esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental System and Loading Method  
The experimental system comprised a loading system, an acoustic emission system, and a data acquisition and monitoring system, as depicted in Figure 2.  
The MTS electronic universal experimental machine was employed in the loading system, offering a displacement loading speed ranging from 0.001 mm/min to 1000 mm/min, with a maximum load capacity of 100 kN. The experimental accuracy could achieve 0.2%, meeting the relevant requirements of the rock mechanics experiments. The acoustic emission equipment was composed of an acoustic emission instrument, acoustic emission sensors, a preamplifier, and signal lines. The acoustic emission instrument used was a Beijing Soft Island DS5-16B acoustic emission signal analyzer (Beijing Soft Island Technology Co., Ltd., Beijing, China), as shown in Figure 3a. The device has the advantages of a high sampling rate, complete data acquisition, long acquisition time, and easy data export. It can collect and store the acoustic emission waveform in real-time during the experiment. The acoustic emission sensor is shown in Figure 3b. The size of this type of acoustic emission sensor is small, and multiple sensors can be deployed in a limited space. The main advantage of the AE sensor is high sensitivity, which can detect small energy release events inside the material, and the collected acoustic emission signal is rich and comprehensive. The bandwidth is 1 KHz-3 MHz. The Hsu-Nielsen test was performed on the AE sensor to calibrate the sensitivity of the sensor before the formal start of the test. Because there is always ineffective noise interference in the experiment, the wavelet transform method was used to filter the AE signal.  
Figure 2: Experimental system.  
With a sampling frequency of 3 MHz and a threshold value set at 100 mv during the experiment, effective external noise shielding was ensured. Three acoustic emission sensors were uniformly arranged at places 3 cm away from each end-face of the specimen, and a total of 6 acoustic emission sensors were arranged for each specimen. The position diagram of the acoustic emission sensors is shown in Figure 4.  
When loading the rock-like specimen, the force control method was used to slowly load the specimen to 10 N, ensuring that the upper indenter of the press made contact with the upper surface of the specimen. Subsequently, displacement control was employed for further loading, with a predetermined loading speed set at 0.5 mm/min. The pressure loading of the experimental machine and acoustic emission monitoring were carried out at the same time. The system automatically collected and recorded the monitoring data such as the time, load, and acoustic emission information of the experiment until the specimen was damaged.
## 4 Recent Advances and Future Directions
### Real-Time Data Processing and Multi-Modal Learning  
The increasing demand for real-time insights has driven significant advancements in real-time data processing and multi-modal learning. Real-time data processing frameworks, such as Apache Kafka and Apache Flink, have evolved to handle high-throughput data streams with low latency, enabling ML models to make predictions and decisions on the fly. These advancements are critical in applications such as autonomous driving, financial trading, and fraud detection, where delays in processing can lead to significant consequences [333; 334; 335].  
Multi-modal learning, which involves the integration of various data types (e.g., text, images, audio, and video), has also seen considerable progress. Models that effectively combine information from different modalities can make more accurate and robust predictions. For example, in healthcare, multi-modal models can integrate medical images, genetic data, and clinical notes to provide more comprehensive diagnoses and treatment recommendations. In the entertainment industry, multi-modal learning is used to enhance user experiences by integrating data from video, audio, and user interactions to deliver personalized content [336; 337; 338; 339].  
The future of real-time data processing and multi-modal learning will likely involve further improvements in the efficiency and scalability of these systems. Additionally, as the amount of available data continues to grow, there will be a greater focus on developing models that can effectively learn from sparse or imbalanced multi-modal datasets. Advances in techniques such as transfer learning, domain adaptation, and meta-learning will play a crucial role in enabling these models to generalize across different domains and data types [340; 341; 342].
