esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 4 Methodology
### Phase 1: Collection of Parallel Texts
#### 4.1.1 Corpus Search  
We collected texts through in-person visits and via official indigenous language websites in Mexico.  
* Searches for parallel texts in person. We carried out the in-person searches at the Faculty of Philosophy and Political Sciences of the Autonomous University of Queretaro. In the in-person searches, we obtained a total of 8 books.
* Searches for parallel texts on official websites. Also, we conducted web searches on the official website gob.mx, INPI, and the Federal Telecommunications Institute. We converted texts found on websites in PDF format into plain text format.  
While parallel texts are available, there is a significant shortage of Mixtec language texts necessary for training translations. With this in mind, we generate synthetic data, as described in the next section.
## 1 Introduction
### Related Work  
Fast marching method (FMM) [7], probabilistic road map (PRM) method [8], and rapid exploring random trees (RRT) method [9; 10; 11] are representatives of sampling-based algorithms. In PRM, the shortest path is computed from the graph made by the sampled points using the Dijkstra algorithm [12; 13]. Artificial potential field methods are also popularly used to design the path planning algorithm which leads the robot manipulator from the starting point to the goal point [14; 15]. In the derivation, the gradient of the potential function plays a key role in computing the direction of the optimal path [16]. Since it is based on the gradient descent method, it can suffer from trapping in the local minimum, which makes it difficult to be applied to high-dimensional problems [17]. In addition, artificial intelligence like reinforcement learning is also widely used to design collision-avoiding path planning [18]. In [19], Real-Time RRT* (RT-RRT*) is proposed to solve the path planning problem with a dynamic obstacle in a two-dimensional environment.  
This paper improves the result in [5; 20] in such a way that the deep reinforcement learning-based path planning for multi-arm manipulators with both static and periodically moving obstacles is proposed using the SAC algorithm with hindsight experience replay (HER).
# Data-Driven Control Algorithm for Snake Manipulator
## 3 Simulation of DDPG Control Based on 2-Link Model  
In order to verify whether the DDPG algorithm can be feasibly and effectively applied to the space manipulator, a DDPG control simulation based on two connecting rods was designed, as outlined in this section. In addition, a 2-link model lays the foundation for the establishment of the whole model.
# Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method
## 3 Comprehensive Safety Factor
### Stress Analysis of the Element  
Figure 2 illustrates the stress state of an arbitrary infinitesimal element within the slope. On this element, a pair of normal stresses \(\sigma_{x}\) and \(\sigma_{y}\), as well as shear stresses \(\tau_{x}\) and \(\tau_{y}\), are applied. \(\beta\) is the angle formed between the principal stress direction and the positive direction of the x-axis.  
For the signs of stresses \(\sigma\), \(\tau\), and angle \(\beta\), the following rules apply:  
1. Normal stresses and principal stresses: When they exhibit a compressive state, their values are positive. If they are in a tensile state, their values are negative.
2. Shear stresses: If a shear stress causes the element to rotate counterclockwise, its value is positive. If it causes clockwise rotation, its value is negative.
3. Angle \(\beta\): Its value ranges between \(0^{\circ}\) and \(90^{\circ}\). When rotating from the positive direction of the x-axis toward the outward normal direction of the acting surface, if the rotation is counterclockwise, \(\beta\) is positive. If it is clockwise, \(\beta\) is negative.  
Based on the above rules, analyzing Figure 3 reveals that the normal stress in the figure is positive, the angle is also positive, and the shear stress is negative.  
According to the definition of principal stress, the calculation formulas for the maximum principal stress \(\sigma_{1}\), the minimum principal stress \(\sigma_{3}\), and the included angle \(\beta\) on the principal plane are as follows:  
\[\left.\begin{array}{c}\sigma_{1}\\ \sigma_{3}\end{array}\right\}=\frac{\sigma_{x}+\sigma_{y}}{2}\pm\sqrt{\left( \frac{\sigma_{x}-\sigma_{y}}{2}\right)^{2}+\tau_{x}^{2}} \tag{24}\]  
Figure 3: Stress diagram on inclined plane in differential element.  
Figure 2: Force diagram of the infinitesimal element.  
\[\beta=-\frac{1}{2}\arctan\frac{2\tau_{x}}{\sigma_{x}-\sigma_{y}} \tag{25}\]  
Figure 3 is a schematic diagram for calculating the stress on any inclined plane within the infinitesimal element.  
