esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Experimental identification and multi-mode vibration suppression of a smart flexible manipulator bonded with piezoelectric actuators and strain sensors are conducted in this paper. A reduced order transfer function representing the physical system is proposed, and a first-order inertia element is added to the model for experimental identification. An optimal multi-poles placement controller is proposed and implemented on the structure. Both comparative simulated and experimental results validate the effectiveness and feasibility of the proposed controller. The conclusions of this study can be summarized as follows:  
To describe the smart flexible manipulator system, a reduced order transfer function model with relocated zeros compensating for the neglect of higher modes is proposed. A first-order inertia element is added to the model for system identification. Comparative results show the identified model matches closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved in the identification results. The validation test with an excitation of several sinusoidal signals further validated the excellent performance of the proposed model.  
An optimal multi-poles placement controller is designed and implemented on the structure. A method where the move distance of the closed loop poles is the shortest is proposed, and the optimal locations of the closed loop poles are determined at the best damping ratio 0.707. Compared with different locations of poles, the best performance of the optimal locations of poles is demonstrated by simulations. Experimental results show vibrations of the first two modes of the manipulator are significantly diminished as expected. Moreover, vibrations of the higher modes are also slightly suppressed with the proposed controller. In addition, if a further location of poles is chosen, the spillover effect will be caused due to the control saturation of actuators, and vibrations of the higher modes will be excited. Consequently, the best performance for the optimal locations of the closed loop poles is validated, and the effectiveness and feasibility of the proposed controller are demonstrated.  
This work was supported by the National Natural Science Foundation of China (No. 51505238 and 51375433) and Zhejiang Provincial Natural Science Foundation of China (No. LQ15E050002). The authors would also like to thank the support of Kuan Cheng Wong Magna Fund in Ningbo University, Ningbo, China.  
[PERSON] conceived the experiments; [PERSON] performed the experiments. [PERSON] analyzed the data; [PERSON] Set up the experiments; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 MSP Algorithms and Master Data in Production Planning System
### MSP Algorithm
#### 3.2.1 Linear Regression  
A linear regression algorithm is a regression analysis technique that models the linear correlation between a dependent variable (y) and at least one independent variable (x). It can be a simple linear regression based on an explanatory variable or a multiple linear regression based on more than one explanatory variable [36]. It is called simple linear regression when based on a single explanatory variable, and multiple linear regression when based on more than one explanatory variable. Linear regression uses a linear prediction function to model the regression equation, and the unknown parameters are estimated from the data. Equation (3) a formula is a way to model a linear relationship, and a method is a formula expressed in vector form. The formula for the linear regression algorithm, where \(y\) is the predicted value, \(x\) are the input variables, \(\beta\) are the weights for each variable, and \(b\) is the bias [37]. This form of a linear equation models a linear relationship between the input variables, where the weights represent the importance of each variable. The linear model uses the data to adjust the weights and bias as it is trained. Typically, methods such as Ordinary Least Squares or Maximum Likelihood Estimation are used to estimate the parameters of the model. Linear models are less affected by the scale of the attributes, so data preprocessing is relatively simple.  
In this study, while generating a linear regression formula with tact time as the result of the MSP algorithm, the metadata included the RESOURCE ID and ITEM ID. Meanwhile, the feature was unspecified. Operation tact time, minimum tact time, etc. The target value was the real tact time. Based on these data, a linear regression formula was generated in the form of (3) to obtain the predicted value.  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{n}x_{n}+\epsilon \tag{3}\]  
In this study, the results of the data applied to the production planning system were implemented through machine learning using a formula model (3) to predict the resulting data.  
Figure 6: MSP tree algorithm.
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### The Influence of Uneven Geostress on Casing  
2.1 The Influence of Uneven Geostress on the Maximum Stress Component and Equivalent Stress of Casing  
Figure 12 illustrates that under different eccentricity conditions, as the stress heterogeneity increases, the maximum stress on the outer wall of the casing steadily rises. It can be concluded that eccentricity has a significant influence on casing stress, and increasing stress heterogeneity leads to an increase in the maximum stress on the outer wall of the casing.  
