esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling of Base-Isolated Structure with Piezoelectric Friction Dampers
### Model of Piezoelectric Friction Damper  
As a novel semi-active control device, piezoelectric friction dampers have been widely investigated by many researchers [24; 25]. Recently, the authors also proposed a piezoelectric friction damper and investigated in their performances theoretically and experimentally [26]. Piezoelectric friction dampers utilize piezoelectric stacks to regulate the damping force and provide a satisfied friction force. The controllable friction force is favorable to ensure energy dissipation for various levels of earthquakes.  
The contact force of the damper is expressed as  
\[N(t)=N_{pre}+C_{pz}V(t) \tag{4}\]  
where \(N(t)\) denotes the total contact force, \(N_{pre}\) denotes the constant preload of the piezoelectric friction damper, \(C_{pz}\) denotes the piezoelectric coefficient of the piezoelectric actuator, and \(V(t)\) denotes the input voltage of piezoelectric stack actuator.  
During the movement of the base-isolated structure, a friction damper has two possible motion states: sticking and slipping phases. The friction force of the piezoelectric friction damper is expressed as  
\[f(t)=-\mu N(t)\text{sgn}(\dot{x}),\ \dot{x}\
eq 0 \tag{5}\]  
\[-\mu N(t)\leq f(t)\leq\mu N(t),\ \dot{x}=0 \tag{6}\]  
where \(\mu\) denotes the friction coefficient of the damper, \(\text{sgn}()\) denotes the sign function related to the slip rate of the damper, and \(f(t)\) denotes the damping force of the piezoelectric friction damper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Floor** & **Mass** & **Stiffness (KN/m)** & **Damping Coefficient (KN\text{-}\text{s/m})** \\ \hline Base floor & 6800 kg & 2315 & 7.45 \\
1 & 5897 kg & 33,732 & 67 \\
2 & 5897 kg & 29,093 & 58 \\
3 & 5897 kg & 28,621 & 57 \\
4 & 5897 kg & 24,954 & 50 \\
5 & 5897 kg & 19,059 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the base-isolated structure.  
In the sticking phase, the absolute value of the friction force, \(f_{s}\), can be approximately expressed as follows:  
\[f_{s}=-\mu N(t)\text{sgn}(\dot{x})\text{ when }f_{s}=|f_{i}+f_{r}|,\ |f(t)|\geq f_{s}\]  
\[f_{i}=m_{t}\ddot{x}_{g}=\left(m_{b}+\sum_{i=1}^{n}m_{i}\right)\ddot{x}_{g},\ f_{r}=k_{b}x_{b} \tag{7}\]  
where \(f_{i}\) is the inertial force applied to the mass; \(f_{r}\) is the restoring force provided by the isolation bearing; \(m_{i}\) is the mass of the superstructure; \(m_{b}\) and \(k_{b}\) are the mass and stiffness of the base isolator, respectively; and \(x_{b}\) is the displacement of the base isolator. The parameters \(N_{pre}\), \(C_{pz}\), \(\mu\) and \(V_{\text{max}}\) of the piezoelectric friction damper are set to 2500 N, 2.5 N/V, 0.4 and 1000 V, respectively.
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 3 Proposed Methodology
### Data Partitioning and Randomized Feature Selection  
Consider a dataset represented by  
\[S=\{\,(\mathbf{x}_{i},y_{i})|i=1,2,\ldots,n\}, \tag{15}\]  
where each \(\mathbf{x}_{i}=(x_{i1},x_{i2},\ldots,x_{id})\in\mathbb{R}^{d}\) is a feature vector of dimensionality \(d\) and \(y_{i}\in\{1,2,\ldots,K\}\) denotes the corresponding class label. The dataset is randomly partitioned into a training set \(S_{tr}\) and a test set \(S_{te}\):  
\[S_{tr}=\{\,(\mathbf{x}_{i},y_{i})|i=1,2,\ldots,n_{tr}\},\ S_{te}=\{\,(\mathbf{x }_{i},y_{i})|i=n_{tr}+1,n_{tr}+2,\ldots,n\}. \tag{16}\]  
For each iteration \(t\) of the proposed RFB-NB process, a random subset of features is selected. Let \(m\) denote the number of selected features, where \(m<d\). The indices of selected features are represented as  
\[F_{t}\subset\{1,2,\ldots,d\},\,\text{where}\ |F_{t}|=m. \tag{17}\]  
This random feature selection introduces diversity at each iteration, mitigating feature dependency and enhancing robustness.
