esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Thevenin Model for a Rectenna and Its Application to an RF Harvester with MPPT
## 6 Conclusions  
This work proposed a compact Thevenin model for a rectenna and its application for designing a high-efficiency RF harvester. The rectenna under study consists of an L-matching network and a half-wave rectifier. Explicit expressions for the Thevenin voltage and resistance were derived that offer insight into the operation of the rectenna. An expression was also provided for the power efficiency. The rectenna was implemented and characterized from \(-30\) to \(-10\) dBm at 808 MHz and the results mainly agreed with the derived model, with differences arising from the limited \(Q\) factor of the matching network. High efficiencies were obtained, in particular 60% at \(-10\) dBm. Then, an ensuing MPPT was also added, where the behavior of the rectenna as an equivalent Thevenin circuit allowed the use of a simple FOCV technique. The whole RF harvester (rectenna plus MPPT) showed an overall efficiency near 50% at \(-10\) dBm. Further tests were performed with a nearby transmitting antenna for powering a sensor node with a power consumption of \(4.2\)\(\mu\)W.  
Conceptualization, M.G.; Formal analysis, M.G. and F.R.; Funding acquisition, M.G. and F.R.; Investigation, M.G. and E.R.-V.; Methodology, M.G.; Project administration, M.G. and F.R.; Writing-original draft, M.G.; Writing-review and editing, M.G. and F.R.  
This work was supported by the Secretariat of University and Research of the Ministry of Business and Knowledge of the Government of Catalonia, by the Spanish State Research Agency (AEI) and by the European Regional Development Fund under Project TEC2016-76991-P.  
The authors wish to thank [PERSON], [PERSON], and [PERSON] for their initial support, and the Castelldefels School of Telecommunications and Aerospace Engineering, Barcelona, Spain, for the RF instrumentation needed to perform the experiments.  
The authors declare no conflict of interest.
# In Silico Modeling the Impact of Cartilage Stiffness on Bone Tissue Stress
## 2 Materials and Methods  
### Geometry and Structure of the Model  
The three-dimensional numerical model of the knee joint was developed through several steps. At first, the computed tomography of a 64-year-old female diagnosed with third stage OA was performed. Then, the obtained images were processed using the free open-source software 3D Slicer [13] and further refined using the MeshLab program [14] to remove noise and smooth surfaces. The result of geometry enhancement in MeshLab is shown in Figure 1.  
As we can see from Figure 1, initially, the model was simplified, and the number of constituent triangles was reduced from 150,304 to 25,000. This step was performed to save resources of the computing program. However, since the surface becomes angular with mesh simplification, Laplacian smoothing was performed using the same MeshLab program. Thus, the initial smoothness lost during model simplification was restored. The output STL file from MeshLab was exported to the SolidWorks software [15] environment, where the final rendering of the mesh was performed, and surfaces were transformed into solid-state models of the femur and tibia. In the same SolidWorks environment, cartilage congruent to articular surfaces was formed for bone components, assuming the same thickness (2 mm) throughout the volume. In the next stage, the meniscus and cartilage were added to the existing model components. The final numerical model is shown in Figure 2.  
It is important to note that the initial geometry underwent three processing steps, which led to distortion of initial geometric shape and volume. Although the model reflects the bone's characteristic curvature, errors in geometry are indeterminable due to the numerous refinements made to the initial file. Moreover, the model's limitations include the absence of ligaments, rendering it suitable only for compression load assessment, as calculating cases with rotation and flexion can result in unreliable outcomes.  
The section view of the model is presented in Figure 3. The cortical bones of tibia and femur were modeled as thin features (thickness of both bones was set to 4.5 mm), while trabecular tissue of the tibia and femur was presented as porous structure by applying the regular spherical cuts. The diameter of cutting cuts was set to 6 mm, and the distance of  
Figure 1: Model geometry simplification and smoothening using MeshLab.  
