esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
###### Abstract  
As the core component of in-wheel motor-driven electric vehicles, the in-wheel motor (IWM) directly affects the driving/braking performance of each driving wheel and the driving performance of the vehicle. The IWM operation involves a coupling of multi-fields, including the electromagnetic, temperature, flow, and mechanical fields, which influence each other. It is necessary to study coupling analysis methods to obtain accurate and consistent results. In this paper, a 15 kW in-wheel motor is taken as the research object. Based on the finite element model of the IWM, the coupling factors between the electromagnetic and temperature field, and the influence trend of coupling factors on the two fields are investigated. On this basis, considering the strong coupling factors obtained from the above analysis, the unidirectional coupling and bidirectional coupling analysis methods are used to analyze the electromagnetic-temperature characteristics of the IWM, and the comparative results between the two methods are discussed. It was found that the results showed the temperature of the IWM calculated by the bidirectional coupling method was higher than that obtained by the unidirectional coupling analysis method. The maximum temperature of stator windings calculated by bidirectional coupling was 7.1% higher than that calculated by unidirectional coupling analysis, and the effect on the relative difference of torque could reach 7.4%. Bidirectional coupling can more accurately reflect the variation of variables in the fields and the prediction of motor performance in the process of motor operation. The progress made in the electromagnetic-temperature coupled analysis method can provide a theoretical basis and useful ideas for the multi-fields coupling analysis of IMMs.  
coupled analysis method; electromagnetic; temperature; in-wheel motor
## 2 Study Area and Data Collection  
Guangzhou, with a total area of 7434.4 km\({}^{2}\), is located in southern China (shown in Figure 1). As one of the fastest growing cities in China, Guangzhou has become a typical industrial city, with a high intensity of economic activities causing serious urban environmental problems, including suburban underlying surface anomalies [36; 37].  
One Chinese disaster mitigation satellite constellation-Huanjing (environment in Chinese, including HJ-1A/1B/1C) which was launched in 2008, is capable of monitoring the environment with wide coverage and high revisit interval at medium resolution. This paper used the HJ-1B to investigate further the correlation between the ISA and LST. HJ-1B satellite contains 2 CCD optical cameras, both with 4 bands (30 m resolution) and a broad swath infrared camera (300 m resolution).
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### The Potential of Logistics and Capital Flow Networks in the Greater Bay Area
The comparative analysis of logistics and capital flow networks in the Greater Bay Area reveals that cities are extensively connected within the logistics network. This network exhibits an increasing diameter and decreasing degree centrality. Although overall efficiency declines, high agglomeration persists, indicating that regional logistics depend on robust cooperation. However, the decrease in marginal returns highlights the heterogeneity and expanding distribution of the logistics network, resulting in diversification and increased complexity. This shift narrows the importance gap among cities in the network. The optimization of the network structure facilitates mutual enhancement of circulation and cooperation among elements. The synergy between logistics and capital flow networks underscores the interconnection between transportation and economic activities in the Greater Bay Area. The logistics network underpins the flow of goods, establishing conditions conducive to interregional capital movement. Consequently, the influx of capital from regional cities fosters infrastructure development and enhances logistics capacity.
## 3 Results and Discussion
### Insight into Formation of PDA-Au by Plasma-Assisted Method
Firstly, HAuCl\({}_{4}\) reacts with the catechol moieties on PDA NS's surface to form a Au\({}^{3+}\)-catechol complex. After this, under the plasma treatment, solvated electrons and H\({}_{2}\)O\({}_{2}\) can directly reduce the Au\({}^{3+}\)-catechol complex into AuNPs anchored on PDA NS's surface. Figure 5: Scanning electron microscope images of plasma-synthesized PDA-Au in presence of (**a**) e\({}^{-}\) scavenger and (**b**) \(\bullet\)OH scavenger.
## Methods
### Data  
#### 2.2.1 Flux Tower Data  
The net ecosystem exchange (NEE) was measured using the eddy covariance (EC) technique [45]. Shortly, the measurement system, located 23.3 m above the ground, included an ultrasonic anemometer (Solent Research 1012R2, Gill Instruments Ltd., Lymington, Hampshire, UK) for measuring three wind speed components and temperature and a closed-path infrared gas analyzer (LI-6262, LI-COR Biosciences, Lincoln, NE, USA), which measured CO\({}_{2}\) and H\({}_{2}\)O concentrations. The vertical CO\({}_{2}\) fluxes were calculated over 30 min averaging periods by block averaging approach using the EddyUH software [46]. The fluxes were corrected for high frequency response underestimation by using the co-spectral transfer functions calculated according to [PERSON] et al. [47] together with in-situ parameterization of the co-spectral model derived from ensemble mean of measured temperature co-spectra [47]. The CO\({}_{2}\) flux was then corrected for storage change to obtain NEE. The 30 min averaged NEE was partitioned into total ecosystem respiration (TER) and gross primary productivity (GPP) as described in [PERSON] et al. [48]. The air temperature and the incoming photosynthetically active radiation (PAR) were also measured at the site above canopy.  
