esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
Various methodologies within existing routing and security approaches have been extensively explored in the available literature. In conjunction with the enhanced dragonfly algorithm, the k-medoid clustering model has emerged as a notable contributor to improving energy efficiency within vehicle-to-vehicle communication [20]. The proposed approach entails the selection of medoids through the calculation of distances between data points. Initially, each object is assigned the nearest medoid value, which is subsequently refined to its closest medoid value. The cluster head selection depends on carefully assessing speed, position, and acceleration parameters. By harnessing the k-medoid clustering model alongside the enhanced dragonfly algorithm, the transmission of messages to various vehicles, RSUs, and base stations is efficiently minimized, culminating in substantial energy savings within the vehicular network. This combined approach also contributes to a heightened packet delivery ratio and network throughput, albeit without a specific focus on the hop count metric.  
Furthermore, clustering has been a favored strategy among many researchers for facilitating reliable and secure routing. A credible clustering technique aimed at optimizing communication was proposed by [28]. In this instance, the parameters of velocity and distance are crucial considerations for forming clusters. The determination of a distrust value plays a pivotal role in selecting a trustworthy cluster head. Should the distrust value exceed a predefined threshold, the vehicle in question is added to a blocklist, whereas a distrust value below or equal to the threshold results in the vehicle being added to an allowlist. Emphasis is placed on considering a vehicle with higher trust as the cluster head. Following the selection of a trustworthy cluster head (CH), the clustering quality of service (QOS) value is meticulously computed. This clustering methodology significantly enhances the packet delivery ratio and throughput, concurrently mitigating packet loss and end-to-end delay issues.
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 6 Conclusions  
In this paper, we construct two kinds of polar-like codes (QSPCs and punctured QSPCs), which can be used for JSCC with side information. We then proposed a DJSCC scheme based on QSPC and punctured QSPC. In this scheme, we have transformed the optimization transmission problem into a problem of optimizing JSCC with side information at the receiver, which can be solved by QSPCs and punctured QSPCs. For the infinite code-length, we have proved that the proposed scheme is asymptotically optimal. For the finite code-length, the simulation results have verified that the proposed scheme outperforms the DSSCC scheme with polar codes and the DJSCC scheme with classic SPCs.  
[PERSON] and [PERSON] initiated the idea and designed the experiments. [PERSON] analyzed the data and wrote the manuscript. [PERSON] made the revisions for the paper. All authors have read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 A Case Study
### Test Cases with a Single Dynamic Obstacle
#### 4.1.2 Overtaking  
A two-ship encounter situation of overtaking as viewed from the OS's perspective was considered in this case. The OS's initial position, velocity, and course were set to (50, 300) m, 3.0 m/s, and 0\({}^{\circ}\), respectively, and the corresponding initial position, velocity, and course of TS were set to (175, 301) m, 1.5 m/s, and 0\({}^{\circ}\), respectively. It should be noted that a little offset to the right was added to the position of TS for the uniqueness of the reasonable path.  
Under the provisions of COLREGs Rule 13 [29], the OS as a give-way vessel should keep out of the way of TS, whereas the TS as a stand-on vessel should always maintain its course to avoid confusion in this case. Rule 13 [29] does not clarify that a give-way vessel should alter its course to which side (starboard or port) when overtaking a stand-on vessel. Hence, parameters \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\) were set to 4.0, 1.0, 1.0, and 1.0, and the corresponding safety distance thresholds \(d_{1}\), \(d_{2}\), \(d_{3}\), and \(d_{4}\) were 200 m, 50 m, 50 m, and 50 m, respectively. The same configuration was adopted for the starboard and port when modeling the space representation.  
Figure 8 shows the path planning results and the collision avoidance trajectories in the overtaking scenario. The planned path is indicated with a solid red line, as shown in  
Figure 6: The path planning results and the collision avoidance trajectories in the head-on scenario. (**a**) The path planning results, (**b**) The collision avoidance trajectories.  
Figure 7: The relative distances between OS and TSs in the single dynamic obstacle scenario.  
