esa-sceva/satcom-chunk-collection huggingface dataset:
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 4 Results and Discussion in Weighing on Modular WIM System
### Test Site Modular System Comprehensive Test
#### 4.1.1 Performance Comparison of Modular System Accuracy  
To compare the accuracy of the three systems, the two-axle truck, four-axle truck, and six-axle truck were each weighed 30 times, and the speed was gradually increased from 5 to 30 km/h as listed in Table 5. The error statistics are shown in Figure 8.  
Figure 7: Proving ground test.  
As shown in Figure 8, in terms of weighing accuracy, the five-platform modular system was the best, with the maximum weighing error always being less than 0.5%. This low error was attributed to the individual testing of the vehicles without multiple vehicles traveling through platforms simultaneously. The weighing error of the three-platform modular system was less than 2%, and the one-platform system was the worst, with a maximum weighing error greater than 5%. The modular WIM system exhibited higher accuracy. Furthermore, from the first test to the thirtieth test, with an increase in vehicle speed, the weighing error of the three systems tended to increase. Notably, the five-platform modular system had the least fluctuation in error, whereas the one-platform system exhibited the greatest fluctuation.  
The five-platform modular system used signals from five scale platforms, and the length of the platforms was moderate. It could achieve whole-vehicle-load weighing and ensure efficient weighing times, so it had the highest weighing accuracy, with minimal impact from vehicle speed. The length of the three-platform modular system was reduced by half, resulting in a shorter weighing time, leading to a decrease in weighing accuracy. In addition, the one-platform modular system was more sensitive to speed changes than the five-platform modular system. In the one-platform system, the axle loads of the vehicle fluctuated with the movement of its center of mass, making it more susceptible to the effects of the driving conditions. Consequently, it could not achieve high accuracy weighing, resulting in the largest weighing errors and significant fluctuations.
###### Abstract  
There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth's crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).  
earthquake prediction; natural hazards; SVM; machine learning; TEC
## 3 Part I: Localization of Real and Virtual Sources in the Absence of Visual Cues
### Experiment Design  
#### 3.1.1 Target Sources' Positions in the Room  
A loudspeaker arc of 1.2 m radius, hanging vertically from a turntable installed in the ceiling of the anechoic room, was used to represent the target source positions in TestReal. The center of the loudspeaker arc at 1.24 m height was defined as the listener position. Six loudspeakers (SPEEDLINK XILU SL-8900-GY) were mounted in the arc at elevations 0\({}^{\circ}\), \(\pm 10^{\circ}\), \(\pm 20^{\circ}\) and \(+25^{\circ}\). The loudspeaker arc could be rotated by the turntable to any azimuth. Signals were played back through loudspeakers over an ADI-SDS RME audio interface. Loudspeakers were individually equalized (in amplitude and phase) using 256-tap FIR filters, calculated as the regularized inverse [17] of transfer functions measured with a calibration microphone (GRAS 40 AF).
## 2 Earth Observation Data for Capacity Building
The advent of Sentinel-1 data and increasingly open SAR data licenses for research and capacity building should allow increased research outputs and the operationalization and extension of test applications based on SAR data for monitoring soil moisture  
Figure 2: Examples of satellite data catalogues: (**a**) USGS Earth Explorer: [[https://earthexplorer.usgs.gov/containing](https://earthexplorer.usgs.gov/containing)]([https://earthexplorer.usgs.gov/containing](https://earthexplorer.usgs.gov/containing)) data from Landsat 1–8, Sentinel 2, ResourceSat 1–2, and a variety of AVHRR and MODIS data products; (**b**) SANSA catalogue: [[http://catalogue.sansa.org.za/search/](http://catalogue.sansa.org.za/search/)]([http://catalogue.sansa.org.za/search/](http://catalogue.sansa.org.za/search/)) containing data from SubbandilaSat, SPOT 2–7, Landsat 1–8, CBERS 2–4; (**c**) ESA Copernicus Open Access Hub: [[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)) containing data from Sentinel 1–3; and (**d**) INPE catalogue: [[http://www.digi.inpe.br/catalogo/](http://www.digi.inpe.br/catalogo/)]([http://www.digi.inpe.br/catalogo/](http://www.digi.inpe.br/catalogo/)) containing data from MODIS, Landsat 1–8, CBERS-2–4, ResourceSat 1 and 2.  
