esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## 5 Results and Discussions
### Parametric Analysis
#### 5.2.3 Effect of Cooling Rate  
This section investigates the effects of the cooling rate on frost heave strain and the efficiency of drainage by the WF during the freezing process. Figure 14 illustrates the comparison between frost heave strain in the WWF and NWF models under different cooling rates. Moreover, a comparison of the saturation variations between the NWF and WWF models is presented in Figure 15 for different cooling rates during the freezing process. The WWF model shows the inhibiting effect of frost heave throughout the freezing process compared to the NWF model, regardless of the cooling rate. Moreover, according to the comparison of the simulated frost heave strain in the NWF models under different cooling rates, the frost heave strain increased as the cooling rate decreased. Based on this observation, the cooling rate has a greater impact on reducing frost heave strain during the freezing process. The slower cooling rate allows moisture to migrate upwards to the freezing front and form ice lenses, which leads to a larger amount of frost heave. A high  
Figure 12: Effect of GWL on frost heave and WF inhibiting effect.  
Figure 13: Contour plot of Effect of GWL on frost heave and WF inhibiting effect.  
cooling rate can rapidly convert the water in the soil into ice, thereby reducing the soil permeability and making it more difficult for the moisture to migrate. The insufficient water supply in the high-cooling-rate circumstance will result in a limited frost heave ratio in the freezing process. Therefore, the increase in the cooling rate decreases the efficiency of the WF in inhibiting frost heave strain.  
On the other hand, though the WF is evidently capable of effectively draining water under different cooling rates, as shown in Figure 15, it can sufficiently remove moisture from the soil, especially at the low cooling rate. Consequently, low cooling rates result in a more apparent reduction in frost heave strains of the WWF model during the freezing process. For example, the saturation distribution in the soil is almost similar when the cooling rates are 0.1 \({}^{\circ}\)C/h and 0.2 \({}^{\circ}\)C/h. The saturation distributions of \(U=0.1\)\({}^{\circ}\)C/h and \(U=0.2\)\({}^{\circ}\)C/h in their corresponding NWF models are significantly different, indicating that the WF has a greater drainage effect when the cooling rate is lower.  
Figure 14: Effect of cooling rate on frost heave and WF inhibiting effect.  
Figure 15: Comparison of saturation distribution between NWF model and WWF model in the freezing process.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 5 Conclusions  
Satellite-based distributed antenna technology enables flexible configuration and deployment according to practical needs and scenarios, thereby enhancing system flexibility and scalability. It holds significant importance and promising prospects in modern satellite communication systems. In this paper, we analyzed the impact of grating lobes on distributed satellite communication (DSC) distributed antennas. We conducted simulation analyses of the grating lobes in the far-field pattern of the antenna array by altering the  
\begin{table}
\begin{tabular}{c c c c} \hline Scanning Angle (\(\theta\)) & \(0^{\circ}\) & \(\pm 4^{\circ}\) & \(\pm 8.7^{\circ}\) \\ \hline Sidelobe Level (\(dB\)) & 2.09 & 1.59 & 1.55 \\ Beamwidth (\({}^{\circ}\)) & \(1.4\times 10^{-5}\) & \(1.4\times 10^{-5}\) & \(1.4\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: Sidelobe levels (\(dB\)) and beamwidths (\(\circ\)) corresponding to different scanning angles.  
Figure 16: The antenna pattern at a scanning angle of \(-4^{\circ}\).  
Figure 17: The antenna pattern at a scanning angle of \(8.7^{\circ}\).  
Figure 18: The antenna pattern at a scanning angle of \(-8.7^{\circ}\).  
relative positions of the satellite antennas and the carrier phase of the satellite terminals in two DSC satellite scenarios. The simulation results indicated that the relative positions of the satellites had an influence on the interference fringes, while changing the carrier phase of the antennas resulted in the translation of the interference fringes. To suppress the formation of grating lobes, we adopted a sparse linear array configuration and compared it with a uniform linear array's antenna pattern. Simulation results indicated that the non-periodic array can push the occurrence of grating lobes further away, but it disperses the energy of the grating lobes into the sidelobe region, resulting in an overall increase in sidelobe levels. In this study, the sidelobe level of the distributed antennas decreased by 13.79 dB compared to that of a single satellite antenna. Therefore, to control the overall sidelobe level, further optimization of the array configuration is necessary.  
