esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 4 Fundamental Limits of Caching: A Cross-Layer Perspective
### Communication Gains  
Proactive caching prolongs the transmission time, which enables many possible energy- and/or spectral-efficient physical layer techniques. We are interested in how a content item is pushed given its RDI and what its EE/SE limit is. Quantitative case studies on the EE of pushing over additive white Gaussian noise (AWGN), multiple-input single-output (MISO), and fading channels are presented in [15; 16; 17], respectively. A user that tolerates a maximal delay of \(T\) seconds may request a content item having \(B\) bits. The AWGN channel has a normalized bandwidth and power spectral density of noise. For on-demand transmission, the data rate should be no less than \(R=\frac{B}{T}\) (bit/s), and its EE is upper bounded by \(\eta_{AWGN}=\frac{R}{2^{K}-1}\) (bit/J). When caching is enabled, we shall also refer to the AWGN channel as the pAWGN channel. A decision should be first made whether a content item deserves pushing. To avoid wasting energy, a content item with a demand probability lower than \(\frac{R}{2^{R}+R+\frac{E|X|}{T}+1}\) should not be pushed [15].  
For content items that deserve pushing, we should further optimize the transmission powers and rates of the placement and delivery phases. Given the delay constraint \(T\), the more bits that are pushed, the fewer bits that should be delivered after being requested. To maximize the overall EE, we should balance the rates and EEs of the two phases by solving a one-dimensional DC (difference of two convex functions) program [15]. Scaling properties may further reduce the complexity of scheduling in pAWGN channels. Let \(\eta^{*}\) denote the EE of a pAWGN channel with RDI \(p(x)\) and \(\alpha=1\). Then the EE of a pAWGN channel with RDI \(ap(x)+(1-\alpha)\delta(x-0^{-})\) is lower bounded by \(\alpha\eta^{*}\). Further, a pAWGN channel has the same EE as its normalized version with unit delay constraint, content size \(R\), and scaled RDI \(Tp(Tx)\). The lower-left sub-figure of Figure 1 presents \(\eta^{*}\) of normalized pAWGN channels and enables a table lookup approach to obtain \(\eta^{*}\). In practice, lazy scheduling low-order modulations can be adopted in pAWGN channels.  
The RDI prediction enlightens more quantitative studies of the cross-layer design of energy- and spectral-efficient pushing with opportunistic scheduling, physical-layer multicasting, OSA, and EH. Although these underlying physical-layer techniques support discontinuous data transmission only, caching provides a virtual real-time experience for users as if they receive their requested data immediately.
## 1 Introduction
However, in the presence of large load disturbance and uncertainties, its switching gain becomes higher, which leads to the production of a large amplitude of chattering. The computing techniques yield a high tracking efficiency, but the whole system cost becomes too expensive. The backstepping technique has recently attracted considerable attention. It is a recursive design methodology developed in 1990 by [PERSON] and his coauthors for designing stabilizing controls. It has become an important robust algorithm due to its ability to control chaos and its flexibility in the construction of control law. It is commonly used for numerous applications, especially nonlinear uncertain systems (e.g., PEMFC power systems) [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. In [48], it is used for a smart grid-connected distributed photovoltaic system. It is designed to track the PV array maximum power point in order to power up the telecom towers. Similarly, it is also proposed in [49; 50] to track the MPP reference voltage, which is generated using the incremental conductance algorithm. In [51], it is used to adapt the turbine speed at its maximum generator speed value. However, pitch control and proportional-integral (PI) regulators are used to determine the optimal specific speed at which the turbine generates its maximum power. In [55], it is proposed for a PV water desalination system. It dissipates the maximum produced power in a resistive load to generate heat, which is then used for the desalination process. In [59], it is proposed to reduce the steady state error, which degrades the efficiency of the MPPT controller. The authors used a regression plane to determine the reference voltage, which corresponds to the peak power.The current paper proposes a new MPPT method based on a backstepping algorithm to keep the system functioning at its optimal power point. The main feature of this method is its simplicity, robustness, and high tracking performance, confirmed by the obtained results. The global system including the fuel cell, the DC-DC converter, and the controllers are presented, modeled, identified, and then tested under a Matlab-SimulinkTM environment. The obtained simulation results are analyzed and discussed. Finally, some conclusions are made and future works are suggested.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 3 Results
### Effects of Clustering Method  
Firstly, K-means clustering was implemented on the original data to classify detector links into different groups based on their speed measurements during the training period. Figure 7a shows the results of the elbow method which implied that two is more likely to be the optimal number of clusters, and Figure 7b displays the cluster results for all observations projected onto the dimension of two principal components. As can be seen, there are overlaps between the two clusters. Figure 7c displays the centroids of two clusters, respectively. The red line is the center of the first cluster, with speed around 70 mph, and the speed of the second cluster's centroids, shown in blue, fluctuates around 50 mph.  
