esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to RTCVD System
### Simulation Comparison  
In this study, two modeling methods were selected for comparison: KL-LS [27] and NNC-SVR-3D [10]. The KL-LS modeling approach utilizes the KL decomposition technique to compute space functions and decrease model dimensions. Subsequently, the least squares identification method is employed to determine low-dimensional temporal coefficients. The NNC-SVR-3D fuzzy modeling method uses the nearest neighbor clustering algorithm to learn the preceding components of three-dimensional fuzzy rules, which is reduced with the use of a similarity assessing approach, and uses multiple single-output support vector machines to learn the space base functions of the resulting components. Additionally, the gradient descent approach is utilized to determine the coefficients of Fourier space base functions for the suggested three-dimensional fuzzy modeling based on AP clustering and the Fourier space base function, which is abbreviated as AP-Fourier-GD-3D.  
The prediction errors of the KL-LS model and the NNC-SVR model on the training data and test data can be seen in Figure 11 and Figure 12, and Figure 13 and Figure 14, respectively. The time normalized absolute errors \(TNAE(f)=\sum|e(f,k)|/\sum\Delta k\) of these models on training and test data are shown in Figure 15 and Figure 16, respectively. The RMSE performance indexes of these models are shown in Table 1.  
From Table 1 and Figures 7-16, it is evident that the three-dimensional fuzzy models outperform the KL-LS model. The KL-LS modeling method is traditional modeling methods based on KL decomposition, which require dimension reduction. Consequently, this will unavoidably result in the emergence of unmodeled dynamic characteristics and uncertainties. The suggested three-dimensional modeling approach seamlessly merges space-time separation and space-time reconstruction, eliminating the necessity of reducing dimensions. In three three-dimensional fuzzy models, the proposed AP-Fourier-SPSA-3D model has better performance. It discloses that the Fourier space base function has better spatial characteristics to disclose the spatial relationship of the DPS system, the AP clustering algorithm can adaptively select cluster cores and its numbers based on the distribution characteristics of data, and the SPSA algorithm can successfully avoid becoming stuck in the local optima.  
Figure 10: The predicted errors of the AP-Fourier-SPSA-3D model in test data.  
Figure 11: Prediction error of the KL-LS model on the training set.  
Figure 12: Prediction error of the KL-LS model on the test set.  
Figure 13: Prediction error of the NNC-SVR-3D model on the training set.  
Figure 16: TNAE of the four models on the test data.  
Figure 14: Prediction error of the NNC-SVR-3D model on the test set.  
Figure 15: TNAE of the four models on the training data.
###### Abstract  
The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model-improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model-fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm-grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm-grey neural network model.  
Keywords:grey neural network; fireworks algorithm; improved fireworks algorithm; relay lifetime prediction
## 4 Gas Turbine Test Bench Experiment and Fault Diagnosis
### Experiment and Data Acquisition
Through further analysis, the pre-measurement vibration data obtained from the gas turbine test bench experiment can be divided into four classes, normal state, air flow excitation, unbalanced, and misalignment, and these test data will be used as the dataset for fault diagnosis. The vibration signals of different faults for gas turbines rotor system are shown in Figure 8.  
The time-frequency analyses of gas turbine rotor system vibration signals under misalignment and unbalanced states are shown in Figure 9. It can be seen from Figure 9 that the abnormal frequency (131.5 Hz) is about 1.47 times the low-voltage rotating frequency (89.1 Hz), and the frequency is not fixed. After preliminary judgment, it is determined as faults by rotor misalignment. In addition to the abnormal frequency associated with excessive vibration, the double frequency component 101 Hz of low-pressure rotor frequency 52 Hz appears when the high speed drops to slow speed, as shown in Figure 9b. By analysis, this may be caused by the rotor unbalanced. After discussion and consultation with experts, it was preliminarily determined that the abnormal frequency and the reasons for vibration exceeded the standard. Through examining actual faults and machine maintenance, the fault type was determined, and the label was given as the experimental sample. The rotor misalignment could cause the radial double frequency vibration, and axial vibration of the rotor could cause the fatigue damage of the shaft, as shown in Figure 10. If the rotor misalignment lasts for a long time, it will aggravate and cause other more serious faults; thus, it must be effectively identified. The gas turbine rotor system unbalance will directly lead to excessive vibration of the whole machine, which will cause a series of deformed or even damaged elements. The deformation caused by vibrations will produce rub impact, leading to fatigue damage of the rotor or other parts, such as the fatigue crack shown in Figure 11.  