# Virtualized MME Design for IoT Support in 5G Systems
## 1 Introduction
We have compared our proposed schemes to two other schemes: (i) a baseline virtualized MME design, which does not apply any specific traffic treatment per class; (ii) an overdimensioned virtualized MME. For the cost analysis, we have considered a theoretical model to dimension the required resources, and the data center setup and billing model of the Amazon Elastic Compute Cloud (EC2). For the delay performance evaluation, we have simulated each scheme. The results show that our schemes provide much lower costs than the overdimensioned one. Furthermore, they provide similar delay performance for MBB and low latency M2M communications. The obtained delay satisfies the exigent requirements of MBB and low latency M2M communications. However, our proposed schemes apply a specific delay requirement per traffic class. This allows the saving of processing resources for delay-tolerant M2M traffic. The remainder of this paper is organized as follows. Section 2 gives an introduction to the main topics on which this work is based and poses the addressed problem. Section 3 introduces a detailed description of the system model and assumptions made. The adopted traffic models are explained in Section 4. In Section 5, we explain the considered schemes to study, including our proposed schemes. Section 6 presents the queue model used in the dimensioning of the virtualized MME. Section 7 analyzes the results. Finally, the conclusion is in Section 8.
## 2 Design and Simulation Setup  
### Materials Choice  
The proposed unit cell has a split ring resonator (SRR) and circular ring resonator (CRR) on top of dielectric material. The backside layer is fully blocked with a dielectric material. Tungsten serves as the metal, and silicon dioxide (SiO\({}_{2}\)) is chosen for the dielectric.  
The scattering condition, band structure, and optical characteristic of silicon dioxide (SiO\({}_{2}\)) and tungsten (W) are detailed in this article by [PERSON] [27; 28]. Tungsten is significantly more stable for applications involving a wide frequency band in the optical range, when compared to other commonly used metals in MA. The superior impedance matching characteristics that tungsten possesses in the optical range are the primary reason for selecting it as the material to be used in the resonator layer, which reduces transmission and reflection. The loss-free feature silicon dioxide (SiO\({}_{2}\)) is chosen for the dielectric layer. Tungsten shows flexible absororptivity inside the optical region, and resonance properties help to design impedance matches. Optical wavelengths are essentially lossless; therefore, they ignore absorption. As a result, this geometrical framework is crucial for entrapping the resonance wavelength. Metamaterials, such as SRR and CRR, use an organic or inorganic layer to modify absorption by modifying the resonator structure. The excitation coefficient and the refractive index of a typical sample of tungsten are 2.916877 and 3.63739, respectively, when determined at 557 nm; it is also known as the mid optical window band, and there are no excitation coefficients when viewed from the opposite side of silicon dioxide (SiO\({}_{2}\)); the refractive index is 1.45704 at 557 nm [29].
## 2 Materials and Methods
### Adsorption Methodology  
Batch adsorption experiments on the 4 AZW adsorbent were conducted with either MB or MG aqueous solutions. A stock solution of MB or MG (1000 mg/L) was prepared by dissolving an accurately weighed quantity (1.0 g) of solid dye in 1 L of deionized water; the solutions for adsorption tests were diluted from the stock solution to the desired concentration.  
One hundred milliliters of the dye solution at various concentrations were placed in 250 mL PE flasks with 0.1 g of the adsorbent and shaken (Labwit ZWY-304) at a speed of 200 rpm. The experiments were carried out at different pH (from 3 to 12) and temperature (from 25 to 50 \({}^{\circ}\)C) values. The initial pH of the solution was adjusted to the desired value by using either HCl or NaOH solutions (0.1 mol/L). Kinetics of adsorption was determined by analyzing the dye uptake at different time intervals. From the kinetic experiments, a time contact equal to 110 min was fixed for all following isotherm experiments in order to reach a steady-state or pseudo-equilibrium. Solid and solution were separated using a 0.22 \(\upmu\)m cellulose acetate membrane filter, then the concentration of MB or MG in the supernatant was determined by a double-beam UV/vis spectrophotometer (SPECORD 200 plus). Kinetics of adsorption was determined by analyzing the dye uptake at different time intervals.  
