esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
###### Abstract  
Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1 um. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke.  
fire detection; particle size distribution; smoke characterizing +
Footnote †: journal:  
According to the Mie theory, the particle size distribution of aerosols is one of the most important influencing factors of their optical characteristics [19; 20; 21; 22; 23]. Therefore, it is essential to study the particle size distributions and the differences of typical fire smokes and non-fire aerosols for developing fire detectors with higher alarm accuracy. [PERSON] et al. measured the particle size distribution and rise rates of typical interference aerosols during sanding wood, grinding red brick, blowing cement, and so on [24; 25]. On the other hand, the particle size distributions of standard fire smokes are characterized by [PERSON] et al. using SMPS (Scanning Mobility Particle Size, TSI SMPS-3936, TSI Incorporated, St. Paul, MN, USA) [26; 27; 28]. A further study to extract the difference between fire smokes and interference aerosols needs to be conducted.  
In this paper, an aerosol particle-sizing platform is set up to study the particle size distribution of both typical fire smoke and non-fire aerosols. Thus, the particle size distributions of various typical fire smoke and non-fire aerosols are measured. By combining the experimental data and the related literature, this paper analyzes the differences in the particle size distribution between fire smoke and non-fire aerosols, which provides the basic knowledge for fire smoke identification.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results  
The obtained results from the simulation experiments using various aspects of different scenarios are discussed in this section. The simulation trace file is the subject of our in-depth investigation. RadioBearerStatCalculator from NS-3 is a trace sink that generates the number of transmitted PDUs (Protocol Data Unit), received PDUs, transmitted bytes, received bytes, and other PDU statistics for uplink users.
## 4 Results and Discussion
### Classical Machine Learning Pipeline  
Using the extracted 3D radiomic features, an XGBoost classifier was optimized. The model resulted in an AUC of 0.853 in the internal test set. Shows a higher performance in comparison with the 3D RF model, where the AUC was 0.836.  
The obtained results showed that the XGBoost classifier is capable of achieving a better performance in comparison with the RF when constructed based on a high number of features (2525 features) compared with the number of training samples (292 samples after class balancing using SMOTE). It is important to note that the performance of the ML models can be affected by the high dimensionality of the data space [22].  
Comparing the radiomic features's importance in the 3D XGBoost classifier, the impact of the textural features and the shape-based features is higher when compared with the 3D RF classifier (see Table 9). In addition, looking at Figure 8, one can see that individual feature importance, for the most important features, is higher in the 3D XGBoost classifier when compared with the 3D RF classifier. This results from the XGBoost giving more weight to features that are used in early splits, as these splits affect more samples and reduce the loss significantly; on the other hand, the RF builds trees independently, resulting in a more even spread of feature importance.
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 3 Results
### Effect of the Concentration of Hydrogen Peroxide on Degradation  
Hydrogen peroxide is the main hydroxyl radical provider, so its initial concentration will naturally affect the degradation efficiency. An appropriate amount of hydrogen peroxide was added to the solution. The obtained results are shown in Figure 2. When the concentration of H\({}_{2}\)O\({}_{2}\) is between 50 mM and 100 mM, the higher the concentration of H\({}_{2}\)O\({}_{2}\), and the faster the [C\({}_{4}\)Bth]PF\({}_{6}\) degrades. When the concentration of H\({}_{2}\)O\({}_{2}\) is in the range of 100 mM to 150 mM, the concentration of H\({}_{2}\)O\({}_{2}\) continues to increase and the degradation yield decreases. [C\({}_{4}\)Bth]PF\({}_{6}\) has the fastest degradation rate when the concentration of H\({}_{2}\)O\({}_{2}\) is 100 mM and the half-life of ionic liquids is 12 min. Continuing to increase the concentration of hydrogen peroxide does not always increase the yield of degradation because excessive concentration of hydrogen peroxide often leads to the \"automatic scavenging reaction\" as shown in the following equations (Equations (2)-(5)), which leads to the removal of oxidized hydroxyl radicals [30].  
\[\mathrm{H_{2}O_{2}+hv\to 2\,OH}, \tag{2}\]  
\[\mathrm{H_{2}O_{2}+`OH\to HO_{2}`+H_{2}O}, \tag{3}\]  
\[\mathrm{HO_{2}+`OH\to O_{2}+H_{2}O}, \tag{4}\]  
\[\mathrm{HO_{2}`\to O_{2}`+H^{+}}, \tag{5}\]  
Therefore, considering both degradation efficiency and economy, a suitable initial concentration of hydrogen peroxide is 100 mM.
