esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of the Influence of Friction Coefficient on Synchronous Performance
### Evaluation Index of Synchronization Performance
#### 2.1.2 Shock Degree During Synchronous Phase  
The shock degree (\(j\)) during the synchronous phase refers to the change rate of the angular speed of the synchronizer's input under the action of synchronous torque. In the synchronization phase, the smoothness of the system is closely related to the shock degree. The greater the shock degree, the more serious the vibration and noise will be. The shock degree during the synchronous phase is an important index to measure shift performance. Its calculation formula is,  
\[\left\{\begin{array}{l}j=\frac{d(T_{f}+T_{c})}{j_{\rm max}}\\ j_{\rm max}={\rm max}(j)\end{array}\right., \tag{5}\]  
where \(T_{c}\) is the resistance moment of the transmission, \(j_{\rm max}\) is the maximum shock degree during synchronous phase. It can be seen from Equation (5) that the change rate of synchronous torque determines the magnitude of the shock degree during synchronous phase. During the shift synchronization, the corresponding control strategy should be added to reduce the shock degree of this process, so as to improve the shift quality.
# Metric for Estimating Congruity between Quantum Images +
###### Abstract  
An enhanced quantum-based image fidelity metric, the QIFM metric, is proposed as a tool to assess the \"congruity\" between two or more quantum images. The often confounding contrariety that distinguishes between classical and quantum information processing makes the widely accepted peak-signal-to-noise-ratio (PSNR) ill-suited for use in the quantum computing framework, whereas the prohibitive cost of the probability-based similarity score makes it imprudent for use as an effective image quality metric. Unlike the aforementioned image quality measures, the proposed QIFM metric is calibrated as a pixel difference-based image quality measure that is sensitive to the intricacies inherent to quantum image processing (QIP). As proposed, the QIFM is configured with in-built non-destructive measurement units that preserve the coherence necessary for quantum computation. This design moderates the cost of executing the QIFM in order to estimate congruity between two or more quantum images. A statistical analysis also shows that our proposed QIFM metric has a better correlation with digital expectation of likeness between images than other available quantum image quality measures. Therefore, the QIFM offers a competent substitute for the PSNR as an image quality measure in the quantum computing framework thereby providing a tool to effectively assess fidelity between images in quantum watermarking, quantum movie aggregation and other applications in QIP.  
Keywords:quantum computation; quantum image processing; fidelity metric; PSNR; congruity, quantum watermarking; quantum movie +
Footnote †: Received: 20 July 2016; Accepted: 29 September 2016; Published: 9 October 2016
## 2 Study Area and Dataset
### Dataset  
The data used in this research were acquired by the Landsat 8, ALOS-2 and Sentinel-1 satellites, which are operated by the National Aeronautics and Space Administration (NASA), the Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA), respectively (Figure 1). Landsat 8 was launched on 11 February 2013 with an Operational Land Imager (OLI) sensor and a Thermal Infrared Sensor (TIRS). The Landsat 8 image was acquired on 7 May 2015 and includes a panchromatic band with 15-m resolution and 11 multi-spectral bands with 30-m resolution. ALOS-2 was launched on 24 May 2014 with an enhanced L-band SAR sensor, PALSAR-2. Its center frequency is 1.27 GHz/23.60 cm in Strip Map mode (SM). Sentinel-1 was launched on 3 April 2014 with the C-SAR sensor in the C-band with a center frequency of 5.40 GHz/5.55 cm in Interferometric Wide Swath mode (IW). Both SAR images covering Tehran were acquired in the ascending path in the right-look direction and by dual-polarization. The ALOS-2 image acquired on 14 October 2015 has HH and HV polarizations, with an incident angle of 40.56\({}^{\circ}\) at the center of the image and a spatial resolution of 6.2 m. The Sentinel-1 image captured on 26 October 2015 has VV and VH polarizations with an incident angle of 34.02\({}^{\circ}\) at the center of the image and a spatial resolution of 13.9 m. Moreover, the ground swath widths of ALOS-2 and Sentinel-1 are approximately 50-70 km and 250 km, respectively. Both SAR images cover the entire study area in one scene.