Figure 8: Different vibration signals for gas turbine rotor system faults. **(a)** Normal state. **(b)** Airflow excitation. **(c)** Unbalanced. **(d)** Misalignment.
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 2 Methodologies  
In this study, three different prediction models are discussed. The autoregressive integrated moving average (ARIMA) model is a flexible method that can even describe the behavior of nonstationary and seasonal time series [31]. However, the ARIMA model might be inadequate in certain situations, such as for series describing employment [32, 33]. The state-space representation (SSR) model can represent many statistical models in a unified manner, for example, an analysis of variance, regression analysis, normal linear models, generalized linear models (GLM), generalized linear mixed models (GLMM), ARIMA, generalized autoregressive conditional heteroscedasticity models (GARCH), stochastic variance models (SV), etc. [34, 35, 36, 37, 38, 39, 40, 41]. The machine learning predictive (MLP) model automatically finds patterns in the data and uses those patterns to make predictions [42; 43; 44; 45]. These three models were analyzed using the R software.
## 3 Results of Literature Review and Economic Assessment
### Replacing Air by CO\({}_{2}\) at Liquid-Vapor Equilibrium in a Hydro-Pneumatic UPHES  
#### 3.2.1 Reaching Constant Pressure for a Hydro-Pneumatic UPHES  
To avoid thermodynamic irreversibilities due to warming and cooling of air during compression and expansion, the novel storage offers to replace air pushing on hydro-water by a constant-pressure fluid at liquid-vapour equilibrium (optionally supercritical). Only one fluid, CO\({}_{2}\), was found by this work to match the four criteria of being inexpensive, of having at ambient temperature a saturation vapour pressure compatible with the pressure range of the hydroelectricity industry (3-15 MPa), of being non-flammable and of being environmental-friendly in terms of depletion of the ozone layer. As this fluid happens to generate some greenhouse-effect in atmosphere, undertaking its sequestration underground is also considered beneficial. Although CO\({}_{2}\) implies the complex use of an impermeable membrane to avoid losses due to its high solvability in water at high pressure, it allows to recharge and discharge electricity at constant pressure, roughly 5.7 MPa (570 m head) when ambient temperature is 20 \({}^{\circ}\)C (or roughly 420 m head in configurations where the receiving pump is located at ground surface 150 m above the cavern) or at much higher pressure when heated. Some semi-crystalline polymers such as PET (Polyethylene Terephthalate), Polyamide 11 (Rilsan), Nylon 6, Nylon 66 and metals such as aluminium foil are promising candidates for the assembling of an impermeable multiple-layer membrane, preferably quasi-adiabatic. The French national institute of research IFP Energies Nouvelles has extensive experience with permeation of CO\({}_{2}\) and is interested to assist the definition, assembling and testing of a suitable membrane.  
Although condensation and gasification of CO\({}_{2}\) happens at nearly constant temperature and pressure in the UPHES LRC cavern or in a smaller dedicated LRC cavern located beside the main cavern, back and forth supply and removal of important amount of heat are compulsory to the phase changes of CO\({}_{2}\). The system uses therefore a wide area of heat exchangers at the lower and upper parts of the smaller cavern. Spraying of droplets of CO\({}_{2}\) pumped from the bottom of the cavern toward its cupola shall facilitate heat exchanges. Forced circulation of CO\({}_{2}\) can assist to reduce temperature gradients and to enhance phase changes.  
