esa-sceva/satcom-chunk-collection huggingface dataset:
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 7 Conclusions  
The proposed DTS-Net is a simple approach inspired by the DTS image reconstruction technique used in image and video super-resolution. The proposed DTS-Net implementation is simple and yet achieves extremely high accuracy and an average speed for dense prediction tasks such as semantic segmentation, as proven by the results obtained on the different semantic segmentation benchmarks reported herein. For the simultaneous joint task of semantic segmentation and depth estimation, DTS-Net exhibited promising accuracy for both semantic segmentation and depth estimation while the two tasks were trained simultaneously. These results prove the strength of the DTS module, which is the core of the proposed method, and demonstrate its potential for re-employment in dense prediction tasks. The proposed DTS-Net-Lite also provides the ability to perform real-time processing by using a lightweight encoder achieving relatively good segmentation accuracy.  
Conceptualization, H.I., A.S.; methodology, H.I., A.S.; software, H.I.; formal analysis, H.I.; investigation, H.-S.K.; resources, H.-S.K.; data curation, H.I.; writing--original draft preparation, H.I.; writing--review and editing, H.I., H.-S.K.; validation, H.I., H.-S.K.; visualization, H.-S.K.; supervision, H.-S.K.; project administration, H.-S.K.; funding acquisition, H.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2020R1I1A3A04037680 and in part conducted as a part of the research project of \"Development of IoT Infrastructure Technology for Smart Port\" financially supported by the Ministry of Oceans and Fisheries.  
Not applicable.  
The datasets used in this paper are public datasets. We also provide the test and the evaluation codes of the proposed method at: [[https://github.com/HatemHosam/DTS-Net](https://github.com/HatemHosam/DTS-Net)]([https://github.com/HatemHosam/DTS-Net](https://github.com/HatemHosam/DTS-Net)) which is created and accessed on 21 June 2021.  
The authors declare no conflict of interest.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 1 Introduction
Different from the aforementioned proposals, we focus on the dynamic characteristic of satellite in actual scenario, by constructing the relationship model between user elevation angle, beam angle and distance, a dynamic divide grouping algorithm is proposed to ensure the effective pairing of NOMA users. Meanwhile, during the solution, to ensure the throughput of cell edge users, based on the instantaneous channel gain, the optimal power control factor expression is derived, then the overall optimal resource allocation is solved by changing the variable form and successive approximation method. The main contributions of this paper are summarized as below:  
* To improve user access throughput, we propose a dynamic divide grouping NOMA method for TSINs. Based on the stochastic geometry theory, with the objective of maximizing the network capacity, a NOMA problem is modeled as an optimization problem of joint user grouping, power control and resource allocation. * To ensure the effective pairing of NOMA users, by constructing the relationship model between user elevation angle, beam angle and distance, a dynamic user pairing algorithm is proposed to pair users into groups for the implementation of NOMA. * To solve efficiently the problem, based on the instantaneous channel gain, the optimal power control factor expression is derived, then a joint optimization algorithm of beam channel and base station channel resource allocation is proposed. The remainder of this paper is organized as follows. Section 2 gives the review of related works, then the system architecture and problem formulation are proposed in Section 3. In Section 4, the dynamic divide grouping method is in Section 5. In Section 6, we solve the jointly resource allocation problem of the whole system. The simulation results are shown in Section 6. Finally, Section 7 gives the conclusion.
## 4 Numerical Results
### MISOCP Relaxation Gap and Simulation Setup  
The accuracy of MISOCP relaxation was tested to compare the power flow obtained from conic relaxation with the original nonconvex model by evaluating the relaxation error defined as  
\[Gap=max_{\forall(i,j)}\left|p_{ij}^{2}+q_{ij}^{2}-(l_{ij}u_{i})\right| \tag{66}\]  
The SOC gap for the case is the order of \(10^{-9}\) MW. Simulations were carried out on an Apple Macbook with 32 GB RAM and an Apple M2 Pro CPU with 12 cores. The problem was modeled using MathWorks' MATLAB with YALMIP and Gurobi addons. The MIPGap and the solution time of all simulated cases are shown in Table 4. In the context of the presented results, \"MIPGap\" is a parameter set to define the tolerance level for the difference between the lower and upper objective bounds, allowing the algorithm to terminate once this relative  
Figure 16: Objective function of Cases 0–5.  