Each experiment was carried out in triplicate and all calculations were conducted with their average values; the maximum difference between the three values was less than 3% of the mean. The adsorbed amount of MB or MG at equilibrium was calculated by the straightforward equation:  
\[\mathrm{q_{e}}=\frac{(\mathrm{C_{0}-C_{e}}).\mathrm{V}}{\mathrm{m}} \tag{1}\]  
where \(\mathrm{q_{e}}\) (mg/g) is the equilibrium adsorption capacity of dye adsorbed per gram of 4 AZW, \(\mathrm{C_{0}}\) and \(\mathrm{C_{e}}\) are the initial and equilibrium concentrations (mg/L) of MB or MG, respectively, V (L) is the volume of the dye solution, and m (g) is the weight of 4 AZW. A linear calibration curve was prepared with MB (R\({}^{2}\) = 0.997) and GM (R\({}^{2}\) = 0.999) for UV/vis quantification.  
The adsorption yield (% removal from aqueous solution) of the dye is calculated as:  
\[\mathrm{Yield}\;(\%)=\frac{(\mathrm{C_{0}-C_{e}}).100}{\mathrm{C_{0}}} \tag{2}\]
## 6 Conclusions  
To study the vibration characteristics of compression ignition (CI) engines fueled with biodiesel, this paper explores the dynamic responses of cylinder liners using a finite element model which includes structural modal characteristics, nonlinearities of assembly constraints and time-varying exciting forces. The high similarity of continuous wavelet transform representations between model predictions and vibration measurements have verified the model accuracy. Moreover, the characteristics of vibration response at the cylinder liner for the engine running with biodiesel blends and its causal relationship with combustion behaviors have been obtained and verified with experiments, providing insight understating for vibration based combustion diagnostics. The key conclusions from this study can be summarized as follows:  
1. The dynamic responses of the cylinder liner to combustion shocks appear in a higher frequency band from 4 to 12 kHz, whereas those of the piston impacts are in a lower frequency range from 1 to 4 kHz. Furthermore, the predicted amplitude of local responses is at the order of 0.01 microns due to the combustion shocks, which may be negligible to affect lubrications. However, piston slaps can lead to deformation as high as 0.1 micros, being about 20% of roughness amplitude.
2. The variation trends of combustion shock-induced responses can well correspond to the combustion characteristics of biodiesel regarding to the peak value of the pressure rise rate. The frequency contents of in-cylinder combustion pressure fueled with biodiesel are less than the fossil diesel, and insufficient to cause a clearer modal responses.
3. The extended combustion duration of biodiesel can cause a relative increase in the kinetic energy of piston lateral movement, thereby leading to a rise of vibration intensity close to the exhaust top dead center (TDC).
4. Due to the compound effect between the combustion force and piston inertia force, the rise rate of the piston side-thrust force close to the combustion top dead center (TDC) shows a nonlinear decrease with the increase in speeds, thereby resulting in a nonlinear trend in the root mean square curve of liner local responses.  
**Acknowledgments:** The support of the Natural and Scientific foundation of China NO. 51375326, are gratefully acknowledged. Thanks are due to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], of Taiyuan University of Technology, for their help in modelling and testing.  
**Author Contributions:** All authors contributed to the research work. [PERSON] and [PERSON] established the finite element method-based model and designed the experiments. [PERSON] and [PERSON] analysed the simulation results and reviewed the research work. [PERSON] participated in the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 4 Traffic State Analysis and Assessment Using ETC Data  
Having established the technical foundations for ETC data processing and feature extraction, this section focuses on how these capabilities enable comprehensive traffic state analysis and assessment.  
Compared with traditional traffic state analysis methods, ETC data-based analysis approaches possess unique advantages, including extensive coverage, strong temporal continuity, and trackable vehicle identity, providing robust technical support for refined urban traffic management.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 7 Conclusions  
To meet the requirements of vehicle speed, latency, and communication overhead in the actual environment of vehicle networking, this paper modified the original PBFT consensus structure and designed an extensible double-layer PBFT consensus algorithm. In addition, a multi-weight subjective logic model CRMWSL for calculating reputation value is proposed, in which factors such as interaction frequency, event timeliness, and distance are taken into account to achieve accurate calculation of RSU node reputation value. Meanwhile, suitable nodes are elected into the committee to participate in consensus according to their reputation values, which further reduces communication overhead and improves blockchain scalability.  
