With the proposed CC structure, the algorithm separate the reliable symbol estimates in the initial vector from unreliable symbols. 3. A algorithm is developed for generating a new candidate set and neighborhood searching space and their implementation with RTS detection. 4. The simulation results of the proposed scheme are given to verify that similar BER performance can be achieved with significant lower computational complexity. The rest of this paper is organized as follows. The system model is introduced in Section 2, while the proposed algorithm in the massive MIMO configuration is described in Section 3. In Section 4, the attainable performance of our scheme is verified. Finally, the conclusion is given in Section 5.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 5 Challenges and Open Research Areas
### Network Coverage  
Many studies have been conducted to improve the network coverage for UAVs. However, a significant reduction in network coverage can be seen when UAVs and drones  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scope**} & **Autonomous** & **Computational** & **Channel** & **Interference** & **Security and** & **Reference** \\  & **Features** & **Intelligence** & **Modeling** & **Management** & **Safety** & **Reference** \\ \hline Compressed & & & & & & & \\ hydrogen, fuel cells & Energy management & \(\surd\) & - & - & - & [89] \\ \hline Hydrogen, fuel & & & & & & \\ cells & Energy management & \(\surd\) & - & - & - & [90; 93] \\ Solar power & Energy management & \(\surd\) & - & - & - & [91; 92] \\ \hline hybrid fuel & Energy management & \(\surd\) & - & - & - & [94] \\ \hline UAV backhaul & & & & Free-space & & & \\ network & & & & optical link & \(\surd\) & - & [95] \\  & & & & (FSO), LOS & & & \\ \hline Genetic algorithm & Energy efficiency & \(\surd\) & - & - & - & [98] \\ \hline Genetic algorithm & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Genetic algorithm \\ Physical layer \\ security \\ \end{tabular} } & Cooperative path & & \(\surd\) & - & - & - & [99] \\  & optimization & & \(\surd\) & - & - & - & [99] \\ \cline{1-1}  & & & & & & \\ Physical layer & & & & & & \\ security & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Secure UAV \\ communication \\ \end{tabular} } & & & & Free-space & & & \\  & & & pathloss with & - & \(\surd\) & [103] \\ \cline{1-1}  & & & LOS & & & \\ \hline Co-channel & & & Free-space path & & & \\ interference & Transmission power & \(\surd\) & loss with line of & \(\surd\) & - & [39] \\ \cline{1-1} management & & & & sight (LOS) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._are moving at a low speed [107]. This can be considered an open research area where the network coverage can be increased by introducing some changes to the existing mobility models.  
Furthermore, to provide network coverage to disaster-affected areas right after earthquakes and tsunamis, potentially emerging technologies such as 5G and UAVs can be used. Certainly, this is another open research area where network coverage can be provided in the affected areas using UAV-mounted small cellular base stations.
## 4 Discussion
### Comparison with Previous Studies
#### 4.1.2 Completeness  
In the present study, completeness was defined as the extent to which the chatbot-generated responses covered the information provided in the IADT guidelines. To ensure standardized evaluation, assessors were supplied with the current IADT guidelines and instructed to rate each response accordingly. Although no previous studies have specifically assessed completeness in chatbot responses related to dental trauma, several studies have examined this parameter in other areas of dentistry. For example, in a study involving dentistry-related questions posed to ChatGPT-3.5, expert evaluators reported a mean completeness score of 2.07 out of 3 (median: 2) [29]. Similarly, when ChatGPT was asked open-ended theoretical and case-based questions on interceptive orthodontics, the model achieved an average score of 2.4 out of 3 [30]. In the study by [PERSON] et al. [31], ChatGPT was evaluated using 50 questions across oral surgery, preventive dentistry, and oral cancer. The corresponding completeness scores were 3.2/5 for oral surgery, 4.1/5 for preventive dentistry, and 3.3/5 for oral cancer. Another investigation, which included 144 clinical questions from various head and neck surgery subspecialties and 15 comprehensive clinical scenarios, reported a median completeness score of 3 out of 3 for open-ended questions [32]. As seen in most of these studies, ChatGPT and its various versions have been the primary focus, and completeness has often been measured using a 3-point Likert scale--similar to the approach adopted in the present study. Consistent with previous findings, ChatGPT-4o achieved the highest completeness score in our analysis, with a mean of 2.74 out of 3 and a median of 3. Although the other chatbots also demonstrated mean scores above 2.5, Gemini Advanced obtained the lowest average score, which was significantly lower than those of the other models. Furthermore, our results revealed a statistically significant positive correlation between accuracy and completeness. In line with this, Gemini Advanced exhibited the lowest performance in both domains, with statistically significant differences observed in comparison to the other chatbots.
