esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Various studies have been conducted on large-scale parallel program optimization for the Sunway TaihuLight. A nonlinear seismic simulation, based on the Sunway TaihuLight, has been implemented. Using register communication, SIMD and other optimization schemes, a performance of up to 18.9 PFlops has been realized [14]. A highly scalable full-implicit solver, based on the strong time-dependence problem of atmospheric dynamics, in which a memory-related block optimization of the local data memory (LDM) is utilized to reduce the data movement overhead, has been designed [15]. A performance-aware model for general sparse matrix-sparse matrix multiplication (SpGEMM) to select an appropriate compressed storage formats, according to the performance analysis of SpGEMM kernels, has been proposed [16]. The new projected entangled pair states method (PEPS++) for strongly correlated quantum many-body systems, based on a carefully designed tensor computation library for manipulating high-rank tensors and optimizing them by invoking various high-performance matrix and tensor operations, has been implemented [17]. A 50-m resolution earthquake simulation of the Wenchuan Earthquake (Ms 8.0 China) on the Sunway TaihuLight has been performed [18]. A sparse level tile data layout for controlling all data reuse and a producer-consumer pairing method have been proposed [19]. The training process of convolutional neural networks (CNNs), in which customizeddeep learning (swDNN) and customized Caffe (swCaffe) by architecture-oriented optimization methods are proposed, have been optimized [20]. However, few optimization studies have been conducted on the fluid mechanical algorithm for the Sunway TaihuLight. The SunwayLB for the Lattice Boltzmann Method (LBM) has been proposed [21]. However, this study only optimized LBM and did not implement a specific fluid dynamics algorithm program. Therefore, an efficient algorithm and framework for fluid machinery algorithm is proposed in this paper. For the SW26010 many-core processor, according to the characteristics of the application and based on the Athread library, an SPM access strategy-based direct memory access (DMA) and Serpentine register communication are proposed to fully utilize the slave computing resources. In addition, the framework is implemented and tested by the proposed axial compressor rotor algorithm on a real-world dataset. The proposed algorithm and framework have great significance for the efficient optimization of the fluid mechanical algorithm on computing platforms with the Sunway TaihuLight many-core architecture. The remainder of this paper is organized as follows: The proposed algorithm framework and the optimization method are described in detail in Section 2. Next, Section 3 presents the results of the results and performance comparison analysis of the algorithm and swHFPM framework. In Section 4, comprehensive experiments are conducted to discuss and evaluate the performance of the algorithm swHFPM parallel framework in detail. Finally, the conclusions of this work are presented in Section 5.
## 4 Design Procedure and Application Example
### SMC Control Law  
Since the transversality value is negative, the reachability conditions are defined in (33), which also defines the control law. The reachability condition \(\lim_{\Psi\to 0^{-}}\left.\frac{d\Psi}{dt}\right|_{u=0}>0\) requires that \(u=0\) when \(\Psi<0\), and the reachability condition \(\lim_{\Psi\to 0^{+}}\left.\frac{d\Psi}{dt}\right|_{u=1}<0\) requires that \(u=1\) when \(\Psi>0\). However, the hysteresis band \([-\Delta\Psi,+\Delta\Psi]\) introduced in Section 3.5 modifies the limits of \(\Psi\) to produce changes on \(u\), which defines the final control law, as follows:  
\[u=\left\{\begin{array}{l}\text{if }\Psi\leq-\Delta\Psi\ \rightarrow\ u=0\ \wedge\ \overline{u}=1\\ \text{if }\Psi\geq+\Delta\Psi\ \rightarrow\ u=1\ \wedge\ \overline{u}=0\end{array}\right. \tag{53}\]Evaluating Equation (44) using the characteristics of Table 2, and the parameters previously calculated, leads to \(\Delta\Psi=0.3\) A.
## 2 Materials and Methods
### System Components  
A residual echo suppression system comprises a linear AEC and an RES model. Two linear AECs were compared: NSLMS and NLMS. For residual echo suppression, two alternatives were considered: the proposed RES model and a pre-trained speech-denoising model.
## 2 Literature Review  
### Overview of PV Power Prediction  
These days, there are three primary types of PV power forecasting techniques. Physical approaches based on the PV power model are the first. Techniques like time series analysis, ML, and DL fall under the second group, statistical approaches. Lastly, hybrid approaches that combine statistical and physical methods have been created to improve prediction accuracy [17].
