esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 3 Ride Comfort Criteria
This paper mainly considers the influence of eight vibration accelerations on driving comfort under the sitting posture model: the vertical and lateral vibrations of the seat  
Figure 6: Vibration model for human sitting posture. Figure 7: Weighting curve for different participating axes. surface, backrest, and floor and the pitch and roll of the seat position. The eight vibration accelerations can be converted from the vehicle acceleration:  
\[ \begin{array}{l}a_{zb}=a_{zs}=a_{zf}=\ddot{y}_{z}\\ a_{yb}=a_{ys}=a_{yf}=\ddot{y}_{y}\\ a_{rx}=\ddot{\theta}_{yz}\cdot y_{s}+\frac{1}{2}\ddot{\theta}_{yz}\cdot h_{s} \\ a_{ry}=\ddot{\theta}_{xz}\cdot d_{s}\end{array} , \tag{33}\]  
where \(a_{ij}\) indicates the vehicle acceleration; \(i=z\), \(y\), \(r\); \(j=b\), \(s\), \(f\). The subscripts \(z\) and \(y\) represent the vertical and horizontal directions, respectively, while the subscript \(\mathbf{r}\) represents the direction of rotation, and the subscripts \(b\), \(s\), and \(f\) represent the seat back, seat surface, and floor surface. \(\ddot{y}_{z}\), \(\ddot{y}_{y}\), \(\ddot{\theta}_{yz}\), and \(\ddot{\theta}_{xz}\) represent vertical, lateral, rolling, and pitching accelerations at the vehicle centroid; \(d_{s}\), \(y_{s}\), and \(h_{s}\) are the longitudinal, transverse, and vertical distances between the vehicle centroid and the driver's seat. The ISO 2631-1 standard [14] uses the OVTV to evaluate vibrations ranging from 0.5 Hz to 80 Hz, while MSI assesses low-frequency vibrations below 0.5 Hz. Considering that vertical bending vortex vibrations in long-span bridges typically have frequencies below 0.5 Hz, it has been observed that drivers experience motion sickness and discomfort during vortex vibrations on the Humen Bridge. Consequently, this article incorporates the motion sickness index and analyzes the driving comfort of vortex vibrations considering both comfort and motion sickness. The calculation methods for the two indicators are as follows.
## 3 Proposed Method
### Results of Discretization and Grouping
#### 4.2.2 Partitioning for IQ Subtests  
To arrive at meaningful conclusions, the developed algorithm was applied not only for the original model with the general IQ as a response, but also for models built for all nine IQ subtests. The discretization and grouping results for the IQ subtests differ quite strongly from each other, which complicates the process of interpreting the results.  
For the convenience of interpretation, it seems expedient to analyze the results for each of the nine IQ subtests, as well as for the general IQ test, with the same partition of the year and department values. As such a common partition, we have chosen the \(3\times 3\) one given in Table 4 and obtained for the general IQ with the _BIC_ goodness function. In order to understand how much this partition deviates from the optimal one, the values of the _BIC_ for each IQ component are compared for the following partitions:  
* Q0: Natural partition (without discretization and grouping);
* Qopt: Optimal partition for the corresponding subtest;
* Q33: Optimal partition for general IQ (\(3\times 3\) partition in Table 3).  
As can be seen from Table 4, the _BIC_ value for the \(3\times 3\) partition chosen for interpretation is close enough to the _BIC_ value for optimal partition, built specifically for the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subtest** & **Q0** & **Qopt** & **Q33** & **(Q33-Qopt/(Q0-Qopt)** \\ \hline IQ1 & 45,347.10 & 44,410.53 & 44,540.93 & 13.92\% \\ IQ2 & 47,304.71 & 46,246.06 & 46,292.62 & 4.40\% \\ IQ3 & 46,440.84 & 45,381.67 & 45,453.85 & 6.81\% \\ IQ4 & 46,376.30 & 45,580.15 & 45,622.01 & 5.26\% \\ IQ5 & 48,866.38 & 47,991.48 & 48,178.74 & 21.40\% \\ IQ6 & 48,135.41 & 47,103.49 & 47,249.93 & 14.19\% \\ IQ7 & 45,913.20 & 44,893.94 & 44,945.67 & 5.07\% \\ IQ8 & 48,095.02 & 47,085.38 & 47,159.35 & 7.33\% \\ IQ9 & 46,556.39 & 45,620.72 & 45,703.43 & 8.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the _BIC_ values for the IQ1–Q9 subtests.  