variability, natural disasters (such typhoons and floods), and detecting illegal activities in the oceans such as oil dumping, unlicensed fishing and mineral exploration. Therefore, developing SAR capability is of paramount importance in taking advantage of its unique properties for supporting the optimization of agriculture and ending hunger (i.e., AU-Agenda 2063, Goal 5), sustainable blue/ocean economy in Africa (i.e., AU-Agenda 2063, Goal 6), and ensuring biodiversity conservation and sustainable natural resource management (i.e., AU-Agenda 2063, Goal 7). In addition, various users need to be equipped with theoretical knowledge and practical skills to ensure a wider utilization of SAR data.
## 3 Results and Analysis
### Coupling Effects of Pressure and Inclination on Upward Flame Spread
With increasing \(\theta\), the length of the preheated zone would be approaching flame length as \(\delta_{p}\to L_{f}\), and combining pressure effect on flame length \(L_{f}\sim p^{\beta}\), correlation of \(V_{p}\) vs. \(p\) in Equation (9) could be,  
\[V_{p}\sim p^{2\alpha+\beta} \tag{10}\]  
We now return to the experimental results in Equation (3); where \(V_{p}\sim p^{1.49}\) and \(L_{f}\sim p^{0.39}\), it could be found to agree well with the theoretical prediction of Equations (6) and (10). Moreover, the reason for similar pressure indexes between the FSR and burning rate of downward spread in Equation (2) is,  
\[\dot{m}_{dw}\sim S_{dw}\frac{\dot{q}^{\prime\prime}_{rad}+\dot{q}^{\prime \prime}_{conv}}{\Delta H_{g}}\sim w\cdot\frac{dx_{p}}{dt}\rho_{FPU}\sim V_{p}, \ for\ downward \tag{11}\]  
where burning area \(S_{dw}\) is mainly determined by cross-sectional area of board, and insensitive to both ambient pressure and inclination, which play an essential role in the tenability of Equation (11). On the contrary, the burning area of upward spread, depending on FSR, is distributed on the board surface rather than cross-section (Figure 1) as,  
\[\dot{m}_{uw}\sim S_{uw}\cdot\frac{\dot{q}^{\prime\prime}_{rad}+\dot{q}^{\prime \prime}_{conv}}{\Delta H_{g}},\ for\ upward \tag{12}\]  
Due to the closer relationship of \(S_{uw}\) (rather than \(S_{dw}\)) vs. \(p\), relations \(\dot{m}_{dw}\sim p^{0.77}\) and \(\dot{m}_{uw}\sim p^{1.02}\) in Equations (2) and (3), respectively, would not be surprising. In fact, theoretical analyses on relations of burning rate or the FSR vs. inclination were proposed from another perspective by a fluid dimensionless quantity (\(Pr\), \(Nu\), \(Ra\) and \(Gr\) number) in previous research, e.g., [23] with reasonable prediction for inclination effect, whereas, it also indicated limitation for the pressure effect because of insufficient heat transfer mechanisms. Additionally, the pressure effect on \(L_{f}\) of upward spread showed much less than downward as stated above. This is probably attributable to the stronger stretching effect on the flame by enhanced air entrainment from the larger burning area, which hid the pressure effect partly.
## 2 Material and Methods  
### Preparation of Polyphenol-Rich Sugar Cane Extract  
The polyphenol-rich sugarcane extract (PRSE) used in this study (Polygain(tm), The Product Makers, Melbourne, Australia) originated from sugarcane (_Saccharum officiparum_) and was made using a patented process. Briefly, clarified sugarcane juice was vacuum-evaporated and crystallised to produce molasses. The molasses was then fermented in the presence of baker's yeast (_Saccharomyces cerevisiae_), and ethanol was removed by vacuum evaporation and condensation. The remaining vinasse was diluted in water and sieved through a series of filters (5, 1, 0.5, and 0.1 um). The vinasse was then further concentrated under vacuum and hot-filled (80 \({}^{\circ}\)C) into plastic drums.  