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## Appendix B Frequency Domain Iterative Receiver
During the decoding process, binary code decoders such as BCJR decoders can be used to return the extrinsic LLRs of the code bits, denoted as \(\lambda_{e}^{c}[c_{t}^{i}]\), which will then be used to generate the soft estimates:  
\[\overline{c}_{t}^{i}=\tanh\left(\frac{1}{2}\lambda_{e}^{c}[c_{t}^{i}]\right). \tag{11}\]  
The soft estimates of the transmitted symbols via the bits-to-symbol mapping followed by the respective rotation can be generated and fed to the MMSE-FDE described above.
# Biodiesel and Biolubricant Production from Waste Cooking Oil: Transesterification Reactor Modeling
## 3 Results and Discussion
### Effects of Pressure, Temperature, and Catalyst Concentration on WCO-TMP Production
Taking into consideration all the discussion above, the following reaction conditions were considered as suitable for the production of WCO-TMP: FAME/TMP molar ratio = 3; working pressure = 510 mmHg; T = 120 \({}^{\circ}\)C; stirring rate = 350 rpm; catalyst concentration = 1%; reaction time = 2 h. Under these conditions, a high conversion rate of WCO-FAMEs was obtained (exceeding 98%), with WCO-FAMEs possessing the following properties: a density of 951 kg\(\cdot\)m\({}^{-3}\); viscosities at 40 and 100 \({}^{\circ}\)C of 127 and 14 cSt, respectively; an acidity value of 0.43 mg\({}_{\text{KOH}}\cdot\)g\({}^{-1}\); flash and combustion points of 225 and 232 \({}^{\circ}\)C, respectively; and an oxidation stability of 6 h. As the oxidation stability is not quite long lasting, the use of an antioxidant additive is advisable. TBHQ was chosen for this purpose, as it is one of the most effective and efficient antioxidants used to stabilize similar products, like biodiesel [56]. In order to determine the presence of some functional groups, the IR profile is shown in Figure 4. As a result, a spectrum corresponding to esters was obtained, with the typical C=O band at 1741 cm\({}^{-1}\) and the absence of the prominent peak corresponding to OH groups (at around 3500 cm\({}^{-1}\)). These spectral features point out the fact that fatty acid esters corresponding to biolubricants were generated, with the high conversion rate of the TMP and purification of the unreacted alcohol. Figure 4: IR profile of the biolubricant (WCO-TMP). Regarding the thermal stability of the resulting biolubricant (WCO-TMP), TG/DTG tests were carried out, and the main results are included in Figure 5. Clearly, different thermal events can be observed, pointing out the absence of moisture in the sample (according to the negligible weight loss at around 100 \({}^{\circ}\)C), including one event between 200 and 300 \({}^{\circ}\)C (possibly because of methyl ester degradation on account of the presence of unreacted biodiesel) and another one between 375 and 475 \({}^{\circ}\)C (with a DTG peak at 435 \({}^{\circ}\)C), which was the main thermal event in this case. This behavior was similar to that in previous studies, where the maximum weight loss took place at slightly lower temperatures [39]. Consequently, a product with high thermal stability was obtained, which is essential to retain the main properties of a biolubricant at high temperatures.