IQ subtests. The average deviation is about 10% of the difference between the optimal partition for a particular subtest and the \(3\times 3\) partition presented in Table 3 (the maximum deviation reaches 21.4 percent for the subtest IQ5).  
In addition to the larger value of the goodness function, we observe an increase in the homogeneity of variances in the groups, due to the use of the proposed method. Table 5 shows the p-value when testing the hypothesis of uniformity of variances across the groups using Levene's Test. The most striking increase in uniformity is observed for the general IQ, which indicates the effectiveness of the proposed approach. A negative result of this test is observed only for IQ9, which can be explained by deviations from normality for this subtest.
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
###### Abstract  
The Internet of things (IoT) is the third evolution of the traditional Internet that enables interaction and communication among machines. Many IoT platforms and networks have been developed, and recently, market sectors have started to develop specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly with the cellular networks, is a great demand. IoT represents one of the main use cases of the fifth-generation (5G) cellular system as announced by the 3 rd Generation Partnership Project (3 GPP) and the International Telecommunication Union (ITU). Integrating IoT networks with 5G networks face many challenges related to dense deployment and a massive number of expected connected devices. Thus, IoT network availability and scalability are the main requirements that should be achieved. To this end, this work provides a framework for integrating heterogeneous IoT networks with the 5G networks. The proposed system considers dense deployment and system scalability and availability requirements as announced by ITU and 3 GPP. Our proposed structure deploys three main communication paradigms; mobile edge computing (MEC), device-to-device communications (D2D), and software-defined networking (SDN). Our proposed system is evaluated over a reliable environment for various deployment scenarios, and the results validate the proposed structure. The proposed IoT/5G reduces the percentage of blocked tasks by an average of 30% than other traditional IoT networks. This increases the overall system availability and scalability since IoT networks can have more devices and tasks than existing IoT networks. Furthermore, our proposed structure reduces the overall consumed energy by an average of 20% than existing IoT networks, which is an effective metric for IoT networks.  
Internet of things; 5G; mobile edge computing; software-defined networking; device-to-device; scalability +
Footnote †: journal:  
_Acticle_  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], N.F. Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN  
12 March 2021  
15 April 2021
## 3 Results and Discussion  
### Characterization of PANI/TiO\({}_{2}\) Composite  
The FTIR spectrum of prepared PANI and PANI/TiO\({}_{2}\) composite are shown in Figure 2. The peaks of PANI appeared at 788, 1292, 1481, and 1563/cm, which correspond to a 1,4 substitution pattern of the benzene ring, C-N stretching vibration of primary amines, C=C stretching vibrations of quinone and the benzene ring, and C=N stretching vibrations of quinone and the benzene ring, respectively [14; 15]. This observation indicates that PANI was synthesized successfully. The broad band of the PANI/TiO\({}_{2}\) composite at 500/cm is attributed to the TiO\({}_{2}\) vibration [12]. Morphological observations of PANI/TiO\({}_{2}\) composite by TEM (Figure 3) revealed relatively small-sized TiO\({}_{2}\) particles dispersed and anchored on the PANI chain. The result of the TEM analysis suggests that PANI/TiO\({}_{2}\) composite was completely fabricated. XRD analyses were used to identify the crystalline structure of the PANI/TiO\({}_{2}\) composite (Figure 4). The characteristic peaks at 25.28, 37.80, 53.89 and 62.69 matched well with the (101), (004), (105) and (204) crystal planes of the synthetic anatase (JCPDS #21-1272) [16]. The crystal size estimatedby the Scherrer equation was about \(16.27\pm 3.13\) nm [17; 18], which was well matched with the size derived from the TEM image (\(16.59\pm 3.59\) nm).  
