esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mobile Object-Following System Using LiDAR
### Object Detection and Visualization  
The message system in ROS (Robot Operating System) is a core mechanism for data communication between nodes. ROS messages (Table 3) are simple data structures that follow a defined format with a series of fields. The 2D LiDAR sensor collects and processes data through an ROS node, continuously publishing distance data about the surrounding environment to the ROS. These data are represented in the'sensor_msgs/LaserScan' message format. This message type includes the following information about the LiDAR scan.  
The controller receives distance and angle data from reflected LiDAR points and analyzes sequential data points to identify followable objects. This process involves detecting the width of continuous objects that meet threshold conditions and generating object data based on compliant points. Real-time visualization of object information was achieved using the ROS open-source software RViz 1.13.19, as shown in Figure 5, enabling intuitive understanding of the distance and orientation of objects relative to the robot, as well as distinguishing additional objects. Figure 6 provides real-time insight into sensor readings, offering detailed monitoring and analysis capabilities for data gathered from the LiDAR sensors.  
Figure 5: 3D visualization of a tracked object using ROS RViz.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Messages** \\ \hline header & Includes the message’s timestamp and frame ID \\ angle\_min & The angle at which the scan starts \\ angle\_max & The angle at which the scan ends \\ range\_min & The minimum distance the LiDAR can detect \\ range\_max & The maximum distance the LiDAR can detect \\ ranges & An array of distance data; each element represents the \\  & distance measured from the LiDAR \\ \hline \hline \end{tabular}
\end{table}
Table 3: ROS message type includes the following information about the LiDAR scan.
## 3 Simulation Performance Analysis
### Impact of Electrode Thickness on SAW Propagation Properties  
When the piezoelectric film's surface is covered with a metallic IDT, the mass loading effect of the metallic electrode becomes non-negligible [31]. This section aims to investigate the impact of the IDT electrode on the propagation characteristics of SAW when h1 and h2 are set to 0.4 \(\lambda\) and 0.2 \(\lambda\), respectively. The \(v_{\rm p}\) and \(k^{2}\) of the Rayleigh and Sezawa modes are compared and analyzed for various electrode normalized thicknesses (h3/\(\lambda\)).  
For the Rayleigh and Sezawa modes excited by this structure, as h3/\(\lambda\) increases from 0 to 0.2, Figure 6 illustrates how various electrode thicknesses affect both admittance characteristics. The admittance value of the Sezawa mode first increases and then decreases with the electrode thickness. The cause of this phenomenon is a decrease in electrode resistance and an increase in conductance when h3/\(\lambda\) is increased from 0.04 to 0.08; the reason for the decrease in conductance in the range of h3/\(\lambda\) from 0.16 to 0.20 could be an increase in acoustic impedance. Moreover, the Sezawa mode has significant advantages over the Rayleigh mode. As shown in Figure 7a, the \(v_{\rm p}\) of the Rayleigh mode changes from 2838 m/s to 2615 m/s, and as depicted in Figure 7b, the \(v_{\rm p}\) of the Sezawa mode changes from 5064 m/s to 4623 m/s. In addition, the \(k^{2}\) of the Rayleigh wave decreases gradually with increasing electrode thickness, from approximately 1.2% to approximately 0.4%. The \(k^{2}\) of the Sezawa wave increases gradually, from approximately 4.2% to approximately 5.4%. In order to obtain a higher \(k^{2}\) value while keeping \(v_{\rm p}\) at a higher value, h3 = 0.08 \(\lambda\) is chosen in this paper, i.e., \(k^{2}\) is 4.9% and \(v_{\rm p}\) is 4892 m/s.  
Figure 5: _TCF_ of Sezawa wave variation with SiO\({}_{2}\) normalized thickness (h2/\(\lambda\)).  
Figure 6: Rayleigh and Sezawa modes’ admittance dependence on electrode normalized thickness (h3/\(\lambda\)).  
In summary, in this paper, under consideration of electrode loading, the Sezawa mode still exhibits a larger \(k^{2}\) and \(v_{\text{p}}\) compared to the Rayleigh mode, which makes it possible to use the Sezawa mode in the fabrication of SAW devices with high frequencies and low _TCFs_.
