esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hybrid Dispatching Scheme
### User Incentive Scheme  
The user incentive dispatching algorithm proposed in this paper is based on the idea of Crowdsourcing, which assigns the task of bicycle dispatching to various users of the shared bicycle, so as to achieve the goal of bicycle dispatching. The user incentive dispatching is a kind of micro-cycle dispatching with low cost and wide range, which is suitable for situation with a few bicycles and small dispatching scale. There are two difficulties in user incentive dispatching:  
* The first is how to devise effective incentive strategy to encourage users to accept the Crowdsourcing dispatching task;
* The second is how to choose the target station of the detour to make the distribution of bicycle more balanced and reasonable.  
For the first question, we do not consider it in the paper due to some complicated inducements like the personal condition, rental moment of bicycle, and the weather. The user incentive dispatching proposed in the paper is to make appropriate strategies to enable users to pay back their bicycles for a better choice and maximize the operator's revenue.  
As shown in Section 4.2, we transformed user incentive dispatching into the following mathematical problems: A trip record shows the user going from \(start\_station\) to \(end\_station\). In path1, the user travels directly from \(start\_station\) to \(end\_station\). In path2, the user selects the detour station \(d_{1}\) and then goes to \(end\_station\). In path3, the user selects the detour station \(d_{2}\) and then goes to \(end\_station\). When the user chooses to detour, he will get a incentive \(cost_{u}\) (\(cost_{u}\) is assumed to be fixed here for the sake of system simplification). In Figure 3, we select all bicycle stations in the \(r\) field of the \(end\_station\) in STG as the candidate detour station set \(d=\{i\ |\ |\ \textbf{i}-end\_station\ \|\leq r\}\), where \(r\) is the maximum distance a user can walk when detouring. Then, we select the station which has maximum \(pred_{i}(t)\) in the detour station set \(d\) as the detour node. The details of user incentive dispatching are shown in Algorithm 2.  
```
0: The state information of all nodes in STG right moment \(S(t)\), the destination of the current user \(end\_station\).
0: Recommended detour node for user. Initialize\(t=t_{n}\),\(end\_node=end\_station,detour\_node=\{end\_station\},r\) for each vertex \(i\) in \(G\)do if\(\parallel i-end\_station\parallel\leq r\)&&\(i\
eq end\_station\)then \(detour\_node\bigcup\{i\}\); endif endfor \(candidate=end\_station\); if\(\max_{i\in detour\_node\,imb_{i}>0\)then \(candidate=\max_{i\in detour\_node\,imb_{i}}\); endif  Return candidate.
```  
**Algorithm 2**\(Man\_dispatch(end\_station,S(t_{n}))\)  
Figure 3: User incentive dispatching scheme.
# Two-Dimensional Constellation Shaping in Fiber-Optic Communications
###### Abstract  
Constellation shaping has been widely used in optical communication systems. We review recent advances in two-dimensional constellation shaping technologies for fiber-optic communications. The system architectures that are discussed include probabilistic shaping, geometric shaping, and hybrid probabilistic-geometric shaping solutions. The performances of the three shaping schemes are also evaluated for Gaussian-noise-limited channels.  
constellation shaping; probabilistic shaping; geometric shaping; fiber-optic communications; quadrature amplitude modulation; mutual information; generalized mutual information +
Footnote †: journal: Applied Physics Letters
# NOMA Clustering for Improved Multicast IoT Schemes
## 3 System Model and Problem Formulation
### System and Channel Models
Where \(|g_{ij}|\) are modeled following a zero-mean, unit-variance, Rayleigh distribution, \(d_{ij}\) is the distance between \(\text{UE}_{i}\) and \(\text{UE}_{j}\), and \(\alpha_{ij}\) is the path loss exponent. In the considered context, we assume that the relative speed between the nodes and the BS is not considerable. Thus, the coherence times of the different fading channels are assumed to be long enough to span over the execution time of the proposed algorithms.
## 4 Result
### Landslide Detection by SBAS
Similarly, for the descending data, the method detected 32 landslides, while the SBAS detected 25. These results show that the multi-direction phase gradient stacking method detects more landslides on both the ascending and descending orbits than the SBAS. The difference in detection results is mainly due to the limitations of SBAS in effectively capturing deformations in low-coherence regions that have been represented as real landslides in previous studies [42]. However, the method in this paper is excellent at detecting landslides in these challenging areas, and helpsto identify more landslides. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Number of Landslides Detection**} \\ \cline{2-3}  & **Ascending** & **Descending** \\ \hline Multi-direction & & \\ phase gradient stacking & 26 & 32 \\ SBAS & 19 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The phase gradient stacking method and the SBAS method landslide quantity detection statistics. Figure 6: LOS deformation results of the ascending and descending tracks obtained by the SBAS method. (**a**) ascending track; (**b**) descending track. In these representations, the blue line is the Bailong River and the dashed box indicates the two key areas of analysis.
