esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Analysis and Discussion
### Calculation and Correlation Analysis  
#### 3.3.1 Results of the Driving Behavior Risk Measurements  
Based on the obtained vehicle trajectory data, the risk assessment results of each driving behavior could be obtained by using four risk measurement index calculation formulas. The results of the driving behavior risk metrics are shown in Figure 4.  
Figure 4 shows the distribution of the evaluation results of the four driving behavior risk indicators. As shown in Figure 4, the values of each indicator are concentrated in the lower numerical range, and only a small part of the data are concentrated in the larger numerical range. The upper boundary of each indicator was considered as the risk  
Figure 4: Distribution of driving behavior risk measurement results.  
Figure 3: Mean and standard deviation of vehicles’ acceleration.  
threshold, which means that the risk of driving behavior was greater at the distribution points in the area with larger values.  
The change in lateral displacement is closely related to the lateral driving stability of a vehicle during driving, and the change in the longitudinal speed of a vehicle can reflect speed stability. A lower value of the indicator \(R_{1}\) indicates that the lateral driving stability of a vehicle is better, and a lower value of the indicator \(R_{2}\) indicates that the longitudinal driving stability of a vehicle is better. For the evaluation of the indicator \(R_{3}\) for the car-following risk, a larger value indicates that the car-following risk is greater, and the possibility of collision is greater. The value of the lane-changing risk indicator \(R_{4}\) is relatively small in Figure 4, and the number of sample data with a low value was far greater than that with a high value. A larger value of the indicator \(R_{4}\) indicates a higher risk for the vehicle during lane changing.
## 1 Introduction
Various methods, such as the feed-rate evolutionary algorithm [25], the equidistance quaternion method [26], and the improved Adams-Malton algorithm [27] are employed to decrease speed fluctuations and guarantee steady, continuous-trajectory operation. Although the effectiveness of the method has been validated experimentally, is cannot be applied directly to optical polishing because the effect of acceleration and deceleration on the realisation precision of runtime is not considered. Driven by the practical needs to improve the quality of optical polishing, this paper presents a systematic trajectory planning method that particularly enhances the realisation precision of runtime. Following this introduction, Section 2 calculates all the control points of NURBS through numerical solution to overcome the problem of calculation efficiency. In Section 3, an optimisation algorithm of fairing NURBS is established by taking the shear jerk of trajectory as an evaluation index. Section 4 then proposes an interpolation scheme with which to minimise the realisation error of runtime by planning the feed-rate of trajectory according to the given runtime between adjacent NC codes. Section 5 reports experiments on a prototype machine which shows that the proposed trajectory planning method is more accurate than the linear interpolation method. Conclusions are drawn in Section 6.
## 4 Discussion
### Comparison between the Spectral Method Algorithm and the COMSOL Simulation
Figure 7 shows the radial distribution of the displacement components \(u_{r}\), \(u_{\theta}\), \(u_{z}\) of some non-axisymmetric modes in the tube wall at \(f=8\) kHz. The modes are indexed as \((n,m)\), where \(n\) is the circumferential order. The transverse axis \(r\) represents the radial component in cylindrical coordinates, and the longitudinal axis is the normalized value of each displacement component. It can be seen from the figure that the distribution of the displacement components of the non-axisymmetric modes (\(n\
eq 0\)) in the tube wall obtained by the spectral method algorithm was consistent with the COMSOL simulation results. In conclusion, the dispersion curves of axial propagation modes in the water-filled tube obtained using the spectral method algorithm are consistent with the results from the COMSOL simulation. In addition, the algorithm can find all the axial propagation modes at any frequency correctly, and obtain the precise mode displacement distribution in the tube wall. The spectral method algorithm consumes much less time and computation resources to acquire such results.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 2 Problem Statement  
Two points are explained before establishing the cooperative guidance model. First, during the flight, the thrust of the UAV is adjusted in a small range, which can keep the velocity of the UAV basically unchanged, and the terminal attack distance is short, usually only a few kilometers to more than 10 kilometers. Therefore, it can be assumed that the velocity of each UAV is a constant. Second, in the process of designing the guidance law, the guidance law can be designed separately in the longitudinal plane and the horizontal plane. The guidance law designed in the longitudinal plane is to keep the UAV flying at a fixed height, and the cooperative guidance law designed in the horizontal plane to is meet the relevant cooperative strike requirements. Therefore, it can be assumed that the UAV and the target are in the same horizontal plane. Therefore, four assumptions can be made, as below:  
**Assumption 1:**_The UAV velocity can be considered as a constant value._  
**Assumption 2:**_The UAV and target are considered as ideal point-mass models._  
**Assumption 3:**_The target is stationary._  
**Assumption 4:**_Compared with the guidance loop dynamics of UAV, the dynamic response speed of the UAV detection device and autopilot is fast enough, so it can be ignored._  
Based on the above assumptions, it is assumed that the UAV in the two-dimensional plane attacks the stationary target at a constant speed, and the relative motion relationship is shown in Figure 1.  