Figure 8: The consensus latency of the three consensus algorithms.  
Figure 9: The throughput of the three consensus algorithms.  
The experimental results show that the proposed CRMWSL model has great advantages over TSL and MWSL in improving the detection rate of malicious nodes, and the proposed C-PBFT consensus algorithm is superior to PBFT and TLPBFT in terms of transaction latency, throughput, and communication overhead.  
In our future work, we will think about adding more weights to further increase the node reputation value's correctness and extending the double-layer consensus method to multi-layer research to further reduce consensus time and increase consensus performance. In order to provide more safe and effective services for the Internet of Vehicles, we will also aim to employ blockchain technology to solve network security issues in the Internet of Vehicles.  
Methodology, J.Z.; Formal analysis, J.Z.; Investigation, Z.D. and Y.L.; Resources, Y.F.; Data organization, M.H. and L.L.; Writing--original draft, J.Z.; Writing--review and editing, J.Z., Y.F. and Z.D.; Visualization, Y.F.; Supervision, Z.D.; Funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the National Foreign Expert Program of the Ministry of Science and Technology (Grant No.G2023041040L).  
Not applicable.  
All the data are included in the article.  
The authors declare no conflict of interest.
## 2 The Proposed Combined Method and Principles  
### The Proposed Combined Method  
The load sequence can be extracted using STL to identify a trend component, seasonal component, and residual component. Specifically, the trend component signifies the evolving pattern of the load, the seasonal component illustrates the cyclic variations in the load, and the residual component captures the stochastic and uncertain aspects of load fluctuations. Each component manifests distinctive characteristics of change. Employing a single model for predicting each component might hinder the ability to capture the unique attributes of the individual load components, consequently reducing the precision of load forecasting.  
The trend component exhibits gradual changes concealed within the dynamically shifting load time series, rendering them challenging to capture. For this reason, the network model needs to have long-term memory and the capacity to analyze and extract the inter-relations of information at distinct time junctures. LSTM has flexible memory and generalization capabilities, making it suitable for predicting relatively smooth trend components. Within the load sequence, there are fixed similar patterns and regularities, namely the periodic patterns of load changes. While LSTM can transmit information from different time nodes, capturing global features, it lacks perception of the local features of the load. On the other hand, the key feature of a CNN is local connectivity and weight sharing, having the ability to convey information from different time points. Given this characteristic, this paper applies a CNN to the prediction of the load's seasonal component. GPR is a non-parametric method able to capture the characteristics of sequences with complex nonlinear patterns and significant uncertainty, and it provides useful uncertainty quantification, making it suitable for predicting residual components with high randomness. In conclusion, in the proposed combined method, LSTM, a CNN, and GPR are used to predict the trend component, seasonal component, and residual component, respectively.  
The proposed combined method consists of three steps: load decomposition, component prediction, and component summation. The specific prediction method flowchart is shown in Figure 1. First, the load sequence is decomposed into three components using STL: the trend component, the seasonal component, and the residual component. Next, LSTM, CNN, and GPR models are constructed to forecast the trend, seasonal, and residual components, respectively. Finally, the predicted results of the three components are aggregated to obtain the final load forecast.
