esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples
### Circular Ti/ZrO\({}_{2}\) Plate  
Consider a clamped circular FGM plate made of titanium (Ti) and zirconia (ZrO\({}_{2}\)), as depicted in Figure 10. The power law used for comparison purposes is \(\
u_{\rm m}=(1/2-z/h)^{g}\), \(\
u_{\rm c}=1-\
u_{\rm m}\), as described in Reddy et al. [51]. The radius, the thickness, and the gradient index are taken as \(r=1\), \(h=0.2\), and \(g=10\), respectively. The top surface of the plate is subjected to a uniform load with amplitude \(\varrho_{0}=2\times 10^{9}\).  
Using fourth-order NURBS basis functions with a \(10\times 10\) mesh in each patch, taking the SDF truncation numbers \(M=5\), \(N=6\), Figure 11 shows the converged displacement and von Mises stress results. It is seen that the results are continuous and smooth along the interfaces of adjacent patches, which means the patches are well coupled by the penalty method. In addition, a reasonable stress concentration phenomenon is displayed on the boundary of the plate.  
Figure 10: Three-dimensional geometry and 2D NURBS midplane of a circular plate. (**a**) Three-dimensional geometry; (**b**) 2D NURBS midplane.  
Figure 9: Nondimensional responses through the thickness of an E-FGM plate with various gradient indexes (\(a/h=4\), \(b/a=3\)). (**a**) Variation in \(\overline{u}\) through thickness; (**b**) Variation in \(\overline{v}\) through thickness; (**c**) Variation in \(\overline{w}\) through thickness; (**d**) Variation in \(\overline{\sigma}_{xx}\) through thickness; (**e**) Variation in \(\overline{\tau}_{xy}\) through thickness; (**f**) Variation in \(\overline{\tau}_{xz}\) through thickness; (**g**) Variation in \(\overline{\tau}_{yz}\) through thickness.  
The nondimensional deflection is defined as \(\overline{w}=\frac{16h^{3}E_{c}}{3r^{4}\epsilon_{0}\left(1-v_{c}^{2}\right)}w\). In Table 3, the central deflection results for various thickness-to-radius ratios and gradient indexes obtained using the SDF-based IGA are compared with the reference solutions in the literature. The reference solutions include FSDT [51], which are analytical solutions based on the FSDT; sFSDT-IGA [52], which are obtained using a simple FSDT with IGA method; and HSDT-IGA [53], which are obtained using Reddy's TSDT with the IGA method. It is seen that the SDF-based IGA results agree well with the HSDT-IGA results, while the FSDT and the sFSDT-IGA are less accurate. Furthermore, as the thickness decreases, the disparity between the results significantly reduces.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
###### Abstract  
Opportunities networks' message transmission is significantly impacted by routing prediction, which has been a focus of opportunity network research. The network of student nodes with smart devices is a particular type of opportunity network in the campus setting, and the predictability of campus node movement trajectories is also influenced by the regularity of students' social mobility. In this research, a novel Markov route prediction method is proposed under the campus background. When two nodes meet, they share the movement track data of other nodes stored in each other's cache in order to predict the probability of two nodes meeting in the future. The impact of the node within the group is indicated by the node centrality. The utility value of the message is defined to describe the spread degree of the message and the energy consumption of the current node, then the cache is managed according to the utility value. By creating a concurrent hash mapping table of delivered messages, the remaining nodes are notified to delete the delivered messages and release the cache space in time after the messages are delivered to their destinations. The method suggested in this research can successfully lower the packet loss rate, minimize transmission latency and network overhead, and further increase the success rate of message delivery, according to experimental analysis and algorithm comparison.  
routing prediction; opportunistic network; utility value; cache management; centrality 2023135217.12.