Keeping the eccentricity constant, as the stress heterogeneity increases, the changes in hoop stress and equivalent stress become more pronounced, while the changes in radial stress and shear stress remain relatively stable. Table 8 presents the increase in stress on the outer wall of the casing as the stress heterogeneity \(\lambda\) increases from 1.0 to 1.6. From this, we can conclude that when the eccentricity is fixed, the changes in hoop stress and equivalent stress become more pronounced as the stress heterogeneity increases. Circumferential stress and equivalent stress are more sensitive to the non-uniformity of in situ stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\varepsilon\)**=0.0** & \(\varepsilon\)**=0.3** & \(\varepsilon\)**=0.6** & \(\varepsilon\)**=0.9** \\ \hline \(\sigma_{\text{Max,e}}/\text{MPa}\) & 176.23 & 177.18 & 178.61 & 180.23 \\ \(\sigma_{\text{Min,e}}/\text{MPa}\) & 137.42 & 139.05 & 138.96 & 138.62 \\ K & 1.28 & 1.27 & 1.29 & 1.31 \\ \hline \end{tabular}  
* Footnotes: \(\sigma_{\text{Max,e}}\) is the maximum equivalent stress value, \(\sigma_{\text{Min,e}}\) is the minimum equivalent stress value, and \(K\) is the stress non-uniformity coefficient.  
\end{table}
Table 7: Equivalent maximum stress and stress non-uniformity coefficient under different eccentricities.  
Figure 11: Equivalent stress distribution on the outer wall of casing under different eccentricities.
# A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
## 3 Results
### Antenna Prototype Fabrication and Measurement  
A prototype of the proposed antennas was developed in the Applied Electromagnetics Research Lab at the University of South Alabama, and reflection coefficients were measured using an Anritsu 37369A Vector Network Analyzer (VNA). Figure 5 shows the fabricated prototype of the proposed antenna. Figure 5,b show the S-band and X-band microstrip patches etched on two separate dielectric substrates. These two dielectric layers were placed on top of each other, and four nonconductive screws and bolts were used to ensure that there were no air gaps between them. Nonconductive spacers ensured that a uniform distance between the patch and the partially reflective surface was maintained.  
Figure 3: Performance of the S-band patch of the proposed antenna, (**a**) Return loss (**b**) Axial Ratio and peak RHCP gain vs. frequency (**c**) RHCP gain and LHCP gain patterns at 2016 MHz in the \(\varphi=0^{\circ}\) plane (**d**) RHCP gain and LHCP gain patterns at 2016 MHz, in the \(\varphi=90^{\circ}\) plane.  
Figure 6 compares the return loss from measurement and simulation results. From the return loss graphs, it appears that the fabricated antenna prototype produced similar results. The measured \(S_{11}<-10\) dB bandwidth in the S-band was 16 MHz, which was 30 MHz in the simulation. This difference was due to the slightly higher reflection coefficient of the fabricated antenna compared to the simulated one. In the case of the X-band patch, the measured return loss bandwidth was 270 MHz, whereas the simulation showed a 210 MHz bandwidth. The differences in the simulation and measurement results could be attributed to some fabrication errors, including the misalignment of various layers of this antenna and maybe the presence of some air gaps between layers. The fabricated prototype exhibited good isolation between ports of the S-band and X-band patches (below \(-20\) dB in the S-band and \(-15\) dB in the X-band).  
Figure 4: X-band patch antenna simulated results, (a) return loss (b) AR and peak RHCP gain vs. frequency (c) RHCP, LHCP gain patterns at 8460 MHz in the \(\varphi=0^{\circ}\) plane (d) RHCP, LHCP gain patterns at 8460 MHz in the \(\varphi=90^{\circ}\) plane.  
