esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Impacts of Valve Designs on Hemodynamic Characteristics  
#### 3.3.1 Wall Shear Stress (WSS)  
The WSS distributions on the entire aorta of each case are illustrated in Figure 8. All the cases showed similar WSS distribution patterns at corresponding time points.  
The specific values of SAWSS and low WSS areas on the entire aorta are listed in Table 3. Increased SAWSS as well as decreased low WSS area were observed in all three cases during the acceleration phase. The low WSS areas (\(<\)0.4 Pa) [35] on the aorta with SPAC tubular and SPAC molded designs was 64.29% and 16.39% smaller than that with the conventional at acceleration peak (t\({}_{4}\)), respectively.  
Figure 8: The distributions of wall shear stress (WSS) in three valve models at different time points.  
Table 4 further lists the SAWSS magnitudes of all the cases on different segments of the aorta at the acceleration peak (t\({}_{4}\)). On the AAo segment, the SAWSS of SPAC tubular valve case is 20.24% and 29.63% higher than that of the SPAC molded valve and conventional valve, respectively. In contrast, the percentage difference of SAWSS on the aortic arch and DAo are within 11% among the cases. The highest SAWSS was observed in the aortic arch segment in all the cases
## 2 Materials and Methods
### Software Choice  
Once all the equations have been obtained, it is necessary to process them in an equation solver program. Due to the large number of equations and relations that had to be implemented, only software capable of processing the entire volume of data in an agile way has been considered. After considering several options (Mathematica, Matlab  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block** & **Equation** & **Left-Side Variable** & **Variable Definition** & **Equation** \\  & **(SI Unit)** & **Variable Definition** & **Number** \\ \hline \(E_{ac}=\left(\frac{m_{tot}a_{\frac{1}{2}}+\overline{\rho}_{av}}{\overline{t} _{t}_{t}_{t}}\right)t_{ac}\) & \(E_{ac}\) (J) & Energy consumed during acceleration & (34) \\ \(E_{gen}=-\eta_{EDS}(m_{tot}a_{\frac{2}{2}}\frac{\overline{\mathrm{p}}}{2}- \overline{p}_{av})t_{dec}\) & \(E_{gen}\) (J) & Energy generated during deceleration & (35) \\ \(\overline{p}_{av}=\frac{p_{0}}{4}+\frac{p_{0_{t}}}{3}\) & \(\overline{p}_{av}\) (W) & Mean power dissipated by running resistance & (36) \\ Energy consumption & \(E_{v}=\frac{p_{v}}{\overline{t}_{t}_{t}}t_{v}\) & \(E_{v}\) (J) & Energy consumed throughout the travel at the speed v & (37) \\ \(E_{bat}=\frac{m_{t+}c_{br}\frac{t_{br}}{t_{br}}}{\overline{t}_{bat}}\) & \(E_{bat}\) (J) & Energy consumed by the batteries & (38) \\ \(E_{t}^{\prime}=\frac{E_{v}+E_{pv}+E_{sv}+E_{bat}}{L_{t}}\) & \(E_{t}^{\prime}\) (J-m\({}^{-1}\)) & Total energy consumed per unit length & (39) \\ \(e_{t}^{\prime}=\frac{E_{t}^{\prime}}{m_{step}\sum_{i=1}^{t-1}f_{i}}\) & \(e_{t}^{\prime}\) (J-m\({}^{-1}\)-kg\({}^{-1}\)) & Total energy per unit length and payload & (40) \\ \(l_{e}=\frac{E_{v}+E_{pv}+E_{pv}}{m_{step}\sum_{i=1}^{t-1}f_{i}}\) & \(l_{e}\) (J-kg\({}^{-1}\)) & Energy consumption per payload mass & (41) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Final equation block, coming from reference [24].  
and Engineering Equation Solver), Engineering Equation Solver [25] has been chosen, as it is used in other models that involve thermodynamical equations [26; 27]. This program takes the equation blocks along with their inputs and obtains their outputs by means of iterations. These results are obtained after an undetermined number of iterations, depending on adjustable stop criteria such as the relative residuals, which can be as low as \(10^{-10}\), or the limit of iterations. The specific version with which the results were obtained is Engineering Equation Solver Professional V9.457-3D (EES). The chosen program, besides solving equations, can create parametric tables and graphs derived from those equations.
