esa-sceva/satcom-chunk-collection huggingface dataset:
_applied_  
_sciences_  
_Article_  
Automatic Wheels and Camera Calibration for Monocular and Differential Mobile Robots  
[PERSON]\({}^{\ast}\)\({}^{\copyright}\), [PERSON]\({}^{\copyright}\), [PERSON] and [PERSON]\({}^{\copyright}\)  
[PERSON]'s International Innovation Institute for Artificial Intelligence, Cybersecurity and Communication, SPREIU \"LEIT\", 197376 Saint Petersburg, Russia, [EMAIL_ADDRESS] (A.F.); [EMAIL_ADDRESS] (A.F.); [EMAIL_ADDRESS] (K.K.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
[PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Automatic Wheels and Camera Calibration for Monocular and Differential Mobile Robots. _Appl. Sci._**2021**, _11_, 5806. [[https://doi.org/10.3309/app1113806](https://doi.org/10.3309/app1113806)]([https://doi.org/10.3309/app1113806](https://doi.org/10.3309/app1113806))  
Academic Editor: [PERSON]  
Received: 29 April 2021  
Accepted: 18 June 2021  
Published: 23 June 2021  
**Published: Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**C copyright:**  
**Copyright:**  
© 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Abstract: Mobile robotic systems are highly relevant today in various fields, both in an industrial environment and in terms of their applications in medicine. After assembling the robot, components such as the camera and wheels need to be calibrated. This requires human participation and depends on human factors. The article describes the approach to fully automatic calibration of a robot's camera and wheels with a subsequent calibration refinement during the operation. It consists of placing the robot in an inaccurate position, but in a pre-marked area, and using data from the camera, information about the environment configuration, as well as the ability to move, in order to perform calibration without external observers or human participation. There are two stages in this process: the camera and the wheel calibrations. The camera calibration collects the necessary set of images by automatically moving the robot in front of the fiducial markers template, and then moving it on the marked floor, assessing its trajectory curvature. Upon calibration completion, the robot automatically moves to the area of its normal operation and it is proposed to refine the calibration during its operation without blocking its work. The suggested approach was experimentally tested on the Duckietown project base. Based on test results, the approach proved to be comparable to manual calibrations and is capable of replacing a human for this task.  
**Keywords:** automatic calibration; Duckietown; wheel robot; camera calibration; wheel calibration; artificial intelligence; mobile systems; monocular systems
## 3 Proposed Scheme
### Encryption and Aggregation  
#### 3.2.1 Data Encryption  
1. Partition of individual data Each node randomly partitions its individual data into \(k\) partitions and sends them to \(k\) pairwise nodes along with the masking keys. The partition form is as follows: \[x_{d}^{i}=\sum_{j\in ind_{i}[s]}x_{(j,d)}^{i}(1\leq s\leq k)\] (3)
2. Generation of pairwise nodes and masking keys For any node, it chooses randomly any \(k\) nodes in one round as its pairing nodes such that if \(user_{i}\) selects \(user_{j}\), then \(user_{j}\) also selects \(user_{i}\). With the session key between them, the two pairwise nodes generate a common key \(r\) from RC4; \(user_{i}\) adds \(r_{(j,d)}^{i}\) to \(x_{(j,d)}^{i}\), and \(user_{j}\) adds \(r_{(i,d)}^{j}\) which satisfies: \[r_{(i,d)}^{j}=-r_{(j,d)}^{i}(i\in ind_{j}[s];j\in ind_{i}[s])\] (4)  
For \(user_{i}\), the generated noise set at the timestamp \(d\) can be denoted as \(r_{(ind_{i}[s],d)}^{i}\) (\(s=1,2,\ldots,k\)). Note that in order to facilitate the temporal aggregation, the pairwise key generated by an SM at the \(T^{th}\) timestamp should satisfy the following equation:  
\[r_{(i,d)}^{j}=-r_{(j,d)}^{i}(i\in ind_{j}[s];j\in ind_{i}[s]) \tag{5}\]
3. Encryption process At the timestamp \(d\), \(user_{i}\) adds the pairwise noise to the partitioned data to generate the encrypted message \(E_{(j,d)}^{i}=x_{(j,d)}^{i}+r_{(j,d)}^{i}(j\in ind_{i}[s])\) to \(k\) pairwise nodes separetely as well as receiving the encrypted message they sent. The Figure 2 illustrates an example for spatial and temporal aggregation among pairwise users in multi-region groups.  