Figure 5: Fabricated prototype of the proposed antenna, (**a**) S-band patch (**b**) X-band patch (**c**) Proposed antenna (Isometric view) (**d**) Ground plane and SMA probes (viewing from bottom).  
Figure 6: Comparison of measured and simulated return loss of the proposed antenna and measured isolation between two ports: (**a**) S-band patch; (**b**) X-band patch.
## 2 Theoretical Background
### Neural Networks
The output signals from the second layer are used as inputs to the third layer, and so on. Normally, the neurons in each layer only have the outputs of the previous stage as inputs. The set of output signals from the last layer constitutes the generated response of the network to the activation pattern supplied by the nodes of the input layer. Figure 4 shows an example of the structure of a neural network. The learning process involves adjusting the synaptic weights between layers. This adjustment enables the neural network to optimize its performance by minimizing the error between the predicted and actual outputs. We used the stochastic gradient descendent [25; 26] learning algorithm, with backpropagation of the error that consisted of a supervised learning algorithm, in which the error between the predicted and actual outputs is calculated. This error is propagated backward, updating the weights and reducing the error iteratively. The objective of our neural network is to perform tomographic reconstruction from ultrasonic projections, and for this purpose, our inputs were the projections (sinograms), while the outputs were the reconstructed images associated with the projections (see Figure 4). However, one of the main challenges of neural networks is obtaining enough training examples. Therefore, training with real samples could entail a very high cost in terms of both time and money. In this article, as noted, we propose training the neural network with simulated cases in order to reconstruct real cases. For this purpose, some experiments were performed to test the reconstruction method, as discussed in the following section. During the training process, the inputs were our projections or sinograms, while the outputs were the images used to generate the simulated sinograms (with the assignment of different materials based on colors).
# Macroscopic Traffic-Flow Modelling Based on Gap-Filling Behavior of Heterogeneous Traffic
## 5 Simulation Results and Discussion
### Straight Road with No Ingress Traffic  
In this case, there is no incoming traffic, and the existing traffic, which has been introduced to the road section as an initial condition, is leaving the road segment at a very high velocity. The total simulation time, in this case, was reduced to just 2 s. This is because, after a few seconds, all the traffic has left the road section and no vehicles are left on the road. A sudden vacuum is produced, resulting in negative densities, which is not possible. First, the PW model is simulated. The density behavior is shown in Figure 8, which is well within the defined range. However, a very interesting observation can be made here. Despite all the traffic leaving the road section and no vehicles entering it, the density is observed to increase at some distance steps with the passage of time, which is unrealistic. The velocity behavior of the PW model is shown in Figures 9 and 10. Again, the negative velocities, which are highlighted in red in Figure 9, can be observed. Velocities get as low as \(-4.1\) m/s (14.76 km/h) at 0.1 s. The negative velocities are exhibited till 0.35 s. In this case, however, the maximum velocity remains within the defined range and the model does not display any unrealistic behavior as far as the maximum velocity is concerned.  
Figure 8: Density behavior of PW model on straight road with no ingress traffic.  
Figure 7: Velocity behavior of proposed model on straight road with ingress traffic.  
Conversely, when simulated for similar conditions, the proposed model produces robust and realistic density and velocity results, which are within the defined ranges. The density behavior can be observed in Figure 11, while the velocity behavior is depicted in Figures 12 and 13. In the case of velocity, unlike the PW model, there is no negative velocity at any instant of time despite the considerable traffic density. The negative velocities, highlighted in red in Figure 12 and observed towards the end of the simulation period at the start of the road section, are rather meaningless, since there is no traffic density, implying that there are no vehicles on the road at that moment on that particular road section.
