esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 1 Introduction
Considering that the multi-scale sample entropy (MSE) [30; 31; 32] can estimate the complexity of time series, accurately measuring the complexity of different modal functions with strong noise resistance ability and excellent consistency, and at the same time, a stable entropy value can be obtained by using fewer data segments. Therefore, the MSE is used as the basis for eliminating the noise component, but the accuracy of the MSE is related to the scale factor. So, a method is proposed to determine the best scale factor in the MSE by the simulation signal with different signal to noise ratio (SNR), and the best factor is placed in the multi-scale sample entropy to calculate the MSE value of random noise, simulation signal, and vibration signal. This is firstly done by calculating the multiscale sample entropy value of 10,000 sets of random noise, extracting the minimum value and using it as the threshold of the noise component in CEEMD; then, the effective IMFs component is reconstructed, and the reconstructed signal is CEEMD decomposed again; this is followed by calculating the MSE value of each component signal, comparing the obtained MSE values with the threshold value, and if there are IMFs component signals greater than the threshold value, remove them, then reconstruct the remaining signal and perform CEEMD decomposition again; after multiple iterations, the MSE values of the component signal that are less than the threshold can be obtained, and the iteration is terminated. Finally, the resulting signal is analyzed by the spectrum to determine the fault characteristics. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted. At the same time, the feasibility of the method is verified by comparison with the traditional CEEMD.
## 2 Simulation Methodology  
The safety of transportation networks can be assessed using surrogate safety metrics that come from microscopic traffic simulations (e.g., [21; 22; 23; 24; 25; 26; 27; 28; 29], etc.). Using simulation modeling has many benefits, one of which is the ability to test potential treatments prior to implementation [30]. Although, most micro-simulation software, such as VISSIM [16], PARAMICS [31], AIMSU [32], Simulation Of Urban Mobility (SUMO) [33; 34], INTEGRATION [35], CORSIM [36], MITSIMLab [37], etc., can be used to model different types of roads and 2+1 designs [38; 39], almost all of them cannot model driving (i.e., overtaking) in the opposite direction.  
The SUMO microscopic traffic simulation model [33; 34] can model overtaking in the opposite direction. In addition, it provides all the modeling features required to put in place suitable substitute safety precautions. Therefore, in this study, the evaluation of the safety and mobility effects of the 2+1 and the conventional TLTW roads were assessed using the SUMO model. For vehicles equipped with Surrogate Safety Measure (SSM) devices, SUMO enables the extraction of simulated conflicts as a direct output of the simulation. SUMO can also produce floating car data (FCD), which can be used by the Surrogate Safety Assessment Model (SSAM) software to obtain conflicts [40].  
Several substitute safety indicators can be used when conducting road safety studies using micro-simulations, like time-to-collision (TTC), deceleration rate to avoid the collision (DRAC) [41], time-to-accident (TTA) [42], encroachment time (ET) [43], crash potential index (CPI) [23; 30], etc., must be used [44; 42]. In this analysis, the TTC is used as the surrogate safety indicator to reflect the crash potential, as it is simple to calculate. TTC can be defined as the time difference between two vehicles, assuming they follow their respective trajectories at their current speeds before they crash [44]. According to the lowest TTC value, the severity of a two-vehicle crash can be determined. [PERSON] (1990) [45] stated that, when the TTC value is less than 1.50 s, there may be a risk of a crash. [PERSON] (2005) [46] also suggested TTC \(\leq\) 1.50 s as a critical value for road safety in urban areas.  
The AASHTO (2011 and 2018) suggested that the TTC value for brake reaction time, which is utilized in the construction of highway stopping sight distances, be 2.5 s [47; 48].
# New Orthogonal Transforms for Signal and Image Processing
## 4 Signal Approximation--Orthogonal Expansion  
The set of rows of matrix \(A(n)\) defined by Equation (15) or Equation (4) represents the set of orthogonal discrete basis functions. We assume that these functions are defined within the interval \([0,T]\). In relation to the rows of matrix \(A(n)\) we have:  
\[\sum\limits_{i,j}\varphi_{i}\cdot\varphi_{j}=\begin{cases}0&\text{for $i\
eq j$}\\ C&\text{for $i=j$}\end{cases}\quad C=\sum\limits_{i=1}^{N}(a^{i})^{2} \tag{20}\]An example of \(N=4\) orthogonal basis functions based on matrix \(A(2)\) for parameter \(a=\frac{1}{2}\) is illustrated in Figure 1.  