Figure 7: Clustering results: (**a**) elbow method showing total with-cluster sum of squares versus number of clusters; (**b**) clustering results showing two principal components of observations; (**c**) cluster centroids versus time index.  
After clustering, the normal CUR process, with three different options, was conducted to explore the effects of incorporating clustering into CUR decomposition. Figure 8 displays the comparison of the performance of the three options with and without clustering method. It was found that clustering almost failed to improve the performance for the SVD-based leverage and weighted average option, while the L2 norm-based option saw a minor enhancement when the CR was less than 16. This finding is somewhat intuitive and contradictory to [PERSON]'s work [10], partly because [PERSON] only tested his method on CRs from 2 to 10. The reason why clustering failed to contribute will be discussed in the discussion section.
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
## 4 Adaptive Control of the Aligning Process for Large Cylindrical Components
* **Adaptive control aligning strategy based on geometric and physical aligning state information:** the aligning state of large components needs to be characterized simultaneously based on the pose error of geometric quantities and the assembly stress of physical quantities. Thus, in the control strategy, both geometric and physical assembly information, i.e., the basic positioning error constraint and the aligning stress constraint for the large component, need to be considered. A comprehensive evaluation function for the quality of large component aligning is constructed, thereby obtaining the optimal pose-adjustment parameters. The basic positioning error constraint (\(\Delta e^{t}(x,y,z,\alpha,\beta,\gamma)\)) is the difference between the current positioning accuracy and the target positioning accuracy of the large component in the current adjustment cycle \(t\). The assembly stress constraint (\(F^{t}(x,y,z,\alpha,\beta,\gamma)\)) refers to the influence of different pose states of the large component on the variation of the aligning stress. Based on the mapping relationship between the pose and aligning stress, the aligning stress at the end of the next cycle  
In summary, based on the above algorithm, through the multi-source heterogeneous data acquisition, state perception, pose-adjustment parameter planning and optimization, force position collaborative control, and trajectory error compensation during the aligning process of large cylindrical components, the autonomous decision and accurate execution of the adaptive aligning of large components are realized.
## 2 Modeling and Parking Path Planning
### Optimization of Parking Path Curvature  
Based on the preview theory, the curvature outlier can be found in the path, i.e., the abrupt change point of the steering wheel. During the advanced and delayed time \(\tau\) of the steering wheel angle step input, connect the curves before and after the curvature outliers with a sinusoidal that conforms to the changing law of the steering wheel angle. In this way, the curvature of outliers is corrected from step signal to gradient signal [28; 29; 30].  
In order to correct the curvature of the path, it is necessary to obtain the coordinate of the curvature outliers in the path in advance, and make sure that the driving speed is low and stable during parking. Therefore, the preview distance can be set to \(v_{0}\tau\). In the case that there is no curvature outlier within the preview distance, no path correction will be carried out. In contrast, if there is a curvature outlier within the preview distance, the path curvature will be corrected with the correction algorithm [31]. As shown in Figure 1, the curvature \(\rho_{0}\) refers to the input data at time \(t_{0}\), which is predicted before time \(\tau\). It is assumed that there is a step signal of curvature from \(\rho_{0}\) to \(\rho\) in the preview distance. The curvature of the planned parking path is corrected immediately. That is, the steering wheel angle input time is corrected and changed in terms of rate. The effect after correction is shown in Figure 3 by dotted line.  
The correction curve is part of the sinusoidal. Based on the diagram in Figure 6, it is assumed that the expression of the correction curve is as follows:  
\[y=\frac{\rho-\rho_{0}}{2}\sin(\omega t+A)+B \tag{8}\]  
The period of the sine function is \(T=4\pi\), hence \(\omega=~{}2\pi/T=\pi/2\tau\), therefore substituting \(\omega\) into Equation (1), the following equation can be obtained:  
\[y~{}=~{}\frac{\rho-\rho_{0}}{2}\sin(\frac{\pi}{2\tau}t+A)+B \tag{9}\]  
Figure 5: Parallel Parking Path Simulation Result.  
