esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 2 Methodology  
### GNSS-IR Soil Moisture-Detection Principle  
Due to the multipath effect, the receiver antenna will simultaneously receive the direct signal and the reflected signal, and produce a certain phase difference between the two--the geometric relationship is shown in Figure 1. In the Figure, \(h\) represents the height of the phase center of the GNSS receiving antenna relative to the reflective surface, and \(\theta\) represents the satellite altitude angle.  
The direct and reflected signal components are superimposed and interfered with to form a composite signal. In a simplified model, the composite SNR generated by the direct signal plus multipath reflection can be represented by the following equation [4; 13]:  
\[\text{SNR}^{2}=A_{m}^{2}+A_{d}^{2}+2A_{d}A_{m}\cos\varphi, \tag{1}\]  
in the equation above, \(A_{d}\) and \(A_{m}\) represent the amplitudes of the direct and reflected signals from the satellite, respectively, while \(\varphi\) represents the phase difference between the direct and reflected signals. Before extracting characteristic parameters, the direct signal component in the original SNR data is removed by performing a low-order polynomial fit to the composite SNR data. The multipath reflected signal \(dSNR\) after removing the direct signal component can be expressed as [14]:  
\[dSNR=A_{m}\cos\left(\frac{4\pi h}{\lambda}\sin\theta+\varphi_{m}\right), \tag{2}\]  
in the equation, \(h\) represents the height of the phase center of the GNSS receiving antenna relative to the reflective surface, \(\theta\) is the satellite elevation angle, and \(\lambda\) is the wavelength of the GNSS signal. \(A_{m}\) denotes the amplitude characteristic parameter, and \(\varphi_{m}\) is the phase characteristic parameter. Setting \(t=\text{sin}\theta\) and \(f=2h/\lambda\), Equation (2) can be further simplified to:  
\[dSNR=A_{m}\cos(2\pi ft+\varphi_{m}) \tag{3}\]  
Subsequently, Lomb-Scargle spectral analysis (LSP) was utilized to extract the frequency value \(f\) of individual SNR sequences, and the \(dSNR\) data were non-linearly least-squares fitted according to the cosine function relationship given in Equation (3) to obtain the characteristic parameters such as frequency, amplitude, and phase [15]. It has been shown that there is a strong correlation between phase and soil moisture, and this study focuses on the phase characteristic parameter data for soil moisture retrieval [7].  
Figure 1: Schematic diagram of GNSS-IR interference. \(h\) is the distance of the phase center of the GNSS receiving antenna from the ground and \(\theta\) is the satellite altitude angle.
# Optical Interference Suppression Based on LCD-Filtering
## 3 Interference Reduction  
In the following, we distinguish two classes of interference and analyze the impact of LCD-based filtering in both cases by simulation. We assume a square LCD with an edge length of \(l=53.3\,\mathrm{mm}\) and \(240\times 240\) pixels. Likewise, the PD is square and of size \(a=3.6\,\mathrm{mm}\). With these assumptions and a focal length of \(f=15\,\mathrm{mm}\), the field-of-view (FOV) of the filter is approximately \(120^{\circ}\).
# Influence of Fracture Geometric Characteristics on Fractured Rock Slope Stability
## 2 Methodology  
### DFN-DEC Model  
In recent years, there has been a significant increase in the use of discrete representations of fracture networks to model the heterogeneity of a rock mass. This is due to the widespread availability of DFN software generators, as well as the widespread availability of powerful computing resources. Because individual fracture properties such as the position, trace length, and aperture are represented, DFNs provide a more realistic simulation of rock mass heterogeneity than the more established continuum or equivalent medium (EM) approaches. The DFN model employed in 3 DEC software is called the DFN-DEC model.  
The method first generates data of fracture geometric characteristics with given statistics based on MATLAB, then inputs these data into 3 DEC software to generate fractures. The specific steps are as follows (Figure 1):  
1. Sufficient data are collected about the fracture characteristics (e.g., strike, dip, trace length, spacing, and roughness) via core logging, exposure mapping (e.g., scan line or window mapping), or other methods. Then, these fractures are divided into different sets according to the strike and dip.
2. For each fracture set, statistical analysis of each fracture characteristic is carried out separately, and their statistical parameters are obtained (i.e., the mean \(\mu\), the standard deviation \(\sigma\), and the probability distribution model).
3. Based on the statistical parameters, the values of fracture characteristics for each fracture set are reproduced using the Monte Carlo sampling method in MATLAB. Note that setting specific dip and spacing thresholds filter out data that tend to cause mesh dissection errors.
4. The slope model with discrete fractures is constructed using the DFN command through the recognizable txt file. Then, the factor of safety \(F\)s of the rock slope is calculated using the strength reduction method (SRM) embedded in 3 DEC software.
