esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 5 Example of Coal Seam Gas Content Prediction
### Predicting Gas Content  
Firstly, we employ the computational capabilities of the intelligent auxiliary software (V1.0) specifically designed for mine gas prediction. Within the context of the mining engineering's coordinate grid diagram, a square grid measuring 100 m by 100 m is utilized to determine the gas content value of the coal seam. Subsequently, the software's contour mapping functionality is leveraged to interpolate and delineate the contour of the coal seam's gas content using the obtained data points (Figure 1). Finally, this contour is seamlessly translated and integrated into the CAD mining engineering plan, as depicted in Figure 2.
###### Abstract  
Mesoporous silica nanoparticles (MSN) are widely used as pesticide carriers to enhance their effective utilization, since it can promote the solubility and absorption of pesticides by plants. For plants, the particle size of pesticides influences their absorption and efficacy. Herein, is our research work of the size effect of MSN on the loading, release, and delivery behavior of pyraoxystrobin (Pyr) in cucumber plants. The well-ordered Pyr-loaded carbon quantum dots-MSN (Pyr@M) with sizes of 15, 100, and 200 nm were prepared. A comparative study among different particle sizes of Pyr@M was carried out on the aspects of control release performance, loading content, uptake, and transportation performance in cucumber plants. It was found that the loading content increased as the particle size increased. The nanoparticles as carriers increased the solubility of insoluble Pyr, but the nanoparticle size had no clear difference impact on the release rate. The efficiency of the cellular uptake strongly depended on the particle size. The smaller the MSN size, the easier it was to be absorbed and transmitted by cucumber plants. Compared to the free Pyr, the upward transportation rate of Pyr from Pyr@M in plant increased by 3.5 times. These findings provide new theoretical basis to design the MSN pesticide delivery system.  
size effect; mesoporous silica nanoparticles; pesticide delivery; pyraoxystrobin; cucumber plates +
Footnote †: journal: Applied Sciences  
1
# Effective Mechanical Properties of a Composite Material Reinforced by Oil Shale Ash Particles
## 4 Mechanical Properties of the Concrete-OSA Composite
### Analytical Calculation for the Concrete-OSA Composite  
Analytical data were obtained using the rule of mixtures and FEM in the same way as for the epoxy-OSA composite. However, in the case of concrete for each category, concrete with 0% ash content was taken as the matrix. For example, for Group C, the matrix properties of C0 concrete plus the properties of the OSA filler were used. This method simplifies the model significantly because the other filling elements were already averaged into the matrix of the composite material. Apart from this difference, everything else was made similar to the epoxy matrix.  
The experimental data were obtained by converting the compressive strength measurements into elastic modulus values using IS 456:2000 Clause 6.2.3.1 (24). The experimental data on the compressive strength are given in Table 13. The data on elastic moduli are given in Table 14.  
