esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Fuzzy T-S Identification and Modeling
A method for Takagi-Sugeno fuzzy modeling using clustering algorithms to identify premise parameters is presented in [14]. The approach enhances generalization and approximation in dense input regions, achieving better control performance. The identification of nonlinear multivariable systems is further explored in [15], where the T-S fuzzy method based on backpropagation with gradient descent is used to model a quadcopter's dynamics. The method demonstrated high identification accuracy (>98%)and computational efficiency. Similarly, ref. [16] applies T-S fuzzy models to describe the infectious dynamics of HIV with high accuracy using Gaussian fuzzy membership functions. Leveraging type-2 fuzzy T-S (T2-ETS) systems, a new technique for identifying nonlinear systems is presented in [17], balancing model complexity and prediction accuracy effectively. Moreover, ref. [18] introduces a combined methodology for the identification and adjustment of a Q-function, concluding that the proposed initialization reduces convergence problems and enables parameter optimization. The review of T-S fuzzy models for predictive control applications in [19] highlights the precision and adaptability of these models in controlling nonlinear systems, such as heat exchangers. The authors of [20] developed a method for identifying fuzzy systems based on entropy, demonstrating higher precision and efficiency in modeling the nonlinear dynamics of small helicopters. Addressing the control of uncertain nonlinear systems, hybrid neuro-fuzzy systems are proposed to overcome the drawbacks of individual fuzzy logic and neural network approaches. References to these methods are found in works such as [21; 22; 23]. An efficient procedure for nonlinear system identification using Takagi-Sugeno neuro-fuzzy models is proposed in [24], achieving high accuracy and robustness. The study in [25] integrates fuzzy stochastic configuration networks with Takagi-Sugeno inference, significantly enhancing the inference capability for complex nonlinear systems. Additionally, ref. [26] introduces type-2 Takagi-Sugeno fuzzy neural networks, showing the superior handling of uncertainty and improved system modeling accuracy.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## 3 Results & Discussion
### Case 4: Water-Based Nanofluid in Straight Tube with Constant Wall Temperature Using SPH  
In this case water-based nanofluids are used at a constant wall temperature (T\({}_{\rm w}\) = 313.15 K) condition with an inlet fluid temperature of T\({}_{\rm o}\) = 293.15 K, at Re = 500. Figure 9 demonstrates the effect of the nanoparticle concentration on the T\({}_{\rm m}\) for constant wall temperature condition.  
It can be observed that the T\({}_{\rm m}\) decreases with increase in the concentration of nanoparticles due to the improved heat capacity (up to 23%). The T\({}_{\rm m}\) for a highest nanoparticle concentration (\(\phi_{b}\) = 10%) was lower by 10 K as compared to the water. The diminution of temperature reflects the improvement in the thermal properties of the resulting mixture caused by the Brownian motion. The effect of the different water based nanofluids was investigated using different nanoparticles viz Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), CuO, SiO\({}_{2}\) and ZnO, as shown in Figure 10a. It may be noted that the Al\({}_{2}\)O\({}_{3}\)-water nanofluid has a higher heat transfer rate than all other nanofluids. The enhancement in Nu for the Al\({}_{2}\)O\({}_{3}\)-water is 14% higher as compared to ZnO-water nanofluid. Figure 10b shows the overall effect of the different base fluids on Nu for constant temperature boundary condition. The Nu at Re = 950 enhanced by 16% for Al\({}_{2}\)O\({}_{3}\)-water nanofluid as compared to water, 10% for Al\({}_{2}\)O\({}_{3}\)-EG nanofluid as compared to ethylene  
Figure 8: Proposed correlation vs computed results and experiments of Nu in a straight tube.  
Figure 9: Effect of nanoparticle concentration in tube flow: axial development of wall and T\({}_{\rm m}\).  
glycol and 8% for Al\({}_{2}\)O\({}_{3}\)-turbine oil nanofluid as compared to turbine oil. The enhancement in the Nu is mainly due to the thermophysical properties of the nanoparticles.  