Figure 15: Grid service restoration in fault for Scenario 2, Case 5.  
gap is attained, thereby ensuring that the obtained solution is close to optimality within this threshold.
## 3 Optimization of Distribution Scheme
### Optimization of Frequency Domain Selection Extension
The offset frequency groups of alignment A1 and E2 are both converted three times. Other schemes generated by A2, C1, C2 and E1 are extremely similar, with the difference in the number of conversion days for the frequency groups being around one or two days. This optimization method not only offers more options for phase-locked schemes and alignment patterns under various frequency domain settings but also enriches the selection of frequency domains. Experiments show that when the frequency domain is set too narrow, there is no fixed offset frequency to keep the beat frequencies within its range. Figure 4: Distribution Frequency for Different Phase Locking Schemes. Figure 5: Distribution Frequency of Different Alignment Patterns. The smallest acceptable frequency domain setting for phase-locking scheme 1 is [7 MHz, 22 MHz]. Moreover, the distribution scheme of the narrowest frequency domain is given by the phase-locking scheme 3. When the alignment pattern is C1, scheme 3 can satisfy the frequency scheme generation of [7 MHz, 17 MHz]. This setting requires 15 switching of the offset frequency group over five years, with a maximum duration of 503 days and a minimum duration of 40 days. If the lower bound is still set to 5 MHz without raising, the upper bound can be reduced to 15 MHz, at which point the program needs to be switched 16 times with a minimum and maximum offset frequency duration of 40 days and 266 days. It is already the frequency limit achievable by the current distribution scheme.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 1 Introduction
### Contributions of the Paper  
We present a decentralized decision-making framework (in the form of a model for virtual forces) for a self-organizing drone swarm. This framework tackles two challenges simultaneously: without a central control element in the MANET, the swarm (1) dynamically creates a network that connects (potentially mobile) terminals and (2) maintains a topology that ensures all drones are connected to the base station through an unambiguous multi-hop path. We found and chose to report in this paper that the collective movement pattern of individual units (an emergent property of the pseudo-forces model) results in the access points forming a topology that reflects the heterogeneous distribution of clients (and subsequent modification thereof) while also preserving unambiguous multi-hop connectivity to a central Internet gateway. The performance of the distributed algorithm is investigated from a systemic point of view (collective behavior) using a simple LQM to study variability across multiple simulation runs, featuring different client movement patterns.
# Continuous-Wave THz Imaging for Biomedical Samples
## 3 CW THz Full-Field Imaging
### CW THz Ptychography Imaging  
Although the advantages of the digital holographic imaging are full-field and real time while the field-of-view (FOV) is limited by the size of the illumination beam. CW THz ptychography is a lensless coherent diffraction large field-of-view phase contrast imaging method, which can retrieve the complex amplitude distribution of the sample robustly from a set of diffraction patterns originating from overlapping sample illumination areas. Both the probe function and the complex transmittance function of the sample can be retrieved respectively by the extended ptychographical iterative engine (ePIE) algorithm without a reference beam. However, the long acquisition time of the ptychography imaging method limits its wide use.  
In 2019, [PERSON] et al. applied a CW THz ptychography imaging system consisting of an FIRL 295 at 2.52 THz and a pyrocam III detector to image the forewing of cicadas, as shown in Figure 11[70]. The reconstruction quality was seriously affected by the incorrect probe position during the process of recording the diffraction information. The translation error was minimised to 0.01 pixel by a probe position correction method based on cross-correlation registration on overlapped regions of the object wavefront. It was easy to distinguish between the subcostal and the first radius vein by correction. The optical thickness of the fourth meridian R4 measured was 9.30 \(\upmu\)m, which was in good agreement with the results measured by the self-referencing common-path CW TDH method in the previous section.  
Figure 10: Schematic layout of the setup of self-referencing CW TDH based on [PERSON]’s mirrors. O: object beam; R: reference beam.
## 3 Space--Time Dynamic Analysis Method Based on DCM
### Stability Analysis of Transfer Matrix \(\mathbf{Q}\)
Let the initial displacement and initial velocity of the beam be respectively  
\[\left(y\right)_{\tau=0}=\sin(\pi\xi) \tag{27}\]  
\[\left(\partial y/\partial\tau\right)_{\tau=0}=0 \tag{28}\]  
The time-history displacement patterns of free vibration with Equation (21) as initial conditions in 6 cases in Table 1 are studied, and the curves are shown in Figure 6.  