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 4 Modeling Reconnaissance Activity
### Metrics
#### 4.6.2 Node Occurrence Probability  
The probability of occurrence of a node shows how likely it is for a node to exist in the path the intruder will follow.  
Given a specific network topology and an incomplete level of knowledge of the intruder's behavior, we end up with a set of multiple paths, one of which is the path the intruder will follow. As there are more than one probable paths that the intruder may follow, the probability of having a specific node in the valid path that will be instantiated is equal to the portion of paths that include the specific node.  
For a given path \(path_{i}\) we calculate a weighted probability \(wp(path_{i},n)\) regarding the occurrence of node n in it. This is equal to zero if node n does not exist in the path.  
For paths that include the node, the weighted probability is inversely proportional to the distance from the start of the path to the position of the node.  
\[wp(path_{i},n)=\begin{cases}\frac{len(path_{i})-index(n)}{len(path_{i})},\text{ if }n\in path_{i}\\ 0,\text{ if }n\
ot\in path_{i}\end{cases} \tag{12}\]  
The total node occurrence probability is the average weighted probability for the node along all paths.  
\[occ^{n}=\frac{\sum_{i=0}^{i=|paths|-1}wp(path_{i},n)}{|paths|} \tag{13}\]  
If a node is present in multiple paths (preferably early along those paths) it has a higher occurrence probability.
## 4 Discussion
### Research Uncertainties and Future Studies
Thus, this assumption is still not fully applicable in the complex vegetation structures of forests or shrubs since they exhibit three-dimensional features. The current methodology is unable to address this limitation, and further in-depth research is required to improve upon this shortcoming. The results of this paper show that the accuracy of FAPAR\({}_{\text{LAI}}\) calculated based on MODIS LAI is better than that of FAPARMOD, and FAPAR\({}_{\text{FVC}}\) considering the effect of FVC is clearly better than FAPAR\({}_{\text{LAI}}\). This FAPAR algorithm can also be used for regional-scale GPP estimation, thus obtaining more accurate GPP and providing more reliable data for the analysis of GPP tempo-spatial patterns and its drivers [72]. However, the results also showed that even though the accuracy of FAPAR\({}_{\text{FVC}}\) with FVC taken into account was clearly improved, a systematic overestimation still persisted, and the main source of error may be related to the error of MODIS LAI data, especially in biomes with significant underestimation of LAI, such as tropical broadleaf evergreen forest and boreal forest. Therefore, it is necessary to pay more attention to reducing the error of MODIS LAI and obtaining more accurate NDVI data in the subsequent GPP estimation.
# Emergence of Nano-Dentistry as a Reality of Contemporary Dentistry
## 4 Discussion
### Conservative Nano-Dentistry
#### 4.1.4 Nanotechnology in Periodontology  
Chronic generalized periodontitis it is a mainly bacterial dependent disease together with other co-reasons. Due to long lasting inflammation, it can lead to loosening of teeth or tooth loss. The current treatment options include mechanical removal of pathogenic biofilms and providing some drug treatment, both local and systemic. Dental researchers attempted to generate an effective and satisfactory drug delivery system for the treatment of periodontal diseases that will be better than conventional ones. Current trends are the use of microparticles [71] and nano-based delivery systems [72; 73].  
Microspheres of \"Arestin\" (minocycline HCl), 1 mg, is a concentrated, locally applied antibiotic that remains active in the pocket for an extended period of time and reduces pocket depth [74]. The microspheres release antibiotic over time, targeting bacteria to reduce pocket depth, so gums can heal better than with scaling and root planing alone.  
Nano-based delivery systems can find significant application for eliminating bacterial pathogens. The use of charged nanoparticles is extremely useful since many of the microorganisms are themselves charged. The charged nanoparticles could be used to directly affect the bacteria or alter the microenvironment of it [75; 76; 77].
