esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Uncertainty Modeling Techniques
### Monte Carlo Simulation  
The integration of Monte Carlo simulation with photovoltaic systems has garnered attention due to the need for robust tracking techniques under variable environmental conditions, such as partial shading.  
In [23], a heuristic approach combined with Monte Carlo simulations for optimizing the reliability and cost of stand-alone photovoltaic systems in off-grid areas is introduced. The method efficiently sizes PV installations, achieving high reliability and minimized costs with low iteration requirements. In [24], Hamiltonian Monte Carlo is introduced to enhance sampling efficiency and accuracy for voltage sag assessments, outperforming traditional methods like importance sampling and Metropolis-Hastings. Reference [25] has used the Monte Carlo simulation for the techno-economic evaluation of energy storage systems in concentrated solar power plants, demonstrating that combined sensible-latent heat storage systems have the lowest levelized cost of electricity and uncertainty.  
To estimate the expected value of a random variable (\(X\)) by Monte Carlo simulation, the probability distributions for all input variables should be determined. Let us assume that there are \(n\) random variables \(X_{1},X_{2},\ldots,X_{n}\), each with known probability distributions \(P_{X_{1}},P_{X_{2}},\ldots,P_{X_{n}}\). A number of simulations (\(M\)), which often is a large number, should be conducted to compute the output variable. For each simulation \(i\), a set of random values \((x_{1}^{(i)},x_{2}^{(i)},\ldots,x_{n}^{(i)})\) based on their probability distribution should be generated. The output variable (\(\gamma\)) as a function of the input variables can be described as follows [26]:  
\[y^{(i)}=f\Big{(}x_{1}^{(i)},x_{2}^{(i)},\ldots,x_{n}^{(i)}\Big{)} \tag{29}\]  
To analyze the results, the expected value of outputs can be calculated as follows:  
\[\mathbb{E}(Y)=\frac{1}{M}\underset{i=1}{\overset{M}{\sum}}y^{(i)} \tag{30}\]
# RFID Authentication System Based on User Biometric Information +
## 2 Related Work  
User authentication methods can be divided into three main categories: information, possessions, physiological and behavioral characteristics.  
_Information_, such as passwords or security codes. The information-based approach is designed to use traditional cryptographic algorithms to perform authentication and use encryption technology to protect tags from illegal access [17; 18; 19; 20; 21]. Most of these methods require modifications to commercial communication protocols or tag hardware, making it difficult to apply them to lightweight passive tags.  
_Possessions_, such as various certificates or ID cards. The physical information-based approaches identify and verify the tags by taking advantage of the differences in the tag circuit characteristics, which will be reflected in the backscattering signal. [PERSON] et al. propose a reader authentication solution based on physical layer signals, namely Arbitrator [22; 23], which can effectively prevent unauthorized access to tags. [PERSON] et al. [24] propose a new physical layer recognition system based on internal similarity, GenePrint. [PERSON] et al. [25] use additional phase offset as a new fingerprint called Tagprint for identifying a pair of readers and tags. [PERSON] et al. explore a new fingerprint called Fingerprint [26] to authenticate passive tags in the commodity RFID system, which uses the electrical energy stored in the tag circuit as a fingerprint. [PERSON] et al. explore a verification method called Hu-Fu [8]. The authors observe inductive coupling between two adjacent tags [27; 28]. When two tags are placed very close and the coupling effect occurs, Hu-Fu can achieve the purpose of verification.  
_Physiological or behavioral characteristics_. In recent years, the mainstream user authentication methods are based on different biological characteristics (e.g., fingerprint, face and retina) for identification. Compared with traditional authentication technology, the biometric authentication technology (especially fingerprint authentication) has achieved more excellent performance owing to its universality, uniqueness, permanence and anti-counterfeiting characteristics. RF-Mehndi [10] uses the physical characteristics of the tag and the biometric characteristics of the holder to verify the user's validity. When the user touches the tag, the physical layer information of the tag and the user's body impedance are combined to achieve verification.  