A continuous signal with finite energy \(x(t)\) can be approximated by linear combinations of \(N\) orthogonal basis functions represented by rows of \(A(n)\)[17]. Such an orthogonal expansion is expressed as follows:  
\[x_{A}(t)=\sum\limits_{i=1}^{N}d_{i}\cdot\varphi_{i}(t) \tag{21}\]  
Multiplying both sides of the Equation (21) by \(\varphi_{j}(t)\), then integrating both sides and accounting for the orthogonality of the basis functions we obtain:  
\[d_{i}=\frac{1}{C}\int\limits_{0}^{T}x_{A}(t)\cdot\varphi_{i}(t)dt \tag{22}\]  
where \(C\) is defined by Equation (20).  
Figure 2 shows the approximated signals for a sinewave for the given \(N\) and selected value of \(a\).  
Figure 1: Illustration of 4 orthogonal basis functions.  
As can be seen from the results in Figure 1 (as expected), increasing the number of basis functions \(N\) decreases the value of the mean square error. For instance, for \(N=4,\ 8,\ 32,\ 64,\ 128\) the _mse_ equals: \(2.245\times 10^{-1},3.09\times 10^{-2},1.6\times 10^{-3},4.0096\times 10^{-4},\)\(9.8877\times 10^{-5},\) respectively.
## 4 Deterministic Transformation of mUAQMSTP  
A deterministic model of the CCM for the suggested mUAQMSTP is demonstrated here.  
**Theorem 2**.: _Assume that \(\xi_{i}\) and \(\zeta_{ij}\) are two uncertain variables with regular distributions, denoted as \(\Psi_{\xi_{i}}\) and \(\Psi_{\xi_{ij}}\), respectively. Then Equation (6) reports the deterministic transformation of the CCM, which is subsequently demonstrated in Equation (7) as_  
\[\left\{\begin{array}{l}Min\ \overline{F}_{1}=\sum\limits_{i=1}^{n}\Psi_{ \xi_{i}}^{-1}(\alpha_{1})x_{i}\\ Min\ \overline{F}_{2}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\Psi_{ \xi_{ij}}^{-1}(\alpha_{2})x_{i}x_{j}\\ \text{subject to}\\ \text{constraints of }\eqref{eq:CCM}.\end{array}\right. \tag{7}\]  
Proof.: Given that \(\xi_{cij}\) and \(\xi_{tij}\) are the independent uncertain variables in the Model (6), it follows that the constraints \(\mathcal{M}\big{\{}\sum_{i=1}^{n}\xi_{i}x_{i}\leq\overline{F}_{1}\big{\}}\geq \alpha_{1}\) and \(\mathcal{M}\big{\{}\sum_{i=1}^{n}\sum_{j=1}^{n}\zeta_{ij}x_{i}x_{j}\leq \overline{F}_{2}\big{\}}\geq\alpha_{2}\) at the respective chance levels \(\alpha_{1}\) and \(\alpha_{2}\) can be correspondingly expressed as \(\mathcal{M}\big{\{}F_{1}\leq\overline{F}_{1}\big{\}}\geq\alpha_{1}\) and \(\mathcal{M}\big{\{}F_{2}\leq\overline{F}_{2}\big{\}}\geq\alpha_{2}\). In accordance with Theorem 1, the reconstructed values of \(\mathcal{M}\big{\{}F_{1}\leq\overline{F}_{1}\big{\}}\geq\alpha_{1}\) and \(\mathcal{M}\big{\{}F_{2}\leq\overline{F}_{2}\big{\}}\geq\alpha_{2}\) are, respectively, denoted as \(\sum_{i=1}^{n}\Psi_{\xi_{i}}^{-1}(\alpha_{1})x_{i}\leq\overline{F}_{1}\) and \(\sum_{i=1}^{n}\sum_{j=1}^{n}\Psi_{\xi_{ij}}^{-1}(\alpha_{2})x_{ij}\leq \overline{F}_{2}\). Accordingly, Model (7) becomes the deterministic equivalent of the Model (6).  