Figure 2: (**a**) Numerical knee joint model; (**b**) Model components: 1—tibia, 2—femur, 3—tibia cartilage, 4—femur cartilage, 5—meniscus.  
their geometrical centers was equal to 5 mm. In the result, the ratio of bone volume and total volume (BV/TV) was equal to 0.2. This value is in agreement with the BV/TV ratios reported by [16].
## 2 Related Work
### Disentangled Representation Learning  
The goal of Disentangled Representation Learning (DRL) is to allow a model to learn to separate potentially independent factors in the data. This improves the interpretability, generalization, and controllability of the representation [11]. The Variational Auto-Encoder (VAE) is a popular method for learning data distribution through something called variational inference. This is used in a process called disentangled representation learning, or DRL. Researchers have proposed various methods to improve the Disentangled ability of VAE on complex datasets. For example, \(\beta\)-VAE makes latent variables more independent by adding a \(\beta\) penalty term, which improves disentanglement. However, a large \(\beta\) value can make reconstruction quality worse [21]. FactorVAE forces latent variables to be independent by minimizing the Total Correlation, which improves disentanglement even more [22]. DIP-VAE improves the Disentangled performance by matching the covariance matrix and prior distribution of latent variables, which is useful in situations that require strict mathematical guarantees [23]. JointVAE can disentangle both continuous and discrete latent variables, which is useful in situations that require handling different types of latent factors [24]. RF-VAE improves the decoupling capability by introducing correlation indicator variables to identify important latent factors [25]. By designing the right model structure and loss function, we can encourage VAE to learn a disentangled latent representation [26]. In the medical field, a method called \"disentangled representation learning\" has been used to study medical images and genetic data. This method helps identify what causes diseases and what types of diseases they are [27; 28; 29; 30].
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
###### Abstract  
Snow plays a critical role in hydrological monitoring and global climate change, especially in the Arctic region. As a novel remote sensing technique, global navigation satellite system interferometric reflectometry (GNSS-IR) has shown great potential for detecting reflector characteristics. In this study, a field experiment of snow depth sensing with GNSS-IR was conducted in Ny-Alesund, Svalbard, and snow depth variations over the 2014-2018 period were retrieved. First, an improved approach was proposed to estimate snow depth with GNSS observations by introducing wavelet decomposition before spectral analysis, and this approach was validated by in situ snow depths obtained from a meteorological station. The proposed approach can effectively separate the noise power from the signal power without changing the frequency composition of the original signal, particularly when the snow depth changes sharply. Second, snow depth variations were analyzed at three stages including snow accumulation, snow ablation and snow stabilization, which correspond to different snow-surface-reflection characteristics. For these three stages of snow depth variations, the mean absolute errors (MAE) were 4.77, 5.11 and 3.51 cm, respectively, and the root mean square errors (RMSE) were 6.00, 6.34 and 3.78 cm, respectively, which means that GNSS-IR can be affected by different snow surface characteristics. Finally, the impact of rainfall on snow depth estimation was analyzed for the first time. The results show that the MAE and RMSE were 2.19 and 2.08 cm, respectively, when there was no rainfall but 5.63 and 5.46 cm, respectively, when it was rainy, which indicates that rainfall reduces the accuracy of snow depth estimation by GNSS-IR.  
GNSS interferometric reflectometry; snow depth variations; snow surface characteristics; wavelet analysis; Svalbard 20202123352; doi:10.3390/rs12203352
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 3 Control Algorithms
### Whole Control Algorithm and Its Implementation  
The whole control algorithm proposed in this study has a structure where the feed-forward control, IPC, peak shaving, and nacelle feedback control algorithms are merged into the blade pitch control loop of the baseline controller. The block diagram of the whole control algorithm is shown in Figure 11. The reference bias control (RBC) was implemented to mitigate the transient response between the pitch control and torque control in the rated wind speed region, while the drivetrain damper was not applied to the torque control loop because the target FOWT is a direct-drive type wind turbine [28].  
