esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implementation and Evaluation  
### Implementation of Each Device  
Figure 6 illustrates the smart devices used in the proposed device network, and Figure 7 illustrates the hardware and communication block diagrams. We developed the tag device for sensor data in this study. The stationary and wearable devices were developed for our previous research and have been customized for this study. The stationary device--containing a passive infrared (PIR) sensor and an LF transmitter--is connected to two LF antennae to emit LF signals containing indoor position information. The wearable device includes an LCD, flash memory, and an LF receiver. The wearable device is in sleep mode and wakes up when it receives an LF signal emitted by the stationary device installed in the door of the detection zone; it then starts preparing the ADL recording. The tag device includes sensors for ambient light, acceleration, gyro, flame detection, and temperature and can be attached to household items to measure their usage. The use of an appliance that has an opening or closing operation such as a refrigerator, rice cooker, washing machine, or faucet is detected by the tag device using an accelerometer and gyro sensors. The flame detection sensor and temperature sensor are used to check the use of a gas stove, and the ambient light sensor and temperature sensor can determine the state of a lamp. All devices (wearable, tag, and stationary) have a BLE module to communicate with each other wirelessly. The stationary device operates centrally in BLE communication, whereas the tag and wearable devices operate peripherally. The stationary device drives one-to-many connections with multiple tag devices and configures and manages the edge network of a unit space. It also recognizes a specific family member's entrance into the space by transmitting LF signals and receiving a BLE advertising message from the wearable device.  
Figure 6: Implementation of the three types of portable devices.  
Figure 7: Hardware and communication block diagrams (wearable, tag, and stationary devices).
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 5 Numerical Experiments
### Experiment Settings
#### 5.1.1 Instances Generation and Parameter Setting
Therefore, the parameters \(N\ =\ 100\), \(p_{a}\ =\ 0.25\), \(Iter\ =\ 300\) are chosen in this paper to ensure the efficiency and stability of the algorithm. For the parameter settings for the rest of the algorithm, refer to References [46; 47]. Specifically, the NSGAII algorithm has a crossover rate of \(GAcr\ =\ 0.8\) and a mutation rate of \(GAmr\ =\ 0.8\); the MOPSO algorithm has an inertia weight of \(PSOw\ =\ 0.8\), a learning factor of \(PSOc1\ =\ 2\), and a learning factor of \(PSOc2\ =\ 1\); and the SPEA2 algorithm has a crossover rate of \(SPcr\ =\ 0.8\) and a variance rate of \(SPmr\ =\ 0.2\). All algorithms were tested on the same problem instance and initial conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **CPU Runtime (s)** & \\ \cline{2-6} \(p_{a}\) & \(N\) = 60 & \(N\) = 80 & \(N\) = 100 & \(N\) = 120 & \(N\) = 140 \\ \hline
0.15 & 461.24 & 503.37 & 522.28 & 660.90 & 853.93 \\
0.20 & 480.20 & 557.38 & 544.78 & 636.45 & 846.06 \\
0.25 & 527.75 & 543.71 & 535.50 & 681.12 & 865.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CPU runtime with different parameters. Figure 5: Parameter sensitivity analysis of the IMOCS algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & \(I_{i}\) & \(m_{i}\) & \(q_{i}^{\text{min}}\) & \(q_{i}^{\text{max}}\) \\ \hline Feeder & \(U[8,21]\) & \(U[5,15]\) & 1 & 2 \\ Medium & \(U[21,30]\) & \(U[15,50]\) & 2 & 4 \\ Jumbo & \(U[30,40]\) & \(U[50,65]\) & 4 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specifications for the different types of vessels.
