esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 3 Materials and Methods
### Translation of ISL Sentence from English Sentence
#### 3.2.3 Script Generator and ISL Sentence  
The script generator creates a script for generating an ISL sentence from the English sentence. The script takes a valid English sentence (after semantic parsing) as input and  
Figure 4: Derivation tree for the sentences: (**A**) Come to my home, (**B**) Hello good morning, (**C**) Thank you, (**D**) Please come with me. Note: terminal symbols are represented in red, whereas green and blue refer to the nonterminal symbols of the derivation tree (generated from the above CFG). The start symbol (often represented as S) is a special nonterminal symbol of the grammar.  
generates the sequence tree, where each node of the tree is related to different gestures that are associated with the avatar movement. The sequence tree maintains the order of the motion performed by the avatar model.  
The structures of the English and ISL sentences are quite different. The representation of ISL from the English sentences is done using Lexical Functional Grammar (LFG). The f-structure of LFG encodes the grammatical relation, like a subject, object, and tense of an input sentence. ISL follows the word order \"Subject-Object-Verb\", whereas the English language follows the word order \"Subject-Verb-Object\" [35]. Moreover, the ISL sentence does not consider any conjunction and preposition in the sentence [36]. Some examples of mapping from English to ISL sentences are represented in Table 3.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 5 Conclusions  
Based on the analysis of Typhoon Morakot in Wenzhou, the main findings of this study are summarized as follows:  
Figure 11: Longitudinal turbulence integral scale: variation with wind speed and distribution: (**a**) Variation in longitudinal turbulence integral scale with wind speed; (**b**) Distribution of longitudinal turbulence integral scales.  
Figure 12: Lateral turbulence integral scale: variation with wind speed and distribution: (**a**) Variation in lateral turbulence integral scale with wind speed; (**b**) Distribution of lateral turbulence integral scales.  
1. The longitudinal and lateral turbulence vary with the wind speed. The ratios of longitudinal to lateral turbulence intensity at measuring points 1 and 2 are 1:0.92 and 1:0.90, respectively. The turbulence intensity gradually decreases, and the reduction rate rises with the change in the time interval. Ultimately, the longitudinal and lateral turbulence intensities approach 0.2 and 0.1, respectively.
2. The distribution of the gust factors is relatively scattered when the wind speed is less than 8 m/s. Conversely, they tend to cluster together with the wind speed exceeds 8 m/s. The distribution of lateral gust factors is more scattered compared to that of longitudinal gust factors, and the gust factor reduces as the time interval increases. Under the same wind speed, the longitudinal gust factor is much great than the lateral gust factor.
3. A curve is constructed to fit the measured data to obtain the link between the turbulence intensity and the gust factor. Measuring point 1 has fitting parameters a = 0.66 and b = 1.30, while measuring point 2 has fitting parameters a = 0.69 and b = 1.32.
4. When the time interval is 3 s, the observed peak factor remains constant as the mean wind speed fluctuates. The peak factor reduces with increased time intervals, and the peak factor substantially varies under different wind speeds.
5. With increasing mean wind speed, the longitudinal and lateral turbulence integral scales rise slightly. The turbulence integral scales generally tend to cluster together in areas with low wind speeds, but the distribution gradually becomes scattered as the mean wind speed increases.  
Conceptualization, Y.W. and Y.L.; methodology, Y.W.; software, Y.L.; validation, Y.L.; formal analysis, Y.W., B.F. and G.F.; investigation, C.Z.; resources, C.Z.; data curation, C.Z.; writing--original draft preparation, Y.W., X.W. and Y.L.; visualization, Y.L. and Q.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Province Natural Science Foundation Project (LY19E080022), the Natural Science Foundation of China (51508419, 51678455), and the Zhejiang Provincial Department of Education Project (The title of the Project: Wind Field Characteristic Monitoring and Wind Load Monitoring Research on Coastal Low-rise Buildings and the number: Y202147409).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Text Theme Model  
Figure 4 illustrates the perplexity function's output results for different themes. It is evident that perplexity gradually decreases as the value of \(K\) (number of themes) increases. According to the elbow method, a significant inflection point occurs around \(K=8\), where the decreasing trend of perplexity slows down. Therefore, the optimal value for K, in this corpus selected for our study, is identified as 8. Utilizing the LDA model, the generation quantity of LDA themes was set to 8.  
