esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods PVEH Model
### Numerical Procedure  
The CFX and Mechanical APDL module with Piezoelectric Extension added under commercial ANSYS software were used to conduct this research. The turbulence model applied in this simulation was the SSG Reynolds-Stress Turbulence model with scalable wall function set because this turbulence model solving transport equations for each Reynolds Stress had proved to work better on flow involving separation and vortex compared to traditional two-equation \(k\)-\(\varepsilon\) and \(k\)-\(\omega\) turbulence model [51]. The ANSYS MultiField option of external solver coupling was chosen to carry out FSI analysis. Transient scheme and advection scheme was both set with Second Order Backward Euler and High Resolution respectively. The FSI coupling for this simulation was a strong two-way  
Figure 6: **(a)** Unstructured fluid domain mesh; (b) inflation layer near wall of model; (c) close-up on tip of model with inflation layer; (d) structured mesh on model.  
coupling approach where the exchange of data between structural and CFD module occurred in a single coupling iteration and a converged solution was obtained before moving to the next time step.  
The time step size for the run was set at \(5\times 10^{-4}\) s to run for 0.3 s or equivalent to 600-time steps with 5-10 coupling iterations for each time step. Convergence was achieved when residual reaches the target of \(1\times 10^{-4}\) with an under-relaxation factor of 0.75. This time step set was able to collect data with a frequency up to 2000 Hz and thus being able to capture frequency range lower than 100 Hz with decent resolution in this simulation conducted. A maximum of 10 iterations in coupling solver and 5 iterations in CFD solver were set for each time step.  
The simulation of electrical performance is conducted using the ANSYS Harmonic response module with a Piezoelectric extension. Previous FSI simulation results act as input for obtaining the electrical result considering the vibration of PVEH plate and optimum load resistance. The range of frequency settings were from 20-100 Hz with an interval of 0.2 Hz. The optimum load resistance for simulation study was calculated using Equation (18). The voltage generated for each VIV case was obtained.
###### Abstract  
This study aimed to compare the dosimetric effect of traditional metallic ports and radio frequency identification (RFID) ports (Motiva Flora(r)) on post-mastectomy volumetric modulated arc therapy (VMAT) planning for left-sided breast cancer. Computed tomography (CT) simulation was performed on an anthropomorphic torso phantom by attaching two types of tissue expander on the left chest wall. For the comparison of CT artifacts, five points of interest were selected and compared: point A = central chest wall, B = medial chest wall, point C = lateral chest wall, point D = axilla, and point E = left anterior descending artery. VMAT planning using two partial arcs with a single isocenter was generated, and dosimetric parameters were investigated. Compared to metallic ports, RFID ports tremendously decreased distortion on CT images, with the exception of point D. The V\({}_{5 Gy}\), V\({}_{10 Gy}\), V\({}_{15 Gy}\), V\({}_{20 Gy}\), V\({}_{30 Gy}\), and D\({}_{\text{mean}}\) values of the heart in RFID ports were lower than those in metallic ports. The V\({}_{5 Gy}\), V\({}_{15 Gy}\), V\({}_{20 Gy}\), V\({}_{30 Gy}\), and D\({}_{\text{mean}}\) values of the ipsilateral lung in RFID ports were also lower than those in metallic ports. RFID ports showed superior dosimetric results for doses to the heart and lungs as compared to that in metallic ports.  