## 3 Results
### Effect of CLC Process on Nitrogen Contaminations  
As a new combustion mode, CLC provides a new path for analyzing the migration mechanism. However, most studies focused on the migration mechanism in the CLC of coal or biomass instead of briquette cyanobacteria [35; 36; 37]. In the CLC process, a fuel reactor is under an air-free environment; thus, NO\({}_{x}\) derived from fuel N is the main component. During the fuel combustion process, HCN and NH\({}_{3}\) are commonly believed to be two precursors or intermediary compounds. In the process of CLC based on hematite OC, a series of competitive reactions about NO\({}_{x}\) emission are shown as follows.  
\[HCN+\frac{15}{2}Fe_{2}O_{3}=\frac{1}{2}N_{2}+5 Fe_{3}O_{4}+CO_{2}+\frac{1}{2}H _{2}O \tag{15}\]  
\[HCN+9 Fe_{2}O_{3}=\frac{1}{2}N_{2}O+6 Fe_{3}O_{4}+CO_{2}+\frac{1}{2}H_{2}O \tag{16}\]  
Figure 22: Comparison of gas yield between quartz sand and hematite at 750, 800, 850, 900 \({}^{\circ}\)C.  
\[HCN+\frac{21}{2}Fe_{2}O_{3}=NO+7 Fe_{3}O_{4}+CO_{2}+\frac{1}{2}H_{2}O \tag{17}\]  
\[NH_{3}+\frac{9}{2}Fe_{2}O_{3}=\frac{1}{2}N_{2}+3 Fe_{3}O_{4}+\frac{3}{2}H_{2}O \tag{18}\]  
\[NH_{3}+6 Fe_{2}O_{3}=\frac{1}{2}N_{2}O+4 Fe_{3}O_{4}+\frac{3}{2}H_{2}O \tag{19}\]  
\[NH_{3}+\frac{15}{2}Fe_{2}O_{3}=NO+5 Fe_{3}O_{4}+\frac{3}{2}H_{2}O \tag{20}\]  
Figure 23 shows the NO concentration change from the outlet of a single fluidized bed in the conventional combustion process and CLC process, respectively. In the conventional combustion process, the selected biriquette cyanobacteria of 0.5 g is the sample densified at the compressive load of 1 ton and the moisture content of 10%. The fluidizing agent is 1.5 L/min O\({}_{2}\). In the CLC process, the fluidizing agent is the mixture of 1.5 L/min CO\({}_{2}\) and 0.8 g/min steam, and hematite of 30 g acts as bed materials. According to Figure 23, NO emission concentration increases with the experimental temperature no matter in the conventional combustion process or the CLC process. In addition, NO emission concentration in the conventional combustion process is evidently higher than that in the CLC process under the same experimental condition. In the conventional combustion process, the NO emission is caused by thermal NO\({}_{\rm x}\) from airborne nitrogen and fuel NO\({}_{\rm x}\) from briquette cyanobacteria. However, NO emission from CLC is lower because the airborne nitrogen is nearly or completely eliminated in the fuel reactor. Additionally, relatively low reaction temperature in the fuel reactor compared to conventional air-fired process is also conducive to reduce NO\({}_{\rm x}\) emission. It is worthwhile to note that the thermal NO\({}_{\rm x}\) in the conventional combustion process would increase more evidently when the temperature reaches to above 950 \({}^{\circ}\)C. In addition, due to the compact structure of briquette cyanobacteria, NO emission maintains for a longer time compared to the combustion process of powder cyanobacteria.
## 2 The CLOPE Algorithm
### Terminologies  
Let \(D=\{t_{i},t_{z},\cdots,t_{z}\}\) denotes as a database containing \(n\) transactions, and \(I=\{i_{i},i_{z},\cdots i_{n}\}\) to be all the attributes in \(D\). On this basis, we have the following definitions.  
**Definition 1** (Transaction). _A transaction \(t\) in \(D\) is a tuple \(<\)_key, \(A>\), _where_ key _is the title of the transaction and \(A=\{i_{a},i_{b},\cdots\}\subseteq I\) is the attribute set in this transaction_.  
**Definition 2** (Cluster). _A cluster \(c\) is a tuple \(<\overline{T},\overline{H}>\) where \(\overline{T}=\{t_{a},t_{b},\cdots\}\in D\) is a set of transactions and \(\overline{H}\) is the histogram of cluster \(c\)_.  