\[\left\{\begin{array}{l}Min\ \overline{F}_{1}=\sum\limits_{i=1}^{n}\varphi_{i}x_{i }\\ Min\ \overline{F}_{2}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\varrho_{ij}x_{i }x_{j}\\ \text{subject to}\\ \text{constraints of }\eqref{eq:CCM}.\end{array}\right. \tag{8}\]  
\(\square\)  
**Corollary 1**.: _Consider the independent zigzag uncertain variables \(\xi_{i}=\mathcal{Z}(r_{i},s_{i},t_{i})\) and \(\zeta_{ij}=\mathcal{Z}\big{(}u_{ij},v_{ij},w_{ij}\big{)}\) where, \(r_{i}<s_{i}<t_{i}\) and \(u_{ij}<v_{ij}<w_{ij}\), and \(r_{i},s_{i},t_{i}\), \(u_{ij},v_{ij},w_{ij}\in\mathfrak{R}\). Therefore, Model (7) can be analogously framed as Model (8)._  
Here, \(\varphi_{i}\) and \(\varrho_{ij}\) are verbalized as below.  
\[\varphi_{i}{=}\begin{cases}(1-2\alpha_{1})r_{i}+2\alpha_{1}s_{i}&;if\ \alpha_{1}<0.5\\ (2-2\alpha_{1})s_{i}+(2\alpha_{1}-1)t_{i}&;if\ \alpha_{1}\geq 0.5\end{cases}\]  
and  
\[\varrho_{ij}{=}\begin{cases}(1-2\alpha_{2})u_{ij}+2\alpha_{2}v_{ij}&;if\ \alpha_{2}<0.5\\ (2-2\alpha_{2})v_{ij}+(2\alpha_{2}-1)w_{ij}&;if\ \alpha_{2}\geq 0.5.\end{cases}\]
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
###### Abstract  
When Internet of Things (IoT) big data analytics (BDA) require to transfer data streams among software defined network (SDN)-based distributed data centers, the data flow forwarding in the communication network is typically done by an SDN controller using a traditional shortest path algorithm or just considering bandwidth requirements by the applications. In BDA, this scheme could affect their performance resulting in a longer job completion time because additional metrics were not considered, such as end-to-end delay, jitter, and packet loss rate in the data transfer path. These metrics are quality of service (QoS) parameters in the communication network. This research proposes a solution called QoSComm, an SDN strategy to allocate QoS-based data flows for BDA running across distributed data centers to minimize their job completion time. QoSComm operates in two phases: (i) based on the current communication network conditions, it calculates the feasible paths for each data center using a multi-objective optimization method; (ii) it distributes the resultant paths among data centers configuring their openflow Switches (OFS) dynamically. Simulation results show that QoSComm can improve BDA job completion time by an average of 18%.  
IoT Big Data Analytics; SDN; QoS +
Footnote †: journal: Applied Sciences
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Advantages of Multi-Connectivity
#### 3.4.1 Enhanced Throughput  
The UE receives communication over two independent RF links, and this can be fully utilized to sum up the data on both links to obtain higher throughput. In ideal conditions, this is the total theoretical value addition of the two independent throughput; however, the channel and subsequent RF conditions always have a negative impact. A challenge is often related to the delay difference between both RF paths or the out-of-order arrival of packets at the destination, which can affect the performance of upper layers, thereby, reducing the throughput.
## 1 Introduction
The proposed method improved the ranging accuracy by a factor of 91. For dynamic targets, the ranging accuracies of the proposed and traditional methods were 0.13 mm and 0.20 mm, respectively, and their maximum ranging deviations were \(-\)0.38 mm and \(-\)1.26 mm, respectively. Furthermore, as the target distance increased, the distance deviation of the traditional method reached a maximum value before gradually decreasing, exhibiting periodicity, with a period equal to one code width. For speed measurements, the proposed method achieved an accuracy of 0.58 mm/s and a precision of 0.44 mm/s.