First, Figure 6a-c are observed, which is the time history displacement of the CGL distributed orthogonal grid with \(m=n=5\) at different time steps. Figure 6a shows a normal harmonic free vibration with the value of \(R(\mathbf{Q})\) close to 1. When the step increases, the spectral radius decreases, and the time-history displacements of cases 2 and 3 show the attenuation form, and the smaller the \(R(\mathbf{Q})\) value is, the faster the attenuation speed is. Then Figure 6d-f are observed, where the orthogonal grid with \(m=5\), \(n=7\) is given, including two kinds of distribution form of nodes at different time steps. Figure 6d also shows a harmonic free vibration the value of \(R(\mathbf{Q})\) close to 1. The time-history displacement amplitude in Figure 6e increases with time and reaches three orders of magnitude of 10 at 4 s, showing a state of instability. Corresponding to the situation that the spectral radius value of the uniformly distributed orthogonal grid in Figure 4c is greater than 1 when \(m=5\) and \(n=7\). Time history displacement in Figure 6f attenuates with time, corresponding to the case 6 where \(R(\mathbf{Q})\) far less than 1. The rules of time history displacement correspond  
Figure 6: Time-history displacement (free vibration) in six cases. ing to different spectral radius values in orthogonal grids can be found by combining above six figures:  
1. When the spectral radius \(R(\mathbf{Q})\) is close to 1, the time-history displacement shows a stable form. To a certain extent, when it is less than 1, the time-history displacement of free vibration will gradually decay, and the decay rate is negatively correlated with \(R(\mathbf{Q})\). 2. When the spectral radius \(R(\mathbf{Q})\) is greater than 1 to a certain extent, it will lead to the accumulation and amplification of errors, and eventually lead to instability. Based on the above rules, in order to make the time history displacement tend to be stable over time, we need to make \(R(\mathbf{Q})\) as close to 1 as much as possible when using orthogonal grids as space-time discretization schemes, and, according to Figure 5, increasing the number of nodes in the time domain direction of the grid is a more effective method to expand the stability range.
# Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement
## 2 Experimental Setup
### Submicron Aerosol Particles  
In this study, a defined aerosol of sodium chloride (NaCl) is used for the investigation of the particle mass concentration. NaCl is characterized by its environmentally and user-friendly chemical properties and easy handling. For this reason, it is a commonly used material for aerosol investigations [48; 49; 50; 51; 52; 53; 54; 55; 56].  
The aerosol mass concentration is regulated by the sodium chloride concentration of the water solution in the Collison nebulizer. The mixing ratio (B:A in Figure 1) is set at a constant of 6:1, resulting in a total volume flow of 30 L\(\cdot\)min\({}^{-1}\). The particle size distribution (PSD) of the generated aerosol is measured with the SMPS at position Sample 1. For  
Figure 2: Specific electrical energy depending on the voltage of the heating stage at a volume flow of 0.17 L\(\cdot\)min\({}^{-1}\).  
the lowest-used NaCl concentration (0.25 g\(\cdot\)L\({}^{-1}\)), the PSD is shown in Figure 3, with an image from the scanning electron microscope (SEM, Model SU8000, Hitachi High-Tech Corporation, Krefeld, Germany). The sample was taken by the Nanometer Aerosol Sampler (Model 3089, TSI GmbH, Aachen, Germany).  
The measured particle size range is between 14 and 800 nm, and the modal diameter is about 30 nm. The SEM image in Figure 3 demonstrates the particle shape, which can be assumed to be spherical. The course of the PSD stays the same for different concentrations of sodium chloride, only the total particle number changes [45]. The smallest particle size corresponds to the detection limit of the measuring device. However, the distribution curve indicates that smaller nanoparticles are also in the system. The broad size distribution of NaCl particles is suitable for all size ranges of the sensors in Table 1 and thus enables investigations under the same conditions.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 3 Location and Velocity Prediction
### Initialization and Modeling of Node Movement
According to [8], the velocity \(v_{k,i}\) of node \(n_{k,i}\) belonging to the \(k\)-th group can be obtained by adding a random motion vector \(\Delta v_{r,i}\) to the center speed vector \(\mathbf{v}_{k}^{C}\) as shown in Figure 3.  