The UAV and the target are denoted by \(M\) and \(T\), respectively. The equations describing the motions between the UAV and the target can be expressed as follows:  
\[\dot{r}=-V_{M}\cos\sigma_{M} \tag{1}\] \[\dot{\lambda}=-\frac{V_{M}\sin\sigma_{M}}{r}\] (2) \[\dot{\gamma}_{M}=a_{M}/V_{M}\] (3) \[\sigma_{M}=\gamma_{M}-\lambda \tag{4}\]In the above equations, \(V_{M}\) is the UAV velocity, the symbol \(r\) is the relative distance between the UAV and target, namely the range-to-go. Symbols \(\gamma_{M}\), \(\lambda\) and \(\sigma_{M}\) represent the flight path angle, the line of sight (LOS) angle and the leading angle, respectively. Symbol \(a_{M}\) is the acceleration command.
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 4 Experiment and Analysis of Surface Defects Detection Based on MB-LBP
### Experimental Design
#### 4.1.2 Algorithms of Image Segmentation in Image Preprocessing  
The steps of image segmentation in image preprocessing is as follows:  
(1) Integral image segmentation.  
The concept of integral images was proposed by [PERSON] and [PERSON] [23]. With the integral image, the grayscale sum of pixels in a rectangular area can be quickly calculated, which represents the features of this area. The value of any point in the integral image is the grayscale sum of all pixels from the upper left corner to this point, that is:  
\[p(i,j)=\sum_{i^{\prime}<-i\&p^{\prime}<-j}pix(i^{\prime},j^{\prime}) \tag{7}\]  
where \(pix(i,j)\) is the grayscale of the pixel located at \((i,j)\).  
As shown in Figure 10, first, the original image is equally divided into blocks. The length and width of the block can be changed as a power of two (eight pixels in this paper). With the integral image, the mean grayscale of each block can be quickly calculated, which represents the features of this block.  
(2) Differential calculation  
Compare each block with four neighbor blocks and obtain all difference values. Then, select the difference value with the largest absolute value as the feature value of the current block. All feature values form a block difference matrix. The result is shown in Figure 11. The difference value in the block difference matrix reflects the change of the grayscale in the image. Blocks with a small difference value and gentle grayscale changes represent a no defect area; while blocks with a large difference value and sharp grayscale changes mean a suspected defect area.  
Figure 11: Results of the four neighbor block difference.  
Figure 10: Divide original image into blocks.  
3. Threshold setting  
Now, the question is how to select a reasonable threshold to determine whether the difference value is large or small. Set two thresholds, high and low, to divide the grayscale difference. As shown in Figure 12, the upper plane represents the high threshold and the lower plane represents the low threshold. The blocks with a difference value greater than the high threshold is identified as \"master\", blocks with a difference value less than the low threshold is identified as \"abort\", and blocks with a difference value between high and low threshold is identified as \"candidate\". The \"master\" blocks are considered to be a defect area; the \"candidate\" block is a candidate defect area, and only when it has a \"master\" eight-neighbor block, will it be promoted to a \"master\" block.
4. Regional growth  
Usually, a complete defect area is composed of multiple blocks. Therefore, it is necessary to merge adjacent \"master\" blocks into larger areas. That is, when there is a \"master\" block in eight-neighbor blocks of a \"master\" block, merge them as a new \"master\" block. Repeat the operations above until the new \"master\" block has no \"master\" block in its neighborhoods.