## 3 Real-Time Implementation of FCS-MPC Techniques
### FCS-MPC: Variable-Switching-Frequency Operation  
The FCS-MPC in this section is shown in Figure 4. Voltage and current measurements from the \(LC\) filter establish predictions with a horizon of \(N=1\), determining current and voltage at instant \(k+1\). A delay compensation mechanism enhances controller performance to counteract delays in measurement and calculation.  
The state vector of the power converter in the current period is used to predict voltage and current variables for period \(k+1\). All potential states are then evaluated for a forecast in period \(k+2\), reducing computational load. Values are checked using all state combinations (\(S_{a1}\ldots S_{c2}\)). Figure 4 shows the block diagram of the FCS-MPC for simulations.  
The cost function (\(g\)) represented in Equation (12) is used to minimize the error between the predicted capacitor voltage (\(v_{c}(k+2)\)) and the reference of the capacitor voltage (\(v_{c}^{*}\)) in the reference frame \(\alpha\beta\).  
\[g=(v_{c\alpha}^{*}-v_{c\alpha}(k+2))^{2}+(v_{c\beta}^{*}-v_{c\beta}(k+2))^{2} \tag{12}\]  
Figure 4: Block diagram of the classic FCS-MPC scheme for voltage control of the three-phase 2L-VSI with delay compensation.  
\(v_{\alpha}^{*}\) and \(v_{c\beta}^{*}\) are the reference values for the voltage of the capacitor in the \(\alpha\beta\) reference frame, and \(v_{\alpha}(k+2)\) and \(v_{c\beta}(k+2)\) are the predicted values for the voltage of the capacitor in the time period of \(k+2\) in the reference frame (\(\alpha\beta\)).  
The necessary steps to implement control of the three-phase, two-level VSI, with a primary focus on maintaining the voltage across the capacitor (\(v_{c}\)) of the \(LC\) filter, are clearly depicted in Figure 4 and detailed here:  
1. Obtain measurements of the values for time instant \(k\) of the current in the filter (\(i_{f}\)) and the voltage in the capacitor (\(v_{c}\)).
2. Make predictions for the value of the output voltage and current in the filter for time instant \(k+1\) considering the voltage that the converter applies in the current period.
3. Make predictions of the voltage (\(v_{c}\)) for time instant \(k+2\) for all possible combinations that the converter can generate, using the values calculated at the previous point.
4. Select the state vector that minimizes the cost function (\(g\)).
5. Apply the states found from the minimization of the cost function.
6. Wait for the start of the next sampling time and repeat.
## 3 Results
### Validation of Pleiades-Retrieved Water Reflectances
OLI data were processed using ACOLITE using both DSF and SWIR atmospheric correction methods [33; 39], which both showed a good correspondence to the Pleiades-retrieved reflectances (Figure 6a,b) with R\({}^{2}\) of -0.9 and slope of \(\sim\)1.00. SWIR-derived water reflectances were slightly lower than the DSF-derived ones, with intercepts of 0.018 and 0.011, in the green and red bands. The SWIR atmospheric correction was finally selected for OLI data processing as it provides the best agreement with Pleiades. Pleiades and OLI water reflectances show good agreement (Figure 6c,d): the linear regression slopes are 1.00 for both the green (\(\rho\)561/\(\rho\)558) and red (\(\rho\)655/\(\rho\)655) bands with small intercepts, 0.0016 and -0.005. R\({}^{2}\) values are 0.82 and 0.84, with RMSE of 0.0048 and 0.0079 with MAPE of 5% and 13%. Water reflectance values derived from Pleiades, MODIS/Terra and MODIS/Aqua were also compared over test site 2 (Figure 7). Two clusters are found in the scatter plots, which correspond to the turbid river plume (higher reflectances) and the clearer oceanic waters (lower reflectances). MODIS/Terra and Aqua products show some difference (MAPE \(\leq\) 14%) due to the time difference between data acquisition (Figure 7a,b). Water reflectance values from MODIS and Pleiades data show a good agreement with linear regression slopes of the best-fitted linear relationships close to 1 in the red band, and slightly higher than 1 in the green band, with no significant intercept in either band. The corresponding MAPE is less than 16% (red) and less than 23% (green). This correspondence is reasonable due to the resolution and overpass time differences of the satellites. Figure 6: Comparisons between SWIR and DSF derived water reflectance values for OLI (\(\rho\)561) (**a**) and OLI (\(\rho\)655) (**b**). Comparisons between OLI (\(\rho\)561) and Pleiades (\(\rho\)558) (**c**), OLI (\(\rho\)655) and Pleiades (\(\rho\)655) (**d**) reflectance values over test site 2 on 7 April 2017.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
#### 2.2.1 Logistic Regression  
Logistic regression is one of the well-known techniques introduced from the field of statistics by machine learning [74]. Logistic regression is an algorithm that constructs a separate hyper-plane between two datasets utilizing the logistic function [75]. The logistic regression algorithm takes features (inputs) and produces a forecast according to the probability of a class suitable for the input. For instance, if the likelihood is \(\geq\)0.5, the instance classification will be a positive class; otherwise, the prediction will be for the other class (negative class) [76], as given in Equation (1). In [77; 78; 79; 80; 81], logistic regression was used in the implementation of predictive cyberbullying models.  