By performing simple calculations on enthalpy of CO\({}_{2}\) latent heat, we can assess that the amount of heat displaced to perform a full change of phase of the CO\({}_{2}\) mass at work (optimal CO\({}_{2}\) mass to maximize the mechanical work by displacement of the maximum volume of water) is very significant and roughly equal to 500% of the hydroelectric energy charged in the cavern. The Pressure-Enthalpy diagram related to CO\({}_{2}\) is appended in Figure 4. We intend to provide this figure just as an illustration of 4 different conceptual cycles, with no availability of actual data tables at this stage.
## 5 Experiments
### In-Depth Analysis
#### 5.6.2 Analysis of Positive/Negative Threshold for Contrast  
In the semantic-boundary-smoothing contrastive learning, the setting of threshold \(\beta\) determines the division between positive and negative examples, representing the model's strictness in distinguishing between them and influencing the gradient updates. Previous research [37] indicates that the robustness of the model to sample perturbations is an important aspect of model performance. The threshold for positive and negative samples  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**DuEE**} & & & & & & & & & \\ \hline \multicolumn{1}{c}{**Methods**} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{**1**} & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline \multicolumn{1}{c}{ICD (Ours)} & **98.55** & **92.03** & **92.93** & **93.82** & **90.39** & **98.55** & **94.93** & **93.43** & **94.60** & **92.49** \\ -replay & 98.55 & 90.66 & 88.89 & 84.17 & 81.98 & 98.55 & 89.20 & 89.93 & 91.66 & 86.79 \\ -replay & 98.55 & 92.03 & 92.07 & 92.81 & 87.24 & 98.55 & 94.39 & 91.16 & 92.99 & 91.13 \\ -replay & 98.55 & 91.78 & 90.53 & 93.54 & 88.26 & 98.55 & 94.93 & 93.94 & 93.82 & 91.59 \\ -replay & 98.55 & 92.16 & 89.53 & 92.54 & 86.21 & 98.55 & 94.93 & 90.47 & 91.74 & 89.94 \\ -contrast & 95.65 & 90.66 & 91.79 & 92.17 & 84.71 & 95.65 & 92.95 & 92.33 & 92.08 & 88.39 \\ -distill & 98.55 & 91.20 & 92.76 & 93.12 & 88.68 & 98.55 & 94.93 & 92.42 & 93.05 & 90.43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Recall_@3 performance of every sub-task on ACE2005-Chinses for ablation study.  
Figure 5: _Recall_@3 performance on old and new tasks in each sub-task on ACE2005-Chinses and DuEE.  
determines the distinction between them, representing the model's strictness in differentiating these samples, which can be seen as a form of perturbation in the training samples. Given that this hyper-parameter is closely related to event patterns and dataset annotation styles, selecting an appropriate threshold is crucial.  
We tested five different threshold values and reported their recall rates on the final task. The results are shown in Table 7. and it indicate that a threshold of 0.2 yields the best overall performance. A threshold that is too high may result in too few positive examples, leading to limited effectiveness with a small sample size, whereas a threshold that is too low might lead to an excessive number of positive examples, affecting the differentiation between positive and negative cases. Therefore, we selected 0.2 as the final threshold setting.
## 3 Results and Discussion
### SEM-EDS Analysis
This confirms the ability of this residue to desorb and re-adsorb As and, additionally, Cr. It is important to mention that during the adsorption process, jarosite is formed by amorphous iron hydroxides with a rather porous morphology. It also contains elements such as Si, Zn, Cu, and Al, which can form complexes with both As and Cr, promoting, to a certain extent, the adsorption of these heavy metals, or forming reactive species with iron (Al, Zn, and Si), which can be more reactive toward As(V) and Cr(III). Likewise, Cu can act as a catalyst and, at some point, oxidize Cr(III) into Cr(VI), which can affect the adsorption of this element. However, Cu can also form complexes with As(V), and this would lead to an increase in the adsorption of As. Finally, it is important to highlight that the influence of each and every one of these elements on the adsorption of As(V) and Cr(III) will depend on various factors, such as the concentration of each element, the pH value of the solution, temperature, and the presence of other ions [65].