## 5 Application of ZPLTS-ELECTRE II Method in Ontology Ranking
### Discussion
#### 5.4.4 Weaknesses and Limitations of ZPLTS-ELECTRE II  
The ZPLTS-ELECTRE II method has some weaknesses and limitations as follows:  
1. The different decision-makers are assigned equal weighting, and the final decision matrix is composed by combining the individual decision-makers' evaluation matrices with equal importance given to all decision-makers. This may not be the case in some decision-making problems.
2. The ZPLTS-ELECTRE II method is dependent on the decision-makers for the specifications of the thresholds and parameters, such as the criteria importance weights, the concordance thresholds, and the discordance thresholds.
3. The ability of the ZPLTS-ELECTRE II method to model both linguistic and numerical data has the implication of individual discordance thresholds for each criterion. This is an advantage as it expands the method's capability of modeling decision-problems. However, when comparing the method to other ELECTRE II methods, ZPLTS-ELECTRE II has the disadvantage of having a larger number of discordance thresholds to be analyzed and defined as opposed to other methods that may only require a smaller number of discordance thresholds.
## 6 Numerical Simulation, and Experiment  
Here, the proposed SHANM-based DoA estimation method is evaluated and analyzed against the \(l_{1}\) norm dependent approach in [51], the SH-ESPRIT in [52], TSDA (elevation estimations with U-SH-ESPRIT and azimuths estimation with U-SH-RMUSIC) in [53], CV-VSBL in [54] and Cramer-Rao bound (CRB) in [55]. Comparisons were also made among the methods using real measurement data from the experiment, which in the end, is the acceptable ground to test any procedure.  
Firstly, an SAA with radius, \(r=1.8\) cm, and made up of 32 radiators operating at 8.4 GHz (Figure 1) that are uniformly positioned on a rigid sphere, is utilized during numerical experiments or simulations [6; 8; 11]. The range between two consecutive samples in the uniform sampling framework remains constant. The uniform sampling framework gives rise to small platonic solids. The event appears majorly for a specific number of elements [6]. The highest order of the SAA is 4. A total of 600 different Monte Carlo simulations were conducted using Matlab software (2019b version) operating on a laptop personal computer (PC); Intel CPU, Core i7-8565U, 8 th Gen., RAM 16 GB, 1 Terabyte. We employed two iterations in the simulations. To avoid aliasing problems, \(kr\) was maintained to be smaller than order \(N\). In addition, narrowband amplitude modulation (AM) waves at far field were employed in each simulation case. Both the SHANM approach and SH-ESPRIT are referred to as SH-ANM-ESPRIT. The approaches that are based on discretion sparsity estimated DoAs via searching for the \(l\) biggest spectrum peaks using \(0.2^{\circ}\) grid interval. The performance metric used was the root mean square error (RMSE), and it is expressed as  
\[RMSE=\sqrt{\frac{1}{M!}\sum_{i=1}^{I}\sum_{j=1}^{M}\left(\hat{\theta}_{ij}- \theta_{i}\right)} \tag{39}\]  
where \(M\) is the number of simulation trials, and \(I\) is the number of source. \(\hat{\theta}_{ij}\) and \(\theta_{i}\) represent the estimates and the true value of the \(i\)th DoA at the \(j\)th attempt.
