esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Velocity Profile Measurement at Two-phase Bubbly Flow  
#### 4.3.1 Measurement and Comparison  
The experiment was conducted at \(U_{L}\) = 200 mm/s and \(U_{G}\) = 5.3 mm/s as shown in Figure 14. The bubble diameter in this experiment was about 2-3 mm. Figure 15a shows the measurement results of two-phase bubbly flows. Bubbles rise mainly near the wall region. The graph shows the average velocity profile data. Liquid velocity distribution is the mean of 5000 profiles and bubble rising velocity is averaged by the amount of data obtained as shown in Figure 16. The result of bubble velocity distribution after separation by the Developed-UVP technique was verified using the PIV method. The velocity profiles of bubble measured by both methods were almost coincident with each other. It was confirmed that discrepancy of both systems was within an acceptable range of \(\pm\)10% as shown in Figure 15b. Furthermore, the liquid velocity profile was obtained and separated from the bubble phase obviously. The result was compared with the liquid phase result of the PIV. The discrepancy between these methods was within the acceptable range of \(\pm\)10% except a few points at the near-wall zone as illustrated in Figure 15c. However, the liquid velocity profile measurement of Developed-UVP in two-phase bubbly flow can be confidently trusted owing to deletion of the bubble velocity data and had good agreement with PIV method.  
Figure 13: Doppler amplitude threshold setting on probability density function (PDF) of Doppler amplitude reflected by nylon particles 80 μm and microbubble <1 mm; (**a**) at \(U_{L}\): 200 mm/s and (**b**) at \(U_{L}\): 300 mm/s.  
Figure 16: Amount of bubble data.  
Figure 14: The image of bubble distribution in the two-phase bubbly flow.  
Figure 15: Average liquid and bubble velocity distribution in two-phase bubbly flow at \(U_{L}\) = 200 mm/s and \(U_{G}\) = 5.3 mm/s: (**a**) velocity profile, (**b**) discrepancy of bubble velocity, (**c**) discrepancy of liquid velocity.  
Next, The \(U_{L}\) increased to 300 mm/s, but gas superficial velocity remained similar to the previous case. The bubble diameter was still about 2-3 mm. Figure 17a represents that the velocity profile of bubble and liquid was obtained separately. Good agreement in the velocity profile measurement of both phases was observed between the Developed-UVP and PIV method due to the discrepancy of both systems was within an acceptable range of \(\pm\)10% as illustrated in Figure 17b,c, respectively.  
From the results at both velocity levels, the measurement error of the bubble and liquid velocity could be caused by measuring equipment, methodology and the experimental condition. Also, the amount of bubble data and void fraction affect the averaging result of bubble velocity. Furthermore, the velocity distribution of bubble and liquid separated had an error at near the wall region (near-wall effect). The error analysis is explained in the detail in Section 4.4.
## 2 Vehicular Networks
### Cellular-Based Networks
Wireless communications over 5G will be highly reliable, with ultra-low latency and very high throughput. For future and existing applications, 5G is expected to support a large number of wireless connections. Proximity service (ProSe) [34], a critical feature in 5G, allows for awareness of nearby devices and remote services and is provided through D2D communications. It also provides data management services for cloud computing, software-defined networks (SDN). The 3 GPP has built a backward and forward compatible 5G standard using current LTE technologies [35] and will soon reveal new air interfaces for 5G technology. In [36], the authors discuss the new radio access technologies for connected and autonomous vehicles, such as visible light communication (VLC), millimeter wave (mmWave), C-V2X, and 5G, along with the challenges and opportunities. The new directions in research related to seamless connectivity, edge, fog, SDN, and security are also focused. An in-depth survey and comparison of vehicular MAC routing protocols was conducted in [37]. It addressed routing protocols with a cross-layer approach with MAC for VANETs. The MAC-aware routing solutions were classified as contention-free and contention-based. It provided a fully standardized, cross-layer communication model that is fully compliant with the existing vehicular service and application layers and messaging sets established by automotive and standards communities.