# Synthesis, Characterization and Biological Investigation of New N-Modified Spinorphin Analogs
## 2 Results and Discussion  
### Chemistry  
The synthetic route for obtaining the novel spinorphin peptides: rhodamineB-Leu-Val-Val-Tyr-Pro-Trp-Thr-NH\({}_{2}\) (Rh-S), rhodamineB-Leu-Val-Val-Tyr-Ac5c-Trp-Thr-NH\({}_{2}\) (Rh-S5), and rhodamineB-Leu-Val-Val-Tyr-Ac6c-Trp-Thr-NH\({}_{2}\) (Rh-S6) is shown in Scheme 1. The peptides were synthesized by the most widely used and proven method in peptide chemistry, the solid-phase peptide synthesis (SPPS)-Fmoc (9-fluorenylmethoxycarbonyl) strategy. The basic concept of this procedure is the stepwise construction of the peptide chain assembled on an insoluble polymeric carrier, Rink-Amide MBHA (4-methylbenzhydrylamine) resin. The chain extension starts from the C-terminal residue. The preparation of C-terminal amides is a widely used approach in the preparation of biologically active peptide molecules, as it thus increases the resistance of peptides to enzymatic degradation [21; 22; 23]. The peptide formation undergoes a repetitive amidation reaction between an activated carboxyl group of one amino acid and an amino group of the other, as shown in Scheme 1.  
This strategy is based on the reaction between rhodamine B with the N-terminal amino group of the spinorphin analogs directly to the resin. Herein, the coupling is accomplished by activation with (2-(1H-benzotriazol-1-yl)-1,1,3,3-tetramethyluronium tetrafluoroborate) (TBTU) and hydroxybenzotriazole (HOBt) in the presence of N,N-diisopropylthelamine (DIPEA). The Fmoc group protects the amino group of all used amino acids. The Fmoc-group was easily removed with a 20% piperidine solution. The final stage of the synthesis is the cleavage of the synthesized peptides from the resin by trifluoroacetic acid (TFA), triisopropylsilane (TIS), and water.  
The three spinorphin derivatives were purified and their structures proved by appropriate modern methods and techniques described in detail in the experimental part. The IR analysis of the studied compounds showed the main vibrational characteristic lines for functional groups and bonds as follows: 3336 (N-H stretching vibration (\(\
u_{\text{NH}}\))), 1664 (s) NCO (amide) stretching and 1678-1722 cm\({}^{-1}\) a high-intensity peak of \(\
u_{\text{C}=0}\), 1539-1531 cm\({}^{-1}\) (\(\delta_{\text{NH}}\)) (Figure 1). The physicochemical data (\(m/z\), \(t_{\text{R}}\), dissociation constants and isoelectric points) of the newly synthesized neuropeptide analogs are shown in Table 1. SEM analyzes of the compounds isolated in pure form were also carried out (Figure 2). The results of SEM measurement showed that the Rh peptide derivative morphology was a porous
## 3 Description of the Study Site and Collected Dataset  
Since ANN is a supervised machine learning approach, a set of experimental data with observed CC values needs to be collected to train the CC prediction model. In this study, 441 samples of soil with 12 properties were collected during the construction phase of the Vinhomes housing project (Hai Phong city, Vietnam) in 2016. The study area ha illustrated in Figure 2. The 12 soil properties include sample depth (X1), sand percentage (X2), loam percentage (X3), clay percentage (X4), moisture content (X5), wet density (X6), dry density (X7), void ratio gravity (X8), liquid limit (X9), plastic limit (X10), plastic index (X11), and liquid index (X12). It is noted that 31 boreholes, ranging from 1.8 m to 52.8 m, are used to investigate the geological condition of the study area. The geological survey and laboratory tests in this study were performed according to instructions stipulated in the Vietnam Standards including TCVN9155:2012 (Hydraulic structures: Technical requirements for drilling machines of the geological survey) and TCVN 9140:2012 (Hydraulic structures: Technical requirements for preserving samples in the work on geological investigation). Moreover, the standard penetration test [65] was used to collect data on soil layers. The statistical characteristics of CC's influencing factors used in this study are shown in Table 1. Table 2 illustrates samples of the collected dataset. Histograms of the variables in the collected dataset are shown in Figures 3 and 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variables** & **Unit** & **Notation** & **Min** & **Mean** & **Median** & **Std** & **Skewness** & **Kurtosis** & **Max** \\ \hline Sample depth & m & X1 & 1.80 & 17.99 & 17.80 & 10.02 & 0.39 & 2.88 & 52.80 \\ Sand percentage & \% & X2 & 17.90 & 33.31 & 30.30 & 9.95 & 1.90 & 6.64 & 71.00 \\ Loam percentage & \% & X3 & 22.10 & 43.32 & 44.80 & 5.61 & \(-\)1.72 & 5.95 & 51.40 \\ Clay percentage & \% & X4 & 5.20 & 23.22 & 24.10 & 5.50 & \(-\)1.30 & 5.24 & 37.50 \\ Moisture content & \% & X5 & 20.30 & 41.38 & 42.00 & 8.05 & \(-\)0.24 & 2.78 & 63.50 \\ Wet density & g/cm\({}^{3}\) & X6 & 1.55 & 1.75 & 1.74 & 0.08 & 0.84 & 4.23 & 2.05 \\ Dry density & g/cm\({}^{3}\) & X7 & 0.95 & 1.25 & 1.23 & 0.13 & 0.80 & 3.53 & 1.67 \\ Void Ratio & Unitless & X8 & 0.58 & 1.15 & 1.16 & 0.20 & \(-\)0.26 & 3.12 & 1.79 \\ Liquid limit & \% & X9 & 23.20 & 43.92 & 44.50 & 5.88 & \(-\)0.60 & 3.52 & 58.30 \\ Plastic limit & \% & X10 & 16.80 & 30.23 & 30.50 & 5.01 & \(-\)0.20 & 2.63 & 43.60 \\ Plastic index & \% & X11 & 5.20 & 13.69 & 14.10 & 2.61 & \(-\)1.40 & 5.21 & 19.10 \\ Liquid index & Unitless & X12 & 0.11 & 0.81 & 0.77 & 0.29 & 0.85 & 4.36 & 1.99 \\ Compression coefficient & cm\({}^{2}\)/KG & Y & 0.01 & 0.06 & 0.07 & 0.02 & 0.15 & 2.89 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistical description of the collected dataset. Figure 2: Location of the study area. Figure 4: Histograms of the compression coefficient as the response variable.
## 5 Empirical Evaluation: Setup and Findings
### Discussion
* The total number of instances is 768 in PIDD, which is not fair enough to build a stable model using deep learning in the hold-out validation method. However, this can be used in cross-fold validation, although this method is not used for our experiment;
* Despite the importance of PIDD data preprocessing, which was clarified and documented in the proposed methodology, it was inferred for its importance for building a classification model before and after the preprocessing. However, the researchers who dealt with data processing and the creation of a deep learning model did not deal with data preprocessing and were interested in accuracy only, which negatively affected the confidence in the applied results of their model;
* The uncertainty-based membership function is tackled for outlier values which are difficult using DL approaches;
* The ROCs and available AUC of the deep learning models are increased in response to a small number of cross-validation and vice versa;
* The pretrained well-known models (e.g., AlexNet, VGG-16, VGG-19...etc.) which are capable of transfer learning and which are fine-tuned were not fit to help in PIDD. This facilitied deep learning model construction. While there are slight improvements in accuracy using the deep learning approaches compared with the fuzzy KNN, the proposed approach fits the uncertainty of the data based on fine-tuned hyperparameter and grid search, leading to model stability and the uncertainty classification. The authors of [34] designed a limited number of layers that start from one layer to four layers and used SoftMax and a rectifier activation function without fine-tuning the hyperparameters, which generally leads to instability of the results and an overfitting problem [35]. Moreover, in [36], even though the accuracy achieved by the authors is greater than the tuned fuzzy KNN, it is based on 5-fold cross-validation, which is considered the simplest validation approach of the machine learning pipeline. In contrast, the tuned fuzzy KNN classifier is validated randomly using hold-out training and a test set. In the same mentality, the deep learning model is subject to be affected by the number of cross-fold batches proved by the ROC figures [57], in which the ROC values decrease with an increase in the number of validation folds.
# Seismic Response of a Three-Dimensional Asymmetric Multi-Storey Reinforced Concrete Structure
###### Abstract  
This study discusses the seismic behavior of a geometrically asymmetric three-storey reinforced concrete (RC) building, considering torsional effect and material nonlinearity. The building is a test structure that was used for seismic performance evaluation in the SMART 2013 (Seismic design and best-estimate Methods Assessment for Reinforced concrete buildings subjected to Torsion and nonlinear effects) international benchmark. To begin with, nonlinear stress-strain relationships that were set up for concrete and reinforcing steel are validated by finite element local tests with a representative volume element. A modal analysis shows that the first three calculated natural frequencies are close to the ones that are obtained by modal experiments. The finite element modeling is further validated by comparing the calculated displacement and acceleration due to a low-intensity ground motion with the responses from the corresponding shaking table test. Using the validated model, a blind nonlinear seismic analysis is performed for a series of Northridge earthquakes in order to estimate the behavior of the asymmetric RC structure to high-intensity ground motions. The calculated displacement and acceleration, as well as their response spectra at various sampling points, agree well with the results of a three-dimensional benchmark shaking table test. By investigating the seismic torsional behavior of the asymmetric RC structure, it is shown that the seismic response of an asymmetric structure is larger than that of a hypothetical symmetric structure. The result indicates that a larger seismic response should be considered in the seismic design of an asymmetric structure compared to a symmetric structure with similar design conditions.  