Keywords:tissue expander; breast cancer; radiotherapy; volumetric modulated arc therapy +
Footnote †: journal: Medical Imaging
## 1 Introduction
This study aims to fulfill these requirements by integrating real-time 3D scanning, surface-normal-based path planning, and robotic automation into a cohesive, scalable system. Beyond technical limitations, the need for automation is further emphasized by the ergonomic and health concerns associated with manual gluing. Workers must handle heavy lasted uppers for extended periods and are exposed to adhesive fumes, increasing the risk of repetitive strain injuries and respiratory issues. Automating this process would reduce occupational hazards while improving consistency and efficiency. To date, few studies have tackled the end-to-end challenge of customizing adhesive paths for individually shaped lasted uppers, especially considering curvature variation, path orientation, and actual production deployment. To address these challenges, this study proposes a fully integrated 3D vision-guided robotic gluing system for automated adhesive spraying on lasted uppers. The system leverages structured-light 3D scanning, OpenCV and PCL-based point cloud processing, and real-time trajectory generation to customize robotic paths for each shoe. A six-axis robotic arm performs the adhesive spraying while dynamically adjusting the orientation of the lasted upper to align the surface normal with the fixed nozzle, ensuring uniform adhesive coverage. In addition, the system includes UV-based fluorescence detection to verify glue application quality. This research addresses this gap by integrating real-time 3D vision, dynamic path planning, and robotic execution, validated in a production-line scenario. The main contributions of this work are as follows: (1) The development of a multi-angle laser scanning module capable of capturing the 3D profile of various lasted upper styles in real time. (2) The design of a vision-based algorithm to determine the bonding line through 3D model alignment with an outside sidewall reference. (3) The integration of a six-axis robotic arm for dynamic path execution based on surface normal vectors. (4) The deployment and validation of the system in a production setting, demonstrating consistent adhesive coverage and repeatability within \(\pm\)0.5 mm, well below industry tolerances. This work offers a scalable and intelligent solution to replace manual adhesive application in footwear production lines, improving process stability, safety, and labor efficiency. Unlike prior work, this system has been successfully deployed in a live production line in Hanoi, Vietnam, demonstrating its industrial feasibility and robustness in handling real-world variability without relying on CAD data or manual path teaching. This research marks a significant advancement toward intelligent and scalable adhesive automation in the footwear industry.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 1 Space-Air-Ground Integrated Network
### Architecture for Space-Air-Ground Integrated Network
#### 1.2.2 Comparison of Different Networks  
Each network layer possesses strengths and weaknesses. The satellite network provides global coverage on the Earth, but there is a long delay in transmission. The ground network has a small propagation delay, so it complements the satellite network. However, the ground network is easily influenced by natural disasters and human factors. Compared with traditional terrestrial networks, SAGINs have an aerial layer so that they have wider coverage, stronger openness, and resistance to various forms of damage [5].
## 2 Materials and Methods
### Bioreactor Setup and Operation  
Three ECSB reactors with a height-diameter ratio of 3 units were employed in a continuous operation mode. The reactors were inoculated with 20 L of mesophilic inoculum with a spherical shape and dark green color. The ECSB reactors were operated at six different HRTs for 15, 10, 7, 5, 3, and 1 day(d). The HRTs were automatically altered by changing the feeding time of the pump at a constant flow. The HRT was calculated by Equation (1) [19; 25].  
\[\mathrm{HRT}=\frac{\mathrm{V_{R}}}{\mathrm{Q}} \tag{1}\]  
where the HRT, volume of the reactor (V\({}_{\mathrm{R}}\)), and influent volumetric flow are in day(s), m\({}^{3}\), and m\({}^{3}\)/day units, respectively.  
The recirculation pump was continuously working at an up-flow velocity of 5 m/h. Each reactor was connected to a 100 L tank that was kept under a nitrogen atmosphere and  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reactor** & **Substrates** & **Dry Matter (wt.\%)** & **Organic Dry Matter (wt.\%)** & **Carbon-to-Nitrogen Ratio (\%)** \\ \hline \multirow{3}{*}{1} & Pellets 1 & 7.79 \(\pm\) 0.16 & 87.59 \(\pm\) 0.06 & \multirow{3}{*}{13.70} \\  & PM+CWM & 2.92 \(\pm\) 1.40 & 60.08 \(\pm\) 9.32 & 13.70 \\ \hline \multirow{3}{*}{2} & Pellets 2 & 8.84 \(\pm\) 1.75 & 89.47 \(\pm\) 1.45 & \multirow{3}{*}{16.32} \\  & PM+CWM+SWW & 1.76 \(\pm\) 0.94 & 58.33 \(\pm\) 9.07 & 16.32 \\ \hline \multirow{3}{*}{3} & Pellets 3 & 7.93 \(\pm\) 0.29 & 87.56 \(\pm\) 0.10 & \multirow{3}{*}{18.87} \\  & PM+CWM+SBT & 3.14 \(\pm\) 0.99 & 64.55 \(\pm\) 8.52 & 18.87 \\ \hline \end{tabular}
\end{table}
Table 1: Characterization of the mixtures and their inoculums.  
temperature of 4 \({}^{\circ}\)C to prevent premature aerobic degradation. The scheme for a single reactor is shown in Figure 1.  