For any SM node \(j\), it will store the encrypted data sent from one of its pairwise node \(i\) in a series of \(T\) timestamps in the form of matrix as follows:  
\[\left[\begin{array}{ccccc}E^{1}_{(j,1)}&E^{2}_{(j,1)}&\cdots&E^{i}_{(j,1)}& \cdots&E^{n}_{(j,1)}\\ E^{i}_{(j,2)}&E^{2}_{(j,2)}&\cdots&E^{i}_{(j,2)}&\cdots&E^{n}_{(j,2)}\\ \vdots&\vdots&&\vdots&&\vdots\\ E^{1}_{(j,T)}&E^{2}_{(j,T)}&\cdots&E^{i}_{(j,T)}&\cdots&E^{n}_{(j,T)}\\ \end{array}\right]\left(\begin{array}{ccccc}E^{i}_{(j,1)}=(x^{i}_{(j,A)}+r^ {i}_{(j,A)})&j\in ind_{i}[s]\\ E^{i}_{(j,A)}=0&j\
otin ind_{i}[s]\\ \end{array}\right) \tag{6}\]
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 2 Random Field Theory  
### Overview  
A random field, \(\mathbf{H}(\mathbf{z},\omega)\), is defined as a random function of variables \(\mathbf{z}\in\Omega\), where \(\Omega\) is the geometry of the spatial domain, and \(\omega\) is a coordinate in the sample space [30; 32]. A given spatial coordinate, \(\mathbf{H}(z_{0},\omega)\) is a random variable that defines the spread in \(\mathbf{H}\) at \(x_{0}\). For a given outcome, \(H(x,\theta_{0})\) defines a realization of the field, which is a deterministic function in \(x\). A specific random field \(\mathbf{H}(\mathbf{z},\omega)\) is usually based on its marginal distribution \(H\) (\(\mathbf{z},\omega\)) \(\sim\)\(f\) (\(\mu(\mathbf{z})\), \(\sigma(\mathbf{z})\)) and autocorrelation function (ACF) \(\rho(x,\,\mathbf{z}^{\prime})=\rho(\|\mathbf{z}\mathbf{z}^{\prime}\|,\,\delta)\), where \(\mu(\mathbf{z})=E[\mathbf{H}(\mathbf{z},\omega)]\) and \(\sigma^{2}(\mathbf{z})=E[\left(\mathbf{H}(\mathbf{z},\omega)\right)-\mu(\mathbf{ z}))^{2}]\) are the mean and variance, respectively; \(\|\mathbf{z}\mathbf{\cdot}\mathbf{z}^{\prime}\|\) is the relative distance between any two points in the field, and \(\delta\) is the scale of fluctuation (SOF). Different types of anisotropic random fields have different autocorrelation functions. As for two-dimensional random fields, the SOFs can be divided into horizontal and vertical, defined as \(\delta_{h}\) and \(\delta_{v}\), respectively. According to previous studies [33; 34], \(\delta_{h}\) belongs to 10-80 m and \(\delta_{v}\) is 0.2-6 m. The mean value \(\mu(\mathbf{z})\), variance \(\sigma^{2}\) and CDF \(F(\mathbf{z})\) can be obtained from the in-situ data. When \(\mu(\mathbf{z})\) and \(\sigma^{2}\) are assumed to be unchanged with soil depth, this is called a stationary random field.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Prediction Model
#### 4.2.2 Forecast Results and Analysis  
The MAPE, MAE, RMSE of DO, COD\({}_{\text{Mn}}\) and NH\({}_{3}\)-N in 2016 under the single model and combined model prediction, as well as the degree of improvement of the combined model compared with the single model prediction ability were obtained according to the modeling process described above, as shown in Tables 6 and 7, respectively.  
For the three water quality indicators, the MAPE, MAE and RMSE values of the combined model are lower than those of the single model, and the prediction effect is better. In the prediction of DO, COD\({}_{\text{Mn}}\) and NH\({}_{3}\)-N, the prediction accuracy of the combined model is improved by 5.49% and 53.68%, 39.10% and 7.79%, and 14.40% and 20.40% compared with the ARIMA model and WNN, respectively.  