## 3 Results and Discussion
### Quality Evaluation  
Those quantitative evaluation metrics, including MI [22], QAB/F [23], QLLSIM [24], and VIFF [25] are often used to evaluate the performances of different fusion methods. A cell ROI region segmentation method based on the improved Otsu algorithm in Section 2 is applied to make the results more convincing.
## 1 Introduction
Various differently sized networks are considered, and our approach gives better results than EEABR algorithm in terms of energy consumption. The main goal of our study was to maintain network life time at a maximum, while discovering the shortest paths from the source nodes to the base node using a swarm intelligence based optimization technique called ACO. A multi-path data transfer is also accomplished to provide reliable network operations, while considering the energy levels of the nodes. We also implement our approach on a hardware component to allow designers to easily handle routing operations in WSNs. The preliminary report on this work may be seen in [18]. The rest of the paper is organized as follows. In Section 2, the proposed routing scheme using ACO is explained by an illustrated example scenario. In Section 3, performance results obtained from the simulations are given. In Section 4, implementation of the approach is presented with hardware simulation results. Finally, in Section 5 we conclude our study and give our future work plan.
# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 4 Experimental Results
### Amplification Rate and Non-Specific Rate  
When a cell sample collected from a blastocyst (i.e., day 5/6 embryos) contains cumulus cells, the DNA from the cumulus cells can contaminate the embryonic DNA, reducing the accuracy of the PGT test. The amplification rate and non-specific rate were regarded as two important metrics of the PGT test. The amplification rate was calculated as the percentage of the number of amplified genetic samples containing target bands after gel electrophoresis over the total number of amplified genetic samples. The non-specific rate was calculated as the percentage of the number of amplified genetic samples containing non-specific bands after gel electrophoresis over the total number of amplified genetic samples. The target bands and the non-specific bands were distinguished based on the target band size.  
A total of 120 mouse blastocysts were collected after cumulus cell removal and ICSL Among them, there were 60 blastocysts in the robotic group and 60 blastocysts in the manual group. For each experiment, DNA from five blastocysts was amplified into one target-gene sample by using a single target-gene primer in the PGT procedure. In total, twelve target-gene samples were obtained in either the robotic group or the manual group for gel electrophoresis. The testing results of four target genes are summarized in Table 3.  
The amplification rate of the robotic group was higher than that of the manual group (97.9 \(\pm\) 4.2% vs. 85.4 \(\pm\) 8.0%), and the non-specific rate of the robotic group was lower than that of the manual group (6.3 \(\pm\) 6.1% vs. 29.2 \(\pm\) 10.8%). The representative banding patterns produced by gel electrophoresis are shown in Figure 11.  
Figure 11: Representative banding patterns produced by gel electrophoresis. (**a**) E6 and E7. (**b**) XBP1 and \(\beta\)-Actin. # represents the sample number, M represents the manual group, R represents the robotic group, and N represents the negative control group without target-gene samples, ensuring that the PGT procedure was free from contamination.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Target Genes**} & \multirow{2}{*}{**Target Band Size (bp)**} & \multicolumn{2}{c}{**Amplification Rate (\%)**} & \multicolumn{2}{c}{**Non-Specific Rate (\%)**} \\ \cline{3-6}  & & **Manual** & **Robotic** & **Manual** & **Robotic** \\ \hline E6 & 644 & 91.7 (11/12) & 100 (12/12) & 25.0 (3/12) & 16.7 (2/12) \\ E7 & 753 & 75.0 (9/12) & 91.7 (11/12) & 41.7 (5/12) & 0 (0/12) \\ XBP1 & 113 & 83.3 (10/12) & 100 (12/12) & 16.7 (2/12) & 0 (0/12) \\ \(\beta\)-Actin & 96 & 91.7 (11/12) & 100 (12/12) & 33.3 (4/12) & 8.3 (1/12) \\ Mean \(\pm\) SD & – & 85.4 \(\pm\) 8.0 & 97.9 \(\pm\) 4.2 & 29.2 \(\pm\) 10.8 & 6.3 \(\pm\) 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PGT results of target genes.
## 4 Experiment  
In this section, we present the experiment we conducted to assess the capability of our SGIM-PB algorithm. The experimental setup features the 7 DOF right arm of an industrial Yumi robot from ABB which can interact with an interactive table and its virtual objects. Figure 1 shows the robot facing a tangible RFID interactive table from [47]. The robot learns to interact with it using its end-effector. It can learn an infinite number of hierarchically organized tasks regrouped in 5 types of tasks, using sequences of motor actions of unrestricted size. The experiments have been carried out with the physical industrial robot ABB and with simulation software provided with the robot, Robotstudio. While the software provided by the robot can provide static inverse kinematics, it can not provide movement trajectories.  
