esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of CeO2@GNPs on the Corrosion Properties of 2024 Alloy
## 2 Materials and Methods  
_Materials Preparation:_ GNPs with a typical multilayer structure provided by Chengdu Zhongke Group were used as the starting materials to produce the reinforcement CeO\({}_{2}\)@GNPs (Figure 1). The cladding preparation process was carried out using the alcohol-thermal cladding method, as described in Ref. [16]. The matrix material in this study was composed of a 2024 aluminum alloy powder provided by Hunan Tianju, (purity is 99.7%, 600-800 mesh in diameter), and its chemical composition is shown in Table 1.  
Figure 1: (a) SEM image of the graphene; (b) TEM image of the graphene with a CeO\({}_{2}\) nanoparticle layer.  
_Preparation of the composites:_ The different types of GNPs and the as-received 2024 aluminum alloy powder were uniformly mixed using high-energy ball milling (HEBM) under an Ar atmosphere. This process was accomplished using a planetary ball mill with a grinding speed of 400 rpm for 2 h. Then, these mixed powders were added to the graphite mold in preparation for hot-press sintering. After degassing at 673 K, the mixed powder was compacted into \(\varphi\) 35 \(\times\) 50 mm billets and consolidated using hot-press sintering under the conditions of vacuum of 3.9 \(\times\) 10\({}^{-3}\) Pa, a high temperature of 595 \({}^{\circ}\)C and pressure of 23 Mpa to obtain the desired material. The same process (milling, compaction and sintering) was used to prepare the 2024 alloy base material for comparison.  
_Corrosion test:_ The fabricated composites were cut into 10 \(\times\) 10 \(\times\) 3 mm squares for corrosion testing using wire electrical discharge machining. The electrochemical testing of these materials was performed in a 3.5% NaCl electrolyte at room temperature using the CHI604E electrochemical workstation. The corrosion samples were connected to the copper wires and encapsulated by epoxy resin, leaving an opening for contact with the reference electrode. The open-circuit potential (OCP) test was performed for 10 min, followed by the electrochemical impedance spectroscopy (EIS) test and the potential dynamic polarization curve Tafel test when the displayed data were stabilized. The frequency range of the impedance spectroscopy was 0.01 Hz-100 KHz, with a corresponding applied sinusoidal disturbance wave amplitude of 5 mV. The scanning range of the polarization curve was from EOCP \(-\)0.6 V to 0.6 V at a scan rate of 5 mV/s. The experimental data analysis was done in accordance with ASTM G5-14.
# Superconducting Sub-Terahertz Oscillator with Continuous Frequency Tuning
###### Abstract  
The development and approach of a superconducting local oscillator based on a long Josephson junction made it possible to create a fully superconducting integrated receiver in sub-terahertz frequency range, which was successfully tested both on board a high-altitude balloon and in the laboratory. In order to expand the frequency range of a superconducting integrated local oscillator, it is necessary to ensure the continuous tuning of its frequency at an arbitrary bias current, including a so-called resonant mode regime. The resonant mode regime takes place for high-quality tunnel junctions with low leakage; in this regime, stable generation is possible only at Fiske steps, the distance in frequency between which is tens of GHz. A method for suppressing resonances has been proposed and implemented; this method is based on the introduction of normal metal layers into the region near the long Josephson junction. Modeling of the propagation of electromagnetic waves in the proposed integrated structure was carried out; experimental samples were fabricated, and their comprehensive study was performed. The complete suppression of resonances and the possibility of the continuous tuning of the frequency of a superconducting local oscillator in the range of 200-700 GHz have been demonstrated. The linewidth of the FFO radiation does not exceed 15 MHz over the entire frequency range, which makes it possible to implement the phase locked loop mode in an integrated receiver intended for spectral studies.  
superconducting local oscillator; long Josephson junction; superconducting integrated receiver; high-quality tunnel junctions; sub-terahertz frequency range; continuous frequency tuning; linewidth and phase-locking of the superconducting local oscillator
# CubeSat-Based Observations of Lunar Ice Water Using a 183 GHz Horn Antenna: Design and Optimization
## 4 Smooth-Walled Horn Antenna Fabrication and Practical Results
### Fabrication Process
These practical results corroborate the performance consistency and suitability of the method for laboratory and experimental high-frequency applications. In Table 2, we present a summarized comparison of key parameters among corrugated, smooth-walled, and fabricated antennas. This comparative analysis provides strong evidence for the viability and effectiveness of our proposed method.
# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
###### Abstract  
The rapid growth of high-quality telecommunications demands enhanced queueing system performance. Traditional bandwidth distribution often struggles to adapt to dynamic changes, network conditions, and erratic traffic patterns. Internet traffic fluctuates over time, causing resource underutilization. To address these challenges, this paper proposes a new adaptive algorithm called Weighted Fair Queues continual Deep Reinforcement Learning (WFQ continual-DRL), which integrates the advanced deep reinforcement learning Soft Actor-Critic (SAC) algorithm with the Elastic Weight Consolidation (EWC) approach. This technique is designed to overcome neural networks' catastrophic forgetting, thereby enhancing network routers' dynamic bandwidth allocation. The agent is trained to allocate bandwidth weights for multiple queues dynamically by interacting with the environment to observe queue lengths. The performance of the proposed adaptive algorithm was evaluated for eight queues until it expanded to twelve-queue systems. The model achieved higher cumulative rewards as compared to previous studies, indicating improved overall performance. The values of the Mean Squared Error (MSE) and Mean Absolute Error (MAE) decreased, suggesting effectively optimized bandwidth allocation. Reducing Root Mean Square Error (RMSE) indicated improved prediction accuracy and enhanced fairness computed by [PERSON]'s index. The proposed algorithm was validated by employing real-world network traffic data, ensuring a robust model under dynamic queuing requirements.  
multi-queue systems; adaptive algorithms; resource utilization; EWC; bandwidth allocation; WFQ; continual learning 2025
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 2 Coordinated Optimal Scheduling Model of Hybrid System
### Objective of Optimal Scheduling Model in Hybrid System  
To comprehensively consider economy, environment protection, and the renewable energy consumption level, the day-ahead optimal scheduling model is constructed as follows:  
\[C=\sum_{s=1}^{NS}\pi^{s}\left[\sum_{t=1}^{NT}\left(\sum_{g=1}^{NG}C_{CHP}^{g,t,s}+ C_{BESS}^{t,s}+C_{grid}^{t,s}\right)\right]+\beta\times CVaR \tag{1}\]  
The above objective is to minimize the operation cost consisting of four distinct terms, including the generation cost of all CHPs, the cost of BESS, the cost of power exchange with the utility grid, and the cost of risk valued by CVaR. The indices g, t, and s represent the CHPs, time periods, and the scenarios. The objective is subject to many system equality and inequality constraints, including the power balance constraint in Equation (2), utility grid power exchange limit in Equation (3), and the constraint of risk in Equation (4). Meanwhile, these costs are calculated by the following model of different energy resources, respectively:  
\[P_{W}^{t,s}+P_{solar}^{t,s}+P_{h}^{t,s}+\sum_{g=1}^{NG}P_{CHP}^{g,t,s}+P_{discharge}^{t,s}-P_{charge}^{t,s}-P_{ev}^{t,s}+P_{grid}^{t,s}=P_{load}^{t,s} \tag{2}\]  
\[-P_{grid}^{\max}\leq P_{grid}^{t,s}\leq P_{grid}^{\max} \tag{3}\]  
\[\tau^{s}\geq\left[\sum_{t=1}^{NT}\left(\sum_{g=1}^{NG}C_{CHP}^{g,t,s}+C_{BESS}^ {t,s}+C_{grid}^{t,s}\right)\right]-\epsilon \tag{4a}\]  
\[CVaR=\epsilon+\frac{1}{NS(1-c)}\sum_{s=1}^{NS}\tau^{s} \tag{4b}\]  
\[\tau^{s}\geq 0 \tag{4c}\]  
The power balance in Equation (2) ensures that the sum of power generated by local generations and exchanged with the utility grid matches the local load. The power exchange with the utility grid is limited by the flow limits of the associated connecting line, as presented in Equation (3). Equation (4) denotes that the factor of risk with auxiliary variables of CVaR.
# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 3 Results and Discussion
The use of different varieties of potatoes in our study may have had some impact on the results obtained. It was noted that the spectral responses differed because of the different characteristics obtained from the various types of potatoes (data not shown). Nevertheless, the results presented here establish that hyperspectral imagery and PSLR regression can be used to provide an estimation of moisture content in potatoes. However, this is a preliminary study, and further  
Figure 5: Raw spectral signatures when considering wavelength from 700 nm to 1000 nm. Figure 6: Measured vs. predicted moisture content in the case of selected optimum wavelengths using PLSR regression. investigation is needed to improve the model quality. One important observation was that the predictive ability of the model was increased when using wavelengths between 733 and 970 nm, rather than the entire wavelength (400-1000 nm). This suggests that future work could focus on this wavelength range, potentially reducing the need for more expensive HSI sensors which can cover a wider spectral range. This study aims to investigate the feasibility of HSI in measuring the moisture content of unpeeled potatoes for determining the type of sensor. Thus, we could execute this method utilizing remotely sensed data by focusing on the moisture-specific bands identified in this study.