Within the infinitesimal element, an arbitrary inclined plane is selected. Based on the static equilibrium conditions, it can be concluded that if the values of \(\sigma_{1}\) and \(\sigma_{3}\) are known then on an inclined plane at an angle \(\alpha\) (where the angle \(\alpha\in[0^{\circ},90^{\circ}]\)) relative to the acting surface of \(\sigma_{1}\), the normal force \(\sigma_{n}\) and shear stress \(\tau\) will exhibit specific numerical relationships, as described below:  
\[\sigma_{\rm n}=\frac{\sigma_{1}+\sigma_{3}}{2}+\frac{\sigma_{1}-\sigma_{3}}{2 }\cos 2\alpha \tag{26}\]  
\[\tau=\frac{\sigma_{1}-\sigma_{3}}{2}\sin 2\alpha \tag{27}\]  
\[\alpha=45^{\circ}+\varphi/2 \tag{28}\]
## 4 Discussion
### Impacts of Other Driving Factors on GPP, ET, and WUE  
The impacts of other driving factors on GPP, ET, and WUE were greater than those of climatic factors, with the influence of the LAI being the most pronounced, particularly on the Loess Plateau and the Inner Mongolia Plateau. The results aligned with those of previous studies, which were mainly due to large-scale ecological restoration programs,such as the Grain for Green Program, which started in 1999 as a pilot, and was formally implemented in 2000. For nearly 20 years, significant human and material resources have been invested, leading to noticeable improvements in China's ecological environment [76; 77]. This improvement was especially evident in the core areas of these projects, such as the Loess Plateau [78; 79], where there has been a significant increase in GPP and ET. Previous studies have shown that LAI was more highly correlated with GPP than ET because the variation in LAI could alter photosynthetic rate and thus effectively affect vegetation productivity. However, this effect showed a weaker impact on the long-term changes in ET over the years, as the annual ET was controlled by the supply of energy and water [80; 81]. Therefore, in arid and semi-arid regions of the Loess Plateau, the increase in GPP was much greater than that in ET, resulting in a significant increase in WUE [82]. On the contrary, in the southern part of China, vegetation coverage is higher, and the vegetation type is mainly dominated by evergreen broad-leaved forests. However, previous studies illustrated that the increased LAI may reduce GPP, mainly due to the mutual shading of the canopy leaves. At the same time, evapotranspiration changes less under these conditions, thus WUE may present a downward tendency in some areas [83].
## 3 Developed UWSN Ecosystem
### Energy-Harvesting Model  
The implementation of the energy-harvesting model is introduced [6] in this section. By using this model, it is possible to simulate several days, weeks, months or even years, using real solar radiation day curves observed in specific world locations. This data can be easily obtained by using public databases, such as the PANGAEA project [7].  
The implementation of the energy model is divided into four sub-models, each represented by one class in the ns-3 simulator. A simplified class diagram is depicted in Figure 2. The _CapacitorEnergySource_ class models capacitor charge and discharge curves. The _SensorEnergyModel_ class describes sensor energy consumption (temperature, humidity, _etc._). The _RadioEnergyModel_ class represents radio energy consumption at different modes, and the _SolarPanelEnergyModel_ class models energy obtained from solar panels.  
Capacitor energy source:This class manages super-capacitor charge and discharge curves using a linear model. In the _CalculateVoltage_ method, the voltage that a capacitor can deliver after \(\Delta t\) is calculated using expression (1):  
\[Vscap_{t+\Delta t}=Vscap_{t}-\frac{Iscap_{t}}{C}\Delta t \tag{1}\]where \(Vscap_{t}\) is the capacitor voltage at a certain time, \(t\), and \(Vscap_{t+\Delta t}\) is the capacitor voltage after a configurable time, \(\Delta t\). \(Iscap_{t}\) is the difference between the current demanded by the capacitor and the current coming from the solar panel.  
Component demands and current delivered by solar panels are calculated by the _CalculateTotalCurrent_ method, which, at the same time, calls to the _GetCurrentA_ method--which returns the current being used (or delivered, in the case of the solar model) by each component.  
The _CalculateVoltage_ method is always called when a mode change occurs in either the radio or sensor models. Any change causes an event to recalculate capacitor energy based on the previous state (mode) of the radio (or sensor) and the amount of time since the previous calculation was performed.  
Radio energy model:This class models the energy consumption of a wireless radio transceiver. Four different modes have been set: reception (RX), transmission (TX), IDLE and SLEEP. Each of these modes consumes a configurable amount of current, and consumption is estimated depending on the current radio mode.  
Sensor energy model:This class models the energy consumption of a sensor attached to the node. It has two states: enabled and disabled. In an enabled state, the sensor consumes a configurable amount of current. Meanwhile, in a disabled state, energy consumption is zero.  