We made preliminary tests of our SGIM-PB learner on a physical simulator of the robot. We will call this the _simulation setup_. In the simulation setup, the end-effector is the tip of the vacuum pump below its hand. The simulation setup will be modified as a setup called _left-arm setup_ in Section 5.5.  
We also implemented this setup for a physical experiment, to compare both interactive strategies more fairly using SGIM-ACTS and SGIM-PB. For that, we modified the procedural teacher strategy so they have the same limited repertoire from which to draw demonstrations as action teachers. We also added an extra more complex task without demonstrations to compare the autonomous exploration capability of both algorithms. The setup, shown on Figure 1, will be referred to as the _physical setup_. In the physical setup, the end-effector is the bottom part of the hand.  
In the following subsections, we describe in more details the physical setup and the simulation setup and their variables, while mentioning their differences. Then we end this section by presenting the teachers and evaluation methods.
## 2 Theory and Calculation Model
### Sputtering Model  
The erosion of the channel wall primarily comes from the impingement of the energetic ions. The erosion rate \(q\) of the channel wall is related to the sputtering yield of the ceramic wall as well as the velocity and angle distribution of the incident particles, and it can be described as [22]:  
\[q=\frac{I_{l}Y(E_{ni},\theta_{i})\cos\theta}{N} \tag{7}\]  
where \(I_{l}\) is the ion incident flux density, \(N\) is the atomic density of the sputtered wall material, \(\theta_{i}\) is the angle between the ion incident direction and the surface normal direction, and \(Y(E_{ni},\theta_{i})\) is the sputtering yield. For the erosion of the channel wall, the sputtering yield represents the number of eroded particles per incident ion at energy \(E_{ni}\) and at normal incidence \(\theta_{i}\), and the influence of \(E_{ni}\) and \(\theta_{i}\) is independent [23]. Therefore, the expression of \(Y(E_{ni},\theta_{i})\) can be written as [24]:  
\[Y(E_{ni},\theta_{i})=S(E_{ni})\cdot Y^{\prime}(\theta_{i}) \tag{8}\]  
Here, \(S(E_{ni})\) is the ion energy sputtering coefficient, and it is related to the sputtering threshold \(E_{th}\) of the wall material. In this paper, the value of \(E_{th}\) is set as 30 eV and the universal semi-empirical formula for \(S(E_{ni})\) based on the Sigmand theory is adopted [24,25]. \(Y^{\prime}(\theta_{i})\) is the ion incident angle-dependent sputtering yield. It is based on a Yamamura empirical formula, which is summarized by experimental results on a large number of materials sputtering [26].  
In the simulation, the change of wall morphology caused by the sputtering erosion is not considered. All the results presented in this paper are obtained after convergence. The time at which the simulation reaches the steady state is denoted as \(t=0\), and all statistics mentioned below start from this moment.
# Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results
## 1 Introduction
### Background and Motivation
To facilitate the development and evaluation of new diagnostic ultrasound methods and algorithms, researchers require a dedicated system that features the full configurability of transmit patterns and acquisition sequences, provides access to raw echo prebeamformed received data (RF data) and has sufficient computational resources to allow for real-time imaging implementation [18]. In addition, to support the SWE technique, research platforms should also allow for the generation of long transmit bursts for push beam generation. To date, the Vantage system with an \"extended transmit option\" (Verasonics, Kirkland, WA, USA) is the only research scanner available that features all of these functionalities. However, this scanner is bulky and expensive. In this work, we present a dedicated high-power TX add-on board (TXPB-256) for a portable and low-cost ultrasound research platform (us4R-lite), which enables the implementation of the SWE technique. In the experimental part of the study, the system performance of the shear wave speed measurement was validated using an industry-standard elastography phantom and various push beam generation techniques. The quality of the resulting stiffness map images was also evaluated. The obtained results showed that SWE implementation is feasible on a portable and limited hardware platform.
## 4 Discussion of Results  
The optimum operating conditions of both cycles were analyzed in Section 3. As mentioned, both refrigeration cycles respond to the same scheme of operation (Figure 1) and may be able to operate with one scheme or the other if some components of the plant are over-sized. However, in practice, only one design of the plant is implemented due to economic reasons, for example: if the plant is designed to be operated in both modes the cascade/subcooler heat exchanger must be sized as cascade heat exchanger, while if it were designed to be operated as MS cycle the subcooler would be size reduced. The same happens for the gas-cooler, a gas-cooler of a cascade system is smaller than that of the MS cycle. Furthermore, if optimum COP results of both solutions are compared (Figures 6 and 10) it can be seen that the MS cycle offers the best results at low environment temperatures and the cascade at high temperatures. Thus, this section is devoted to compare the COP values offered by both solutions. First, the recommended operating range of each cycle is analyzed in terms of COP, and then the results are translated to the different climatic regions of Spain through the computation of the average annual COP. The objective is to obtain conclusions about which system would be more recommended for a given evaporating level in a given climatic region.