The capacity to absorb light is an important factor for the activation of the visible-light photocatalyst [19]. To compare the light absorbance of the pristine TiO\({}_{2}\) and 4% PANI/TiO\({}_{2}\) composite in the visible-light region, DRS analysis was conducted in the wavelength range of 200-800 nm. The reflectance results (Figure 5) indicated a weaker photo-reflectance of 4% PANI/TiO\({}_{2}\) composite than TiO\({}_{2}\) at wavelengths above 400 nm, which means the composite has a stronger absorption  
Figure 3: Field-emission transmission electron microscope (FE-TEM) image of PANI/TiO\({}_{2}\) composite.  
Figure 2: Fourier-transform infrared (FTIR) spectrum of prepared polyaniline (PANI) and PANI/TiO\({}_{2}\) composite.  
capacity of visible light and the possibility of photocatalytic activity under visible-light irradiation. The red-shift in the absorption edge of 4% PANI/TiO\({}_{2}\) in this study could be elucidated by a color change [20; 21; 22]. PANI/TiO\({}_{2}\) composites appeared blue and grayish, which had a positive effect on the visible-light absorption. By contrast, pristine TiO\({}_{2}\) powder was white. The band gap energies (\(E_{g}\)) of samples were estimated from the intersection point of the absorption onsets and the baseline of the absorption spectrum using Kubelka-Munk remission function [2; 23]. The \(E_{g}\) of pristine TiO\({}_{2}\) and PANI/TiO\({}_{2}\) composites were 3.21 and 3.15 eV, respectively. A similar band gap energy reduction in the PANI/TiO\({}_{2}\) composite (3.07 eV) has also been reported in the literature [12], which could lead to higher photocatalytic activity.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### LKAS Control Strategy
#### 2.2.3 Kalman Filter  
The Kalman filter is a recursive minimum variance estimation method that efficiently manages real-time signals in control systems [22]. It is widely used in linear systems, and the discrete and continuous forms of these systems can be mutually converted. Suppose that the state variable \(x\in\mathbb{R}^{n}\) can be used to estimate and discretize the entire control process. Then, the discrete stochastic differential equation is used to express the system state, \(x_{k}\), and the observation value, \(z_{k}\):  
\[\left\{\begin{array}{l}x_{k}=Sx_{k-1}+Gu_{k-1}+w_{k-1}\\ z_{k}=Hx_{k}+v_{k}\end{array}\right., \tag{20}\]  
where \(u_{k}\) is the control vector, \(S\) is the state transition matrix, \(G\) is a random signal of the control input matrix, \(H\) is the state observation matrix, and \(w_{k}\) and \(v_{k}\) are process excitation and observation noises, respectively. Normally distributed white Gaussian noises can be obtained as:  
\[\left\{\begin{array}{l}w_{k}=N(0,\;M_{k})\\ v_{k}=N(0,\;O_{k})\end{array}\right., \tag{21}\]  
where \(M_{k}\) and \(O_{k}\) are the covariance matrices of the process excitation and observation noises, respectively.  