###### Abstract
Recently, fault monitoring of inverter-fed IMs has been proposed using differential-mode (DM) and common-mode (CM) impedance analysis [5; 29; 30; 31; 32]. In [29; 30], DM and CM impedance modeling and measurement methods are presented to facilitate high-frequency impedance analysis for stator health monitoring. The influence of stator faults on DM impedance was investigated, and variations in DM noise were accurately predicted [31]. In contrast, CM impedance measured at specific frequencies has proven to be effective for detecting ITFs. This approach is particularly advantageous because it does not require direct electrical connections to the motor terminals [32]. Instead, it relies on externally injected radio-frequency sinusoidal signals, offering high reliability and robustness, as it remains unaffected by the supply voltage quality. Although impedance analyzers can measure up to 120 MHz [33], additional equipment, such as inductive couplers and impedance analyzers, is necessary for implementation. In this paper, a fault diagnosis algorithm based on the current vector pattern is proposed for detecting ITFs in IMs. When an ITF occurs in IM, the fault resistance occurs in the parallel connection of the faulted phase in the IM. Based on a mathematical model of an IM with an ITF, the current in the steady state is analyzed. Due to the faulted phase, a negative-sequence current is generated, even though a positive-sequence voltage is applied to the IM. Since both positive- and negative-sequence currents are generated, the current vector pattern in the stationary coordinate frame has a specific shape, which can be obtained from the mathematical model. Furthermore, the co-simulation with a finite element analysis (FEA) and circuit simulation is implemented to analyze the current vector pattern, and an ITF diagnosis algorithm is proposed based on the current vector analysis. An experimental setup is implemented using a 12 kW, four-pole, three-phase IM, inverter, fault resistance, and terminal box for winding connection. The effectiveness of the proposed ITF diagnosis algorithm is validated through experiments conducted under the fault conditions. The rest of this paper is organized as follows. The current vector analysis of an IM with an ITF based on a mathematical model is discussed in Section 2. Based on the current vector analysis, an ITF diagnosis algorithm is proposed, and the co-analysis is described in Section 3. Section 4 presents experimental verification using the 12 kW, four-pole, three-phase IM and terminal box. Section 5 provides the conclusion of this study.
# Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement
## 1 Introduction
Combined with the extended Kalman filter (EKF) strategy, it is successfully used to compute the global position of UAVs. In [26], the authors proposed a consensus-like relative localization scheme to estimate the relative position between the agents; the numerical simulations show that the relative position estimate exponentially converges to its true value. However, there is no discussion on the influence of measurement noises. In [27], the authors fused the obtained direct and indirect RL estimates by using a consensus-based fusion method to generate the relative positions. Their proposed RL estimation was applied to formation control. The abovementioned research works study the area coverage problem and multi-UAV RL problem separately. However, in many real environments, such as in GPS-less environments, most UAVs' locations cannot be known continuously or known with uncertainty. In this case, we have to consider the area coverage and multi-UAV RL problem together, which is the main focus of this work. To be more specific, the locations of a team of UAVs are unknown a priori when they perform the area coverage tasks. The main challenging issue will fall on (1) how to obtain the UAVs' location when they perform the coverage task and (2) how to compromise the efforts of locational optimization placement between the relative localization and area coverage. We assume that only one UAV can access its global position. Distance measurements are available through the distributed networks by using UWB technology. The relative localization problem is solved by combining the UWB measurements and UAV's odometry measurements. The distributed coverage placement algorithm is proposed in [1], which is widely used in area coverage problems. For compromising the efforts of locational optimization placement between the relative localization accuracy and area coverage placement, the control inputs of the UAVs are obtained by solving the optimal UAVs coverage placement problem under the motion and communication constraints. The coverage placement problem comprises the location uncertainty optimization problem and locational optimization problem, and the solving process will adapt sequential quadratic programming (SQP) methods. The contribution of this paper are summarized as follows:  
1. We propose an algorithm for multi-UAV to achieve the coverage task collaboratively based on the relative localization. The optimal multi-UAV coverage placement is obtained by considering the compromise between the coverage placement and the sensor placement. The remainder of this paper is organized as follows. Section 2 provides our considered relative localization-based area coverage problem formulation. Section 3 proposes the localization algorithm-based RL for coverage task performing. Section 4 proposes optimal multi-UAV placement for improving the coverage performance. The simulation and analysis are proposed in Section 5. Finally, the conclusions are drawn in Section 6.
## 3 Measurement, Control, and Communication Architecture  
The architecture developed for remote monitoring and control of the SWT is structured into two core components: a custom-designed hardware platform and a layered software system that manages measurement, control, and communication tasks. The system integrates embedded hardware with open-source tools to support real-time data acquisition, supervisory control, and user interaction.  
Figure 2 presents an overview of the system's communication and control structure [37]. It shows how the architecture follows a client-server model, in which a local embedded controller (server) installed at the wind turbine site manages low-level measurements and control actions, while a remote application (client) handles supervision and command dispatch.  
