esa-sceva/satcom-chunk-collection huggingface dataset:
# Examination of Viscosity Effect on Cavitating Flow inside Poppet Valves Based on a Numerical Study
## 4 Discussion
### Potential Core Pattern  
The last issue associated with the viscosity effect refers to Re Number effect. According to the evolution of potential core, the water-flow case of higher Re produces disruption of the potential core at the rear part, contributing to the puff pattern as proposed by [PERSON] and [PERSON] (1971). In comparison, the oil case of low Re produces waving of the potential core at the rear part, contributing to the waving pattern as proposed by [PERSON] and [PERSON] (1971). Such a difference of potential core is a result of the advancing Re Number effect, or specifically the shearing layer thickness and transition process. It also could be noticed that flow separation at poppet wall side is quite evident in the oil-flow case for both sharp and chamfered valve seat structure. But in the water case, such flow separation is negligible even in the case of chamfered valve seat structure due to the much smaller size of the vortex at the wall shearing layer. In the water-flow case, the stronger transition process disrupts the coherent vortex ring, and the produced highly 3-dimensional bubble cluster tends to tears the potential core apart, responsible for the establishment of the puff-like potential core at the rear region. As reported by [PERSON] [23], the water-flow case with puff pattern is similar to the jet column mode with penetration of the vortex through the jet potential core, while the oil-flow case of waving pattern shares similitude to the shear layer mode without an overall disruption of the potential core by the vortex, as explained in the aforementioned interaction between the two paired vortexes. Such difference in vortex cavitation as a consequence of Re Number variation is found in a previous investigation regarding to circular jet flow by [PERSON] et al. [24]. Potential core dynamics could be fundamentally explained as a consequence of Re Number effects.  
Figure 9: Detailed view of vortex structure in different poppet valves of 0.6 mm openness and different operating conditions, and sensitivity study on influence of viscosity on flow dynamics (**a**) oil flow inside poppet valve of sharp valve seat structure, with pressure drop of 45 bar; (**b**) oil flow inside poppet valve of sharp valve seat structure, with pressure drop of 38 bar; (**c**) oil flow inside poppet valve of chamfered valve seat structure, with pressure drop of 44 bar; (**d**) oil flow inside poppet valve of chamfered valve seat structure, with pressure drop of 30 bar; (**e**) water flow inside poppet valve of sharp valve seat structure, with pressure drop of 45 bar; (**f**) water flow inside poppet valve of chamfered valve seat structure, with pressure drop of 44 bar; (**g**) sensitivity of jet flow dynamics to variation of kinematic viscosity, pressure drop of 45 bar, sharp valve seat.
## 2 Safety Analysis Approach
### Failure Rate Derivation via Fault Tree Analysis  
An avionic system's failure rate should consider all possible failure and degradation modes derived from all its parts. For the present safety analysis, two possible failure conditions are considered for the air data system functions: (1) loss (i.e., undetected or unannuciated loss) and (2) erroneous (i.e., detected or annunciated loss).  
With the SSA, FTAs are analysed for each ADF in order to calculate the quantitative failure rates of air data functions at the system level. The known failure rate is imposed at the lowest level (e.g., failure of one of the identified ADS sub-parts), and the ADS function's safety budget is calculated using a _bottom-up_ approach.
## 1 Introduction
The present study investigates the optimal planning of the EH to achieve the maximum benefit of energy generation in the day-ahead market in partnership with EGHNs. In this study, the size of EH equipment, including photovoltaic and solar renewable energy sources, CHP, boilers, energy storage, and electric vehicles, is determined using a new and improved Fick's law algorithm. According to NFL theory [28], a meta-heuristic algorithm may work well in solving some optimization problems, but the same algorithm cannot achieve the optimal solution in solving other problems. On the other hand, improving the performance of meta-heuristic algorithms by using special techniques can prevent them from premature convergence and strengthen their ability to quickly reach the global optimal solution. For this reason, in this article, a new meta-heuristic algorithm is adopted to find a solution to the EH scheduling problem. The conventional FLA [29] imitates Fick's diffusion law. Traditional FLA has problems in the form of an imbalance between exploration and exploitation, as well as being caught in premature convergence. In this study, to improve the performance of the traditional FLA against these problems, [PERSON]'s direct rotational (RDR) method [30] is used. In this study, the performance of the suggested IFLA to solve the optimal and coordinated EH scheduling problem with the aim of profit maximization is compared with the conventional FLA, PSO, and MRFO methods. The changes in daily power and profit for different energies for electricity, heating, and gas markets, the impact of different load levels, and also the exit rate of equipment on energy profit are evaluated. The major highlights of this paper are listed below:  
1. Coordinated energy scheduling and management strategy in the energy hub considering the economic optimal performance based on profit maximization. 2. Investigation of the load levels and forced output rate of renewable energy resources and CHP equipment on the energy hub economic performance. 3. Use of a new meta-heuristic algorithm named the improved Fick's law algorithm (IFLA) algorithm based on [PERSON]'s direct rotational method to solve EH scheduling. 4. Evaluating the superiority of the proposed EH scheduling framework compared with conventional FLA, PSO, and MRFO. The following presents the organization of the paper. In Section 2, the formulation of the problem including the profit function and the operation and hub constraints are presented. Section 3 describes the current meta-heuristic algorithm and how it can be used to solve the problem. Section 4 presents the simulation results and the study findings are concluded in Section 5.