# Dynamic Characteristics Analysis of a Rigid Body System with Spatial Multi-Point Supports
## 2 Dynamic Model of a Rigid Body System with Multi-Point Elastic Supports  
### Simplified Model Description  
A simplified rigid body system with spatial multi-point elastic supports is illustrated in Figure 1. The rigid body structure is with the mass (m), the moment of inertia tensor is \(\mathbf{J}=\left(J_{x},J_{y},J_{z}\right)^{\mathrm{T}}\). A global coordinate system \(O\)-\(XYZ\) with the origin (\(O\)) located at the center of the mass is adopted. The rigid body displacement \(\mathbf{u}=\left(\mathbf{r},\mathbf{\theta}\right)\) is composed to the displacement \(\mathbf{r}=\left(r_{x},r_{y},r_{z}\right)^{\mathrm{T}}\) and the rotation \(\mathbf{\theta}=\left(\theta_{x},\theta_{y},\theta_{z}\right)^{\mathrm{T}}\) around the center of mass. The rigid body is mounted by N supports, and the distance vector from each support to the center of mass is \(\mathbf{d}_{n}=\left(d_{nx},d_{ny},d_{nz}\right)\). Each support provides a three-dimensional elastic constraint \(\mathbf{k}_{n}=diag(k_{nx},k_{ny},k_{nz})\). A local coordinate system is built located on each support to facilitate subsequent stiffness component calculations for each support. Take the top-left support for example, and the local coordinate system A\({}_{0}\)-X\({}_{\mathrm{A}}\)Y\({}_{\mathrm{A}}\)Z\({}_{\mathrm{A}}\) is built, where \(k^{\prime}{}_{Ax}\), \(k^{\prime}{}_{Ay}\), and \(k^{\prime}{}_{Az}\), respectively represent top-left support stiffness components along three directions of the local coordinate axes \(x_{\mathrm{A}}\), \(y_{\mathrm{A}}\), and \(z_{\mathrm{A}}\), as shown in Figure 2.
## 4 Conclusions  
In this study, six samples with known volume fractions of compositions were imaged by laboratory X-ray CT equipment. Optical microscopic images of the samples showed the presence of a large number of particles smaller than the CT voxel size. Volume fractions of each composition in the samples were computed using DCM, and the results were compared with the results of image threshold segmentation algorithms. The main conclusions are as follows:  
1. The X-ray theoretical linear absorption coefficients of different compositions in the DCM can be determined effectively by the equivalent energy method. For the sample containing a single silica composition, combining DCM with laboratory micro-CT allows accurate determination of the volume fractions of silica and pores in the sample.
2. For silica samples with added iron particles, the volume fractions of pore and iron particles can be obtained by constraining the self-energy parameters of silica in the DCM. The calculation accuracy of the volume fractions is related to the content of iron. The accuracy of DCM calculations decreases slightly when the iron particle contents are 10.24% and 16.90%, due to the influence of the partial volume effect and the point spread function.
3. A comparison between DCM and automatic image threshold segmentation algorithms reveals significant advantages of DCM when handling images containing numerous particles smaller than CT voxels. Unlike simple binary segmentation (0 or 1), DCM provides volume fractions of each composition in each voxel. This enables the quantitative assessment of the multi-scale structural information of the samples. This method facilitates the establishment of multi-scale structural three-dimensional grids for numerical simulations of the physical and chemical properties of the samples.  
Conceptualization, H.W. and Y.-S.Y.; methodology, H.W. and Y.-S.Y.; software, X.M.; validation, X.Z.; formal analysis, X.Z.; investigation, X.M.; resources, H.W.; data curation, X.M.; writing--original draft preparation, H.W. and X.M.; writing--review and editing, Y.-S.Y.; visualization, X.Z.; supervision, H.W. and Y.-S.Y.; project administration, H.W. and Y.-S.Y.; funding acquisition, H.W. and Y.-S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the carbon-based electronic technology research and development project of the Institute of Carbon-Based Thin Film Electronics, Peking University, Shanxi.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors declare no conflicts of interest.