Substitute the points \((t_{0},\rho_{0})\) and \((t_{0}+2\pi,\rho)\) on the curve into Equation (2), the parameters of A and B can be obtained by solving the equations:  
\[A=-\frac{\pi}{2}(\frac{t_{0}}{\tau}+1),\ B=\frac{\rho+\rho_{0}}{2}\]  
The expression of the curvature correction curve can be derived as follows, by substituting the parameters of A and B into Equation (9):  
\[y=\frac{\rho-\rho_{0}}{2}\sin\left[\frac{\pi}{2\tau}(t-t_{0}-\tau)\right]+ \frac{\rho+\rho_{0}}{2} \tag{10}\]  
As shown in Figure 7, when \(\tau=1.0\) s, the steering wheel angle change the curve before and after preview correction. The blue dotted line indicates the case before the preview correction, and the red solid line indicates the case after the preview correction. As per the results, the corrected steering wheel angle changes uniformly without step changes.
## 3 Methods and Techniques
### Proposed Method for Lung Nodule Localization
#### 3.1.3 Noise Removal  
The image's visual and overall quality is improved by noise reduction. The image filtering methods used in this strategy are intended to enhance the overall picture quality. The quality of a picture degrades when there is noise present, which also has the effect of decreasing the colorization performance requirements. An adaptive median filter is used to remove noise. This filter uses the average formula to help change the pixel value to correspond with the area/cluster-wide weighted average. An image's quality increases as noise is removed from it. The adaptive median filter is simple and understandable. It reduces the density variance and modulates the clusters' mean values. Additionally, it eliminates noise by using each picture pixel's definition. A weighted average value is used to replace each missing pixel value, which is then adjusted based on how close it is to other pixels in the picture.
###### Abstract  
The efficiency and stability of perovskite solar cells (PSCs) depend not only on the perovskite film quality, but they are also influenced by the charge carriers of both the electron and hole transport layers (ETL and HTL). Doping of the carrier transport layers is considered one of effective technique applied to enhance the efficiency and performance of the PSCs. FK209 cobalt TPSI and lithium TPSI salt were investigated as dopants for mesoporous TiO\({}_{2}\) (M-TiO\({}_{2}\)) in the ETL. Herein, FK209 cobalt doping offers improved conductivity, reproducibility and stability compared to other doping or undoped M-TiO\({}_{2}\) control device. It has been found that an optimum concentration of 2.5 mg FK209 cobalt in the M-TiO\({}_{2}\) has resulted in an efficiency of 15.6% on 0.36 cm\({}^{2}\) active device area, whereas, the undoped M-TiO\({}_{2}\) yielded an average efficiency of 10.8%. The enhanced efficiency is due to the improved conductivity of the ETL while maintaining high transparency and low surface roughness with FK209 doping. The M-TiO\({}_{2}\) doped with FK209 has a transparency of the 90% over the visible range and its measured energy gap was 3.59 eV. Perovskite films deposited on the M-TiO\({}_{2}\) doped with FK209 has also a lower PL intensity indicating faster charge extraction. The measured lifetime of the perovskite films deposited on the optimised M-TiO\({}_{2}\) film was 115.8 ns.  
electron transport layer; titanium dioxide; FK209 cobalt; perovskite solar cells; doping technique
## 3 Experimental Results
### Experimental Data Processing
#### 3.1.1 Image Preprocessing  
Low contrast and noise often appear in the transmission process of surface defect images of strip steel, so it is necessary to filter and enhance the original images. In this paper, the bilateral filter method [39] removes the low-frequency scanning noise in the image without destroying the edge information of the image, and a gray histogram is used to determine the gray transformation mapping curve [40]. The gray value of the image is adjusted and its contrast is stretched to enhance the image, and the OTSU is used to perform adaptive threshold segmentation. To facilitate observation, the defect areas with prominent features are marked with red wireframes, and the defect features are enhanced after preprocessing. The image preprocessing results are shown in Table 1.
