esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 4 Structural Design of 2 DOFs TMD
### Parametric Design of 2 DOFs TMDs
\right] \tag{34}\]  
Figure 10: Coordinate systems of the slender beams: (**a**) local coordinate; (**b**) upper view of global coordinate; (**c**) front view of global coordinate. where \(\mathbf{T}\) is the translation matrix. \(\mathbf{R}_{x,y,z}(\theta)=\mathbf{R}_{x}(\theta)\mathbf{R}_{y}(\theta)\mathbf{R}z(\theta)\), which is the multiplication of rotation matrices. \(\mathbf{R}_{x}(\theta)\), \(\mathbf{R}_{y}(\theta)\), and \(\mathbf{R}z(\theta)\) stand for the rotation matrices by an angle \(\theta\) about the \(x\), \(y\), and \(z\) axis, respectively. They are given in Equation (35):  
\[\mathbf{R}_{x}(\theta)=\left[ \begin{array}{ccc}1&0&0\\ 0&\cos\theta&-\sin\theta\\ 0&\sin\theta&\cos\theta\end{array} \right],\quad\mathbf{R}_{y}(\theta)=\left[ \begin{array}{ccc}\cos\theta&0&\sin\theta\\ 0&1&0\\ -\sin\theta&0&\cos\theta\end{array} \right],\quad\mathbf{R}_{z}(\theta)=\left[ \begin{array}{ccc}\cos\theta&-\sin\theta&0\\ \sin\theta&\cos\theta&0\\ 0&0&1\end{array} \right]. \tag{35}\]  
For the vector \((\Delta x,\,\Delta y,\,\Delta z)^{T}\) between two position, the translation matrix can be expressed as  
\[\mathbf{T}=\left[ \begin{array}{ccc}0&-\Delta z&\Delta y\\ \Delta z&0&-\Delta x\\ -\Delta y&\Delta x&0\end{array} \right], \tag{36}\]  
Based on Equations (33)-(36), the global compliance matrix of TMD is computed by:  
\[\mathbf{C}_{s}=\frac{L_{2}}{EI_{y}}\left[ \begin{array}{cccc}c_{11}&&&\\ &c_{22}&&&\\ &&c_{33}&&&\\ &&&c_{44}&\\ &&&c_{55}&\\ &&&c_{66}\end{array} \right]. \tag{37}\]  
The principal diagonal elements of \(\mathbf{C}_{s}\) are selected as follows:  
\[\begin{array}{l}c_{11}=\frac{L_{2}^{2}\beta_{2}s^{3}}{16}\Big{(}\frac{1}{ \beta_{2}L_{2}^{2}s^{3}+\beta_{2}L_{2}^{2}s^{2}+3\beta_{2}L_{3}s^{2}+\frac{1}{ 3\beta_{2}L_{2}d_{1}}s^{3}+3\beta_{2}L_{1}^{2}+3\beta_{2}L_{3}^{2}s^{3}+3\frac {d_{4}^{2}s^{2}}{4}}\Big{)}\\ c_{22}=\frac{L_{2}^{2}s^{3}}{16}\Big{(}\frac{1}  
In general, the size of TMD should be smaller and not occupy the space of bridge as much as possible. Thus, considering the processing conditions and the size of deck, we choose \(t=w=1\)mm. The mass block is iron and \(m_{d}=0.18\) kg. The material of slender beams is Acrylonitrile Butadiene Styrene (ABS), and the elastic modulus \(E=2\) GPa, \(\chi=0.37\), the [PERSON]'s ratio \(\
u\) is 0.394. In addition, in order to simplify calculation and TMD design, let \(d_{4}=d_{2}\). The calculation results of 2 DOFs TMDs are summarized in Table 5. It is noticed that the three-order natural frequency \(\omega_{3}\) are about 7 times greater than \(\omega_{2}\), and \(\omega_{4}\) is much larger than \(\omega_{1}\) and \(\omega_{2}\). Therefore, the undesired DOFs can be neglected, and the expected 2 DOFs TMDs are obtained. According to the dimension parameters, FE model of 2 DOFs TMDs are built as shown in Figure 11. The mode shapes of two expected TMDs are shown in Figures 12 and 13, respectively.
## 5 System Implementation and Configuration
### Technical and Economic Criteria of the SPV/WTG System
#### 5.4.2 SPV  
The _\"Sharp\"_ solar model is considered in this study; this model has highly efficient, affordable systems. Moreover, the Sharp solar module incorporates an advanced surface to increase light absorption and improve efficiency [32]. The technical specifications and costs used in the HOMER simulation based on the proposed solar model are listed below:  
1. Economic issues: the initial installation costs of SPV, the replacement costs, and the annual O&M costs per 1 kW are $1000, $1000, and $10, respectively.