# Assessment of Novel Modal Testing Methods for Structures Rotating in Water
## 4 Results
### Results in Water
#### 4.2.2 Influence of Rotation on the Natural Frequencies of the Disk in Water  
Figure 16 shows the spectra of the structural response measured by different sensors mounted at different locations when the disk was not rotating and rotating in water. As the sensors measured different physical magnitudes, i.e., acceleration or strain, all the spectra were normalized using their maximum values in the range of 0 to 100 Hz. The spectrum at 0 rpm of the strain gauge SG3_9 shows two frequency peaks corresponding to the 1 ND at 13.1 Hz, with a higher amplitude, and to the 2 ND at 22 Hz, with a lower amplitude. At 50 rpm, the single frequency peaks associated with the 1 ND and the 2 ND were split into  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode Shape**} & **Numerical** & **Experimental** & \multirow{2}{*}{**Discrepancy**} \\  & **Simulation** & **Modal Test** & \\ \hline
0 NC & 13.0 Hz & 12.1 Hz & 7.1\% \\
1 ND & 12.9 Hz & 13.1 Hz & 1.4\% \\
2 ND & 24.3 Hz & 22.0 Hz & 9.2\% \\
3 ND & 55.8 Hz & 51.8 Hz & 7.3\% \\
4 ND & 104.9 Hz & 101.0 Hz & 3.8\% \\
1 NC & 110.6 Hz & 122.6 Hz & 10.8\% \\
1 NC+1 ND 1 & 128.2 Hz & 128.9 Hz & 0.5\% \\ \hline \hline \end{tabular} \({}^{\dagger}\) Combined mode with 1 nodal circle (1 NC) and 1 nodal diameter (1 ND).  
\end{table}
Table 2: Natural frequencies obtained from numerical and experimental modal analyses of the non-rotating disk in water.  
Figure 15: Numerical frequency response obtained from a harmonic analysis of the non-rotating disk in water.  
two frequency peaks. This split of a natural frequency detected from an on-board position was not observed in air, so it indicates that the hydrodynamic forces induced by the disk vibration in water were responsible for this effect. The splitting of natural frequencies measured from an on-board position when a disk was submerged in water and rotated was also observed by other authors [17].
## 7 Performance Evaluation  
The performance evaluation was done through four application versions, which we used to simulate and compare multiple linear regression to simple linear regression and to the original version of a monitoring application. This monitoring application simulates the gathering of three variables from the environment: temperature, humidity and light. The application versions to achieve the simulations are:  
1. First version: original application version, which sends temperature, humidity and light readings periodically every 1,024 clock shots from the sensor node, without performing prediction. This version was created to serve as a reference application for us to compare the energy consumption in the later versions, which uses prediction for data reduction.
2. Second version: enhanced version of the original application through a simple linear regression model. It sends only \(\beta\) and \(\alpha\) coefficients for each dependent variable. It uses a counter (time variable) as independent variable to predict temperature, humidity and light. This version was designed to verify the energy consumption when simple linear regression is used to reduce data sent to the sink. It was also implemented to calculate SSerr and R\({}^{2}\) to compare to the next versions. The counter is used as time variable, so it does not send any variable samples to the sink. This version is based on the method proposed by current works as [PERSON] _et al._[7].
3. Third version: enhanced version of the original application through a simple linear regression function, but using the temperature as independent variable, instead of time variable. It sends reading samples of the temperature variable and the \(\beta\) and \(\alpha\) coefficients for each dependent variable (except temperature) to predict the dependent variables humidity and light. This version was designed to verify the impact of this model on energy consumption when simple linear regression was sending an independent variable to reduce data communication. It was also created to check SSerr and R\({}^{2}\) compared to the second and third versions. The temperature was chosen as independent variable due to the results obtained from coefficient \(\mathbf{r}\), which can be seen later in the next section.  
* Fourth version: enhanced version of the original application through a multiple linear regression function, using counter and temperature as independent variables. It sends reading samples of temperature and \(\beta\) and \(\alpha\) coefficients for each dependent variable (except temperature) with \(\beta=(\beta_{0},\beta_{1},\beta_{2})\) where \(\alpha=\beta_{0}\). It predicts the dependent variables light and humidity. This version was designed to verify SSerr and R2 compared to the second and third versions. Our proposed method is based on this version.
