esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Modeling and Simulation of MICEPH  
The fluid structure interaction (FSI) and UNDEX phenomena are implemented in non-linear finite element code ABAQUS V6.13 (Dassault Sys Simulia Corp, Providence, RI, USA)/Explicit. The MICEPH used in this study consists of five cross-ellipses, as demonstrated in Figure 4. The optimized structural hull and water domain are imported from ANSYS Program as (* igses). Since the water domain does not have internal space for the pressure hull, it therefore needs to be modified to have identical internal shape for the hull. This can be simply done using the merge/cut function in the CAE model part in ABAQUS. The pressure hull was modelled using the shell elements. The stiffeners were added in the radial and longitudinal directions to provide structural integrity. The fourth node shell element (S4R) was used to model the pressure hull and stiffeners. An assemblage of 4-node acoustic tetrahedral elements (AC3D4) was used to represent the external fluid. The total horizontal length of the fluid model with the spherical ends was 11.56 m. The vertical length of the fluid domain was 7 m. The modeled water domain part includes the hull structure. The model and the charge mass were located at depth 100 m below the free surface. In dynamic problems that involve fluid and coupled solid medium, the interface between the two domains must be identical. Also, the water domain must have bulk modulus and density since it is acoustic domain. Figure 5 shows the finite element model and the meshing technique for the MICEPH surrounded by the fluid. The boundaries of the fluid around the MICEPH may cause shock wave reflection or refraction, which may cause a change in its superposition or cancellation by the incident wave [56; 57]. To overcome this problem, the boundary condition of the fluid is executed as a non-reflective boundary condition during the analysis. The pressure hull was exposed to UNDEX produced by various amounts of explosives and offset distances (30 kg TNT at 5.5 m, 20 kg TNT at 5.5 m, 15.5 m and 20.5 m). In this study, the stand-off distance is located at the right side of the cross-elliptical submersible pressure hull. The stand-off point represents the location where the incident wave defined and represented by reference point1 (RP\({}_{1}\)). The location of the charge (source point) defined as reference point 2 (RP\({}_{2}\)), which represents the position of the charge as illustrated in Figure 6. There are three types of input parameters for UNDEX: physical charge, material and bubble model. Defining the UNDEX bubble in the interaction module was applied by defining the source point, stand off point which specifying the wave properties and charge depth. Taking into consideration that the infinite surface shouldn't reflect the shock wave; thus, acoustic impedance (non-reflecting) has to be applied. The initial boundary condition for the infinite surface of water domain (acoustic wave) was set to zero, which means that the domain is calm water, i.e., no interference [58].  
Figure 4: Miceph.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 4 Network Clustering  
### Number Determination of Clusters  
Based on the estimated information of positions and velocities, we aim to group these nodes into several clusters by using the \(k\)-means++ algorithm, which is an unsupervised learning method [20]. Before performing clustering, it is essential to determine the cluster number. Herein, we adopt the widely used Silhouette coefficient, which does not need to know the labeling of the dataset [21; 22]. It gives a metric of the separation between clusters and thus provides a way to assess the number of clusters. For each node, the corresponding Silhouette coefficient is expressed as  
\[s(i)=\frac{b(i)-a(i)}{\max\{a(i),b(i)\}}, \tag{13}\]  
where \(a(i)\) is the average distance between node \(n_{i}\) and the rest members belonging to the same cluster, \(b(i)\) denotes the average distance between node \(n_{i}\) and all other members belonging to the nearest cluster. The detailed expressions of \(a(i)\) and \(b(i)\) are presented as  
\[a(i)=\frac{1}{|C(i)|-1}\sum_{C(i),i\
eq j}D(i,j), \tag{14}\]  
\[b(i)=\min_{i\
eq j}\{\frac{1}{|C(j)|}\sum_{j\in C(j)}D(i,j)\}, \tag{15}\]  
where \(|C(i)|\) is the total member number belonging to the cluster assigned to \(n_{i}\). According to Equation (13), it can be learned that this metric has a range of \([-1,\,1]\). To determine the optimal number of clusters, we iterate different numbers of cluster and calculate the average silhouette coefficient of all nodes for a given number of cluster. The number with the maximum value is regarded as the optimal choice. Furthermore, multiple rounds are conducted and the average number is used to eliminate the randomness error.