## 5 Conclusions  
1. Mathematical models are important in the analysis, design, and fault diagnose of rotor-bearing systems. However, due to the complex structure and other factors, it is impossible to establish an accurate physical model. Thus, in this paper, an identification method of the nonlinear, rotor-bearing system based on NARX model is proposed.
2. The experimental results indicate that the NARX model can reproduce the underlying nonlinear, rotor-bearing system accurately. Furthermore, the method enriches the nonlinear, rotor-bearing modeling theory and provides a reliable model for dynamic analysis, design, and fault diagnosis of the rotor-bearing system, which is of practical significance.
3. The NARX model under multi-harmonic excitation can reflect a broad range of system characteristics. The MPO and OSA methods are used in model validation. The results indicate that it is inappropriate to use the OSA method when establishing the nonlinear, rotor-bearing system.  
Figure 13: Output comparison in time and frequency domain of the test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Term** & **Coefficient** & **ERR\%** \\ \hline
1 & \(y(k-1)\) & 1.8444 & 99.4393 \\
2 & \(y(k-2)\) & \(-0.8595\) & 0.5177 \\
3 & \(u(k-2)u(k-2)\) & 0.1788 & 0.0027 \\
4 & \(u(k-1)u(k-2)\) & \(-0.2439\) & 0.0017 \\
5 & \(u(k-1)u(k-1)\) & 0.0642 & 0.0138 \\
6 & \(y(k-2)y(k-2)\) & 0.6580 & 0.0003 \\
7 & \(y(k-1)u(k-1)\) & 0.1268 & 0.0006 \\
8 & \(y(k-1)y(k-2)y(k-2)\) & \(-0.5515\) & 0.0003 \\
9 & \(u(k-2)\) & \(-0.0243\) & 0.0003 \\
10 & \(y(k-1)y(k-2)\) & \(-0.7159\) & 0.0001 \\
11 & \(y(k-1)u(k-2)\) & \(-0.1019\) & 0.0001 \\
12 & \(u(k-1)\) & 0.0250 & 0.0001 \\
13 & \(u(k-2)u(k-2)\) & \(-0.0020\) & 0.0001 \\
14 & \(y(k-1)u(k-1)u(k-1)\) & 0.1688 & 0.0002 \\
15 & \(y(k-2)u(k-1)u(k-2)\) & \(-0.1482\) & 0.0005 \\ Total & - & - & 99.9778 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification result of the test.  
**Acknowledgments:** This work was supported by the National Science Foundation of China (grant numbers 11572082); the Fundamental Research Funds for the Central Universities of China (grant numbers N160312001, N150304004); and the Excellent Talents Support Program in Institutions of Higher Learning in Liaoning Province of China (grant numbers LJQ2015038).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the study; [PERSON] and [PERSON] performed the experiments; [PERSON] wrote the manuscript. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
**Conflicts of Interest:** The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Materials and Methods
### Measurement Procedure  
The actual dimensions of the printed parts were measured using a Tesa Micro-Hite 3D coordinate measuring machine (CMM) manufactured by Hexagon Manufacturing Intelligence, Renens, Switzerland, with an accuracy of \(\pm\)0.001 mm. This machine is equipped with an automated probing system, allowing precise dimensional data collection for each part. The associated software enables detailed deviation analysis and the generation of comprehensive reports.  
Technical specifications of the measuring machine:  
* Model: Tesa Micro-Hite 3D.
* Accuracy: \(\pm\)0.001 mm.
* Measuring range: 400 \(\times\) 500 \(\times\) 450 mm.
* Probing method: Tactile system with an interchangeable probe head.
* Software used: TESA Reflex v.2022, Renens, Switzerland and TESA Power-Inspect v.2022, Renens, Switzerland for dimensional analysis.  
Measurements were conducted along three different directions to evaluate dimensional deviations caused by material shrinkage and variations in the layer deposition process:  
* 0\({}^{\circ}\)--Axis 1-1: The primary longitudinal direction, corresponding to the alignment of the part on the printing platform.