## 6 Conclusions  
In this paper, a practical design method of high-performance square-loop and circular-loop FSS filters for use of mm-wave imaging applications is presented. The filters consist of periodic loop structures fabricated on both sides of the thin dielectric substrate. The effects of design parameters on the performance of the FSS filters are discussed and analyzed. Also, a comprehensive comparative study on the performance of square- and circular-loop FSS structures at mm-wave frequency range in the presence of fabrication tolerances is presented. With a high-accuracy fabrication, the square-loop and circular-loop FSS filters perform equivalently. In the presence of appreciable fabrication tolerances, however, it is concluded that the circular-loop based FSS filter produces more consistent, predictable and accurate results. As a design example, square-loop and circular-loop FSS filters resonating at 105 GHz were deigned, fabricated and measured. The circular-loop FSS filter resulted in more accurate and consistent performances and is proved to be a useful configuration when a large-size FSS structure in required, for example, for protection of sensitive receiver electronics of mm-wave imaging systems.  
[PERSON] performed the study, interpreted the data and drafted the manuscript. [PERSON] and [PERSON] helped to perform measurements. [PERSON] and [PERSON] critically revised and finalized the intellectual contents of the manuscript.  
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF - 2017M1A7A1A03064220). This study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011). The authors declare no conflict of interest.
# Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm
## 3 Results and Discussion
### Experiment Setup  
The experimental platform is shown in Figure 6, where a M4313M2B force/torque sensor was mounted on the end effector of manipulator UR10 and used to collect raw force/torque data from the environment. Notice that the raw force/torque data were processed with a gravity compensation algorithm to obtain the real contact force and torque. Furthermore, the peg was mounted on the force sensor, and the hole was fixed on the experimental table. The diameter and length of the peg were 10 mm and 40 mm, respectively. The diameter of the hole was 13 mm.  
To verify the validity of the variable stiffness admittance control based on the RL algorithm, a series of experiments for the peg-in-hole assembly was carried out. In the first experiment, the assembly task was realized by using position control. Then, the compliance assembly experiment using fuzzy control was implemented in the second experiment. Finally, compliance assembly experiments based on the SARSA and DDPG algorithms were performed separately. In all experiments, the targets of the assembly tasks were to push the peg into the expected position into a hole. Before the assembly, the peg was positioned randomly near the hole, where the rotation angle \(\theta\) was set from 5 to 10 degrees (the angle errors caused by positioning were usually less than 5 degrees). In the assembly process, when the depth was larger than 30 mm and the force value was less than 2 N, the task  
Figure 5: The training process of DDPG algorithm.  
was realized successfully. Obviously, the insertion depth could be used to indicate the assembly success rate. Meanwhile, we also focused on the assembly efficiency, where fewer adjustment steps meant a higher assembly efficiency. Furthermore, the cumulative reward could reflect the assembly effect well. Therefore, the reward, steps, and depth were selected to evaluate algorithm performance in this article.  
Furthermore, the action selection during the initial training stage had strong randomness in the reinforcement learning. Thus, the velocity and acceleration of the robot were limited (0.001 m/s\({}^{2}\)) to ensure the assembly's safety in the experiment. Additionally, the contact force threshold was set to 40 N. Moreover, in each adjustment step, the maximum translation offset along the \(X_{1}\)-axis and \(Y_{1}\)-axis was set to 0.001 m, and the maximum rotation degree was set to 5 degrees.
# Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling
## 3 Simulation Validation
### Network Architecture Definition  
The 3D workspace of the IRB140 robot is shown in Figure 5, where the black scatter pattern is the J2 space, the red dots represent the J3 workspace, and the blue region describes the J4 space. It is noted that the complete workspace is reduced since the J1 axis is constrained within the frontal region at [\(\pi/2\); \(-\pi/2\)] range from the Home-pose.  
