esa-sceva/satcom-chunk-collection huggingface dataset:
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### Adding Users to the Simulation Area  
Several nodes representing UEs can then be placed randomly on a preconfigured square/rectangle area. Once the total number of nodes in the simulation is defined, it is possible to add all the nodes either during the simulation launch or gradually according to preconfigured time intervals. In our simulations, we opted for the second method and added a new UE every 100 s, as illustrated in Figure 13. This strategy of linearly adding users seems to well suit the reality of the disaster scenario where rescue teams should join the disaster area gradually. This allows us to better observe the evolution of the group as well as the many parameters related to the connectivity levels, the average CQI, and the number of topology updates.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Transmitter range & Randomly from 100 to 200 m \\ \hline CQI & 1 to 15 (0 for unreachable UE) \\ \hline Simulation time & 7200 s (2 h) \\ \hline Node pause time when boundary is reached & Randomly 0 to 60 s \\ \hline Topology size & 500 m \(\times\) 500 m \\ \hline Number of nodes & From 2 to 70 (new added node every 100 s) \\ \hline Node speed (Vmax) & 0 to 4 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the Parameters used in the simulation experiments.  
Figure 12: Overview of D2D direct communication in loosely coupled topology implementing: (**a**) “less cost” and (**b**) “optimized tree”.
## 3 System Model and Performance Analysis
### Channel Utilization  
The channel utilization U is the ratio of valid packets correctly received to the total packets. In this paper, the number of valid packets correctly received is the sum of the number of packets successfully transmitted when high-priority patients and low-priority patients compete fairly, the number of packets successfully sent by high-priority STA on the remaining subchannels, and the number of packets successfully transmitted by low-prioritypatients in the PU feedback phase and the next round of the backoff phase. Thus, the expression for channel utilization can be obtained as follows:  
\[U=\frac{n\theta(1-p_{1})+n_{1}\theta p_{1}(1-p_{2})+n_{2}\theta p_{1}(1-p_{3})}{r}. \tag{38}\]
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### 5.2.3 Extreme Edge Network-Level Contribution (Solutions)  
The extreme edge network-level solutions use the perspective of the D2D technique for smooth hand-off operations. Various studies [87; 93] have proposed approaches to tackle the mobility of D2D pairs. Furthermore, for the exploration of D2D mobility, a Markov chain-based model [108] and a simulation-based model [93; 109] have also been proposed. In the following discussion, we highlight the advantages, drawbacks, and considerations of the D2D approach concerning its applicability in light of the parameters mentioned in Table 2 of this article.  
**Advantages of the D2D communication approach**  
* Offers the option of decentralization at the time of MM.
* Offers D2D handoff controlling policies [92]  
**Drawbacks of the D2D communication approach**  
* The feasibility concerning delay and energy conservation for the MM mechanism has not been explored yet.
* We have the challenge of the increased number of control messages in the network.  
**Considerations for the D2D communication approach**  
Owing to the discussions and the advantages/drawbacks mentioned above, the D2D approach fulfills the parameter for dependability criteria.  
Likewise, the parameter AL2 (offering mobility provision at the edge network level will execute in cooperation with core and access network-based approaches) fulfills the adaptability criteria. Next, the D2D approach fulfills parameter EL1 for fulfilling extensibility criteria. Furthermore, the D2D method does not satisfy any parameter from a power-saving perspective.  
It has been derived from the literature and discussion that the D2D methods also contribute to MM through the CP support facility.
## 4 Discussion  
Application of 3D DIC in composite structural testing is presented using a few case studies performed at CONCOM over the years. It was found that the accuracy of the technique in strain measurements of composites was limited to about 200 microstrains; therefore, it may not be an appropriate measurement technique in structural testing in which few strains are expected.  
Furthermore, two new applications for DIC in manufacturing and inspecting of thermoplastic composites developed at CONCOM are presented, and proof of concepts are demonstrated. It was demonstrated that 3D DIC can be used for composite ply inspection during manufacturing using AFP. However, an optical random pattern projector needs to be developed for this purpose.  
Figure 23: AFP defect detection by DIC: (**a**) twisted tow and (**b**) damaged tow.  
Figure 22: (**a**) Overlap between two tow detections. (**b**) Gap detection, z-axis represents thickness direction.  
**Author Contributions:** F.S.: conceptualization, methodology, investigation, analysis, writing--original draft preparation, and editing. S.V.H.: conceptualization, methodology, writing--review and editing, supervision, project administration, and funding acquisition.  
**Funding:** The financial contributions from the Natural Sciences and Engineering Research Council of Canada (NSERC), industrial chair on Automated Composites, Center for Research in Polymers and Composites (CREPEC), Bell Helicopter Textron Canada Ltd., Bombardier Aerospace, and Concordia University are appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 1 Introduction
### Contributions  
The main contributions of this paper are summarized as follows:  
* Cognitive interference detection and cancellation schemes are proposed. The proposed approaches can be distinguished from the existing interference cancellation methods because they are based on a digital channelizer which is an essential part in a satellite, needed to utilize transponders bandwidth-efficiently.