## 2 Materials and Methods  
Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders were synthesized through the solid-state reaction method. Stoichiometric amounts of SrCO\({}_{3}\), ZnO, MoO\({}_{3}\), and Eu\({}_{2}\)O\({}_{3}\) were weighed according to the composition formula of (\(2-x\)) SrCO\({}_{3}\) + ZnO + MoO\({}_{3}\) + (0.5 \(x\)) Eu\({}_{2}\)O\({}_{3}\), where \(x\) = 0, 0.04, 0.06, 0.08, 0.10, and 0.12, respectively. After being mixed in acetone, dried, and ground, the solid-state reaction method was used to heat the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) compositions in an air atmosphere. The Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders were heated to 900 \({}^{\circ}\)C, 1000 \({}^{\circ}\)C, 1100 \({}^{\circ}\)C, and 1200 \({}^{\circ}\)C for 4 h. When the synthesizing temperature was equal to 1300 \({}^{\circ}\)C, the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) was melted and gathered together to not be used as part of the phosphors. The crystalline structures of the synthesized Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders were measured using X-ray diffraction (XRD) (Bruker, Boston, MA, USA ) patterns with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5418 A) and with a scanning speed of 2\({}^{\circ}\) per minute. Photoluminescence (PL) properties were recorded at room temperature in the wavelength range of 450-800 nm on a Hitachi F-4500 fluorescence spectrophotometer (Hitachi, Tokyo, Japan). In the past, we had found that 271 nm had a better excitation effect on BaZr\({}_{1-x}\)Eu\({}_{x}\)O\({}_{3}\) powders [16]. This result suggested that we would also need to find the optimum optical wavelength for exciting the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders. In this study, the three-dimensional (3D) scanning process using a spectrophotometer (Hitach, Tokyo, Japan) was used to find the optimum optical wavelength, and this value was dependent on the compositions of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors and the synthesizing temperature. We found that the optimum exciting optical wavelength for all of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders was 350 nm, and the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) powders excited by other wavelengths had the weaker PL intensities.
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 2 Method  
As shown in Figure 1, the PTRNet model consists of an encoder and a decoder. Point cloud data are first encoded and then inputted into an encoder, which converts the input points into high-dimensional spatial features. Mapping the points into a high-dimensional space simplifies the extraction of the local and global features of the point cloud. The second part of the task uses the two features from the encoder as input for point cloud registration. Specifically, the six-layer fully connected network predicts the relative pose, combining the translation and rotation quantities and outputs seven data elements: three translation quantities and four unit quaternions obtained through normalization.
# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 2 Method
### Integral Structure  
The RL-Unet is a U-shaped network that is structured with an encoder, decoder, and skip connections. In the process of residual connection, local inductive bias module is added to build RLSwin-Transformer module. Meanwhile, inspired by TFCNs [31] network, the MLP module is changed into Res-MLP module. It can learn local feature information better and realize cross-group information blending. In the decoder stage, a new densely linked double up-sampling module is proposed, which contains two existing up-sampling methods: one is bilinear up-sampling and the other is introduced by Deeplabv3+ [32] ASPP module. Through the combination of the two, multi-scale information can be effectively learned and model generalization ability can be improved. Figure 1 depicts the RL-Unet's general structure.  
Firstly, in the coding part, the medical image data are separated into non-overlapping image blocks of equal size and input into Patch Partition, and the image blocks are embedded and encoded to complete the feature dimension conversion and other operations. Then, the converted image blocks are input into the RLSwin-Transformer module for feature learning, and the down-sampling function is implemented by Patch Merging [33; 34; 35]. The feature extraction part is divided into four stages. As the network depth increases, the size of the image blocks halves, and the number of channels increases by twice. The output of the four stages is processed by a standard 1 x 1 convolution to obtain the four hierarchical features of the RLSwin-Transformer (ST1, ST2, ST3, and ST4). In the decoding part, a dense connection double up-sampling module is proposed, which can restore the image's original resolution and complete the final segmentation prediction through cross-scale connection and attention module.
## 3 Results and Discussion
### Optimization Results
The screw speed modifications exhibited contrasting results, as Minitab increased it by 46%, while DE decreased it by 22%. Regarding the results, DE proved to be more efficient in reducing weight and energy consumption. It achieved a 2% decrease in weight and a 14% decrease in energy consumption, while Minitab only achieved a 1% decrease in weight and an 8% decrease in energy consumption. The results emphasize the better performance of DE in optimizing both operational parameters and efficiency metrics for the injection molding process. The DE algorithm, utilizing its predetermined configurations, effectively optimized the weight and energy consumption of the injection molding machine. The algorithm's capacity to efficiently explore and utilize the search space was demonstrated by the rapid early improvements followed by stabilization in both objective functions. This led to convergence within the set parameters. The results demonstrate that the selected DE settings were suitable for the specific optimization challenge, resulting in substantial enhancements in both weight and energy consumption.