2. Technical issues: the lifetime of an SPV array is 25 years; a derating factor of 0.9, a reflectance of 20%, and a dual-axis tracking system for the SPV array are used in this paper.
3. SPV size: the sizes of the simulation values of the applied SPV panels are 4, 4.5, 5, 5.5, 6, 6.5, and 7 kW.
## 3 Results and Discussions
### Revisiting GRACE-Derived Non-Tidal DeformationWith GNSS Station PositionTime Series
#### 3.3.1 Horizontal Deformations Variations  
Firstly, we compare the IG2 horizontal time series with GRACE-derived horizontal deformations to address the relations for the horizontal deformations. Figure 9 displays _WRMS_ reductions for the east, north, and vertical components, respectively (red depicts a reduction larger than 20%, yellow between 0 and 20% reduction, and blue if signal _WRMS_ is added). The results indicate significant disagreement with the horizontal deformations at the global scale, especially for the east component (the noticeable blue arrow), while for the vertical component, the IG2 displacement is fairly well predicted by GRACE solutions. Figure 10 states mean _WRMS_ values and percentages of stations at which the _WRMS_ is reduced when correcting the deformations with GRACE solutions, which can further confirm the discrepancy between the vertical and horizontal predictions of observed GNSS signals [13]. A common argument to explain this discrepancy is the spatial resolution of GRACE, a larger part of horizontal deformations being attributed to high spherical harmonics degree loads--that is, short wavelength loads--that GRACE cannot resolve [12; 56]. GRACE-modeled non-tidal loadings from multiple institutions (GeoForschungs Zentrum/Potsdam, Germany (GFZ) and Jet Propulsion Laboratory (JPL)) are also compared with the IG2 solutions. The _WRMS_ variations are listed in Figure 10. An overall consistency among the solutions from the different GRACE institutions is displayed.  
Figure 9: WRMS reductions obtained for the east, north and vertical components using GRACE-derived deformations corrections.
## 2 Prediction Principles of the LSTM and GWOAD Models  
### Long Short-Term Memory (LSTM) Model Principles  
LSTM is a type of recurrent neural network (RNN) with memory functionality that was proposed by [PERSON] and [PERSON] in 1997. Compared to traditional RNNs, LSTM incorporates memory cells that allow for the discrimination of useful information [12]. As shown in Figure 2, LSTM consists of four \"gates\" for information processing: the forget gate \(f\), input gate \(i\), output gate \(o\), and a memory cell \(c\).  
The pigment time series data at a time \(t\), as the input information matrix \(x_{t}\), and the output signal matrix \(h_{t-1}\) at the previous time form the forget gate to select the cell state \(c_{t-1}\) at the previous time. Then, the forget gate matrix \(f_{t}\) is as follows:  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+\ U_{f}h_{t-1}+b_{f}\Big{)} \tag{1}\]  
where \(\sigma\) is the sigmoid function; \(W_{f}\) and \(U_{f}\) are the forget gate weight matrices; and \(b_{f}\) is the forget gate offset parameter matrix. Second, the input gate determines what information should be stored in the current state \(c_{t}\); then, the following is obtained:  
\[i_{t}=\sigma(W_{i}x_{t}+\ U_{i}h_{t-1}+b_{i}) \tag{2}\]  
\[\widetilde{c}_{t}=tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{3}\]  
where \(i_{t}\) is the input gate matrix of the neuron at time \(t\); \(\widetilde{c}_{t}\) is the cell candidate state matrix of the neuron at time \(t\); \(W_{i}\) and \(U_{i}\) are the input gate weight matrix; \(b_{i}\) is the input gate offset parameter matrix; \(W_{c}\) and \(U_{c}\) are the cell candidate state weight matrix; and \(b_{c}\) is the cell candidate state offset parameter matrix. From Equations (1)-(3), the unit state matrix \(c_{t}\) of the momentary neuron is as follows:  
\[c_{t}=f_{t}\otimes c_{t-1}+i_{t}\otimes\widetilde{c}_{t} \tag{4}\]  
Figure 2: Structure of the LSTM network unit at time \(t\).  
where \(\otimes\) is the dot product of the matrix. Finally, the output gate determines the output control of the cell state \(c_{t}\) to \(h_{t}\) at the current moment; then, the following is obtained:  
\[\small o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+\ b_{o}) \tag{5}\]  
\[\small h_{t}=o_{t}\otimes tanhc_{t} \tag{6}\]  
where \(o_{t}\) is the output gate matrix of the neuron at time \(t\); \(W_{o}\) and \(U_{o}\) are the output gate weight matrix; \(b_{o}\) is the output gate offset parameter matrix; and \(h_{t}\) is the output matrix of the neuron at time \(t\).  