All three reactors were operated under mesophilic conditions with temperatures between 37 and 40 \({}^{\circ}\)C and pH values close to 8 by regulating the feed of the reactor, which is mainly possible due to the buffer capacity of the manures [26; 27]. The procedure for the settings and monitoring of the continuous operation was as described in reference [28]. The measured and set variables are summarized in Table 2. Other relevant values were calculated from registered variables such as the organic loading rate (OLR) (\(\mathrm{kg_{COD}}/\mathrm{m^{3}}/\mathrm{d}\)), methane productivity (MPR) (\(\mathrm{L_{CH4}}/\mathrm{L_{reactor}}/\mathrm{d}\)), methane yield (MY) (\(\mathrm{L_{CH4}}/\mathrm{kg_{VS}}\)), removal efficiencies of chemical oxygen demand (\(\mathrm{\SIUnitSymboleta}_{\mathrm{COD}}\)) (%), and biological oxygen demand on the fifth day (\(\mathrm{\SIUnitSymboleta}_{\mathrm{BOD5}}\)) (%).  
Figure 1: Schematic diagram of the reactor. The parts are (1) feed tank, (2) three-way sampling valve, (3) eccentric screw pumps, (4) mixer for influent and recirculation, (5) bioreactor, (6) recirculation, (7) bell separator, (8) biogas outlet, (9) foam trap, (10) gas flow-meter, (11) three-phase separator or settling zone, (12) transition zone, (13) digestion zone, (14) effluent, (15) siphon, and (16) digestate storage.
## 4 Results and Discussion
### Fatigue Damage: Distribution of the Maximum Peak-to-Peak Pressure Region  
As shown in Figure 10a-d, when \(\varepsilon\in\) [0.1, 0.4] (passing through a tunnel longer than 500 m), the MPPR is located in the region of [0.33,0.67]. As seen from Figure 10e-h, when \(\varepsilon\in\) [0.4,0.8], the MPPR is located in the region of [0.33,0.71]. When \(M\in\) [0.1,0.4] (corresponding to \(v_{tr}<\) 500 km/h), the MPPR is located in the region of [0.38,0.71]; when \(M\in\) [0.4,0.8], the MPPR is located in the region of [0.33,0.67]. Thus, MPPR is mainly located in the region \(x^{\star}\in\) [0.33,0.67], indicating the most probable location of accidents caused by fatigue failure. As there are some pressure-bearing facilities in the tunnel, such as fire doors, control boxes, and signal lights, it is suggested to reinforce the tunnel lining and facilities and avoid placing auxiliary facilities in the MPPR.  
Figure 10: Distribution of the maximum peak-to-peak pressure region under different train/wave velocity ratios \(M\) and train/tunnel length ratios \(\varepsilon\) (\(M\leq\) 0.8, \(\varepsilon\leq\) 0.8): (**a**) \(\varepsilon\) = 0.1; (**b**) \(\varepsilon\) = 0.2; (**c**) \(\varepsilon\) = 0.3; (**d**) \(\varepsilon\) = 0.4; (**e**) \(\varepsilon\) = 0.5; (**f**) \(\varepsilon\) = 0.6; (**g**) \(\varepsilon\) = 0.7; and (**h**) \(\varepsilon\) = 0.8.  
In addition, Figure 10 shows that when \(M=0.5\) with \(\varepsilon=0.1\)-0.8 (the corresponding train speed is approximately 600 km/h), and when \(M=0.25\) with \(\varepsilon=0.1\)-0.5 (the corresponding speed is approximately 300 km/h), both the MPP and MNP appear at the midpoint of the tunnel axis in the worst conditions. This result indicates that under the conditions calculated above, no matter how long the tunnel is, the midpoint of the tunnel axis is the worst pressure fluctuation point in the tunnel associated with failure damage.
## 4 The Concept Alignment Example Setup and Modeling
### CAE-ATG Systems Behavior Specification
#### 4.4.1 District CS Behavior  
As Figure 6a shows, the behavior of the district modelled the requests of a firefighting machine (_evOnFire_) as soon as a fire hazardous event (_heOnFire_) occurred. The system switched back to a normal state of operation (state NOMINAL) as soon as a fire extinguishing event (_evEst_) was received.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 4 Numerical Results and Discussion
### Wear Depth Distribution  
The wear depth distribution over the contact patch, displayed in Figure 7, was calculated using (11)-(16). When the contact starts, the wear depth grows smoothly and quickly, and a large amount of worn material is removed. When the wheel is approaching the end of the contact zone, the wear depth function rapidly decreases.  