## 1 Introduction
In a word, as far as China is concerned, there are still some problems in the research and prevention of asphalt UV aging. * The influence of UV radiation on asphalt has not been expressed uniformly and clearly, and there is no standard test method for the UV aging of asphalt in the current specification;
* There is a lack of practical climate zoning for asphalt UV radiation;
* The equivalent relationship of asphalt UV aging between different UV radiation areas has not been established;* There is a lack of anti-UV aging prevention methods for asphalt in different UV radiation areas. Therefore, it is urgent and meaningful to put forward the corresponding zoning for asphalt UV aging in China and the corresponding prevention measures and prevention standards for different zones. The purpose of this paper is to solve the above four problems, and then to achieve the climate zoning for asphalt UV radiation. On the basis of the above research status, in order to provide reasonable prevention and control measures for asphalt UV aging in different areas of China, based on the average annual UV radiation amount in China in the most recent 30 years and the characteristics of asphalt UV aging, this paper puts forward the climatic zoning of asphalt UV radiation in China. A variety of base asphalts and styrene-butadiene-styrene block copolymer (SBS) modified asphalts were selected to carry out an UV radiation test, and the relationship between UV radiation amount and viscosity, low-temperature performance, and rheological properties of asphalt were studied. The equivalent aging indexes of asphalt during UV radiation were selected, the UV aging equation of asphalt was proposed, and the equivalent UV aging relationship among different UV climate zones was established. The prevention and control strategies of UV aging of asphalt among different zones was proposed, and the above theory was verified using a trial road. The research results can provide necessary technical support for improving the durability of asphalt pavements in China.
## 3 Parameter Analysis and Theoretical Calculation
Based on the law of refraction (\(n_{0}\sin\theta=n\sin\theta_{1}\)), \(\Delta L_{2}\) can be simplified to  
\[\Delta L_{2}=\Delta h(\frac{n^{2}}{\sqrt{n^{2}-n_{0}^{2}\sin^{2}\theta}}-\frac {n_{0}}{\cos\theta}). \tag{7}\]  
The phase difference is  
\[\Delta\varphi_{2}=\frac{2\pi}{\lambda}\Delta h(\frac{n^{2}}{\sqrt{n^{2}-n_{0} ^{2}\sin^{2}\theta}}-\frac{n_{0}}{\cos\theta}). \tag{8}\]  
Therefore, compared to a BPPF placed perpendicular to the optical axis, a BPPF placed obliquely adds an additional phase difference (\(\Delta\varphi=\Delta\varphi_{2}-\Delta\varphi_{1}\)):  
\[\Delta\varphi\ =\frac{2\pi}{\lambda}\Delta h(\frac{n^{2}}{\sqrt{n^{2}-n_{0}^{2} \sin^{2}\theta}}-\frac{n_{0}}{\sqrt{1-\sin^{2}\theta}}-n+n_{0}). \tag{9}\]  
As shown in Equation (9), \(\Delta\varphi\) is related to the oblique incident angle \(\theta\), and the transmittance function can be written as  
\[T^{\prime}(\theta)=\exp[i(\varphi(\beta)+\Delta\varphi)]. \tag{10}\]  
Figure 3: Microstructure diagrams of a BPPF placed (**a**) perpendicular and (**b**) oblique to the optical axis. IB: incident beam; EB: ejected beam. The optical field distribution near the focus under the condition of oblique incidence can be obtained by substituting Equation (10) into Equations (1) and (2).
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 5 Simulations
### Simulation Method
#### 5.2.2 Impacts of PUs  
Figure 6 shows the probability of connection \(p_{con}\) versus distance \(r\) with different intensity of PUs \(\lambda_{p}\). _We observe that \(p_{con}\) decreases with the increased intensity \(\lambda_{p}\)._ This may owe to the fact that the higher intensity of PUs leads to the lower spectrum availability of SUs.  
Figure 7 shows the probability of connection \(p_{con}\) with different values of the transmission power of PUs \(P_{p}\). _We observe that \(p_{con}\) decreases with the increased transmission power \(P_{p}\)_. This is because PUs with higher \(P_{p}\) can cause higher interference to SUs (in other words, they are more easily detected by SUs according to the detect and avoid protocol), and consequently SUs have less chance to transmit.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
### Objectives and Research Framework  
In the new era of Industry 4.0, numerous approaches have emerged to integrate predictive maintenance methods into intelligent maintenance systems, thereby supporting the development of smart factories. Modern technologies and methods, such as the implementation of intelligent sensors [29] and the development of predictive maintenance models, facilitate the monitoring of key machine variants for maintenance inspections. Furthermore, a novel approach involving rule learning for automated predictive maintenance has also been proposed [30]. In the field of artificial intelligence technology, research has been conducted on the application of machine learning for fault detection. Additionally, studies on emerging technologies, including the Internet of Things (IoT), digital twins (DTs), and heuristic approaches, have shown that they can significantly reduce infrastructure maintenance costs [31; 32]. This research presents an alternative perspective by applying a combination of predictive maintenance (PDM), computerized maintenance management systems (CMMSs), and intelligent maintenance systems (IMSs) alongside state space search (SSS) algorithms to enhance the efficiency of maintenance predictions.