Therefore, the results extracted from the LDA-Gibbs model encompass eight themes. For each theme, the feature words are sorted based on their generation probability within that theme. The top 30 words for each theme are selected and presented in Table S2. The LDA-Gibbs model randomly selects text themes and compares the similarity of words in the comments. After marking similar words, the model assesses the frequency of word occurrences. Simultaneously occurring high-frequency words are grouped into the same theme. In a given comment dataset, characteristic words from different themes may appear, with the proportion of high-frequency words determining the thematic attributes of that comment. By drawing inspiration from the coding results of Nvivo and integrating comments with high-frequency feature words, the following themes were systematically summarized: visit time, visitor types, visit motivation, visitor activities, characteristics of urban river corridor, maintenance and management, accessibility, and visitors' attitude, as outlined in Table 1. Subsequently, a thematic overview was provided for the eight themes), accompanied by illustrative comment examples (Table S3).  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Theme** & **Category** \\ \hline
1 & Visit time & Season, month, weekday and weekend, holiday, time of a day \\
2 & Visitor types & Children, adults, the elderly, parents, friends, couples, etc. \\  & & Admiring the scenery, exercising, spending time with family or friends, \\
3 & Visit motivation & discovering and experiencing nature, relaxing, escaping city life, engaging in outdoor recreation and services \\ \hline \end{tabular}
\end{table}
Table 1: Summarized themes and categories.  
Figure 4: Number of theme–perplexity line graph.
# Simulation and Experimental Analysis of Pressure Pulsation Characteristics of Pump Source Fluid
## 6 Simulation and Experimental Analysis
### Analysis of the Test Results of the Fluid Pressure Pulsation Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Experiment** & **Simulation** & **Error (\%)** \\
**Multiplication** & **Frequency (Hz)** & **Frequency (Hz)** & **Error (\%)** \\ \hline
1 & 210.5 & 211.3 & 0.4 \\
2 & 421.1 & 419.9 & 0.3 \\
3 & 631.5 & 631.2 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Frequency comparison analysis. Figure 15b extracted the first three frequencies of fluid pressure pulsation, as shown in Table 10.  
Through the comparative analysis of the experiment and simulation, it can be concluded that the simulation results were very consistent with the experimental measurement results. When the engine speed was 1800 rpm, which is 30 Hz, considering 7 plungers, it can be easily calculated that there are 7 \(\times\) 30 = 210 pressure pulses per second, so 210 Hz is the first natural frequency that one would expect. Consequently, further higher-order frequencies happen to be positioned at 2, 3,... times 210 Hz. Similar aspects occur in the design of aircraft propellers, where the so-called \"Blade Passage Frequency\" is a key parameter for the study of acoustics and vibrations produced by rotating engine rotors [6]. Under the load pressures of \(p_{1}\) = 4.375 MPa, \(p_{2}\) = 8.38 MPa, and \(p_{3}\) = 12.87 MPa, the third-order frequency errors before simulation and experiment were about 1.3%, 0.3%, and 1.5%, respectively. The main reason for the small deviation between the two is that in the AMESim simulation process, the outlet of the plunger pump and the throttle valve are directly connected with a wire, which is equivalent to no pipeline, and the damping effect of the pipeline was not considered. In addition, the plunger pump and hydraulic valve leakage were not considered in the AMESim simulation process. Additionally, there were other environmental factors and manual operation factors during the experiment. In summary, the frequency error of the pump fluid pressure pulsation frequency of the simulation analysis and experiment test was about 1%, which proved the correctness of the simplified system model.
# EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling
###### Abstract  
Glenohumeral stability is essential for a healthy function of the shoulder. It is ensured partly by the scapulohumeral muscular balance. Accordingly, modelling muscle interactions is a key factor in the understanding of occupational pathologies, and the development of ergonomic interventions. While static optimization is commonly used to estimate muscle activations, it tends to underestimate the role of shoulder's antagonist muscles. The purpose of this study was to implement experimental electromyographic (EMG) data to predict muscle activations that could account for the stabilizing role of the shoulder muscles. Kinematics and EMG were recorded from 36 participants while lifting a box from hip to eye level. Muscle activations and glenohumeral joint reactions were estimated using an EMG-assisted algorithm and compared to those obtained using static optimization with a generic and calibrated model. Muscle activations predicted with the EMG-assisted method were generally larger. Additionally, more interactions between the different rotator cuff muscles, as well as between primer actuators and stabilizers, were predicted with the EMG-assisted method. Finally, glenohumeral forces calculated from a calibrated model remained within the boundaries of the glenoid stability cone. These findings suggest that EMG-assisted methods could account for scapulohumeral muscle co-contraction, and thus their contribution to the glenohumeral stability.  
calibration; electromyography; glenohumeral stability; musculo-tendon unit; shoulder
## 2 Submillimeter-Level Microwave Ranging
### MWR Measurement Modeling
Dual ranging \(R(t)\equiv\lambda\Theta(t)\), with  
\[\lambda=c/(\ddot{f}_{1}+\ddot{f}_{2}) \tag{5}\]  
Subsequently, we have  
\[R(t)=\rho(t)+\rho_{TOF}(t)+\rho_{err}(t)+N^{\prime}+I^{\prime}+d^{\prime}+\varepsilon ^{\prime} \tag{6}\]  
where \(\rho(t)\) is the instant distance, \(\rho_{TOF}(t)\) is the correction of time of flight (TOF), \(\rho_{err}(t)\) is the ranging error due to oscillator and time tag, \(N_{i}^{j}\) is the ambiguity, \(I^{\prime}\) is the ionosphere error, \(d^{\prime}\) is the phase bias due to other reasons, and \(\varepsilon^{\prime}\) is the ranging error due to measurement noise. The one-way phase measurement of \(\phi_{i}^{j}(t+\Delta t_{i})\) is transferred to instant distance \(\rho(t)\), and DOWR ranging \(R_{K}\)&\(R_{Ka}\) can be derived while using K&Ka band measurement. Finally, DOWR ranging without the influence of the ionosphere can be given as  
\[R=\frac{\ddot{f}_{Ka}^{2}R_{Ka}-\ddot{f}_{K}^{2}R_{K}}{\dot{f}_{Ka}^{2}-\ddot{ f}_{K}^{2}} \tag{7}\]  
The principle of differential frequency phase measurement is to change the reference signal and measured signal into intermediate frequency (IF), or lower frequency, by mixing the radio frequency (RF) and local frequency, followed by a phase comparison. Because the IF signal entering the phase measurement system is much lower than the RF, the phase period is expanded, which greatly improves the phase measurement accuracy.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology
### Spatiotemporal Graph Attention (ST-GAT) Model
#### 2.1.1 Graph Attention Layer  
The graph attention layer (GAT layer) [46] is designed to dynamically learn the importance of neighboring nodes in a graph by applying attention mechanisms. Unlikestandard convolution layers that treat all neighbors equally [47], the GAT layer assigns different weights to each neighboring node based on its relevance to the target node. This is achieved through a self-attention mechanism, which calculates attention scores for each neighbor, allowing the model to focus more on important nodes and ignore less relevant ones. As a result, the GAT layer can capture complex spatial relationships in a graph-structured dataset more effectively.  
The detailed steps for constructing a GAT are as follows. The first step is to determine the structure of the graph, specifically how the nodes are connected. For excavation-induced deformation issues, each monitoring point can be considered a node. The connections (edges) between nodes can be determined based on the actual physical location, functional connections, or previous data correlations. For example, if the correlation between two monitoring points is high, they can be connected by an edge. The feature vector \(h\) of each node \(i\) consists of the wall deflection data \(d\) and axial force \(\text{data}\,f\) from that monitoring point, which are represented as  
\[h_{i}=[d_{i},f_{i}] \tag{1}\]  
These features undergo a transformation through a shared linear matrix \(W\) to enhance the expressiveness of the model, producing transformed features:  
\[h_{i^{\prime}}=Wh_{i} \tag{2}\]  
Attention coefficients are then computed by focusing on pairs of nodes and considering their transformed features \(h_{i}\) and \(h_{j}\) through a single linear layer followed by LeakyReLU activation function. The calculation is structured as follows:  
\[e_{ij}=\text{LeakyReLU}(a^{\text{T}}[h_{i^{\prime}}\parallel h_{i^{\prime}}]) \tag{3}\]  
where a represents the learnable parameter vector, which is used to consider the interactions between various monitoring points. The attention coefficients \(e_{ij}\) are normalized using a softmax function to yield the final attention scores \(\alpha_{ij}\), as shown in Equation (4).  