For a particular set of data, a higher weight is assigned to a single model of the combined model, which indicates that the method has a better predictive ability. Taking DO as an example, the prediction accuracy of the ARIMA model is higher than that of WNN, and it has a better prediction ability. In the ARIMA-WNN model, the bat algorithm was used to calculate the weights of the two individual models as 0.8505 and 0.1495, respectively, meaning that the individual model with better prediction ability had more weight and proving the reliability of to the bat algorithm to determine the weight coefficient in the combined model. The combined model of water quality prediction proposed in this paper was compared with the prediction results of each individual model, the BP neural network and LSSVM. The results are shown in Figures 14-17:  
As shown in the figures presented above, the MAPE, MAE and RMSE of the combined model are significantly lower than those of the comparison models. The fitting figures of the real and predicted values also show that the water quality prediction model proposed in this paper has a higher fitting degree to the real data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Quality Index** & **Prediction Accuracy Index** & **ARIMA** & **WNN** & **Combined Model** \\ \hline \multirow{4}{*}{DO} & MAPE & 2.73\% & 5.57\% & 2.58\% \\  & MAE & 0.2395 & 0.4716 & 0.2248 \\  & RMSE & 0.2867 & 0.558 & 0.2758 \\  & MAPE & 19.64\% & 12.97\% & 11.96\% \\ \multirow{4}{*}{COD\({}_{\text{Mn}}\)} & MAE & 0.4885 & 0.3517 & 0.3160 \\  & RMSE & 0.6853 & 0.4611 & 0.4079 \\ \multirow{4}{*}{NH\({}_{3}\)-N} & MAPE & 16.18\% & 17.40\% & 13.85\% \\ \cline{1-1}  & MAE & 0.0320 & 0.0307 & 0.0268 \\ \cline{1-1}  & RMSE & 0.0427 & 0.0324 & 0.0324 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of water quality index predictions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DO** & **COD\({}_{\text{Mn}}\)** & **NH\({}_{3}\)-N** \\ \hline AI\({}_{\text{ARIMA}}\) & 5.49\% & 39.10\% & 14.40\% \\ AI\({}_{\text{WNN}}\) & 53.68\% & 7.79\% & 20.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Improvement in the predictive ability of the combined model relative to the single model.
## 2 Mathematical Formulation  
Consider a steady, two dimensional, laminar, incompressible fluids over a thin moving needle. The influence of variable heat source/sink and hh reactions should also be considered. The geometry of the problem and its cylindrical coordinates \((x,r)\) are demonstrated in Figure 1. The axial direction \(x\) is parallel to the moving thin needle, and radial direction \(r\) is in the direction of the flow that is normal to it. The width of the needle is smaller than the thickness of the boundary layer formed over it. The impact of the curvature in the transverse direction is of utmost prominence, as the anticipation of the needle is thin. The pressure variation is unkempt along the surface of the needle [36; 37; 38].  
Let \(r=R(x)=\left(\frac{vmx}{U}\right)^{0.5}\) stipulate the radius of the needle. Following [PERSON] and [PERSON] [39; 40], the cubic autocatalysis homogeneous reaction is stated as:  
\[A^{*}+2B^{*}\to 3B^{*},rate=k_{c}a^{*}b^{*}, \tag{1}\]  
and on a catalyst surface, the heterogeneous reaction is indicated as:  
\[A^{*}\to B^{*},rate=k_{s}a^{*}, \tag{2}\]  
where \(A^{*},B^{*}\) are two chemical species with \(k_{c},k_{s}\) as the respective concentrations of these chemical species.  
Keeping in view the prior assumptions, the non-linear partial differential equations (PDEs) that govern the problem are as follows [2; 41; 42; 43]:  
\[\frac{\partial(ru)}{\partial x}+\frac{\partial(rv)}{\partial r}=0, \tag{3}\]  
\[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial r}=\frac{v}{r} \frac{\partial}{\partial r}\bigg{(}r\frac{\partial u}{\partial r}\bigg{)}-Fu^ {2}, \tag{4}\]  
\[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial r}=\frac{\alpha}{ r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial T}{\partial r}\bigg{)}+\frac{1}{ \rho C_{p}}h^{\prime\prime\prime}, \tag{5}\]  
\[u\frac{\partial a^{*}}{\partial x}+v\frac{\partial a^{*}}{\partial r}=D_{A} \bigg{(}\frac{\partial^{2}a^{*}}{\partial r^{2}}+\frac{1}{r}\frac{\partial a^ {*}}{\partial r}\bigg{)}-k_{c}a^{*}b^{*2}, \tag{6}\]  
\[u\frac{\partial b^{*}}{\partial x}+v\frac{\partial b^{*}}{\partial r}=D_{B} \bigg{(}\frac{\partial^{2}b^{*}}{\partial r^{2}}+\frac{1}{r}\frac{\partial b^ {*}}{\partial r}\bigg{)}+k_{c}a^{*}b^{*2}, \tag{7}\]  
The suitable associated boundary conditions [11; 44; 45; 46] are:  
\[u=u_{w},v=0,T=T_{w}=T_{0}+ex,D_{A}\frac{\partial a^{*}}{\partial r}=k_{s}a^{ *},D_{B}\frac{\partial b^{*}}{\partial r}=-k_{s}a^{*},\text{at}\ r=R(x)\]  
\[u\to u_{\infty},T\to T_{\infty}=T_{0}+fx,a^{*}\to a_{0},b^{*}\to 0\,\text{ as }r\to\infty. \tag{8}\]  
The variable heat source/sink \(h^{\prime\prime\prime}\)[47] is articulated as:  
\[h^{\prime\prime\prime}=\frac{\kappa u_{w}}{xy}(C(T_{w}-T_{0})f^{\prime}( \zeta)+D(T-T_{\infty})). \tag{9}\]  
Figure 1: Flow model.