# Virtual Try-On Systems in Fashion Consumption: A Systematic Review
## 3 Results and Discussion
### Future Agenda
#### 3.5.4 Technological Challenges and Future Directions  
Despite significant advancements in VTO systems, their further development still faces several critical technological challenges that require in-depth research and innovation. Future studies should address these challenges to ensure the effective implementation and broader adoption of VTO systems. First, robust privacy protection mechanisms are needed to address consumer concerns regarding the use of personal data, such as body measurements and photos, in VTO platforms [104]. Second, improving the realism of virtual garments remains a pivotal task. Key areas for advancement include fabric simulation, size adaptation, and occlusion management, all of which are fundamental to delivering an authentic and immersive consumer experience [26; 97]. Lastly, the issues associated with immersive environments, such as cyber-sickness in VR/AR applications [105], demand significant improvements in hardware and software technologies to reduce latency and ensure high-resolution, seamless interactions.
## 3 Results  
After a number of preliminary measurements, it was found that a single drop of barbell only produces a small dose value. However, the value of dose recorded using the Decibel X application is only to one decimal place; thus, not every drop of barbell causes a change in the value of dose. In order to get a more accurate dose value, the measurement method adopted in this project was not to directly measure the dose value produced by a single drop. Ten groups of measurements were conducted, with each group recording the noise generated by 20 drops and the total dose generated in turn, before averaging the results to obtain a more accurate dose value generated by a single drop.  
Ten sets of measurements were conducted for each activity at each weight. By calculating and comparing the standard deviation of the data, four sets of data with the least fluctuation were selected, and the average values of the four groups of data are presented in Table 3. The details of these four sets of data are listed in Table 3 (Appendix A).  
According to the widely accepted safety noise level [6; 10], 85 dBA was also adopted as a safe standard in this project. It can be seen from the data in Table 3 that all the measured data exceeded the safety standard, which is very likely to cause hearing loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Combination\(\times\)** & **Weight (kg)** \\ \hline A & Barbell (15 kg) + 2 \(\times\) 2.5 kg metal plate & 20 \\ \hline B & Barbell (15 kg) + 2 \(\times\) 5.0 kg metal plate + 2 \(\times\) 2.5 kg & 30 \\ \hline C & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 2 \(\times\) 2.5 kg & 40 \\ \hline D & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 2 \(\times\) 5.0 kg & 50 \\ \hline E & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 4 \(\times\) 5.0 kg & 60 \\ \hline F & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 6 \(\times\) 5.0 kg & 70 \\ \hline G & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 8 \(\times\) 5.0 kg & 80 \\ \hline H & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 6 \(\times\) 5.0 kg & 80 \\ \hline I & Barbell (15 kg) + 2 \(\times\) 10.0 kg metal plate + 4 \(\times\) 5.0 kg & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combinations of various weight plates.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multirow{2}{*}{**Weight**} & \multicolumn{4}{c}{**Average Noise Level (dBA)**} & \multicolumn{4}{c}{**Average Dose for a Single Drop (\%)**} \\ \cline{3-10}  & & **Clean and Jerk** & **Snatch** & **Deadlift** & **Squat** & **Clean and Jerk** & **Snatch** & **Deadlift** & **Squat** \\ \hline A & 20 & 91.7 & 90.2 & 86.2 & 89.3 & 0.060 & 0.055 & 0.030 & 0.045 \\ B & 30 & 97.0 & 96.8 & 92.0 & 95.5 & 0.085 & 0.080 & 0.060 & 0.075 \\ C & 40 & 98.7 & 98.9 & 93.2 & 96.9 & 0.100
# Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks
## 5 Conclusions  
In this paper, extensive simulations were conducted at mmWave frequencies (28 GHz, 39 GHz, 60 GHz, and 73 GHz) within an indoor environment to investigate the propagation behavior at LOS and NLOS scenarios using different antenna configurations. The investigated parameters include PLE, RMS-DS, number of clusters, and number of rays per cluster. It was found that PLE shows dependency on frequency, the omnidirectional PLE was found to be less than the directional ones in both LOS and NLOS scenarios, and the LOS PLE was less than two due to the waveguiding effect. DS generally is lower at higher frequencies; in LOS scenarios, omnidirectional RMS-DS is bigger than directional ones due to having more propagation paths. The number of clusters follows a Poisson distribution for NLOS scenarios, while, for LOS scenarios, it follows a decaying exponential distribution; omnidirectional channels tend to have fewer clusters. The number of rays per cluster for NLOS scenarios follows a decaying exponential distribution. As seen from the literature, depending on the type of indoor environment (i.e., offices, halls, corridors, etc.), PLE, RMS-DS, and other channel parameter values will vary significantly. This work presents a comparative study that will enhance our understanding of indoor channel behavior at different operating frequencies within the same environment. This simulation is important as it provides the omnidirectional channel behavior at different frequencies, which is important for indoor channel planning. The work is helpful to network engineering designers in considering the effect of walls on network indoor coverage. In the future, the work can be extended to consider more indoor environments and operating frequencies.  