## 6 Conclusions and Future Work  
This study proposed the RoBERTa-MHARC model and constructed a dataset of tens of thousands of items to address the detection of telecom fraud texts. The experimental results demonstrated the strong performance of the model on three datasets, supported by the use of multi-head attention to enhance the understanding of the text and residual connections to promote gradient flow and accelerate the convergence. The combination of the inconsistency loss function with the cross-entropy loss function also proved effective in improving the model performance.  
Future work will focus on addressing the limitations identified in this study. First, we plan to expand the dataset to cover a broader range of telecom fraud text categories, thereby improving the model's generalizability. Second, optimizing the model's complexity to reduce training and deployment challenges in resource-limited environments will be a priority. Additionally, we aim to explore alternative model architectures and loss functions to further improve the performance. Another direction for future work is investigating alternative position encoding methods, such as integrating RNNs or other feature embeddings, to capture more complex sequential dependencies. Lastly, future studies should investigate the feasibility and ethical implications of deploying this model in real-world applications, ensuring its effectiveness and fairness in practice.  
**Author Contributions:** Conceptualization, J.L. and C.Z.; methodology, J.L. and L.J.; data curation, C.Z.; formal analysis, L.J.; funding acquisition, J.L. and L.J.; investigation, C.Z.; resources, J.L.; software, C.Z.; project administration, J.L.; visualization, C.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Guangxi Natural Science Foundation (No. 2022 GXNSF-BA035510), the Guangxi Key Research and Development Program (No. Guike AB23075178), the National Natural Science Foundation of China (No. 62267002), the Innovation Project of GUET Graduate Education (No. 2024 YCXS046), and the Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi (No. 2024 KY0213).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data used in this article are provided throughout the article.  
**Acknowledgments:** The authors would like to express gratitude to all of the participants who have contributed to this research and the funding support from numerous projects.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building
## 2 Related Work
### Modeling Tool of Spatial Graph  
In regard to the modeling tools of sensor deployment, standard practical solutions are still lacking. Building Information Modeling (BIM) is a framework to support the planning and construction of buildings. Industry Foundation Classes (IFC) standard [9], which is a well-known representation of BIM, considers the elements inside a building as objects that are defined by a 3D geometry and normalized semantics. The Green BuildingDXML (gbXML) [10] emerged to allow sharing information between BIM and energetic analysis software. However, the main intentions of these tools are the modeling of the physical structure and used materials, which is static. Their main focus is on the physical environment setup. That means they are often used for structural analysis or 2D/3D modeling by using CAD tools. The functional aspects of knowledge extraction of building systems are not covered by these approaches.  
Many research projects are actually elaborating semantic models for facilitating building management, such as rule-based methods for supervision [11], definition or classification of metadata schema for facilitating building management [8,12,13]. An ontology is a vocabulary based method for defining the concepts and relationships used to describe an area of concern based on RDF (Resource Description Framework) [14]. A few of specific ontologies have been proposed for the domain of smart homes and buildings [11; 15; 16; 17; 18; 19]. Most of these ontologies focus on realizing specific applications like energy management [18; 19; 20], or automated design and operation [11; 16; 17; 18; 19].  
The SOSA (sensor, observation, sample, and actuator) ontology [21] and semantic sensor network (SSN) ontology are W3C recommendations, providing an approach to describe hardware, observation of physical entities and actuation, etc. The BOnSAI [15] ontology was proposed for describing the functionality of sensors, actuators, and appliances. However, it does not provide sufficient information on spatial relationships among the sensors and other building assets. The Building Topology Ontology (BOT) [17] defines the relationships between the sub-components of a building. It also follows general W3C principles and was suggested as an extensible baseline for reuse.  
To summarize these ontology-based studies, whether merely for modeling the resource description or knowledge extraction, ontology that constructs the physical relations in the smart building is considered as suitable to present graph concepts. Moreover, aiming for removing ambiguity, and pursuing application portability, unifying the sensor data format is one of the most important considerations. In addition, the W3C endorsed ontologies demonstrate high potentials of upper-layer application development. Thus, rather than proposing a new ontology, our approach preferred to reuse the existing ontologies, and extend them by adding other necessary specific information.