Although RF-Mehndi combines the physical characteristics of the tags with the user's biometric information, it still cannot handle the case where the use's ID card is lost, which brings trouble to the user. Therefore, we design a system that can verify the user by combining its password with its biometric information, without the need for an ID card.
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 2 Basic Knowledge  
### Motion Control System  
As shown in Figure 1, a servo motor control system typically consists of a closed-loop circuit, which mainly includes seven components: human-machine interface, motion controller, driver, actuator, driving mechanism, load, and feedback.  
The motion controller is the \"brain\" of the entire system, which receives the feedback from the motor and calculates the servo error between the actual and given state, then generating a control signal to adjust the state of the motor and reduce the error according to its control strategy. After that, the driver amplifies these signals to a high-power voltage and current to meet the needs of the motor operation [2].  
In this work, we mainly focus on the control strategy in the controller, and our goal is to design a risk-sensitive control algorithm to avoid the overcurrent problem during the training process.
## 5 Simulation Results
### Wavelet Method Analysis Results  
Applying Equation (27) to determine the number of layers \(n_{ls}\) for the DWT, with \(f_{c}=\) 10,000 Hz and \(f_{s}=\) 100 Hz, results in \(n_{ls}\) being 6. Table 2 presents the frequency range of each of the discrete wavelet coefficients. The ITSCF voltage data extracted from Figure 9 are subsequently fed into the DWT to acquire wavelet coefficients for each frequency range.  
By employing the simulated data presented in Figure 9, the wavelet coefficients of \(V_{q}\) for various \(\mu\) values within the ITSCF framework are extracted via wavelet transform. Subsequently, signal reconstruction is conducted based on these coefficients, and the results are depicted in Figures 18 and 19. It can be observed that after performing the DWT and IDWT, the signals for each frequency range were analyzed. The reconstructed signal in the fifth range (156 Hz-312 Hz), which corresponds to twice the fundamental frequency (200 Hz), exhibited a significantly larger value compared to other frequency ranges. The  
Figure 16: Voltage responses of various components at \(r_{f}=0\)\(\Omega\). (a)Voltage across the ITSC resistance and inductance; (b) ITSC back electromotive force.  
Figure 17: Response of phase-a coil voltage and current at \(r_{f}=0\)\(\Omega\). (a) \(V_{q\bar{q}}\); (b) \(i_{a}\).  
analysis reveals a pronounced amplification of \(V_{q}\) in specific wavelet frequency components of ITSCF.  
However, the DWT alone is insufficient for effectively comparing the trends of \(V_{q}\) variations in ITSC. Therefore, the values for each frequency band are converted into energy percentages using Equation (39), resulting in the bar charts shown in Figures 20 and 21. The results demonstrated that a larger \(\mu\) value was associated with a more significant second harmonic distortion, leading to a higher proportion of energy concentrated in the 156-312 Hz frequency range.  
Figure 19: ITSC voltage IDWT reconstruction at 1500 rpm, \(\mu=0.7\).  
Figure 18: ITSC voltage IDWT reconstruction at 1500 rpm, \(\mu=0.3\).
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### System Description  
Figure 1 depicts the schematic diagram of the proposed HetNet driven by the grid-tied hybrid solar PV/BG system. HetNet comprises different types of BSs equipped to meet the necessary data demands and has a specific size and power consumption. In general, a narrow area covering a small-size BS needs low-radiated power. The pico- and femto-BSs are therefore supplied with standalone solar PV, which consumes little power. A battery bank is linked to guarantee a constant energy supply, to counteract the RES deficiency or failure. The energy supply is regulated by an energy management unit (EMU) for each base station.  
The major components of the system along with their mathematical models are briefly described below.