Conceptualization, Y.H. and C.W.; methodology, Y.H.; software, C.W.; validation, Y.H. and C.W.; formal analysis, C.W and C.Q.; investigation, C.W.; resources, C.W.; writing--original draft preparation, C.W.; writing--review and editing, Y.H.; visualization, C.Q.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset is not applicable.  
The authors declare no conflict of interest.
## 5 Analysis for Torsional Stiffness Properties of Leaf Spring Torsional Vibration Damper
### Influence of Leaf Spring Size on Torsional Stiffness Properties  
The stiffness of the leaf spring group governs the torsional stiffness of the torsional vibration damper. From the leaf spring group's structure, it is evident that the spring's length and thickness impact its stiffness. Maintaining the other parameters constantly, altering the leaf spring length reveals its impact on the damper's torsional stiffness, illustrated in Figures 12 and 13. Figure 12 highlights the considerable influence of spring length on the damper's torsional stiffness. When the spring length is shorter, the damper's torsional stiffness curve displays pronounced nonlinear characteristics. However, as the leaf spring length increases, the curve's nonlinear aspects gradually diminish. At a leaf spring length of 260 mm, the curve tends towards linearity. Figure 13 demonstrates the significant influence of leaf spring length on torsional stiffness at equivalent torsion angles, with this influence amplifying as the torsion angle increases. At smaller torsion angles, increasing the leaf spring length leads to a gradual decline in torsional stiffness. Yet, at torsion angles of 1.6\({}^{\circ}\) and 1.8\({}^{\circ}\), extending the leaf spring sharply reduces torsional stiffness. Shorter springs exhibit increased stiffness in the spring group, resulting in conspicuous nonlinear characteristics in the torsional stiffness of the damper.  
Figure 11: Influence of clamping groove arc radius on torsional stiffness of damper under the same torsion angle.  
Figure 10: Influence of clamping groove arc radius on torsional stiffness of damper.  
Given a uniform change in the thickness of the leaf spring, Figures 14 and 15 illustrate the impact on the torsional stiffness of the leaf spring torsional vibration damper by altering the thickness of the thin edge while maintaining the thickness of the thick edge. The curves in Figure 14 exhibit noticeable nonlinear characteristics, and these properties become more pronounced as the thickness of the thin edge of the leaf spring increases. Figure 15 indicates that, under the same torsion angle, the impact of leaf spring thickness on the torsional stiffness of the damper is as follows: when the torsion angle is small, the effect of spring thickness on torsional stiffness is relatively modest; with an increase in torsion angle, the influence of spring thickness on torsional stiffness also grows. Consequently, the thickness of the spring has a discernible impact on the torsional stiffness and nonlinear characteristics of the damper.  
Figure 12: Influence of leaf spring length on torsional stiffness of damper.  
Figure 13: Influence of leaf spring length on torsional stiffness of damper under the same torsion angle.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 7 Conclusions  
Recent studies on traffic engineering and multicast protocols on SDN mostly focus on single source multicast. This paper studies the problem of delay bounded multi-source multicast for SDN. The objective is to use multiple source to minimize the total delay cost, and hence increase transmission efficiency, utilizing the content replica design. Compared with unicast, multicast can naturally reduce the bandwidth consumption and reduce the load on the source. We first model our DBMS scheme into MILP problem and prove its NP-hard hardness. We then designed DBMS algorithm to solve the MILP model. Our DBMS algorithm is able to find the minimum delay cost path from each potential source node to its multicast nodes. We have done intensive simulations to evaluate the performance of our DBMS scheme, and our results demonstrate that DBMS scheme tremendously reduce the total delay cost when compared to other schemes. Our algorithm ensures that the execution time of our DBMS scheme is much lower than that of the modified-RAERA, STP, ST and KPP, and is very closer to the optimal solution. DBMS scheme has been tested for different network topologies, delay bounds, network sizes, \(|S|\) and \(|D|\) sets. These parameters do affect the simulation results to various degrees. However, DBMS performs well in all situations when compared to other algorithms.  
**Acknowledgments:** This work was supported by the Department of Tertiary Education Financing (DTEF) in Botswana and the Botswana International University of Science and Technology (BIUST).  