# Experimental Study of See-Saw Mode Nano-Vibration on Orifice-Type Restrictors
## 1 Introduction
[18] used the two-way coupled fluid-structure interaction to analyze the stability characteristics of a soft rotor and gas bearings, deriving the dynamic parameters for varying bearing capacity, rotational speed and temperature. [PERSON] et al. [19] proposed a two-round optimization design method for aerostatic spindles considering the FSI effect. They revealed the deformation law of guideways for different materials and gas film thicknesses. Unfortunately, such discussions have rarely included FSI vibrations in restrictors. To reduce vibrations and optimize performance, several applications have been proposed. [PERSON] et al. [5] showed that to suppress micro-vibrations, the air-supply pressure should be kept as small as possible. Small air-pockets and orifice diameters are also needed. [PERSON] et al. [20; 21] studied the gas-pressure fluctuation and vibration characteristics inside an aerostatic thrust bearing equipped with a pocketed orifice-type restrictor, and a flow-field disturbance structure was created for the air chamber, changing the internal microscopic flow field [22]. These theoretical models were established for different conditions under a variety of restrictor types, such as orifice, slot or porous. When studying static performance, pocket structures, recesses and grooves are often found on the restrictors, which complicates the models. Hence, although more time-consuming than solving the governing air-film equations, the computational fluid dynamics (CFD) domain analysis has become popular [23]. Highly precise sensors (e.g., linear variable differential transducer accelerometers [8], piezoelectric acceleration sensors [10], laser displacement transducers [22], and capacitive displacement sensors [24]) have been employed to measure the vibration displacement and acceleration. Compared with experimental environments and device limits, micro- and nano-vibration experiments are complicated. This paper examines new FSI vibrational performance at the nano-scale from a microscopic perspective. In Section 2, an equivalent model of a one-orifice-type restrictor is described, and a finite element model (FEM) based on the COMSOL FSI model was used for simulation. The simulation results are discussed as well. In Section 3, two types of restrictor vibration characteristics are inspected from experiments. A POLYTEC OFV505/5000 high-performance laser vibrometer is employed to obtain nanometer vibrations, and the time-domain results and frequency-domain results of restrictor vibration are analyzed to reveal the orifice-type restrictors. Discussion and conclusions are in Sections 4 and 5.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 2 Methods and Materials  
### Real-Time Kinematic Positioning  
RTK positioning is based on double-differenced (DD) GNSS observations between observational stations and the time to reduce the effects of the spatially correlated errors, such as the ionospheric and tropospheric delays and clock errors, over a short baseline. The DD equations of the pseudorange and carrier-phase observations are [31]  
\[\mathsf{P}_{br}^{j,g}=\mathsf{\mu}_{r}^{ij,g}s_{r}-\mathsf{\rho}_{br}^{ij,g}+ \delta_{p} \tag{1}\]  
\[\lambda\Phi_{br}^{ij,g}=\mathsf{\mu}_{r}^{ij,g}s_{r}-\rho_{br}^{ij,g}-\lambda N _{br}^{ij,g}+\delta_{\Phi} \tag{2}\]  
where P and \(\Phi\) are, respectively, the pseudorange and carrier phase DD observations; \(\delta\) represents the noise in the pseudorange and carrier phase observations: \(\mu\) is the LOS (line-of-sight) unit vector from a receiver to a satellite: \(s\) is the coordinate components: \(\rho\) denotes the geometric distance between the receiver and the satellite; \(N\) is the integer phase ambiguity; \(\lambda\) is the wavelength of the carrier phase observations; \(i\) and \(j\) are the two satellites tracked by both the base (\(b\)) and rover (\(r\)) stations, where \(i\) represents the reference satellite; and superscript \(g\) indicates the satellite system, i.e., Global Positioning System (GPS), BeiDou navigation system (BDS), or Galileo and GLONASS. Detailed computation steps for RTK are not given here for brevity. Interested readers may refer to [31].
