esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 4 Results
### Doppler Shift Analysis  
The analysis extends to the Doppler shift observed at the GPS L1 frequency across varying ranges of elevation angles, while considering effects during both day and night periods. Figure 13 illustrates the distribution of the Doppler shift during low solar activity. The electron density variations in grazing angle reflectometry can induce a maximum Doppler shift of \(\pm\)2 Hz in the GPS L1 signal during daytime. The attenuation in the Doppler shift demonstrates a strong correlation with diurnal cycles, resulting in a reduction during nighttime periods. This phenomenon can be attributed to the decrease in the rate of electron density changes, which in turn leads to a corresponding decrease in the magnitude of the Doppler shift.  
The Doppler shift histograms reveal a symmetrical distribution centered around approximately 0 Hz with a distinct separation in very-low-elevation cases. The distribution is also influenced by the transmitter motion relative to the specular point elevation angle. In Figure 14, it becomes evident that at very low elevation angles, a rising transmitter (ascendant elevation) induces a positive Doppler shift, while a setting transmitter (descendant elevation) results in a negative Doppler shift. However, at higher elevation angles (20\({}^{\circ}\) to 30\({}^{\circ}\)), the relationship may vary or even reverse.  
Figure 12: Altimetric uncertainty due to uncertainty in ionospheric delay model depending on elevation, daytime (DT), and nighttime (NT) using NEDM2020 during LSA.  
The distribution of the Doppler shift for F10.7 = 180 exhibits an increase in dispersion, approximately doubling in all scenarios. In the elevation range of 5\({}^{\circ}\)-10\({}^{\circ}\) within the tropics region, the range of \(f_{d}\) is more extensive during daytime, reaching maximum values of up to \(\pm\)4 Hz. The rising and setting event analyses present similar behavior, with negative magnitudes primarily observed during rising events and positive magnitudes during setting events.
## 2 Design and Simulation Results
### Structural Analysis
and 8.2 GHz and shown in Figure 6. It can be observed that the currents are mainly concentrated at these frequencies around the slot resonators which results in severe impedance mismatches around the resonant frequencies leading to the quintuple band-notching behavior of the antenna. Figure 4: Simulated reflection coefficient and VSWR (Voltage Standing Wave Ratio) of the proposed quintuple band-notched UWB antenna. Figure 5: Simulated antenna gain from the radiator, percentage radiation efficiency, and realized antenna gain including the effects of the input mismatch of the quintuple band-notched UWB antenna. Figure 6: Surface current distributions of the proposed antenna at the corresponding center frequencies of the notched bands (**a**) 3.5 GHz; (**b**) 4.5 GHz; (**c**) 5.25 GHz; (**d**) 5.7 GHz; (**e**) 8.2 GHz.
## 4 Current Research Status of Energy Management Techniques  
As can be seen from the aforementioned studies, much attention and research have been directed toward energy management techniques for hydraulic hybrid vehicles and have been proposed, simulated, analyzed and tested in a HIL or an actual vehicle in previous research. The results of the application of these energy management techniques prove that control strategies are vital to enhance energy efficiency and reduce emissions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Researcher** & **Method** & **Recycled Energy** & **Energy-Saving Technology** & **Findings** \\ \hline \multirow{3}{*}{1} & [PERSON] et al. & & & & & \\  & [87; 73]–Series hybrid & Simulation & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{2} & [PERSON] et al. [75]–Series hybrid & Simulation & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{3} & [PERSON] et al. [68]–Series–parallel hybrid & Simulation & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{4} & [PERSON] et al. [77]–Series hybrid & HIL & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{5} & [PERSON] et al. [85]–Hydraulic & Simulation & & & & & \\Generally, the objectives of the energy control techniques are aligned with the objectives of the whole vehicle, which are primarily: maximum energy economy, minimal emissions or even zero emissions, minimum system cost and good drivability and comfortability, namely, more complete energy utilization for better performance, power and drivability.  
Therefore, to pursue such goals, the energy management techniques of hydraulic hybrid vehicles must be designed one-to-one with their configuration and circumstance in the application, where the relationship of the three is constrained and reinforced by each individually. The following is an overview of the current state of the energy strategy. It is true that the current application of hydraulic energy storage technology in vehicles has yet to be significantly developed. Therefore, an overview of the current control technology is highly desirable. Energy management control techniques integrated into hydraulic hybrid vehicles can normally be separated into two primary types: rule-based and optimized control strategies, as shown in Figure 20.