**Author Contributions:** [PERSON] conceived and wrote the paper; [PERSON], [PERSON] and [PERSON] performed experiments; [PERSON] and [PERSON] provided technical support and revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The Proposed Method
### Contrastive Learning
#### 3.5.2 Label-Level Contrastive Learning  
The Label-Level Contrastive Learning (LL-CL) strategy narrows the distance of samples with the same labels, as shown in Figure 3c. Specifically, let \(B=\{x_{1},x_{2},\ldots\,x_{b}\}\) denote a collection of samples; \(b\) is the batch size. \(\{x_{i},y_{i}\}\) represents a sample and label pairs in \(B\). The LL-CL loss on the batch \(B\) is defined as:  
\[P_{B}(i,c)=\frac{exp(sim^{+}(h(x_{i}),h(x_{c})/\gamma)}{\sum_{x_{i}\in B,x_{i} \
eq x_{i}}exp(sim^{-}(h(x_{i}),h(x_{k})/\gamma)} \tag{10}\]  
\[L_{LL-CL}=-\sum_{i\in B}\log\frac{1}{C_{i}}\sum_{y_{i}=y_{c}\
eq i}P_{B}(i,c) \tag{11}\]  
where \(P_{B}(i,c)\) represents the likelihood that \(x_{c}\) is the most similar sample to \(x_{i}\), \(h(\cdot)\) represents the heterogeneous graph neural network, \(x_{c}\) is a document in \(B\) that belongs to the same category as \(x_{i}\), \(x_{k}\) is a distinct sample from \(x_{i}\) in the same batch, thereby serving as a negative example for comparative analysis during the contrastive learning process, and \(C_{i}\) represents the set of samples in \(B\) that share the same label as \(x_{i}\). The LL-CL loss function is designed to maximize the similarity between examples within the same category while minimizing the similarity between examples from different categories.  
The three loss functions mentioned above are combined and jointly trained in XCLHG. Specifically, the overall training loss, \(L_{XCLHG}\), is calculated as a combination of the heterogeneous graph neural network loss, \(L_{CE}\), the multi-view contrastive learning loss, \(L_{TL-CL}\), and \(L_{LL-CL}\) on batch \(B\); the coefficients \(\lambda\) and \(\mu\) are introduced to balance the optimization objectives:  
\[L_{XCLHG}=L_{CE}+\lambda L_{TL-CL}+\mu L_{LL-CL} \tag{12}\]
## 1 Introduction
### Medical Screening and Certification Prior to Space Travel
AI-improved augmented reality/virtual reality to reduce/prevent Spaceflight-Associated Neuro-Ocular Syndrome (SANS), space motion sickness (SMS), and post-flight motion sickness (PFMS) [116; 117; 118]  
AI identification of an individual's radiosensitivity (genotype-phenotype) using multimotics to prevent the negative effects of ionizing radiation [119; 120; 121]  
Emerging technology could allow for the transfer of large volumes of data at faster rates in order to facilitate medical research in space [122]  
AI can be used to create novel metrics (e.g., travelers' satisfactions, post spaceflight measurements for travelers) and ethics (e.g., health insurance)  
\begin{table}
\begin{tabular}{c} \hline \hline
**Potential AI Applications**  
AI-based models for the identification of causal relationships between various exposures and subsequent cancer development (e.g., CRISP) [84]  
**Medical Screening Preflight and Inflight Monitoring**  
AI monitoring for gut microbiota dysbiosis using smart toilets during pre-, intra-, and post-flight periods [2; 85; 86]  
Screening for latent virus infection or reactivation based on immunological variables, which can then be used as a biomarker for assessment of adaptive immune function [87; 88]  
The Edge TPU and other similar low-power AI ASICs could be used to monitor genomic imprinting (e.g., DNA methylation signatures or epigenetic/transcriptomic-based tests) for inflight lung cancer screening (may be equivalent to low-dose CT chest screening) [63; 89; 90]  
AI screening for mental illness through facial recognition and vocal analysis [68; 69]  
A combination of AI and imaging could potentially identity abnormal or misfolded proteins [91; 92]  
Deep learning algorithms for the inflight telediagnosis of various conditions (e.g., skin lesions or SANS) [93; 94; 95]  
AI-integrated wearable technology for space suits (e.g., exoskeletons) to monitor biodata (e.g., electrocardiogram, blood pressure, sleep cycle, body temperature, etc.) [96; 97]  
AI monitoring of metabolomics-based biomarkers for assessment of sleep quality and circadian rhythm [98]  
AI can be used to analyze retinal fundus photography to identify and monitor space anemia [99]  
**Medical Diagnostic Tools**  
The Edge TPU and other similar low-power AI ASICs (i.e., Google Coral Edge TPU, NVIDIA Jetson Nano, Intel Neural Compute Stick 2) could advance image segmentation AI models, allowing for these models to be feasibly deployed in ultrasound point-of-care settings (e.g., detection of DVT, structural heart disease, hemodynamic changes), even procedural guidance [100].