The embedded controller consists of an Arduino(r) MEGA microcontroller for analog signal acquisition and control output generation, and a Raspberry Pi(r) single-board computer that handles protocol translation and communication with the user interface. These devices are interconnected via universal asynchronous receiver-transmitter (UART) and  
Figure 2: Communication model adapted from the IEC 61400-25 standard [37] to the proposed SWT remote-control system divided by layers.  
operate in coordination to perform data acquisition, boost converter control, and wind speed measurement.  
At the software level, the system employs a WebSocket-based communication protocol to enable bidirectional, low-latency interaction between the embedded controller and the MATLAB(r)-based user interface. Data is continuously exchanged using standardized information models inspired by the IEC 61400-25 framework, which defines the semantics for wind power system communication.  
This architecture enables seamless integration between physical sensors, control logic, and user supervision, while maintaining modularity and scalability for future enhancements.
## 3 Results and Analysis
### UGB Integrated Ecological Constraint  
In this study, the excellent and good REEI areas were used to construct the Sanya ecological constraint zone, and the PLUS model was used to simulate the urban built-up growth in 2030 and to delineate the UGB (Figure 4). The results show that under the ecological constraints, the built-up area in 2030 is 218.4 km\({}^{2}\), accounting for 11.92% of the city, and the built-up land is 79.6 km\({}^{2}\) less than under the natural growth scenario (Figure 5). The urban built-up growth in 2030 based on the ecological constraints is different from the sprawl trend under the natural scenario. It is characterized by connotative growth, and the main growth space is within the built-up area of the city. Since the industrial development layout of Sanya focuses on key areas, such as the Yazhou Bay Science and Technology City, the Sanya Central Business District, and the Haitang Bay National Coastal Leisure Park, the key industrial development areas are the central part of the city, Haitang Bay, and the Yazhou District. The built-up areas still expand, and these areas are the focus of Sanya's urban development in the future.  
Figure 4: Urban built-up growth simulation and UGB delineation in 2030.  
The central part of the city is mainly characterized by connotative growth, and the built-up land in the Yazhou and Haitang districts exhibits a connotative and epitaxial growth trend, with growth rates of 21.80%, 36.52%, and 33.73%, respectively, in these three regions compared with 2018.  
Compared with the growth under the natural scenario, the UGB delineation coupled with the REEI not only effectively restrains the blind expansion of the built-up land but also effectively protects the ecological and agricultural security. In addition, there is still room for urban development in the northwestern part of the Yazhou District, which reserves space resources for future urban industrial layouts.
## 3 Analysis of Product Function-Structure Correlation
### Analysis of Structure-Function Correlation  
Structure is real object mapping of function. It can be discerned from correlation that the adaptive modification of structure layer may cause change of function layer, so a product structure behavior function reverse conduction model (SBF model) needs to be constructed based on the multi-layer FBS vertical decomposition model, to realize mechanism of reverse conduction from structure layer to function layer. The related feedback conduction steps from structure to function in the SBF model are illustrated in Figure 5, and each step is described as follows:  
Step 1: Modification of product structure layer. Select a prior modification trigger unit from the parts in the function domains V\({}_{+}\) and V\({}_{-}\) of product based on the affair-element  
Figure 4: Feature matter-element correlation matrix.  
Figure 5: The structure-behavior-function (SBF) feedback model.  
implication reasoning and implement modification operation for the feature structure of the trigger unit.  
Step 2: Structure-behavior conduction. The modification of structure layer itself may not influence the behavior layer. When the product is needed to realize corresponding function, the difference of structure layer before and after modification is conducted to the behavior layer.  
Step 3: Change of behavior layer. When such changes as increase, substitution, and decrease, occur in the original structure layer owing to transformation operation, such changes as behavior increase, behavior parameter value change, and behavior decrease, may also occur in the corresponding behavior layer, and the change of behavior child layer is fed back first to the behavior parent layer and finally to the target function layer.  
Step 4: Behavior-function conduction. The change effect of behavior layer is conducted to the target function layer so that quantitative change effect and qualitative change effect occur in the target function layer.  
Step 5: Change of product function layer. For the function elements in the function domains V\({}_{+}\) and V\({}_{-}\), the quantitative change effect and qualitative change effect of function are realized under the conduction of behavior change effect. For the function elements in the V\({}_{++}\), the function intensity generally remains unchanged due to the principle of structure independence followed in the early decomposition.  