## 4 Measurement
### Measuring Efficiency Comparison  
The efficiency of a five-phase induction motor was first measured. The measurement was carried out in accordance with the description in the introduction of Section 4.  
Figure 9 depicts the motor efficiency characteristics for all four conditions mentioned. The study is performed out with a nominal motor torque of 3.5 Nm. The blue curve in the figure represents the efficiency of the machine operating without failure and the red curve represents the efficiency of the motor operating in one-phase failure. The gray and yellow curves represent the efficiency during the fault-tolerant control for unbalanced and symmetrical currents, respectively.  
The efficiency of the motor decreased by 22.51% in the one-phase failure based on these waveforms for the rated parameters of the motor. This is consistent with the theory presented in Section 1, where previous research has shown that a single-phase failure reduces motor power and efficiency by 20%, respectively. This can be improved by employing the fault-tolerant control described in Section 3.2. The motor efficiency characteristics in Figure 8 show that the modification aided in improving motor efficiency. The greatest loss of efficiency occurs at higher speeds (higher power). For example, at a speed of 2500 rpm, the decrease in motor efficiency was 15.65% for unbalanced currents and 11.43% for symmetrical currents.  
The efficiency characteristics for 2 Nm and 1 Nm show that control editing is still effective and improves motor efficiency with a one-phase failure. In Figures 10 and 11, fault-tolerant control implemented on symmetrical currents has better properties than control implemented on unbalanced currents. Because the symmetrically distributed currents ensure a symmetrically distributed load of the individual motor phases, and the machine is also symmetrically heating.  
As shown in Figure 11, the fault-tolerant control to symmetrical currents provides the same efficiency as the fault-free state at reduced torque.
## 3 Proposed Broadcast Event-Triggered Consensus Control Scheme
### Proposed Theorems  
**Theorem 1**.: _Given the linear discrete system dynamics (Equation (1)) with the control input of communication as represented in Equation (16), if the system satisfies ETF (Equation (14)), where the measurement error is represented by Equation (15), the information will not be sent to the agents unless it violates the function. The information will be sent to the neighbors to update the control input. The average consensus (Equation (6)) can asymptotically marginally stabilize, which is achieved at \(t\to\infty\), and reach a steady state error when the eigenvalue of Perron matrix is equal to \(\lambda\), which is shown in the Gershgorin circle (Proof 1) and Lyapunov stability, indicating \(V<0\), which is a definite negative (Proof 2)._  
**Remark 1**.: _The uniqueness of this ET is that the event is triggered at the periodic sampling time, which is during odd time intervals. This is known as the sampled-data ET system, where the sampled data of the agent's state position will determine the condition of the event (Equation (14)). The difference between this study and [51, 52] is the integration of ET into the broadcast system, whereas in comparison with [60], the communication issue is taken into consideration for multi-agent consensus in a broadcast communication environment._  
**Remark 2**.: _The threshold value \(\sigma_{i}\) of the triggering function (Equation (14)) is state-dependent, where the agent's state position depends on its neighbor's state position._  
**Assumption 4**.: _The network of topology is assumed to be strongly connected among inter-agents under the undirected graph to achieve a marginally stable consensus._  
**Proof of Theorem 1.** Refer to Appendix A. \(\square\)  
**Theorem 2**.: _For BET communication, suppose that the objective function \(P\) is given and assumed that \(P\) is changing satisfying \(\
abla P(x_{d})=0\) with ETF, \(f(e,x)\) (Equation (14)) applied in the agent and neighbors' communication system using a standard distributed protocol (Equation (16)). Let \(L_{i}\) and \(G\) be given by Equations (7), (8) and (11). If the BET satisfies the condition of Theorem 1 and Lemma 1, then \(\lim\limits_{t\to\infty}x(t)=x_{d}\) w.p.1 is achieved._  
**Proof of Theorem 2.** Refer to Appendix B. \(\square\)  
Figure 5: Agent \(i\) event-triggered controller, \(\gamma\).
# High-Frequency and Spectrum-Clean Shear-Horizontal Acoustic Wave Resonators with AlN Overlay
## 5 Conclusions  
In this study, high-frequency nonleaky SH SAW resonators on AlN/LiNbO\({}_{3}\)/Si are demonstrated with thermal stability, large coupling and spurious-free. The high-velocity Si substrate was used to reduce the propagation leakage into substrate, and 15-30\({}^{\circ}\) rotation angles from _YX_-LiNbO\({}_{3}\) were selected to provide the ultralarge \({k_{\text{eff}}}^{2}\). A careful trade-off analysis is provided on the AlN coating thickness between the enhanced \({v_{\text{p}}}\) (_f_ for given transducer) and the cost of extra \({k_{\text{eff}}}^{2}\). Furthermore, the out-of-band and near-band spurious modes in the layered SAW structures were analyzed by using FEM simulations and the requirements for the substrate were derived to avoid the presence of spurious modes. Based on this analysis, a new nonleaky SH resonators with optimized substrate design are obtained, demonstrating the ability with a high frequency of 3 GHz at 1 \(\upmu\)m IDT pitch, a high \({k_{\text{eff}}}^{2}\) of 14.4% and a spurious-free response throughout 0-6 GHz, showing a great potential for 5G/6G RF communication systems.  