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
## 3 Results
### Simulation Method of Hemolysis Characteristics of the Impeller with Three Speed Patterns  
The fitting results of the blood pump flow rate and AOP, under the condition of heart failure in the second cardiac cycle, were used as the boundary conditions of the blood pump inlet and outlet to simulate the internal flow field and hemodynamic characteristics of the blood pump in auxiliary work. The speed of the blood pump was set according to the three speed patterns. The flow rate, pressure boundary conditions, and impeller speed were set using the user-defined function. The hemolysis model was solved in the form of a user-defined scalar transfer equation. The HI was set to zero at the inlet of the blood pump, and the hemolysis was calculated using the mass-weighted average at the outlet. The simulation was carried out in ANSYS Fluent. The relative motion between the impeller and the volute was simulated with a sliding mesh. The time step was 0.001 s, and the solution step was 1600 steps with a total time of 1.6 s. Considering the instability of the simulation results of the first cardiac cycle, this study compared mainly the difference between the convective flow field and the blood damage of the impeller with three speed patterns in the second cardiac cycle.
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods  
This study was part of a collaborative journalism experiment for event coverage, carried out in three days in the town of Almagro (Ciudad Real, Spain) during the International Festival of Classical Theater. Almagro is a small town in central Spain (Castilla La Mancha) with a population of around 9000 residents and it is a candidate to become a World Heritage Site, having been already declared a Historical-Artistic Zone. Its urban structure and the abundance of old stone buildings are a challenge for radio transmissions. The experiment performed was led by the Spanish Public Radio and Television (RTVE), with the participation of the Autonomous University of Barcelona (UAB), and by the Universityof Castilla La Mancha (UCLM), within the framework of the Chair of the Observatory for Innovation in News of the Digital Society (OI2) of the UAB-RTVE.
## 3 Results
### Analysis of Data Received from GlobalStar and SatNOGS  
The temperature variations obtained in the month of July 2020 using only the system based on intersatellite communication can be seen in Figures 8 and 9. The location of the sensors is in each of the solar panels for external temperatures and on the onboard computer, electrical power system, and radio boards for internal temperatures.  
These figures depict the temperature variations observed during one month of satellite operation. Throughout this period, the satellite experienced maximum and minimum temperatures of 45 \({}^{\circ}\)C and \(-\)25 \({}^{\circ}\)C respectively in the external environment. The internal temperatures ranged from 0 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, with an abnormal data point of 85 \({}^{\circ}\)C on 18 July 2020 possibly indicating an error in reading the sensor or a component failure as shown in Figure 9a.  
Figure 7: Velocity of the AztechSat-1 during its last days in orbit [26].  
\begin{table}
\begin{tabular}{r} \hline
**Two Line Element (TLE)** \\ \hline
1 45261U 98067 RE 20274.58306181 +0.00017834 +00000-0 +28188-3 0 9998 \\
2 45261 051.6407 182.8369 0007640 038.2637 321.8894 15.53524114034841 \\ \hline \end{tabular}
\end{table}
Table 3: NORAD Two-Line Element of the AztechSat-1.  
Figure 10 illustrates the relationship between the battery voltage and the input and output current of the EPS system. The behavior of the battery voltage can be observed in comparison to the levels of input and output current. It is evident that the battery charging process occurs when the input current surpasses the output current. Additionally, an anomaly in the charge level is noticeable, potentially correlated with the anomaly depicted in the internal temperature as shown in Figure 9a.  
Figure 8: Temperature variations on the external panels of the nanosatellite: (**a**) Temperature variations on panels \(-\)X and \(-\)Y; (**b**) Temperature variations on panels X+ and Y+.  
Figure 9: Temperature variations on internal and external components: (**a**) Temperature variations on internal boards of the satellite; (**b**) Temperature variations on solar panel Z.
## 5 MDP Model for the Multi-UAV Task Planning
### UAV Flight Model  
In the simulation environment, in order to facilitate the analysis of the problem, the UAV and the obstacles in the air area are set to the shape of a sphere, the radius of the UAV is \(r_{uav}\), and the radius of obstacles is \(r_{adv}\). The landmark is set as a cube in order to distinguish it from the UAV, and then one of the landmarks is set as the target. The radius of its effective range is \(r_{aim}\). When the UAV approaches the target area, for example \(D\leq D_{aim}=r_{uav}+r_{aim}\), it is determined that the UAV reaches the target area. At this time, the position of the UAV is \(P_{t}=[x_{t},y_{t},z_{t}]^{T}\), and its speed is \(V_{t}=[v_{t,x},v_{t,y},v_{t,z}]^{T}\).  