# Effect of Various Proportions of Rice Husk Powder on Swelling Soil from New Cairo City, Egypt
## 3 Results and Discussion
### Geotechnical Characteristics Evaluation after Using RHP
#### 3.2.2 Effect of RHP on S and SP  
The SP and soil S results in different percentages of RHP are presented in Table 5. The variables of SP and S of the soil with different parentages of RHP are presented in Figures 7 and 8. The SP and S of the test sample were reduced by adding 5% to 15% of the RHP. For example, Figures 7 and 8 show that the SP decreases from 1003 kN/m\({}^{2}\) to 800, 653, and 489 kN/m\({}^{2}\), while S decreases from 48% to 45.5, 44.7, and 34.6% by adding the RHP from 0 to 15%. The addition of the stabilizer shows an unmistakable decrease in S and SP. The reduction in S and SP values for stabilizing soils was due to the addition of low-plastic  
Figure 6: Variation of liquid limit, plastic limit, and plasticity index with different percentages of RHP.  
RHP contains 90 percent silicon dioxide. Where the total percentage composition of silicon dioxide, aluminum oxide, and ferric oxide was 94.51 so, this value was well in excess of the minimum requirement of 70% of ASTM C618 Pozzolans [40]. Once added to the RHP, the granular structure of the expanding soil changes. This high amount of silica provides good pozzolan action, which leads to a significant improvement in soil characteristics. RHP has a certain degree of replacement effect when added to expansive soil. On the one hand, due to the low plasticity and low expansion and water absorption, expansive soil's physical properties, particularly PI and S, will change.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 4 COMSOL Multiphysics Finite Element Method (FEM) Validation  
To compare the results of the temperature evaluation performed on the basis of the thermal LNM calculations with simulations performed by an FEM, the cable construction, materials and surrounding conditions must be identical. The FEM is a computer-based calculation method solving partial differential and integral equations. It is based on a unique connection between the field sizes in nodes and field sizes anywhere in the element and solved as a mathematical interpolation problem. The purpose of this validation is to analyse the transient temperature behaviour in the centre of the cable and compare the results between the two methods.  
No-Load Full-Load Test of a 220 kV 3\(\times\) 1800 [mm\({}^{2}\)] Al XLPE SL Type Submarine Cable  
In this subsection, a validation of the proposed method is presented with an estimated 220 kV 3\(\times\)1800 [mm\({}^{2}\)] Al XLPE submarine cable. The validation was performed using the cable data shown in Table 1 and surrounding conditions in Table 2 in Appendix A. The test is a full-load no-load test, where the cable is applied to two long-term step sequences. This test aims to show the entire transient temperature progress due to a step applied to the cable until it nearly reaches steady-state temperature conditions. The first step is a 2000 [h] step with a current loading of 930 [A] corresponding to 100% load, and the second step is similar: a 2000 [h] step with a current loading of 0 [A] corresponding to 0% load, as shown in Appendix A, Figure 11. The test result is illustrated in Figure 18 and shows major agreement between the two methods used to evaluate the transient temperature behaviour in the same cable. The FEM-based approach is represented by the blue line and the LNM-based method is represented with orange. Comparing the transient temperature behaviours caused by the full-load and the no-load step shows almost identical temperature behaviours.  
The temperature progress of the FEM-based and LNM-based evaluation shows that the transient temperature curves cross each other twice on each step. The first part of the transient is almost identical until they cross the first time, after which temperature differences occur. From Figure 16, it can be concluded that the deviation at the end of the temperature progression is caused by the modelling of the surroundings.  
The difference in the calculation methods causes the deviation. The transient temperature evaluation using the FEM is divided into an infinite number of small thermal capacitances that are continuously charged along the layer of the cable. Using the LNM, the thermal capacitances are modelled as two dominant thermal capacitances \(Q_{A}\) and \(Q_{B}\). The maximum negative temperature deviation in this test is 1.19 [\({}^{\circ}\)C].
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 3 System and Signal Model for the Receivers
### Linear Feedforward Non-Iterative FDE Receivers  
#### 3.1.1 Zero Forcing  
The signals of each transmitting antenna are considered the target signal by the ZF receiver, while the other outputs are considered interferers. The main job of this receiver is to reduce the interference to zero by inverting the channel response and fitting the result to the closest letter in the alphabet being looked at. The inverse channel, \(H^{-1}\), is then multiplied by the received signal, \(Y_{k}\), as described above.  