**Definition 3** (Histogram). _If_ occ(\(i\), \(c\)) _represents the occurrence of attribute \(i\) in cluster \(c\)_, _then the histogram of cluster \(c\) could be symbolized as \(c.\overline{H}=\{(i,\,\)occ(\(i,c\))\()\)\(\,|\,\)occ(\(i,c\))\(\,>\)\(\,0,i\in I\}\)_.  
In addition, we have the following properties for histogram \(\overline{H}\) :  
(1) The _size_ is the total number of attributes in cluster \(c\), defined as:  
\[S(c)=\sum_{i_{x}=1}^{N}\text{occ}(i_{x},c) \tag{1}\]  
(2) The _width_ is the total number of different attributes in cluster c, defined as:  
\[W(c)=\left\{\text{occ}(i,c)>0\,|\,i\in I_{3}\,|\right. \tag{2}\]  
(3) The _height_ is the ratio between the _size_ and the _width_, defined as:  
\[H(c)=S(c)\,/\,W(c) \tag{3}\]  
(4) The number of transactions in cluster c, defined as \(|\,c\,|\).  
**Definition 4** (Clustering). _A clustering \(C\) is a set of all the clusters_, _i.e._, \(C=\{c_{i},c_{z},\cdots c_{p}\}\).  
**Definition 5** (Profit of Clustering). _Suppose a clustering \(C\) contains \(p\) clusters, the profit value is defined as_:  
Figure 1: Histograms of the two clustering.  
\[\text{Profit}_{r}(C)=\frac{\sum_{k=1}^{p}\frac{S(c_{k})}{W(c_{k})}\times\mid c_{k} \mid}{\sum_{k=1}^{p}\mid c_{k}\mid} \tag{4}\]  
According to the CLOPE paper, the theoretical purpose of CLOPE algorithm is to find a clustering \(C\) that maximizes Profit(\(C\)) to produce the best histogram, given a database \(D\) and the repulsion factor \(r\). In addition, the author pointed out that the profit value of clustering \(C\) is only affected by the distribution of transactions in each clusters, which is the numerator part of Equation (4).  
The CLOPE algorithm contains two phases. In the initialization phase, each transaction is sequentially read and placed into the best cluster that would maximize the profit value of current clustering. The iteration phase includes a loop that reads transactions from head to tail and tries to move each of them into the best cluster that maximizes the profit value of clustering. The loop does not end until no transactions are moved into a new cluster. Unfortunately, the proposed CLOPE algorithm is unable to find a clustering \(C\) that maximizes Profit(\(C\)) as it claimed. Details of this core problem are discussed in the next subsection.
## 4 Results
### Analysis and Discussion
With a single run, it is possible to obtain management plans with varying degrees of grid dependence. For example, in a military base, management modes with low grid dependence are suitable for wartime energy system management, while those with low operational costs are suitable for the economical operation of energy systems during peacetime. Furthermore, the results demonstrate that the traditional NSGA-II algorithm had difficulty meeting various preferences and failed to converge under stringent constraints, resulting in high costs and substantial reliance on the grid. In contrast, the proposed NSGA-II-MC algorithm can efficiently manage these constraints and generate satisfactory energy management solutions tailored to diverse preferences.
## 4 Simulation Tests and Results
### Case Study 6  
The aim in the sixth case study was to maximize the coverage of a two-dimensional, squared area of \(50\times 50\). The number of the available sensors was equal to 20 and their sensing range was equal to 5. The grid dimensions were \(1\times 1\), the size of the population in the GA algorithm was equal to 600, and the swarm size in the PSO algorithm was adjusted to 600. The results derived are synoptically presented in Table 8.
## 5 The Query Management Scheme
### The Query Transmission Phase (QTP)  
This is the last phase of the query management scheme. In this phase, the Distributor receives queried data from the NM, and each query response is sent to the appropriate user applications. According to the users' demands, the Distributor can organize the queried data into time sequences, or it can simply deliver query responses whenever it receives a message from the NM.