The predicted value of the system state, \(\overline{x}_{\overline{k}}\), and the optimal estimate of the state, \(\overline{x}_{k}\), can be expressed as:  
\[\left\{\begin{array}{l}\overline{x}_{\overline{k}}=S\overline{x}_{k-1}+Gu_{ k}\\ \overline{x}_{k}=\overline{x}_{\overline{k}}+K_{k}\big{(}z_{k}-H\overline{x} _{\overline{k}}\big{)}\end{array}\right., \tag{22}\]  
because the estimation principle of the Kalman filter is to minimize \(D_{k}\), the covariance of \(\overline{x}_{k}\), making it increasingly close to the true value. Then, the cost function, \(J_{k}\), is established:  
\[J_{k}=\sum\
olimits_{min}D_{k} \tag{23}\]  
By combining Equations (22) and (23), the filter gain of the Kalman filter, estimation error variance, and state estimation covariance are calculated:  
1. Kalman filter gain: \[K_{k}=D_{k}^{-}H^{T}\Big{(}HD_{\overline{k}}^{-}H^{T}+O_{k}\Big{)}^{-1}.\] (24)
2. Estimation error variance: \[D_{k}=(E-K_{k}H_{k})D_{k}^{-}.\] (25)
3. State estimation covariance: \[D_{k+1}^{-}=SD_{k}S^{T}+M_{k}.\] (26)
# A Two-Phase Ensemble-Based Method for Predicting Learners' Grade in MOOCs
## 3 Methodology
### Centroid Distance Features and Selection Method  
Following data preprocessing, eight features are utilized to train the model for the grade prediction. The features consist of two categorical variables, 'edu' and 'age', and six numerical features that were created by the system. Based on the number of 'grade' classes, five extra features are produced during the feature engineering process. The distance features are derived from the distance between each data point and the centroid point of each 'grade' class cluster. The distances are computed utilizing the Euclidean distance, with all of the training data being placed in the vector space, and Equation (2) is as follows:  
\[d(p,q)=\sqrt{\sum_{i=1}^{n}(q_{i}-p_{i})^{2}} \tag{2}\]  
where \(d(p,q)\) is the distance between the data points \(p\) and \(q\). \(n\) is the number of dimensions of venter space. \(p_{i}\) and \(q_{i}\) are the value of each datum at the \(i^{th}\) dimension.  
Finally, each dataset contains thirteen features from eight selected original dataset and five new distance features. The example of the CNPC dataset is illustrated in Figure 3.  
However, the results of the practical experiment indicate that including all of the new features as training features reduces the prediction model's effectiveness. The method of feature selection is utilized to solve this problem. A silhouette coefficient is used to choose the appropriate features. The silhouette coefficient is derived using the average intra-class distance and the average nearest cluster distance. Typically, it is employed to assess the quality of the clustering [24; 25]. The formula provided in Equation (3) is used to compute a dataset's silhouette score of clusters.  
\[\textit{silhouette coefficient}=\frac{(b-a)}{\max(a,\ b)} \tag{3}\]  
where \(a\) is the mean cluster centroid distance. \(b\) is the average nearest cluster distance for every sample.  
For the purpose of understanding the silhouette score, numbers approaching zero imply overlapping clusters, but negative numbers indicate that a different cluster is more similar. The silhouette score will be closer to one the more clearly the clusters are recognized. After calculating the silhouette score based on the data from the HMPC and CNPC datasets, the results are displayed in Table 3.  
To utilize the silhouette coefficient in the feature selection procedure, each pair of grade clusters is analyzed. Because a low silhouette score represents overlapping data between the examined clusters, the grade clusters with the lowest scores will be utilized as additional training features for the prediction model. In addition, the 'cen_c0' attribute will always be used to support binary classification. Consequently, the HMPC dataset's new features are 'cen_c0', 'cen_c3', and 'cen_c4', whereas the CNPC dataset's new features are 'cen_c0', 'cen_c1', and 'cen_c3'.  
Figure 3: Examples of data records after pre-processing.
## 2 Antenna Modelling
### Reflections or Obstructions  
The performance of antennas can be drastically impacted by environmental conditions and surrounding objects. Signal reflections and obstacles are just two problems that can be caused by these external factors. Radio waves can be reflected by nearby surfaces, including water, air, and solid things like buildings and automobiles. Multipath propagation and signal attenuation due to interference introduced by reflections are possible outcomes. It is possible to reduce the effects of reflections through careful antenna sitting and design, such as the use of beamforming techniques. Signal attenuation and decreased range can occur when an antenna's line of sight to its intended target is blocked by, for example, trees, buildings, or terrain. Obstructions can be reduced with careful site planning that takes into account the antenna's height and direction. If you want your antenna to work well and reliably under real-world settings, you need to take into account its surroundings and eliminate any potential interference.