Canopy light use efficiency was calculated based on GPP (in \(\upmu\)mol CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\)) and incoming PAR (\(\upmu\)mol photons m\({}^{-2}\cdot\)s\({}^{-1}\)) as done by [PERSON] et al. [24] after Monteith [49]:  
\[\mathrm{GPP}=\mathrm{LUE}\ \times\ \mathrm{PAR}\ \Leftrightarrow\mathrm{LUE}= \ \frac{\mathrm{GPP}}{\mathrm{PAR}}\]
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 3 Results of the Review
### Key Issues to Be Addressed
It should be noted that IoT is a promising technology not only in the evolution of Industry 4.0 and Industry 5.0 but also in smart home automation (SHA), smart sustainable cities, energy management systems, and municipal resources (water, waste), which will enable better integration of the above-mentioned areas, extending M2M wireless communication beyond smart factories to many other fields, including monitoring of the product life cycle, its carbon footprint, and full recycling. One noteworthy solution is a real-time location monitoring system based on Bluetooth low energy (BLE) technology for wide applications, including even underground communication as part of industrial positioning systems. This allows us to look anew at automatic storage and high-bay systems, which will be able to grow not only upwards but also downwards (Figure 8) [22]. To sum up, the 5G networks currently in use will not satisfy the requirements expected to be necessary for operation from 2030 onward in terms of delays, throughput, and traffic density [23]. Currently, much research is being carried out on both the physical layer and higher layers of the International Organization for Standardization/Open Systems Interconnection (ISO/OSI) model, the results of which are intended to provide 6G networks with higher performance and the ability to implement new functions in M2M connections, thanks to global coverage, very low delays, high reliability, mass connectivity, improved intelligence and security, non-orthogonal multiple access, MIMO, energy harvesting, hybrid terrestrial and satellite relays, improved IIoT-based automation, millimeter wave band utilization, interference management, hybrid precoding, and statistical shaping beams [24]. Additional requirements for 6G networks are imposed by the sources of industrial data (including medical data, as in the case of the Internet of Medical Things--IoMT) and, on the other hand, by the methods of their use: mass processing, analysis, classification, inference, and prediction from data, including for the purposes of automation, computerization, and  
Figure 8: Key challenges of 6G [22]. technical control as well as analysis of the life cycle of products and carbon footprint of processes [25; 26; 27].
[PERSON] et al. [37] proposed a new simple but practical boundary identification-load evolution (BILE) model to deal with structural optimization problems under pressure loads. Using this method, the design of an underwater pressure hull was explored. The considerable advantage of the topology optimization method involving pressure loads based on the BILE model is that it can complete the iteration within 80 to 100 iteration steps. The computational efficiency is greatly improved compared with the previous method, and the optimal configuration of the result is similar to that of the previous method. The density gradient method proposed by [PERSON] and [PERSON] [38] to track a loading surface was similar to a multi-physics model. Design-dependent loads were applied through the area integration of the density gradient without the need for a loading surface search. The SIMP method is an optimization solver that is more mature than other methods, and it can solve a number of topology optimization problems. Although buckling-constrained topology optimization has been studied in previous work, the problem has not been considered under design-dependent loads using the SIMP method. Likewise, research has been conducted on topology optimization under design-dependent loads. Based on the above discussion, the boundary searching method plays a significant role in solving this problem, and the DRLSE loading surface search algorithm based on image segmentation, which is easier to apply, is used in this study. However, the objective has always been to obtain the maximum stiffness structure subjected to volume constraints. The goal of this research was to solve structural topology optimization problems considering buckling and volume fraction constraints under design-dependent loads. A comparison with the compliance minimization results is also provided in this paper. In addition, the method used in this work is preliminarily applied to designing underwater pressure structures. This paper is organized as follows. Section 2 gives the problem statements for buckling-constrained compliance minimization. Section 3 describes the derivations of the expressions for the sensitivity of the constraint functions and objective functions. The methods for pressure loads are described in Section 4. The optimization algorithm is given in Section 5. In Section 6, numerical examples are presented, as well as an analysis of the differences between with and without bucking-constrained solutions. Section 7 provides concluding remarks.