# Superparamagnetic Fe/Au Nanoparticles and Their Feasibility for Magnetic Hyperthermia
###### Abstract  
Today, magnetic hyperthermia constitutes a complementary way to cancer treatment. This article reports a promising aspect of magnetic hyperthermia addressing superparamagnetic and highly Fe/Au core-shell nanoparticles. Those nanoparticles were prepared using a wet chemical approach at room temperature. We found that the as-synthesized core shells assembled with spherical morphology, including face-centered-cubic Fe cores coated and Au shells. The high-resolution transmission microscope images (HRTEM) revealed the formation of Fe/Au core/shell nanoparticles. The magnetic properties of the samples showed hysteresis loops with coercivity (HC) close to zero, revealing superparamagnetic-like behavior at room temperature. The saturation magnetization (MS) has the value of 165 emu/g for the as-synthesized sample with a Fe:Au ratio of 2:1. We also studied the feasibility of those core-shell particles for magnetic hyperthermia using different frequencies and different applied alternating magnetic fields. The Fe/Au core-shell nanoparticles achieved a specific absorption rate of 50 W/g under applied alternating magnetic field with amplitude 400 Oe and 304 kHz frequency. Based on our findings, the samples can be used as a promising candidate for magnetic hyperthermia for cancer therapy.  
Fe/Au nanoparticles; superparamagnetic; core/shell; magnetic nanoparticle hyperthermia 10.390/app1146637
## 2 Materials and Methods
### Data Synthesis and Analysis  
Meta-analyses were conducted using R software (version 3.5.0; R Project for Statistical Computing). The standardized mean difference (SMD), along with the 95% confidence interval (CI), was employed as the primary summary statistic. A random-effects model was applied to account for variability among the included studies. Statistical significance was defined at a threshold of 0.05. To assess heterogeneity, the \(l^{2}\) statistic and chi-square test were calculated. The analysis was conducted in line with Cochrane Handbook guidance to manage intra-patient correlation in cluster-structured data. A correlation coefficient of 0.07 was applied to inflate the standard error where clustering corrections were absent.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 1 Introduction
### Related Works
Recent publications [15; 16; 17; 18; 19; 20], among others, have looked at the usage of RISs in OFDMA systems, as listed in Table 1. The works in [15; 16; 17; 18; 19; 20] have focused on maximizing the sum-rate in an OFDMA network. The work in [15] proposes a RIS-aided multi-user THz MIMO system with OFDM access, aiming to maximize the weighted sum-rate via joint analog/digital beamforming at the base station and reflection matrix optimization at the RIS. In [16], the authors propose a novel uplink communication system with a transmissive RIS transceiver, employing OFDMA for multiple users, and utilize alternating optimization to maximize the system sum-rate while meeting QoS constraints. The work in [17] investigates the joint design of MU beamformers and RIS's programmable reflecting elements (PREs) for quality-of-service in RIS-aided OFDM networks. In [18], the authors propose a resource allocation algorithm for RIS-aided MISO OFDMA multicell networks targeting URLLCusers, ensuring QoS while maximizing system throughput through a sub-optimal iterative approach. The authors in [19] propose the optimal beam reflection based on the the federated learning (OBR-FL) algorithm for RIS-assisted communications, utilizing federated learning to optimize beam reflection based on sparse CSI while preserving user privacy. The work in [20] proposes dynamic passive beamforming for RIS-integrated OFDMA systems, optimizing reflection coefficients and resource allocations to maximize users' common rate while flexibly adapting to channel variations and outperforming fixed passive beamforming approaches. On the other hand, the authors in [21] delve into the investigation of maximizing energy efficiency fairness in an active RIS-aided cell-free network. In contrast to the studies mentioned, recent research is oriented towards formulating an energy-efficient problem in the DL direction of a RIS-aided single input single output (SISO) OFDMA system, taking passive RIS into account. The consideration of a single-antenna BS and users allows for a more straightforward simplification of our proposed problem. The presented problem is intricate, involving high-dimensional discrete and continuous variables. Given the complexity, suggesting a model-based optimal solution proves impractical. Additionally, the unavailability of accessible data for training hinders the utilization of machine learning methods. Consequently, in comparison to other solutions outlined in the literature and presented in Table 1, a sub-optimal solution is advocated by us through the lower-bound optimization of the EE problem.