* 45\({}^{\circ}\)--Axis 2-2: A diagonal direction, used to identify irregular shrinkage variations.
* 90\({}^{\circ}\)--Axis 3-3: A direction perpendicular to the primary longitudinal axis.  
These measurement directions were chosen to provide a comprehensive assessment of dimensional deviations, allowing a precise evaluation of errors induced by the 3D printing process, Figure 2. Measurements were performed 24 h after printing to allow material stabilization and to avoid errors caused by the thermal relaxation of the PLA filament.
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 3 Screening for Sleep Apnea in Hospitalized Patients
### Pulse Oximetry  
Overnight pulse oximetry (ONO) during sleep has inconsistent sensitivity in the outpatient setting and is not recommended as a primary screening method for OSA [50; 51]. ONO can be used to calculate the oxygen desaturation index (ODI), which refers to the number of events per hour with a fall in oxygen saturation from baseline by a predefined percentage (3-4%; ODI 3% and ODI 4%) [52; 53]. ODI may be comparable to AHI for the diagnosis of OSA in an inpatient setting [25].  
For inpatient settings with limited testing capabilities, ONO is a feasible test to set up and can serve as a screening method to stratify patients for the urgency of a referral for sleep studies and attempting empiric treatment while inpatient [24; 25]. A limitation of ONO is that a positive test cannot be used for prescribing CPAP. In addition, using ONO to screen for OSA in the hospital may not be feasible for acutely ill patients with cardiopulmonary pathologies because it may be inappropriate for them to tolerate hypoxemia.  
High-resolution pulse oximetry (HRPO) uses oximeters calibrated to have a shorter averaging time and a higher resolution unit for SPO2 signal detection than standard pulse oximeters. It is also less affected by motion artifacts. HPRO is also feasible to set up in an inpatient setting and may have a sensitivity similar to home apnea screening tests [54; 55].
## 3 Imaging Attitude Control
### Imaging Attitude Analysis
#### 3.2.2 Imaging Attitude Analysis  
An example is illustrated to analyze the characteristics of the imaging attitude for the three-axis platform and two-axis platform. The longitudinal angle of the view of the area camera is 15\({}^{\circ}\) in the example, the latitudinal angle of view of the area camera is 20\({}^{\circ}\), and the relative height of the aircraft is 5000 m. Three images will be taken during each sweeping cycle. The cross-flight overlap angle for two successive images is 4\({}^{\circ}\), and the along-flight distance between two successive images is 500 m. Table 1 presents the three-axis platform gimbal angles for the imaging attitude control using Equations (6)-(8) and the two-axis platform gimbal angles using the algorithm proposed by [PERSON] and [PERSON]. [14]. According to the gimbal angles and aircraft attitudes in Table 1, the coverage area on the ground for each imaging step during one cycle could be obtained (Figure 4). The quadrangle, colored blue or red in Figure 4, represents the coverage area for one image, the black dot in the center of the quadrangle represents the projection of the LOS, and the dashed line represents the projection of \(Y_{S}\).  
perpendicular to the flight direction, and the direction of the LOS is aligned with the target direction, which verifies the validity of the imaging attitude control. The algorithm for the two-axis platform controlled the LOS correctly, but the orientation of the projection maps could not be controlled, and full coverage of the survey area could not be guaranteed.
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 2 Atomization Model and Basic Theory
### Mathematical Model  
In this paper, the STAR-CCM+ 2022.1.0 software with the VOF and VOF-DPM models is used to carry out the numerical simulation of the centrifugal atomization process of the rotating disk. The VOF algorithm is mainly implemented to capture the morphology change in the interface between the aluminum alloy liquid film and the surrounding gas phase. The VOF-DPM model combines the fluid volume method and the multiphase flow model of the Euler-Lagrange method. The VOF method can obtain the distribution characteristics of the alloy liquid film and the DPM method can be used to trace the discrete motion trajectory of the droplets formed by rotating disk atomization. The VOF-DPM model combines the advantages of the two methods and can achieve the transformation of the Eulerian continuous to the Lagrange discrete particle phase. Therefore, the model can be used to simulate the whole process of the centrifugal atomization of aluminum alloy, in order to study the spreading motion of the molten metal on the surfaces of different disk structures and the breakage law of the molten metal thin liquid film.  