The resulting PRSE had the following specifications provided by the manufacturer: 55\({}^{\circ}\) brix, pH 4.4, density of 1.28 g cm\({}^{-3}\), \(\geq\)38,000 mg total polyphenols L\({}^{-1}\) (as gallic acid equivalents), \(\geq\)10,000 mg flavonoids L\({}^{-1}\) (as catechin equivalents), 20-40 g K kg\({}^{-1}\), 30-50 g Mg kg\({}^{-1}\), 3-5 g Ca kg\({}^{-1}\), 0.5-2 g Na kg\({}^{-1}\), 100-150 mg Fe kg\({}^{-1}\), 40-46 g total amino acids kg\({}^{-1}\) (\(\approx\)20% were essential amino acids), and organic acids at 4-5% \(w/w\) of sugars.  
According to the provided specification, the following polyphenols and flavonoids have been identified in PRSE: chlorogenic acid, caffe acid, sinapic acid, syringic acid, vanillin, homoorientin, orientin, vitrein, swertisin, diosmin, apigenin, tricin, and diosmetin. The LC-MS analysis allowed the tentative identification of 7 apigenin-C-glycosides, 3 methoxyluteolin-C-glycosides, and 3 tricin-O-glycosides.
## 2 Research Background
### Knowledge Gap and Hypotheses  
The objective of this study was to demonstrate the application of statistical methods for data mining. The research framework and dataset involve the analysis of ICT implementation and application in enterprises. The paper addresses two crucial knowledge gaps. Firstly, there is a lack of studies analyzing ICT implementation in enterprises from transitional countries. Furthermore, existing studies that partially address this subject do not utilize data mining algorithms. Secondly, this paper provides a significant overview of how basic statistical methods compare to more complex approaches.  
This study presents the application of statistical methods in data mining, and the results obtained are equally significant. The results highlight factors that potentially affect an enterprise's intention to implement and apply ICTs.  
Based on the literature review and the goal of the paper, the following hypotheses are established as guidelines for data mining:  
* H\({}_{\text{1a}}\): Manager/director age positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{lb}}\): Manager/director gender positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{1c}}\): Manager/director education positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{1d}}\): Enterprise size positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{2}}\): Business performance positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{3}}\): Awareness of ICT importance positively affects intentions to implement and apply ICTs within an enterprise.
* H\({}_{\text{4}}\): Intellectual capital positively affects intentions to implement and apply ICTs within an enterprise.  
* H5: Market assessment positively affects intentions to implement and apply ICTs within an enterprise. The purpose of these hypotheses is to provide a research framework and guide the application of statistical methods on the acquired dataset.
# Implementation of a Breast Phantom with Acoustic Properties for Ultrasonic Thermometry
###### Abstract  
Breast cancer remains one of the leading causes of death among women globally. Early detection is critical for improving patient outcomes, making the development of accurate and efficient detection methods essential for facilitating timely treatment and enhancing patients' quality of life. Lesion sites are often associated with localized temperature increases, which can be identified by characterizing thermal gradients using thermometry tools. Ultrasound-based techniques are preferred for obtaining thermal patterns due to their noninvasive, non-ionizing nature and cost-effectiveness compared to methods like magnetic resonance imaging. This study focuses on developing breast tissue models with varying acoustic properties, specifically the speed of sound across temperatures ranging from 32 \({}^{\circ}\)C to 36 \({}^{\circ}\)C in increments of 0.5 \({}^{\circ}\)C for ultrasonic inspection and diagnostic applications. These models simulate healthy and tumorous breast tissue, including the fat, gland, and tumor layers. Signal variations were analyzed using cross-correlation methods to assess the changes in the speed of sound as a function of temperature. The proposed methodology offers a cost-effective, rapid, and precise approach to phantom production, facilitating the detection of temperature changes in 0.5 \({}^{\circ}\)C intervals through cross-correlation analysis of the acquired signals.  