## 3 Results
### Performance of PPI Prediction
#### 3.4.3 Comparison with Other Methods  
There have been many prediction approaches developed for detecting PPIs. To further estimate the capacity of the proposed model, we compared our method with other existing methods. Table 6 shows the results of diverse approaches to the Yeast PPI dataset. The accuracies obtained by other previous methods range from 86.15% to 94.72%. In contrast, our method achieves an average accuracy of 95.64%. Our method obtained a higher PE and Sen than that of eight other methods. In conclusion, compared with all methods from Table 6, our method obtained the highest _Acc_ and _MCC_ in the Yeast PPI dataset.  
Similarly, we compared our method with five other existing methods on the Human dataset. Based on Table 7, which shows the results of diverse approaches on the Yeast PPI dataset, the accuracies obtained by other previous methods range from 89.30% to 95.70%. In contrast, our method achieves an average AUC of 0.9518.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Feature** & **Classifier** & _Acc (\%)_ & _PE (\%)_ & _Sen (\%)_ & _MCC (\%)_ \\ \hline You’s work [50] & MLD & RF & 94.72 \(\pm\) 0.43 & **98.91 \(\pm\) 0.33** & 94.34 \(\pm\) 0.49 & 85.99 \(\pm\) 0.89 \\ You’s work [45] & Multiple & E-ELM & 87.00 \(\pm\) 0.29 & 87.59 \(\pm\) 0.32 & 86.15 \(\pm\) 0.43 & 77.36 \(\pm\) 0.44 \\ You’s work [51] & MCD & SVM & 91.36 \(\pm\) 0.36 & 91.94 \(\pm\) 0.62 & 90.67 \(\pm\) 0.69 & 84.21 \(\pm\) 0.59 \\ [PERSON]’s work [15] & PR-LPQ & RoF & 93.92 \(\pm\) 0.36 & 96.45 \(\pm\) 0.45 & 91.10 \(\pm\) 0.31 & 88.56 \(\pm\) 0.63 \\ [PERSON]’s work [14] & ACC & SVM & 89.33 \(\pm\) 2.67 & 88.87 \(\pm\) 6.16 & 89.93 \(\pm\) 3.68 & N/A \\ [PERSON]’s work [14] & AC & SVM & 87.36 \(\pm\) 1.38 & 87.82 \(\pm\) 4.33 & 87.30 \(\pm\) 4.68 & N/A \\ [PERSON]’s work [52] & LD & SVM & 88.56 \(\pm\) 0.33 & 89.50 \(\pm\) 0.60 & 87.37 \(\pm\) 0.22 & 77.15 \(\pm\) 0.68 \\ [PERSON]’s work [53] & LD & KNN & 86.15 \(\pm\) 1.17 & 90.24 \(\pm\) 1.34 & 81.03 \(\pm\) 1.74 & N/A \\ Our Method & _PSSM_ & RFEC & **95.64 \(\pm\) 0.52** & 96.75 \(\pm\) 0.45 & **94.47 \(\pm\) 0.47** & **91.30 \(\pm\) 1.03** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Different methods tested on the Yeast PPI dataset using 5-fold cross-validation. The numbers which are bold means the largest values of performance.  
Figure 5: ROC curves of the SVM method on the Human dataset. The curve provides Sensitivity (TP Rate) against 1-Specificity (FP Rate).  
Figure 6: ROC curves of the SVM method on the _H. Pylori_ dataset. The curve provides TP Rate (Sensitivity) against 1-Specificity (FP Rate).  
average accuracy of 96.59%. Accordingly, our method outperforms better than most other approaches, which are also based on ensemble classifiers.
