esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method
## 1 Introduction
[PERSON] [17] proposed an RRP scheme incorporating random perturbations to address the state estimation problem in dynamic networks under random noise conditions. In addition, the research shows that the event-triggered strategy and non-fragile controller design can realize the dissipative synchronization problem of MJNNs with inertial terms [18; 19]. Although numerous studies on discrete-time NN state estimation exist in the literatures [20; 21; 22; 23], [PERSON] proposed a state estimation approach for MJNNs with mixed delays to guarantee that the resulting error system exhibits extended stochastic dissipativity [24]. The study of using RRP to deal with the problem of discrete-time s-MJNN observers has not been fully developed, which is one of the important motives for our research. In recent years, many researchers have achieved significant advancements in the field of fault diagnosis. Example, the extended state approach is utilized to address the issue of fault detection in discrete systems, while the singular system method is used to solve the H\(\infty\) fault detection problem of mixed models [25]. Other researchers have proposed fault detection methods for Markov jump systems and designed observers capable of simultaneously estimating actuator faults, sensor faults, and system states [26; 27; 28]. Additionally, new observer design methods have been introduced for switched fuzzy stochastic systems with sensor and actuator faults, enabling the simultaneous estimation of system states and faults [29; 30]. Although some scholars have investigated communication protocols in discrete-time s-MJNNs and others have focused on fault estimation in Markov jump systems, no existing research has addressed fault estimation in discrete-time s-MJNNs while simultaneously considering both communication constraints and delays using a novel extended state method. Therefore, it is necessary to investigate fault estimation in s-MJNNs based on the extended state method, which carries significant theoretical and practical implications. Based on the above background, our research focuses on the fault estimation problem for discrete-time s-MJNNs with time delays. To achieve an \"average\" distribution of data transmission among sensors, a scheduling scheme based on the RRP is studied in this paper. The main innovations of this paper are as follows:  
1. A novel state augmentation approach is proposed, incorporating sensor faults into the system as augmented state variables. 2. To avoid data congestion and save communication resources, the RRP scheme is adopted to schedule sensor data transmission. 3. A discrete-time extended state observer is designed to simultaneously estimate system states, actuator faults, and sensor faults under time-varying delay conditions. 4. Triple summation terms are introduced into the Lyapunov function, and their forward differences are bounded using the reciprocal convexity method, ensuring that the error system exhibits enhanced stochastic dissipativity.
## 7 Conclusions  
The fast growth of telecommunications and the NGN networks based on a hybrid/multi-layer architecture make the study of satellite platforms of fundamental importance for the development of a network architecture that is able to satisfy the requests of new users' applications. This work provides, after a brief description of the propagation problems of LMS communication links and the statistical resolutions, an overview on LMS channel models provided in the scientific literature. Adequate knowledge of propagation impairments and channel models is required for designing and evaluating the performance of the advanced technologies used to establish reliable communication links in LMS communication systems. The main objective is to highlight the effects and the related propagation patterns that must be considered for LMS communication links in order to accurately estimate the propagation issues. The performance of LMS communication systems depends on several factors, including operating frequency, elevation angles, geographic location, climate, etc. Thus, the main task of this paper is to collect the main literature works about satellite channel models, starting from the 1980s and progressing up to the present day, providing a comprehensive review about the stochastic model approach to the channel design. Different methodologies, such as more realistic physical-statistical channel models, may be used to determine the impact of these elements on LMS communications, but they need extensive and complex simulations, whereas stochastic approaches are easy, more desirable, and need less computing work. Furthermore, due to the different natures of propagation environments, a Markov-based approach can represent an important tool for modeling satellite channel characteristics, guaranteeing the QoS and performance required by users. As shown in the paper, different Markov models using a different number of states have been proposed in order to try to capture, in a better way, the propagation characteristics of satellite links. Through this review, it was discovered that the most-used Markov model is that which proposes three different states, as is also suggested by the ITU recommendation in [58], where the states represent deep-shadow, intermediate-shadow, and good-state link conditions.  
