esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## Appendix A Segment Merging  
The criterion used to select the candidates for merging is based on proximity of two segments and convexity of their union. Two segments \(C_{i}\) and \(C_{j}\) satisfy the proximity criterion if:  
\[\min(\mu_{ij},\mu_{ji})\geq-\tau_{prox,1},\]  
where \(\tau_{prox,1}=0.2\) is an experimentally determined threshold. The convexity of the union of two segments is evaluated by computing its convex hull and counting the outlier points, i.e., points that do not lie on the convex hull within a predefined threshold. The outlier percentage is defined by:  
\[\eta(C_{i},C_{j})=\frac{\big{|}C_{i}\cup C_{j}\big{|}-\big{|}inliers\big{(}C_ {i}\cup C_{j}CH(C_{i}\cup C_{j})\big{)}\big{|}}{\min\big{(}|C_{i}|,C_{j}| \big{)}},\]  
where \(|X|\) denotes the cardinality of a set \(X\), \(CH(X)\) denotes the convex hull of a set \(X\), and \(inliers(X,Y)\) denotes the inlier set. The inlier set contains all points \(p\in X\) that are close to the surface of a convex polyhedron \(Y\). Furthermore, \(p\in inliers(X,Y)\) only if the local surface in the vicinity of \(p\) is similarly oriented as the surface of \(Y\) in the vicinity of \(p\). This is evaluated by the following criterion based on the point-to-plane distance between \(p\) and the tangential plane of \(Y\) with the normal parallel to the local surface normal of \(p\):  
\[\max_{p^{\prime}\in Y_{Y}}(n_{p}^{T}(p^{\prime}-p))\leq\tau_{prox,2},\]  
where \(n_{p}\) is the local surface normal of \(p\), \(V_{Y}\) is the set of vertices of \(Y\), and \(\tau_{prox,2}\) is an experimentally determined threshold. In the experiments reported in Section 7, \(\tau_{prox,2}\) was set to 5% of the radius of the model's bounding sphere, where an approximate minimum bounding sphere was computed by the algorithm proposed in [35]. If \(\eta(C_{i},C_{j})<\tau_{new}\), then a new segment is created from the union of \(C_{i}\) and \(C_{j}\). If \(\eta(C_{i},C_{j})<\tau_{merge}<\tau_{new}\), then the new segment replaces \(C_{i}\) and \(C_{j}\), i.e., the two original segments are rejected. The values of thresholds \(\tau_{new}\) and \(\tau_{merge}\) used in the experiments reported in Section 7 were 0.2 and 0.05, respectively. This merging process was applied not only to the original segments, but also to the new segments created by merging the original segments. In that case, the candidates for merging were always one original and one new segment. If one candidate was an original segment \(C_{i}\) and another was a new segment composed of original segments \(C_{j}\), then the proximity and convexity criterion were evaluated for each pair of segments \((C_{i},C_{j})\).
# TSCH Multiflow Scheduling with QoS Guarantees:
## 2 Materials and Methods
In this case nodes 8, 9 and 10 transmitted all the flows assigned simultaneously to node 1, which acts as sink. The packet inter-arrival time were then observed. This allows us to know the behaviour of the node with flows of different priorities typical in the industrial sector. In TSCH the bandwidth depends on the amount of transmission timeslots in each slotframe, therefore, a lower number of timeslots in the slotframe increases the bandwidth. Since Orchestra and MSF by default send one packet per slotframe, a length of 7 timeslots will be used, as this is the minimum length configurable for the number of nodes without Orchestra over-allocating the cells. This slotframe size allows a higher bandwidth to adapt to the flow with the packet period of 70 ms. The parameters used in the simulations are summarised in Table 2.