This relationship between group center and member velocities can be expressed as  
\[v_{k,i}=\mathbf{v}_{k}^{C}+\Delta\mathbf{v}_{r,i}, \tag{5}\]  
\[\Delta\mathbf{v}_{r,i}=v_{r} \begin{pmatrix}\cos\phi_{r}\sin\theta_{r}\\ \sin\phi_{r}\sin\theta_{r}\\ \cos\theta_{r}\end{pmatrix} , \tag{6}\]  
where \(v_{r}\) is distributed uniformly within a specified maximal speed \(v_{r,\max}\), i.e., \(v_{r}\in[0,v_{r,\max}]\). The random elevation \(\theta_{r}\) angle is uniformly distributed within \((0,2\pi]\) and the azimuth angle \(\phi_{r}\) varies evenly within a range of \((0,\pi]\).
## 3 Lab Testing Protocol
### Indirect Tensile Strength and Stiffness Modulus Evaluation
The highest magnitude of stiffness modulus is shown by 0.3% BF at 8.21 GPa, followed by PF at 7.68 GPa. SBS-7%, however, exhibits the least amount of deformation resistance. BF shows a 31% higher performance in terms of gained stiffness modulus when compared to SBS-7% asphalt mixture. Furthermore, BF outperforms LF and PF mixtures by 9% and 6%, respectively. The addition of basalt fiber improves cohesion between the aggregates and asphalt mixtures, thereby increasing the deformation resistance of asphalt mixtures. The performances of BF and PF align with the previous study performed by [PERSON] et al. [1], where BF slightly outperforms in terms of increased stiffness modulus under high-temperature conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SBS-7\%** & **LF** & **PF** & **BF** \\ \hline Load factor & 0.5904 & 0.5967 & 0.5989 & 0.6087 \\ Rise time (ms) & 123.4 & 123.8 & 124.1 & 124.7 \\ Deformation (mm) & 0.0053 & 0.0045 & 0.0042 & 0.0039 \\ Stiffness modulus (GPa) & 6.01 & 7.43 & 7.68 & 8.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Stiffness modulus results. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Indirect Tensile Strength [MPa]** \\ \cline{2-3}  & **Dry** & **Saturated** \\ \hline SBS-7\% & 936.1 & 1231.6 \\ LF & 1213.8 & 975.2 \\ PF & 1324.2 & 1105.8 \\ BF & 1382.4 & 1258.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ITST results.
## 3 Results
### Quality, Reliability and Content  
Intra-examiner and inter-examiner reliability was good to excellent, with all intraclass correlation coefficients being above 0.86 for quality and content analysis scores.  
The mean VIQI total score was 11.24. The mean m-DISCERN was 1.83. The mean JAMA score was 1.89. The mean total content score was 2.67.  
Figure 4 shows the individual scores for videos categorized by different sources. As for the VIQI, the videos uploaded by laypeople (mean value = 7.92) showed significantly lower values compared with those uploaded by healthcare professionals (mean value = 12.38) (\(p\) < 0.0001) and by commercial companies (mean value = 11.21) (\(p\) = 0.009). Regarding m-DISCERN, videos uploaded by laypeople had significantly lower scores (mean value = 1.15) compared to videos by healthcare professionals (mean value = 2.13) (\(p\) = 0.003) and by hospitals/universities (mean value = 3.00) (\(p\) = 0.002); m-DISCERN was significantly lower also for videos by commercial companies (mean value = 1.43) compared to those uploaded by hospitals/universities (\(p\) = 0.005). Regarding JAMA, no statistically significant difference  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Video Metrics** & **Mean** & **SD** \\ \hline Video length (min) & 4.64 & 2.59 \\ Number of views & 34,149.21 & 132,175.72 \\ Number of likes & 701.56 & 4369.44 \\ Number of dislikes & 0 & 0 \\ Number of comments & 45.71 & 190.31 \\ Interaction Index & 35.06 & 266.55 \\ Video Power Index & 34.73 & 165.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: General characteristics of videos. SD, standard deviation.  