# Biosynthetic Pathways of Hormones in Plants
## Abbreviations  
1\({}^{\prime}\)-OH-MeCLA 1\({}^{\prime}\)-hydroxymethyl carlactonoate  
3-DT 3-dehydroteasterone  
4 DO 4-deoxyyrobanchol  
5 DS 5-deoxystrigol  
6-deoxy-3-DT 3-dehydro-6-deoxyatesterone  
6-deoxyCT 6-deoxycathasterone  
ABA  Abscisic acid  
ACC 1-aminocyclopropane-1-carboxylic acid  
ACC ACC oxidase  
ACS  ACC synthase  
ADC  Aginine decarboxylase  
Agm  Agmatine  
AO1  Aldehyde oxidase 1 AOS & Allene oxide synthase  
Arg & Arginine  
ASMT & \(N\)-acetyl serotonin \(O\)-methyltransferaseraserase  
BL & Brassinolide  
BR & Brassinosteroid  
CCD7 & Carotenoid removal dioxygenase 7  
CK & Cytokinin  
CL & Carlactone  
CLA & Carlactonoic acid  
CN & Campestanol  
COMT & Caffe acid \(O\)-methyltransferaseraserase  
CPP & \(Ent\)-copalyl diphosphate  
CPS & \(Ent\)-copalyl diphosphate synthase  
CR & Cholesterol  
CS & Castasterone  
CT & Cathasterone  
_cZ_ & _Cis_-zeatin  
DAD1 & Defective in Anther Dehiscence 1  
dcSAM & Decarboxylated S-adenosyl-L-methionine  
DL & Dolicholide  
DMAPP & Dimethylallyl pyrophosphate  
dn-OPDA & 2,3-dinor-12-oxo-10,15(Z)-phytodienoic acid  
DS & Dolichosterone  
DOXP & 1-deoxy-D-xylulose-5-phosphate  
DXS & DOXP synthase  
FPP & Farnesyl pyrophosphate  
GA & Gibberellin  
GGPP & Geranylgeranyl pyrophosphate  
GGPPS & Geranylgeranyl pyrophosphate synthase  
GPP & Geranyl pyrophosphate  
HMBDP & \(E\)-4-hydroxy-3-methyl-but-2-enyl diphosphate  
IAA & Indole-3-acetic acid  
IAM & Indole-3-acetamide  
IAN & Indole-3-acetonitrile  
IAOX & Indole-3-acetaldoxime  
ICS & Isochorisamate synthase  
ICS-Glu & Isochorisamate-9-glutamate  
iP & \(N^{6}\)-\(\Delta^{2}\)-isopentenyl adenine  
IPA & Indole-3-pyruvate acid  
IPP & Isoopentenyl diphosphate  
JA & Jasmonic acid  
KAO & \(Ent\)-kaurenoic acid oxidase  
KO & \(Ent\)-kaurene oxidase  
KS & \(Ent\)-kaurene synthase  
LBO & Lateral Branching Oxidoreductase  
MAX1 & More Axillary Growth 1  
MeJA & Methyl! jasmonate  
MEP & Methylerythhriol phosphate  
MTA & \(5^{\prime}\)-methylthioadenosine  
MVA & Mevalonate  
NCPA & \(N\)-carbamoyl! putrescine  
ODC & Ornithine decayylase  
OPC-8 & 3-oxo-2-(20-[Z]-penenyl)-cyclopentane-1-octanoic acid  
OPDA & 12-oxo-2-phytodienoic acid  
OPR3 & Oxidoreductase OPDA reductase 3  
Orn & Ornithine  
PA & Polyamine  
PAL & Phenylalanine ammonia-lyase
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 3 Method
### Data Pre-Processing  
To fully utilize the hyperspectral imaging information and dielectric characteristics of apples for non-destructive detection of apple core browning, a multi-view learning approach was adopted to fuse the optoelectronic data of apples. The feature vectors from both views were merged using the concatenation method, and then the samples of core browning in the dataset were augmented using the SMOTE (Synthetic Minority Over-sampling Technique) method [34].  