Remark: due to the regular wear map, this graph is also regular. Discontinuous wear maps may also generate discontinuous wear depth distributions. Figure 8 displays the dependence of the power dissipation on the friction coefficient. The amount of dissipated power is strongly correlated with the change in the friction coefficient. For higher values of the friction coefficient, the dissipated energy amount is also higher. It implies that the creep force and creepage are also increasing when the friction coefficient is increasing. Since the wear rate is strongly dependent on the power dissipation by means of the friction work, the dependence of wear on the friction coefficient is similar to that found in the power dissipation results in Figure 8. The data in Figure 9 confirm this expectation.  
Figure 6: Vertical temperature distribution along \(x_{2}\) axis.  
Figure 7: Wear depth distribution.  
Figure 10 and 11 display the results of the fretting wear investigation. In this example, the wheel moves forward and back longitudinally along the \(x_{1}\) axis with the amplitude \(\delta x=75\cdot 10^{-6}\) m. Due to plasticity occurrence, the length of the contact zone and the wear depth increase as the number of cycles increases. This has the same impact as the temperature, i.e., it results in a slight increase in the wear depth function.  
Figure 8: The dependence of power dissipation on friction coefficient.  
Figure 10: The wear depth update for different material models.  
Figure 9: The wear rate as a function of friction coefficient.  
The method to solve the mechanical subsystem (19)-(22) is quickly convergent. It required 30 iterations to find the solution of this system. At the beginning of iterations, the active sets were calculated not precisely, and they changed during iterations. Closer to the final solution, these sets were stable, which ensures convergence.
## 4 Discussion
### Limitations  
Using lower reflectance values to delineate areas of Fe(III) iron deposits became problematic, as false positives on Wheat Maid's higher terrain were detected, and AMD was not usually found there [29]. This has been the case in other research using basic spectral indices [16; 61]. Ref. [14] observed this when mapping ferrous and Fe (III) iron distribution over two sites known for their banded iron ore deposits, where red soils were flagged as potential iron deposits, even though these did not correspond to iron mineralization itself. The issue of false positives is not only restricted to geological band ratios, but also to water indices such as NDWI, where cloud, snow, and terrain and cloud shadows are often mistaken for water pixels [68]. Predetermined areas of interest can be created and binary hard-thresholding can be used to limit these 'false-positives', but all potential sources of Fe(III) iron deposits were of interest in this study. Although it is known that mine waste particles can be re-suspended from mine waste piles by wind or redistributed by recreational activities, and that the upper lagoon had remedial seeding to prevent this [27; 61], the hypothesis that low reflectance values could be due to wind redistribution was not tested. The ASD TerraSpec Halo performed well during ground sampling campaign, with a quick and user-friendly interface, calibration, and in-built GPS system with an internal spectral library and useful geochemical scalars; however, the Halo struggled with high albedo and moist surfaces, and thus, should be used alongside a GNSS system. The GNSS system should reach full epochs before moving to the next sample to decrease 2 DRMS circular error when measuring near waterbodies residing in mine sites. The Halo cannot successfully register an accurate sample if the surface is not homogeneous or contains traces of vegetation.
# Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping
## 7 Conclusions
In order to achieve intelligent and adaptive evaluation, we designed an intelligent index system for rehabilitation movement evaluation, established a five-point score mechanism based on movement similarity, and achieved quantification and adaptability of movement quality assessment through polynomial fitting. Faced with the challenges of algorithm complexity and modeling difficulties, this article selected the DTW algorithm with low complexity and strong adaptability to achieve efficient and accurate rehabilitation action assessment, which has important reference value for the development of intelligent rehabilitation systems. The experimental results demonstrate the following findings: Firstly, compared with action evaluation based on the feature matrix DTW algorithm, action evaluation based on the FW-DTW algorithm significantly improves the similarity of four types of actions, thereby enhancing the overall performance of action evaluation. Second, the difference in similarity between patients' action data and the template action data is large enough to evaluate patients' limb disability status. Finally, the confidence level of the established score mechanism reached 99%, confirming its applicability for rehabilitation action evaluation. Conceptualization, M.Y. and X.L. ; methodology, M.Y. ; software, N.G. ; validation, Z.L., M.Y. and N.G. ; formal analysis, X.L. ; investigation, Z.L. ; resources, Z.L. ; data curation, X.L. ; writing--original draft preparation, M.Y. ; writing--review and editing, M.Y. ; visualization, Z.L. ; supervision, N.G. ; project administration, X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This work was financially supported by a key research and development program project of Hubei province (No. 2020 BCB054). This study was approved by the Ethics Committee of Wuhan Wuchang Hospital (No. 2022001, 2 March 2022) and complied with the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available upon request from the corresponding author due to privacy. We thank all the participants for their time, effort and valuable feedback. The authors declare no conflicts of interest.