The OSA weight and volume fraction were calculated using the mass obtained from the recipe and density of OSA and are shown in Table 15.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & \multicolumn{2}{c}{**Compressive**} & \multicolumn{1}{c}{**Standard Deviation**} & \multicolumn{1}{c}{**Standard Deviation in \%**} \\ \cline{3-5}  & **A0 0\%** & 72.0 & 1.45 & 2.02 \\  & **A25 25\%** & 64.8 & 3.87 & 5.97 \\  & **A30 30\%** & 64.0 & 3.10 & 4.84 \\ \hline  & **B0 0\%** & 37.7 & 3.71 & 9.83 \\  & **B30 30\%** & 41.4 & 3.94 & 9.51 \\  & **B35 35\%** & 37.8 & 4.34 & 11.48 \\ \hline  & **C0 0\%** & 17.3 & 0.68 & 3.95 \\  & **C10 10\%** & 17.4 & 0.62 & 3.54 \\  & **C15 15\%** & 16.7 & 0.67 & 4.01 \\  & **C20 20\%** & 15.5 & 1.10 & 7.06 \\  & **C25 25\%** & 16.3 & 0.57 & 3.49 \\  & **C30 30\%** & 15.9 & 0.82 & 5.14 \\  & **C35 35\%** & 15.7 & 0.95 & 6.02 \\ \hline \end{tabular}
\end{table}
Table 13: Compressive test results used to obtain the modulus of elasticity.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Group A**} \\ \cline{2-4}  & **A0 0\%** & **A25 II 25\%** & **A30 II 30\%** \\ \hline Elastic modulus (GPa) & 42.43 & 40.25 & 40.00 \\ \hline  & **Group B** & \\ \hline  & **B0 0\%** & **B30 30\%** & **B35 35\%** \\ \hline Elastic modulus (GPa) & 30.70 & 32.17 & 30.74 \\ \hline  & **Group C** & \\ \hline  & **C0 0\%** & **C10 10\%** & **C15 15\%** & **C20 20\%** & **C25 25\%** & **C30 30\%** & **C35 35\%** \\ \hline Elastic modulus (GPa) & 20.80 & 20.86 & 20.43 & 19.69 & 20.19 & 19.94 & 19.81 \\ \hline \end{tabular}
\end{table}
Table 14: Elastic moduli obtained using the data from Table 11 and Equation (24).
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 6 NN Architecture Experiments
### Phase One  
This phase was designed as a proof of concept: can a NN learn the spectral warping function paths? For this phase, a single phrase was selected to be trained using the two input models mentioned above. This phase also provided a basis for exploring different NN architectures. For all these tests, the phrase, \"Author of the danger trail, [PERSON], etc.\" was selected from the CMU-ARCTIC database.  
We experimented with both input models on different number of layers in order determine a good architecture. Table 2 shows the number of nodes in each layer and the output function used for that layer for both architecture. The notation 256L 500R 900R 1000R 964L means that this NN had 5-layers with 256 inputs, 964 output nodes with 500, 900, and 1000 neuron nodes in the hidden layers. L and R represent \"linear\" and \"ReLU\" activation output functions, respectively. This table also shows results for architectures described in later phases of development.  
Figure 10 shows how the NN learned warping paths at various segments of the selected speech signal for different training iterations. In this figure, each subplot represents the mapping for one spectral segment of speech. In each of these, the blue plots shows the \"truth\" from DW the paths; the NN-learned paths are in orange. In this figure, the horizontal axes are the sequence of indices (1-468) and the vertical axes are the values of the \(\mathbf{a}_{F}\), truth and learned. Figure 10a uses the 19-layers-1-IP\(/1\)-OP architecture. There was fairly good matching between the training data and the NN output, but Figure 10b, produced using the 19-layers-3-IP\(/1\)-OP architecture showed better results, with the training data being virtually indistinguishable from the NN outputs. Figure 10c,d shows how the NN learned a \(\mathbf{b}\) warping path at various segments of the selected speech signal after 5000 training iterations. Again an excellent match was achieved with 3-IP\(/1\)-OP architecture. Not only was the NN better trained as demonstrated by these figures, but the quality of the audible transformed signal was significantly better when the three-input NN was used. Figure 11 shows the MSE between the true and estimated warping paths for the selected architecture (3-IP\(/1\)-OP) with 5000 training iterations, demonstrating that the learning essentially converged by that point.  
The use of three input blocks provided results superior to the single input block model. On the basis of the MCD values from Table 2 and Figure 10,d, we concluded that the NN used in the phases of this experiment should be based on the 19-layers-3-IP/1-OP architecture.  
Figure 10: Learned warping paths, phase one, 5000 training iterations. (**a**) Warping path **a** produced using 1-IP/1-OP architecture. (**b**) Warping path **a** produced using 3-IP/1-OP architecture. (**c**) Warping path **b** produced using 1-IP/-1-OP architecture. (**d**) Warping path **b** produced using 3-IP/1-OP architecture.
