esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
###### Abstract  
Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.  
kernel method; extreme learning machine; distributed framework; fusion; aircraft engine; failure diagnostic +
Footnote †: journal: Applied Sciences
## 3 Proposed Detection Mechanism
### Packets Capturing  
At the initial stage, the nodes are deployed in a network to form a WSN. These nodes constantly sense the data by investigating changes in the environmental parameters. These parameters can be air pressure, temperature, sound, heat, motion, etc. Once the data is collected, these nodes send their sensed data to BS through various routes. The data is sent in the form of packets. The sizes and number of packets depend on the size and nature of the data to be transmitted. A particular time interval is used between the transmission of data by each node. The BS captures the data in a time period that makes it capable of not losing any packet transmitted by the network nodes. The data packets received by the BS possess various properties such as the size, arrival time, source and route information, interpacket arrival time difference, etc. All these records are maintained by the BS for analysis.
# Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues
## 3 Problem Modeling and Algorithm Modeling
### Queue Length Analysis  
In the management of the ship queue in the anchorage, for the queue length distribution at the departure time in the anchorage system, considering that there are K types of ships with different priorities in the system, the problem of the queue length of the \(k\)-th type of ships is transformed into the problem of reward accumulation during the busy period of a special Markov fluid model. The LST expression of the queue length at the departure time is  
\[f_{x}^{(k)*}(s)=\hat{\kappa}^{(k)}\Phi^{*}(s)\mathbf{I} \tag{6}\]  
where \(\hat{\kappa}^{(k)}\) is obtained by solving a non-symmetric algebraic Riccati equation, and \(\Phi^{*}(s)\) is obtained by solving a non-symmetric algebraic Riccati equation constructed by \(Q\), which is used to analyze the performance of the waiting time in the anchorage. When analyzing the statistical characteristics of the queue length, the moment information can be used to measure characteristics such as the degree of dispersion of the queue length. Through the derivation of Formula (6), the calculation formula of the \(m\)-th moment of the queue length at the departure time is obtained:  
\[E[\left(\mathrm{x}^{(k)}\right)^{m}]=(-1)^{m}\hat{\kappa}^{(k)}\Phi^{(m)} \mathbf{I} \tag{7}\]  
where \(\Phi^{(m)}\) is derived from the non-symmetric algebraic Riccati equation constructed by the CTMC generator matrix and is obtained by solving recursively in combination with the Sylvester equation, which is used to calculate the \(m\)-th moments, such as the waiting time and queue length. In order to more effectively process and analyze the distribution function of the queue length, the Erlangization method is used for approximation:  
\[F_{\mathrm{X}}^{(n)}(\mathrm{x})=\hat{\mathbf{a}}^{(k)}\mathbf{I}+\hat{\mathbf{ a}}^{(k)}\Phi_{n}(\mathrm{x})\mathbf{I} \tag{8}\]  
where \(\hat{\mathbf{a}}^{(k)}\) is affected by \(Q\). As \(n\) continues to increase, the approximation accuracy will continue to improve, so that the results can more accurately approximate the true queue length distribution function.  