For the average Nu in a straight tube with constant wall temperature a new correlation (Equation (14)) is proposed by fitting the data obtained from the numerical simulations. The correlation covers a wide range of the nanoparticle concentration (0% \(\leq\)\(\phi_{b}\)\(\leq\) 10%) and Prandtl number (6.0-500) under the laminar flow condition (25 < Re < 1500). Figure 11 shows the comparison between the present computed Nu results, with the experimental results from [PERSON] et al. [22], [PERSON] et al. [50] and the computed data from [PERSON] et al. [21] with the present proposed correlation (Equation (14)). A good agreement was attained between the computed and previous experimental work with the predicted values of the Nu with a variation of \(\pm\)5%.  
\[Nu\ =\ 0.257 Re^{0.37}Pr^{0.36} \tag{14}\]
###### Abstract  
Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then,  
the essential features of high-dimensional datasets. This paper summarized the research of deep learning in the field of video applications over CVIS in recent years, compared and analyzed the existing research methods, and discussed the current challenges and prospects for the future of video applications over CVIS based on deep learning.  
This paper summarized the main research methods in the field of video applications over CVIS, and classified them based on the characteristics of their research methods, as shown in Figure 1.  
The rest of this paper is organized as follows. Section 2 analyzes the advantages and disadvantages of traditional methods for solving video application problems in vehicle-road coordination systems. Section 3 focuses on addressing the current challenges faced by video applications through deep learning. Section 4 expounds the solution of video application problems in complex CVIS through deep reinforcement learning. Section 5 presents the datasets and model performance evaluation metrics. Section 6 discusses potential future trends in the field.
# System-Level Statistical Eye Diagram for Signal Integrity
## 2 Statistical Eye Diagram
### Equalizer
Thus, the voltage range right after the equalization becomes narrow. Therefore, the pre- and de-emphasis have a higher probability area after/before the center. As mentioned earlier, the difference between the pre-emphasis equalizer and the CTLE is  
Figure 6: Equalized SBRs depend on the equalizer. (**a**) The SBR is the channel response for the input bits of \(01000\cdots\). (**b**) The DFE mitigates the inter-symbol interference (ISI) based on the previous bits. Thus, the voltage level with a length of UI is attenuated after the single-bit pulse. (**c,d**) The pre-/de-emphasis also equalizes the ISI noise in the time domain. The pre-emphasis boosts the high frequencies; thus, the peak of the single-bit pulse is amplified. Likewise, the de-emphasis attenuates the high frequencies after the single-bit pulse. Therefore, the dip after the single-bit pulse is amplified by the de-emphasis. In other words, the emphasis amplifies the high-frequency signals locally in the time domain. (**e**) The CTLE mitigates the low frequencies or amplifies the high-frequency components in the frequency domain. The high-frequency signals over the whole pulse response are amplified by the CTLE [32]. the range of equalization. Therefore, the statistical eye diagram for the CTLE has a higher level for the ONE as a result of the equalization.
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Model Optimization Settings  
The BVI-DVC [40] dataset, comprising 800 videos with resolutions from 270p to 2160p, was utilized for training a model. This diverse set of training data was compressed under the JVET NNVC CTC using the RA and LD configurations. The videos, initially in MP4 format, were converted to YUV format with chroma sampling 4:2:0 and a bit depth of 10. For ease of processing, 10 frames were extracted from each video, yielding a total of 8000 frames for training. Given that the proposed network cannot manage different input sizes, the chroma channels were up-sampled by a factor of two to match the Luma channel's spatial resolution. Finally, a random patch of size 240 \(\times\) 240 was chosen from each frame to serve as the network input.  
Five separate models were trained using the same network architecture, but with different QP values, specifically 22, 27, 32, 37, and 42, as per the JVET NNVC CTC guidelines. These models were then evaluated for different base QP values, with the same model generation strategy applied to the RA and LD configurations. Each CNN model underwent training for 200 epochs, utilizing the Adam optimizer with a learning rate of \(10^{-4}\). The hyper-parameters \(\beta_{1}\) = 0.9 and \(\beta_{2}\) = 0.999 were employed to calculate the gradient averages during the learning process.  