Figure 8a. The OS is expected to alter its course to the port side to pass on the TS based on the path. The collision avoidance trajectories of this overtaking scenario are shown in Figure 8b. The relative distance is denoted with a filled triangle green line, as shown in Figure 7, and the shortest distance between OS and TS is 50.49 m. The results demonstrate that collision avoidance can be realized when the OS follows the planned path.
## 3 The Conventional Critical Distance Method
### Predicting Fatigue Strength Using the Conventional TCD  
The critical distance method to predict the fatigue limit of the notch usually requires the assistance of finite element analysis software. In the present study, the commercial  
Figure 6: Schematic diagram of the critical distance method.  
finite element software Abaqus was used to obtain the stress distribution near the notched specimen. The ratio of the finite element size of the specimen to the actual size was set to 1:1. The 8-node hexahedral C3D8R solid element was used in the finite element simulation. Since the critical distance method needs to extract detailed stress data near the notch, the mesh size refinement was performed at the critical location of the notched specimens and the minimum size of the mesh was 0.1 mm, as shown in Figure 7. The model has a total of 5,063,121 nodes and 8,400,000 elements. A fixed constraint was applied at one end of the Q345 flat plate, and an axial tensile load was applied at the other end. The elastic stress distribution near the notch of V2 specimen is shown in Figure 8. Stress analysis was carried out in the notch area using element sizes of 0.1 mm, 0.05 mm, and 0.02 mm to evaluate the effect of mesh size. Figure 8 shows that finite element meshing has good convergence. The stress concentration factor Kt of each notch can be determined from the finite element results, as shown in Table 2. The stress concentration factor of the Q345 qD notched specimen obtained by the stress concentration factor expression of Noda's double-sided V-notch [40] is also listed in Table 2. The results show that the two are almost identical, with a maximum deviation of only 1.3%. [PERSON] et al. [41] have given the stress intensity factor threshold for Q345 qD material when the stress ratio was 0.1 (\(K_{th}=11.07\) MPa-m\({}^{0.5}\)). Therefore, the conventional critical distance under 1 million cyclic loading can be obtained, \(L=0.498\) mm, for the Q345 qD steel used. According to the characteristics of the critical distance method, when the stress in the critical distance range is equal to the fatigue strength of the smooth specimen, the corresponding applied load is the fatigue strength of the notch. Table 3 shows the prediction results of the notched specimens V1-V4 based on PM and LM and compares them with the test results. It can be seen from Table 3 that the conventional critical distance method does not provide reasonable fatigue strength prediction results for the low-carbon steel Q345 qD small-notch plate specimen. In particular, the prediction ability of the point method is obviously inferior to that of the line method, something which has been supported by other researchers [11; 19; 22].
## 2 Materials and Methods
### Methods
#### 2.3.3 Gpp, NPP, and VT Trend Analysis  
Theil-Sen's slope analysis was performed to depict the long-term (2001-2020) annual GPP, NPP, and VT trends over the mining patches across Rajmahal Hills. Theil-slope Sen's is a non-parametric method that determines the linear trend by estimating the median slope between all data points [60; 61]. The [PERSON]'s slope method is effective because it produces correct confidence intervals for data with skewed behavior and heteroscedasticity and is insensitive to outliers [62]. Thus, Theil-slope Sen's analysis has been utilized more frequently to comprehend long-term trends in a variety of investigations pertaining to climatology, ecology, and hydrology [18; 63]. The'spatialEco' Package in R was used for trend analysis of vegetation productivity indicators [64]. The \"spatialEco\" Package can handle autocorrelation and seasonality effects in the data, accounting for more significant uncertainty in trend estimations for a time series of atmospheric parameters.  
\[\beta=Median\left(\frac{X_{j}-X_{i}}{j-i}\right),j>i \tag{3}\]  
where \(\beta\) is the tendency of the vegetation productivity indicators sequence, \(x_{i}\), \(x_{j}\) are the sequences of vegetation productivity indicators, \(i\), and \(j\) is the year of the vegetation productivity indicators. When \(\beta\) > 0, the time series shows an upward (increasing) trend; when \(\beta\) < 0, the time series shows a downward (decreasing) trend.  