There is a close relationship between the queue length distribution at a random time and the queue length distribution at the departure time. The relationship between the queue length distributions at a random time and the departure time is  
\[f_{\mathrm{y}}^{(k)}(u)R^{(k)}-F_{\mathrm{y}}^{(k)}(u)Q=\lambda^{(k)}f_{ \mathrm{x}}^{(k)}(u) \tag{9}\]In the LST domain, Formula (9) can be further expressed as  
\[f_{y}^{(k)*}(s)(sR^{(k)}-Q)=\lambda^{(k)}sf_{x}^{(k)*}(s) \tag{10}\]  
Secondly, based on the \(k\)-class queue length embedded at the departure time, the expression of the \(k\)-class queue length is derived, and the moment is obtained by taking the derivative of the LST:  
\[E(y^{m})=(-1)^{m}\frac{\mathrm{d}^{m}}{\mathrm{d}s^{m}}\underline{f_{y}^{*}(s )}|_{s\to 0}\mathrm{l} \tag{11}\]
# Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils
## 3 Results and Discussion
### Characterization of the Soil Properties
The samples were collected from both topsoil (<1.20 m) and deeper soil layers, where higher OC content was expected in the topsoil. However, no significant correlation was observed between depth and OC content, with most samples showing OC concentrations consistent with those typically found in deeper soil layers (corresponding to a fraction of organic carbon (\(f_{\text{OC}}\)) < 0.005) [3]. The only exceptions were samples 8, 10, 7, and 30, where the \(f_{\text{OC}}\) values ranged from 0.006 to 0.008. The range of SSA values varied considerably (0.41-64.39 m\({}^{2}\) g\({}^{-1}\)), indicating differences in the sorption capacity of the soils. Additionally, the observed SSA values were generally lower than those typically associated with highly effective sorbents [42]. The elemental composition, particularly Si and Al content, suggests the presence of various clay minerals and metal oxides, which can contribute to sorption through mechanisms such as cation bridging and surface complexation, thereby impacting overall soil properties and potential interactions with contaminants [43; 44]. XRD analysis revealed that the soils predominantly comprised quartz, clinochrysotile, muscovite, and various feldspars (including albite, orthoclase, microcline), and zeolites, along with clay minerals such as kaolinite and montmorillonite (Table S1). Fine-grained soils demonstrated a higher abundance of phyllosilicates, particularly clay minerals (kaolinite, montmorillonite, and illite) and muscovite, which are known for their high sorption capacities due to large specific surface areas and cation exchange properties. In contrast, these sorption-active minerals were often absent in coarse-grained soils, suggesting potentially lower sorption capacities in these samples. This mineral distribution pattern aligned with the particle size distribution results, reinforcing the textural classification of the samples. Before the experiments, all soil samples were analyzed for PCE content. The results indicated that PCE concentrations were below the detection limit, confirming the absence of the compound in all samples.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
## 4 Modulation and Demodulation Model Based on a Vector Quantization LSTM Autoencoder
### LSTM-AE-Based Modulation and Demodulation Framework
As shown in Formula (11), the training direction of the model is guided by comparing the difference between the input \(\mathbf{O}_{e}\) and the output \(\mathbf{O}_{d}\) of the encoder network. In the formula, \(\mathbf{\mathrm{\pi}}\) is the set of all adjustable parameters, and \(B\) is the batch size. Figure 2.5  
The Adam optimizer is widely used as the default optimizer due to its adaptability and stability. We use Adam instead of the AdaDelta optimizer to adapt to the task background with fast convergence.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 2 Materials and Methods
### State Estimation  
Estimates of the system states in \(\Phi\) are required in order to estimate the parameters in \(\Theta\). The simulator used to test the system provides \"truth\" measurements for roll, pitch,  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Description** \\ \hline \(u\), \(v\), \(w\), \(p\), \(q\), \(r\) & Body frame x, y, z, roll, pitch, and yaw velocities \\ \hline \(\phi\), \(\theta\), \(\psi\) & Inertial frame roll, pitch and yaw \\ \hline \(u_{\psi}\) & Yaw control input \\ \hline \(A_{0}\) & Coefficient relating \(u_{\psi}\) to yaw torque \\ \hline \(N_{\dot{a}}\), \(N_{\varphi}\), \(N_{\dot{u}}\), \(N_{p}\), \(N_{\dot{q}}\), \(N_{t}\) & Added mass coefficients \\ \hline \(N_{r}\), \(N_{rr}\) & Linear and nonlinear damping coefficient \\ \hline \(I_{z}\), \(I_{zx}\), \(I_{zy}\) & Moments of inertia \\ \hline \(g_{x}\), \(g_{y}\), \(g_{z}\) & Coordinates of the center of mass \\ \hline \(b_{x}\), \(b_{y}\), \(b_{z}\) & Coordinates of the center of buoyancy \\ \hline \(m\) & Vehicle mass \\ \hline \(g\) & Acceleration of gravity \\ \hline \(V\) & Vehicle volume \\ \hline \(\rho\) & Water density \\ \hline \end{tabular}
\end{table}
Table 1: Variable definitions.  
yaw, and their respective velocities, so those values are used directly. A Kalman filter is used to estimate the attitude accelerations. Since \"true\" measurements are used as the inputs to the Kalman filter and to the RLS estimation, this paper does not attempt to assess the performance of the controller in the presence of noisy measurements.