asymmetric reinforced concrete structure; SMART 2013 international benchmark; finite element model; nonlinear seismic analysis; seismic torsional behavior +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Modeling and Control
#### 2.2.4 Modeling of the CoG Compensation System  
The CoG compensation system consists of a linear guide rail coupled with a DC motor, which is used to drive a slider using a timing belt mechanism. Figure 11 shows a schematic of the CoG compensation system, based on belt-drive models such as in [23].  
Using [PERSON]'s law, the equation representing the counterweight motion is  
\[F=M\ddot{x}+B\dot{x}+Kx, \tag{5}\]  
where \(M,\ B,\) and \(K\) represent the mass, viscous friction damping coefficient, and spring constant representing the belt stiffness, respectively. The timing belt is assumed to be rigid and, as such, we set \(K=0\).  
By applying [PERSON]'s voltage law on the DC motor circuit,  
\[V-iR-L\frac{di}{dt}-V_{m}=0, \tag{6}\]  
where \(V\) is the input voltage, \(i\) is the current, \(R\) is the resistance, \(L\) is the inductance, and \(V_{m}\) is the back emf voltage. Since the electrical time constant is much smaller than the mechanical time constant, the inductance in the motor can be neglected, and Equation (6) can be used to solve for the current.  
The equation describing the pulley rotation is  
\[\tau_{m}=\dot{J}\dot{\phi}+b\dot{\phi}+\tau_{L}, \tag{7}\]  
where \(\tau_{m}\) is the motor torque, \(J\) is the pulley mass moment of inertia, and \(b\) is the rotational damping coefficient. \(\tau_{L}=\frac{Fr}{R\dot{G}}\) is the load torque due to the counterweight, where \(n\) and \(G\) are the efficiency and gear ratios of the mechanism. The motor torque and voltage \(\tau_{m},\ V_{m}\) are given by  
\[\tau_{m}=K_{t}i,\ V_{m}=K_{e}\omega, \tag{8}\]  
where \(K_{t}\) and \(K_{e}\) are the torque and electric constants.  
Figure 11: Schematic representing a model of the CoG compensation system.  
To combine the mechanical and electrical systems, we substitute \(i\) obtained from Equation (6) into \(\tau_{m}\) from Equations (7) and (8). Noting that \(\omega=\frac{\dot{x}}{r}\), where \(r\) is the radius of the pulley, we substitute \(F\) from Equation (5) into \(\tau_{L}\), so that Equation (7) can be written as  
\[\left(J+\frac{Mr^{2}}{nG}\right)\ddot{x}+\left(b+\frac{Br^{2}}{nG}+\frac{K_{t} K_{t}}{R}\right)\dot{x}=\frac{K_{t}r}{R}V. \tag{9}\]  
By taking the Laplace transform of Equation (9), the system transfer function with an input \(V(t)\) and output \(x(t)\) is given by  
\[\frac{X(s)}{V(s)}=\frac{K_{t}rnG}{s^{2}(JnG+Mr^{2})R+s\big{(}bRnG+Br^{2}R+K_{t} ^{2}nG\big{)}}. \tag{10}\]
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 5 5G Technology Integration
The term cooperative means that users closer to the RSUs and/or base stations act as decode-and-forward relay (DFR) to help other users with less favorable channel conditions. This is also addressed in [102; 103]. SDN is also an effective enabler of IoEV, where the intelligence of the EVs can be utilized by the SDN controllers to optimize different vehicular decisions, including optimal EV routing to charging stations, EV charging scheduling, load balancing, etc. On the other hand, the authors in [91] show the effectiveness of a heterogeneous network that utilizes DSRC to support V2V communication and 5G mmWave for V2I communication, aiming to manage the required vehicular connectivity and optimize the selection of the different radios. With these additional capabilities, heterogeneous networks of different architectures are expected to address the varying requirements of different EV charging modes while catering to the expected data congestion, payload variations and latency requirements.