The following Equation (5) illustrates the models developed for different QP ranges.  
\[CNN\ Models=\left\{\begin{array}{ll}M_{\mathrm{QP}=22},&if\ \ \mathrm{QP}<24.5\\ M_{\mathrm{QP}=27},&if\ \ 24.5\ \leq\mathrm{QP}<29.5\\ M_{\mathrm{QP}=32},&if\ \ 29.5\ \leq\mathrm{QP}<34.5\\ M_{\mathrm{QP}=37},&if\ \ 34.5\ \leq\ \mathrm{QP}<39.5\\ M_{\mathrm{QP}=42},&if\ \ \mathrm{QP}\geq 39.5\end{array}\right. \tag{5}\]  
where \(M_{\mathrm{QP}}\) denotes the model associated with each QP values (i.e., 22, 27, 32, 37, 42). According to the range of base QP values, each model is selected.  
The L\({}_{2}\) loss function, also referred to as the mean squared error (MSE) loss, is a widely used loss function in machine learning. It calculates the average of the squared differences between the predicted outcomes and the actual values, providing a measure of the model accuracy.  
The model utilizes the L\({}_{2}\) loss function, specified by Equation (6), to measure its performance.  
\[L_{2}\ or\ MSE=\sum_{j=1}^{k}\bigl{(}Y_{j}-\mathbb{Y}_{j}\bigr{)}^{2} \tag{6}\]  
where \(Y_{j}\) denotes the original pixel value and \(\mathbb{Y}_{j}\) represents the corresponding enhanced pixel value resulting from the application of the CNN filter.
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 1 Introduction
Although previous researchers made some contributions to the combination of uniform temperature and earthquake loads, they regarded thermal load and seismic excitation as two separate actions. As a matter of fact, temperature action already exists in structure when an earthquake happens. Meanwhile, the seismic responses of these bridges are complicated, and some components may enter into nonlinear states during strong seismic excitation. It may not be reasonable to linearly add the two actions without considering the nonlinear properties of structures. Therefore, it is necessary to analyze the seismic responses of cable-stayed bridges based on the existing thermal load, and nonlinearity of bridge components should be considered in this process. A three-dimensional finite element model of the prototype bridge using OpenSees program is established. Then, three ground motion records based on the design response spectrum of the site of the prototype bridge are selected from the PEER Ground Motion Database. Nonlinear time history analysis of the prototype bridge with varying thermal loads and different deck to pylon connections are conducted to obtain the responses of the critical elements of the bridge. Finally, comparisons are made between the responses of the bridge with and without accounting for uniform temperature to evaluate the effects of the uniform temperature on the seismic responses of the bridge.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### PLS Schemes for IoT: State of the Art
#### 2.2.5 Physical Layer Encryption  
Instead of communicating a secret message straight away using the aforementioned PLS techniques, [PERSON] and [PERSON] can also opt to exploit the noisy channel to generate a secret key and use the key as a one-time pad to ensure information-theoretic security. From a practical perspective, the design of physical-layer encryption schemes from correlated channel observations turns out to be a simpler problem than the construction of codes for the wiretap channel. The procedure of physical layer key generation mainly includes four steps: (1) Channel probing: the communicating users measure the channel using a public pilot; (2) Parameter quantization: the analog to digital (binary) conversion; (3) Information reconciliation: error correction via public discussion; and (4) Privacy amplification: removing information leakage [56]. Ref. [57] presented a comprehensive review of the physical layerencryption techniques, highlighting the major technical challenges and solutions. Ref. [58] investigated the secret key generation issue for the ultra-wide band (UWB) channels, where the pulse response of the legitimate channel is used as the random source to distill the keys. Ref. [59] developed a key agreement strategy based on LLR thresholding. Ref. [60] utilized the received signal strength as the common randomness for the legitimate users, which enjoys a low implementation complexity. In addition to the above \"deterministic\" encryption techniques where the one-to-one mapping is required between the plain text and the cipher text, there is also another encryption method called probabilistic ciphering. As was described in [61], the sensor observations are randomly mapped to a set of discrete quantization levels, with the corresponding mapping probabilities only known to the LFC but unknown the EFC. By optimizing the probability distribution, a high error floor is created for the signal detection at the EFC, thereby guaranteeing transmission security. [PERSON]. [62] applied the idea of probabilistic ciphering to distributed estimation, which significantly improves the system security performance.