Step 6: Product function-customer requirement matching. Re-match the function parameter value corresponding to product function with that corresponding to the customer required function, to judge whether the condition that the dependent function value \(k\)\(\geq\) 1 is tenable. If yes, it indicates that the product function satisfies the actual customer requirement, and the modification of structure layer is stopped. If not, further modification of structure layer is needed till the judgment condition is tenable based on the feedback from the SBF model.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 1 Introduction
### Motivation and Contribution  
To guarantee a required achievable rate, with perfect CSI available, [PERSON] et al. show that the transmit power of the CUEs can be made inversely proportional to the number of antennas in the uplink of massive MIMO systems [3]. Inspired by this fact, we limit the maximum transmit power of the CUEs to suppress the interference from the CN to the D2D, and then guarantee the CN performance with the aid of large-scale antennas.  
In this paper, we consider an uplink massive MIMO system with D2D underlay. The main contributions of this work are summarized as:  
* The probability density function of the signal-to-interference-plus-noise ratio (SINR) at the CUEs is approximated in closed-form.
* The outage probabilities and the achievable rates of the massive MIMO system and the D2D link are derived in closed-form.
* Constrained by the SE of the D2D link and CUEs, the EE of the massive MIMO system is maximized by jointly optimizing the transmit power of CUEs and the number of BS antennas.
## 2 Data and Methods
### Methods
#### 2.2.3 Parameter Sensitivity Analysis  
Sensitivity of simulated GPP to parameters in three LUE models was analyzed using the factorial approach [104-106]. This method facilitates the statistically based representation of combinations of errors in several parameter sets. For a two-level complete factorial design, each of the model parameters is assigned upper and lower values based on specified perturbations of the magnitudes of the parameters, and the model is run using all combinations of parameter values. For \(n\) different parameters, this would require 2\({}^{\mathrm{n}}\) simulation runs. Each parameter here was perturbed by an arbitrary magnitude \(\pm\)10% [105].  
The main effect of a parameter, which is also referred to the parameter sensitivity, is calculated as the average difference between a run in which the parameter is at its upper level (+10%) and a run in which the parameter at its lower level (-10%), but other parameters remain unchanged. For example, there are 4 simulation runs for TL-LUEn when considering the parameters \(\varepsilon_{m}\) and \(\beta\). They are both \(\varepsilon_{m}\) and \(\beta\) at their lower levels (simulation #1), \(\varepsilon_{m}\) at its upper level and \(\beta\) at its lower level (simulation #2), \(\varepsilon_{m}\) at its lower level and \(\beta\) at its upper level (simulation #3), and both \(\varepsilon_{m}\) and \(\beta\) at their upper levels (simulation #4). The main effect of \(\varepsilon_{m}\) in TL-LUEn are the average of the difference between simulation #2 and simulation #1, and the difference between simulation #4 and simulation #3. A larger value of main effect indicates higher sensitivity of simulated GPP.
at the current time will influence a future time. Second, bus traffic flow has specific patterns among other traffic flows; for example, it has a two-peak traffic flow during the morning and evening, and its daily traffic volume is extremely large compared with other transportations. Researchers have long been testing various methods to predict traffic flow. The autoregressive integrated moving average (ARIMA) model and its variants are widely used time-series approaches to predict traffic flow [1; 2; 3; 4; 5]. However, these methods cannot capture the spatial correlation of traffic flow.  
Recently, as a result of the accumulation of big data and improvements in machine computing capabilities, deep learning has been greatly successful in the sectors of image classification [6], natural language processing [7], as well as in other fields [8]. This has inspired many researchers to try to use the deep learning methods to predict traffic flow. For example, [PERSON] et al. [9] used convolution neural networks to model traffic predictions. Later, they [10] used residual networks [11; 12] to capture spatio-temporal correlation more effectively. However, these studies do not fully consider the scenario pattern of traffic flow.  
In this study, we build improved spatio-temporal residual networks for bus traffic flow prediction, which captures both the spatio-temporal correlation and scenario patterns of bus traffic flow. Specifically, we used two fully connected neural networks to capture the scenario patterns and improved residual network block to capture the spatio-temporal correlation, which together predict the bus traffic flow. Experiments on Beijing transportation smart card data show that our proposed model outperforms the four baseline methods.  
In summary, our contributions are as follows:  
* We find that bus traffic flow has specific scenario patterns, which is important for bus traffic flow prediction. We use two fully connected neural networks to capture these specific scenario patterns.
* We improve the residual network block to capture the spatial and temporal correlation of bus traffic flow. We build an improved spatio-temporal residual network model to predict bus traffic flow effectively.
* We evaluate our model on Beijing transportation smart card data, which shows that our proposed method achieves better results than the four baseline methods.