## 2 Materials and Methods
### Data Analysis  
The _TS_ data was analyzed from the 16 live mullets. The measured _TS_ data from the mullet among the total _TS_ data within the water column were extracted using the specific _Target collection and TS distribution_ module in Visual Analyzer software (Ver. 4.3) provided by the equipment manufacturer [15].  
To remove unwanted _TS_ data and to obtain the valid data, we used criteria that satisfied both conditions simultaneously; target depth and off-axis position. Considering that the depth of activity of the tethered mullet was 4.5-5.5 m, only the _TS_ data that satisfied the depth range among all _TS_ data was extracted. The half beam widths of the transducers used in the _TS_ experiments were \(\pm 4.6^{\circ}\) at 38 kHz, \(\pm 3.9^{\circ}\) at 120 kHz, \(\pm 3.45^{\circ}\) at 200 kHz, and \(\pm 3.5^{\circ}\) at 420 kHz. Considering the compensated _TS_ value with beam width, only the _TS_ data to be positioned within the half beam-width angle at each frequency were extracted. We calculated the mean _TS_ value using only the _TS_ data that satisfied both of the above conditions simultaneously.  
All selected _TS_ data was converted to the backscattering cross-section (\(\sigma_{bs}=10^{\text{TS}/10}\)) for the linear value, which was used to calculate the mean _TS_ values [16]. The mean _TS_ was defined by  
\[\text{mean }TS=10\text{ log}_{10}(\Sigma\sigma_{bs}/n_{\text{i}}) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **38 kHz** & **120 kHz** & **200 kHz** & **420 kHz** \\ \hline Beam type & \multicolumn{4}{c}{Split-beam} \\ Source level (dB) & 212.6 & 219.6 & 220.5 & 219.0 \\ \(-3\) dB Beam width (\({}^{\circ}\)) & 9.2 & 7.8 & 6.9 & 7.0 \\ Absorption coefficient (dB/m) & 0.00876 & 0.03916 & 0.05666 & 0.10118 \\ Pulse duration (ms) & \multicolumn{4}{c}{0.2} \\ Ping rate (pps) & \multicolumn{4}{c}{2} \\ Temperature (\({}^{\circ}\)C) & \multicolumn{4}{c}{13} \\ Salinity (psu) & \multicolumn{4}{c}{32} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Several scientific echo-sounder parameters used for the target strength (_TS_) measurements of Redlip mullet (_Chelon haematochelus_).  
The analysis was performed using three linear fitting models (Equations (2)-(4)) [17]. The _TS_ functions were fitted to the mean _TS_ data using the least-squares regression with _FL_ and _W_:  
\[TS=m\ \text{log}_{10}(FL,\text{cm})+b \tag{2}\]  
\[TS=20\ \text{log}_{10}(FL,\text{cm})+b_{20} \tag{3}\]  
\[TS=m\ \text{log}_{10}(W,\text{g})+b \tag{4}\]  
where \(m\) is the slope (20 is the standard slope) and \(b\) and \(b\)20 are the intercepts, respectively [17].  
The _TS_ value is strongly affected by the tilt angle of the fish. In order to select the _TS_ data that can judge the tilt angle, only the underwater images that can visually measure the angle were extracted. From the captured underwater image data, the tilt angle was estimated from the gradient of a straight line connecting the head to the tail. The tilt angle between the incident wave and the fish was defined as a positive angle (+) when the head was upward and a negative angle (-) when the head was downward.
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 4 Analysis on Eavesdropping Probability
### Analysis of Fri-Jam Schemes
#### 4.2.2 Case II: FJ-Ran Scheme  
We then analyze the case of FJ-Ran, in which all the jammers are randomly distributed in the network. Recall that both jammers and legitimate users are distributed according to PPP while they have the different distribution parameters. In particular, we denote the density of legitimate users by \(\lambda_{1}\) and the density of friendly jammers by \(\lambda_{2}\). Based on the well-known stochastic geometric results [31], we can obtain Theorem 3 on the probability \(P_{e}\) that one transmission has been eavesdropped as follows.  