## 2 Stress Analysis Method of the Arch Ring Section
### The Arch Ring Section Stress Optimization Method
#### 2.2.1 Ansys-Based Objective Optimization Algorithm Flow  
In Ansys, there are two types of objective optimization algorithms: zero-order optimization and first-order optimization. The zero-order optimization method is based on the idea of \"limit approximation\", in which the accuracy of the calculation results is limited. The first-order optimization method is based on solving the minimum value to achieve the optimization procedure with high computational accuracy [22]. Ansys' first-order optimization function module is applied to study the joint effect of temporary prestressing and buckle force. The main procedure is as follows.  
1. Determining parameters such as objective function, design variables, and state variables.
2. Establishing a finite element parametric model to load and solve.
3. Entering the Design Opt first-order optimization module, setting the optimization initial sequence, specifying the loop control parameters, and invoking the Batch batch start mode.
4. Optimization iterative analysis and calculation: determining reasonable iterative convergence conditions, and entering the post-processing module to view the results after the program converges.  
According to the optimization target and the actual construction situation, the parameters are selected as follows.  
Objective function: To control the peak tensile stress in the cross-section of the arch ring sections during construction, the maximum tensile stress at the top and bottom flanges of the cross-section of each section of the main arch ring during construction is now taken as the objective function: \(\min/(x)\).  
Design variables: the buckle cable force and the quantity of prestressing are selected as design variables, and the cable force and prestressing are optimally solved based on the influence matrix, where the quantity of prestressing is expressed in the form of area function.  
Constraints: the design value of tensile strength of the main arch ring material is used to customize the maximum tensile stress constraint index of the top and bottom flanges during the construction of the main arch ring as constraint 1: \(\{\sigma_{max}\ \leq\ \{\sigma_{0}\}\}\); the buckle cable safety factor of 2.5 is used as constraint 2: \(K_{Ti}\geq\) 2.5; the buckle tower offset of 40 mm is used as constraint 3.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 2 Related Work  
Sun glare detection approaches can be broadly categorized into three major families: hardware-based techniques, traditional image processing-based methods, and deep learning-based architectures. Figure 1 illustrates the taxonomy of sun glare detection approaches.
## 4 Results
### Analysis Results
#### 4.3.3 Analysis of Redundancy Ratio  
According to the statistical calculation results based on the DEA-SBM model, DEA is effective for all seventeen sample sites. However, the input-output redundancy ratio varies, necessitating separate analysis for each. The calculation results from MaxDEA Pro 8.0 software show that the Crste of low-efficiency station areas is low due to the excessive investment and insufficient output of commercial service facilities. To enhance spatial efficiency in these station areas, it is essential to reduce wasteful investments in economic, social, and spatial efficiency. In addition, a reasonable resource input allocation structure should be established, and the efficient allocation of resources should be realized.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 6 Conclusions
As a result, there is no need to perform the de-noising of the waveform prior to the calculation of AE entropy. AE entropy provided the same information as the traditional AE features in our laboratory-based experiments. However, before it can be successfully implemented to monitor the primary wall of LNG tanks, further theoretical and experimental work is required. Firstly, the nature of noise in the primary wall during service operation needs to be understood so that an effective filtration of the AE data can be carried out. Secondly, the performance of AE entropy in a noisier environment needs to be assessed. Finally, to avoid the effect of attenuation on the AE entropy there has to be an optimum network of sensors deployed on the primary wall to effectively capture elastic waves from any part of the tank where damage is occurring. This can only be achieved by having a clear understanding of the attenuation of elastic waves in the primary wall. Conceptualization, F.T.S. and T.P.S. ; methodology, F.T.S. ; software, G.E. ; validation, F.T.S. and G.E. ; formal analysis, F.T.S. ; investigation, F.T.S. and G.E. ; resources, T.P.S. ; data curation, F.T.S. ; writing--original draft preparation, F.T.S. ; writing--editing, T.P.S. and G.E. ; visualization, T.P.S. ; supervision, T.P.S. and G.E. ; project administration, F.T.S. ; funding acquisition, G.E. This work was funded in part by Lloyd's Register Foundation, in part by London South Bank University, in part by TWI Ltd, and in part by National Structural Integrity Research Centre. The authors declare no conflict of interest.