## 4 Result Verification
The verification strategy for the longitudinal force estimation results of 4 WIDEV is shown in Figure 9. The accuracy of the estimation is verified by comparing the results output by the estimation model with the data collected during testing. The longitudinal tire force estimation results under the chassis dynamometer test are shown in Figure 10. According to the overall trend and local amplification comparison chart of the estimated result curve, it can be seen that the CFF-SIR algorithm has higher estimation accuracy in three situations: no noise and interference, noise influence, and interference influence. Then, quantitative statistics were also used to compare and analyze the performance of estimation errors, and the results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working Condition** & **Method** & **Mean Error** & **Mean Square Error** \\ \hline \multirow{3}{*}{Without noise or interference} & CFF-SIR & 1.1684 & 0.5311 \\  & C-SIR & 2.3588 & 1.2069 \\ \multirow{3}{*}{With noise} & CFF-SIR & 1.2639 & 0.8682 \\  & C-SIR & 2.8692 & 1.7646 \\ \multirow{3}{*}{With interference} & CFF-SIR & 1.3417 & 0.5934 \\  & C-SIR & 2.9902 & 1.5006 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean error and mean square error of estimation results in sine-steering maneuver. Figure 10: Estimation results of longitudinal tire force under chassis dynamometer test. (**a**) Without noise and interference effects, (**b**) with noise effects, (**c**) with interference effects. Figure 9: Verification strategy for longitudinal tire force estimation results of 4 WIDEV. In the three cases of no noise and interference, noise influence, and interference influence, the average estimation errors under the C-SIR algorithm are 3.9118, 4.6788, and 4.7903, respectively, while the average estimation errors under the CFF-SIR algorithm are 2.3745, 2.6787, and 2.6854, respectively. In addition, the mean square error under the CFF-SIR algorithm is also less correlated. By comparison, it can be seen that the proposed improved algorithm can effectively suppress the estimation error and error fluctuation of the identification model, and the overall estimation effect has been significantly improved. By combining simulation data and chassis dynamometer test data and calculating their mean, it can be concluded that compared to the C-SIR algorithm, the CFF-SIR algorithm has improved the comprehensive estimation accuracy by 8.37%.
## 2 Introduction to Logistic Regression  
Logistic regression is a statistical model that expresses its input-output relationship stochastically through the logistic function, where the output of the logistic regression model is categorical. It is characterized by high analytical power and adaptability in determining the relationship between two or more variables. This method has already been applied in many research fields because of its high performance in a binary classification problem [14].  
In this study, the \(d\times 1\) vector \(x_{i}\in R^{d}\) consisting of \(d\) feature values represents the \(i\)-th feature vector while the scalar \(y_{i}\in\{0,1\}\) represents its corresponding category label (e.g., clutter or target). The binary logistic regression model considered in this study has the learning parameters \(d\times 1\) vector \(\mathbf{\beta}\in R^{d}\) and scalar \(b\in R\). For a set of \(N\) labeled data points \((x_{i},y_{i})\), the cost function for the parameters \(\mathbf{\beta}\) and \(b\) is the log-likelihood of the defined by  
\[\text{cost}\left(\mathbf{\beta},b\right)=\sum_{i=1}^{N}\left(1-y_{i}\right)\log \left(1-\alpha_{i}\right)+y_{i}\log\alpha_{i}, \tag{1}\]  
where \(\alpha_{i}=P(y_{i}=1|x_{i})\) represents the probability that the output \(y_{i}\) belongs to the category of 1 given \(x_{i}\) and \(N\) is the number of the feature vectors. For the binary logistic model considered here, the probability \(P(y_{i}=1|x_{i})\) is given by  
\[P(y_{i}=1|x_{i})=\frac{1}{1+e^{-(\mathbf{\beta}^{T}x_{i}+b)}}, \tag{2}\]  
where the superscript \(T\) represents transpose.  
In order to find the optimal learning parameters \(\mathbf{\beta}\) and \(b\) that maximize the likelihood function for a given training set, the trust region Newton scheme [21] is employed in this study. This scheme relies on approximate Newton stages in the initial stage but obtains fast convergence. The performance of the model is evaluated by measuring the AUC of the ROC curves [22], the classification tables for the test data set and the detection rate according to the target-to-clutter ratio (TCR), which is defined by  
\[TCR=\frac{\sum\limits_{q=0}^{Q-1}P_{t}(f_{q})}{\sum\limits_{q=0}^{Q-1}P_{c}(f_ {q})}, \tag{3}\]  
where \(P_{T}(f_{q})\) and \(P_{C}(f_{q})\) are the power spectral densities of the target signal and the seabed clutter at frequency bin \(f_{q}\), respectively, and Q is the total number of frequency bins in the section of the signal bandwidth.
# Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
## 3 Proposed Scheme
### Smart Device Key Updating Process  
* After completing certification sensor group formation and session key distribution, the sensor node carries out the group key distribution and renewal process as follows. Using the polynomial distribution of PCGR, it makes MN do most of the calculation, distributes pieces of the specific value through the \(f(y)\) function, and verifies whether the node is contaminated or needs to be withdrawn.
* The group's representative node, MN, defines and generates polynomials \(g(x)\) and \(f(y)\) for group distribution and node verification. After that, it generates verification values S and \(D_{n}\). Next, it generates \(P_{1}\) for transmitting to nodes and deletes \(g(x)\) and \(e(x)\) to prevent them from being exposed by an attacker.
* The sensor node that received \(P_{1}\) decodes this, and obtains the group key and secret piece. It then informs MN that the group key was successfully received.
* After a certain period of time, MN transmits a message for updating the group key to the nodes within the group. Nodes that receive the key-update message send MN an encrypted secret piece with the session key in response, and MN checks the validity of the received value through a Lagrangian polynomial and sends a \"success\" message. If the value differs, it informs specific nodes of contamination.
* Nodes that received the message from MN send nodes around \(P_{n}\) for updating the key through Equation (2):\[g\left(c\right)=g^{\prime}\left(c\right)+e_{u}\left(c,\ N_{id}\right) \tag{2}\]  
After the key is updated, it finishes the verification process with nodes through the group key. The whole process of Smart Device Key Updating is shown in Figure 6.
# Future Wireless Networking Experiments Escaping Simulations
## 5 Our Wireless Experiment on Each Testbed
### W-iLab1.t  
1. **Linear Topology:** In W-iLab1.t, more nodes were available. A 10-node setup was used in W-iLab1.t based on the availability at the time of reservation as displayed in Figure 7. F1, F2, F3 are 2.4 GHz channels, i.e., 1, 6, 11, while F4, F5, F6, F7 are 5 GHz lower channels, i.e., 36, 40, 44, 48, and F8, F9 are 5 GHz upper channels which are 149 and 153.
2. **Ring Topology:** The ring topology setup is similar to W-iLab1.t linear topology, the only difference is the addition of one more 5 GHz upper channel in F10 which is 157 to connect node 9 to node 0, as can be seen in Figure 8.  
Figure 8: W-iLab1.t ring topology node setup.  
Figure 7: W-iLab1.t linear topology node setup.  
3. **Grid Topology:** The formation of grid topology is designed in such a way that it uses the two available wireless interfaces to form a grid between 10 nodes. The network setup is shown in Figure 9. F1, F2, F3 make use of channel 1, 6, 11. F4, F5, F6 make use of channel 36, 40, 44, i.e., 2.4 GHz and 5 GHz lower frequency channels.
## 3 System Architecture
### Smart Homes Appliance Models
#### 3.1.2 Deferrable Appliances  
The household appliances in this category can be interrupted or shifted in any allowable time range according to their requirements for achieving the optimal scheduling in a smart home. The set of deferrable appliances is represented by \(A_{d}\), and \(a_{d}\in A_{d}\) denotes each appliance in this category. The total energy consumed by all the appliances of this category is represented by \(E_{d}\) in Equations (13) and (14).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Population size & 200 \\ Number of iterations & 100 \\ Probability of crossover & 0.9 \\ Probability of mutation & 0.1 \\ Number of appliances & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Genetic algorithm (GA) parameters.  
Figure 3: Status of the appliances’ execution pattern.