The LSTM model selectively discards, retains, and controls the output values of the pigment data at the previous moment by introducing a cell state through the forget gates, input gates, output gates, and a memory cell. This approach helps to avoid overfitting and the delivery of interference information and alleviates the issues of exploding gradients and vanishing gradients encountered in traditional recurrent neural networks (RNNs). Furthermore, LSTM resolves the problem of the long-term dependency nature of RNNs, causing it to be suitable for short-term memory time series regression tasks.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 3 Methodology
### Path Curve Optimization and Path Restoration Optimization  
#### 3.3.1 Path Curve Optimization  
Path smoothing is a key technology in solving space. It aims to achieve a smooth transition of connection points by introducing curves to ensure the continuity of each path segment and the connection points. Since the final path generated by the algorithm in this paper usually has tortuosity and unevenness in a complex map environment, this hurts the path tracking of mobile robots.  
Therefore, it is necessary to smooth the generated path to maintain the original path shape more accurately. This paper adopts the Catmull-Rom spline curve to smooth the path, which can effectively improve path continuity and trackability.Specifically as shown in Figure 1.  
The specific formula is as follows:  
\[M_{t}=\begin{bmatrix}-\tau+2\tau t^{2}-\tau t^{3}\\ 1+(\tau-3)t^{2}+(2-\tau)t^{3}\\ t+(3-2\tau)t^{2}+(\tau-2)t^{3}\\ -\tau t^{2}+\tau t^{3}\end{bmatrix} \tag{20}\]  
By substituting four points of the generated path into the formula in sequence, interpolation calculations between the path points can be carried out, thus achieving curve smoothing. The goal is to smooth the generated path. To maintain the original path shape as much as possible, it is only necessary to make the Catmull-Rom spline curve pass through each path point and construct the corresponding curves by using three adjacent points in turn. The smoothed path has a continuous curvature, which can effectively reduce the problems of jitter and sharp turns of the mobile robot caused by sudden changes in the path curvature. In addition, this smoothing process can accurately restore the original path, thus significantly improving the smoothness of the path.
## 4 Theoretical Error Analysis
### Projection Accuracy
#### 4.1.3 Errors in Depth Measurement  
At an underwater environment, the depth information is usually obtained by measuring water pressure. The knowledge of the pressure-depth relationship measures the depth that is associated with the medium of interest. Even small amounts of error in depth measurement cause imperfections in the localization accuracy. Fortunately, current water pressure sensor technologies can provide very accurate underwater depth measurement [15,38]. Hence the effects of errors in depth measurement may cancel out.
# Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
## 3 Proposed Self-Evolving FLC Design Method
### Online Stage
#### 3.3.6 Algorithm  
Algorithm 1 presents the steps of the proposed self-evolving method to design the FLC controller, and Figure 3 presents the respective flowchart.  
Figure 3: Flowchart of the proposed self-evolving FLC design method.  