# Survey of Artificial Intelligence Model Marketplace
## 2 AI Marketplace Overview
Such flexibility not only fosters innovation but also ensures that the AI models are aligned with the exact expectations and objectives of the buyer, enhancing overall market efficiency and satisfaction. * _AI model platform:_ Similar to the way traditional application stores offer a platform for users to browse and select applications, the AI marketplace provides a platform for potential customers to review and choose existing AI models. AI marketplace even allows customers to test key features, functionalities, and performance metrics before making a selection [39; 40]. * _Pricing Service with multiple payment options:_ Unlike application stores, where sellers solely define the price, the AI marketplace offers multiple pricing strategies for sellers and buyers to ensure the price reflects the actual value of the AI model, benefiting both sellers and buyers [41; 42]. The AI marketplace provides multiple flexible payment options, such as pay-per-use, subscription, or milestone-based payments, while the application store generally limits payment options to in-app purchases, upfront payments, or monthly subscription fees. Application store payments are based on a fixed design that may include features that may not be of buyers' interest. * _Protection:_ The AI marketplace protects sellers' intellectual property, preventing unauthorized access or misuse. Additionally, all transactions within the AI marketplace are conducted in full compliance with relevant legal and regulatory requirements such as HIPPA [9; 20; 38]. Figure 1: Characteristics of AI model marketplace. To comprehensively describe the AI marketplace from multiple perspectives, we design problem spaces, aiming to capture the complexity and diversity of AI trading environments by considering trading models, pricing strategies, and customization options. Best practices for AI marketplace development involve implementing robust pricing schemes, user-friendly designs, and vigorous policy enforcement to ensure transparency, accessibility, and trust among stakeholders. Successful marketplaces should support model customization for domain-specific needs, provide performance metrics for informed decision-making, and integrate security measures like blockchain to protect transactions. Additionally, standardizing formats and enabling seamless integration with popular tools enhance usability. Incorporating feedback loops and catering to cross-industry use cases further expand the marketplace's reach and relevance, fostering a balanced ecosystem that benefits buyers and sellers. A detailed checklist/matrix showing critical success factors for AI marketplaces is presented in Table 3. Each factor is explained with examples and implementation suggestions.
## 2 State of the Art
Furthermore, only a few consider HRC, but only one considers tasks where other actors besides humans and robotic arms interact (such as AGVs or other automation systems). The framework presented in this paper focuses not only on assembly planning but also on the broader area of process planning. We separate the process definition from the simulator. This makes the framework modular and reusable and, thus, flexible enough for testing different process configurations on the same simulator or even for trying the same process on a simulator developed with other technologies. The framework also considers HRC with various levels of automation. These features are important in the simulation of many industrial processes. Nowadays, these processes require the simultaneous interplay of workers, machinery, and other automation devices such as collaborative robots or AGV. Thus, the definition of a task requires detailed scheduling to make it safe and optimal. Our work proposes an organized solution and definition for this schedule. Also, using a process definition separated from the simulator allows every industry to develop its own solution with reusable modeling which is easily portable to any language, allowing an agile implementation of any process. The framework has some limitations. Firstly, as it was conceived as a fast and simple way for modeling processes for industrial VR/AR simulators, it is not intended as a mathematical theory or formal language with which to model an industrial process. Secondly, the framework adapts to the actual level of expertise of the modeler, and thus the resulting process can vary in terms of organization and granularity. Thirdly, networking issues among the devices that may be used in an industrial AR simulation are not considered. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & \\ \hline
13 & x & S & x & & x & x & x & & & x & x & x & AR \\
14 & x & S & & & & x & & & & & x & x & AR \\
15 & & & S & & & x & & & x & x & x & x & AR \\  
& & & x & & x & & & & & & NI \\
35 & & & G & & & & x & & & & & & & N \\
36 & & G & & & & x & & & & & & & & \\ \hline \hline \end{tabular} Legend: Fl, flexible; Fr, framework; TM, task modeling (N, not present; S, specific; and G, general); HRC, human–robot collaboration; Mod, modularity; Mon, monitoring; MA, multi-actor; MD, multi-domain; MP, multi-purpose; RT, real-time; Re, reusable; SI, simulation engine/manager independence; UF, user feedback; and Tech, technology (N, none; NI, non-immersive; VR, virtual reality; and AR, augmented reality).  