* In the proposed interference cancellation, a range of the interference power which should be cancelled out is evaluated. For too weak interference, the interference cancellation causes more severe signal distortion than the interference level and the cancellation is not needed.
* The detection and false-alarm probabilities of the proposed interference detection schemes are analyzed. The validity of the analysis is also verified through simulations. Based on the analytical results, we can provide a guideline in designing the cognitive interference cancellation system under various conditions.
## 1 Introduction
[16] investigated the topology optimization problem considering fatigue resistance properties by introducing a modified Goodman criterion and Sines equivalent mean stresses and showed that the optimized structure was able to avoid some stress concentrations due to geometric singularities. However, the paper only shows the results of two-dimensional examples and does not extend the algorithm to three-dimensional examples. Therefore, it remains to be seen whether the algorithm can be applied to practical engineering 3D structures. [PERSON], et al. [17] converted the fatigue constraints of the structure to stress constraints based on the Palmgren-Miner linear cumulative damage criterion, as well as using the strategy of separating fatigue analysis from topology optimization to design the structure with the goal of minimum mass to ensure that the structure did not fail during operation. Although the overall stress level was reduced in the optimization results, the reduction of the stress value at the geometric singularities was not obvious. [PERSON], et al. [18] applied the BESO method to study the topology optimization fatigue problem under a high cycle for research, which improved the computational efficiency of the algorithm by introducing a modified Goodman criterion and a modified equation; numerical examples were presented to prove the effectiveness of the algorithm. There are various criterion mentioned in paper [17; 18], the basic idea of which is to control the stress amplitude of the structure within the range allowed by the criterion in the process of structural design. This paper utilizes a similar design idea which ensures that the von-Mises stress values of the redesigned bracket are within a reasonable range. A fatigue test is also performed on the redesigned bracket after 3D-printing and the test result shows that the redesigned bracket can satisfy the expected engineering specifications. This article mainly considers the application of topology-optimized structural design for fatigue characteristics. The article is presented as follows. In Section 2, the detailed design requirements of the aerospace bracket are presented. Section 3 introduces the design model of the aerospace bracket considering fatigue characteristics. The topology optimization model considering fatigue characteristics and its sensitivity analysis are presented. In Section 4, the result of the proposed method is given, in addition to the geometry model reconstructed by reverse engineering. Meanwhile, the simulation results of the new geometry and the original geometry under the same load are compared. In Section 5, the experimental procedure and the experimental result of the new bracket fabricated using 3D-printing are shown. In Section 6, the article is summarized.
###### Abstract  
The aim was to evaluate the influence of artificial canal size on the results of cyclic fatigue tests for endodontic instruments. Dynamic cyclic fatigue at body temperature using continuous tapered nickel-titanium F6-SkyTaper instruments (Komet, Lemgo, Germany), size 25/.06 with an amplitude of 3 mm, was tested in four different simulated root canals: (A) size of the instrument +0.02 mm (within the tolerances of the instruments); (B) +0.05 mm; (C) +0.10 mm; (D) parallel tube with 1.25 mm in diameter. The artificial canals (angle of curvature 60\({}^{\circ}\), radius 5.0 mm, center of curvature 5.0 mm) were produced by a LASER-melting technique. Time and cycles to fracture, and lengths of the fractured instruments were recorded and statistically analyzed (Student-Newman-Keuls; Kruskal-Wallis test). Time to fracture significantly increased with increasing size of the artificial canals in the following order: A \(<\) B, C \(<\) D (\(p<\) 0.05). Length of separated instruments continuously decreased with increasing canal sizes. The parallel tube produced the significantly shortest fragments (\(p<\) 0.05). Within the limitations of this study, dynamic cyclic fatigue of endodontic instruments depends on the congruency of the instruments' dimensions with that of the artificial canals. In future cyclic fatigue testing, due to the closer match of canal and instrument parameters, it is necessary to adjust the artificial canal sizes to the size of the instruments within the manufacturing tolerances of the instruments.  