Figure 3: Sources of upload of included videos.  
was found among the source categories, with all showing low scores (mean value = 2.00). As for the content, the videos uploaded by laypeople (mean value = 1.54) showed significantly lower scores compared to videos uploaded by healthcare professionals (mean value=3.25) (\(p\) = 0.003). The results are summarized in Table 2.  
In detail, the analysis of single components of the customized content score revealed that, among all videos, the most mentioned topics were the definition and indication regarding the use of MAD (respectively, 75% and 77% of videos). The comparison with alternative treatments was quoted in 41% of videos. A total of 17% of videos cited long- or short-term side effects of treatment with MAD. The duration and cost of the therapy with MAD were cited only in 9.5% of the videos. The instructions on the usage of MAD were provided in 20% of the videos. Contraindiications about the use of MAD were mentioned in 9.5% of the videos. The need for a multidisciplinary care team was mentioned in 9.5% of the videos, and the need to consult a dentist qualified in sleep medicine was reported only in 8% of the videos.
## 6 Experiment
### Experimental Results and Discussion  
The channel of the system in this experiment is measured at the middle of the \(x\)-axis. See Figure 16. The values shown represent the frequencies from DC up to 500 kHz. The channel response cuts off at very low frequencies due to the DC-bias-Tee circuit used in this experiment. So, the first data-carrying subcarrier of the ACO-OFDM signal is not used.  
The BER results for each quadrant and the BER results for the combined signal as a function of the transmitter displacement along the \(x\)-axis are shown in Figure 17. The transmitter is moved horizontally along the \(x\)-axis, while the receiver is fixed. This changes two channel parameters simultaneously: the incident angle and the distance between the transmitter and the receiver. The transmitter is moved along the \(x\)-axis between \(-200\) mm and \(200\) mm as shown in Figure 15. This corresponds to incident angles between \(-26.5^{\circ}\) and \(26.5^{\circ}\). The distance is minimum when the transmitter is in the middle and it increases as the transmitter moves to the left or the right.  
As the transmitter moves from left to right, the BERs for quadrants one and four decrease until the transmitter reaches the middle and then it starts to slightly increase as it moves to the right. This is because, as the transmitter moves from left to the middle, the light spot partially overlaps both quadrants and when the transmitter is in the middle it fully overlaps both quadrants. After that, the light spot fully overlaps both quadrants as the transmitter moves to the right (see Figure 15). However, as the transmitter moves from the middle to the right, the distance increases which causes the BERs to slightly increase. Similarly, we can describe the BER changes for quadrants two and three; however, for these, the BERs decreases as the transmitter moves from right to left. After combining the four signals  
Figure 16: Experiment channel frequency response.  
Figure 17: Experimentally measured BERs for each quadrant and for the combined signal using MRC versus the angle of arrival as the transmitter is moved along the \(x\)-axis.  
using the MRC algorithm, the BER is significantly reduced for most \(x\)-axis displacements. For example, the BER drops below \(10^{-4}\) compared to a BER of \(0.5\times 10^{-3}\) without combining at a displacement of 40 mm. This demonstrates the advantage of using diversity combining in improving the performance of the QADA receiver.
# Design Approach and Mechanism Analysis for Cavitation-Tolerant Torque Converter Blades
## 4 Discussion
### Effect of Slot Width  
The influence of different slot widths on cavitation characteristics is shown in Figure 14. A larger slot width leads to a smaller cavitation volume in the stator. There is a strong linear relationship between the slot width and \(T_{\text{bg0}}\), and there is a high-order functional relationship between the slot width and the cavitation volume in the stator. As for the slotting design between P0.1 and S0.3, when the slot width is 2 mm, the large cavitation volume of the stator means that the cavitation effect is still heavy. When the slot width reaches 3 mm, the cavitation volume of the stator is greatly reduced, and the cavitation effect is largely decreased. The slotting design is implemented between S0.05 and S0.4. The widening of the slot passage does not fully suppress the stator cavitation; in particular, the cavitation suppression effect is very limited. The slotting design is applied between S0.15 and S0.6, and the cavitation volume of the stator is greatly reduced as the slot widens. When the slot width reaches 2 mm, the stator cavitation is fully suppressed. Overall, the slot width plays a crucial role in suppressing the stator cavitation, especially in the slotting scheme of Plan 4.  
Figure 14: _Cont._  
Figure 13: Stator suction surface load distribution with different suppression slots under stall operating condition.