The unitary linear regression approach was also used to analyze the long-term annual trends of GPP, NPP, and VT according to the Ordinary Least Squares (OLS) over different mining locations in Rajmahal Hills. The slope of trends was estimated using Equation (4).  
\[\theta_{\text{slope}}=\frac{n\times\sum_{i=1}^{n}i\times P_{i}-\sum_{i=1}^{n}i \sum_{1}^{n}P_{i}}{n\times\sum_{i=1}^{n}i^{2}-(\sum_{i=1}^{n}i)^{2}} \tag{4}\]  
where \(\theta_{\text{slope}}\) = the increasing or decreasing rate of NPP/GPP/VT; \(n\) = the total number of observations (21), \(i\) = the year order from 1 to 21 in the study period; and \(P_{i}\) = NPP/GPP/VT in the ith year. When \(\theta_{\text{slope}}\) > 0, then the trend increases; When \(\theta_{\text{slope}}\) < 0, then the trend decreases.
# Contingent Task and Motion Planning under Uncertainty for Human-Robot Interactions
## 1 Introduction
This part of the computation is done offline and aims to prune infeasible actions due to geometric constraints and to obtain a feasible set of actions in the tree of plans. As the basic contingent planner considers only symbolic reasoning, this module enables it to incorporate geometric reasoning to deal with practical applications. The reasoning process provides feasible initial and goal configurations for motion planning queries, improving its success rate and thus the overall performance of the planner in the generation of a feasible manipulation plan. * _Human-robot collaboration._ There are some actions which can be executed by the robot and others that require the collaboration of a human operator. The proposed relaxed geometric reasoning is extended to inform the planner about which actions cannot be executed by the robot, and hand over them to the human operator, allowing the planner to handle those cases where the selection of actions to be performed by a human operator is required. In these cases, the geometric world resulting by these actions is simulated and used in the planning system for further geometric reasoning evaluation. This step makes the basic planner flexible to consider the result of human actions, extending its performance to situations it is not able to be handled autonomously. * _State observation._ To observe the binary outcomes of actions, two modules are proposed: _perception_ and _human knowledge._ Perception is used to detect, e.g., the actual locations of the objects or some objects feature like color. The knowledge provided by the operator is required for more difficult observations like determining if a can is filled or empty, or if a glass contains a given drink. Action observation takes place at execution time. The combining of both modules widens the capacity of the planner to identify the current situation of the robot's world and decide the best course of actions in execution, thus improving the planner performance in finding feasible solutions. One of the main advantages of the proposed framework is that the offline computation is valid and works despite the actual values of the uncertainty variables or the actual outcomes of the executable actions. The rest of the paper is structured as follows. First, Section 2 summarizes some related work and Section 3 explains a proposal for contingent task and motion planning. Afterwards, Section 4 presents and illustrates the proposed relaxed geometric reasoning for mobile manipulators, Section 5 demonstrates contingent heuristic computation using relaxed information, Section 6 details tree-based planning using search space, and Section 7 presents manipulation plan execution using sensing and human interaction. Finally, Section 8 shows some implementation issues as well as empirical results, and Section 9 sketches the conclusions and future works.