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 6 Conclusions  
In this paper, we proposed and evaluated novel ADT-enhanced MIMO-VLC systems, where the ADT was designed to consist of one center LED and multiple inclined side LEDs. The LOS channel gain using ADT was calculated, and the average achievable rate of the MIMO-VLC system applying ADTs was further derived. Our simulation results verified that the average achievable rate of the ADT-enhanced MIMO-VLC system can be maximized by selecting the optimal combination of the inclination angle of the side LEDs and the spacing between adjacent ADTs.  
We also demonstrated that the average achievable rate of the ADT-enhanced MIMO-VLC system can be further enhanced by equipping the ADT with more LEDs under the conditions of increased complexity and cost. Therefore, it is promising to apply optimized ADTs for substantial performance enhancement of high-speed MIMO-VLC systems.  
Conceptualization, B.Q. and C.C.; Formal analysis, W.W., M.L., Y.Z. and C.C.; Funding acquisition, C.C.; Investigation, B.Q.; Methodology, Y.Z.; Project administration, C.C.; Software, Y.Z.; Supervision, W.W., M.L. and C.C.; Writing--original draft, B.Q.; Writing--review and editing, W.W. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61901065), the Natural Science Foundation of Chongqing (cstc2021 jcyj-msxmX0480 and cstc2021 jcyj-msxmX0458), and the Fundamental Research Funds for the Central Universities (2021 CDJQY-013).  
Not applicable.  
The authors declare no conflict of interest.
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 3 Smart Chatter Suppression in Wheel Reprofiling
### Process Damping Maximization  
For the case described in Figure 3, \(k=207\). This implies that the working conditions were in Zone A, or the process damping zone in the stability lobe diagram (Figure 5). The effectiveness of process damping grows as the ratio between the tooth passing frequency and the chatter frequency decreases, therefore, the lower the spindle speed is set, the higher the stability boundary is. The physical effect for this stability increase is the rubbing phenomenon between the flank face and the wavy surface (see Figure 6).  
In order to take advantage of the process damping effect, two practical alternatives are feasible. On one hand, the spindle speed can be reduced to increase the number of vibration waves per revolution, and thus increase process damping. However, the application of this technique can reduce productivity and can be limited by machinability constraints. On the other hand, worn tools can be used to avoid chatter, as the process damping increases with flank wear. However, worn tools increase the static cutting loads, and excessive wear may lead to tool breakage and poor surface finish. Special edge  
Figure 5: Relative location on the stability lobe diagram.  
Figure 6: Process damping due to rubbing of the flank face with the wavy surface left [10; 18].  
geometries can be also used to avoid sharp edges and reproduce the performance of a worn tool. Several authors [18; 30] have studied the effect of cutting edge geometry on process damping.  
Process damping was proven to be a valid solution for chatter avoidance in the studied portal lathe case. The applied solution consisted of reducing the cutting speed and thus the revolutions per minute of the wheelset. Figure 7 shows how reducing the cutting speed from 60 m/min to 40 m/min made the chatter vibration disappear, leading to a stable cutting condition.  
This technique was effective and easy to implement, but it reduced the productivity as the spindle speed was also decreased. Feed per tooth could be increased at the same time to preserve productivity, but there was a risk of side effects such as early tool wear or breakage.