artificial canal; congruence; body temperature; dynamic fatigue tests; fracture resistance; ISO; nickel-titanium instruments +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
Due to the limitations of data acquisition, the conflict risk between drones and manned aircraft has seldom been estimated and the risk level is unclear. Furthermore, the reliability of the Monte Carlo simulation results needs to be further verified due to limited iterations. The purpose of this paper is to provide a conflict risk assessment method between manned aircraft and drones in the terminal area based on actual trajectory data of manned aircraft and drones. This method is oriented to the different flight stages and flight altitudes of manned aircraft. Based on the trajectory prediction results of manned aircraft and the drone, the collision probability is calculated. The main contributions and innovations provided in this paper are as follows:  
First, due to the flexibility and changeable range of activities of drones, their position at the next moment is uncertain. Therefore, the use of deterministic trajectory prediction may bring a large error to the collision prediction results. This paper proposes a quartile regression bidirectional gate recurrent unit neural network (QRDGRU) to predict the location of the drone. The model integrates the bidirectional gated recurrent unit structure and the quartile regression structure, which can obtain the position distribution of the drone. Considering the uncertainty of drone position, the collision probability prediction can be more accurate. Second, most of the existing prediction methods regarding collision probability are based on the assumption that the error follows a normal distribution. The collision probability is calculated considering the error caused by the uncertainty of the predicted position. In this paper, a Monte-Carlo-based collision probability prediction method between manned aircraft and drones is proposed. Time to the closest distance at the encountering point and the collision probability at the closest point are calculated. The rest of the paper is organized as follows. The data collection and preparation process are presented in Section 2. Section 3 exhibits the prediction method of manned aircraft trajectory in terminal areas. Section 4 provides the prediction of trajectory distribution for drones. The conflict risk assessment between drones and manned aircraft is presented in Section 5. Section 6 summarizes the conclusions and proposes future works.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model
### Scenarios of the Experiments  
We have simulated a network with 49 nodes, physically arranged as a grid topology of \(7\times 7\) nodes with \(5\) m between each node and its neighbor horizontally and vertically, see Figure 3. This is because we would have a specific topology and we can play with positions of the jammer node and co All nodes in the network has the same traffic rate (10 packets per second in each node). Packet loss in the links is between \(0.5\)-\(1.5\%\). We have used the standard IEEE 802.15.4 for the MAC layer. This standard describes very low energy to work. On the other side, data transmission rate is very low. The energy model presented in this analysis is the model studied in [11]. It consists of starting, microcontroller energy, transmission energy, receiving energy, switching energy, algorithm CSMA/CA energy, and shutting down energy. These are the main tasks that a node executes on the network and are considered in active mode, without sleep times.  
Table 1 describes the parameters of the simulations. We use an event-driven simulator based on C++ language designed and implemented by us, proven in [29]. This is a network simulator with parameters of MAC and Network layer, and it has punctual features of the Physical layer such us: percentage of interference in the channel and RSSI (Received Signal Strength Indicator) parameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \multicolumn{2}{c}{**Physical Layer Parameters**} \\ \hline Sensitivity threshold receiver & \(-94\) dBm \\ Transmission power & \(4.5\) dBm \\ Propagation model & Free Space \\ \hline \multicolumn{2}{c}{**MAC Layer Parameters**} \\ \hline Waiting time for ACK packet & \(30\) ms \\ Maximum retransmission number & \(3\) \\ Maximum retry number & \(5\) \\ Maximum number of tries to reach a node from the collector & \(9\) \\ Packet error rate & \(1\%\) \\ Average frame length & \(22\) bytes \\ Maximum number of backoffs & \(4\) \\ MAC protocol & IEEE 802.15.4 \\ MAC layer & CSMA/CA \\ \hline \multicolumn{2}{c}{**Network Layer Parameters**} \\ \hline Number of nodes & \(49\) \\ Maximum number of neighbors & \(16\) \\ Discovery neighbor time & \(30\) s \\ Update time neighbors table & \(30\) ms \\ Maximum data rate & \(250\) kbps \\ Routing & Hierarchical \\ Scenario & Static nodes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation and real network parameters. CSMA/CA, carrier sense multiple access with collision avoidance [2].
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 7 Conclusions  
In this study, a prediction model was developed for the effective beam width for the initial stiffness of the wall-slab joints in TWTS structures based on the EBWM concept. Additionally, by applying experimental data from the literature, a practical application procedure using the proposed model is as well suggested. Thus, the following conclusions can be drawn from this work:  
* The prediction model of the effective beam width for the initial stiffness which is defined as the stiffness at 0.05% inter-story drift ratio of wall-slab joints in TWTS structures has been proposed as shown in Equation (18).
* By applying the results from an experimental test, a correction coefficient \(\rho\) to estimate the unknown value of the lateral force for the initial stiffness has as well been proposed as shown in Equation (32).
* Using the proposed analytical prediction model, a practical design procedure has been suggested.
* The effects of parameters that are involved in the prediction model, such as the height and length of bearing wall, and the span length of slab on effective beam width have been discussed.
* Due to several assumptions being made for the model processing, the applicable scope of this proposed model has been carefully defined. Moreover, one of the indexes of this model is estimated using limited experimental data from the literature. This model can give a good prediction for similar target specimens in practical design, but there is a need to perform further experimental tests to enlarge the usage scope of the proposed model.  