Conceptualization, F.D.R. and M.T.; methodology, F.D.R.; formal analysis, M.T.; writing--review and editing, F.D.R. and M.T., supervision, F.D.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
###### Abstract  
Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.  
manufactured sand concrete; adaptive neuro fuzzy inference system; compressive strength; teaching-learning-based optimization; mixture proportion; principal component analysis +
Footnote †: journal: Applied Sciences
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 2 Materials and Methods
### Mitochondrial Respiration and Energy Metabolism Evaluation  
To evaluate changes in energy metabolism, oxygen consumption using the Oxygen-2K system (Oroboros Instruments, Innsbruck, Austria) was measured. Trypsinized cells were resuspended in culture medium and introduced into the chamber of the system for analysis. Following the sealing of the chambers, baseline routine respiration was recorded. To assess non-ATP-linked respiration, ATP synthase activity was inhibited by the addition of oligomycin (2.5 uM). Subsequently, to evaluate the non-phosphorylating capacity of the electron transport system (ETS), mitochondria were uncoupled using a stepwise titration of carbonyl cyanide 4- (trifluoromethyloromethoxy) phenylhydrazone (FCCP), by administering increments of 0.5 uM. The maximum respiratory capacity before sequentially adding rotenone (0.5 uM) and antimycin A (2.5 uM) was determined. All reagents used were obtained from Sigma Aldrich (St Louis, MO, USA). Therefore, complexes I and III were inhibited, allowing to measure residual oxygen consumption. Prior to these measurements, cells were pre-incubated with 10 uM and 50 uM CoQ10 for 24 h.
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 2 Materials and Methods
### Data Processing and Analysis  
Raw marker trajectories from the Vicon system were labelled, gap-filled, and low-pass filtered at 10 Hz using a fourth-order zero-lag Butterworth filter in Nexus 2.15 (Vicon Motion Systems Ltd., Oxford, UK) as per established protocols [19]. Joint angles were constructed using Bodybuilder code, with joint angles adjusted as needed to match the Xsens' joint angle convention. Flexion-extension, abduction-adduction, and external-internal shoulder and hip joint angles; flexion-extension and external-internal elbow and ankle; flexion-extension and ulnar-radial deviation wrist; and flexion-extension knee joint angles were then exported to MATLAB R2022b (MathWorks Inc., Nantucket, MA, USA), where further analysis was conducted. To ensure comparability, the Vicon data were down-sampled from 240 Hz to 60 Hz by retaining every fourth frame, matching the Xsens sampling rate. Xsens data underwent proprietary fusion of accelerometer, gyroscope, and magnetometer signals via the Xsens Kalman Filter [8], while the LXsolver algorithm--automatically executed by the system--applied biomechanical constraints during reconstruction to reduce soft tissue artifact and improve joint estimation. The calculated Euler angles in degrees followed the YXZ (+/ -) cardan sequence, which corresponded to the medio-lateral, antero-posterior, and axial rotations, respectively.  
The start of the movement was defined at the heel strike of the step preceding the layup jump (0% of the task) and the end at the instant of ground contact on landing following ball release (100% of the task) (Figure 2B). Event detection was performed manually through identifying the local minima and maxima around key foot contacts within the linear acceleration profiles of the foot and toe segments [20]. Whole-body COM location of the VICON data was computed using [PERSON]'s [21] anthropometric conventions to obtain 3D coordinates of the COM pathway. COM velocity, which was used to calculate resultant jump take-off angle, was obtained from the numerical differentiation of the COM position data. Jump height was calculated as the difference between COM position in the vertical direction (z) at the maximum height achieved during the jump and the COM vertical position during standing. Joint angle data and COM path data were interpolated and time-normalised to 101 points to represent 0-100% of the movement. To eliminate the inherent system offset between the joint angle COM trajectory data captured by the Vicon and Xsens systems, an adjustment process was implemented based on the method of [22]. The difference between the two traces was calculated, and the average difference was determined across all time points. The Vicon data were then adjusted by subtracting the average difference. Finally, RMSD was computed between the original Xsens data and the adjusted Vicon data to evaluate alignment accuracy.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results  
### Mission Profile  
For the vehicle mission to succeed, the mission profile needs to be established. As such, an orbital node is defined and the flight path is determined. The orbital node height is 500 km above the Martian surface. This node can either be a Mars planetary space station, another big transport vehicle coming from Earth, or another. The altitude of the node hasbeen arbitrarily selected as a high estimate of where such space station or big transport vehicle would be placed around Mars.  