Figure 12: Penetration loss through a concrete wall.
## 3 Applicability of Standard Solutions
### SLAAC/NDP  
_StateLess Address AutoConfiguration_ (SLAAC) [6] is a standard that allows the automatic auto-configuration of an IPv6 address without the need of a router node. For this, it needs help from the protocol _Neighbour Discovery Protocol_ (NDP) [7], a standard to transmit the messages and to discover its neighbours. A node automatically creates an IPv6 address joining its _host_ identifier (it is usually the MAC address) with a local well-known prefix and undergoes a DAD process by _broadcasting_ NDP messages to the neighbours.  
If the IPv6 address is not unique, the auto-configuration process will stop and it will be necessary to do it manually. If on the contrary the address is unique, it will have to ask for the network prefix through NDP messages and, then, if its IPv6 address is effectively unique, it will verify with DAD again.  
The applicability of this protocol in mobile _ad hoc_ networks is limited because it uses the NDP protocol to send the messages and NDP assumes that all the nodes are connected to each other in the network. Consequently, it only supports one-hop transmission, whereas mobile _ad hoc_ networks are most frequently multi-hop, thus not reaching the majority of the nodes to carry out the DAD processes and not being able, therefore, to ensure that the obtained IPv6 address is unique.
# Data-Completion and Model Correction by Means of Evanescent Regularization
## 3 A Hybrid Modeling Approach
### A Single Unmeasurable Quantity
The results are illustrated in Figure 3 for both the measurable and the unmeasurable quantities of interest, when performing the training over 100 time steps only, and an initial guess \(\mathbf{y}_{0}^{h,nm}=0.5\), while 1 was the correct initial condition in this example. The used values of the regularization coefficients in this example are \(\lambda_{1}=10^{-3}\), \(\lambda_{2}=10^{-3}\), \(\lambda_{3}=10^{-3}\), \(\lambda_{4}=10^{-4}\), and \(\lambda_{5}=10^{-2}\). The mean relative errors in the updated model are found to be:  
* Relative error on \(\mathbf{y}_{0}^{h,nm}\), the initial condition of the non-measurable quantity: 0.15%
* Relative error on the measurable quantities \(\mathbf{y}^{m}\): 0.0093%
* Relative error on the unmeasurable quantities \(\mathbf{y}^{nm}\): 0.104%  
We can conclude that the hybrid model is able to outperform the simulation correction, with a lower number of time steps. In fact, increasing the number of the available time steps increases the information that is used by the machine learning algorithm to correct the simulation. On the other hand, the increase in the number of time steps integration requires an increase in the computation time. The approach illustrated in Section 2.2 did correct the simulation, but even after 1000 time steps available, the results were less accurate than those of the hybrid model after 100 available time steps only.
## 3 Materials and Methods
### SPI-Net Architecture
#### 3.2.2 Cartesian Coordinates Features Branch  
As the Geometric branch only takes as input relative Euclidean distances between key-points, the Geometric branch is location-viewpoint invariant. Hence, it does not contain any global spatial motion information of the pedestrian. Solely using the Geometric feature branch is therefore unsubstantial as it does not take any information about the spatial information of the pedestrian in the scene. To overcome this issue, we develop a Cartesian Coordinates features branch that is made to retain such spatial information. Moreover, the Geometric features branch treats no explicit sequential modeling at all, but only treats the question of representation of an action in the embedding. Our Cartesian Coordinates features branch is therefore designed to extract both spatial and temporal features: features that are not explicitly learned in the Geometric branch.  
Since we take the model speed as one of our priorities, we use a 2D-convolution-ready representation format of the sequence to represent human pose sequences allowing us to extract spatio-temporal features using standard computer-vision deep-learning methods. Human pose sequences are converted to a 2D image-like spatio-temporal continuous representation based on a spatial joint reordering trick [74; 75] called Tree Structure Skeleton Image (TSSI) [76]. Such representation preserves both spatial and temporal relationships by repeating the joints and re-indexing them. TSSI is described in more detail in Figure 6.  