```
1:Range, minimum, and maximum values, of the variables \(x_{j}^{-}\) and \(x_{j}^{+}\) (\(j=1,\ldots,n\)) and \(u^{-}\) and \(u^{+}\);
2:Thresholds: \(\delta\) (Criterion 1), \(\eta_{j}\) for \(j=1,\ldots,n\) (Criterion 2); \(\epsilon\) (Criteria 3 and 4), sliding window's size \(T_{M}\), and \(\gamma\);
3:Design the initial fuzzy controller (Section 3.2);
4:**Antecedent part**: design the membership functions \(A_{j}^{i_{j}}\) (\(i_{j}=1,2\)):
5:**for all all input variables \(j=1,\ldots,n\)do
6: parameters of \(A_{j}^{1}\): \(a_{j,1}=b_{j,1}=x_{j}^{-}\) and \(c_{j,1}=x_{j}^{+}\);
7: parameters of \(A_{j}^{2}\): \(a_{j,2}=x_{j}^{-}\) and \(b_{j,2}=c_{j,2}=x_{j}^{+}\);
8:endfor
9:**Consequent part**: define all consequent parameters as the minimum control value \(\theta_{j}^{i_{j}}=u^{-}\) (\(i_{j}=1,2\));
10:**Online Stage:
11:**while the controller is turned on, \(k=1,2,\ldots\)do
12: Update the consequent parameters by (12) (Section 3.3.1);
13:**if sliding window M is filled then
14: Obtain the estimated control error \(e_{c}(k)\) using (13);
15:**if Criterion 1 is met then
16: Select, using (15), the candidate input variable \(x_{j}\). in which a new control rule can be added (Section 3.3.2);
17: Obtain the center of the candidate MF (\(A_{j}^{new}\)) by (16);
18:**if Criterion 2 is met then
19: Add \(A_{j}^{new}\) (new MF) to the selected input variable \(x_{j}\). (Section 3.3.3);
20: Update the nearest left (\(A_{j}^{left}\)) and right (\(A_{j}^{right}\)) membership functions (Section 3.3.3);
21: Define the consequent parameter \(\theta_{j}^{new}\) of the new fuzzy control rule using (17);
22:**endif**
23:**endif**
24:**if Criteria 3 and 4 are met then
25: Delete the fuzzy control rule \(R_{j-}^{del}\) (Section 3.3.5);
26: Delete membership function \(A_{j-}^{del}\) (Section 3.3.5);
27: Update the nearest left (\(A_{j-}^{left}\)) and right (\(A_{j-}^{right}\)) membership functions (Section 3.3.5);
28:**endif**
29:**endif**
30: Apply the command signal of the current designed fuzzy logic controller, and read the output variable \(y(k)\).
31: Update M (sliding window);
32:**endwhile**
```  
**Algorithm 1** Proposed self-evolving FLC design method.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 5 Experimental Results and Discussion  
The structure and analysis designed in this paper were tested on the experimental platform, as shown in Figure 8. The FPGA chip from Xilinx xc7 vx690 Uffg1761 worked at 100 MHz clock frequency, and the DDR3 from Micron (Boise, ID, USA) MT41K512M16 HA-125 worked at 200 MHz. The development tools were Vivado2017.4 and HLS2017.4. Both SAR images used in this paper for testing had 16,384 \(\times\) 4096 points with floating-point complex numbers.  
The working course of our experimental platform was designed as follows. First, two SAR images were sent from the ground verification system through the TLK2711 interface and written in the two DDR3 SDRAM chips of FPGA, respectively. Then, algorithmic processing, i.e., coarse registration and interferogram generation, started until all calculation results were saved. Then, the calculation results were sent to the ground verification system and verified with the MATLAB calculation results. The IP core AXI-Timer provided by Xilinx recorded the time used during algorithmic processing. The veri  
Figure 8: Experimental environment and platform. (**a**) Ground verification system; (**b**) Experiment platform.  
Figure 7: \(\
abla G\) of matrix transposition schemes under the condition of \(c^{\prime}\).  
was divided into two parts. Sections 5.1-5.3 verified the validity of the analysis in Section 4, while Section 5.4 verified the efficiency and accuracy of the structure designed in Section 3.
## 6 The Description of Contextual Data for E-Service Provision of Multimodal Freight Transportation
### Short Description of Case Study
Although the cost and complexity of these technologies vary, they play a key role in optimizing the logistics supply chain, stimulating economic growth, and reducing the impact on the environment. As these technologies mature, it will become increasingly important to integrate them into logistics operations in order to remain competitive in the global market. Automatic determination of the level of service plays an important role in monitoring the provision of multimodal logistics services. This process uses software to measure and monitor the approved level of service to ensure that logistics operations meet the expected operational standards within a set time frame. By automating this process, companies can continuously monitor KPIs, thereby increasing the transparency and efficiency of their logistics operations. Service level automation is divided into partial and conditional automation. It handles complex logistics operations, especially last-mile deliveries where tracking and managing parcels are critical to customer satisfaction. The main advantage of this technology is that it improves operational efficiency and indirectly contributes to environmental sustainability by optimizing the use of transport. Delivery notification systems provide customers with automatic real-time updates on the status of their orders. These messages, which can be sent by email, SMS, or mobile application, are necessary to ensure the transparency of the delivery process. Notifications usually indicate important milestones, such as the estimated delivery time and the electronic waybill. Semi-automated delivery notification systems play an important role in last-mile deliveries, ensuring that customers are informed about orders in a timely manner. These systems contribute to economic growth by improving the visibility of the supply chain, reducing bottlenecks, and improving customer satisfaction. By optimizing resource use, they also help reduce inefficiencies and environmental impacts. ETA (estimated time of arrival) systems are needed to predict when a vehicle, cargo ship, or other means of transport will arrive at its destination. This information is necessary to enable customers and logistics companies to plan and optimize their activities, especially in multimodal environments such as ports where it is necessary to coordinate the activities of different modes of transport. ETA systems are semi-automated and integrated with logistics to improve planning and decision-making. These systems are especially useful for improving port operations and last-mile logistics. By providing the exact arrival time, they increase the safety and efficiency of logistics operations and contribute to the competitiveness and economic growth of the logistics industry. The ETA also helps businesses avoid operational and environmental gaps by ensuring dynamic supply chain management.