In the 3D dynamic environment, the motion model is set as follows to enable UAVs and obstacles to operate in this environment with a motion model:  
\[\begin{bmatrix}v^{\prime}x\\ v^{\prime}y\\ v^{\prime}z\end{bmatrix}=\begin{bmatrix}v_{x}\\ v_{y}\\ v_{z}\end{bmatrix}+a\times\Delta t, \tag{11}\]  
where \(v^{\prime}x,v^{\prime}y,v^{\prime}z\) represents the new velocity components of the UAV in the X-axis, Y-axis and Z-axis, respectively. \(a\) is the acceleration of the UAV, and \(\Delta t\) is the motion interval time. In the event of a collision \(a=f/m\), \(f\) is the force generated during the collision, and \(m\) is the mass of the UAV. Then, the position is updated to \(P^{\prime}=[x^{\prime},y^{\prime},z^{\prime}]\):  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{bmatrix}=\begin{bmatrix}x\\ y\\ z\end{bmatrix}+\begin{bmatrix}v^{\prime}x\\ v^{\prime}y\\ v^{\prime}z\end{bmatrix}\times\Delta t, \tag{12}\]  
Figure 6: The MDP model of the multi-UAV task planning.  
In addition, the turning angle, climb angle or descent angle of the UAV are defined as Formula (13) and Formula (14), respectively.  
\[\theta=\arccos\] \[\frac{\left(x_{t}-x_{t-1}\right)\left(x_{t-1}-x_{t-2}\right)+\left(y_{t} -y_{t-1}\right)\left(y_{t-1}-y_{t-2}\right)}{\sqrt{\left(x_{t}-x_{t-1}\right)^ {2}+\left(y_{t}-y_{t-1}\right)^{2}\cdot\left(x_{t-1}-x_{t-2}\right)^{2}+\left(y _{t-1}-y_{t-2}\right)^{2}}} \tag{13}\]  
\[\psi=\arctan\frac{\left|z_{t}-z_{t-1}\right|}{\sqrt{\left(x_{t}-x_{t-1}\right) ^{2}+\left(y_{t}-y_{t-1}\right)^{2}}}, \tag{14}\]  
At the same time, the movement of the UAV and obstacles also needs to meet the constraints of speed, acceleration, flight height, turning angle, climb angle or glide angle, as shown in Formula (15), wherein \(h\) is the flight height of UAV.  
\[\left\{\begin{aligned} & v\leq v_{\max}\\ & a\leq a_{\max}\\ & h_{\min}\leq h\leq h_{\max}\\ &\theta\leq\theta_{\max}\\ &\psi\leq\psi_{\max}\end{aligned}\right. \tag{15}\]  
Some parameters of the UAV 3D model in the experiment are set as shown in Table 2.
## 6 Simulation Results
By increasing the number of ABSs above four, the interference among users rises, which decreases the received SNR below the threshold. Moreover, by comparing the proposed scheme with the conventional one for a varying number of deployed ABSs, we observe that the number of outage users are less in the proposed scheme. Hence, the proposed scheme performs better in terms of user association than the conventional scheme. To verify the efficacy of the proposed scheme, we also compare the mean sum-rate performance of the proposed prioritized-based user association scheme with the following schemes:  
* Benchmark: we tried every possible combinations of ABS user associations, which resulted in the highest mean sum rate compared to other schemes. * SINR-based user association: users are associated with the ABS from which they received the maximum SINR. * Distance-based user association: users are associated with the nearest ABS. * Random user association: users are associated randomly with any ABS in the vicinity without caring about any requirement. Figure 5: Comparison of sum-rate performance under the proposed and the conventional SNR-based user association scheme. From the results in Table 4, we can clearly notice that the benchmark algorithm has the best mean sum rate because it tries every deployment positions and associate users with the ABS that maximizes the sum rate. The proposed scheme has the 2 nd best performance because it prioritized the users with emergency communication requirements. In distance-based association, performance degrades because users can be associated with the nearest base station, but this does not guarantee good channel conditions. Obviously, in random association, the performance is worst because it disregards any QoS requirements.