\end{table} 
Table 1: This table presents a summary of the features considered and their presence in the works reviewed.
## 4 Numerical Example: Transient Fin Problem
### Sensitivity Analysis of Temperature History for a Fin with Temperature-Dependent Specific Heat  
When any coefficient of Equation (1) is temperature-dependent, the transient heat transfer problem becomes nonlinear and analytical equations are no longer available [82]. To test the capacity of Transient Thermal ZFEM to handle nonlinearities, a model containing temperature-dependent specific heat was created and solved. For this analysis, the specific heat was selected as the variable of interest because it is temperature-dependent in the model and it directly modifies the thermal capacitance of the material. Moreover, according to Equations (1) and (7), this property is expected only to affect the process in the transient state. The values for \(C_{P}(T)\) used in the model are presented in Figure 8a and correspond to Ti64 alloy [90]. Due to the lack of analytical equations, the response obtained from  
Figure 7: The heating rate in time for fin tip: solid black line, rate using analytical differentiation in Equation (23), yellow star, rate using Transient Thermal ZFEM, blue square, FD using Abaqus built-in element solution.  
built-in Abaqus functions combined with FD was used as a reference to calculate the NRMSE. To calculate the reference response for \(S_{C_{P}}^{\theta}\) with FD, one model was run with the nominal parameters from the curve shown in Figure 8a, and for the second model, all the values in the curve defining \(C_{P}(T)\) were shifted 5.5 J/Kg K which corresponds to 1% of the minimum value of the specific heat. The convergence analysis for these models is presented in the Supplementary Material. For the calculation of the sensitivities using Transient Thermal ZFEM, the procedure described in Section 3 was followed as: (a) first interpolating the experimental values for \(C_{P}(T)\) in the temperature range of the solution, and then (b) applying an imaginary perturbation, \(h=10^{-10}\) to all values as shown in the inset of Figure 8a. Finally, to normalize the sensitivity results, we used \(|\phi|=580\) J/Kg K for Equation (10) which corresponds to the specific heat reported in Table 1.  
The \(\theta\) response for Transient Thermal ZFEM and built-in Abaqus functions is reported in Figure 8b, and the sensitivity response is presented in Figure 8c. A good agreement was found between both models with an NRMSE of 0.003 for \(\theta\), and 0.007 for \(S_{C_{P}}^{\theta}\). These results show that the Transient Thermal ZFEM can accurately determine sensitivities considering material nonlinearities and temperature-dependent parameters.  
Figure 8: (**a**) Temperature-dependent specific heat of Ti64. Experimental data obtained from [90]. The inset shows the original and the perturbed property in the \(T-C_{P}-i\) space; (**b**) dimensionless temperature history of fin tip for temperature-dependent specific heat; (**c**) sensitivity of \(\theta\) to specific temperature-dependent specific heat. Yellow star, sensitivity using Transient Thermal ZFEM, blue square, sensitivity using 1% FD and Abaqus built-in element solution, black dashed line, steady-state solution.
## 4 Computer Simulation Experiments
### C/N\({}_{0}\) Mutation Scenario Evaluation  
The motion form of the receiver was set to static. In the simulation process, the signal C/N\({}_{0}\) was changed in step form to evaluate the estimation performance of the ASTKF C/N\({}_{0}\) estimator during C/N\({}_{0}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantization Bit** & **Noise Figure** & **IF Frequency** & **Sampling Frequency** \\ \hline
2 bits & 2.8 dB & 3.996 MHz & 16.369 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: RF front-end main parameters.  
Figure 4: Simulation experiments data generation and processing flow.  
Figure 5: Global Positioning System (GPS) radio frequency (RF) signal simulation and digital intermediate frequency (IF) signal acquisition.  
hopping. At the beginning of the simulation, _C/N\({}_{0}\)_ was set to 45 dB-Hz. At 60 s, _C/N\({}_{0}\)_ was increased to 55 dB-Hz in steps. At 120 s, _C/N\({}_{0}\)_ was reduced to 15 dB-Hz in steps, and at 180 s, it was restored to 45 dB-Hz.  