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 3 CFD Modeling and Verification
### CFD Model
\(Y_{m}\) represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate. \(C_{2}\) and \(C_{\varepsilon}\) are constants. \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the turbulent Prandtl numbers for \(k\) and \(\varepsilon\), respectively. \(S_{k}\) and \(S_{\varepsilon}\) are user-defined source terms. The eddy viscosity is:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{11}\]  
Compared with the standard \(k\)-\(\varepsilon\) model, \(C_{\mu}\) is no longer a constant in the realizable \(k\)-\(\varepsilon\) model and its formula is as follows:  
\[C_{\mu}=\frac{1}{A_{0}+A_{S}\frac{kU^{*}}{\varepsilon}} \tag{12}\]  
and  
\[ \begin{array}{ll}U^{*}\equiv\sqrt{S_{ij}S_{ij}+\widetilde{\Omega}_{ij} \widetilde{\Omega}_{ij}}\\ \widetilde{\Omega}_{ij}=\Omega_{ij}-2\varepsilon_{ijk}\omega_{k}\\ \Omega_{ij}=\widetilde{\Omega}_{ij}-\varepsilon_{ijk}\omega_{k}\\ A_{0}=4.04,A_{s}=\sqrt{6}\cos\varphi\\ \varphi=\frac{1}{3}\cos^{-1}\left(\sqrt{6}W\right),W=\frac{S_{ij}S_{3}S_{3}}{ \widetilde{S}^{3}},\widetilde{S}=\sqrt{S_{ij}S_{ij}},S_{ij}=\frac{1}{2}\left( \frac{\partial u_{j}}{\partial x_{i}}+\frac{\partial u_{j}}{\partial x_{j}}\right) \end{array}  \tag{13}\]  
where \(\widetilde{\Omega}_{ij}\) is the mean rate-of-rotation tensor. It can be concluded that \(C_{\mu}\) is a function of the mean strain and rotation rates, the angular velocity of the system rotation, and the turbulence fields. All fluids in the computational domain are water in this study. The grids of all the models are 2D unstructured grids and created by MESH. The mesh at the suction chamber inlet is refined. Velocity inlet boundary conditions are adopted for all fluid inlets. Turbulence specification method is given by turbulent intensity and hydraulic diameter. The outlet is set as outflow. All annular jet pump CFD simulations are conducted by the ANSYS Fluent R18.0 (Houston, PA, USA) which uses the finite volume method as a numerical solution technique. The pressure and velocity coupling method adopted the semi-implicit method for pressure linked equations (SIMPLE) algorithm with the pressure-based solver. The spatial discretization of the convection terms selected the second order upwind scheme. All simulations are performed in parallel on a computer with an Inter Core (TM) i5-9400 CPU (6 processor, each with a clock speed of 2.9 GHz). The number of complete convergence iterations is 3000, and the computation time is 30 min per case. The contingent conditions in the CFD process are controlled to achieve strict consistency to ensure the reliability of the computation results. When the iteration converges, global scaled residuals for continuity and momentum equations drop to \(10^{-6}\).
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 2 System Structure
### Accent/Speaker Embeddings  
I-vector is a popular technology in the field of speaker recognition. It was motivated by the success of the Joint Factor Analysis (JFA) [23]. JFA is used to construct the subspace of speaker and channel separately, proposing powerful tools to model the inter-speaker variability and to compensate for channel/session variability in the context of GMM. However, [PERSON] et al. [24] proved that channel factors estimated using JFA, which are supposed to model only channel effects, also contain information about speakers. Thus, i-vector methods construct a low-dimensional subspace, termed the total variability space. This space contains factors of both speaker and channel variability. In this way, the i-vector models both speaker and channel information and characterizes most of the useful speaker-specific information in a fixed and low dimensional feature.  
Due to the powerful representation capabilities of DNNs, x-vector is now a powerful representation for speaker recognition. On one hand, the TDNN-based architecture models the short-term context; on the other hand, the statistics pooling layers process all the information across the time dimension, so that subsequent layers operate on the entire segment. The DNN architecture used to extract x-vectors is outlined in Table 1. The splicing parameters for the five TDNN layers were: \(\{t-4,t-3,t-2,t-1,t,t+1,t+2,t+3,t+4\}\), \(\{t-2,t,t+2\}\), \(\{t-3,t,t+3\}\), \(\{t\}\), and \(\{t\}\). The statistics pooling layer was used to calculate the mean and standard deviation on all frames of the input segment, followed by upper layers at the segment level with a softmax output layer. Segment-level embeddings  
Figure 1: Schematic diagram of the multistream CNN acoustic model architecture.  
were extracted from the 512-dimensional affine component of the first fully connected layer. Speaker embeddings, which capture both speaker and environment specific information, have been shown to be useful for the ASR task. For accented speech, the information of tone and speaking habit of each accent is especially important. We have explored four types of representations as auxiliary inputs in a neural network to further improve the accuracy of the accented ASR system; the procedure is shown in Figure 2. In the procedure, we first extracted embeddings of each utterance and then concatenated them to each frame feature of the utterance as a compensatory input for the neural network.