Finally, it should be noted that the prediction model of the effective beam width for the initial stiffness of wall-slab joints in TWTS structures proposed in this study is more engineering-oriented, by which the seismic performance design and assessment can be achieved in a reasonable and simplified manner.  
All authors conceived and designed the study. Methodology, calculation, analysis, and writing: J.W.; Methodology, reviewing, and editing: K.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The authors acknowledge the support of the Inai laboratory from Yamaguchi University for providing the experimental test data, and [PERSON] from Chubu University and [PERSON] from The University of Tokyo for very helpful discussions and valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Exchange Flows in Inclined Pipes with Different Viscosity Ratios
## 3 Results and Discussion
### Velocity Changes  
#### 3.2.1 Light Fluid Velocity Changes  
Subsequently, we also investigated the variation in the velocity (V) of the light fluid with tilt angle (\(\theta\)) for different viscosity ratios (\(\beta\)). We calculated the velocity of the watery fluid in terms of the distance it reaches along the upper end of the pipe, as well as the time of its movement, and the results obtained are shown in Figure 7. We observe that the light fluid velocity increases and then decreases as \(\theta\) increases, which is consistent with [PERSON] et al. (2018), who observed a change in the rate of the rise of bubbles in an inclined pipe.  
We also found that when the viscosity ratio is smaller, the change in the rate of rise for the light fluid in the pipe is smaller. This is because, at this time, the viscosity ratio of the two fluids is relatively small, and the watery fluid produced by the viscous force is rather large. At this time, due to the density difference between the two fluids being small, the light fluid produced by the inertia force is also relatively small. Under the joint action of the two, no matter how inclined the pipeline, the change in the fluid velocity will not be large. When the viscosity ratio is relatively large, the speed at which the light fluid rises in the pipeline is larger. This is because, at this time, the viscosity ratio of the two fluids is relatively large, although the light fluid produced by the viscous force change is not. However, the difference in the density between the two fluids is very large at this time, leading to the light fluid's inertia force being larger. Additionally, the rising speed of the watery fluid is mainly dominated by the force of inertia. When the pipeline is tilted, because the inertia force is larger, the changes in the tilted axial inertia force of the pipeline will be larger. This will this lead to a large change in the velocity of the rising fluid under this viscosity ratio.
## 2 Optimization Problem  
### Cost Analysis of Three-Stage Helical Gearbox  
In practice, the cost of a gearbox depends on many cost elements, including the cost of the casing, shafts and gears, and bearings. However, due to the complicated cost calculation, the cost of bearings has not been considered in this study. As a result, the cost of a three-stage helical gearbox, namely \(C_{gb}\), can be calculated as the Equation (1):  
Figure 1: Gear mass versus second stage gear ratio [1].  
Figure 2: Partial gear ratios versus total gearbox ratio.  
\[C_{gb}=C_{g}+C_{gh}+C_{s} \tag{1}\]  
where \(C_{g}\), \(C_{gh}\), and \(C_{s}\) indicate the cost of gears, the cost of gearbox housing and the cost of shafts respectively.  
Theoretically, the cost of a gear (the price of a gear) includes material costs, machining costs, heat treatment costs, labor costs including management and overhead costs, etc. The gear cost also depends on the gear shape and the gear size. The above component costs help to calculate the cost of a gear. In addition, in practice, the gear cost is usually calculated by unit price per kilogram and it varies by company policy and periodically. Therefore, in this study, the gear cost is investigated as a variable and calculated by the Equation (2):  
\[C_{g}=c_{g.m}\cdot m_{g} \tag{2}\]  
in which, \(c_{g.m}\) is the cost per a kilogram of gears (USD/kg), and \(m_{g}\) is representative for the mass of all gears in the gearbox (kg).  
The cost of gearbox housing can be determined by Equation (3):  
\[C_{gh}=c_{gh.m}\cdot m_{gh} \tag{3}\]  
in this situation, \(c_{gh.m}\) is the cost per a kilogram of gearbox housing (USD/kg), and \(m_{gh}\) is the mass of the gearbox housing (kg).  
Finally, the cost of shafts is determined by Equation (4):  
\[C_{s}=c_{s.m}\cdot m_{s} \tag{4}\]  
where \(c_{s.m}\) is the cost per a kilogram of shaft (USD/kg), and \(m_{s}\) is the mass of all shafts in the gearbox (kg).  
Based on previously mentioned equations, it can be drawn that in order to get the cost of the gearbox (\(C_{gb}\)) two factors should be identified. The first is the cost per a kilogram of gears, gearbox housing, and shafts which are varied according to the market. The second is the mass of gears, the gearbox housing, and shafts corresponding to \(m_{g}\), \(m_{gh}\), and \(m_{s}\). However, it is noticed that the first factor is beyond the scope of this study, because it depends on the price of commercial markets. Then the later will be obtained by the detailed calculations in the next part of this study.