The ZF criterion considerably increases the channel noise in sub-channels with local deep notches during the channel equalization process. Using the ZF algorithm, \(F_{k}\) becomes [10]:  
\[F_{k}=\left(H_{k}^{H}H_{k}\right)^{-1}H_{k}^{H} \tag{4}\]
## 5 Experimental Results
### Channel Capacity Analysis  
The results shown in Figure 5 are evaluated by the Shannon-Hartley equation, considering the PLC measured spectral density \(S_{PLC}\). Figure 7 illustrates how the channel capacity changes as the modulation index increases, while keeping the spreading factor constant at 15%. In the case of periodical signals (sawtooth and sine wave), it is noticed that the channel capacity decreases severely in the case of lower peaks of EMI, especially when the modulation index is between 30 and 3000. This occurs because, within this range, the sampling frequency of the modulating signal \(f_{s}\) approaches the G3-PLC sub-carrier frequency (\(f_{sub}=1500\)). In contrast, for the non-periodic signal, the channel capacity loss decreases across the modulation index due to the random operation of the frequency change. Figure 8 shows the channel capacity loss percentage with the modulation index \(\beta\).
# Semantic Multigranularity Feature Learning for
## 2 Related Work
### Remote Sensing Image Scene Classification Methods  
CNN-based methods have achieved impressive improvement for RS image scene classification. In [30], a feature-selection method was proposed based on the deep belief network (DBN). Due to the effectiveness in feature abstraction, this method works well on a relatively small dataset. [31] proposed an adaptive deep pyramid matching (ADPM) model that took advantage of information from all convolutional layers in the network. ADPM achieved superior performance on the 21-Class Land Use dataset [32] and the 19-Class Satellite Scene dataset [33]. Multiple convolutional layers were also used in [34] to compute a covariance matrix. Each entry of covariance matrix stands for the covariance of two feature maps; this could exploit the complementary information from different convolutional layers. As a complete novel network, the capsule network is applied in RS image scene classification. In [35], the capsule network was added to a CNN without fully connected layer. In [36], a Multigranularity Multilevel Feature Fusion Branch (MGML-FFB) was proposed to extract multigranularity features by a custom module named the channel-separate feature generator (CS-FG). Further, diversified predictions were provided by an ensemble module integrated in the network. [PERSON] et al. conducted a series of studies in RS Image Scene Classification. In [37], a multilayer feature fusion network was developed with a data augmentation approach integrated into training to improve the generalization ability of the model. In [38], the authors proposed a global-local dual-branch structure (GLDBS) that allows a network to explore global and local discriminative features. In addition to CNN, Graph Convolutional Network (GCN)-based methods have also been explored. The paper [39] discussed a deep feature aggregation framework driven by graph convolutional network (DFAGCN) for high-spatial-resolution scene classification. The framework utilizes a pretrained CNN to obtain multilayer features, which are fed into a GCN to obtain patch-to-patch correlations between the feature maps. The output is then passed through a weighted concatenation operation and a linear layer to make a prediction. Our investigation shows that the method involving feature map rearrangement has not been extensively studied.  
This method is tested on larger datasets, such as NWPU-RESISC45 [25]. Some of the mentioned efforts are used as baselines of this study.
# Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm
## 1 Introduction  
### Research Background  
With the development of robotics and artificial intelligence, the application of robots can be seen in all walks of life. The most important manifestation of mobile robot intelligence is autonomous navigation, which is mainly to solve the problem of \"where am I\", \"where should I go\" and \"how should I go\" of robots. The key technology to solve this problem is path planning, which is also a difficult technology. Path planning requires the robot to find the best collision-free path from the starting point to the target point in different map environments, and the quality of the path planning can directly affect the robot's task completion.  