Following the proposed methodology, the ANN architectures with the GA algorithm is defined using Matlab Parallel Computation to reduce the computational time. The GA optimization includes a set of hyperparameters for the ANN creation and training: \"scaled conjugate gradient\" as the training function and the MSE as the performance coefficient. In Table 4, the main inputs for the generated models are presented. In particular, the adopted technique allows the optimized mix network to obtain the best configuration of the number of layers and number of neurons for each joint.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**IRB140**} \\ \hline
**Joint** & \(\theta_{i}\) **[rad]** & \(d_{i}\) **[mm]** & \(a_{i}\) **[mm]** & \(\alpha_{i}\) **[rad]** \\ \hline
1 & 0.0 & \(3.52\times 10^{2}\) & \(7.00\times 10^{1}\) & \(\pi/2\) \\
2 & \(\pi/2\) & \(2.18\times 10^{-8}\) & \(3.60\times 10^{2}\) & 0.0 \\
3 & 0.0 & \(2.95\times 10^{-7}\) & \(8.94\times 10^{-7}\) & \(\pi/2\) \\
4 & 0.0 & \(3.80\times 10^{2}\) & \(-3.51\times 10^{-6}\) & \(-\pi/2\) \\
5 & 0.0 & \(-4.50\times 10^{-7}\) & \(2.34\times 10^{-6}\) & \(\pi/2\) \\
6 & 0.0 & \(6.50\times 10^{1}\) & \(-4.45\times 10^{-7}\) & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: D-H parameters for the IRB140.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Robot** & **Case** & **MEE [\(\upmu\)m]** & **Low** & **High** \\ \hline \multirow{4}{*}{IRB140} & Circle & 0.015 & +9.0\% & +2.0\% \\  & Spiral & 0.017 & +1.0\% & \(-8.0\%\) \\ \cline{1-1}  & S-Shape & 0.014 & \(-2.0\%\) & \(-5.0\%\) \\ \cline{1-1}  & Rectangle & 0.013 & +7.0\% & +12.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: FK error—MEE in perturbed conditions.  
The mentioned settings show an offline computational time close to 70 h in comparison with the global approach that requires \(\sim\)30 h. The computational time influence is neglected in the present work due to its offline nature. The obtained network architecture from the robot base frame to the TCP is expressed as:  
\[\begin{array}{ccccc}I_{1}\left\{65&65&45&45&15\right\}\\ I_{2}\left\{65&65&60&55&15\right\}\\ I_{3}\left\{35&50&70&25&35&25\right\}\end{array}\]  
The network architecture from the robot TCP to the base frame is defined as:  
\[\begin{array}{ccccc}I_{1}\left\{70&65&65&35&40&20\right\}\\ I_{2}\left\{75&75&10\right\}\\ I_{3}\left\{45&50&55&55&35&50&30\right\}\end{array}\]
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.3 Antenna Selection  
Though phased arrays, with sometimes metasurface combinations, dominate the NTN satellite applications, several innovative designs employed come to solve challenges via elegant ways. Some novel designs addressing these challenges are the metasurface loaded mesh reflector (ref. [118]), waveguide antennas (refs. [119; 123]), lens antennas (ref. [120]), microstrip patch antennas (refs. [124; 126; 127; 128; 129; 130]), phased array antennas (ref. [122]), and uniform rectangular arrays (URAs) (ref. [125]).
## 2 Description of the Measurement System
### Motor Test Bench
The torque measured by the sensor is linearly proportional to its output voltage, and it is 200 Nm at 5 V (Figure 5). Two shunt resistors (Figure 6) with voltage drops of 60 and 75 [mV] at 100 and 300 [A] nominal current intensities are used for measuring the intensity of the electric current flowing through the windings of the analyzed motor. Thus, the maximum current intensities we can measure safely are 100 and 300 [A], respectively. Figure 4: The optical LED sensor and its output voltage signals. Figure 5: The rotary torque sensor and its output voltage signal. The NI 9239 voltage input module has four channels with an allowed input voltage range of \(-10\) V to \(10\) V for each channel. If the voltage to be measured is outside this range, a voltage divider with a division ratio of 1:11 is used. The module's maximum sample rate and analog input resolution are 50 kHz and 24 bits, respectively. The module has an analog input isolation on a channel-to-channel basis; thus, the entire system is protected from harmful voltage spikes up to the isolation rating. In addition to safety, isolation eliminates measurement errors caused by ground loops because the front end of the module is floating. The voltage input module is connected to the PC through an NI USB-9162 device (National Instruments, Debrecen, Hungary), which is a Single-Module Carrier Compact DAQ Chassis. The self-developed software component of the MS is implemented in NI LabVIEW, Java SE and VBA with an underlying MySQL database. Applying the software component, the measured quantities can be monitored in real time, displayed graphically, and stored in a database for later use. A detailed description of the software component can be found in Reference [25].