## 1 Introduction
Initial or periodic monitoring can be carried out using rope access to check the condition of the steel sheet pile [9]. Inspection techniques are usually limited to accessible areas that enable visual and tactile inspection, with some thickness measurements done using ultrasonic testing (UT) equipment. Figure 1: “Corrosion conditions at five different vertical exposure zones”, adapted from [2]. These inspection methods are not only costly but time-consuming and labour-intensive. Alternative testing methods, such as integrity testing and crosshole sonic logging, have been used on cylindrical concrete piles [10]. However, they are not directly relevant to the technique used in this study. This paper reports an investigation carried out using an advanced non-destructive testing (NDT) technique that uses ultrasonic guided wave (UGW) signals to inspect the full length of steel sheet piles. This technique is known as guided wave testing (GWT) [11]. Substantial effort has been expended on the development of such inspection systems due to their ability to screen long distances from a single source to assess a structure's integrity. This method is extensively used for the inspection of pipelines in the oil and gas industry, as it can identify defects such as corrosion and erosion up to 50 m in each direction from a single location [12]. GWT has been used previously to inspect steel sheet piles in the dead zone, and this is the first time an attempt has been made to inspect the active zone [13]. The major difficulty is that the waveguide has a complicated transverse profile that is significantly different to those of pipes and rails, where the cross-sectional area is finite and small. The waves are bounded by the upper and lower surfaces of the sheet pile in the thickness direction. However, waves could be reflected from edges in the transverse direction as well as from potential defects that are intended to be detected. Nonetheless, it is possible to arrange phase delays between transducers to focus on sound energy in particular areas in order to avoid reflections from edges on the sheet piles. This paper is organized as follows. The theoretical background of UGWs is presented in Section 2. The FEA theory and methodology implemented for numerical modelling is reported in Section 3, while numerical modelling and results are discussed in Section 4. Conclusions and future suggested work follow in Section 5.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 3 The Proposed Algorithm
### Event Classification  
Once the Random Forest model is trained, it is ready to classify incoming sensor data into pre-defined event categories. The goal of this step is to predict the type of event based on the feature vector derived from the real-time sensor data.  
The event classification process involves the following key steps:  
* Feature Extraction: For each new observation, the relevant characteristics are extracted in the same manner as during training, that is, using statistics and frequency domain characteristics.  
* Prediction with Random Forest: The feature vector for each new observation is passed through each decision tree in the forest. Each tree makes a prediction, and the final event classification is determined by majority vote.
* Majority Voting: After all decision trees make their predictions, the final class label is the one that is predicted by the majority of trees.  
Mathematically, the classification can be expressed as follows:  
\[\hat{y}=\text{Majority Voting}(T_{1}(X_{\text{new}}),T_{2}(X_{\text{new}}), \ldots,T_{N}(X_{\text{new}})) \tag{6}\]  
where  
* \(\hat{y}\) is the predicted event label for the new observation.
* \(T_{j}(X_{\text{new}})\) is the prediction made by the \(j\)th decision tree for the characteristic vector \(X_{\text{new}}\) from the new observation.
* \(N\) is the total number of trees in the forest.  
The event classification process is described in Algorithm 4. This algorithm outlines the steps taken by the trained Random Forest model to classify incoming sensor data into predefined event categories based on extracted features.  
```
1:Input: Feature vector \(X_{\text{new}}\) for the new observation
2:Output: Predicted event label \(\hat{y}\)
3: Extract features \(X_{\text{new}}\) from the new sensor data
4: Initialize a list to store predictions from each tree: predictions \(=[]\)
5:for each tree \(T_{j}\) for \(j=1,2,\ldots,N_{T}\)do
6: Pass \(X_{\text{new}}\) through tree \(T_{j}\)
7: Store the prediction in predictions
8:endfor
9: Perform majority voting on the predictions
10: Assign \(\hat{y}\) as the class with the majority votes in predictions
11:return\(\hat{y}\)
```  
**Algorithm 4**Event Classification with Random Forest
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Laser-Induced/Scribed Graphene ECG Electrodes  
Producing graphene rapidly and in large quantities is a challenging problem that must be solved if graphene ECG electrodes are to be implemented on a large scale. Laser induction and laser scribing are rapid and low-cost graphene production methods that have developed rapidly in recent years [59, 60]. Not only can laser-induced graphene (LIG) and laser-scribed graphene (LSG) be prepared rapidly and at low cost on a large scale, but, most importantly, the preparation of graphene and fabrication of corresponding graphene electronics can be accomplished simultaneously. Based on these techniques, a series of laser-induced/scribed graphene ECG electrodes have been proposed [61, 62, 63, 64, 65, 66, 67], whose patterns can be designed flexibly and processed quickly according to attachment position, impedance, and other requirements, which has good application potential.