###### Abstract  
The objective of this study was to compare the external load of training sessions using as a reference an official competition match in women's football in order to find if the training sessions replicate the competition demands. Twenty-two semi-professional football players were analyzed during 17 weeks in the first phase of the competitive period of the 2020-2021 season of Spanish women's football. In addition to the competition (Official Matches, OM), four types of sessions were distinguished: strength or intensity (INT), endurance or extensity (EXT), velocity (VEL), and activation or pre-competitive (PREOM). The external load variables recorded were total distance (TD), high-speed running (HSR), sprint (Gprint), accelerations (ACC2), deceleration (DEC2), player load (PL), distance covered per minute (TDmin), high metabolic load distance (HMLD), and total impacts. The main results were that the external load demanded was different according to the type of session, being, in all cases, much lower than OM. The variables referring to the neuromuscular demands (ACC2 and DEC2) were higher in the INT sessions, the TD variable in the EXT sessions and the velocity variables (HSR and Sprint) in the VEL sessions. We can conclude that there was an alternating horizontal distribution of training loads within the competitive micro-cycle in women's football, although the order was not the usual one for tactical periodization.  
team sport; women; external load; periodization; electronic performance; tracking systems +
Footnote †: journal: Journal of Computational Research and Public Health
## 4 Bayesian Optimization of ResNet18
### Validation of Model Immunity to Interference  
In the production environment of a hydrogen refueling station, the bearings experience varying loads while the signal acquisition process is affected by on-site mechanical noise contamination. Therefore, to simulate real working conditions and verify the BOA-ResNet18 network's generalization ability for various signals, this study introduces Gaussian noise to the original vibration signals to model mechanical noise in the hydrogenation plant.  
\[SNR=10\times lg\frac{Ps}{Pn} \tag{22}\]  
The average signal power (\(Ps\)) and average noise power (\(Pn\)) are used to calculate the signal-to-noise ratio (SNR) on a logarithmic scale expressed in decibels (dB), whereby dB = 10 \(\times\) lgSNR. To simulate actual working conditions, this study adds three different values of SNR (70 dB, 80 dB, and 90 dB) since the bearing vibration signal typically has an SNR between 70 dB and 90 dB. The added noise signal is shown in Figure 19.  
Figure 17: Troubleshooting accuracy of two signals in the BOA-ResNet18 network.  
Figure 18: Troubleshooting loss values for two signals in the BOA-ResNet18 network.  
Additionally, the harsh operating conditions of hydrogen refueling stations and the continuous operation of compressors cause bearings to typically experience fatigue rupture, scoring, or wear caused by foreign object intrusion. As a result, this proposes adding random pulse signals to simulate the sudden changes in vibration signal waveforms that result from faults. The resulting simulated fault signal can be expressed as follows:  
\[y(t)=x(t)+noise(t)+f(t) \tag{23}\]  
where \(x(t)\) represents the original bearing signal and \(noise(t)\) represents the Gaussian noise signal:  
\[noise(t)=A\times e^{(-((t-t_{0})^{2}\ /\ (2\times sigma^{2})))} \tag{24}\]  
where \(f\) is the frequency of the signal, \(A\) is the amplitude of the noise signal, \(t_{0}\) is the center time of the noise signal, and \(sigma\) is the standard deviation of the noise signal. \(f(t)\) is a random pulse signal:  
\[f(t)=A\times P(t-t_{0})\times rand \tag{25}\]  
where \(A\) represents the pulse's amplitude, \(P(t-t_{0})\) represents the pulse function, \(t_{0}\) represents the pulse's start time, and rand represents a random number.  
Existing experiments indicate that the amplitude range of random pulse signals of  
ResNet18 is resistant to interference thanks to its residual structure and capacity to extract features from abstract imagery. Bayesian optimization enhances the network model's data fitting ability and further improves its generalization capabilities. The model maintains a computational accuracy of 92.95% when subjected to both Gaussian and random impulse noise, demonstrating its ability to withstand interference in the operational environment of a hydrogen refueling station.