## 4 Discussion
### Opportunities and Future Directions  
We quantified cue response rates by applying a static threshold value to acceleration magnitude data from the two arms. This activity detection technique provides a simple quantifiable measure of upper limb movement but, in isolation, does not allow an assessment of movement quality or accuracy. Adding a more complex activity recognition capability to our current system is plausible, as others have reported high accuracy in assessing goal-directed upper limb movement quality with activity recognition algorithms [62; 63]. Movement quality information has the potential to provide valuable feedback to users to further engage them in the activity program, especially if movement quality feedback is provided in real-time. In one exemplar study, [PERSON] et al. (2022) explored the effects of providing real-time feedback on hand and wrist movement \"counts\" to chronic stroke survivors using a wearable wristwatch/ring system compared to a control group of survivors that used the devices without feedback. Their system used a threshold method to quantify active movement similar to that used in the current study. The authors found a significant increase in hand counts among the group receiving real-time feedback and a non-significant increase in hand counts in the control group [28]. Stroke survivors who received real-time feedback with complimentary count goal setting were also found to have worn the device more hours per day on average compared to the control group [28]. Those results align with widely accepted views that feedback can positively influence behavior and performance in rehabilitation [10].  
Although real-time feedback or more advanced activity recognition features may be advantageous to user experience, drawbacks of these features on an affordable wearable system must be considered. That is, an assessment of movement quality typically requires higher sampling frequencies compared to activity detection alone [64], and the integration of real-time movement recognition into wearable systems likely will require greater processing power (c.f., [65]). Adding these system functions could decrease device wear-time (and therefore system usability) for any given battery capacity. Thus, advanced features must be carefully balanced with system power requirements, and they must demonstrate significant advantages in eliciting a desired behavioral response before adoption into a practical wearable system.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
###### Abstract  
The calculation of the main circuit parameters is the basic part of the engineering design for high voltage direct current (HVDC) transmission systems. Compared to the conventional line commutated converter (LCC), the application of the capacitor commutated converter (CCC) can reduce the probability of commutation failures and the shunt capacitor reactive compensation. This paper proposes a calculation method of main circuit parameters for the CCC-based HVDC system. Firstly, the topology of a CCC-HVDC transmission system is described. Secondly, based on the steady-state mathematical model of the CCC, the paper proposes the calculation method of the commutation capacitor to satisfy the system requirements, and the calculation formulas of the main circuit parameters are also given. Then the calculation procedure of the main circuit steady-state parameters is described in detail considering system parameters, control modes, calculation tolerances and operating conditions. Finally, a two-terminal \(\pm\)500 kV/3000 MW LCC-CCC HVDC transmission system is presented to verify the validity of the main circuit parameter calculation method. The proposed method has great significance for the AC filter design in practical engineering application.  
main circuit parameter calculation; capacitor commutated converter (CCC); line commutated converter (LCC); LCC-CCC HVDC; calculation procedure
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 5 Experimentation Including the Results and Discussion
### Retraining the Model  
As observed in Table 11 and Figure 6, as newer data with a higher proportion of anomalies is introduced, the model's accuracy, recall, and F1-score begin to decline. This degradation indicates that the existing model is becoming less effective at distinguishing between normal and anomalous patterns. To quantify this decline, we identified a threshold at which retraining becomes necessary and tracked performance metrics across multiple batches. When significant drift was detected, model retraining was triggered. Retraining the model is elaborated in Figure 4d. The model was retrained using updated data, that is, by expanding the training dataset with new data.  
With reference to Table 13, to evaluate batch 'N', the model would be trained on (Initial Training data + Batch1 data +...+ Batch N-1 data). This would update the model parameters and track performance improvements. To validate the performance, the newly trained model is tested against upcoming batches and compared with previous results. Performance validation ensures that the retraining enhances predictive accuracy.  
As per Table 12, since a drift was detected in batch 2, batches 1 and 2 in Table 13 do not reflect results of retraining. In Table 13, retraining was started at batch 3, and Table 13 presents the results of batches 1 and 2 and the rest of the batches after retraining. As can be observed in Table 13, overall, the accuracy improves across all batches after retraining; precision remains high, near 100%, as false positives are well controlled. Recall also improves, indicating that the retrained model is better at identifying positive cases. The  
Figure 6: F1-score and accuracy before retraining.  