The proposed control algorithm was designed as an in-house code using MATLAB/Simulink (Version R2023b, 2023) and Visual Studio (Version 16.11.22, 2019) for im  
Figure 11: Block diagram of the whole control algorithm for the target FOWT.  
Figure 9: Steady-state curve of the blade pitch angle and rotor thrust with the peak shaving.  
plementation in OpenFAST. The control algorithm designed in MATLAB/Simulink is converted into C++-based source code through the 'Code Generation' tool in MATLAB and is then integrated into the source code for creating the controller file, which is compiled in Visual Studio. To facilitate parametric studies, coding was carried out to generalize the required control parameters in the controller file (DLL: dynamic link library file) so that they could be modified through the in-house developed controller input file (IN: input file).  
The flowchart for the controller implementation is shown in Figure 12. The controller file and the control input file are input into the ServoDyn module in OpenFAST. These files output blade pitch control commands and generator torque control commands, interfacing with other dynamic modules in OpenFAST. Although the proposed control algorithm was designed using in-house code, the process for implementing the controller files in OpenFAST followed the methods verified and suggested by NREL, the provider of OpenFAST [18]. The proposed control algorithm, in the form of the C++ source code prior to the creation of the controller files as shown in Figure 12, can be included and uploaded to the commercial Programmable Logic Controller (PLC). This facilitates the link between simulation-based verification and the experimental validation of the proposed control algorithm. Therefore, the results presented in this study hold significant industrial applicability and practical value. In fact, the same approach has been adopted in other studies, where it was used to implement wind tunnel experiments or field tests utilizing commercial Bachmann PLC [28; 29].
## 4 Conclusions  
The SnSe thermoelectric materials should have highly reliable metallization layers which could decrease contact resistances at the interfaces with electrodes and protect element diffusion which were hardly solved with a single-layer metallization technique. As a solution, the Ag/Co/Ti multi-layer metallization was applied on SnSe thermoelectric materials where each component layer has its own role such as Ag layer for low electrical contact resistance, Co layer for thermal coefficient matching between Ag and Ti layers, and Ti layer for diffusion barrier. The multi-layer metallization resulted in a robust interfacial bonding property as well as a low SCR of 1.53 m\(\Omega\cdot\)cm\({}^{2}\) at the multi-layer/SnSe interfaces, because the electrical resistivity of SnSe is relatively higher compared to the other intermediate-thermoelectric materials. Moreover, it maintained its microstructures even after the rigorous heat treatments at 723 K for 20 h. The Ag/Co/Ti multi-layer metallization was demonstrated to successfully fabricate the thermoelectric devices or modules with high ZT SnSe material, due to its multi-functionality. Nonetheless, in case of the practical thermoelectric devices under operation, the metallized layers should withstand rigorous environments for a long time at the operation temperature. Thus, in following studies, long-term stability and thermal cycling tests need to be conducted for more practical use of Ag/Co/Ti multi-layer metallization on SnSe materials.  
This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (NRF-2015R1A5A1036133). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2016R1D1A1B01007074). All the authors have equally contributed to the development of multi-layer metallization and its analysis.  
The authors declare no conflict of interest.  
Figure 7: **(a)** SEM image and EDS line scanning of the Ag/Co/Ti multi-layer metallization; and **(b)** the graph of SCR after heat treatment at 723 K for 20 h.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Discussion of Outcomes  
The overall experimentation results are discussed as follows:  
* Based on parameter tuning experimentation, we found that the minimized values for the probability of mating rate \(M_{R}\) provides better route discovery. Therefore, we suggest that the use of smaller \(M_{R}\) values enhance the diversification of our proposed JMA and therefore eradicates local optima and premature convergence.
* Based on population size varying experimentation, we suggest that using population size \(POP=50\) achieves best route discovery reliability. However, maximizing population size will lead to high computation complexity rather than reduced complexity, as well as lower accuracy.