agar gel; cross-correlation; phantom; thermometry; ultrasonic speed Article
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 2 Related Work
### Modality Fusion with Attention Mechanism  
To focus on the relationship between audio and visual modalities, [PERSON] et al. proposed an AV align model in which one of the two decoder-side attentions of the AVSR model in [7] was moved to encoder-side cross-modal alignment [13]. On the encoder side, the cross-modal alignment fused the modalities by computing attention using audio and video encoder outputs as queries and values, respectively. The output of the attention mechanism is similar to (7), which is written as  
\[\tilde{h}_{u}^{av}=\tanh(W_{c}^{a}[c_{u}^{v},h_{u}^{a}]), \tag{11}\]  
where \(c_{u}^{v}\) and \(h_{u}^{a}\) denote a video context vector using an audio encoder state as a query and the audio encoder state at encoder time step \(u\), respectively. Therefore, the video context vector and the audio encoder state are fused by tanh and trainable parameters \(W_{c}^{a}\), which results in an attentional vector obtained by the merged modalities, \(\tilde{h}_{u}^{av}\), at the top layer of the audio encoder.  
Unlike the AV align, [PERSON] et al. [16] proposed modality attention to obtain attention weights between modalities in a decoding step. The modality attention is similar to the conventional attention methods except that the modality attention is calculated and combined over the modality axis. The modality attention fusion process can be summarized as [16]  
\[z_{i}^{m}=Z(f_{1...i}^{m})=\sigma\left(W\cdot\text{LSTM}\left(f_{1...i}^{m} \right)+b\right), \tag{12}\]  
where \(f_{1...i}^{m}\), \(z_{i}^{m}\), and \(Z\) denote the feature vectors corresponding to the encoder outputs of modality \(m\) up to decoder time step \(i\), scores for the feature vectors of modality \(m\) at decoder time step \(i\), and the scoring function composed of the LSTM, the feed-forward network with \(W\), \(b\), and the sigmoid function \(\sigma(\cdot)\). Then, the attention weight for the \(m\)-th modality, \(\alpha_{i}^{m}\), and the fusion output feature vector \(v_{i}\) at decoder time step \(i\) can be computed by  
\[\alpha_{i}^{m}=\frac{\exp(z_{i}^{m})}{\sum_{j=1}^{M}\exp(z_{i}^{j})}, \tag{13}\]  
\[v_{i}=\sum_{m=1}^{M}\alpha_{i}^{m}f_{i}^{m}, \tag{14}\]  
where \(M\) denotes the number of modalities.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Attribution to Climate and Land Use  
After obtaining the vegetation growth that excludes CO\({}_{2}\) concentration effects, NDVI\({}_{a}\) is then used to disentangle the climate effects (CV and CC) and land use (LU) on vegetation growth. The TSS-RESTREND method was developed by [PERSON] et al. [31]. One major advantage of TSS-RESTREND is its ability to handle non-linear trends, making it useful in cases where trends are not strictly linear. This method also provides a measure of statistical significance for the detected trends, allowing for more robust conclusions to be drawn. Other methods, such as linear regression and Mann-Kendall trend tests, are simpler and more straightforward, but may not be suitable for complex, non-linear trends. Overall, the choice of attribution method depends on the specific research question, data availability, and the characteristics of the trend being analyzed. We apply the version that contains both precipitation and temperature to estimate the Vegetation Climate Relationship (VCR) pixel-by-pixel [32]. LU effects are calculated using an ordinary least squared regression between the residuals of VCR and time. A similar attribution method can be found in IPCC. A detailed description can be found in ref. [32].
## 2 Theory
### Wavelet Decomposition  
Wavelet decomposition refers to the decomposition of a sample into a high-frequency part and low-frequency part. Each layer of wavelet decomposition decomposes the sample into high-frequency and low-frequency parts. The wavelet decomposition process is shown in Figure 1. After the first layer wavelet decomposition of the sample, high-frequency D1 and low-frequency A1 are obtained. Next, in each layer decomposition, the low-frequency sample obtained from the previous layer is decomposed into two parts, high-frequency Dn and low-frequency An. Dn is the high-frequency signal decomposed by the \(n\)th layer, while An is the low-frequency signal decomposed by the \(n\)th layer.  