Nowadays, the rise of mobile robots, unmanned vehicles, and the field of UAVs has led more and more scholars to work on path planning algorithms. Although there are many algorithms proposed for path planning, there are still great challenges in the research of path planning, especially about the trajectory optimization of paths, and there are still many problems that need to be solved. The paths planned by the current mainstream pathplanning algorithms are shown in Figure 1, where path A and path B are connected by discrete path points, so there are problems such as paths turning at acute angles and the planned paths are not the actual shortest paths, which are not ideal for the movement of mobile robots. In the actual application of mobile robots, it is necessary to avoid sudden sharp turns or stops, and to ensure the continuity of posture, speed and acceleration at the turns during the movement. For example, in unmanned driving, the discontinuity of the planned path interruptions leads to sudden turns and sudden changes in speed and acceleration during the driving process, which can affect the safety of passengers. Secondly, the path planning based on grid map will restrict the robot to search the direction on the map, so that the planned path is not the actual shortest path, which will generate many useless inflection points, and these inflection points will affect the operation efficiency of the robot. As shown in Figure 1 for path C, a shorter, smoother path represents less energy loss and higher efficiency while ensuring the safety of the mobile robot. For example, in the process of transporting goods by logistics AGVs, the efficiency of the AGVs directly affects the overall efficiency of the logistics warehouse operation.  
To address the above problems, this paper will propose a solution by adding multi-angle pathfinding idea and path smoothing technique to the existing path planning algorithm. Multi-angle pathfinding is to break the constraints of the traditional path planning algorithm in the search direction brought by the discrete grid map, so that its path can be extended from any angle. The path smoothing process will make it easier to realize the continuous change of position transformation, moving speed, and acceleration of the robot in the process of motion.
## 4 Result
### Efficiency Analysis Results
On the other hand, Verizon Wireless and AT & T shows the lowest performance in efficiency in 2011 and 2012, but have improved their network quality efficiency from 2013. T-mobile exhibits the highest efficiency in all years. Based on the Mann-Whitney U test and Wilcoxon W test, the three hypotheses were verified at a significance level of 0.05. The summary of the hypothesis test results are tabulated in Table 4 below. The verification rejected H1, and confirmed H2 and H3. That is, the wireless carriers' efficiency in establishing network quality and their management efficiency as well as their management efficiency and market efficiency show the same distribution. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Null Hypothesis** & **U Value 1,2** & **W Value 1,2** & **Sig.** & **Decision** \\ \hline \multirow{2}{*}{H1} & Management Efficiency \(\leftrightarrow\) & \multirow{2}{*}{22.00} & \multirow{2}{*}{100.00} & \multirow{2}{*}{0.0029} & \multirow{2}{*}{Reject} \\  & Service Quality Efficiency & & & & \\ \hline \multirow{2}{*}{H2} & Management Efficiency \(\leftrightarrow\) & \multirow{2}{*}{42.00} & \multirow{2}{*}{120.00} & \multirow{2}{*}{0.0887} & \multirow{2}{*}{Accept} \\  & Network Quality Efficiency & & & & \\ \hline \multirow{2}{*}{H3} & Management Efficiency \(\leftrightarrow\) & \multirow{2}{*}{63.00} & \multirow{2}{*}{141.00} & \multirow{2}{*}{0.6300} & \multirow{2}{*}{Accept} \\  & Market Efficiency & & & & \\ \hline \end{tabular} \({}^{1}\) U value: Mann-Whitney U, W value: Wilcoxon W; 2 Asymptotic significances are displayed. The significance level is 0.05.  
\end{table} 
Table 4: Hypothesis test results. Figure 3: Efficiency score of wireless carriers.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
## 3 Results
### Test Dataset Description  
During the study, the continuous monitoring of the behavior of the variables involved in the process, such as pressure, wave velocity, amplitude, and travel time, was performed during each iteration cycle to evaluate the behavior of these parameters in the acoustic analysis developed in the oilfield region. This technique allowed the comparison of data obtained in the field with those provided by the mathematical model, to make the appropriate adjustments in the mathematical model that fit the existing boundary conditions in the area of operation. As a result, these adjustments and refinement of the pseudocode allowed us to understand the behaviors of the wave dispersion in different subsurface geological conditions.  
The geological conditions in the oil zones influenced the characterization of the geological variables that defined the mathematical model when considering the readings obtained in the measurement process conducted in the field and comparing it with the results obtained using the finite difference approximation method in the seismographic study of wave propagation in the oil zones.