## 2 Theoretical Insight
### Introduction of Axial Force in Beams  
Initial axial effort is now introduced in the previous model. If it is tensile, it increases the bending stiffness, whereas if it is compressive, it lowers it, leading to buckling instability. Only compression is considered. In the case of axial compression, the force term contributes to the balance of moments, and the Euler beam equation is supplemented by the second order spatial derivative term associated with the force \(P\) as  
\[E\frac{\partial^{4}u}{\partial x^{4}}-P\frac{\partial^{2}u}{\partial x^{2}}= \rho S\omega^{2}u \tag{20}\]  
for which the solution for the transverse displacement is now  
\[u(x,\omega)=C_{1}\sin(k_{1}x)+C_{2}\cos(k_{1}x)+C_{3}\sinh(k_{2}x)+C_{4}\cosh( k_{2}x) \tag{21}\]  
with \(k_{1,2}\) as the bending wavenumbers  
\[k_{1,2}=\pm\frac{1}{\sqrt{2 EI}}\left(-P\pm\sqrt{P^{2}+4E\mu S\omega^{2}}\right) ^{1/2} \tag{22}\]  
From this solution form and considering the same boundary conditions as in Section 2.4, the analytical description of the structure is achieved, although equations become lengthy and are not presented here. In addition to the previous case, Figure 7 shows the isofrequency line as the axial load increases, resulting in the lowering of eigenfrequencies. Augmenting the axial load in the beam from 0 to 0.14 \(P_{crit}\) shows the frequency shift in the fundamental frequency to the low frequencies. For a given mass ratio, decreasing the stiffness ratio makes it possible to lower the fundamental frequency of the resonator. In this context, this effect is obtained by the axial compression of the beam rather than by decreasing the positive stiffness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{\(m_{b}/m\)} \\ \hline  & **2** & **1** & **0.5** & **0.1** \\ \hline Analytical & 1.92 & 1.72 & 1.50 & 1.04 \\ Finite element & 1.92 & 1.72 & 1.49 & 1.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: First root \(kl/2\) of Equation (17) calculated analytically and by finite elements as a function of the ratio \(m_{b}/m\).
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### The Need for CR-Based UAVs
#### 4.1.4 Application Requirements  
UAVs may often be deployed in missions where they are expected to broadcast live video and to send high definition pictures to the base station. However, live stream broadcast tolerate packet loss, but require a high bandwidth for the timely data delivery. Sending high definition pictures is the opposite operation, as the UAV may need a low bandwidth with no packet loss tolerance, but with delay tolerance [9].  
While traditional UAVs may face this problem, CR-based UAVs may easily deal with it. CR-based UAVs may change their communication frequency accordingly to application requirements. Thus, a UAV equipped with CR technology may be live streaming video and switch to a low bandwidth to send a large file when required. This feature not only optimizes the overall network performance of CR-based UAVs, but it also opens new application opportunities for these aircraft. For instance, they can be used to improve the communication performance of terrestrial users in 5G, through the UAV-assisted 5G communications [32].
## 2 Materials and Methods
### Determination of Volatile Compounds
#### 2.4.2 HS-SPME-GC-MS  
The study made a slight modification to the method of [PERSON] [24]. The cooked embryo rice (2.00 g) was transferred into a 20 mL (22.5 \(\times\) 75 mm) glass headspace vial (Gerstel, Baltimore, MD, USA). The headspace bottle was placed in a constant-temperature heating device and maintained at 40 \({}^{\circ}\)C for 5 min to achieve a distribution balance between the sample matrix and the headspace phase. Then, the solid-phase micro-extraction fiber head (50/30 \(\upmu\)m DVB/CAR/PDMS, Supalco, PA, USA) was inserted and extracted at 60 \({}^{\circ}\)C for 40 min to adsorb the target volatile compounds in the overhead space. An ISQ series single-quadrupole (Thermo Fisher Scientific, Waltham, MA, USA) column gas chromatograph equipped with a DB-Wax capillary column (30 mm \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness; Agilent Technologies, Santa Clara, CA, USA) was used to separate the components at 40 \({}^{\circ}\)C for 5 min, followed by a gradient of 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C to ensure effective separation of the components. The temperature was held at 230 \({}^{\circ}\)C for 10 min, the solvent delay was set to 3 min, the inlet temperature was kept constant at 260 \({}^{\circ}\)C, helium with 99.999% purity was selected as the mobile phase, the flow rate was controlled to 1.0 mL/min, and the detection sensitivity was improved by adopting the non-shallow-flow injection mode. When the interface temperature was 260 \({}^{\circ}\)C, the ion source (EI) temperature was 200 \({}^{\circ}\)C, and the electron bombardment energy was 70 eV, scanning was carried out in the range of \(m/z\) 35.0-600.0, and the solvent delay time was 3 min to eliminate column loss interference.  