In this study, wavelet decomposition will be performed on the sample feature set after dimensionality reduction by factor analysis. A three-layer wavelet decomposition will be performed on the sample feature set after dimensionality reduction to obtain four decomposed sample feature subsets.  
Figure 1: Schematic diagram of the wavelet decomposition principle.
# How _Pseudomonas nitroreducens_ Passivates Cadmium to Inhib Plant Uptake
## 1 Introduction  
Cadmium (Cd) is a non-essential element of the human body that is carcinogenic [1; 2; 3; 4] and mutagenic. It can cause lung damage and fragile bones [5] and affect calcium regulation in biological systems, and it is one of the most common toxic heavy metals. Even at very low doses, through biomagnification and bioaccumulation [6], cadmium can also pose great harm to humans and the environment [7; 8]. With the high speed of developments in industrial operations, large amounts of waste are released into the environment, and free heavy metals cannot be converted into bioavailable forms, resulting in the accumulation of heavy metals in soil and water and the long-term poisoning of the ecological environment on which humans depend [9]. Therefore, research on the presence of cadmium in the environment is useful. A method for controlling heavy metal pollution is necessary, and it is of great significance to protect the soil ecological environment and guarantee the output of agricultural products and food safety.  
At present, there are many ways to remove heavy metals, such as electrochemical removal [10; 11; 12; 13; 14], chemical precipitation [15; 16], ion exchange [17; 18] and solvent extraction [19]. However, most of these methods are not suitable for practical application because of their limitations of high cost, high energy requirements, low efficiency, secondary pollution and so on [20]. Compared to the conventional methods mentioned above, the biosorption of heavy metals using environmental microorganisms has become one of the hotspots in the environmental bioremediation field due to the advantages of environmentally friendliness and low operating costs. Biosorption mainly utilizes microbes to reducethe bioavailability of metal ions, thereby inhibiting their accumulation in plants. Bacteria [21; 22], fungi [23; 24], algae [25; 26] and _Saccharomyces_ [9; 27; 28] are found to be capable of accumulating and removing heavy metals from aqueous solutions and soil, but the underlying mechanism and adsorption properties are not fully understood. The application of the microbial remediation of heavy-metal-contaminated sites is rarely reported.  
In this work, bacterium 11830, which was stored in our laboratory, was identified as _Pseudomonas nitroreducens_ through morphological observation, physiological characteristics, 16 SrDNA sequencing and phylogenetic tree analysis. Langmuir isotherm, Freundlich isotherm and biosorption kinetics modeling were used for research on the biosorption characteristics of Cd. Then, the SEM, TEM, EDS and protein differential analysis were used to determine the biosorption mechanisms. Finally, the pot test confirmed the ability of strain 11830 to inhibit the adsorption of cadmium by the hyper-enriched plant _Houttuynia cordata Thumb (H. cordata)_, which is of practical application value for reducing the accumulation of cadmium in crops.
## 2 System Model
### Communication Model  
For the communication model, we adopt the Vehicle-to-Everything (V2X) standard based on the 5G New Radio (NR) air interface for data transmission [38]. Given the OFDMA mechanism, interference is ignored due to exclusive subcarrier allocation. Therefore, for all \(j\in\mathcal{N}\), the transmission rate between the \(i\)-th task vehicle and the \(j\)-th service node for task offloading in time slot \(t\) is expressed by  
\[r_{ij}(t)=B\sum\
olimits_{s\in\mathcal{K},j\in\mathcal{N}}w_{s,i,j}\log_{2} \left(1+\frac{p_{i}^{\max}h_{i,j}(t)}{\sigma^{2}}\right), \tag{1}\]  
where \(h_{i,j}(t)\) is the channel quality between the \(i\)-th task vehicle and the \(j\)-th service node in time slot \(t\), \(\sigma^{2}\) is the noise power, and \(p_{i}^{\max}\) is the upper limit of the transmission power of the \(i\)-th task vehicle.