* Based on varying the velocity of vehicles, we suggest that using velocity of 20 km/h and lower will achieve the best reliable route discovery for the efficient transmission of information over the vehicles.
* Finally, JMA achieves the satisfactory performance over the all the different density scenarios. Among these three different scenarios, JMA provides the best results in low- and medium-density test cases, and kept the pace in high-density test cases.  
Figure 8: High Vehicular Density (50 vehicles). (**a**) Best route discovery achieved using JMA; (**b**) Mean route reliability results achieved across 1000 Monte Carlo trails.  
Figure 7: Medium Vehicular Density (30 vehicles). (**a**) Best route discovery achieved using JMA; (**b**) Mean route reliability results achieved across 1000 Monte Carlo trails.
## 2 Physically Distributed Logically Centralized (PDLC) SDN Architecture and Components
### Data Layer  
At the lowest layer depicted in Figure 2, network switches and routers are represented as blue boxes. These forwarding hardware devices store the state of the network data plane Forwarding Information Base (FIB), such as configured port speeds and TCAM (Ternary Content Addressable Memory) entries and the associated meta-data, including flow ports and packet counters. Additionally, they offer an interface facilitating the controller's ability to modify the network state by adjusting forwarding table entries that dictate their behavior. These network components merely require software support for this interface, such as OpenFlow, and basic connectivity. The collective set of switches directly linked to a specific SDN controller, along with all hosts connected to those switches, constitutes the domain of that controller [15; 16; 22].
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 6 Conclusions  
In this paper, we propose a scheme that adds XOR bit operations into the transmitter and receiver designs of conventional NOMA to alleviate performance degradation caused by the power distribution of the original signal. The addition of the XOR bit operation can improve the overall sum rate of users while mitigating the error performance degradation caused by power allocation. Additionally, by improving the BER and data rate of users with poor channel conditions, the user fairness of the entire system can be improved. Therefore, in systems where the required SNR is high for users with poor channel conditions, high communication performance can be expected by applying the proposed scheme. In this paper, as we have demonstrated through simulations that the addition of this XOR operation reduced the required power by 5 dB, the proposed transceiver model can be expected to achieve higher energy efficiency and increased network capacity.  
Conceptualization, S.-W.P. and H.-D.K.; methodology, S.-W.P. and K.-H.S.; software, S.-W.P. and J.-W.K.; validation, S.-W.P. and H.-K.S.; formal analysis, S.-W.P.; investigation, S.-W.P. and S.-H.S.; resources, S.-W.P. and Y.-J.C.; data curation, S.-W.P.; writing--original draft preparation, S.-W.P.; writing--review and editing, S.-W.P. and [PERSON]-K.S.; visualization, S.-W.P.; supervision, Y.-H.Y. and H.-K.S.; project administration, Y.-K.M. and H.-K.S.; funding acquisition, Y.-H.Y. and H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government [Ministry of Science and ICT (MSIT)], South Korea, under the Metaverse Support Program to Nurture the Best Talents under Grant IITP-2024-RS-2023-00254529; in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2020R1A6A1A03038540; and in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00219051).  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts 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  
Figure 9: BER performance comparison of user 1 and user 2 in proposed scheme.
# High-Precision Elastoplastic Four-Node Quadrilateral Shell Element
## 6 Conclusions  
In this study, we enhance the four-node quadrilateral shell element to mitigate the effects of film self-locking, shear self-locking, and the zero-energy mode. The zero-energy mode is addressed by incorporating normal rotational degrees of freedom into the shell element and introducing additional stiffness through a penalty function. Shear self-locking is resolved using a mixed tensor interpolation strain method. Through analyses involving in-plane fragment inspection, curved fragment inspection, plane shells, curved shells, and twisted shells, we compare the results with those obtained from the ANSYS software. The findings demonstrate that the improved element effectively eliminates the influences of film self-locking, shear self-locking, and the zero-energy mode, resulting in satisfactory stability and calculation accuracy.  