To fairly evaluate the advantages of the strong tracking filter over the classical Kalman filter, a measurement noise adaptive technique that is based on the Allan variance is introduced into the standard amplitude Kalman filter. A _C/N\({}_{0}\)_ estimation algorithm that is based on adaptive Kalman filter (AKF) is constructed as a benchmark. The averaging time for both methods is set to 0.5 s.  
Figure 6 presents the _C/N\({}_{0}\)_ estimation results of the AKF and ASTKF methods. The results of the AKF and ASTKF methods are approximately consistent when _C/N\({}_{0}\)_ is stable. However, when _C/N\({}_{0}\)_ shows a large jump (at 60 s, 120 s and 180 s), the ASTKF estimation method can track the _C/N\({}_{0}\)_ change more quickly, while the AKF estimation result shows a significant lag.  
When the signal amplitude changes abruptly, _C/N\({}_{0}\)_ changes substantially. The system model that is represented by Equation (20) has a severe mismatch with the real model. The mean square error that is calculated by the classical Kalman filter in Table 1 is smaller than the true value, thereby resulting in a small filter gain value, and the impact of the measurement innovation on the state update is weak. According to Equation (22) and the calculation process of time-varying fading factor, we can see that the STKF utilizes the measurement innovation when calculating the one-step prediction mean square error matrix. Therefore, when _C/N\({}_{0}\)_ jumps, the filter gain of the ASTKF method is larger than the filter gain of the AKF method, and the response to the sudden change in state is more rapid.  
Figure 6: Comparison of the estimation results in a _C/N\({}_{0}\)_ mutation environment (0.5 s averaging time): (**a**) a global figure; (**b**) a zoomed-in view of part 1; (**c**) a zoomed-in view of part 2; and (**d**) a zoomed-in view of part 3.
## 1 Introduction
This study addresses the challenge of two-dimensional positioning in tunnel environments, focusing on maintaining GNSS positioning continuity for vehicles inside and outside the tunnel, as well as enhancing the broad applicability of the positioning system. By leveraging existing GNSS receivers, this paper proposes a two-dimensional differential positioning algorithm for tunnels, based on GNSS frequency division relay forwarding and a dual T-shaped LCX cable configuration. The algorithm combines GNSS signal frequency division relay forwarding with a dual parallel LCX cables layout to achieve two-dimensional positioning in tunnel environments. Mixed GNSS signals from open environments are received through antennas and processed via frequency division relay forwarding. The GNSS signals from different systems are then used to independently excite different ports of the LCX cables deployed inside the tunnel. By leveraging the uniform radiation characteristics of the LCX cable, consistent GNSS signal coverage is achieved throughout the tunnel. Based on the signal propagation paths, this paper derives the pseudorange observation equation for the receiver within the tunnel. The two-dimensional positioning of the receiver is determined by calculating the differences in pseudorange measurements between satellites. The simulation results indicate that the system achieves meter-level positioning accuracy. This paper is structured as follows: Section 1 provides an introduction, outlining the current state of development in indoor positioning and tunnel positioning technologies. Section 2 presents the system proposed in this study, detailing the structure and operational characteristics of the LCX cable. Section 3 describes the implementation of the positioning algorithm. Section 4 analyzes the simulation results. Section 5 concludes the paper with a summary of the key findings.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 2 Magnetic Field Measurements at Low Earth Orbit Altitudes
### Swarm  
_Swarm_ is an ESA mission (launched on 22 September 2013) consisting of three identical satellites in near-polar, circular orbits with the goal to study the Earth's magnetic field in all aspects. The implemented orbital constellation are two satellites closely spaced side-by-side (i.e., 1.4 deg longitudinal separation and only a few seconds differential delay in orbit) at an altitude of \(<\)470 km (Alpha, Charlie) and the third one (Bravo) a bit higher at \(<\)520 km (altitude decrease during life time). The Level-1b magnetic field products are derived from two instruments, the absolute scalar magnetometer (ASM) and the vector field magnetometer (VFM) with 1 Hz data rates for both instruments (low rate) and in addition 50 Hz (high rate) for the VFMs. A detailed description, including nomenclature, ephemeris- and plasma-products together with derived Level-2 products is given by [7].