# Rapid Laser Reactive Sintering Synthesis of Colossal Dielectric CCTO Ceramics
###### Abstract  
Calcium copper titanate (CCTO) ceramics were successfully synthesized using a rapid laser reactive sintering method without conventional long heat treatment times. The microstructure, dielectric properties, and impedance spectroscopy results for CCTO sintered at laser power rates of 25-85 W were investigated in detail. The X-ray diffractometry results showed that prepared CCTO is polycrystalline in a cubic structure with high purity. Scanning electron microscopy showed that CCTO sintered at 85 W has a dense microstructure with an average grain size of 30 nm. The dielectric permittivity of CCTO ceramics increased with increasing laser power over the entire frequency range and achieved a value of almost 10\({}^{5}\) in the low-frequency region. The dielectric permittivity maintained almost constant values from 10\({}^{2}\) Hz to 10\({}^{7}\) Hz, with lower dielectric loss (\(\sim\)0.1) from 10\({}^{3}\) Hz to 10\({}^{6}\) Hz, demonstrating good frequency stability. The impedance spectroscopy study showed that grain and grain boundary resistance decreased with rising laser power based on two parallel Resistor-Capacitance (RC) equivalent circuits in series. The activation energies for the grain boundaries were calculated from the impedance using the slope of ln\(\sigma\) versus 1/T and were found to be in the range of 0.53-0.63 eV. CCTO synthesized by rapid laser reactive sintering is competitive for practical applications.  
CCTO; ceramic; permittivity; impedance; laser Article
## 3 Results and Analysis
### Morphological Analysis and Precision Evaluation of Landslide Area  
The previous experimental treatment successfully enabled the identification and extraction of the Jiangdingya landslide area within the study area, which is despite the interference caused by the abundance of land types in the complex terrain. The morphological processing image utilized the Canny edge detection algorithm to identify and extract the boundary of the landslide at Jiangding for enhancing the visualization of the experimental effect. This boundary was then overlaid with the DOM images, as depicted in Figure 9. The slope derived from the mentioned DEM can be used to identify the \"tongue\"-shaped distribution of the landslide area. The main body of the landslide exhibits a gradual incline at the top and a steep decline at the bottom, with the landslide behind the wall and a clearly defined moving area and tongue.  
Figure 8: Extraction results of the morphological landslide.  
This study aims to assess the accuracy of landslide information extraction areas. This research utilizes high-precision DOM data and previous Jiangdingya landslide data as references to evaluate experimental accuracy. The range of features is determined through artificial vectoring [28]. Accuracy, precision, and recall are used as evaluation indices to identify the extraction of landslides in the complex terrain of Jiangdingya. The accuracy of landslide extraction in the area shown in Figure 8 is evaluated by constructing a confusion matrix to assess precision. This process involves correctly extracted landslide areas and erroneously extracted areas. The classification accuracy evaluations for the landslide area are 98.02%, 85.24%, and 84.78%. The blue area denotes accurate extraction, while the light-yellow area indicates erroneous extraction, as shown in Figure 10.
## 1 Introduction
Considering samples with weak surfaces, the ASE can be described using computational stochastic fracture mechanics [59] and the strain energy model [60]. [PERSON] et al. suggest that the ASE is mainly influenced by fractal characteristics, while weak surface properties are considered a secondary factor [61]. On this basis, the modified SEL and the original SEL are combined to explain the mixed type ASE-NSE of nominal strength, which is denoted as the unified SEL (USEL). However, the USEL is not capable of describing the finite strength of large uncracked samples, so it is enhanced by replacing the notched SEL with the MFSL [14]. Nevertheless, the reasons for the impact of fractal characteristics on the ASE remain uncertain. [PERSON] and [PERSON] [62] and [PERSON] et al. [63] argued that the ASE of small samples is related to surface flaws or imperfections that are introduced during sample preparation. In fact, the volume fraction of surface flaws or imperfections decreases as the sample size increases, resulting in the well-known anomalous size effect (ASE). Some research [64; 65] indicates that smaller samples can quickly slide along a flaw on the surface, resulting in a lower compressive strength. [PERSON] et al. [66], [PERSON] et al. [67], and [PERSON] [68] observed that regarding the uniaxial compression of basalt, sandstone, high-strength gypsum, limestone, and granite, cracks originating on one surface of smaller samples can promptly propagate to the opposite surface. Moreover, micromachining research has found that surface effects, such as the Rehbinder effect, the effect of solid coatings, and the effect of extrusion cutting, can cause the ASE of nominal strength and failure strength for metallic material [69; 70]. Thus, surface effects play a crucial role in influencing the ASE of nominal strength. Readers should note that this research does not address the size effect of nanomaterials resulting from surface stress caused by the atomic-scale architecture [71; 72; 73]. The analysis presented above indicates that the surface effect is the primary reason for the anomalous size effect (ASE), while the deterministic size effect resulting from an FPZ controls the normal size effect (NSE). Hence, this study introduced a combined size effect model (CSE model) that incorporates the surface effect and the fracture process zone (FPZ) to elucidate both the NSE and ASE for rocks under compression. Next, we conductedanalyses on various parameters to enhance our understanding of the CSE model. Finally, validation of the CSE model was conducted, and it was compared with other models.