# Using Imaging Techniques to Analyze the Microstructure of
## 5 Summary and Recommendations
These techniques provide high-resolution data on aggregate characteristics, detect anomalies or defects, and enhance understanding of microstructural behaviour under different loading conditions with acceptable accuracy. These imaging techniques can assist in developing more durable and long-lasting AC mixtures. These image techniques can be used in conjunction with traditional tests for AC mixtures to provide a comprehensive understanding of the behaviour of AC mixtures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Imaging Technique** & & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Digital camera imaging} & \(\bullet\) & High-resolution imaging & \(\bullet\) & No depth perception \\  & \(\bullet\) & Cost-effectiveness & \(\bullet\) & Subjectivity in image interpretation \\  & \(\bullet\) & Ease of use & \(\bullet\) & Limited quantitative analysis \\  & \(\bullet\) & Rapid data acquisition & \(\bullet\) & Sensitivity to lighting conditions \\ \hline \multirow{4}{*}{Scanning electron microscope (SEM)} & \(\bullet\) & High-resolution imaging & \(\bullet\) & Costly and complexity \\  & \(\bullet\) & Three-dimensional visualization of & \(\bullet\) & Complex sample preparation \\  & \(\bullet\) & the surface & \(\bullet\) & Vacuum requirements \\  & \(\bullet\) & Elemental analysis & \(\bullet\) & Limited sample size \\  & \(\bullet\) & Microstructural characterization & \(\bullet\) & Limitations on access to equipment \\ \hline \multirow{4}{*}{X-ray CT scan} & \(\bullet\) & High-resolution imaging & \(\bullet\) & Limitations on access to equipment \\  & \(\bullet\) & Three-dimensional visualization & \(\bullet\) & Radiation exposure \\ \cline{1-1}  & \(\bullet\) & Quantitative analysis & \(\bullet\) & Image artifacts such as noise, \\ \cline{1-1}  & \(\bullet\) & Material differentiation & \(\bullet\) & streaking, and beam hardening \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The advantages and disadvantages of each imaging technique. Imaging techniques can assist in initial screenings, quality control, and research, but certain performance-related parameters may still require validation via traditional testing methods. Writing-original draft, M.A. ; Writing-review & editing, H.S. ; Supervision, H.S. All authors have read and agreed to the published version of the manuscript. This research and APC was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), grant number 717754. Not applicable. No new data was created. The data in the article is based on the literature. Details of the data are available from the corresponding author. The authors would like to acknowledge Al-al-Bayt University--Jordan and the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this research. The authors declare no conflict of interest.
## 5 Conclusions  
The effectiveness of the numerical simulation approach combining discretizing and solving the Powell vortex sound equation with CFD is verified by experiment. Then the temporal distribution and evolution characteristics of acoustic field inside a centrifugal pump are revealed, and various operation condition study is conducted. The conclusion of this study is listed as follows,  
1. In the centrifugal pump, the acoustic source and acoustic pressure are less than 0 basically, and the minimum value of the two parameters is distributed near the solute tongue. Additionally, the concentration, break, extend, migration and reaggregation phenomena of the minimum acoustic source region exist during the fluid motion process. Specifically, the minimum acoustic source region concentrates on the tongue firstly, then extends and migrates downstream slightly, and aggregates again near the tongue, which leads to the similar evolution characteristics of acoustic pressure. Moreover, the distribution and fluctuation characteristics of the two parameters have little change with increasing rotational speed, while the minimum value of the two parameters decreases and the fluctuation range extends generally. The acoustic source distribution and evolution are the fundamental causes of the acoustic pressure distribution and evolution.
2. The maximum standard deviation (STD) of acoustic source inside the impeller is located near the pressure side of blade tail, while that inside the volute is located near the volute tongue, which is also the region with the largest STD of the whole pump. Compared with the STD of acoustic source component induced by the stretching of the vortex caused by change of velocity (Source-1), the STD of acoustic source component induced by the non-uniformity of fluid kinetic energy (Source-2) is closer to that of overall acoustic source, which is the main noise source. Take the volute tongue as an example, at different rotational speeds, the STD of Source-1 and Source-2 accounts for about 5% and 95%, respectively, which shows that the change of operation condition has little effect on the proportions of different acoustic source components.  
Briefly, the numerical simulation approach is suitable to calculate the parameters of flow and acoustic fields simultaneously, then the generation, distribution and evolution characteristics of acoustic field are revealed. The study lays a foundation to analyze the propagation mechanism of acoustic field in centrifugal pumps.  
Figure 16: The STD ratios of different acoustic source components under various rotational speeds.  
**Author Contributions:** [PERSON] conducted the simulation and experiment, and wrote the manuscript; [PERSON] and [PERSON] reviewed and edited the manuscript.  