F1-score improves, showing a better balance between precision and recall. True positives increase, which means that more anomalies are actually being caught. False negatives remain low, meaning fewer anomalies are missed. Figure 7 presents the trends in accuracy before and after training.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 8 Case Study
### Results
#### 8.2.4 Fatigue Life Prediction  
As shown in Table 14, the cumulative fatigue damage could be calculated as 0.202, then the pavement fatigue life which is the reciprocal of cumulative fatigue damage was calculated as 4.95, meaning the HMAC overlay might generate reflective cracking in nearly 5 years. Moreover, our in situ survey also showed that a part of the pavement surface cracked almost 4 years after overlaying. This indicates that the proposed method to predict service life of pavement agreed with practical conditions. Furthermore, the current research suggested that a stress absorbing layer should be set before HMAC overlaid between HMAC and concrete pavement.
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 1 Introduction
### Literature Review  
This work goes through the analysis of a simple transmission line (a one-dimensional electrical network) connecting a source of communication to a receiver, where undesirable signals attack the line and the communication is not working properly. Depending on the strength of the alien signal, the receiver and the source can be damaged. This is typically a problem of electromagnetic interference (EMI) caused by electromagnetic emissions coming from external/internal origins [1; 2]. Techniques to protect the system against these interferences are called electromagnetic compatibility methods, which are described in classical references on EMI/EMC [3; 4; 5; 6; 7], succinctly put in [8]. In this research, our analysis uses the transmission line model, known as the TLM method [8; 9; 10; 11; 12], which is a finite difference method based on space discretization that allows computational solutions based on matrix algebra techniques [13]. The TLM method was initially developed to solve one-dimensional problems, as in a transmission line propagation, and evolved to two [14] and three-dimensional models [15; 16] through complex 3D cells [17], which can solve EMI/EMC spatial problems [18] and other applications, as long as computers are becoming more powerful [19; 20; 21; 22; 23]. We apply the TLM concept to deal with a nonlinear filter known as \"varistor\" [24; 25], developing an algorithm to compute, through an iterative procedure, the dynamical analysis of the network under study [26]. The varistor comes to improve network communication performance. It behaves as a nonlinear filter [27; 28; 29; 30; 31; 32] connected to the electrical network to respond to the alien disturbances. Based on this simple and practical electrical problem, the paper points out suggestions to a generalized dynamical network model, considering complex systems frameworks [33; 34] and complex network measurements [35; 36; 37] as basic paradigms.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Controller Systems Using IT2 FLC and Neural Networks  
In [57], a bilateral teleoperation system was controlled through a T2 fuzzy wavelet neural network (T2 FWNN). This teleoperation system allows a human operator to send commands to a local master manipulator where it then drives a slave manipulator in a remote location. This is highly non-linear, as communication delays and corrupt data affect the data link. The main advantage of this controller is that it can handle unknown environmental interactions; it can handle uncertainties associated with information and data in the knowledge base; and it can remove most noise added to data transmitted through the communication channel. This controller was compared to the T1 FWNN controller and conventional FC, and it was more efficient.  
In [42], the feedback error learning of magnetic satellites was controlled using a T2 FWNN with elliptic MFs. The learning is based on the feedback error learning method, and the stability of the learning is proven, but also the stability of the overall system, to ensure robustness. The simulation of the proposed control scheme shows that the adaptive T2 FNN when it is used in parallel with a conventional PD controller can control the system effectively, and it outperforms conventional PD controller when it is used alone. It is also shown that the use of T2 FNN with elliptic Type-2 MFs added makes it possible to cope with uncertainties in terms of measurement noise more effectively.
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 4 Results and Discussion  
### Cyclic Behaviour  
Cyclic tests were performed until failure, as illustrated in Figure 5. In the research carried out in [24], it was found that 51 CrV4 steel exhibits cyclic softening behaviour. Figure 5 shows the hysteresis loops for the first cycle and the cycles corresponding to 50% and 90% of the total life. The consideration of the hysteresis loop for 50% of the total life is widely used for the development of fatigue resistance curves; however, the 51 CrV4 steel revealed in the analysis in [24] that for high strain amplitude conditions (higher strain amplitudes than 0.5%), the hysteresis loops do not stabilise completely, which is also proven by Figure 5, with a continuous cyclic softening and mean stress relaxation; however, the differences and variations are quite low. Thus, the analysis of the elasto-plastic transient given by the isotropic and kinematic cyclic laws for a life of 90% of the total life has been considered. Assuming 90% of the total life, it is still ensured that the hysteresis loops do not consider the final loops containing crack initiation and propagation phenomena.