_applied_  
_sciences_  
_Article_  
Prediction of Pavement Maintenance Performance Using an Expert System  
[PERSON] 1,*(r), [PERSON] 2 and [PERSON] 1,*(r)  
1 Civil Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia  
2 Civil and Environmental Engineering Department, College of Engineering, University of Rhode Island, Kingston, RI 02881, USA; [EMAIL_ADDRESS]  
*Correspondence: [PERSON]DDRESS] (A.A.-M.); [EMAIL_ADDRESS] (A.H.A.-Q.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
**Abstract:** The pavement experiences deterioration due to traffic and environment, i.e., unsatisfactory riding quality and structural inadequacy, over time. Thus, predicting pavement performance over time is one of the key elements of any pavement maintenance management system (PMMS). It can be used as an efficient tool to program/schedule the maintenance applications and expenditures, and thus the necessary funds can be allocated. Using a combination of independent variables for any selected pavement section can generate section-wise condition assessment and prediction models. Moreover, these models can be used to select the most cost-effective maintenance alternative to be applied to that pavement section. The present study developed an expert system based on pavement performance models which combines the available maintenance data with the knowledge acquired from the experts of the General Administration of Operation and Maintenance in Riyadh, Saudi Arabia. Eight regression models were first developed for four maintenance and rehabilitation (M&R) strategies, i.e., no maintenance, routine maintenance, overlay, and reconstruction for low and high traffic. Then, a practical expert system was developed to aid pavement maintenance engineers in finding the most effective and efficient M&R strategies and suitable time for the application. The regression models revealed that the effect of routine maintenance and reconstruction is greater in low traffic than in high traffic, while the effect of overlay is greater in high traffic than in low traffic. Based on this initial system, another improved one can be developed using the machine learning technique.  
**Keywords:** pavement maintenance management system; expert system; pavement performance; cost-effective maintenance alternatives
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 6 Theoretical Performance Bounds with Simple Models
### Upper Limit of Mean User Interests  
We first evaluate the upper limit of mean user interests. Given that the users' interest is uniformly distributed, the total interest in the highest \(N\) content scores for each user is:  
\[S=X_{(F+1-N)}+X_{(F+2-N)}+\ldots+X_{(F)} \tag{31}\]  
where \(X_{(i)}\) represents the \(i\)-th order statistics. According to [40; 41; 42], the PDF of the \(i\)-th order statistics of the standard uniform distribution is:  
\[f_{i}(x)=\frac{F!}{(i-1)!(F-i)!}x^{i-1}(1-x)^{F-i},0\leq x\leq 1. \tag{32}\]  
That is, \(X_{(i)}\) obeys the beta distribution with parameters \(i\) and \(F+1-i\), where \(F\) is the total number of contents.  
Given that \(\mathbf{X}=(X_{1},X_{2},\ldots,X_{F})\) is a uniform distribution in \([a,a+h]\), where \(a\in\mathbf{R}\), \(h\in(0,\infty)\), then for \(i\in\{1,2,\ldots,F\}\), \(X_{(i)}\) obeys a beta distribution with left parameter \(i\), right parameter \(F-i+1\), position parameter \(a\) and scale parameter \(h\). In particular, we have [42]:  
\[\mathbb{E}(X_{(i)})=a\ +h\frac{i}{F+1} \tag{33}\]  
Figure 2: Comparison of the exact outage probability obtained by Monte Carlo simulations and the estimated outage probability calculated by Algorithm 3 (the file size distribution is subject to \(Lognorm(10,1)\), \(U=5\), \(F=500\), \(N=50\), \(K=256\)).  
\[\text{var}(X_{(i)})=h^{2}\frac{i(F-i+1)}{(F+1)^{2}(F+2)}. \tag{34}\]  
It follows that the mean of \(S\) is given by:  
\[\begin{split}\mathbb{E}(S;F;N)&=\mathbb{E}(X_{(F+1-N )}+X_{(F+2-N)}+\ldots+X_{(F)})\\ &=\mathbb{E}(X_{(F+1-N)})+\mathbb{E}(X_{(F+2-N)})+\ldots+ \mathbb{E}(X_{(F)})\\ &=(a+h\frac{F+1-N}{F+1})+\ldots+(a+h\frac{F}{F+1})\\ &=Na+\frac{h}{F+1}\frac{(F+1-N+F)F}{2}\\ &=Na+\frac{Nh(2F+1-N)}{2(F+1)}.\end{split} \tag{35}\]  
Here, \(\mathbb{E}(S;F;N)\) is the total interest shown on the recommended list of an individual user. If there are \(U\) users, the total interest becomes:  
\[I_{total}^{est}=U\cdot\mathbb{E}(S;F;N). \tag{36}\]