Since a sequence is represented with a 3-dimensional \((300,14,2)\)-shaped tensor, we can easily apply the TSSI normalization [76] on the input and transform the original sequences into a multi-channel redundant image of shape \((300,25,2)\). A few sequences of pedestrian actions in the TSSI-format are plotted with their ground truth intentions in Figure 7 for illustration.  
We then classify these images using standard computer vision deep-learning methods, such as in [27; 28; 29; 30; 31], while preserving spatial and temporal relationships.  
Therefore, after the normalization of its input, the second branch corresponds to any other image classifier based on convolutions and pooling blocks for features extractions and fully-connected layers at later stages of the network. Similarly to the Geometric features branch, we evaluate the capability of discrimination of that branch alone for the C/NC task and we then concatenate the two branches and evaluate the approach as its whole.
## 3 Selected Results of Optical Metrology Techniques
### Multiple Simultaneous Viewing Directions Using Four Digital Holographic Microscopes (MuVie)
#### Investigation of Functional Surfaces within the Cavity of a Micro Component  
The second example is a digital holographic measurement system, which has been developed in collaboration with the company _VEW GmbH_ to investigate functional surfaces within cavities. The system is shown in Figure 4a. It consists of a single microscope, which inspects the object in a normal observation direction at a rate of one part per second. Again two wavelengths \(\lambda_{1}=632\) nm and \(\lambda_{2}=637\) nm are used to arrive at a synthetic wavelengths of \(\Lambda=80.5\,\upmu\)m. In contrast to the first system, the two holograms are recorded in quick succession using a fiber optical switch between the wavelengths. The recording time of a single hologram is approximately 1 ms to enable the system to also operate in a production environment. The object is a micro deep drawing part with a functional surface inside of a cylindrical tube, as shown in the inset of Figure 4a. The highlighted surface inside the cylinder is the region of interest. In the center of the object, we find a hole with a diameter of 0.9 mm. Figure 4b shows a measurement result with a clearly visible scratch in the upper left part of the surface. Similarly to the first system, the shape of the object  
Figure 3: Digital holographic microscope for the inspection of micro cold formed micro cups: (**a**) The measurement system consists of 4 digital holographic microscopes to provide a full view of the top area of the object. (**b**) The measurement result shows a micro cup with diameter and height of approximately 1 mm. The lateral resolution is 2 μm over an extended range of 700 μm. The system can be used to quantify local imperfections, such as dents and deformations. The measurement uncertainty in the current configuration is 5 μm. Reprinted according to Creative Commons license from Reference [21].  
can be determined with a measurement uncertainty of approximately 5 \(\upmu\)m and a lateral resolution of 2 \(\upmu\)m.
## 4 Selection and Modeling of a Case-Study Super-High-Rise Building  
### Case-Study Building  
To further investigate the potential application of GSPCW walls in real structures, a super-high-rise building was selected for the case study. Figure 12 shows the floor plan of the structure. The structural system of the selected building was a reinforced concrete (RC) frame-core wall system, which is the most commonly used system for super-high-rise buildings in China. This system normally combines core walls (in the middle of the plan) and columns (at the perimeter of the plan) to resist later loads, obtaining a balance between lateral resistance and stiffness, along with architectural functions. In this study, the frame columns of the building were concrete-filled steel tube (CFT) columns, and the frame beams were RC beams and steel beams. The coupling beams of the core walls were steel-concrete coupling beams and RC coupling beams. The floor slabs and roof slab were reinforced concrete slabs, and the thicknesses of the floors were 100 mm and 120 mm, respectively. Outriggers and mega-braces were used at several stories along the building height to increase the lateral stiffness. The total height of the building was 400 m, with 96 stories. Structural concrete strength was C60 (based on the Chinese design code) for the columns and core walls, and C35 for the floors. The structural steel was Q345, and the reinforcing steel was HRB500 for force-resisting bars and HRB400 for hoops and stirrups. In accordance with the Chinese Code for seismic design of buildings [17], the building was assumed to be on a location representing high seismicity, with a peak ground acceleration (PGA) for the designed earthquake of 0.2 g. The site class was III, and the seismicity group was I, resulting in a characteristic period for the site of 0.45 s. The dead and live loads for both the floor and the roof were 5.0 kN/m and 2.0 kN/m\({}^{2}\), respectively. The details of  
Figure 11: Influence of concrete compressive strength: (**a**) load–displacement hysteresis curves, and (**b**) skeleton curves.  
Figure 10: Schematic diagram of the aspect ratio of grille-type steel plate.