## 5 Effect of Slope Inclination
To examine the effect of inclination of slopes having the same mean strength, the probability density function of horizontal permanent displacements \(u_{x}\) corresponding to the three slopes are compared in Figure 17a. It is evident from Figure 17a that the effect of  
Figure 16: Computed permanent displacements for slope inclination 1:1: (**a**) horizontal displacement \(u_{x}\), (**b**) vertical displacement \(u_{y}\), and (**c**) total displacement \(u\) fitted by Weibull, Extreme Value, and Gamma distributions. slope inclination on \(u_{x}\) is substantial, as both its mean value and variation of the results increase significantly with slope steepness. Indeed, the mean values of \(u_{x}\) are 0.51 m, 0.94 m, and 1.28 m for the 2:1, 4:3, and 1:1 slopes, respectively. (It is noted that the mean values of horizontal permanent displacement \(\overline{u}_{x}\) of homogeneous slopes from Table 8 are 0.39, 0.41, and 0.76 m, respectively). Moreover, the corresponding standard deviations are 0.138, 0.304, and 0.344 for the three slopes. Similar conclusions for the effects of inclination of heterogeneous slopes on the vertical permanent displacements \(u_{y}\) and total permanent displacements \(u\), are derived from Figure 17b,c, respectively. Figure 17: Probability density of computed permanent displacements for slope inclinations 2:1, 4:3, and 1:1: (**a**) horizontal displacement \(u_{x}\), (**b**) vertical displacement \(u_{y}\), and (**c**) total displacement \(u\) (Weibull distribution). Moreover, Figure 18a plots the probability of exceedance of a given value of horizontal permanent displacement \(u_{x}\) for slope inclinations 2:1, 4:3, and 1:1. Similar results for the vertical permanent displacement \(u_{y}\), and total permanent displacements \(u\) are given in Figure 18b,c, respectively. Figure 12:0 Probability of exceedance of a given value \(\upu^{\star}\) of permanent displacement for slope inclinations 2:1, 4:3, and 1:1: (**a**) horizontal displacement \(u_{x}\), (**b**) vertical displacement \(u_{y}\), and (**c**) total displacement \(u\) (Weibull distribution). Finally, it should be noted that the numerical results presented in Figures 14-16 correspond to seismic excitations having a peak ground acceleration of \(a_{\mathrm{gmax}}=0.3\) g. To investigate the effect of material nonlinearity of permanent displacements of slopes, similar parametric investigations have been conducted for a wide range of peak ground accelerations from 0.05 g to 0.5 g ([PERSON], 2017) [24]. It has been shown that the seismic permanent displacements of a homogeneous slope (\(\overline{u}_{x}\), \(\overline{u}_{y}\)) in equation (2) increase with the magnitude of peak ground acceleration \(a_{\mathrm{gmax}}\) and can be described with an exponential function of the form. Figure 12:1  
where \(a_{i}\), \(b_{i}\) are constants ([PERSON], 2017) [24].
## 3 Materials and Methods
### Data Denoising Based on Statistical Analysis  
Due to the sensor and the complexity of the acquisition environment, MLS point clouds inevitably have some noise. This can have a very large impact on the calculation of the characteristics of local points, such as the calculation of surface normals or curvature changes and can produce many unforeseen errors. Therefore, it is necessary to preprocess the original point cloud before road surfaces extraction to remove obvious outliers and noise values to then improve the efficiency and accuracy of the data processing. In this study, a statistical filtering method [39] was used to achieve this purpose. A statistical analysis was carried out on the neighborhood of each point in the collected original point cloud data, and the distances between each point and their mean \(\mu(\mu=\frac{1}{n}\sum\limits_{i=1}^{i}D_{i})\) and standard deviation \(\sigma(\sigma=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{i}\left(D_{i}-\mu\right)^{2}})\) were calculated; the standard deviation multiple is \(std\). Only two thresholds, \(m\) and \(std\), need to be input in the algorithm implementation process. When the average distance of a point near the \(m\) point is within the standard range \((\mu-\sigma std,\mu+\sigma\cdot std)\), the point is retained, and the point is defined as an outlier point if it is not within the range. Figure 2 shows the results of removing large-scale noise and outliers, and it can be seen that statistical filtering has a good effect on removing large-scale noise points. After removing noise and outliers, there are still some unrelated points in the point cloud (such as moving objects), which significantly affect the voxel-based region growing algorithm. Therefore, these were further removed in this study using CloudCompare [40] software (Version 2.13).