Qualitative analysis: After mass spectrometry deconvolution of GC-MS spectra, compounds with a matching degree greater than 75 were selected for analysis and compared with the NIST database. Under the same chromatographic conditions, the retention index of the target was calculated and compared with the NIST database RI to identify the target.  
Quantitative analysis: The relative content of each compound was calculated according to the peak area normalization method of each volatile compound.
## 1 Introduction
These algorithms are applied to three engineering structures of increasing complexity for damage detection: a forty-story shear building (updating 40 parameters), a two-span continuous steel beam (updating 30 parameters), and a steel pedestrian bridge (updating 15 parameters). The comparative study evaluates their performance in updating numerous uncertain parameters, sampling efficiency, and computational cost. This paper aims to provide guidance to less-experienced users and new researchers in the field of structural damage detection using BMU with advanced sampling techniques. The study serves as a valuable reference for understanding the concepts and practical implementations of these three MCMC algorithms. The main contributions of this paper are as follows:  
* Comprehensive Tutorial: We introduce a novel and comprehensive tutorial on three advanced MCMC algorithms (DRAM, TMCMC, and DREAM) tailored for the complex field of structural damage detection. This work not only presents these algorithms but also bridges the gap between theory and practical application in this specialized area. * High-Dimensional Parameter Updating: Our study is the first to effectively demonstrate the updating of a large number of uncertain parameters (up to 40) within the context of structural damage detection. This capability marks a significant advancement in the field, enhancing both the accuracy and resolution of damage identification, which is crucial for practical applications. * Open-Source Resources: All models, codes, and data from our study are fully open-source and accessible at [[https://github.com/Jice1991](https://github.com/Jice1991)]([https://github.com/Jice1991](https://github.com/Jice1991)) (accessed on 19 September 2024) in the respective repositories for DRAM, TMCMC, and DREAM. By providing these resources, we aim to empower other researchers to build upon our work, foster innovation, and expedite the development of new algorithms and methodologies in this domain. This paper is organized as follows. In Section 2, the background of Bayesian model updating and structural damage detection is presented. Section 3 elaborately introduces the three advanced MCMC algorithms (i.e., DRAM, TMCMC, DREAM). In Section 4, three typical engineering structure examples are used to demonstrate and compare the efficacy of damage detection with limited measurements by the three MCMC algorithms. Discussions of each MCMC's advantages and limitations are also included. Finally, Section 5 provides the conclusions.
# Nozzle with a Feedback Channel for Agricultural Drones
## 5 Conclusions  
In this study, an advanced nozzle for an agricultural drone with a feedback channel was developed. The spraying performance was evaluated in terms of the droplet size and uniformity and compared with that of a fan-shaped commercial nozzle. The droplet size reduced by at least six to eight times with the proposed nozzle, making it suitable for use in sprayers. Variations in the spraying amount and the corresponding FWHM indicate that the developed nozzle has a better spraying uniformity than the commercial nozzle. To investigate the effect of the feedback channel, the spraying performance of the nozzle without the feedback channel was further investigated. Although the non-feedback nozzle diameter was smaller than that of the commercial nozzle, the droplet size increased as the feedback channel was removed, regardless of the load condition. The droplet diameter of the non-feedback nozzle was not appropriate for use in sprayers. In addition, the spraying uniformity of the non-feedback nozzle was lower than that with feedback. Considering both the droplet size and spraying uniformity, it was confirmed that the 30 kg load condition is optimal because the FWHM is almost independent of loads exceeding 30 kg, while the droplet size increased with increasing load. In addition to spraying performance, including droplet size and uniformity, the proposed nozzle reduces the consumption of water and pesticide. The flow rate for the developed nozzle with a 50 kg load is 150 mL/min, which is significantly lower than that of the commercial nozzle (500 mL/min). The developed nozzle would help improve the spraying performance and contribute to expanding the drone market in agricultural fields.  