\[\alpha_{ij}=\frac{\exp(e_{ij})}{\sum\limits_{k\in\mathcal{N}(i)}\exp(e_{ik})} \tag{4}\]  
The attention weights between different nodes are obtained through a multi-head attention mechanism, as shown in Figure 1. In addition, these attention weights, \(\alpha_{ij}\) allow the weighted aggregation of neighbor features \(h_{j}\) to update the feature vector of each node \(h_{j^{\prime}}\), culminating in the refined representation of each node \(h_{i^{\prime}}\) within the network.  
\[h_{i^{\prime\prime}}=\sum\limits_{j\in\mathcal{N}(i)}\alpha_{ij}h_{j^{\prime}} \tag{5}\]  
Figure 1: Structure of graph attention layer. (**a**) Attention calculation; (**b**) multi-head attention.
## 1 Introduction
In summary, we propose a rolling bearing intelligent diagnosis scheme based on the Archimedes optimization algorithm (AOA), to optimize the parameters of VME and a 1D-MRCNN. Initially, WCK serves as the objective function to optimize the penalty factor \(\alpha\) and center frequency \(\omega\) of the VME, aiming to extract the desired mode components and eliminate noise in the vibration signals. Then, the processed vibration signals are input into the 1D-MRCNN for fault diagnosis. The fusion of VME with the 1D-MRCNN maximizes the advantages of both techniques, thereby enhancing recognition accuracy. The following is an overview of this paper: Section 2 presents related works (pertaining to AOA, VME, and 1D-MRCNN); Section 3 employs simulated signals to validate the feasibility of parameter-optimized VME using WCK as the objective function; in Section 4, the experimental signals are analyzed and compared to demonstrate the practicality and superiority of the proposed method; and Section 5 presents our conclusions.
###### Abstract  
The Republic of Djibouti has untapped potential in terms of renewable energy resources, such as geothermal, wind, and solar energy. This study examines the economic feasibility of green hydrogen production by water electrolysis using wind and geothermal energy resources in the Asal-Ghoubbet Rift (AG Rift), Republic of Djibouti. It is the first study in Africa that compares the cost per kg of green hydrogen produced by wind and geothermal energy from a single site. The unit cost of electricity produced by the wind turbine (0.042 \(\$\)/kWh) is more competitive than that of a dry steam geothermal plant (0.086 \(\$\)/kWh). The cost of producing hydrogen with a suitable electrolyzer powered by wind energy ranges from \(\$\).672/kg H\({}_{2}\) to \(\$\).10.63/kg H\({}_{2}\), while that produced by the high-temperature electrolyzer (HTE) powered by geothermal energy ranges from \(\$\).31/kg H\({}_{2}\) to \(\$\).78/kg H\({}_{2}\). Thus, the AG Rift area can produce electricity and green hydrogen at low-cost using wind energy compared to geothermal energy. The amount of carbon dioxide (CO\({}_{2}\)) emissions reduced by using a \"Yinhe GX113-2.5 MW\" wind turbine and a single flash geothermal power plant instead of fuel-oil generators is 2061.6 tons CO\({}_{2}\)/MW/year and 2184.8 tons CO\({}_{2}\)/MW/year, respectively.  