**Funding:** This research was funded by Key Research and Development Project of Shandong Province, grant number 2019 CSF109084 and Young Scholars Program of Shandong University (YSPSDU), No. 2018 WIJH73. And the APC was funded by Key Research and Development Project of Shandong Province.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Indoor Positioning Systems
### Systems Based on Sound
#### 3.3.2 Inaudible Sound  
Sound signals considered inaudible to the human ear are emitted in bands below or above the audibility threshold. The human ear can perceive sounds between 20 and 20,000 Hz. Signals with a frequency below 20 Hz are called infrasound, and those with a frequency above 20,000 Hz are called ultrasound.  
Indoor infrasound location systems are not quite common for several reasons, such as the cost and complexity of devices that work at this frequency. For example, [PERSON] explored the infrasound model to authenticate the identification of users in indoor environments by speech [25]. The authors described the difficulty in finding devices that work at this frequency since most available solutions are in the human audible range or above (ultrasound). For this reason, the authors described a way to obtain ultra-low frequencies of standard recordings generated by smartphones through acceleration-based cepstral features. To validate the experiment, the authors mapped more than 30 locations on a fingerprinting map. The level of accuracy of the system was over 90% in identifying the assigned areas.  
Ultrasound location systems are quite common in automation approaches and have several hardware options available at a low cost. An evident advantage of ultrasonic signals over audible sound signals is that they are not detectable by humans [97]. [PERSON] et al. used ultrasonic signals to determine the location of targets in indoor environments [97]. This study indicated that the model is limited by the need to establish a direct line of sight between the sensor and the monitored target. Even so, the results obtained in the experiments indicated a maximum error of 1.00 m only for the ultrasonic transducer and 0.35 m when the ultrasonic sensor was associated with a TDoA model, which explored the ultrasonic reflections of the sites. [PERSON] led a study group on the use of ultrasonic sound and infrasound as tools to enable the identification of positions of places and people [4]. The experiments indicated an accuracy level of almost 0.01 m in the direct line of sight between the sensor and the monitored target.
## 3 Results and Discussion
### Tin Layer Chromatography (TLC)
The IRL then started to decay from the sixth (515-640) nm band-pass filter to the seventh (545-675) nm band-pass filter, and from the eighth to tenth band-pass filters (585-720 nm, 605-730 nm, and 645-800 nm), no luminescence could be observed. On the other hand, the D1 dye had strong IRL under excitation using the first filter to the tenth, with a peak luminescence under excitation using the middle-range filters. Similar to crystal violet, the D2 dye showed a very weak luminescence under the first excitation \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{\(\mathbf{R_{f}}\)} & \multicolumn{4}{c}{**Subgroup I**} & \multicolumn{4}{c}{**Subgroup II**} & \multicolumn{4}{c}{**Subgroup III**} & \multicolumn{4}{c}{**Subgroup IV**} \\ \cline{3-14}  & & **PE1** & **TO1** & **RO4** & **RX1** & **PR2** & **RE1a** & **VN1** & **PK1** & **BV1** & **SA1a** & **ZR1a** & **TO3** \\ \hline Crystal violet & 0.77 & X & X & X & X & X & X & X & X & X & X & X & X \\ D1 & 0.55 & X & & X & X & & & X & & & & & & \\ D2 & 0.66 & & & & & X & X & & & X & & & & X \\ D3 & 0.76 & & & & & & X & & & X & & & X \\ D4 & 0.53 & & & & & & & & X & & & & X & X \\ D5 & 0.48 & & & & & & & X & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the TLC bands and dye distribution of the solvated ink samples of subgroups I–IV. Figure 7: TLC plates of solvated ink samples of (**a**) subgroup I, (**b**) subgroup II, (**c**) subgroup III, and (**d**) subgroup IV writing pen samples. filter. It then started to exhibit luminescence under excitation beginning from the second filter, with higher luminescence than that in the case of crystal violet. At the same time, the luminescence could still be observed at excitation using the eighth and ninth filters, unlike the crystal violet. Figure 9 displays the IRL images of TLC plates of solvated ink samples of subgroups III and IV under white light and different preset optical excitation filters. The IRL properties of the dyes indicated on the TLC plates of subgroup III samples are similar to those observed in the case of subgroup II. Moreover, the D3 dye that distinguishes the PK1 sample exhibited strong luminescence under excitation using the first (400-485 nm) bandpass filter and across the remaining excitation filters, similar to D1 and D2. For solvated ink samples of subgroup IV, which have been shown to contain crystal violet, D2, and D4 dyes, luminescence could be observed for crystal violet and D2, similar to samples of subgroups I-III. In addition, the D4 did not express any luminescence and seemed black under different preset optical excitation filters. Figure 8: IRL of TLC plates of samples of (a) subgroup I and (b) subgroup II under white light and different preset optical excitation filters. Figure 9: IRL images of TLC plates of samples of (a) subgroup III and (b) subgroup IV under white light and different preset optical excitation filters.