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
###### Abstract  
Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spatial scan statistic was upgraded to identify statistically significant communities at each snapshot by considering the effects of the communities discovered at the previous snapshot. Finally, different rules defined based on the Jaccard coefficient were used to identify the evolution of the communities. Experimental results on both simulated and real-world vehicle movement datasets showed that EACO performs better than three representative dynamic community detection methods: FacetNet (a framework for analyzing communities and evolutions in dynamic networks), DYNAOGA (dynamic multi-objective genetic algorithm), and RWLA (random-walk-based Leiden algorithm). The dynamic communities identified by EACO may be useful for understanding the dynamic organization of urban structures.  
dynamic community; ant colony optimization; evolutionary clustering; significance test; vehicle movements +
Footnote †: journal: Applied  
doi: 10.3390/app12157608  
Academic Editor: [PERSON]
## 3 Results and Discussions
### Prediction Results of the Multi-Parameter Empirical Model  
Due to the different characteristics of the equilibrium lines of mixed gas hydrate in different temperature ranges, this study conducted fitting for the three regions in Figure 3 respectively to obtain more accurate empirical models. In Figure 3, it was found that \(\ln P\) exhibited a clear linear relationship with \(T\) in region (I), (II), and (III), and the slope of the straight lines in the three regions also gradually increases as the temperature increases.  
For regions (I), (II), and (III), the coefficients B and C in Equation (6) fitted for different CO\({}_{2}\) mole fraction are shown in Figure 4a-c. The variation of the coefficients B and C with CO\({}_{2}\) mole fraction indicates a strong correlation. Among them, B and C exhibit a monotonic increasing and decreasing trend with increasing CO\({}_{2}\) mole fraction, respectively. Specially, in region (III), when the CO\({}_{2}\) mole fraction exceeds 0.8, the C value drops sharply. To accurately describe this trend of change, we fitted the relationship between C and CO\({}_{2}\) mole fraction in two stages, namely 0-0.9 and 0.9-1. As shown in Figure 4, the fitted curves match the data points well, and the values of the fitted five parameters a-e in Equation (7) are listed in Table 3. Thus, a multi-parameter empirical model for predicting the phase equilibrium conditions of CO\({}_{2}\)-CH\({}_{4}\) gas hydrates was established.  
Figure 4: Variation of the fitted coefficients B and C with CO\({}_{2}\) mole fraction: (**a**) below the freezing point, (**b**) temperature range of 0–10 \({}^{\circ}\)C, (**c**) temperature range of 10–20.85 \({}^{\circ}\)C.  
To verify the accuracy of our empirical model, this study compared its predicted values with experimental data, the Chen-Guo model, and the vdW-P model, as shown in Figure 5, and the AARDs of this work were listed in Table 2. From Figure 5, it can be observed that the proposed empirical model has a good prediction accuracy, with an AARD of 3.09% for the whole temperature and pressure ranges. It is slightly better than the results of the two theoretical models. Compared to the Chen-Guo model, there is a significant improvement in prediction accuracy of pressures below freezing point. The proposed multi-parameter empirical model is simpler to use and has high accuracy and wide temperature and pressure ranges, making it suitable for on-site applications
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques
#### 2.3.2 Centroid  
The centroid localization technique estimates the receiving node's position as the centroid of all transmitting nodes' positions with minimum computations. Similar to the proximity technique, the receiver node first identifies whether it is located within the communication range of the known transmitting nodes. Next, the receiving node determines its location by using the mean of all coordinates of all the known transmitting nodes located within the threshold region by using the following equations [50]:  
\[x_{u}=\frac{\sum_{i=1}^{m}x_{i}}{m}, y_{u}=\frac{\sum_{i=1}^{m}y_{i}}{m}, \tag{19}\]  
where \((x_{u},y_{u})\) are the coordinates of the receiver node, \((x_{i},y_{i})\) are the anchor nodes' coordinates, and \(m\) is the total number of known transmitting nodes located within the coverage region of the receiver node.  
Although the centroid localization technique is easy to implement, its accuracy depends heavily on the environmental propagation conditions and the density of anchor deployment.  
Figure 6: Proximity localization technique, reproduced with permission from [PERSON], Recent Advances in Wireless Indoor Localization Techniques and System; published by Hindawi Limited, 2013.