# THz Rectennas and Their Design Rules
###### Abstract  
The increasing demand for more efficient energy harvesting solutions has urged research for better harvesting solutions than the presently-available ones. While p-n junction solar cells have become commercially widespread, they are expensive and suffer from poor efficiency figures hardly reaching 20%. Other radiation-electricity converters such as rectennas have a theoretical limit in excess of 80%. However, no efficient rectenna solution for the terahertz frequency band has been commercialized or presented in the academic literature. In fact, there are many obstructions to an efficient solution. The aim of this paper is to address the key points towards an efficient and commercially-available solution by briefly reviewing the relevant literature and so identifying five factors that should be addressed in order to reach an efficient solution.  
Keywords:THz rectennas; tunneling diode; geometric diode; waste heat; transfer matrix method; QTBM model; non-equilibrium Green function model; Seebeck effect; rectenna detectors; nonlinear circuit +
Footnote †: journal: Accepted: 1 November 2017; Published: 17 November 2017
# Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction
###### Abstract  
Student grade prediction (SGP) is an important educational problem for designing personalized strategies of teaching and learning. Many studies adopt the technique of matrix factorization (MF). However, their methods often focus on the grade records regardless of the side information, such as backgrounds and relationships. To this end, in this paper, we propose a new MF method, called graph regularized robust matrix factorization (GRMF), based on the recent robust MF version. GRMF integrates two side graphs built on the side data of students and courses into the objective of robust low-rank MF. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. The resulting objective problem can be effectively optimized by the Majorization Minimization (MM) algorithm. In addition, GRMF not only can yield the specific features for the education domain but can also deal with the case of missing, noisy, and corruptive data. To verify our method, we test GRMF on two public data sets for rating prediction and image recovery. Finally, we apply GRMF to educational data from our university, which is composed of 1325 students and 832 courses. The extensive experimental results manifestly show that GRMF is robust to various data problem and achieves more effective features in comparison with other methods. Moreover, GRMF also delivers higher prediction accuracy than other methods on our educational data set. This technique can facilitate personalized teaching and learning in higher education.  
robust matrix factorization; student grade prediction; educational data mining; side information graph; personal teaching and learning Article
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### eNodeB Support for Microgrid Blackout Recovery  
The eNodeB is the LTE/5G node which operates the radio cells. The UEs connect to the eNodeB. Blackout conditions can reduce the availability of the mobile core network. Loss of the mobile core network normally means that the eNodeBs cease to handle traffic. However, the Isolated E-UTRAN Operation for Public Safety (IOPS) [17] feature of 3 GPP Release 13 [15] allows eNodeBs to continue to handle traffic within their own radio cell even if the mobile core network is unavailable. IOPS requiresadditional hardware and software in the eNodeBs. IOPS enables the MAS agents to communicate as long as the eNodeB is available (regardless of the availability of the rest of the PLMN), thus enhancing the communications service availability during blackouts.  
In order to give MAS MTC devices preferential handling during blackouts, the eNodeBs should know that a blackout has occurred and distinguish it from other emergency situations and congestion situations. If there is a large-scale blackout, an administrative procedure can be performed centrally in the PLMN so that all eNodeBs pre-emptively give priority access to MAS MTC devices, even if there is no local blackout. To cover the eventuality that local eNodeBs may, due to a sudden blackout, lose contact with the mobile core network and must act autonomously, eNodeBs should themselves be able to detect a local blackout. To aid their situational awareness, the eNodeBs may themselves act as agents in the MAS. Such an agent is like a UE located at the eNodeB: it has an interface towards the equipment it represents in the MAS (the rest of the eNodeB) and a communications interface towards the rest of the MAS (acting like a UE). During blackouts, eNodeBs may fail, in which case the UEs they were hosting will roam into surviving eNodeBs' cells. Network dimensioning should ensure that enough eNodeBs have standby power supplies or battery packs to support the traffic during blackouts.