\[\mathrm{h}_{\theta}\ (\mathrm{x})\ =\frac{1}{1+\ e^{-\theta^{T}x}}, \tag{1}\]  
if \(\mathrm{h}_{0}\ (\mathrm{x})\geq 0.5\), \(\mathrm{y}=1\) (Positive class)  
and if \(\mathrm{h}_{\theta}\ (\mathrm{x})\leq 0.5\), \(\mathrm{y}=0\) (Negative class)  
As stated in [82], LR works well for the binary classification problem and functions better as data size increases. LR iteratively updates the set of parameters and attempts to minimize the error function [82].
## 5 Numerical Analysis Results and Comparison of Strong Wind Analysis with Real Tornado Action
### Anchorage Analysis of the Rafter  
The analyzed element of the structure as a potential place of destruction is the assembly connection of the rafters. This is a sensitive place, where damage can occur in several ways. The main ones are cutting the mounting anchor, tearing out the fastening, cutting  
Figure 14: Area of projected area.  
the wall plate, or cutting the rafter, which is cut out at this point at a maximum of 1/4 of the cross-section height.  
The following parameters were assumed: rafters 2 \(\times\) 6\", wall lath 4 \(\times\) 4\", anchors 2\(\times\) \(\updownarrow\)12 with a length of 6\". The connection is by means of metal sheets attached to the rafters, as shown in Figure 15.  
At the beginning, the possibility of cutting wooden elements was checked, taking into account the weakening of the profiles.  
1. Fixing weakened by the anchor hole The surface area was reduced by 14%, and the shear stresses (arising from the force calculated in Point 6.3) are equal to \(\tau=2.82\) MPa.
2. Rafter weakened by shear stresses The surface area was reduced by 25%, and the shear stresses (arising from the force calculated in Point 6.3) are equal to \(\tau=4.17\) MPa.  
Comparing the shear stresses with the characteristic shear strength of \(f_{v,k}=4\) MPa, it can be concluded that at the maximum allowable cut of the rafters, the cross-section is exposed to shear failure, as shown in the Table 6.  
The next element is the anchor connecting both beams.  
Based on the forces obtained in Point 6.2, the stresses in the anchor bolt were calculated:  
\[\tau=\frac{F}{S}=\frac{4.368\text{ kN}+0.401\text{ kN}}{\pi\times\left(\frac{12\text{ mm}}{2}\right)^{2}}=\frac{4.77\text{ kN}}{1.13\text{ cm}^{2}}=42.17\text{ MPa} \tag{16}\]  
Comparing the stress value with the shear strength of S235 steel, we obtain:  
\[\frac{\tau}{k_{t}}=\frac{42.17\text{ MPa}}{0.65\times 120\text{ MPa}}=54\% \tag{17}\]  
The calculations show that shearing off the fixing anchor was not the direct cause of the structural failure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & **Stress** & **Shear Strength** & **Bearing Capacity** \\ \cline{2-4}  & **[MPa]** & **[MPa]** & **[\%]** \\ \hline Fixing & 2.82 & 4 & 71\% \\ Rafter & 4.17 & 4 & 104\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bearing capacity due to shear force.  