## 6 Classification of Harmonic Emission Profiles
### Robustness of Performance  
The process of randomly splitting into training and test samples was repeated 100 times in order to evaluate the robustness of the classification system independently from one specific split, as presented in Table 1. The average and per class classification rates are represented as box whisker plots in Figure 10, and separately for the training samples and the test samples.  
The median of the average classification rates was 97.0% for the training samples and 92.3% for the test samples. The different classification results varied for different splits. Nevertheless, the lowest classification rates per class did not fall below 72%.
# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
## 6 Conclusions  
PLPS generally adopts a pseudo-random pulsing signal structure to mitigate the near-far effect. This signal pattern leads to irregular pulsing periods in the tracking loop when estimating the code phase, doppler, and carrier phase. However, traditional VTL is established on a continuous CDMA architecture. Thus, there exist two problems when directly adopting VTL to tracking PRPS.  
* The discriminator model is affected by irregular pulsing periods in PLPS.
* The sampling time of the navigation filter inputs is inconsistent and time-varying in PLPS.  
They will cause jitter degradation and biases in tracking PRPS. Thus, traditional VTL is incompatible with tracking PRPS.  
Therefore, this paper proposes an optimized VTL architecture that includes IUP pre-filters and a navigation filter based on PM. According to the timeslot intervals, the IUP pre-filter based KF adjusts the update cycles, avoiding the impact of the pseudo-random pulsing signal. So, it can estimate the measurement accurately. Meanwhile, the navigation filter predicts the measurements of each pre-filter to the same sampling time. So, the measurements can reflect the errors of the sampling time, reducing the measurement noise of the navigation filter.  
Additionally, a simulation test is carried out to compare the proposed IUPPM-VTL with traditional VTL based pre-filter and IUP-VTL based IUP-prefilter. The reference trajectory includes static, uniform acceleration, uniform speed, turning, and uniform deceleration. The maximum speed is 31.6 m/s, and the maximum acceleration is 18 m/s\({}^{2}\). Therefore, this comprehensive scenario can better evaluate the performance of the VTLs. The results show that the proposed IUPPM-VTL has a higher tracking accuracy than IUP-VTL and VTL. Meanwhile, their position errors are 0.12 m, 0.30 m, and 0.63 m, and their velocity errors are 0.24 m/s, 0.40 m/s, and 0.50 m/s. Thus, the proposed IUPPM-VTL performs the highest position and velocity precision among the three VTLs. In future work, it is necessary to concentrate on implementing and applying this method to real-time PRPS.  
Conceptualization, L.T.; methodology, L.T.; software, L.T.; validation, L.T. and G.L.; formal analysis, L.T.; investigation, L.T.; data curation, L.T., G.L., and J.S.; writing--original draft preparation, L.T.; writing--review and editing, L.T., G.L., J.S., and B.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Velocity errors of three VTL structures in XYZ directions.  