Conceptualization, Z.W., S.W., F.B. and J.Z.; methodology, Z.W., S.W.; software, Z.W. and J.Z.; formal analysis, Z.W. and S.W.; resources, Z.W. and J.Z.; data curation, Z.W. and S.W.; writing--original draft preparation, Z.W.; supervision, J.Z. and F.B.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of Shanghai under Grant No. 21 YF1402500.  
The data that support the finding of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 1 Introduction  
Managing the large data volumes that arrive continuously can often exceed the capabilities of individual machines. Stream data processing requires continuous calculation without interruption and places high reliability requirements on resources. In this regard, it is important to develop efficient task-scheduling algorithms that reduce costs, improve resource utilization and increase the platform stability of Cloud services. Naturally, responsive schedules are required to keep pace with the transmission of massive data for large-scale tasks, and this aggravates the difficulty of the workflow scheduling problem [1,2]. An important challenge presents itself when the system parameters (especially the number of available nodes and executing tasks) need to change during runtime. This is a quite natural scenario, considering the fact that an application may require more resources from the Cloud when they are available or, in the reverse scenario, some resources (such as nodes) may become temporarily unavailable while an application is running.  
Several data stream processing systems (DPSs) that take advantage of the inherent characteristics of parallel and distributed computing, such as Apache Storm [3], Spark Streaming [4], Samza [5] and Flink [6], have specifically emerged to address the challenges of processing high-volume, real-time data. Specifically, the default Storm scheduler has become the point of reference for most researchers, who compare their proposed schemes against this simplistic scheduling algorithm. The main drawbacks of the Storm scheduler are as follows:  
1. It does not offer optimality in terms of throughput;
2. It does not take into account the resource (memory, CPU, bandwidth) requirements/availability when scheduling;
3. It is unable to handle cases in which system changes incur.  
In this work, we propose a pipelined modular arithmetic-based approach (the PMOD scheduler), which is based on the idea of each node receiving tuples for processing only from one other node at a time. The PMOD scheduler is proven to have some important advantages such as almost perfect load balancing, which is very important in today's Cloud systems [7], minimized buffer requirements and higher throughput. The PMOD organizes all the required operations (tuple transfer, tuple processing and tuple packing, which will be discussed in Section 5) in a pipeline fashion, thus decreasing the overall execution time compared to other known schemes, as the experimental results show.  
The remainder of this work is organized as follows: Section 2 briefly summarizes some important dynamic scheduling approaches and describes the methodology on which their scheduling strategy is based. Section 3 gives a brief motivating example so that the reader can better understand the motivation behind a dynamic strategy. Section 4 describes the necessary mathematical background. Section 5 presents the PMOD scheduler. In Section 6, we present our comparison results, and Section 7 concludes the paper and offers perspectives for future work.
## 2 Numerical Model and Validation
### Computational Domain and Boundary Conditions  
Figure 2 shows the grid distribution and the computational domain. The computational domain is a rectangle area with a length of 60\(D\) and a width of 40\(D\), and the center of the cylinder is at the coordinate origin. The cylinder diameter is 0.12 m. The upstream inlet is 20\(D\) ahead of the coordinate origin, and the downstream outlet is 40\(D\) after the coordinate origin. The distances between either of the upside and downside and the coordinate origin are 20\(D\). Unstructured grid is adopted to discretize the computational domain into four layers. The grids in the regions with large gradients of flow parameters are locally refined, i.e., the region near cylinder surface and wake region. The geometric model and meshing are performed by pre-processing software ICEM.  
The boundary conditions are set up as follows, the inlet is set as the velocity-inlet with a uniform velocity \(U_{\infty}\), the outlet is set as the pressure-outlet with the relative pressure of 0, the upper and lower sides are set as the symmetry boundary, and the cylinder surface is set as the no-slip wall.  