## 5 Discussion
### Limitations of the Study
#### 5.2.3 Experimental and Simulation Parameter Control  
A further limitation lies in the control of experimental parameters. While the deep rolling process was carefully monitored, slight variations in tool pressure, speed, or offset could introduce experimental errors. For instance, minor deviations in tool pressure or rolling speed could lead to inconsistent stress application across different trials. These variations, although minor, might explain some of the discrepancies observed between the FEA predictions and the experimental results, especially in terms of stress uniformity and deformation patterns. This issue is consistent with findings from [PERSON], [PERSON] et al. (2018) who reported that even slight fluctuations in rolling tool alignment or surface roughness could lead to significant variations in residual stress patterns. Their work suggests that further automation and tighter control over experimental parameters might minimize these inconsistencies and improve result accuracy. Moreover, [PERSON] et al. (2019) highlighted that surface roughness before deep rolling could affect the residual stress distribution, which could be another potential source of variation not fully controlled in our experiment.
## 3 Methodology
### Evaluation
#### 3.5.2 Partial Dependency Plot (PDP)  
A partial dependency plot (PDP) shows the marginal effect of variables on the predicted outcome of a machine learning model [21]. It is a simple and effective way to visualize interactions of variables and their importance.  
The formulation for a partial dependency plot (PDP) involves plotting the average predicted outcome of a model as a function of a specific variable while holding other variable fixed.  
The following is the formulation for a PDP [21]:  
\[PDP(x_{j})=\frac{1}{n}\sum_{i=1}^{n}\hat{f}(x_{j},x_{-j}^{(i)})\]  
In this equation, \(PDP(x_{j})\) represents the partial dependency for variable \(x_{j}\). \(\hat{f}(x_{j},x_{-j}^{(i)})\) represents the predicted outcome of the model for the given variable value \(x_{j}\) keeping the remaining variables, \(x_{-j}^{(i)}\), fixed. The result is averaged over all \(n\) instances in the dataset.  
By calculating the PDP for a specific variable, we can analyze how the model's predictions change as that variable varies. This helps to understand the relationship between the variable and the predicted outcome while controlling for other variable. Another important use of PDPs is for identifying and detecting potential non-linear relationships or interactions between variables.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 4 Performance Analysis  
This section provides efficient methods of analyzing the proposed scheme. In the first instance, we develop the use of decoding success probability (DSP) to investigate theperformance of the regular LT codes as well as the proposed scheme. Then, we use the DSP to optimize the parameters of the regular LT codes.
# Study on Preparation and Rheological Properties of 3D Printed Pre-Foaming Concrete
## 3 Results and Discussion
### Pore Structure  
The pore structure of 3D printed pre-foaming concrete directly affects the properties of hardened paste, including density, compressive strength and thermal conductivity. Figure 10 presents the pore structure (pore size distribution and volume of Hg intruded) of 3 DFCs as measured by Mercury Intrusion Porosimetry (MIP) for 28-day cured foamed mortar. According to the pore size, the pores can be divided into micropores (diameter \(<\) 10 nm), mesopores (10-50 nm), capillary pores (50-1000 nm) and macropores (>1000 nm) [35]. Form Figure 10a, it can be seen that compared with 3 DFC0, with the increase of surfactant content, there are more capillary pores and macropores in 3 DFCs. At a low-level content of surfactant, the sample of 3 DFC0.5 added more capillary pores and a small number of macropores. With a higher level of surfactant, the critical pore size of capillary pores of sample 3 DFC1, 3 DFC1.5 and 3 DFC2 is lower than that of 3 DFC0. However, the critical pore size for them at macropores increases significantly. This phenomenon can be explained by the rupture between the bubbles introduced by surfactants and the formation of gap separation, resulting in the formation of larger micropores. As can be seen from Figure 10b, the cumulative number of pores of 3 DFCs increases with the increase of surfactant dose. This shows that the foam produced by physical foaming of surfactant can obviously increase the number of pores in cement mortar, so as to achieve the purpose of reducing the density of cement mortar.  
The scanning electron microscope for 3D printed pre-foaming concrete with two different amounts of surfactants is shown in Figure 11. From the scanning electron microscope images of 50 and 100 magnification, it can be seen that there are a large number of 10-500 \(\upmu\)m large pores in the hardened foam mortar, which can not be captured by the MIP test [20]. The micrograph shows the clear difference between the foam pore size and the pore distribution among different hardened mortar. It can be observed from Figure 11, the pore size and the number of large pores are increased with the increase of surfactant dose from 1% to 2%. It is obvious that the increase of foam content increases the pore size and quantity of 3D printed pre-foaming concrete. In addition, it can also be observed that the increase of bubbles leads to the thinning and weakening of the pore wall in the hardened mortar, resulting in the decrease of compressive strength [36].  
Figure 10: _Cont._