Conceptualization, S.K.K. and S.Y.J.; methodology, S.K.K. and S.Y.J.; validation, S.K.K., H.A., and J.W.M.; formal analysis, S.K.K.; investigation, S.K.K.; data curation, S.K.K.; writing--original draft preparation, S.K.K. and J.W.M.; writing--review and editing, S.K.K. and S.Y.J.; visualization, S.K.K., H.A., and J.W.M.; supervision, S.Y.J.; project administration, S.Y.J.; funding acquisition, S.Y.J. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a research fund from Chosun University, Korea, 2020.  
Not applicable.  
The authors declare no conflict of interest.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Measurement Repeatability Analysis  
In order to quantify the measurement repeatability, five independent samplings were performed on each scene. The calculated standard deviations \(\sigma\) are 28.9 m\({}^{2}\) (football field), 156.7 m\({}^{2}\) (dormitory area), and 547.8 m\({}^{2}\) (downtown area), respectively, and the corresponding coefficient of variation CV = 0.40 \(\sim\) 0.64%, which is lower than the industry standard limit. The Q-Q plot of the three types of areas is shown in Figure 15. Comparing the quantile of the measured value with the theoretical normal distribution, it can be seen that the scatter points are basically distributed along the y = x line. The Shapiro-Wilk normality test shows that a test statistic of W = 0.92-0.97 (critical value W \({}_{critical}\) = 0.762, \(n\) = 5, \(\alpha\) = 0.05), a significance level of \(p>0.1\), and assumes that the measured data obeys the normal distribution.  
Figure 14: Deji Square area test trajectory.  
Figure 13: The actual tread track in Deji Square. (**a**) Actual field scene (light rain); (**b**) Schematic satellite view of the urban center. Red line in-dicates the walking path during sampling.  
Figure 15: Q-Q plots drawn for three types of areas. (**a**) Football field (open area), (**b**) Dormitory zone (semi-urban), (**c**) Urban center (dense environment). Diagonal reference lines indicate expected normal distribution; closer point alignment implies better normality.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 1 Introduction
Section 3 is fully devoted to the development of an \(H_{\infty}\) control system for an industrial Comau manipulator axis. General control requirements are derived, and the Linear Matrix Inequality (LMI) technique is adopted to determine the controller. The simulation of motor and link responses shows the inability of the standard design procedure to damp link oscillations, due to the plant dynamics' cancellation within the control action, as discussed in Section 4. A new implementation approach is then proposed in Section 5, based on different definitions of the weighting functions, leading to robust and satisfying behavior of both the motor and the link. Section 6 reports experimental results that confirm the effectiveness of the proposed solution. Section 7 finally draws some conclusions and sketches possible future works.
## 5 Conclusions  
Inspired by the recognition capability of the human, Pattern Based Communication System (PBCS) follows the perceptual selectivity of a human being. When a human recognizes sounds, the brain keeps perceiving sounds in the native language while shutting down to the others. The trained brain can selectively focus on to chosen conversation among all other sounds even if its portion is much less than all other heard sounds at same time. In PBCS, the communication system selectively recognizes and recovers the communication signal in known patterns and just like the human brain it ignores the unknown patterns. The objective of PBCS is to optimize the data transmission performance under varying spectral and SNR limitations.  