###### Abstract  
In this work, the effect that two basic air quality indexes, aerosols and tropospheric NO2 exert on surface solar radiation (SSR) is studied, along with the effect of liquid and ice clouds over 16 locations in Greece, in the heart of the Eastern Mediterranean. State-of-the-art satellite-based observations and climatological data for the 15-year period 2005-2019, and a radiative transfer system based on a modified version of the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model are used. Our SSR simulations are in good agreement with ground observations and two satellite products. It is shown that liquid clouds dominate, with an annual radiative effect (RE) of \(-36\) W/m\({}^{2}\), with ice clouds (\(-19\) W/m\({}^{2}\)) and aerosols (\(-13\) W/m\({}^{2}\)) following. The radiative effect of tropospheric NO2 is smaller by two orders of magnitude (\(-0.074\) W/m\({}^{2}\)). Under clear skies, REaer is about 3-4 times larger than for liquid and ice cloud-covered skies, while RENO2 doubles. The radiative effect of all the parameters exhibits a distinct seasonal cycle. An increase in SSR is observed for the period 2005-2019 (positive trends ranging from 0.01 to 0.52 W/m\({}^{2}\)/year), which is mostly related to a decrease in the aerosol optical depth and the liquid cloud fraction.  
surface solar radiation; aerosols; clouds; tropospheric NO2; Greece; SBDART; MODIS; CALIPSO; CM SAF; CERES Article
## 4 Simulation Results
### Thermal Noise  
Figure 11 shows the phase jitters of the slave loop induced by thermal noise. In the simulation, the carrier noise ratio of the signal received from the master antenna is set to be 45 dB-Hz, while the signal received from the slave antenna varies from 10 dB-Hz to 40 dB-Hz. The master loop is a 3 rd order loop  
Figure 10: Phase jitter induced by oscillator vibration.  
with a bandwidth of 15 Hz, while the slave loop is a 2 nd order loop with bandwidth of 0.5 Hz. The coherent integration time in both loops is 10 ms. As shown in Figure 10, the phase jitter of the slave loop with the new structure is larger than that in the independent loop, since extra noise originates from the assistance from the master loop. This phenomenon becomes more apparent when the C/N\({}_{0}\) values of the two loops are close, and become less apparent when the difference of the C/N\({}_{0}\) values is larger. This can be explained by Equation (20). Given that the C/N\({}_{0}\) of the master antenna signal is firmed and high enough, when the C/N\({}_{0}\) of the salve antenna is low, the error mainly depends on the slave loop. However, when the C/N\({}_{0}\) of the salve antenna signal is high enough, the total error variance is about two times of the error of an independent loop.  
The differential phase jitters of the two structures are shown in Figure 12. It can be clearly seen that the differential phase jitter with assisting tracking loop is smaller than the one in the independent loop, especially when carrier noise ratio values of both loops are similar. That is because of the second item of Equation (22) dominants under this condition, compared to that in the independent loop structure. While, the differential phase jitter mainly depends on the slave loop error with the  
Figure 11: Phase jitter of slave loop output induced by thermal noise.  
Figure 12: Phase jitter of differential phase output induced by thermal noise.  
decline, resulting in a similar performance between the two structures. Due to analysis above, however, it should be highlighted that a very small bandwidth such as 0.5 Hz cannot be used in the independent loop due to the satellite motion caused by dynamic and TCXO instability as described above.