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 3 Simulation  
In order to validate the influence of the PPD, an ultrasonic array signal was simulated by modifying the method by Reference [28]. It is assumed that the wave field is 2-dimensional and a downward elastic half-space, and the sections perpendicular to the out-of-plane axis are identical. To simplify the simulation in this research, the effects of beam pattern and power attenuation along the wave path are ignored. The array signal is simulated by using phase shifts applying to the ultrasonic pulse, \(p(t)\), as shown in Figure 1b. The pulse spectrum, \(P(\omega)\), can be calculated by the Fourier transform of the pulse signal, as follows:  
\[P(\omega)=\int_{\infty}^{\infty}p(t)e^{-i\omega t}\mathrm{d}t, \tag{4}\]  
where \(\omega\) is the angular frequency; and \(i\) is the imaginary unit.  
The pulse spectrum, \(P(\omega)\), is then phase-shifted as much as the delayed time from the transmitter to reflector to receiver with the known wave speed.  
\[E_  
The simulated array signal, \(e_{s}\), is then calculated by summing the inverse Fourier transform of the phase-shifted pulse spectrum.  
\[e_{s}(t,x_{T},x_{R})=\frac{1}{2\pi}\sum_{n=1}^{N_{D}}\int_{\infty}^{\infty}E_{s,n }(\omega,x_{T},x_{R})e^{i\omega t}\mathrm{d}\omega, \tag{7}\]  
where \(N_{D}\) is the number of reflectors.  
The simulation parameters are the same as the experimental parameters. The array system is placed at the top surface and has 12 transducers spaced by 30 mm, as shown in Figure 2a. The wave speed in the wave field is 2300 m/s, and the point damage is assumed at coordinates (12.5, 100) mm. The input electronic signal with frequency of 50 kHz is a 1-cycle square wave pulse and is fed to the simulated band-pass filter shown in Figure 1a. The generated signal is fed to both the TFM and the PPD-TFM.
## 2 System Level Analysis of Proposed Converter
### Balancing Range  
As the charge balancing among the battery modules is controlled by the different voltage share among the PMs as previously described, the range of output voltage for the PMs determines the range of imbalance in battery modules SoC that can be handled by the MMPC.  
The voltage contribution of the PM which interfaces to the battery module at lowest SoC is defined as:  
\[V_{\text{min}}=K_{\text{min}}\frac{V_{\text{bus}}}{n} \tag{4}\]  
where \(K_{\text{min}}\) is the sharing factor of the PM that interface to the battery module with the lowest SoC.  
In order to keep the bus voltage at the reference value unaffected by the balancing process, the sharing factor of the PM which interfaces to the highest SoC battery module (\(K_{\text{max}}\)) should satisfy:  
\[K_{\text{max}}=n-K_{\text{min}}-\sum_{i=2}^{n}K_{i} \tag{5}\]Based on Equation (4), \(K_{\rm min}\) can be estimated based on the minimum achievable output voltage which is limited by the voltage of the PV-module at maximum power point (V\({}_{\rm mpp}\)) during sunlight. The voltage share of the PM that interfaces to the battery with the highest SoC (V\({}_{\rm max}\)) should satisfy Equation (6) in order to maintain the bus voltage at the same level.  
\[V_{\rm max}=K_{\rm max}\frac{V_{\rm bus}}{n} \tag{6}\]  
It can be observed from Equations (4) and (6) that, the wider the output voltage range of the PM, the bigger the disparity in battery SoC that can be handled. Accordingly, the topology of the MPC used for the PMs affects the operating range and SoC balancing as will be investigated in Section 3.