## 2 Material and Methods
### Volatile Compound Profiling  
Samples were analyzed using an Agilent 8890 gas chromatography system and an Agilent single quadrupole 5977 gas-mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). Separation of compounds were carried out on a non-polar HP-5 MS capillary column (30 m, 0.250 mm ID, and 0.25 \(\upmu\)m film thickness). Helium was used as the carrier gas and run in splitless mode with the flow rate set to 1.0 mL/min. The initial temperature of the program was 30 \({}^{\circ}\)C, maintained for 3 min, then the temperature was raised to 120 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min, then to 250 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min, and finally maintained at that temperature for 3 min. The ion source temperature was 230 \({}^{\circ}\)C, the quadrupole temperature 150 \({}^{\circ}\)C, the transfer line temperature 250 \({}^{\circ}\)C, and the quality scan range was 45-550. Identification of volatile compounds was performed by comparing the acquired mass spectra with those in the NIST 17 mass spectral library. Only compounds with a similarity index (match quality) of \(\geq\)85% were considered tentatively identified. For additional confirmation, linear retention indices (LRIs) were calculated using a standard series of n-alkanes (C7-C30) analyzed under the same chromatographic conditions, and compared with reference values reported in the literature. For peak detection, the initial threshold was maintained at 12, with the initial peak width and initial area reject set to 0.2 and 1, respectively.
## 3 Advantages, Characteristics and Types of Digital Twin  
Since the first introduction of DT, its popularity has increased as more and more researchers started focusing their research on DT. Figure 3 shows the exponential growth of the number of publications found on Scopus as well as ScienceDirect (limited to the English language) that contain the term 'Digital Twin' in the article title, abstract, or as keywords from 2011 to 2020. The increase in the publications is quite recent, i.e., 2016 onwards, as it can be clearly seen in Figure 3.  
The growing popularity of DT can also be observed as it has been considered as a strategic technology trend for three consecutive years (2017-2019) by Gartner [61; 62; 63], future technology in the field of aerospace and defense by Lockheed Martin [64], and one of the defining technology of next decade by Forbes [65]. According to Juniper Research, by 2021, the global DT market was expected to be USD 12.7 billion [66]. The global DT market share, previously dominated by early adopters of the technology i.e., North America and Europe (>50%), is expected to expand in the Asia Pacific at a compound annual growth rate of 40.0% over the next 5 years [67].  
As the DT market is growing exponentially, every sector or business wants to invest in the technology. However, to leverage the DT technology for any sector, it is important to understand the value it can bring to the business. It is also beneficial to understand the different characteristics and types of DT in order to choose the correct type of DT that can complement the business and can provide maximum profits.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 6 Simulation Analysis
### Simulation Comparison
#### 6.4.2 Formation Obstacle Avoidance Method  
The sparse A * algorithm [30] was selected and compared with the formation obstacle avoidance algorithm proposed in this paper to illustrate the effectiveness of the algorithm. The simulation scenario used scenario 2 as given in Section 6.2. The sparse A* simulation results are shown in Figure 23.  
In order to analyze the complexity of the two algorithms, the number of UAVs in the formation was increased successively, and the two algorithms were used to simulate the formation obstacle avoidance simulation. The relationship between the algorithm running time and the number of drones was obtained as shown in Figure 24.  
Figure 23: The sparse A * algorithm for formation obstacle avoidance.  
Figure 24: The algorithm running time.  
We concluded from Figures 16 and 24 that the sparse A* algorithm was basically the same as the obstacle avoidance path planned by the algorithm in this paper, but the running time of the sparse A* algorithm was much longer than that of the algorithm in this paper, and it doubled with the increase in the number of UAV formations. However, the obstacle avoidance algorithm proposed in this paper based on geometry had a shorter running time and did not change significantly with the increase in the number of UAVs. It could meet online real-time trajectory planning, and has strong engineering applicability.