## 2 Materials and Methods
### Cell Viability  
RAW264.7 cells were seeded at a density of 1 \(\times\) 10\({}^{5}\) cells/mL in DMEM supplemented with 10% FBS with a volume of 200 \(\upmu\)L/well in a 96-well plate. After a 24 h incubation at 37 \({}^{\circ}\)C, media was aspirated and cells were either supplemented with increasing concentrations of BWSPHs (BW-SPH-A to -F) and SGID BWSPHs (BW-SPH-A-GI to -F-GI) with the final concentrations in the culture medium ranging from 0-1.0% (\(w/v\) dw), or DMEM only (control), for 24 h with a final volume of 200 \(\upmu\)L. Cell viability was assessed using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrezolium bromide) assay (MTT I proliferation kit, Roche Diagnostics; Burgess Hill, West Sussex, UK), which consisted of a MTT reagent and a solubilisation solution. The well contents were aspirated and MTT reagent (10 \(\upmu\)L) and DMEM (100 \(\upmu\)L) were added to the wells and incubated for a further 4 h at 37 \({}^{\circ}\)C. The solubilisation solution (100 \(\upmu\)L) was added and following overnight incubation, the absorbance was read at 570 nm using a microplate reader (VarioskanFlash, Thermo Scientific, Waltham, MA, USA). For subsequent assays, a nontoxic concentration of each sample was used, which induced greater than 80% cell viability.
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 3 System Model  
We consider a VANET, as we can see in Figure 1, whereby the vehicles communicate between them. We denote the number of vehicles as \(N\). As we mentioned previously, we aim to optimise the throughput of each vehicle while satisfying certain QoS constraints, such as PRR and rate.To this end, we identify certain wireless parameters as capable of assisting this objective. These are the transmission power and the rate of transmission. The former can be adjusted to strengthen the signal for successful packets' transmissions and to mitigate interference from other neighbouring vehicles. The latter can be optimised to achieve better throughput in the network when multiple packets are required to be forwarded.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 2 Analysis of IGBT Turn-Off Voltage Spike
Therefore, controlling the voltage rise rate becomes the main method for reducing the turn-off overvoltage spike.
## 2 Related Works
### Feature Extraction  
Feature extraction is the process of calculating stable attributes from data that have less intra-class variation and more inter-class variation. It is the process of transforming raw data into a discrete finite set of values with a uniform and lower dimension. Feature extraction is crucial for the precise classification of data into their respective classes. Moreover, feature extraction represents the input file with a selected set of values, excluding redundant information. Researchers have developed their robust and distinctive frameworks for text feature extraction and employed them in text mining and sentiment classification applications [30; 31]. Various frameworks for extracting robust and invariant features from text data have been developed by researchers, which can be broadly categorized into two classes: handcrafted and deep neural networks. The handcrafted descriptors are developed through feature engineering and mostly consist of statistical models. The researcher adopts various statistical analyses to represent the distribution of words in the textual data for precise categorization. The frameworks of shallow learning (SL) enormously comprise machine learning methods with handcrafted feature representation methods. Shallow learning requires a combination of heavy dimensional features provided in [32]. However, the SL techniques are still applicable to textual data classifications. In [33], the bag-of-n-grams and parallel convolution neural networks (CNNs) frameworks are developed to detect and categorize fake and real reviews. In [34], costumer reviews have been classified by employing the eigen-space graph generation and then classifying the graphs by KNN and SVM classification methods. In [35], the authors have developed a deep learning framework for review sentiment classification. This approach incorporates rating information to capture the overall sentiment distribution of sentences. It involves adding a classification layer over the embedding layer and fine-tuning using labeled sentences for supervised learning. The classification models of random forest, KNN, and SVM obtained an F1 score of 95%. The PDF format adopts to visualize various content, such as images, text, and sequential files. The methods showed powerful real-time classification of costumer feedback compared to previously reported work. In [36], gradient-boosted decision tree classification of the log-loss function is developed and achieves an 82% reduction in false positives.