## 5 Experiments and Discussion
### Numerical Simulation Analysis of Single Vehicle  
The numerical simulation analysis of the critical distance was carried out for each of the lane-changing models of different conflict scenarios separately, which were avoiding a rear-end collision and avoiding a side crash between vehicles \(MV/RV\) and vehicles \(LV/MV\). It was assumed that the two vehicles were of the same type and that the vehicle length was 4.8 m. Considering the safety and comfort of driving, the acceleration of the lane-changing vehicle should not be too high. During the simulation, this study set the acceleration value range from \(-2\) m/s\({}^{2}\) to \(2\) m/s\({}^{2}\) and the steering angle from \(2^{\circ}\) to \(5^{\circ}\). Four different motion states of the relevant vehicles were also taken into account, which were four different motion state combinations in terms of velocity and acceleration.
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 2 System Model
### Signal Model  
In the previous section, various signal processing techniques have been addressed by presenting their applicability in this proposed system. In this section, a significant emphasis has been put on the signal model to describe various useful processing techniques. This can be divided in two segments first, which are: (1) the Ground Segment and (2) the UAV Segment.
## 2 Materials and Methods
### Evaluation Methods and Metrics
#### 2.5.2 Evaluation Metrics  
In our experiment, we evaluated the model performance with MSE, RMSE, and \(R^{2}\), which are defined as [21]:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}=\sqrt{MSE} \tag{8}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}}=1-\frac{MSE}{MST} \tag{9}\]  
where \(n\) is the number of samples, \(y_{i}\) is the true value of the \(i\)th sample, \(\hat{y_{i}}\) is the predicted value of the \(i\)th sample, \(\overline{y}\) is the mean value of the target vector, and MST is mean total sum of squares [21].  
Specifically, the _MSE_ and _RMSE_ will have a value of 0 if the regression model fits the data perfectly [47], and the higher the values of MSE and RMSE, the worse the model will be [21]. Furthermore, the \(R^{2}\) will have a value of 1 if the regression model fits the data perfectly, and the positive values of \(R^{2}\) range from 0 to 1 [47], i.e., the closer to value 1, the better the model [21]. However, the \(R^{2}\) will have negative value if the regression model performs poorly. As mentioned above, the best value and worst values of MSE, RMSE, and \(R^{2}\) are shown in Table 5 [47].  
To summarize, an \(R^{2}\) value of 0.8 clearly indicates very good regression model performance, and \(R^{2}\) values such as 0.756 or 0.535 indicate good results [47].
## 3 Experimental Test and Result Analysis
### Hardware Equipment  
In this paper, an Ackerman differential car integrated with ROS is selected as the experimental equipment. The robot integrates a variety of sensors and computing equipment. It is equipped with laser radar for environmental perception, a camera for visual information capture, an inertial measurement unit (IMU) for attitude and motion information, a motor with an encoder for the precise control of motion, and embedded computing hardware for data processing and algorithm execution. Servo motors and stepper motors are used to precisely control the motion of robots. IMU can provide data on robot acceleration and angular velocity, which is crucial for robot positioning and navigation. Laser lidar is commonly used to detect static and dynamic obstacles. The camera is the main visual sensor for object detection, which is used to capture two-dimensional images of the scene. Through the image processing and computer vision algorithms of Raspberry Pi 4B, objects in images can be recognized and classified. The detailed layout and configuration of the hardware structure are shown in Figure 6.
# Real-Time Monitoring of Passenger's Psychological Stress
## Materials and Methods
### Physiological Recording  
The wearable sensor Empatica(tm) E4 wristband was used to record three different physiological signals: blood volume pulse (BVP), skin conductance (SC) and skin temperature; and 3-axis accelerometer data. This device can be used in two separate ways: either in storage mode (data is stored in the wristband's local memory), or in streaming mode (transmission by Bluetooth(r) connection to a suitable smartphone application). The storage mode has been used during the learning phase, the streaming mode during the test phase. The BVP signal is an Empatica's proprietary version of pulse oximetry: by measuring the oxygen saturation in blood vessels at the surface of the skin, it allows an indirect measure of heart rate. In Empatica(tm) E4, BVP is recorded at 64 Hz, SC at 4 Hz, 3-axis acceleration at 32 Hz and skin temperature at 4 Hz. The wristband automatically infers interbeat intervals (IBIs) when data's quality is high enough. Its battery and memory units are sufficient to enable more than one day of continuous recording. The device is also equipped with a trigger button which allows the participant to mark some significant events, and the experimenter to locate them easily in time.