## 4 Experimental Results
### Experimental Study 1  
In this section, the results obtained from classifying the dataset consisting of 157,572 images, each with dimensions of 50 \(\times\) 50 \(\times\) 3, using deep learning methods are presented. For the deep learning methods, an original CNN model using particle swarm optimization in the final classification layer and customized pretrained VGG16, MobileNet, and DenseNet201 models were used. The results obtained by these models are explained in detail. A total of 80% of the dataset was used as training data, 10% as test data, and 10% as validation data.
## 3 Methodology
### Visualization Techniques  
Visualizing traffic data is crucial for effective city traffic management. It aids in comprehending the dynamics of moving entities and uncovering patterns related to traffic, social dynamics, spatial geography, and economic trends [22]. Some aspects of the potential application field of traffic visualization are identifying real-time traffic jams by monitoring traffic situations, discovering mobility patterns of vehicles and pedestrians, and improving route planning according to the traffic density. Commonly used visualization techniques include line charts, bar charts, heatmaps, histograms, and geospatial maps. Geospatial maps represent the most commonly employed method for visualizing traffic data, and 2D visualization is more commonly used in the literature than its 3D counterpart [23].  
While 2D geospatial maps provide a good representation of the spatial dimensions of data, they fail to present the temporal dimension of data, which is especially important in traffic data visualizations. There are different approaches for representing spatial data withtemporal components. The most widely studied method in the literature is the space-time cube (STC) model [24]. In an STC model, the spatial components of the data are depicted along the \(x\) and \(y\) axes, while the temporal dimension is represented along the \(z\) axis. This model works well with small-to-medium-sized data; however, when the data are too large, the visualization becomes difficult.  
Another common approach to represent space and time is to perform animation of a map to display geospatial changes in time. The visualization technique used in this article uses the HeatMapWithTime plugin of Folium library [25]. A heatmap is a powerful visualization method for storytelling, especially for geospatial data. This plugin allows creation of an interactive animation of heatmaps that allows end users to manipulate the visual representation according to their needs. Some of the potential interaction options are overview, zoom, pause, play, loop, and also play at different speeds for a period of time. The heatmap allows us to visualize the traffic density of different sections of the city throughout the day. Every dot in the heatmap represents the location of a vehicle; by analyzing the location of vehicles at different time intervals, potential mobility patterns of vehicles and the traffic density of the streets can be identified. The dataset employed in this article (described in Section 4.1) does not contain vehicle information but only street section data; however, with the provided information, we can manually assign a certain number of vehicles for every street section according to the traffic density of the sections, i.e., higher-traffic-density sections have a higher number of vehicles, and lower-density sections have a lower number of vehicles.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
###### Abstract  
Wireless communication technology adopting electromagnetic waves in an unlicensed spectrum, such as visible light, for communication has attracted wide research efforts. Visible light communication (VLC) utilizes visible light as a communication medium to transmit signals but faces a limited communication bandwidth and low data rate, which is caused by the intrinsic characteristics of LEDs. This paper first studies a mathematical model of limited bandwidth and its effect on transmitted signals and then analyzes the free space and underwater channel loss. With the theoretical analysis, a VLC transceiver system is presented for solving bandwidth limitation by utilizing a pulse-amplitude modulation-8 (PAM-8) scheme and a hybrid equalization method. The proposed hybrid equalization combined a passive equalizer, a neural network (NN)-based feed-forward equalization (FFE), and a radial basis function neural network (RBF-NN). The feasibility of this VLC system was verified through a co-simulation platform with both free-space and underwater channels. Compared with a VLC system adopting a deep neural network (DNN)-based post-equalization method, the proposed VLC system could achieve a data rate of 3.6 Gbps with a bit error rate (BER) of 3.8 \(\times\) 10\({}^{-3}\) over a 3 m free-space channel. The RBF-NN achieved a reduced training time of 10 min, which was 86.7% lower than the conventional DNN-based post-equalization method.  
visible light communication (VLC); neural networks (NNs); feed-forward equalization (FFE); PAM-8 modulation; optical wireless transceiver circuit