## 2 Experimental Description  
Bearing estimation experiments with an underwater acoustic source were performed at three floating ice cap sites (named S1, S2 and S3) in the central Arctic Ocean. During the experiment, at each site, a 3C geophone (named G1) mounted on the ice was used to receive the cross-ice signals. G1 is a wireless vibration sensor, and the brand is iSensor. Its ID number is 701. It is self-contained and can directly measure the vibration velocity and acceleration for particle motion in three dimensions. The receiving frequency band of G1 is 0.01-1000 Hz, and the A/D resolution is 20 bits. The working temperature range is \(-\)20-60 \({}^{\circ}\)C. During the experiment, the sampling frequency of G1 was set to 4 kHz. In addition, an underwater signal recorder (USR) beneath the ice was used to receive the acoustic pressures in the water at the same time. The USR was manufactured by the Institute of Acoustics of the Chinese Academy of Sciences. The model used (USR-2000) has a maximum operating depth of 2000 m. The sampling frequency of the USR was set to 24 kHz, and the analog-to-digital (A/D) resolution was 24 bits. The sensitivity responses to the frequency of G1 and the USR are shown in Figure 1. It can be seen from Figure 1a,b that the sensitivity curves are rather flat. This means that G1 and the USR are ideal for low-frequency measurement.  
The field trial geometry is shown in Figure 2. D is the depth of the acoustic source. The source is a fixed-depth explosion source, which can generate broadband signals. The depth D was 100 m at each of the three sites. R is the horizontal distance between the source and the geophone. At the three different sites, it was 70, 340 and 100 m, respectively. Based on the depth D and horizontal distance R of the source, the three different incident angles \(\theta\) could be calculated as 35\({}^{\circ}\), 73.6\({}^{\circ}\) and 45\({}^{\circ}\), respectively. The ice thickness was mainly 1-2 m.  
It was roughly measured using a meter ruler. But the ice thickness at site S1 was relatively thin. During the experiment, the \(x\) direction of the geophone was toward the source, and the bearing was \(0^{\circ}\). The basic information includes site coordinates, experimental time, real source bearing, R, D and number of sources, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Site Name** & **Site Coordinate** & **Experimental Time** & **Real Bearing** & **Horizontal Distance** & **Source Depth** & **Source Quantity** \\ \hline S1 & \(84.5125^{\circ}\) N 168.6950\({}^{\circ}\) W & 16 August 2020 & \(-5\) & 70 & 100 & 2 \\ \hline S2 & \(86.0430^{\circ}\) N 160.8985\({}^{\circ}\) W & 18 August 2020 & \(-5\) & 340 & 100 & 2 \\ \hline S3 & \(85.4917^{\circ}\) N 179.9095\({}^{\circ}\) E & 24 August 2020 & 90 & 100 & 100 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Basic information of bearing estimation experiments.  
Figure 1: Sensitivity response to the frequency: (**a**) geophone and (**b**) underwater signal recorder. The different colored lines represent x-, y-, z-direction respectively.
## 5 Experimental Results
### Throughput Analysis
#### 5.3.1 Aho-Corasick Algorithm  
Algorithm 1 shows pseudo-code of the parallel implementation of the AC algorithm that executes the pattern-matching process using multiple CUDA threads. Here, InChar represents the character on the target text, cs represents the current state, and pos represents the position on the target text. Pattern-matching tests of the AC algorithm are performed in two different versions. In one of them, network packets are held in global memory (AC), while in the other, network packets are held in shared memory (AC-ps). Figures 9 and 10, respectively, compare the packet-processing times and throughputs of these pattern-matching versions applied to datasets with different numbers of patterns.  