## 5 Conclusions  
This paper conducts an in-depth analysis of the relationship between the local time invariance of motion parameters and the performance of the corresponding dead-reckoning algorithm. Given the awareness that the expected state exhibits superior time invariance compared to the temporary state, a geometric approach is devised. This approach transforms the parameter-estimation problem into a convex optimization problem. To solve the optimization problem and accurately estimate the expected state, a pseudoinverse gradient descent method featuring eight branch directions is employed. The proposed 8B-PGDM achieves excellent estimation accuracy with only three samplings, with an MSE of 0.7906, significantly better than traditional dead reckoning (DR) methods.  
In terms of effectiveness and significance, the following aspects can be highlighted. Firstly, the method is capable of achieving trajectory predictions with fewer sampling times, which is particularly advantageous in scenarios where the target is occluded or communication is intermittent. This feature enables the method to operate effectively under challenging conditions, ensuring reliable performance even when data acquisition is limited. Secondly, it can yield trajectory predictions with a smaller mean squared error. A lower mean squared error indicates a higher accuracy of the predictions, thereby improving the overall quality and reliability of the results obtained through the method. Thirdly, the predicted trajectories are smooth without jaggedness. Smooth trajectories are more desirable in many applications as they conform better to physical realities and are easier to interpret and utilize, enhancing the practical value of the predicted results. Finally, the iterative solution speed of the method is fast. Rapid iteration enables quick convergence and timely results, making the method suitable for time-sensitive applications where real-time or near-real-time processing is required, thereby enhancing its efficiency and responsiveness in dynamic situations.
## 4 Signal Combining Schemes for ADR Solutions
### Receiver Scenario with a Single PD (\(N_{pd}\) = 1)  
In this scenario, the \(R_{x}\) in the underground mining VLC environment will have a single PD installed. As one of the main objectives of this work is to mitigate the ICI in the mining VLC system (see Section 1), one of the most important communications metrics to verify the reduction of this problem is the SINR, which allows evaluation of the capacity and quality of the VLC channel. Thus, to maximize the SINR of the link that involves the \(R_{x}\), the PD in it, must have the ability to choose from all the \(T_{x}\)s of the mining scenario, i.e., the one that achieved the best SINR. To accomplish this, we formulate an optimization problem, where the desired \(T_{x}\), \(T_{x_{ap}}\), where \(a_{op}\in[1,N_{T_{x}}]\), is described as [22; 23; 44]  
\[T_{x_{ap}}=\underset{T_{x_{ap}}}{arg\ max}\ \gamma_{T_{x_{ap}},R_{x}}, \tag{33}\]  
where \(\gamma_{T_{x_{ap}},R_{x}}\) is the SINR of the link between \(T_{x_{ap}}\) and \(R_{x}\). The SINR can be expressed as [22; 23; 44]  
\[\gamma_{T_{x_{ap}},R_{x}}=\frac{\left(\tau\sigma\xi P_{a_{op}}H_{T_{x_{ap}},R_ {x}}\right)^{2}}{\sum\limits_{a=1,a\
eq a_{op}}^{N_{T_{x}}}\left(\tau\sigma \xi P_{a}H_{T_{x_{a}},R_{x}}\right)^{2}+\left(n_{R_{x}}\right)^{2}}, \tag{34}\]  
where \(H_{T_{x_{ap}},R_{x}}\) is the overall DC channel gain for underground mining VLC systems between \(T_{x_{ap}}\) and \(R_{x}\), the sum term represents the interference produced by the \(T_{x}\)s that were not selected in the link, and \(n_{R_{x}}\) is the total noise in \(R_{x}\).  
Receiver Scenario with Pyramidal ADR (\(N_{pd}\) = 4) and Hemi-Dodecahedron ADR (\(N_{pd}\) = 6)  
In this scenario, the \(R_{x}\) will be equipped with the ADRs described in Sections 3.2 and 3.3. Therefore, we will have multiple received signals, one for each PD, to combine these signals into one, or select the best of them, three signal combining schemes, SBC, EGC, and MRC are described below.