**Funding:** This research was funded by the National Key Research and Development Project of China grant number 2020 YFB0505602-02.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
### Commercial Solutions
EyeCad, despite its user-friendly design and visual appeal, lacks synchronous collaborative features and inspection tools, limiting its versatility. MAGIC seeks to address these limitations by combining key features into a comprehensive configurator. It ensures equal access to functionalities regardless of user authority within the scene; dynamically adapts rendering quality to match available hardware through a dedicated graphics management system; and delivers an engaging, emotional user experience. By overcoming hardware constraints, collaboration barriers, and customization challenges, MAGIC offers an inclusive and high-performing solution for VR-based design and configuration. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **VRED Pro** & **Mindesk** & **EyeCad VR** & **Keyshot VR Studio** & **VR Sketch** \\ \hline
**Accessibility** & & & & & \\ \hline User Locomotion & Teleport & Fly Mode & Teleport/ & Teleport/Fly & Teleport/Fly \\  & & & Walkthrough & Mode/Walkthrough & Mode/Walkthrough \\ \hline Viewpoints & ✓ & \(\times\) & ✓ & ✓ & ✓ \\ \hline UI & Intuitive & Limited & Limited & Intuitive & Versatile \\ \hline
**Configuration** & & & & & \\ \hline Aesthetic Variants & ✓ & \(\times\) & ✓ & ✓ & ✓ \\ \hline Geometric Variants & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) \\ \hline
**Object Manipulation** & & & & & \\ \hline Move & \(\times\) & ✓ & \(\times\) & ✓ & ✓ \\ \hline Rotate & \(\times\) & ✓ & \(\times\) & ✓ & ✓ \\ \hline Scale & \(\times\) & ✓ & \(\times\) & ✓ & ✓ \\ \hline Grab and Drop & \(\times\) & ✓ & ✓ & ✓ & ✓ \\ \hline
**Interaction and Inspection** & & & & & \\ \hline Mechanism Simulation & ✓ & \(\times\) & ✓ & ✓ & \(\times\) \\ \hline Measurement & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline Exploded & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline Section Planes & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline Show/Hide & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline Annotation & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Screenshot & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline
**Immersiveness** & & & & & \\ \hline Visualization Method & \
\begin{tabular}{l} Ray Tracing/ \\ Baked Lights \\ \end{tabular} & Shadowed & Baked Lights &
\begin{tabular}{l} Ray Tracing/ \\ Baked Lights \\ \end{tabular} & Shadowed \\ \hline Animations & ✓ & \(\times\) & ✓ & ✓ & \(\times\) \\ \hline Lighting & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Scenario & ✓ & Limited & ✓ & ✓ & Limited \\ \hline Additional Contents & ✓ & Limited & ✓ & ✓ & Limited \\ \hline
**Multi-User Capability** & & & & & \\ \hline Sharing Requirements & High & Moderate & \(\times\) & High & Low \\ \hline Live Chat & ✓ & ✓ & \(\times\) & ✓ & ✓ \\ \hline
**Synchronization** & & & & & \\ \hline VR \(\leftrightarrow\) CAD & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of VR software features.
## 4 Results
### Comparing Satellite Observations and Model Simulations
The period and location of averaged increased XCO\({}_{2}\) (South America: 1.52 ppm and South Asia: 1.09 ppm) over tropical forest areas can be identified, with a 25% increase in local CO\({}_{2}\) emission (averaged as 4.59 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\)/s and 2.28 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\)/s). With an emission increase of 37.5% of averaged local biospheric CO\({}_{2}\) flux (Eurasia: 3.79 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\)/s; North America: 5.52 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\)/s) makes the averaged increased XCO\({}_{2}\) (1.52 and 1.77 ppm) over tropical and northern hemisphere be detectable. Local CO\({}_{2}\) emission and transported CO\({}_{2}\) could make the increased atmospheric XCO\({}_{2}\) detectable over South Africa and Australia. So, the CO\({}_{2}\) variation with significant local CO\({}_{2}\) flux change can be detectable, in XCO\({}_{2}\). Figure 7: Increased XCO\({}_{2}\) (\(\Delta\)XCO\({}_{2}\)) corresponding to enhancements of local CO\({}_{2}\) fluxes (\(\Delta\)CO\({}_{2}\) Flux) in regions of interest. Different color lines indicate different regions (black: Eurasia; green: North America; red: South Asia; blue: South America; cyan: South Africa and magenta: Australia). The black dashed line shows the one-to-one relationship.
# Effects of Illuminance Level of Light Source on White Appearance of a Tablet Display
## 6 Conclusions  
In conclusion, a psychophysical experiment is carried out to examine white appearance using a tablet display under eight different ambient lighting conditions, which includes two levels of correlated color temperature and four levels of illuminance. A total of 6992 whiteness percentage evaluations are collected from thirteen observers to identify the whitest stimulus under these eight lighting conditions.  