## 5 Conclusions  
1. A nested ensemble filtering (NEF) approach has been advanced for parameter estimation and the uncertainty quantification of traffic noise prediction. This improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. Compared with the EnKF method, the proposed NEF approach can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process.
2. The proposed NEF approach was applied to traffic noise prediction on the Trans-Canada Highway in City of Regina. The traffic noise model is incorporated into the proposed NEF approach to  
Figure 14: Variations of parameters (**a**) and (**b**) in Model (20) during the data assimilation process under scenario C.  
Figure 15: Comparison between the predictions and observations in scenario C.  
quantify the parameter uncertainties in the traffic noise prediction model. Such a process is implemented using Matlab. Four scenarios at three observation sites were designed to evaluate the performance of the NEF approach in estimating unknown parameters and quantifying the uncertainty of the empirical traffic noise prediction model. Both new (scenarios A\({}_{2}\) and B) and old (scenarios A\({}_{1}\) and C) pavement condition were considered. The results demonstrate the applicability of the proposed methodology.
3. Comparisons between the nested ensemble filtering approach and maximum likelihood estimation (MLE) method have been undertaken. It is indicated that: (a) the NEF method performed better than MLE in most conditions, (b) the model parameters can be recursively corrected whenever new measurement is available, and (c) the uncertainty in the traffic noise model can be reduced well and quantified through the proposed NEF approach.
4. This study is a new attempt to improve upon the ensemble Kalman filter. Only the empirical traffic noise prediction model was applied to demonstrate the applicability of the proposed NEF method. It is desired that more real-world models (e.g., FHWA traffic noise model) will be undertaken to demonstrate the practical applicability of the proposed NEF method.  
[PERSON] and [PERSON] designed the research; [PERSON] conducted the field measurements and calculation; [PERSON] and [PERSON] wrote the manuscript; [PERSON] made the revision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Brunel University Open Access Publishing Fund.  
We are very grateful for the editor's and the anonymous reviewers' insightful and constructive comments. They are critically helpful for improving this manuscript.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Simulation Results and Comparisons
#### 4.2.2 Pressure Analysis and Comparisons  
The pressure fluctuation signal at P3 in the pipeline is analyzed in Figure 28, presenting a comparative evaluation between the experimental and OLGA simulation results. The thick and solid blue lines represent the experimental results, while the thin and solid blue lines depict the OLGA simulation results. Furthermore, the red dotted lines display the experiment's three stages of slug flow formation, while the red solid lines reveal the same stages in the OLGA simulation. The slug flow formation stage's duration is comparable in the experiment and simulation. However, during the slug eruption and flow backstage, the experiment's duration surpasses the simulation's. As a result, the period from slug generation to discharge is lengthier in the experiment than in the OLGA simulation.  
Moreover, the experiment's pressure values are slightly higher than the simulation, and the formation pressure of each slug in the experiment is not entirely consistent. In contrast, the pressure fluctuations of each slug formation cycle in the simulation remain relatively consistent. It is crucial to highlight that the experimental and simulation results' discrepancies may arise from installation errors, measurement errors, and environmental factors, but they may not potentially impact the slug flow formation stage analysis in this study.
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 4 Comparison between DEM and Experimental Results
### Two Elements under Point-Edge Contact Mode  
From Figure 11, it can be observed that under different loads, the opening size and amplitude of the main peak are similar, and the time of the small wave peak that appears after the first peak in the contact force-time curves obtained from simulation and experiment is also close, with a slight offset in the two curves after the peak. The relative error between the peak contact force obtained from the discrete element simulation and the experimental value is around 13%. The results obtained from the experiment indicate that, after time t = 0.05 s, the contact force does not stabilize near zero but continues to rapidly decrease until it reaches stability in the vicinity of a negative value. This is attributed to the elastic properties of the membrane material in the thin-film pressure sensor. Following the impact load, the membrane of the thin-film pressure sensor may undergo elastic deformation. Once the impact force dissipates, the membrane rebounds, generating a reverse force and causing the collected contact force by the sensor to stabilize near a negative value.  
Figure 10: Comparison of DEM and experimental contact force–time curves under edge-to-edge contact mode (non-collinear).