Figure 15: Scheme of rafter support on the wall: (**a**) front view, (**b**) side view, (**c**) 3D view.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 4 Parametric Analysis
### Effect of \(M_{\rm setup}\) on the Resistance Factor and the Factor of Safety  
\(M_{\rm setup}\) is one of the important factors for analyzing the influence of the resistance factor and the factor of safety. Therefore, in order to study the influence of \(M_{\rm setup}\) on the resistance factor and factor of safety for driven piles in clay and sand, the values of key parameters are determined, such as \(\beta_{\rm T}\) = 2.33 and \(\rho\) = 3.69. It should be noted that the correlation between \(R_{0}\) and \(R_{\rm setup}\) is not considered in this section. Meanwhile, based on the previous discussion of \(M_{\rm setup}\), there are sufficient reasons to make \(M_{\rm setup}\) equal to 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5 and 4.0, respectively. The varying regularity of the resistance factor and factor of safety for the driven piles in clay and sand with \(M_{\rm setup}\) are shown in Figure 3.  
In the distribution curve of \(\phi\) versus \(M_{\rm setup}\), it can be obviously seen that the \(\phi\) of driven piles in sand decreases slowly with the increase in \(M_{\rm setup}\). In the clay, the resistance factor of the driven piles remains basically unchanged with the increase in \(M_{\rm setup}\). Meanwhile, it can be seen from Equations (11) and (12) that the value of \(M_{\rm setup}\) is larger and the value of \(R_{\rm setup}\) is larger. The value of \(M_{\rm setup}\) is determined by the type of soil, so it is concluded that the size of \(M_{\rm setup}\) is adjusted by selecting different soil, and then the size of \(R_{\rm setup}\) is controlled.  
Figure 2: Resistance factor and factor of safety with \(\beta_{\rm T}\) for driven piles in clay and sand.  
In the distribution curve of FOS versus \(M_{\text{setup}}\), it can be obviously seen that the value of FOS of driven piles in sand and clay both increase slowly with the increase in \(M_{\text{setup}}\). It can be further seen that the calculated factor of safety of driven piles in sand is larger than those in clay, and the difference between them is about 20% for a given \(M_{\text{setup}}\). This phenomenon shows that the type of soil has a great influence on the factor of safety for driven piles, which should be emphasized in practical application.  
By comparing the two figures in Figure 3, it can be concluded that the resistance factor of driven piles in sand and clay decreases with the increase in \(M_{\text{setup}}\), while the factor of safety for driven piles in clay and sand increases with the increase in \(M_{\text{setup}}\). Particularly, no matter how \(M_{\text{setup}}\) changes, the resistance factor and the factor of safety remain basically unchanged. Therefore, the final resistance factor of driven piles in clay and sand are 0.25 and 0.19, respectively, and the final factor of safety of driven piles in clay and sand are 1.70 and 2.20, respectively.
# Logistics Space: A Literature Review from the Sustainability Perspective
## 5 Discussion
### Factor Issues  
This paper extracts five factors which can best explain the evolution of logistics space: land price, traffic accessibility, market demand, agglomeration advantage and government policy. In addition, industrial structure, labor level and e-commerce logistics are also important influencing factors in the evolution of logistics space. The original development foundation of the logistics industry comes from the logistics service demand generated by the manufacturing industry, which has derivative characteristics. To select logistics company's site relies on the degree of manufacturing distribution. Because the logistics industry is a labor-intensive industry, the labor cost has an important influence on the location selection or transfer of logistics companies. The rapid development of e-commerce highlights the importance of logistics distribution and supply chain. Online retailers and logistics companies focus on improving the supply chain system and building new types of supply chain infrastructure such as distribution centers and distribution centers in the suburbs. In particular, the popularity of online retailing and logistics companies provide services to end consumers from indirect to direct services. E-commerce warehouses usually occupy a large area to meet the needs of massive commodity categories and one-stop storage. For example: Suning's \"Super Cloud Warehouse\" and Jingdong's \"Asian One.\" Although the automated warehouse system and loft racking system make full use of the storage space, its floor space still exceeds 100,000 m\({}^{2}\), and in 2019, the total storage area of Suning has reached 12 km\({}^{2}\). In order to save operating costs, many e-commerce companies have gradually migrated to the suburbs [76]. The Internet of Things and 5G will promote real-time information-driven logistics activities to optimize logistics resource allocation, reduce energy consumption and distribution distance, and achieve sustainable logistics. Different types of logistics companies are affected by different factors. Warehousing and transportation logistics companies have high requirements for land rent and transportation, while logistics companies engaged in  
Figure 4: The progress of the evolution of logistics space.  
information management tend to be densely populated. This article makes a qualitative assessment of the influencing factors of logistics space. If there is more data to support, quantitative methods can be used to further measure the influence degree of each factor so that to logistics space, a deep understanding of its evolutionary mechanism can be gained.