The appearance of white is critical to the imaging quality of display devices. A proper display white point should be set to neutral to enhance image quality. However, variations in ambient lighting can alter the perception of white. This study investigates changes in perceived whiteness under different ambient illumination levels, including 150, 300, 600, and 1200 lx, and correlated color temperatures of 3500 K and 6500 K. The experimental results indicate that under an illumination level of 150 lx, the adapted white points for light sources at 3500 K and 6500 K tend to shift toward higher color temperatures, with both white points being similar. As the illumination level increases, the adapted white point under the 3500 K light source approaches the light source's color temperature. In comparison, the adapted white point under the 6500 K light source shifts toward an even higher color temperature. This study developed a perceived whiteness prediction model using an artificial neural network based on the experimental data. The testing results show that the model can effectively and accurately predict perceived display whiteness under various environmental conditions. The model can be a design reference for the display and lighting industries.  
H.-P.H., concept provider, project administration, conducting experiments, data analysis, and writing manuscript preparation. H.-C.L., data analysis, modeling, and writing manuscript preparation. Y.-M.F., data analysis and writing manuscript preparation. M.W., concept provider, supervision, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC), grant number NSTC 112-2221-E-263-001.  
This study is exempt from review by the Institutional Review Board (IRB) of National Taiwan University (IRB No: 201911 ES010), with an approval date of 13 April 2020.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the author.  
The authors declare no conflicts of interest.
# Demonstration of Phase Change Thermal Energy Storage in Zinc Oxide Microencapsulated Sodium Nitrate
## 5 Conclusions  
This work presents laboratory and prototype-scale characterization of an innovative PCM for thermal energy storage: (i) thermal properties of NaNO\({}_{3}\)-ZnO microcapsules; (ii) a prototype tank demonstration of phase-change thermal energy storage; and (iii) a methodological framework for design, scaled manufacturing, and prototype testing of microencapsulated NaNO\({}_{3}\). Methodological novelties are: (a) effective manufacturing of microcapsules, (b) a method to measure the high temperature thermal energy stored in a prototype tank, and (c) a software tool (as a document template) to analyze and predict the dynamics of PCM energy storage during charge-discharge cycles of a cylindrical storage tank.  
In summary:  
* NaNO\({}_{3}\)-ZnO microcapsules were prepared by a simple and easy to scale protocol, then characterized for structural and thermal properties;
* Charge-discharge experiments of the storage tank demonstrated the availability of high-temperature thermal energy stored as latent heat in NaNO\({}_{3}\)-ZnO microcapsules;
* The temperature interval to access this energy in the pilot tank is 303 to 285 \({}^{\circ}\)C, as predicted by DSC experiments at microscale;* By the adapted calorimetric method, we estimated that in-tank high temperature specific energy of microcapsules is in the range 80.5-99.5 kJ/kg, in line with expectations for inorganic salts;
* Thermal transfer simulations using the Microsoft Excel template setup for transient nonlinear FEM confirmed that prototype-scale thermal energy storage in microcapsules can be predicted from laboratory measurements on small material samples.  
Future work may use the simulation-prototyping platform to develop microencapsulated phase change materials with enhanced thermal conductivity, ensuring optimal heat transfer rates for specific applications.  
Conceptualization, R.R.P., M.D.R.-S., C.N., and P.S.; methodology, C.N., M.D.R.-S., R.R.P., and P.S.; investigation, C.N., I.A.T., C.B., C.F.C., P.S., and N.Z.-I.; writing--original draft preparation, C.N.; writing--review and editing, C.N., R.R.P., and M.D.R.-S.; supervision, C.B., R.R.P. and M.D.R.-S.; project administration, R.R.P. and M.D.R.-S.; funding acquisition, R.R.P. and M.D.R.-S. All authors have read and agreed to the published version of the manuscript.  
The research results are based on the financial support from National Authority for Scientific Research in Romania: ENERHIGH project (IDP_37_776, MySMIS104730, Contract 93/09.09.2016 under the Competitive Operational Program 2014-2020) and PN 19190101/2019-2022.  
Authors are grateful to [PERSON] for scientific advice and administrative support. Excellent technical assistance was provided by Eng. [PERSON].  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.