Paths for ascent, rendezvous and reentry are simulated. This provides a better understanding of the loads experienced by the vehicle during these phases of the flight, the amount of fuel needed, as well as the control and heating of the vehicle during the mission. Figure 1 shows the mission profile of Charon as it was determined through the analyses.  
The maximum mission duration was chosen to be four days, keeping power and life support requirements in mind. Due to the favourable location, it is assumed that the Mars base will be located at Deuteronilus Mensae, lying at an altitude en latitude of 42.5\({}^{\circ}\) and 25.5\({}^{\circ}\), respectively. The inclination of the node is defined to be 42.5\({}^{\circ}\) accordingly. After the ascent phase, the vehicle reaches its phasing orbit at roughly 42.5\({}^{\circ}\) inclination and 100 km higher than the node orbit. Following this, it will start far range rendezvous by undergoing transfer to the node after which close range rendezvous is initiated and docking is completed. When returning, the vehicle performs an insertion burn with the main engines for controlled entry into the Martian atmosphere. This is followed by atmospheric deceleration and propulsive landing. The separate stages of the mission will be detailed further next.
### Working Principle of the TRUM  
Figure 1 shows the structure of the PMR40 motor. The stator, together with the ceramic piece pasted to its surface, are bolted to the base; the rotor has a vibration-damping sheet on one side and a friction plate on the other side; one end of the shaft is bolted to the rotor base and the other end is designed with a thread connected to the base through a bearing. The lock nut is tightly connected to the shaft end thread to press the base, the stator and the rotor, thereby generating pre-pressure between the stator and the rotor. The vibration on the surface of the stator, which is excited by the piezoelectric ceramic, is converted into rotation of the rotor by the frictional force between the stator and rotor contact interface.  
In our previous research, a high overload experiment of 10,000 g impact acceleration was performed on the PMR40 motor. The results showed that the rotor and stator were deformed, the ceramic was broken and the motor failed to work. These results can only demonstrate the state of the motor after  
Figure 1: Structure of the PMR40 ultrasonic motor.  
impact, but cannot explain the mechanism of the influence of the high-impact process on the motor. Meanwhile, the high overload test is very expensive and difficult to realize. Therefore, it is more economical and effective to establish the impact dynamics model to guide the design of the motor structure of high-overload resistance.  
To reduce the maximum stress or deformation of the stator and rotor during the high-impact process so as to reduce the contact surface variation, the weak rotor structure is improved based on the structure of the PMR40. A disc spring is designed to modify the rotor and four new TRUMs are designed. The impact dynamics simulation model of the TRUM is established to analyze dynamic response of each key part and the results are finally verified by experiments.
## 5 Energy Efficiency and Sustainability
### Energy Efficiency in the Context of RIS and HAP Integration
#### 5.1.5 Operational Longevity and Maintenance  
RIS's passive nature and lack of moving parts also mean less wear and tear, leading to longer operational life and reduced maintenance needs. This aspect indirectly contributes to energy efficiency by reducing the energy and resources required for manufacturing, deploying, and maintaining the HAPs [72; 126].
###### Abstract  
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) combined EMD_CS_ST denoising method is proposed. First, through EMD decomposition of the noise signal, a series of Intrinsic Mode Functions (IMF) from high frequency to low frequency are obtained. By calculating the correlation coefficient between each IMF and the original signal, the boundary component between the signal and the noise was identified, and the boundary component and its previous components were sparsely processed in the discrete wavelet transform domain to obtain the original sparse coefficient \(\theta\). Second, \(\theta\) is filtered by ST to get the reconstruction coefficient \(\theta_{new}\) after denoising. Then, CS was used to recover and reconstruct \(\theta_{new}\) to get the denoised IMF\({}_{new}\) component and then recombined with the remaining IMF components to get the signal after denoising. In the simulation experiment, the denoising process of EMD_CS_ST method is introduced in detail, and the denoising ability of EMD_CS_ST, DWT, EEMD, and VMD_DWT under 10 different noise levels is discussed. The signal-to-noise ratio, signal standard deviation, correlation coefficient, waveform diagram, and spectrogram before and after denoising are compared and analyzed. Moreover, the signals obtained from the underground cavern of the Shuangjiangkou hydropower station were denoised by the EMD_CS_ST method, and the signals before and after denoising were analyzed by time-frequency spectrum. These results show that the proposed method has better denoising ability.  