SMOTE is a method used to address data imbalance issues. It can expand the minority class samples in the dataset via generating synthetic samples. Since the number of core browning samples in the apple core browning dataset was small, the SMOTE method was used to generate new synthetic samples, thereby increasing the number of core browning samples in the dataset and improving the classification performance for detecting apple core browning.
## 3 Materials and Methods
### ML Classification Algorithms
#### 3.3.1 K-Nearest Neighbours  
KNN is based on the idea that neighbors with comparable characteristics should share similar beams [16; 22]. KNN algorithm is applied in the context of classifying samples based on their similarity in terms of beams using the Euclidean distance measure. With a predefined beam codebook, each beam covers a spatial direction within the FoV. Thus, KNN considers that similar or neighbor positions (using the location coordinate tuples) should have similar beams. Following the implementation in [16], the mode of the beams from \(N_{knm}=5\) nearest neighbors is selected as the predicted beam. The k-smallest difference in Euclidean distances is employed as the parameter used in selecting the neighbors and predicting the beams. The true beam (ground truth) is the beam with the highest measured received power for each sample TX point. The accuracy of the algorithm is assessed by comparing its predictions to the ground truth.
## 5 Performance Evaluations
### Results and Discussions
#### 5.3.2 Re-Clustering Frequency  
Figure 6 depicts the frequencies of re-clustering in both the proposed scheme and SEED protocol as a function of the number of rounds (a round is the interval of time during which network topology was created and packets exchanged between source (child nodes), destination (e.g., CH(s), and the final destination (e.g., sink node) takes place). If the frequency of re-clustering was high, control overhead in the network increased. As a result, the overall energy consumption of nodes increased. Figure 6 illustrates that there was less frequent re-clustering in the proposed scheme as compared to the SEED protocol.  
In the proposed scheme, the frequency of re-clustering was comparatively low up to 50 rounds and increased slowly in the next 50 rounds for both packet rates (e.g., 5 pkts/s and 10 pkts/s). However, in SEED, the number of re-clustering linearly increased, which meant that the CHs changed more frequently than that in the proposed scheme. The reason is that the proposed scheme reduced the frequent re-clustering by employing a certain threshold value for energy. The re-clustering triggered only when the value of energy fell below a certain threshold of residual energy. However, in SEED, re-clustering happened very frequently without taking into account the status of residual energy. As a result, SEED generated additional control overheads in the network and increased energy consumption.
## 3 Materials and Methods
### MobileNet  
MobileNet, developed by Google researchers in 2017, was proposed to provide lower computational costs and better performance on mobile devices with low hardware specifications. The MobileNet model reduces the number of learnable parameters by using different types of convolution layers, such as depthwise separable \(1\times 1\) convolutions, to process each input channel. It also uses this convolution to reduce the size of the feature map during feature extraction. MobileNet's distinctive feature is its use of depthwise separable convolutions, which are essentially standard convolutions divided into depthwise and \(1\) by \(1\) pointwise convolutions as can be seen from Figure 3. Every input channel is filtered in depthwise convolution independently. The \(1\) by \(1\) pointwise convolution that follows combines all of the depthwise convolution outputs in a linear way. This factorization leads to a significant reduction in both the computing cost and the size of the model. Following each convolution layer in the model, batch normalization and the non-linear activation function ReLU have both been applied. Stride processes are used for downsampling in both the depthwise convolution layers and the first convolution. Then, the average pooling layer, fully connected layer, and softmax classifier are added. The basic MobileNet contains a total of \(28\) layers, including depthwise and pointwise convolution layers [39]. The last fully connected layer is trained for 1000 classes of the ImageNet dataset with default image input dimensions of \(224\times 224\times 3\). MobileNet detailed architecture is given as in Table S1.