**Theorem 3**.: _In FJ-Ran scheme, the probability \(P_{e}\) that one transmission has been eavesdropped is_  
\[P_{e}=\int\limits_{r>0}e^{-\mu T_{p}r^{\alpha}\sigma^{2}}\cdot L_{I_{t}}(\mu T _{p}r^{\alpha})\cdot L_{I_{j}}(\mu T_{p}r^{\alpha})e^{-\lambda_{1}\pi R^{2}}2 \pi\lambda_{1}rdr,\]  
_where \(L_{I_{t}}[\mu T_{p}r^{\alpha}]=\exp(-\pi r^{2}\lambda_{1}\rho(T_{p},\alpha))\), \(L_{I_{j}}[\mu T_{p}r^{\alpha}]=\exp(-\pi r^{2}\lambda_{2}\rho(T_{p},\alpha))\) and \(\rho(T_{p},\alpha)=T_{p}^{-2/\alpha}\int_{T_{p}^{-2/\alpha}}^{\infty}\frac{1} {1+\mu^{2/2}}d\mu\)._  
Proof.: According to the channel model defined in Section 3.2, we have the  
\[\begin{split} P_{e}&=\int\limits_{r>0}P\left[ \frac{P_{t}hr^{-\alpha}}{\sigma^{2}+I_{t}+I_{j}}>T|r\right]e^{-\lambda\pi r^{2} }2\pi\lambda rdr\\ &=\int\limits_{r>0}P[h>T_{p}r^{\alpha}(\sigma^{2}+I_{t}+I_{j})|r ]e^{-\lambda_{1}\pi R^{2}}2\pi\lambda_{1}rdr.\end{split} \tag{10}\]  
Following the similar analysis procedure to [31], we then have  
\[P[h>T_{p}r^{\alpha}(\sigma^{2}+I_{t}+I_{j})|r]=e^{-\mu T_{p}r^{\alpha}\sigma^{ 2}}\cdot L_{I_{t}}(\mu T_{p}r^{\alpha})\cdot L_{I_{j}}(\mu T_{p}r^{\alpha}). \tag{11}\]  
Substituting \(P[h>T_{p}r^{\alpha}(\sigma^{2}+I_{t}+I_{j})|r]\) in Equation (10) by RHS of Equation (11), we finally prove the above result.  
It is shown in Theorem 3 that the probability \(P_{e}\) heavily depends on both the node density \(\lambda_{1}\) of legitimate users and the node density \(\lambda_{2}\) of jammers, and the channel conditions.
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 3 Our Method
### Featured Challenges and Solutions
#### 3.1.3 Necessity for Efficacious External Interaction and Shared eHMI  
Wheelchairs cohabitate with pedestrians in non-motorized zones. Pedestrian apprehension, stemming from trust deficits and ambiguity regarding wheelchair intentions, mirrors automotive scenarios [46]. Pedestrians, analogously to drivers responding to external signals, prefer real-time insights into autonomous wheelchair motion trajectories. Our on-ground projection system, functioning as a form of augmented reality, meets eHMI requisites by projecting the imminent path to inform pedestrians. This transparent communication aids pedestrians in comprehending wheelchair intentions and adjusting accordingly, attenuating potential axxieties.  
The adoption of this shared eHMI strategy significantly enhances reciprocal comprehension and mutual situational awareness, leading to a synergistic and cooperative dynamic among users, pedestrians and the autonomous wheelchair system.
## 3 Methods
### Staging  
This phase is the key contribution of this research, because most current CADs are capable of tumor detection but not staging assessments. Staging measures the severity of lung cancer by determining how large the tumors and how much they have spread. Knowing the exact stage of a tumor can give a great deal of help in providing precise treatments for patients. Hence, it plays a vital role in increasing the patient's survival rate. As we described earlier, our second-stage classification aims to perform the staging of true tumor nodules. To carry out this task, we reused the nodule features of truly predicted tumor by the first-stage classification. Furthermore, we extracted additional locational features to determine the invasion and spread of the tumors.
## 2 Materials and Methods
### Alternative Photogrammetric Processing Scenarios  
To validate our selection of the standard processing parameters, different tests were performed, which are shown in Table 2. The first test focused on image alignment accuracy setting, which was varied from \"Low\" to \"Highest\" and encompassing \"Medium\" and \"High\" accuracy settings, while other parameters were left unchanged. Using \"High\" accuracy, image alignment is performed using original images. Using \"Highest\", the software upscales images by a factor two in each direction, while lower accuracy settings will see image resolution decreasing incrementally by a factor two. A compound test was performed in the case of \"High\" image alignment accuracy to assess the effect of disabling the reference pair preselection, which uses image coordinates to help finding matches.  