## 3 Intermittent Periodic Transmit (IPT)
### Bi-Directional IPT (Bi-IPT)  
Basic function of Bi-IPT is the same as IPT. It is a special case where relay nodes have uplink packets. Even if the sent packet is addressed to the upstream node, the opposite downstream node can receive (overhear) and it is regarded as a forward link packet reception, as shown in Figure 2b. Bi-IPT can also greatly reduce packet loss, however, it may increase packet delay especially in uplink  
Figure 3: Basic throughput performance of IPT on string topology.  
Figure 2: Intermittent Periodic Transmit (IPT). **(a)** Uni-directional IPT; **(b)** Bi-directional IPT.  
traffic [23]. As mentioned above, this is because uplink traffic is carried after the downlink traffic reception. It should be noted that if there is no downlink packet to be sent, the node should transmit a control packet which has no data and it causes extra overhead.
###### Abstract  
The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB(r) is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.  
wind turbine (WT); flexible demand resources (FDR); battery energy storage system (BESS); demand side management (DSM); solar photovoltaic (PV); random forest (RF) +
Footnote †: journal: Future Internet
## 1 Introduction
However, a solution to the problem of the propagation of guided waves in a cylinder is much more numerically demanding than that of guided waves in plates, so these methods often fail due to the necessity of calculating complicated Bessel functions for large arguments and orders, or the high-frequency problem. Moreover, these methods strongly depend on vulnerable root-searching algorithms in the wavenumber domain. In other words, while these matrix methods are accurate and flexible, they require an iterative approach and are prone to numerical instability. In addition, the global matrix method sometimes failed to trace the curves for the complicated system, resulting in the dispersion curves crossing incorrectly at adjacent branches. For these reasons, matrix methods are not effective in solving vascular models with a much lower Young's modulus than normal materials. To obtain the accurate propagation characteristics along the axial direction of guided waves in the arterial wall, we construct the arterial vessel as a hollow cylinder model, and then use the Legendre polynomial to expand the guided wave characteristic equation in the vessel. The polynomial-expansion techniques can obtain the analytical solution of the integral expression involved in the dispersion equation of cylinder guided waves by the recursive and orthogonal properties of the Legendre polynomial. In 2017, [PERSON] et al. [22] first used the Legendre polynomial method in solving the guided waves in anisotropic plates, then they introduced the similar method to hollow cylinders [23]. Compared to the matrix methods, the polynomial-expansion method transforms the solution of the cylinder guided waves into a polynomial eigenvalue problem, which avoids solving any transcendental equation and increases the stability of the method. However, this method does not miss any modes and has the ability to calculate all modes that exist for the given case, and frequency/wave number range, automatically. It also can avoid tracing a wrong mode and reduce or eliminate the mode-jumping (mode-crossing) phenomenon in tracing modes. All these advantages make the polynomial-expansion technique a robust and efficient algorithm. On the basis of using the Legendre polynomial method, we derive the analytical solution of the guided wave in the arterial wall and obtain the propagation characteristics and dispersion curves. Then, the calculated results of the method are analyzed and compared with that obtained by the thin-layer-based method in [15], in order to validate our method and explore the propagation characteristics of guided waves in arterial vessels. The paper is organized as follows. In Section 2, we analyze the propagation characteristics of the guided wave in a hollow cylinder model and derive the state equation by using Legendre polynomial expansion. Section 3 presents the comparison between the proposed method and the thin-layer-based method in [15], along with the dispersion curves with different elasticity. General concluding is in Section 4.
## 3 Preliminaries  
In this paper, the task of traffic flow prediction is to predict the traffic flow in future time periods based on the historical traffic flow and the spatial relationship of nodes. This is defined as follows:  
Nodes Graph: We use \(G=(V,E,A)\) to represent the graph of nodes, \(V\) to represent the sequence of nodes, \(E\) to represent the edges of nodes, and adjacency matrix \(A\) to represent the connection between nodes.  
Feature Matrix: Graph signal vector \(X_{N}^{t}\in R^{N}\), where \(t\) denotes the time step. \(X_{N}^{t}\) represents the observed traffic flow of the nodes at time step \(t\).  
Prediction Problem: The prediction problem can be described as follows: given the spatial-temporal data \(\tau\) time steps in the past, learn a nonlinear mapping function to predict the data \(Q\) time steps in the future. The formula is as follows:  
\[\left(X_{N}^{(t+1)},\cdots,X_{N}^{(t+Q)}\right)=f\Big{(}G;\left(X_{N}^{(t- \tau+1)},\cdots,X_{N}^{(t)}\right)\Big{)} \tag{1}\]  
where \(f\) is the nonlinear mapping function to be learned, and \(\tau\) denotes the number of look back intervals.