## 2 Optimization
### Classical Iterative Optimization Algorithms
#### 2.1.2 Momentum  
Adding a **momentum** term \(\mathbf{m}\) in classical stochastic gradient descent helps to accelerate learning in relevant directions and reduce oscillations during training by slowing down along dimensions where the gradient is inconsistent, i.e., in dimensions where the sign of gradient often changes. The momentum [22] update rule is given by  
\[\mathbf{m}_{0} =0\quad\text{(initialize momentum)}\] \[\mathbf{m}_{t} =\gamma\mathbf{m}_{t-1}-\eta\
abla J(\mathbf{\theta}_{t-1})\quad\text{( compute momentum update)} \tag{3}\] \[\mathbf{\theta}_{t} =\mathbf{\theta}_{t-1}+\mathbf{m}_{t}\quad\text{(apply update)} \tag{4}\]where \(\gamma\in[0,1)\) is the _decay constant_. By setting \(\gamma\) to 0, we get classical SGD without momentum. In iteration \(t\), parameter update is equal to  
\[\mathbf{m}_{t} =-\eta\sum_{i=0}^{z-1}\gamma^{t-1-i}\
abla J(\mathbf{\theta}_{i}) \tag{5}\] \[=-\eta\underbrace{\big{(}\
abla J(\mathbf{\theta}_{t-1})+\gamma\
abla J (\mathbf{\theta}_{t-2})+\cdots+\gamma^{t-1}\
abla J(\mathbf{\theta}_{0})\big{)}}_{ decaying\ sum\ of\ gradients}. \tag{6}\]  
From (6), it can be seen that update in iteration \(t\) takes into account all gradients calculated so far with more weight put on the recent ones. As \(t\) increases, we have lesser and lesser trust in the gradients calculated in iterations at the beginning of the training.  
The \(i\)th component of vector \(\mathbf{m}\), which corresponds to update made to parameter \(i\) of the given network, accumulates speed when partial derivatives \(\partial_{i}J\) point in the same direction and slows down when they point in different directions. This property helps momentum to more quickly escape flat regions where the gradient is close to zero but often points in the same direction. Accumulated speed sometimes leads to overshooting the local minimum, which results in many oscillations back and forth around the minimum before convergence.
## 4 Limitations of the Study  
The monitoring of cruise ships' traffic was carried out in period from August 2014 to June 2015, and although the period of the research does not display the current status, the results showed cruise ships' navigational routines where selected cruise routes are constant as cruise ships operate on circular itineraries. The defined navigational practice in costal navigation has become standard operational procedure for cruise ships. The results are not closely related to the time period; on the contrary, they reveal established cruise ships' navigational practice in coastal navigation.  
Cruise industry trends were analyzed for the period from 2015 to 2019. Further research was not possible due to the COVID pandemic and the subsequent inactivity in the cruise ship industry. However, the obtained results directed research to the central and south part of the Adriatic east coast. The uniqueness of the region gave solid ground for the detailed analysis of cruise ships' routing practice in coastal navigation. Global cruise industry trends and cruise ships' routing practices in costal navigation raised the question of the sustainable development of coastal regions that have not been involved in cruise tourism before.  
The research covered sizes of cruise ships from 50,000 GT and more. The size limit was placed in order to only take large cruise ships with higher environmental impacts and elevated safety risks into consideration. However, smaller cruise ships under 50,000 GT should be taken into consideration in future research, as they are also important factors of navigational safety and the sustainable development of coastal areas.
# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 2 Scheduling Algorithms  
Scheduling of radio resources is a critical process for cellular communications, as it directly affects services' QoS. In the literature, there are well-known algorithms for wireless communications that are widely accepted. Improving the QoS for 5G communication sessions by maximizing the throughput, minimizing the delay and jitter, as well as optimizing the allocated resources have been the key issues for several research efforts. In the following subsections, we present the well-established 5G scheduling algorithms and scheduling algorithms proposed for V2X communications, and we discuss what is still needed especially for V2N communications.