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 2 Objective  
This study aims to prevent overturning accidents of mobile cranes by detecting ground subsidence in advance at construction sites. We developed a ground safety analysis system specifically designed for use with mobile cranes and validated its effectiveness through field tests. To enable real-time monitoring of the ground conditions, we applied an electrical resistivity exploration method [33; 34]. The system used in typical electrical resistivity explorations consists of a main measurement unit, electrodes, cables, and interpretation software [33; 34; 35; 36; 37]. The development of the ERMS was based on the relevant literature. Additionally, a foldable electrode device and a wireless control program were developed to enhance user convenience and operational efficiency. The ERMS can be mounted on mobile cranes and is specifically designed to reduce the measurement time, labor, and costs associated with typical electrode installation for electrical resistivity exploration.  
The focus of this study can be summarized into two key elements. First, the integration of the ERMS with mobile crane enhances worksite safety by enabling preemptive action against potential safety hazards. While previous studies have primarily focused on safety technologies applicable after the start of crane operations, the proposed system offers the advantage of preventing accidents before crane operation begins. Furthermore, the ERMS provides real-time monitoring of the ground conditions and predicts the possibility of ground subsidence, thereby helping to anticipate and prevent overturning accidents. Second, we developed a foldable electrode with a water supply device, which was designed to replace typical electrode installation methods and improve the efficiency of ground exploration. Unlike typical pin-buried electrodes, the foldable electrode device uses a cylindrical mass electrode, which is easier to install and remove. The water supply device reduces contact resistance between the electrode and ground, resulting in more accurate data acquisition. The performance of the developed ERMS was tested in the same environment using commercial equipment, including a MINISTING R1 (Advanced Geoscience Incorporation, Austin, TX, USA) and Terrameter LS2 (ABEM Instrument, Sundbyberg, Sweden). The reliability of the ERMS was confirmed by comparing the error rates of the measurement data from these commercial devices. Moreover, the ERMS was mounted on a mobile crane, and its applicability was further validated through field tests.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 4 Numerical Examples
### Terzaghi's Problem: Convergence Study  
Next, a simplified version of the Terzaghi problem is used as a convergence study. We consider a domain with dimensions of \(w\times h=1\times 1\) with the same boundary conditions as in the previous case. For simplicity we choose the following material parameters: \(\alpha=1\), \(c=0\), \(E=2/3\), \(\
u=0.25\) and \(\kappa=1\). The external load applied is \(p_{0}=1\) and we assume no body forces i.e., \(\mathbf{b}=\mathbf{0}\).  
This case was run with an increasing number of degrees of freedom using polynomial degrees \(p_{\rm P}=1,2,3\) for the pressure and correspondingly \(p_{\rm u}=2,3,4\) for the displacement. In all cases, the time step was kept sufficiently small for the spatial discretization error to dominate and we look at the results at the end of the first time step.  
The convergence study is performed by calculating the relative \(L^{2}\) error of the pressure field. The relative error based on the computed pressure values, \(\rho_{h}\), is calculated from  
\[\rho_{h}=\frac{\|p_{h}^{\rm f}-p^{\rm f}\|_{L^{2}}}{\|p^{\rm f}\|_{L^{2}}} \tag{41}\]  
where \(p_{h}^{\rm f}\) and \(p^{\rm f}\) are the computed and analytical solution pressures, respectively. The results from the mesh convergence study are shown in Figure 6 in terms of plots of the relative error versus the total number of degrees of freedom. The expected convergence rate based on the analytical solution is also shown. We observe from the results that optimal convergence rates are obtained for all polynomial degrees considered.
## 5 Development of an Evaluation System
### Implementation of the Underwater Propagation Attenuation Model in the Low-Frequency Band
#### 5.2.3 Underwater Propagation Delay  
Since ultrasonic waves are used, propagation delay in water is much longer than that of terrestrial radio wave communication. This long delay is not negligible. Ultrasonic waves propagate much more slowly in water. Its speed is expressed by Equation (9) [16].  
\[\mathrm{c}=1412+3.21\mathrm{T}+1.195+0.0167\mathrm{D} \tag{9}\]  
where T is water temperature (\({}^{\circ}\)C), D is water depth (m), S is salt concentration (%). This speed is about 1500 m/s, which is approximately one-200,000 th of that of radio waves. In other words, the propagation delay of ultrasonic waves in water is 200,000 times longer than that of radio waves. It takes about 13.3 ms for ultrasonic waves to travel 20 m. We implemented an underwater propagation model based on the summertime values in the Pacific Ocean near Japan. The assumed underwater environment is shown in Table 2.