## 5 Results Discussion  
Summarizing the obtained results, it is possible to postulate that assembly processes modeled as directed rooted trees can be optimized from the viewpoint of modularity with the above-described objectives. In other words, it is quite likely that assembly processes with higher structural modularity provide advantages, at least from the perspective of MLT and THR parameters, against processes with relatively lower levels of optimal modularity. When analyzing the results from the practical case study, it was found that MLT values varied from 14.27 min to 29.12 min (51%) between optimally modular APS (No.10) and integral APS (No.1). In addition, when comparing the same two APSs (No.10 and No.1), THR values differed from 126.03 parts/hour to 61.81 parts/hour. These significant differences justify our confirmation of the positive impact of process modularity on the efficiency of assembly processes in mass customization environments. By comparison of the three modularity indicators, using them to identify optimal modularity of process structures, the M(G) indicator seems to be more appropriate for the given purpose than Qd or CMI metrics. This assertion can be explained as follows: the M(G) indicator was specially developed to map this modularity attribute of manufacturing and assembly processes, while the Qd indicator was originally created for community networks, and was only subsequently adopted for optimal modularity measurement of manufacturing and assembly processes [48]. Moreover, both the M(G) and Qd indicators are similarly sophistic in their nature, which was rigorously verified in Section 4. Thus, it can be stated that they are applicable in a wide range of applications. The reason to employ the relative modularity indicator, CMI, was to show the differences between this indicator and the two indicators devoted to identifying the most optimal modular structures.  
Based on the obtained research results, answers to the research questions can be formulated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**APSs No.** & **Qd** & **CMI** & **M(G)** & **MLT (min)** & **THR (Parts/h)** \\ \hline
51 & 0.465 & 0.333 & 0.0656 & 14.28 & 126 \\
52 & 0.456 & 0.385 & 0.067 & 14.28 & 126 \\
26 & 0.465 & 0.333 & 0.0685 & 14.28 & 126 \\
13 & 0.472 & 0.333 & 0.071 & 14.28 & 126 \\
31 & 0.456 & 0.385 & 0.0718 & 14.28 & 126 \\
10 & 0.486 & 0.333 & 0.0759 & 14.27 & 126.03 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._Answer to RQ 1. According to findings from the comparison of the process modularity indicators, it can be stated that at least the two measurement methods using indicators Qd or M(G) can be applied to determine an optimal process structure from the realistic alternatives.  
Answer to RQ 2. Based on the obtained results from the both examined cases, one can see that process modularity positively impacts the manufacturing lead time and throughput rate. When the modularity of the manufacturing process increases, then MLT decreases and THR increases.
# FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle
## 4 Design of the IPMSM and IM Using the Proposed Procedure
### IPMSM Design Example  
The number of poles and number of slots should be determined at the first step. Given 3200 rpm as the maximum speed of the machine, the number of poles can be a value in the range of 10 to 20 poles based on the stator fundamental frequency in the range 250 to 500 Hz.  
Based on scaling of a commercial line-start induction machine design to the proposed 370 mm stator outer diameter, 72 slots were chosen for the stator. The selection of a value of slot per pole per phase (SPP) of 2, based on torque ripple and THD considerations, with 72 slots determines the machine number of poles which is equal to 12.  
The inner to outer stator diameter ratio of 0.6 to 0.8 is a typical value for electrical machines (for instance, the IM in the Tesla model S used 0.6 [59]), and a value of 0.7 was used. Given 370 mm for the outer diameter, the inner diameter will be equal to 259 mm. Given the dimensions of the machine and the high operating temperatures, a 1 mm airgap length is used.  
The stator yoke and tooth thicknesses are obtained using the optimization procedure described in the previous section. The resultant yoke and tooth thicknesses are 17 mm and 7.3 mm, respectively.  
Based on the specifications, the maximum DC-link voltage of the machine is 750 V which limits the maximum rms phase voltage (\(E\)) to 286 V. Therefore, the number of series turns (\(N\)) is found using Equation (1) to be 72 turns per phase.  
The 60\({}^{\circ}\) V-shaped topology is selected for the rotor based on the analysis in the previous section. As explained, the amount of PM material is adjusted to reach the 0.85 T airgap flux density. The rated current is then calculated by Equation (2). The machine's thermal performance is examined and if the temperatures are too high, the current is reduced, and the PM volume is increased if necessary to reach the expected performance within the voltage limit.  
The cross-section of the final design of the 100 kW IPMSM was presented earlier in Figure 4 (rightmost design) and Table 3 summarizes its dimensions.