The VOF model algorithm is based on the fact that two or more fluids are impenetrable to each other. For each variable introduced by the volume fraction of the fluid, the free liquid surface is tracked by solving the volume fraction. In each control volume, the sum of the volume fraction of all phases is 1. The interface between two phases can be traced by solving the continuity equation for the volume fraction of one or more phases. The volume fraction of phase fluid \(q\) is \(a_{q}\), and its continuity equation is as follows [30,31]:  
\[\frac{1}{\rho_{q}}\left[\frac{\partial}{\partial t}\left(\alpha_{q}\rho_{q} \right)+\
abla\left(\alpha_{q}\rho_{q}u_{q}\right)\right]=S_{aq}+\sum_{p=1}^{ n}\left(m_{pq}-m_{qp}\right) \tag{3}\]  
In the above formula, \(\rho_{q}\) is the physical density of the \(q\) phase; \(u_{q}\) is the velocity of the \(q\) phase; \(m_{pq}\) is the mass transfer from the \(p\) to the \(q\) phase; \(m_{qp}\) is the mass transfer from \(q\) to \(p\); and \(S_{aq}\) is the source term.  
In this study, the dispersion of atomized droplets is mainly affected by the viscous force and gravity of the atomizing atmosphere, etc. Based on [PERSON]'s second law, the dispersion equation for droplets used by the DPM model equation can be simplified into the following expression [32]:  
\[\frac{du_{d}}{dt}=-18\frac{\mu_{d}}{\rho_{d}D_{d}^{2}}\frac{C_{D}Re}{24}(u_{d} -u_{d})+\frac{g(\rho_{d}-\rho_{a})}{\rho_{d}}+F_{x} \tag{4}\]  
In the formula, \(u_{d}\) is the atomized droplet velocity; \(\rho_{d}\) is the molten metal density; \(D_{d}\) is the atomized droplet diameter; \(\mu_{d}\) is the molten metal dynamic viscosity; \(Re\) is the relative Reynolds number; \(u_{a}\) is the atomized ambient gas velocity; \(C_{D}\) is the drag coefficient; and \(F_{x}\) is the sum of other external forces that are unaccounted for.
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 4 Results and Discussion
### Ten-Bar Planar Truss
#### 4.1.3 GNN-Based Optimization  
The proposed GNN-based optimization was validated by a simple FEM-based optimization, in which the PSO algorithm evaluates each particle via a finite element analysis. It should be noted that the population of the GNN-based optimization was set to 75 and increased to 150 in the final 10% of iterations, compared to the fixed population of 75 for the FEM-based optimization over the course of all 100 iterations. The optimum outcomes for the 100 independent runs of each optimization technique are presented in Table 3, with the GNN-based optimization reaching the optimal weight with a negligible 0.05% deviation from the results of the FEM-based optimization. As for computational efficiency,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training** & **Validation** \\ \hline MSE (in\({}^{2}\)) & 0.236 & 0.240 \\ RMSE (in) & 0.271 & 0.288 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean results of five-fold cross-validation for 10-bar truss.  
Figure 8: Convergence history of the loss function for the trained surrogate model.  
Figure 7: (**a**) Convergence history of the loss function for five-fold validation model; and (**b**) convergence history of the metric function for five-fold validation model.  
the FEM-based optimization required an average of 9.3 s to attain an optimal solution with 7500 analyses. In contrast, the GNN-based optimization took 221.7 s, including 3.2 s for the data preparation and 218.5 s for the optimization process. A total of 2500 FEM analyses were performed by the GNN-based optimization, of which 1000 were to generate training samples and 1500 analyses were in the last ten iterations for 150 particles in each iteration. Despite using fewer FEM analyses than the FEM-based optimization, the GNN-based optimization required more time to achieve the optimal solution. This is because the 10-bar planar truss is a simple problem that lends itself to simple analysis, and therefore, the needed time to analyze the simple truss is less than that required for GNN predictions.
# Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues
## 3 Problem Modeling and Algorithm Modeling  
### Problem Description  
In the process of ship anchorage dispatching, ships arrive at port anchorage one after another through multiple channels to wait for berthing. The scheduling system needs to make decisions based on the ship priority type \(k\in\{1,2,\cdots,K\}\) (the higher the value, the higher the priority), the arrival rate \(r_{i}^{(k)}\) of each type of ship, and the current system state of the anchorage \(\mathcal{S}=\{S_{1},S_{2},\cdots,S_{n}\}\). This \(\mathcal{S}\) covers different states, such as the remaining number of anchorages, the occupancy of berths, the queue lengths of ships with different priorities, and the port service efficiency. The generation matrix \(Q\) of the continuous-time Markov chain (CTMC) is used to determine, under this state, whether  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Merits** & **Insufficiencies** \\ \hline
[12, 13, 14, 15] & Construct a theoretical optimization framework to adapt to different service time distributions & Poor resistance to randomness, complex multi-priority calculation, and low dynamic scene efficiency \\
[16, 17, 18, 19, 20] & Small and medium-sized scenarios are optimized and feasible, with high flexibility & No convergence proof, easy to fall into local optima, hard constraint modeling is weak, difficult to migrate \\
[21, 22, 23] & Provide a theoretical and technical reference for throughput optimization and traffic equalization & Insufficient adaptation of complex priorities, weak generalization of burst scenarios, sensitive parameters \\
[24, 25, 26] & Dynamic prediction of state and behavior & Data privacy constraints, unexplainable decisions, reliance on stationary assumptions, lack of priority modeling \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of ship dispatching methods.  
to arrange for the ship to enter the anchorage and wait, receive services immediately, or temporarily stay in the open sea. Due to the limited resources of the anchorage, when ships of type \(k+1\) arrive continuously, their higher service priority means that they will quickly occupy the anchorage resources, causing ships of type \(k\) to enter the anchorage in a disorderly manner. This will lead to the unreasonable occupation of resources and delay the system's response to ships of type \(k+1\). This complex queuing phenomenon caused by the contradiction between the dynamic arrival of ships and the limited service resources makes it difficult for traditional scheduling methods to accurately predict the waiting times of ships, thus affecting the overall scheduling efficiency. In extreme cases, it may trigger a chain reaction delay in port operations.
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 5 Conclusions  
In this work, we modified the self-attention mechanism to make it more suitable for speech tasks by incorporating features from different dimensions. The results showed that our improvement in the self-attention mechanism was effective. We utilized self-supervised pre-training and data augmentation to enhance the performance of the proposed model. Our model achieved promising results on both ASVspoof 2021 LA and DF datasets, especially in the DF partition, where it outperformed the current state-of-the-art model. This study fills the gap in the research on the ASVspoof 2021 DF dataset. However, due to the use of pre-trained models and self-attention mechanisms, our model has a large number of parameters, which may not be suitable for resource-limited scenarios. Future work will focus on reducing the model size, retaining its effectiveness, and making it more widely applicable.  
Conceptualization, J.Z. and G.T.; methodology, J.Z. and S.L.; software, J.Z.; validation, J.Z. and G.T.; formal analysis, J.Z. and G.T.; investigation, G.T. and Z.C.; resources, G.T. and Z.C.; data curation, J.Z.; writing--original draft preparation, J.Z. and G.T.; writing--review and editing, J.Z.; visualization, J.Z. and S.L.; supervision, G.T. and S.L.; project administration, G.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: All data included in this study are available upon request by contacting the corresponding author.  
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
###### Abstract  
The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities.  
Internet of Things (IoT); radio frequency identification (RFID); Global Navigation Satellite System (GNSS); low-cost localization +
Footnote †: journal: Geo-Inf.