In this study we proposed a signal construction method called SPEC for PBCS so that the user can construct band limited glossaries for different target data transmission rates and SNR values. The signal patterns (waveforms) are constructed according to proposed SPEC method and matched with the binary information in the transmitter in computer simulation and the laboratory tests. The distorted and received communication signal pattern is recovered by an Artificial Neural Network (ANN) implemented on a PC in both cases. The output is then concatenated by the bit sequencer to obtain the original information. During the laboratory tests, generated and distorted signals patterns are sampled in the baseband and processed on a scaled PC platform after digitalization. The low frequency laboratory measurements (Table 3) validate the simulation results when we neglect communication medium property related effects. As a result, SPEC method provides the necessary band limited and perceivable signals for pattern glossaries of PBCS and the proposed system offers advantages for variable communication performance requirements between noise redundancy and high link spectral efficiency.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 3 Methodology
### Resampling
#### 3.4.2 Oversampling  
Figure 1(b) shows the training sample generation process, which leverages oversampling techniques. We selected the three most-popular data augmentation algorithms [57] for synthetic security alert data generation: SMOTE [22], ADASYN [23], and SVMSMOTE [58]. The selected algorithm was adopted to generate synthetic positive samples, which were subsequently combined with the corresponding negative samples to prepare the training samples. The oversampling algorithms are described in detail below:  
* SMOTE utilizes the \(k\)-nearest neighbors (\(k\)-NN) algorithm to create synthetic samples. The method begins by choosing a random sample from the minority class. Subsequently, synthetic samples are generated by interpolating the line segments between the selected sample and its \(k\) nearest neighbors (NNs). It generates new synthetic samples close to the feature space between two sample pairs based on their local density and borders with the other class [59]. Synthetic samples were generated by taking the difference between the selected sample and its neighbor and multiplying this difference by a random number between 0 and 1, resulting in the random selection of a point in the line segment. This procedure broadens the decision region of the augmented class.
* ADASYN is a modification of SMOTE that uses a density distribution to determine the number of synthetic samples generated for each sample. A density distribution measure assigns more weight to samples that are more challenging to learn, forcing the learning algorithm to focus on these samples. In contrast to SMOTE, which generates the same number of synthetic samples for each point, ADASYN generates a different number of synthetic samples based on an estimate of the local distribution of chosen samples.
* SVMSMOTE is also an extension of SMOTE, which uses selective synthetic sample generation to determine which samples should be oversampled. In SVMSMOTE, the borderline area is approximated by support vectors after training an SVM classifier on the original training samples [60]. Synthetic samples are randomly created along  
Figure 2: Security alert training samples generation.  
the lines joining each support vector in the minority class with a number of NNs [58]. This method focuses more on where the data are separated into different classes, and more samples are synthesized away from the overlapping regions of different classes. The important parameters of the oversampling algorithms are listed in Table 1.
# Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization
## 4 Simulation Results
Figure 5: The probability distribution at 250 th sample time. (**a**) Visualization of the probability of values at ZX plane; (**b**) visualization of the probability of values at ZY plane. Table 3 shows the performance of each filter at a different velocity condition. The velocity of the vehicle was set at intervals of 10, from 60 km/h to 120 km/h. The performance of the filter changed slightly because of random environment effects. The RMSE change of noise shows that the vehicle position noise was almost the same at all velocity conditions. This suggests that both filters were in similar environments. From the RMSE of PF, it can be concluded that the previous basic particle filter estimation's mean RMSE is 11.002 m and the extended particle filter estimation's mean RMSE is 6.201 m. The estimation RMSE of PF decreased by about 43.64% compared to the previous PF. The final estimation result is shown in the right column of the PAUKF in Table 3. The mean of RMSE of the previous basic PAUKF was 10.295 m and that of the extended PAUKF was 2.696 m. Compared to the previous basic PAUKF, the extended PAUKF decreased the RMSE by about 73.81%. The extended PAUKF considered the geometry of perception, and improved the weight generation method and selection method. The extended PAUKF may therefore estimate the trajectory precisely. In order to figure out the performance of the extended PAUKF in a huge perception error environment, we changed the perception noise from N(0, 0.09) to N(3, 9) in the x, y, z-directions, meaning that the minimum error of relative distance of every feature and vehicle was always larger than 5.196 m. In this noisy environment, with a velocity of 60 km/h, the RMSE of the total RMSE of the extended PAUKF was 5.771 m. Since the measurement variances were not changed, the extended PAUKF still attempted to rely on the measurement from the PF. The algorithm should perform better if we tune the variance of the measurement matrix. This simulation provides a reference for the extended PAUKF in a huge perceived noise environment. The algorithm requires tuning of the hyperparameters before implementation in the real world.