## 5 IPTMS Modelling  
IPTMSs are highly integrated and interdisciplinary, making it beneficial for designers to access the components' performance in various forms, such as in terms of thermal and electrical energies. The models developed for an IPTMS can be used as analysis tools for researchers to investigate the interactions between different subsystems in order to obtain optimal solutions. To evaluate aircraft performance with an IPTMS, the tip-to-tail (T2T) models are developed, usually including the IPTMS, fuel thermal management system, engine system, and air vehicle system. In addition to the aircraft-level models for subsystems, the T2T models also involve component-level models for some critical components. With the integrated models, the T2T models provide a foundation for the aircraft's future optimisation. In some cases, the T2T models are designed for a certain type of aircraft, but the modelling methods are applicable to other aircraft types.  
[PERSON] et al. [86] proposed a modelling and simulation process for IPTMS to maximise the aircraft's energy utilisation while minimising complexity. The component-level models involve electrical components such as the generator, feeders, and actuators. The integrated model has the capability to assess the transient behaviours of the subsystem and find the drivers of the system's performance to define the design points of the subsystems. Throughtesting, the researchers identified limitations with the assumptions of the design point of the energy storage unit.  
[PERSON] et al. [87; 88; 89] developed a generic T2T model, including the subsystem of an air vehicle system, fuel thermal management system, IPTMS, engine system, and control system. They developed the model without proprietary data so that the tool is highly adaptable for different conceptual designs. To increase the model fidelity, they developed transient models for the integrated power package and TMS components (e.g., fuel and oil pumps and heat exchangers). The researchers found that the shortcomings in the TMS design can be detected through the assessment of the transient interactions between the subsystems, which could be neglected in conventional designs. In [90], the T2T model is adapted to a solid oxide fuel cell (SOFC) to analyse the impact of the SOFC at the aircraft level. It was found that the SOFC can improve the overall aircraft efficiency by a small percentage and can reduce the use of bleed air from the main engine. The T2T offers the capability of a trade-off study by evaluating the relationship between subsystems, but only the configuration of the IPTMS integrated with a SOFC is studied.  
Due to the multidisciplinary interactions within the subsystems of the IPTMS, a T2T model is required for the IPTMS to assess its performance. A versatile model can be used by different design teams, reducing the operating cost of design. Additionally, the development of IPTMS modelling can contribute to the modular design of IPTMSs.
###### Abstract
The main contributions of this work are as follows:  
* An RUL probability prediction neural network based on degradation data is proposed for an aero-engine based on a BiGRU network and an IVBI technology, which can give not only the RUL prediction but also an accurate estimate of prediction uncertainties. * A new IVBI method is proposed by replacing the traditional single Gaussian distribution in the variational Bayesian inference with a Gaussian mixture distribution to improve the generalization capability and prediction ability of the proposed method. * The performance of the proposed model is validated on the CMAPSS data set. Comparisons with five other advanced deep learning methods show that our method is the most effective one under all of the considered evaluation indices. The rest of this paper is structured as follows. Section 2 gives a description of the proposed framework for aero-engine RUL prediction. Section 3 reports our model performance on the CMAPSS data set and discusses the results of comparative experiments. Finally, the conclusions are shown in Section 4.
## 3 Methodology
### Federated Learning Setup  
To simulate a federated learning environment, the training process was distributed across three virtual clients, each receiving a non-overlapping subset of the augmented training dataset. The partitioning was performed randomly to emulate data heterogeneity commonly observed in real-world decentralized systems. As shown in Figure 3, class distribution across the three clients remained relatively balanced, with each subset containing comparable numbers of samples from both the \"Fall\" and \"NoFall\" classes. This setup ensures that each client maintains representative local data while preserving the statistical diversity necessary for robust federated learning.  
Two federated learning strategies were implemented and compared. In the decentralized ring-based configuration, clients are arranged in a circular topology and update a shared model sequentially. After each client completes its local training, it passes the updated model weights to the next client in the ring. Aggregation is performed using standard Federated Averaging (FedAvg), but without a central server. In the centralized server-based configuration, each client trains independently and transmits its model weights to a central aggregator, which performs weighted FedAvg, giving more influence to clients with larger datasets. Across both setups, clients used identical CNN architectures initialized with ImageNet-pretrained weights trained for 25 local epochs per round, and using a mini-batch size of 32. This consistent configuration enables a fair and controlled comparison of model performance under both coordination paradigms.  
Figure 3: Class distribution per client.