# Weighted Optimal Quadrature Formulas in Sobolev Space and Their Applications
###### Abstract  
The optimization of computational algorithms is one of the main problems of computational mathematics. This optimization is well demonstrated by the example of the theory of quadrature and cubature formulas. It is known that the numerical integration of definite integrals is of great importance in basic and applied sciences. In this paper we consider the optimization problem of weighted quadrature formulas  
will be another linear functional. The error functional [1; 2] of the cubature formula will also be linear  
\[(\ell,\varphi)=\int_{\Omega}p(x)\varphi(x)dx-\sum_{k=1}^{N}C_{k}\varphi(x^{(k)})\]  
\[=\int_{\Omega}\epsilon_{\omega}(x)p(x)-\sum_{k=1}^{N}C_{k}\delta(x-x^{(k)})] \varphi(x)dx. \tag{1}\]  
The problem of constructing a cubature formula  
\[\int_{\Omega}p(x)\Omega(x)dx\cong\sum_{k=1}^{N}C_{k}\varphi(x^{(k)}) \tag{2}\]  
in the functional formulation consists of finding such a functional (1) whose norm in the space \(B^{*}\) is minimal.  
Studies of optimal and asymptotic cubature formulas are found in [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Optimization studies of quadrature formulas are presented in [12; 13; 14].  
Currently, there are various methods for constructing optimal approximate integration formulas: the spline method, the \(\varphi\) function method and the Sobolev method.  
In recent years, significant progress has been made in developing optimal quadrature formulas and analyzing their error estimates for the approximate evaluation of regular functions [15; 16; 17; 18; 19; 20; 21; 22; 23; 24], singular [25; 26], fractional [27] and integrals from rapidly oscillating functions [28] using the Sobolev method.  
In this paper, the construction of composite optimal quadrature formulas with weight in the Sobolev space is studied using the variational method. Here, the square of the norm of the error functional of composite quadrature formulas with weight function is computed using the extremal function. By minimizing the given norm concerning the coefficients, a system of algebraic equations is derived. The uniqueness of the solution to this system is established. This approach allows for the optimal coefficients of quadrature formulas involving a weight function to be determined.  
In the case where the weight function is equal to one, the coefficients of the well-known Euler-Maclo mean quadrature formula are obtained from the general formula for the optimal coefficients.  
The developed optimal quadrature formula is applied to obtain approximate solutions of certain linear Fredholm integral equations of the second kind, and the results are compared with those presented in [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46].
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase II: Determining the Routing Group Members  
In order to determine the routing group member, a priority function may be implemented based on the communication group in Phase I. From the routing group, a member is selected to perform the information relay (e.g., the nearest one with respect to the sink node). Accordingly, each node has its own routing group members and the relay vehicle. Thus, we obtain:  
\[R_{u}=\{p^{{}^{\prime}}|p^{{}^{\prime}}\in G_{u},\cos Y_{(p^{{}^{\prime}})}\leq \cos\theta^{{}^{\prime}}\} \tag{6}\]  
\[p^{{}^{\prime}}=\arg\min_{p^{{}^{\prime}}\in R_{u}}D_{p^{{}^{\prime}}\rm sink} \tag{7}\]  
Figure 5: The proposed HDTC scheme (**Left**); the vehicle and its group members (**Right**).  
where \(D_{p^{{}^{\prime}}sink}\) is the distance between vehicle \(p^{{}^{\prime}}\) and the sink vehicle, \(\theta^{{}^{\prime}}=\eta\cdot\theta\) and \(0<\eta\leq 1\). The rationale of the parameter \(\theta^{{}^{\prime}}\) is to take the group moving characteristics into account, such that proper candidates for forwarding the message to the sink vehicle can be determined. Note that the position of vehicle \(p^{{}^{\prime}}\) can be estimated by the angle/distance information associated with the position of vehicle \(u\). Therefore, vehicle \(p^{{}^{\prime}}\) is selected as the relay node for vehicle \(u\).  
Figure 7 depicts the flow graph of Phase II, and Figure 8 (right) demonstrates the communication protocol, including initial selection, routing member selection and data dissemination. Figure 8 (left) shows an example for the proposed algorithm in an intersection. The purple node at (510, 150) is the source node, which is going up, and the second purple node at (200, 490) is the sink node, which is going right. The ocean blue nodes are the relay nodes.