The calculation accuracy and stability of the four-node quadrilateral shell element, which is based on the neutral surface shell element, are comparable to those of the Shell281 element, which is based on the thin shell element in ANSYS. The introduction of rotational degrees of freedom around the z-axis enhances the membrane component of the four-node quadrilateral shell element, resulting in improved accuracy and adaptability. This study presents all the necessary formulas for code development, enabling comprehensive calculations of elastic-plastic problems and contributing to the advancement of independent finite element software.  
This study has achieved significant progress in improving the computational accuracy of four-node quadrilateral shell elements, although there are still some limitations. Due to the singularity of the unit types and the difficulty of unit partitioning, computational analysis in complex structures displays areas for improvement. Future research will address these issues by developing three-node triangular shell elements. These works will aim to improve the comprehensiveness and reliability of shell units, ensuring their wider applicability and effectiveness in practical scenarios.  
[PERSON] was responsible for data collection and processing, as well as writing and revising the article; [PERSON] was responsible for providing technical support and paper review. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available upon request to the author as the project has not been completed and data confidentiality is required.  
There are no conflicts of interest.  
Figure 14: Time variation curve of deflection of motion-strengthened material under impact load.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 6 Simulation Analysis  
### eNodeB Vulnerability Analysis  
Among the three vulnerable nodes,, as eNodeB, receives the average firing rate of the PTP messages sent by the attackers. We take the value of as 0-30 and the other value of as 1, and solve them in turn. The specific calculation results are shown in Table 4. In order to see the impact of the different protocol vulnerabilities more intuitively, the data presented in Table 4 are presented in a schematic diagram as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Steady-State Probability** & **Variation Range** & **Changing Amplitude** & **Change Trend** \\ \hline \(P(M_{5})\) & 0.1429–0.0064 & \(-\)0.1365 & Reduces Sharply \\ \(P(M_{6})\) & 0–0.1911 & 0.1911 & Rises Sharply \\ \(P(M_{33})\) & 0.1071–0.1481 & 0.041 & Rises Slightly \\ \(P(M_{14})\) & 0.0179–0.0247 & 0.0068 & Almost Unchanged \\ \hline \hline \end{tabular}
\end{table}
Table 4: Changes in the steady-state probability of each end state when \(\lambda_{4}\) changes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transitions** & **Corresponding Firing Rate** & **Definition** \\ \hline \(t_{4}\) & \(\lambda_{4}\) & eNodeB receives the Delay\_Req message sent by the OBC \\ \(t_{5}\) & \(\lambda_{5}\) & OBC receives the Follow\_up message sent by eNodeB \\ \(t_{13}\) & \(\lambda_{13}\) & OBC receives the Delay\_Rep message sent by eNodeB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of vulnerable nodes.  
Figure 7: Simulation results of each steady-state probability when \(\lambda_{4}\) changes.  
As shown in Table 4 and Figure 7, the effects of different values on the steady-state probability values of each end state of the clock node are compared. As gradually increases, the steady-state probability values of each end state of eNodeB and the OBC change.Moreover, the steady-state probabilities \(P(M_{5})\) and \(P(M_{6})\) of the abnormal and normal end states of eNodeB change the most drastically. \(P(M_{5})\) drops rapidly by 0.1365,that is, from 0.1429 to 0.0064, and \(P(M_{6})\) increases rapidly by 0.1911 from the initial value of 0. Compared with the change amplitude in eNodeB, the steady-state probabilities \(P(M_{13})\) and \(P(M_{14})\) of the abnormal and normal end states of the OBC barely change, only by 0.041 and 0.0068, which are 1/3 and 1/28 of \(P(M_{5})\) and \(P(M_{6})\), respectively. This shows that the malicious tampering of the message by the attacker has the greatest impact on the device eNodeB of the message receiver.