## 4 Results and Analysis
### Simulation Results of Arch Culvert Construction
#### 4.1.2 Soil Stress and Pore Water Pressure  
Figure 14 shows the vertical stress curve recorded by the data recording point DD (Point DD as shown in Figure 5). Figure 15 shows the vertical stress contour after construction. The distribution of vertical stress exhibits only slight fluctuations in the shallow soil, primarily at the edge of the arch culvert area and the junction of the larger and smaller arch culverts. Because in the excavation area of the foundation pit, the self-weight load of the arch culvert is slightly less than that of soil and water. Therefore, the load generated by the lake filling is regarded as the historical maximum load of the foundation, the load generated by the arch culvert construction after the excavation is the normal consolidation pressure of the foundation. Figure 16 shows the outline of pore water pressure distribution after construction. Based on the results of this study, it can be observed that the pore water pressure of the bearing layer of the composite foundation along the length of the arch culvert is slightly lower. However, the pore water pressure is significantly higher in other areas. It should be noted that the influence of foundation treatment was not considered in this study, and the pore water pressure may not have had enough time to dissipate. The higher pore water pressure observed in the study may be related to the construction time and the lower permeability coefficient of the reinforced area.  
Figure 14: Vertical stress recording curve (unit Pa).  
Figure 13: Horizontal displacement contour (unit: mm).  
Figure 15: Post-construction vertical stress contour (unit: Pa).
# Friction and Wear Performances of Materials for Wind Turbine Sliding Bearing Bushes
## 4 Wind Turbine Sliding Bearing Bush Friction and Wear Properties Analysis
### Comparative Analysis of Friction and Wear Properties of Two Groups of Bearing Bush Materials
Commonly used in material selection, the PSI method integrates performance indexes such as wear rate, average friction coefficient, and standard deviation of the friction coefficient for both groups of coatings. All performance parameters were standardized, and the overall priority index was subsequently calculated. The results indicate that the overall priority index for the CoCrFeNiMo high-entropy alloy coating is 0.71419, whereas the overall priority index for the Ni-Cr-Mo-Si alloy coating is 0.9736. In conclusion, the overall priority index of the CoCrFeNiMo high-entropy alloy coating surpasses that of the Ni-Cr-Mo-Si alloy coating. However, it is essential to note that the friction and wear properties of the Ni-Cr-Mo-Si alloy coating are superior. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing Bush Material** & **Properties Parameter** & **30 N** & **40 N** & **50 N** \\ \hline CoCrFeNiMo & Mean friction coefficient & 0.4987 & 0.4664 & 0.4545 \\ High-entropy alloy coating & Standard Deviation of friction coefficient & 0.03591 & 0.0272 & 0.0202 \\  & Wear rate & 1.81\% & 0.52\% & 0.62\% \\ Ni-Cr-Mo-Si & Mean friction coefficient & 0.4986 & 0.5243 & 0.5144 \\ Alloy coating & Standard Deviation of friction coefficient & 0.0361 & 0.0150 & 0.0098 \\  & Wear rate & 0.30\% & 0.33\% & 0.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of friction and wear data of the two groups of material coatings.
## 3 Single Linear System
### Stabilization of Non-Autonomous Systems  
When \(B(\cdot)\) is a non-zero matrix, and system (6) is non-autonomous, the following theorems are obtained.  