## 2 The Definition of Initial Damage  
The indicators that are defined in rock damage mechanic variables macroscopically have multiple parameters such as density, resistance, elastic modulus, tensile strength, yield stress formula, elongation, ultrasonic velocity, and acoustic emission [23]. The determination methods of tensile strength, yield stress and elongation cause secondary damage to rock, and the damage degree measured by density and resistance is different from the fact. The method of elastic modulus and longitudinal wave velocity to represent initial damage is similar, and the ultrasonic testing methods use the longitudinal waveform to obtain the internal crack information of the rock [44]; these belong to non-destructive testing technologies and have little effect on the material's original state. The damage variable is defined as [45]:  
\[D=1-\left(\frac{v^{\prime}}{v}\right)^{2} \tag{1}\]  
where \(D\) is the longitudinal wave velocity of the rock, \(v\) is the initial wave velocity (m/s), and \(v^{\prime}\) is the faulted condition wave velocity (m/s).  
The non-destructive state of rock under different geographical conditions has no uniform standard. Therefore, the non-destructive state of the research rock should be analyzed according to the formation conditions; in order to better understand the non destructive state, a quantitative definition of non-destructive state wave speed determined from the drilling condition of rock is necessary. The non-destructive initial wave velocity is defined as the maximum value of the test results in relation to the statistical analysis and is given by [PERSON] [46]. The statistical formula for the longitudinal wave velocity is applied here and is defined as:  
\[V_{0}\geq\max\Bigl{(}v_{0}^{1},v_{0}^{2},\ldots v_{0}^{i}\Bigr{)} \tag{2}\]  
where \(v_{0}^{i}\) is the longitudinal wave velocity of the rock in the \(i\) times experiment (m/s), and \(V_{0}\) is the longitudinal wave velocity (m/s).  
Based on the initial damage formula proposed in the above analysis, the rock sample's longitudinal wave velocity test is conducted. Moreover, the mechanical characteristics of limestone with different initial degrees of damage under the static and dynamic compression test are tested.
## 2 Mathematical Model and Inversion Method
### Mathematical Model
\(\varphi,\theta,r\) represent the longitude, geocentric colatitude, and geocentric distance, respectively, in the cone coordinate system. \(P_{n_{k}}^{m}(\cos\theta)\) is the associated Schmidt quasi-normalization Legendre functions of real degree \(n_{k}\) and integer order \(m\). \(K_{p}^{m}(\cos\theta)\) is the Mehler functions. \(P_{0}^{m}(\cos\theta)\) is the particular form of the \(K_{p}^{m}(\cos\theta)\) function when \(n\leq m\)_and_\(n=0\). \(R_{p}(r)\) are the radial functions. \(R_{0}=\sqrt{a}/\sqrt{b-a}\) is the particular form of the \(R_{p}(r)\) function when \(n=0\)[35]. \((G_{n_{k}}^{i,m},H_{n_{k}}^{i,m},G_{n_{k}}^{e,m},H_{n_{k}}^{e,m})\) are the Legendre coefficients, and \((G_{p}^{m},H_{p}^{m},G_{0}^{m},H_{0}^{m})\) are the Mehler coefficients. \(k\), \(p\) are integers that correspond to the sequence number of roots \(n_{k},n_{l}p\) Superscripts \(i,e\) of the Legendre coefficient refer to the components of the internal and external source field. Achieving the separation of the internal and external field sources is more difficult when the R-SCHA algorithm is used with the aid of the boundary conditions of cone \(\Omega\) compared with spherical harmonic analysis. Therefore, careful selection and model correction are conducted on the satellite data to exclude the interference of external source fields as much as possible. The component of the external source field introduced in the algorithm represents neither the ionosphere nor the magnetic field. Instead, it adopts two groups of Legendre coefficients to fit the distribution form of the geomagnetic field in cone \(\Omega\). According to radial function form \((r/a)\) corresponding to the Legendre coefficient \((G_{n_{k}}^{e,m},H_{n_{k}}^{e,m})\) in the formula (2), we can infer that \((G_{n_{k}}^{e,m},H_{n_{k}}^{e,m})\) mainly describes the contribution of the geomagnetic field in the middle and upper space altitude layers and several long wavelength components of the geomagnetic field in the earth's surface space. After testing, [PERSON] et al. found that achieving the best fit is difficult using the model if the component of the so-called external source field was removed from the algorithm, but the independent component of the internal source field was only used even if the observation data was distributed in the lower bottom boundary of the spherical cap. The fitting effect improves significantly if the component of the external source field is added to the Legendre function [36; 37].