Optimum strategies during photogrammetric model optimization were assessed through the declination of different scenarios (cf. Table 2). The Sillon de Talbert field site, with reduced superficiies and a comparatively large number of well-distributed targets, was used as a testing ground. The first scenario (S-GCP) used all ground targets (\(n=21\)) as GCPs with camera information disabled during processing. This scenario is equivalent to the classical approach relying solely on GCPs for camera calibration and model georeferencing. The second scenario (S-RTK-GCP) used all targets as GCPs, as well as camera information (location and attitude), for the optimization. Other scenarios using camera information were implemented by varying the number of GCPs used, hereafter referred to as S-RTK, S-RTK-1 GCP, S-RTK-3 GCP, S-RTK-5 GCP and S-RTK-9 GCP, corresponding to using 0 (i.e., no GCPs), 1, 3, 5 and 9 GCPs, respectively. Scenarios using 1 and 3 GCPs were replicated (differentiation is done using subscripts 1 and 2) in that targets selected as GCPs were changed to test the effect of GCP location on model quality.  
To facilitate results' comparison for the different scenarios tested and to expedite data processing, evaluations were performed on 1 m resolution DEMs created from the sparse point clouds. This decision was supported by the fact that dense matching has limited effect on the overall model georeferencing and geometry (e.g., 3D deformations that may be present in the sparse models following model optimization), improving mainly the representation of fine-scale features through a higher model resolution.
## 4 Case Studies and Process Description  
The investigations are carried out for three design cases of commercial methanol production, which mainly differ in terms of the reforming technology (sub-system II), heat integration concept (sub-system V) and total capacity. Common to all designs is a pretreatment unit for desulphurization of natural gas (sub-system I), a methanol synthesis in conjunction with a recycling of unconverted syngas (subsystem III) and the distillation unit for crude product purification (sub-system IV). The three cases refer to  
1. conventional SMR with low-pressure methanol synthesis for a production capacity of 2200 MTPD,
2. a serial two-step configuration of SMR and ATR with low-pressure methanol synthesis for a production capacity of 4750 MTPD, and
3. a parallel configuration of SMR and DMR with low-pressure methanol synthesis for a production capacity of 5000 MTPD. A flow sheet and the corresponding stream data for Cases I, II and III are given in Figures 3-5, and in Tables 3-5, respectively. The plant designs differ considerably in terms of thermodynamics and economics. The use of different reforming technologies and reforming agents results in a varying effluent composition and mass flow rates from sub-system II. This has implications on the required syngas conditioning technology, the size of the syngas compression unit and on the performance of the methanol synthesis unit. Thus, deviations from the ideal composition signify a lower selectivity and conversion, and consequently higher recycle ratios in the synthesis unit (Figure 2). This affects the size of the compressors negatively and results in increased electricity consumption. In Case I, water is used at a S/C-ratio of 3.5 to result in a low methane slip of 1 mole-% (stream 6). The tubular SMR takes place at 25 bar and is fired by natural gas in a furnace (R-04). Two large compressors CM-02 and CM-03 feed air to the combustion process. The syngas contains excess hydrogen and has a H\({}_{2}\)/CO-ratio of 5, which is far above the stoichiometric requirement of the synthesis (\(S\) module \(\sim\)2). For syngas adjustment, the syngas is blended with a CO\({}_{2}\) (stream 9). Due to the high CO\({}_{2}\) mole fraction, the composition is outside of the ideal composition range (elongated ellipse) in Figure 2, resulting in a low methanol yield of 6.7 mole-%. Accordingly, the recycle-to-feed-ratio (RTFR) has a relatively high value of 7.7. After the synthesis, the process gas passes a flashing unit to separate the liquid crude product from gaseous components (stream 12 and 13). Within the distillation columns, methanol is separated from light ends (butane, propane) and water. A purified stream of 31.3 kg/s methanol is produced according to a methane intensity of 1.86 kg\({}_{\text{CH3 OH}}\)/kg\({}_{\text{CH4}}\). With respect to Case II, syngas generation is conducted in a serial two-step reforming unit, using steam and oxygen as reforming agents (stream 3 and 5 in Table 4). The heat of the ATR (R-05) is recovered as medium pressure steam, which is provided as a heat source to the tubular SMR (R-04).