microseismic signal denoising; empirical mode decomposition; compressed sensing; soft thresholding +
Footnote †: journal: Applied Signal Processing
## 2 Related Work  
### Vehicle and Traffic Element Identification  
In autonomous driving and advanced driver assistance systems (ADAS), the recognition of vehicles and traffic elements is a pivotal task. Recently, numerous research endeavors have focused on crafting efficient object detection and recognition algorithms, prominently including YOLO (You Only Look Once) [23], SSD (Single-Shot MultiBox Detector) [24], and Faster R-CNN (Faster Region-based Convolutional Neural Network) [25]. YOLO, a deep learning-powered object detection approach, operates by segmenting the input image into S \(\times\) S grids, where each grid cell forecasts B bounding boxes along with their confidence scores and C conditional class probabilities. The object detection is facilitated through end-to-end regression optimization.  
YOLO distinguishes itself in terms of speed, accuracy, and computational efficiency. It achieves a remarkable detection speed of 45-60 frames per second (FPS) on standard GPUs, coupled with a mean average precision (Map) of 57.9%, surpassing Faster R-CNN's 42.7% Map. Furthermore, compared to SSD, YOLO offers faster processing rates (30-40 FPS for SSD) and a higher Map (46.5% for SSD). Notably, YOLO boasts a compact model size and minimal computational resource demands, rendering it an ideal candidate for deployment on embedded systems and mobile devices.  
Given our reliance solely on monocular camera data and the imperative to drastically curtail computational overhead while maintaining high recognition accuracy, we opted for YOLO as the core object detection and recognition algorithm for our real-time, efficient vehicle and traffic element recognition framework in the context of autonomous driving and ADAS. Inspired by recent advances in vehicle tracking methods [26], we chose FairMOT-MCVT as our core algorithm for real-time efficient vehicle tracking for applications in autonomous driving and ADAS.
## 5 Results and Discussion
### Convergence Time  
The convergence time for all routing schemes is depicted in Figure 9. It can be seen from the figure that all the schemes show similar results when the node speed is up to 30 m/s. As the node speed increases to 40 m/s, the convergence time of the conventional MP-OLSR and MP-OLSRv2 routing schemes substantially increases. This because other routing schemes select the longer path from source to destination nodes based on the link cost function and multiple disjoint metrics, while the MRLAM chooses the shortest path with more stable nodes and better links which reduces data packet transmission delay. In addition, the MRLAM routing scheme utilizes the mobility and link quality status of the mobile nodes, which leads to minimizing the frequent change of network topology. Meanwhile, it uses the Q-learning algorithm which quickly updates the routing information of the network with any changes occurring in the network, and this reduces the convergence time of the network. In the MRLAM routing scheme, fewer intermediate nodes need to converge, thus the load on any given node or communication link is minimized. Therefore, it reduces the calculating costs of intermediate nodes of the paths and improves communication efficiency. Overall, at the node speed of 60 m/s, the proposed MRLAM routing scheme recorded the lowest convergence time of 16.49% and 11.34% in comparison to the MP-OLSR and the MP-OLSRv2 routing schemes, respectively.  
Figure 8: Node Energy Cost with different speed of nodes.
## 4 Topology Control
### Re-Deployment
#### 4.4.2 Steiner Tree  
Given a weighted undirected graph and a subset of terminal nodes, finding a minimum cost tree spanning terminals is called the Steiner tree problem, which is NP-hard. The difference between the Steiner tree problem and the MST problem is that the Steiner tree allows one to construct or to select intermediate connection points to reduce the cost of the tree. Those intermediate nodes are the optimal new nodes that the network needs to maintain the connectivity and low cost. For now, the best algorithm has \(1.39-approximation\) for a weighted Steiner tree [101]. MST can be used to solve the Steiner tree, but it is not necessary for it to be an optimal solution. Placing the minimum number of nodes for maintaining network connectivity, such that the transmission range of each sensor does not exceed a threshold, is the minimum number of Steiner points (SMT-MSP) [102], which is NP-Hard, as well. Many node placement optimization problems can be reduced to an SMT-MSP problem, e.g., [44; 45; 97; 103; 104].