```
input: InChar, cs, pos whilepos is less than 256 do  GetNextState(cs, InChar); whilecs is equal -1 do  GetStateFailure(cs); endwhile  GetNextState(cs, InChar);  GetOutputState(cs); ifoutput is greater than 0 then  UpdateDetectedPatternCount(); endif endwhile
```  
**Algorithm 1** Aho-Corasick algorithm  
A pattern-matching performance of the AC algorithm is associated with the memory space required by the algorithm and access overheads. An increasing number of examined patterns also increases the memory space required by the algorithm in the pattern-matching process. Therefore, applying the AC algorithm in the pattern-matching process makes the cache space useless for larger state transition tables. As a result, the pattern-matching speed drops for large pattern datasets. Local memory in the GPU have less access latency than global memory. Accordingly, processing packets in shared memory instead of global memory will reduce the packet-processing time (Figure 9). Hence, the tests produce a higher throughput when the network packets are in shared memory (Figure 10).  
Coalesced memory access occurs when all 32 threads in a warp access consecutive locations of a piece of memory. While detecting the patterns using the AC algorithm, threads do not act depending on the access patterns. These threads access random locations of the AC tables to find the next state based on the character on the target text. This causes uncoalesced memory accesses. Moreover, the absence of any access pattern during the pattern detection causes bank conflicts on shared memory.
# Optimising Amber Processing Using 3D Scanning: New Perspectives in Cultural Heritage
## 2 Methodology and Research
### Processing Scanned Models  
The data (post-)processing workflow is a multi-stage process, as depicted in Figure 7.  
Scanning involved acquiring several to a dozen individual scans that collectively covered the entire surface of the scanned object. The data were imported into Artec Studio 15 software in the form of point clouds with (x, y, z) coordinates and textures.  
Figure 6: View of the handheld scanner scanning station: (**a**) general view, (**b**) view of amber on the rotating table.  
Figure 7: Stages of point cloud processing.  
The first stage of data processing involves aligning each scan to the coordinate system and cleaning it from artefacts, such as scanned fragments of the table, reflections and unwanted elements (Figure 8).  
In the next stage, Aligning and Registration, involved aligning and merging individual scans to form a unified whole. Automatic alignment options can be used, but the manual selection of common points on the scans is often necessary, requiring at least three pairs of such points. Registration involves bringing the scans into a single coordinate system and optimising their alignment.  
The fusion stage involves triangulating the scans after alignment. The Sharp Fusion algorithm was applied to achieve sharp edges in the model. The result was a 3D mesh model of the amber composed of triangles. In the case of missing parts in the mesh, a hole-filling procedure was performed.  
For visualisation purposes, the texturing stage involved applying the captured texture to the mesh model. A texture resolution of 2048 \(\times\) 2048 ppi was used, and brightness and contrast values of the texture display could be adjusted. The final stage was the export of 3D mesh models of the amber.  
The OBJ or STL formats were used, which archived the geometry of the models, serving as their digital documentation. In the case of the STL format, it enabled 3D printing.
## 1 Introduction
There are hierarchical connections and shared information between the two levels of labels. With reasonable use, the two labels can complement each other and promote the learning of the corresponding classifiers. As shown in Figure 1, the categories of the coarse-grained labels include \"transportation land\", \"public land\", \"residential\", etc. ; and the categories of the fine-grained labels include \"airport\", \"parking\", \"school\", \"square\", \"dense residential\", etc. The coarse-grained labels include one or more fine-grained labels. For example, the coarse-grained label \"residential\" consists of three fine-grained labels: \"dense residential\", \"medium residential\", and \"sparse residential\". The logical relationship between coarse- and fine-grained labels means that they contain common features. Some features that are difficult to learn in fine-grained classifiers can be easily learned in coarse-grained classifiers, and the model's attention can be focused on features with greater influence [30]. To make full use of hierarchical information among multi-grained scene labels, this paper proposes an attention-embedding, multi-task, multi-grained, network (AEMMN) that uses a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning. Multi-task learning can improve the discrimination of the common features. A gradient control module is designed to control the gradient propagation of two classifiers so as to suppress the negative transfer caused by irrelevant features between tasks. In the feature extraction part, the proposed method uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, the external attention modules are exploited to improve the discrimination of fine-grained and coarse-grained features. Our contributions are as follows: (1) Different from traditional single-grained classification methods, this paper proposes a multi-grained classification scheme that starts from the coarse-grained level before moving to the fine-grained level. (2) This paper proposes an attention-embedding, multi-task, multi-grained, network to explore the hierarchical information in scene labels to improve the discrimination of common features. (3) A gradient control module is designed to facilitate the positive interaction of two-grained labels. (4) The experimental results show that by separating coarse-grained and fine-grained feature learning-gradient backpropagation the model can achieve excellent performance on remote sensing scene classification tasks.