Solar panel energy model:This class models the current delivered by solar panels--which is calculated using values stored in a.csv (comma-separated values) file. Each.csv field corresponds to one-hour of mean solar radiation in \(kJ/m^{2}\). Using weather databases, such as AEMet (Spanish Meteorological Agency) or PANGAEA, we are able to easily obtain real solar radiation values.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 5 Constraints
### Potential Biases  
Data collection bias: Employing specific keywords for tweet collection may introduce selection bias, potentially skewing the representation of certain sentiment groups by either overemphasizing or underemphasizing them.  
Labeling bias: The subjective nature of manual sentiment labeling makes it susceptible to individual biases, influencing the accuracy and reliability of sentiment categorization.  
Model bias: The selection of algorithms and hyperparameters holds the potential to impact model performance, introducing biases that may affect the interpretation of sentiment analysis results.  
Pre-trained word embedding bias: The biases inherent in the training data of pre-trained embeddings could be mirrored in sentiment analysis outcomes, potentially amplifying and perpetuating biases present in the initial word embedding data.  
Although this research offers valuable perspectives on the sentiment analysis of Turkish financial tweets, both researchers and readers must remain cognizant of these limitationsand biases. This awareness is crucial for the accurate interpretation and contextualization of this study's findings.
## 3 Sink-oriented Dynamic Location Service
### Global Grid Construction  
SDLS constructs a shared global grid structure once all the sensor nodes are deployed. Global grid construction can be enabled when all sensor nodes obtain their locations using a local positioning system and maintain their one-hop neighbors' geographic locations in their tables. Specifically, the grid is set up in parallel with the \(X\)-axis and \(Y\)-axis of the positioning system. The positive directions of  
Figure 3: Selection of shortest path relay.  
the _X_- and _Y_-axes of the predefined positioning space are pointing to the east and north, respectively. The baseline coordinate (_Xbase_, _Ybase_) of a pre-defined positioning system is set in the mission messages or hard coded in each sensor node. The all sensor field is divided into small virtual grids. The basic idea of the grid-based approach is to establish a grid using the intersection points called grid points. The distance to the grid point is the cell size and is determined by the user such that grid points are not within transmission range. The sensor field is divided into square-shaped grids of user-defined size \(a\). Each cell is an _a_\(\times\)_a_ square. Based on the global grid, the virtual coordinate (_Xp_, _Yp_) is determined to be grid point, the nearest node to the grid point is called the \"grid node\", and the grid nodes that take the role of the location server for a specific source node are named \"anchors\". The grid points are determined using the baseline coordinate (Xbase, Ybase) as follows:  
\[\{Xp=Xbase+i*\alpha,Yp=Ybase+j*\alpha\};\{i,j=\pm 0,\pm 1,...\} \tag{1}\]  
The first grid point sends a grid node announcement message to each of the four adjacent grid points, using simple geographic forwarding through intermediate nodes. As a result, the node that is geographically closest to each of the grid points becomes the grid node. Finally, several nodes are selected as grid nodes to cover all grid points across the original network. Every grid node will be in charge of a location server and will maintain the location information of the source node in its cache. After selection, the grid nodes will announce their role to the adjacent grid nodes. Then, neighboring grid nodes send notification messages to each other to establish a neighbor grid node table. The main objective of global grid is to deliver location information of the source nodes to the multiple mobile sinks. In the case of multiple mobile sinks, it is difficult to keep tracking the location of mobile sinks, since links between the source node and the sink may be easily broken. In our protocol, once the sink obtains the location of the source node using the global grid structure, the sink can directly send a query to the source node. This will be explained in Section 3.5.
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
## 3 Results and Discussion
### Effect of High-Porosity Sintered Fibrous Metal
#### 3.2.2 Effect of Mass Flux  
The boiling curve of the three different surfaces at mass flux \(G\) = 150, 300, and 600 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) are shown in Figure 10. In general, the bare surface had the property that when the heat flux was constant in flowing subcooled boiling, the temperature boundary layer became thinner, and the onset nucleate boiling (ONB) shifted towards a higher wall heating degree as the mass flux increased [34]. The heat flux also increased as the mass flux increased. This effect is attributed to two main effects: the macro convection promoted by the working fluid motion and the layer evaporation between the heater wall and the water. However, independent of the mass flux, all the boiling curves converged at one point, called the transition to the fully developed boiling. Before this point, the effects of the macro convection became lower; however, the layer evaporation effect prevailed.  
In general, the heat flux, using porous surfaces, increased as the mass flux increased before the onset nucleate boiling (ONB); nevertheless, during the partial developed boiling the macro convection played an important role, and heat flux increased at a higher porous body thickness.  