###### Abstract  
With technological advances in the field of communication, the need for reliable high-speed data transfer is increasing. The deployment of large number of wireless sensors for remote monitoring and control and streaming of high definition video, voice and image data, etc. are imposing a challenge to the existing network bandwidth allocation for reliable communication. Two novel schemes for ultra-wide band (UWB) communication technology have been proposed in this paper with the key objective of intensifying the data rate by taking advantage of the orthogonal properties of the modified Hermite pulse (MHP). In the first scheme, a composite pulse is transmitted and in the second scheme, a sequence of multi-order orthogonal pulses is transmitted in the place of a single UWB pulse. The MHP pulses exhibit a mutually orthogonal property between different ordered pulses and due to this property, simultaneous transmission is achieved without collision in the UWB system, resulting in an increase in transmission capacity or improved bit error rate. The proposed schemes for enhanced data rate will offer high volume data monitoring, assessment, and control of wireless devices without overburdening the network bandwidth and pave the way for new platforms for future high-speed wireless sensor applications.  
UWB; modified hermite pulse; orthogonal pulse; correlation; bit error rate +
Footnote †: journal: Low Power Electronics and Applications
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 3 Network of Networks in Virtualized Networks
### Model of Network of Networks with Network Slicing  
The PV-NoN model defines the availability state of interfaces to deal with traffic fluctuations and interdependence among a PN and VNs. Here, the word _available_ denotes the state in which packets are transmitted at interfaces with no packet overflow. In the following sections, we describe the definitions of the input states of V-interfaces and P-interfaces and the availability states of V-interfaces calculated based on those input states.  
Figure 3: Example of virtualized network based on network slicing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Type-SD** & **Type-UD** & **Type-DD** \\ \hline Interdependence of PN-VN & yes & yes & yes \\ Interdependence of VN-VN & no & yes & yes \\ Utilization guarantee & yes & no & yes \\ Utilization efficiency & no & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of three resource partitioning schemes for PV-NoN model.
## 4 Simulation Results
### Battery State-of-Charge (SOC) Variation  
Figure 3 illustrates the monthly variation in the battery's SOC for the three MPPT methods: ANN-based MPPT, RL-based MPPT, and P and O MPPT method. The SOC is expressed as a percentage and plotted against the months of the year. It can be seen from the figure that the RL-based MPPT method demonstrates the most stable SOC profile throughout the year, maintaining the battery within the optimal SOC range (20% to 90%). This indicates that the RL algorithm effectively balances energy generation and consumption, minimizing energy losses and ensuring reliable battery operation. It is clear that the ANN-based MPPT method also performs well, with slight fluctuations in SOC during the winter months (e.g., December and January). These fluctuations are attributed to lower solar irradiance and shorter sunlight hours, which reduce PV energy production. The conventional MPPT method exhibits the most significant SOC variations, with the battery frequently approaching the minimum SOC limit during winter. This highlights the limitations of traditional methods in adapting to dynamic environmental conditions. The results suggest that advanced MPPT methods, particularly RL, are more effective at managing battery SOC, ensuring reliable energy storage and utilization.
## 3 Results and Discussion  
### Analysis of Past Drought Events  
The occurrence of drought episodes, based on the classification of Vegetation Condition Index aggregated over 3-months (VCI3M) as the proxy variable for drought conditions is shown in Table 4. The thresholds used are documented in [PERSON] and Atzberger [22], [PERSON] et al. [40] and [PERSON] et al. [42].  
Figure 5: Outline of the ANN modelling process. The process sequentially inputs selected GAM models and the panel dataset followed by the iteration of the performance of the models against the data. The data is randomly partitioned in the ratio 70:30 for training and validation, respectively, for each and every iteration of the \(k\) times a model is run against the data. The \(k\)-fold iteration was chosen to minimize impacts of the random initialization of the network weights.  
Over the 178 months period, 377 out of a possible 712 (52%) drought episodes are reported at county level, 29 (4%) of which are classified as extreme (VCI \(<\) 10) and are therefore signaling a possible collapse of community coping capabilities.