Dibouti; wind energy; geothermal energy; hydrogen; Asal-Ghoubbet Rift; cost analysis 2022, 15, 138. [[https://doi.org/10.3390/en15010138](https://doi.org/10.3390/en15010138)]([https://doi.org/10.3390/en15010138](https://doi.org/10.3390/en15010138))
## 3 Experiments
### Texture Classification
To validate our approach (inMGNMM), we compared it with five other methods, namely finite Gaussian mixture (GMM), infinite Gaussian mixture (inGMM), finite generalized Gaussian mixture (GGMM), infinite generalized Gaussian mixture (inGGMM), and finite multivariate generalized Gaussian mixture (MGGMM) [10]. The evaluation results using three different datasets for six different methods are summarized in Tables 1-3, respectively. Accordingly, it is clear that our method has the highest achieved accuracy for the three datasets. In particular, for the case of the UMD dataset, the accuracy of our infinite mixture with DMD features is 92.12% and, therefore,  
Figure 3: Samples of textures from different datasets (**a**) KTH-TIPS, (**b**) UIUCTex, and (**c**) UMD dataset. it outperforms the other methods--GMM, inGMM, GGMM and inGGMM by--6%, 5%, 2.37% and 1.92%, respectively. Likewise, we reached the same conclusion for other datasets, and our model provides very encouraging results using different features as compared to other models. By contrast, the worst performance is obtained with the Gaussian-based classification. We can also conclude that all obtained results using our proposed infinite mixture are increased by around 1% to 2% if comparing it to its finite mixture counterpart (MGGMM). This can be explained by the model's capacity to combine prior knowledge during the learning process, its generalization capability, and its flexibility in terms of making less restrictive assumptions about the number of clusters than the finite model. On the other hand, a superior performance of the visual feature DMD over the SIFT and LBP for texture classification task can be observed in all these datasets. With DMD, the accuracy is improved over SIFT and LBP by around 3% and 2%, respectively. This result is probably achieved by the rich description of DMD obtained by considering all possible fine details at different resolutions and scales. Finally, we note that the performance of all methods for the KTH-TIPS dataset indicates that it is sometimes difficult to identify texture because of its various rotation and viewpoint changes.
# Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm
## 2 Alternative Implementation of the A* Algorithm
### Alternative A* Algorithm
After introducing the obstacle rate \(Q\), the new heuristic function is defined as \(H^{\prime}(n)=H(n)\Big{(}e^{-lnQ}\Big{)}\). To ensure its admissibility, we must adjust this heuristic function. Since \(Q\) is a value between 0 and 1, and \(e^{-lnQ}>1\), the value of \(H(n)\) is scaled up. Directly scaling \(H(n)\) may violate the admissibility condition, so a dynamic adjustment parameter \(\alpha\) is introduced where \(\alpha=\min\) (1, \(e^{-lnQ}\)). The new heuristic function is then defined as \(H^{\prime}(n)=\alpha\cdot H(n)\). When \(Q\) is large, indicating a complex map environment and difficult path search, the heuristic function \(H(n)\) should be reduced to increase the search accuracy so \(\alpha=1\) and \(H^{\prime}(n)=H(n)\), maintaining admissibility. When \(Q\) is small, indicating a simple map environment and an easier path search, \(H(n)\) should be increased to reduce the search space and speed up the process, so \(\alpha=e^{-lnQ}\). To ensure that \(\alpha\cdot H(n)\) does not exceed the actual minimum cost \(H^{*}(n)\), \(\alpha=\min\) (1, \(\frac{H^{\prime}(n)}{H(n)}\)). With this definition, the new heuristic function \(H^{\prime}(n)=H(n)\Big{(}e^{-lnQ}\Big{)}\) satisfies \(H^{\prime}(n)\leq H^{*}(n)\) in all cases, ensuring the heuristic function's admissibility. In different regions, the search direction and speed are dynamically adjusted by modifying the weight of the heuristic function \(H(n)\), allowing the A* algorithm to adapt during the search process \(F(n)\). This approach achieves both the speed and flexibility of path planning, ensuring the planned path is globally optimal. As shown in Equation (5), when there are fewer obstacles, the obstacle rate \(Q\) decreases which then increases the heuristic function value, reduces the search space during the A* algorithm's operation, and increases the path search speed. Conversely, when there are more obstacles, the obstacle rate \(Q\) increases, which reduces the weight of the heuristic function \(H(n)\). This leads to an increase in path nodes during the A* algorithm's search process, thereby improving path search accuracy. Therefore, enhancing the evaluation function of the A* algorithm can improve its speed, accuracy, and adaptability to environmental information when searching different locations on the map.