The enlarged boiling curve at mass flux \(G\) = 150 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) is shown in Figure 11a. We found that there was a small variation in the heat flux when the wall superheat was 10 K or lower; however, the porous surfaces with 1.0 and 0.5 mm deviated from the slope of the forced convection at around 13 and 16 K, respectively. In other words, the wall superheat degree decreased as the porous body thickness increased. Figure 11b shows the boiling curve at the mass flux of 300 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) where the deviation from the convection slope was around 15 and 18 K for the 1 and 0.5 mm porous thicknesses, respectively. The reason is attributed to the development of the thermal boundary layer. There are three main reasons for the difficulty in developing a thermal boundary layer. First is the turbulence effect caused by the porous body. This porous surface is installed on the bare surface, and it leads to stirring the working fluid near the heating surface. Second is the expanding heat transfer area. The porous metal is sintered to the heating surface as the same material, and the heat is easily transferred from the heating surface to the working fluid. Third is turbulence promotion by the bubbles. This porous surface has a higher number of cavity sites; thus, the generation and the evaporation of bubbles also increases. We considered that these  
Figure 10: Boiling curves for each surface at the subcooled temperature \(\Delta T_{\text{sub}}\) = 30 K.  
effects cause turbulence in the thermal boundary layer compared to the bare heat-transfer surface and, therefore, the heat transfer is enhanced even at a low wall superheat.
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 4 Experiments
### Objective Evaluation  
#### 4.1.1 Speaker Similarity Test  
One of the most widely employed methods to quantify the similarity between two speakers' voices used in the field of speaker recognition is cosine similarity. This metric is defined as the inner product of two vectors divided by the product of their magnitudes; i.e., the cosine of the angle between them. In speaker recognition, i-vectors [32] or x-vectors [33] are typically utilized to characterize a speaker. We used the [PERSON] toolkit [34] to extract both i-vectors and x-vectors, and calculated the cosine similarities between the converted and target speeches. A higher cosine similarity value indicates better voice conversion performance.  
Table 1 summarizes the average cosine similarity values along with the corresponding 95% confidence intervals for the converted speeches generated by DiffVC and CycleDiffusion.  
The cosine similarity scores for the i-vectors demonstrated a modest enhancement of 10.8% (from 0.4850 to 0.5376). The cosine similarity using the x-vectors showed only marginal improvement. However, the confidence interval contracted by 39.4% (from 0.0033 to 0.0020), indicating a sharper distribution and therefore diminished variance across speakers. On average, the proposed method outperformed the baseline system in the speaker similarity tests using both i-vectors and x-vectors. This suggests that incorporating cycle consistency loss into the diffusion model-based voice conversion training process is an effective strategy for enhancing speaker similarity and achieving more consistent results.
# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 4 Conclusions  
To summarize, we propose a road pavement damage detection method based on the local minimum of grayscale and the feature fusion of image blocks. By the image preprocessing of the grayscale, we confirm the feature points according to the local minimum of grayscale. Then, the image blocks consisting of the feature points neighborhood are input into the designed CNN model with an SVM classifier. The tested accuracy for four independent datasets increases as the number of feature points increases and can reach at least 88.3% when \(m\geq 5\), which indicates that the proposed model is valid and efficient in identical pavement damage detection. The proposed training model is constructed based on image blocks, is of a small scale and has a small requirement for computing resources. Our model is privileged for cracks and potholes detection, and pavement damage always consists of cracks and potholes. Therefore, our model could be generalized to most damage detection. In practical pavement detection, one can build a dataset of the pavement to be detected with only a few pictures to train the model and obtain recognition results of a relatively high accuracy with little machine time, even at mobile terminals.  
Data curation, G.-H.C. and H.H.; formal analysis, Z.C., Y.-L.S., W.-H.I. and K.-L.Y.; funding acquisition, J.N.; investigation, G.-H.C., W.-W.J. and H.H.; software, Z.C., Y.-L.S. and W.-W.J.; validation, Z.C. and J.N.; writing--original draft, Z.C.; writing--review and editing, Y.-L.S.; discussion and future work, Y.-L.S., W.-H.I. and K.-L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial Natural Science Foundation, grant numbers LGF20F010003 and LQ19A040010.  
Not applicable.  
Figure 5: Accuracy as a function of the number of feature points \(m\) for datasets test0, test1, test2 and test3.  
Data Availability Statement: The data presented in this study are available in the article.  
This work was supported in part by a grant from the Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences (20190909). The work was supported in part by the Research Center for Deep Space Explorations of the Hong Kong Polytechnic University.  
The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.