**Theorem 3**: _The constants are \(T,\rho,\lambda\), \(\alpha>0\) with \(\rho\alpha<1\), and vector-value function \(\eta(t)\succ 0\). If there exist vector-valued functions \(v(t),\theta(t),\pi(t):\mathbb{R}_{+}\to\mathbb{R}_{+}^{n}\) and \(\theta(t):\mathbb{R}_{+}\to\mathbb{R}^{n}\)such that_  
_(1)_ \(\tilde{A}(t)+\delta I_{n}\succeq 0\)_,_  
_(2)_ \(\hat{v}^{\mathsf{T}}(t)+v^{\mathsf{T}}(t)\tilde{A}(t)+\pi^{\mathsf{T}}(t)+ \theta^{\mathsf{T}}(t)\prec 0\)_,_  
_(3)_ \(v^{\mathsf{T}}(t)H(t)+\theta^{\mathsf{T}}(t)-\rho\xi^{\mathsf{T}}(t)\preceq 0\)_,_  
_(4)_ \(\lambda\pi^{\mathsf{T}}(t)+\theta^{\mathsf{T}}(t)+\theta^{\mathsf{T}}(t) \tilde{A}(t)+\lambda\theta^{\mathsf{T}}(t)\succeq 0\)_,_  
_(5)_ \(\lambda\theta^{\mathsf{T}}(t)+\theta^{\mathsf{T}}(t)H(t)\succeq 0\)_, and_  
_(6)_ \(\eta^{\mathsf{T}}(t)C(t)-\alpha v^{\mathsf{T}}(t)\preceq 0\)_,_  
_hold, then under control law \(u(t)=K(t)x(t)\), the closed-loop system (7) is positive and there is IO-FTS w.r.t. \((\omega_{1},\eta(\cdot),T)\). Therefore, the system (6) is positive and stabilizable, where \(\tilde{A}(t)=A(t)+B(t)K(t)\), \(H(t)\succeq 0\), \(C(t)\succeq 0\), \(\delta>0\), \(x(t_{0})=0\), \(v(\cdot)\in\omega_{1}\), and \(t\in[t_{0},T]\)._  
From Lemma 2 and condition (1) of Theorem 3, we know \(\tilde{A}(t)\) is a Metzler matrix, \(H(t)\succeq 0\), \(C(t)\succeq 0\), and \(v(t)\succeq 0\), which means system (7) is positive. Then, under the control law \(u(t)=K(t)x(t)\), replacing \(A(t)\) in Theorem 1 with \(\tilde{A}(t)\), we can get condition (2) of Theorem 3. Therefore, system (7) is positive, there is IO-FTS w.r.t. \((\omega_{1},\eta(\cdot),T)\), and system (6) is positive and stabilizable.  
**Theorem 4**.: _The constants are \(T,\rho,\lambda,\alpha>0\) with \(\rho\alpha<1\), and the vector-value function \(\eta(t)\succ 0\). If there exist vector-valued functions \(v(t),\theta(t),\pi(t):\mathbb{R}_{+}\to\mathbb{R}_{+}^{n}\) and \(\theta(t):\mathbb{R}_{+}\to\mathbb{R}^{n}\)such that_  
1. \(\tilde{A}(t)+\delta I_{n}\succeq 0\)_,_
2. \(\psi^{\rm T}(t)+v^{\rm T}(t)\tilde{A}(t)+\pi^{\rm T}(t)+\theta^{\rm T}(t)\prec 0\)_,_
3. \(v^{\rm T}(t)H(t)+\theta^{\rm T}(t)-\rho\xi^{\rm T}(t)\preceq 0\)_,_
4. \(\lambda\pi^{\rm T}(t)+\theta^{\rm T}(t)+\theta^{\rm T}(t)\tilde{A}(t)+\lambda \theta^{\rm T}(t)\succeq 0\)_,_
5. \(\lambda\theta^{\rm T}(t)+\theta^{\rm T}(t)H(t)\succeq 0\)_, and_
6. \(\tilde{\eta}^{\rm T}(t)C(t)-\alpha v^{\rm T}(t)\preceq 0\)_,_  
_hold, then under the control law \(u(t)=K(t)x(t)\), the closed-loop system (7) is positive and there is IO-FTS w.r.t. \((\omega_{\infty},\eta(\cdot),T)\). Therefore, system (6) is positive and stabilizable, where \(\tilde{A}(t)=A(t)+B(t)K(t)\), \(H(t)\succeq 0\), \(C(t)\succeq 0\), \(\tilde{\eta}(t)=(t-t_{0})\eta(t)\), \(\delta>0\), \(x(t_{0})=0\), \(v(\cdot)\in\omega_{\infty}\), and \(t\in[t_{0},T]\)._  
By similar analysis of Theorem 3, we can obtain the desired results.
## 2 Numerical and Experimental Methods
### Application of XFOIL  
The unsteady simulation of the flow is time-consuming. One popular alternative in the airfoil design is the use of XFOIL to simulate steady-state airfoil aerodynamics. XFOIL is an open-source tool that can be executed in Fortran [22]. Similar to the current VPM, the airfoil body is represented by vortex panels, but with additional source elements to represent the inviscid flow atop the boundary layer. Then, a serious of boundary-layer equations are coupled to obtain the final solution [21]. The advantages of using this program are its speed and the capability of simulating a series of different angles of attack at once.