The numerical calculations are carried out with a Reynold number of 4.1 \(\times\) 10\({}^{4}\), and the turbulence flow is calculated by SST \(k\)-\(\omega\) model. SIMPLE algorithm is adopted to calculate the coupling between the pressure and velocity fields. The pressure interpolation format is set as \"Standard\". The second-order upwind scheme is used for the momentum discretizing because of its accuracy and stability. During the solution process, the convergence residual standard of continuity equation is 1.0 \(\times\) 10\({}^{-6}\), and the convergence residual standard of momentum equation and turbulence parameters is 3.0 \(\times\) 10\({}^{-7}\).  
Figure 1: Schematic of fixed circular cylinder with TWW.  
The CFD general software Fluent is used to calculate the flow around a fixed cylinder. Firstly, the governing Equations (1) and (2) are solved to obtain the flow characteristics around the cylinder, and then the aerodynamic coefficient of the cylinder is obtained. When the amplitude of the aerodynamic coefficient is stable (\(t=1.8\) s), TWW is activated. The wave Equation (3) of traveling wave is solved by User Defined Function (UDF) in Fluent, and the radial velocity at any point P in Figure 1 can be obtained according to Equation (3), and then the radial velocity is decomposed into the x direction velocity and the y direction velocity. The DEFINE_GRID_MOTION macro of UDF is used to assign the velocity for all grid points on the rear semi-cylindrical surface, and the motion of cylinder surface grids are realized by the dynamic mesh technique. When the grids are updated to the specified position, the flow field is calculated to converge with this shape as the boundary, and then the calculation of the next time step begins. This loop calculation ends until the TWW achieves a significant wake control effect.
## 6 Open Challenges, Future Prospects and Research Directions
### Optimizing Spatial Modulation with AI and ML  
SM techniques, which exploit multiple spatial channels, are proving essential in increasing data rates and system capacity without requiring additional bandwidth. Also, AI and ML are emerging as transformative technologies in this domain. The application of AI and ML algorithms can further optimize SM schemes. AI and ML can analyze large volumes of data to predict channel conditions and dynamically adjust modulation parameters in real-time. For instance, ML models can predict periods of high turbulence and preemptively switch to more robust modulation schemes, reducing the risk of data loss. These predictive capabilities will ensure that FSO systems maintain optimal performance in real-time, enhancing their overall efficiency and reliability.
# Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
## 2 Background  
### Data Source  
The work presented in this paper is based on a simplified fuel rig designed to represent the Boeing 777 fuel system. Figure 1 illustrates the schematic of the fuel rig, with fuel flow moving from left to right, powered by a pump. Direct proportional valves (DPVs) were employed to accurately simulate degradation within the system, with DPV1 to DPV5 used to introduce and control five distinct failure modes: pump external leakage, pump internal leakage, FOHE (fuel oil heat exchanger) blockage, FOHE leakage, and clogged nozzle. Pressure sensors monitor the system's pressure, while flow meters track the flow rate.  
As part of another task within this PhD project, a Simulink model (Figure 2) was developed using experimental data to create a digital twin of the fuel rig. Similar in function to the fuel rig, this model simulates five failure modes and their combinations under five specific pump speeds. Although this simulation work is beyond the scope of the current paper, the model has been systematically validated under both healthy and faulty conditions, with a maximum deviation of 3% between simulation results and experimental  
Figure 1: The schematic of the fuel rig.  
data. A more detailed discussion of this work will be presented in a subsequent article, which is currently under preparation. Consequently, the model is employed here to provide the necessary data for training, testing, and validating the ML-based diagnostic algorithms.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 5 Experiment Setup and Results
### Experiment Discussion  
An attack on the Routing Protocol for Low-Power and Lossy Networks (RPL) can have a significant impact on the power usage of the nodes in the network. RPL is designed for low-power and lossy networks, such as those found in Internet of Things (IoT) devices, where nodes often have limited resources and rely on efficient communication to conserve battery life. An attack on the RPL routing protocol can lead to nodes taking longer routes, which can result in higher power consumption due to increased data transmission and reception. Additionally, if the routing protocol is compromised, nodes may be forced to repeatedly transmit data packets, which can further drain their batteries. This can lead to reduced network lifetime, as nodes may run out of power and fail.  
Furthermore, if a malicious node is introduced into the network, it may consume more power than other nodes by participating in attacks, such as Selective Forwarding and Blackhole Attacks. This can further exacerbate the power consumption issues and reduce the overall network efficiency. Therefore, it is crucial to develop effective security mechanisms to prevent attacks on the RPL routing protocol in order to ensure the longevity and reliability of the network.