## 3 Experimental Design and Location  
To investigate the road surface, the framework used for the testing is shown in Table 2. The research was performed in the city of Noida, which comes under the National Capital Region of New Delhi. The city has an elevation of 200 m above sea level and a flat topographical region. Like most of the country, there are extremely contrasting roads in Noida, ranging from excellent roads to terrible roads. An expressway just outside of Noida (Figure 7a) and a slum road in Noida (Figure 7b) were considered for the experiment. Several test runs were performed, yielding different results. The experiment was conducted for three months covering around 150-170 km. For the evaluation of the performance of the framework, the algorithm presented in this research was applied in a Windows environment (Dell XPS 15Z with Intel i7 2600, 8 GB DDR3 RAM on Windows 10 Pro) with the use of Visual Studio 2017 (VC ++ Redistributable) and Open CV3.3 Library. Hanuman, IFS, and DTW algorithms were used across the entire framework. For test runs, a Honda Amaze was used and image processing was done by a cost effective 4k resolution action camera. Table 2 shows the software and hardware specifications used in the experiment  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technique Used** & **DTW + Ultrasonic Sensor + Image Processing** \\ \hline Location of Experiment & Noida City, UP, North India. \\ OS of the Data Collection System & Windows 10 Pro \\ Equipment used for Data Collection & Dell XPS 15z, HP Pavilion DV9000 \\ Vehicle(s) used & Honda Amaze (2016) \\ Distance Covered (km)/Travel time (hours) & (150–170 km/4 h) \\ Detection & Location of potholes, speed bumps on the roads \\ \hline \hline \end{tabular}
\end{table}
Table 2: Software and hardware specifications used in the experimental setup  
Figure 6: Procedure to categorize the image using the proposed method: (**a**) original image; (**b**) processed image; (**c**) further processed image; (**d**) image after Hanuman algorithm; (**e**) report of Hanuman algorithm.
###### Abstract  
A large percentage of the Costa Rican territory is covered with high evergreen forests. In order to compute a 1\({}^{\prime\prime}\) Bare-Earth Digital Terrain Model (DTM) for Costa Rica CRDTM2020, stochastic Vegetation Bias (VB) was reduced from the 1\({}^{\prime\prime}\) NASADEM, Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) data. Several global models such as: canopy heights from the Global Forest Canopy Height 2019 model, canopy heights for the year 2000 from the Forest Canopy Height Map, and canopy density from the Global Forest Change model 2000 to 2019, were used to represent the vegetation in the year of SRTM data collection. Four analytical VB models based on canopy heights and canopy density were evaluated and validated using bare-earth observations and canopy heights from the Laser Vegetation Imaging Sensor (LVIS) surveys from 1998, 2005, and 2019 and a levelling dataset. The results show that differences between CRDTM2020 and bare-earth elevations from LVIS2019 in terms of the mean, median, standard deviation, and median absolute difference (0.9, 0.8, 7.9 and 3.7 m, respectively) are smaller than for any other of the nine evaluated global DEMs.  
CRDTM2020; bare-earth digital elevation model; digital terrain model; vegetation bias; NASADEM; LVIS Article  
Regional \"Bare-Earth\" Digital Terrain Model for Costa Rica Based on NASADEM Corrected for Vegetation Bias. Remote Sens.2022142421. [[https://doi.org/10.3390/rs14102421](https://doi.org/10.3390/rs14102421)]([https://doi.org/10.3390/rs14102421](https://doi.org/10.3390/rs14102421))  
17 February 2022  
Accepted: 10 May 2022  
Published: 18 May 2022  
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**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).