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 2 Materials and Methods
### Continuous Phase Equations  
Given that the flow is incompressible and isothermal, the continuity, and the Reynolds-Average Navier-Stokes (RANS) equation can be adequately expressed as ([PERSON] and [PERSON], 2011):  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\rho\frac{\partial u_{i}}{\partial t}+\rho u_{j}\frac{\partial u_{i}}{ \partial x_{j}}=-\frac{\partial P}{\partial x_{i}}+\frac{\partial u_{i}}{ \partial x_{j}}\left[2\mu S_{ij}-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\right] \tag{2}\]  
where \(u_{i}\)--the mean velocity; \(x_{i}\) the coordinate system, \(P\)--mean pressure, \(\rho\)--density of the gas, and \(\mu\) is the dynamic viscosity of the continuous phase. Equation (2) determines the Reynolds stress tensor, \(\tau_{ij}=-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\)  
\[\text{The strain rate tensor, }S_{ij}=\frac{1}{2}\left[\frac{\partial u_{i}}{ \partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right] \tag{3}\]  
Reynolds Stress Model (RSM)  
The way to solve the Reynolds stress tensor \(\tau_{ij}\) is most important for numerical studies. In the RSM closure model, all six Reynolds stress components are solved along with the dissipation rate. Ignoring the production conditions related to the buoyancy and rotation of the system, the transport equations for RSM can be defined as [42]:  
\[\frac{\partial\tau_{ij}}{\partial t}+u_{k}\frac{\partial\tau_{ij}}{\partial x_{k} }=-\tau_{ik}\frac{\partial u_{j}}{\partial x_{k}}-\tau_{jk}\frac{\partial u_{i }}{\partial x_{k}}+\epsilon_{ij}-\phi_{ij}+\frac{\partial}{\partial x_{k}} \bigg{[}v\frac{\partial\tau_{ij}}{\partial x_{k}}+C_{ijk}\bigg{]} \tag{4}\]  
The right part of the Equation (4) represents the response: derivative of local time and convection. In turn, the left side represents the stress generation, the dispersion tensor, and the pressure-strain correlation tensor, respectively. The first and second components in parentheses represent molecular diffusion and turbulent diffusion tensors.  
\[\text{The dissipation tensor, }\epsilon_{ij}=-2v\frac{\overline{\partial u^{\prime}_{i }}\frac{\partial u^{\prime}_{j}}{\partial x_{k}}}{\partial x_{k}} \tag{5}\]  
\[\text{The pressure-strain correlation tensor, }\phi_{ij}=\frac{\overline{p^{\prime}} \left[\frac{\partial u^{\prime}_{i}}{\partial x_{j}}+\frac{\partial u^{\prime }_{j}}{\partial x_{i}}\right]}{\text{The turbulent diffusive transport tensor, }\rho C_{ijk}=\rho\overline{u^{\prime}_{i}u^{\prime}_{j}u^{\prime}_{k}}+ \overline{p^{\prime}u^{\prime}_{i}}\delta_{jk}+\overline{p^{\prime}u^{\prime }_{j}}\delta_{ik} \tag{7}\]  
The term \(\delta\), in Equation (7), is termed the Kronecker delta. Ignoring the pressure fluctuations, the tensor \(C_{ijk}\) is symmetrical in all three indexes, which makes it invariant in rotation. To close Equation (4), the tensors must be defined \(\epsilon_{ij}\), \(\phi_{ij}\), and \(C_{ijk}\) from Equations (5)-(7) [42,43].
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 6 Conclusions  
This article proposes a multi-vehicle tracking method suitable for advanced driver assistance systems based on monocular cameras in the driver view. The approach is based on the tracking-by-detection paradigm. At the same time, joint detection and appearance embedding are used to improve tracking speed. In the detection phase, we use a modified one-stage detection network equipped with an adjusted BiFPN block with three prediction heads consisting of a sequence of convolutional neural networks. In addition, the k-means algorithm is used on the training dataset to obtain proper priors. On the other hand, the tracking phase mainly consists of a two-step matching method and a Kalman filter. Finally, experimental results demonstrate that our approach achieves state-of-the-art performance on public benchmarks and that it can handle deformation and illumination variations. While maintaining high performance, our tracker can achieve a speed of about 16 FPS on the KITTI tracking dataset. Raw data and demos are available at [[https://github.com/LvpengfeiNJ/MVT-MCDV-ADAS-Tracking](https://github.com/LvpengfeiNJ/MVT-MCDV-ADAS-Tracking)]([https://github.com/LvpengfeiNJ/MVT-MCDV-ADAS-Tracking](https://github.com/LvpengfeiNJ/MVT-MCDV-ADAS-Tracking)) (accessed on 24 October 2022).  
In the future work, we will further explore the number of convolution layers of the prediction head which can make the detection performance the best. We will also study how to deploy this multi-vehicle tracking algorithms to edge AI devices and look into ways to minimize the loss of performance during deployment.  
Conceptualization, P.L.; Investigation, Y.W.; Methodology, P.L.; Project administration, M.W.; Resources, M.W.; Software, P.L.; Supervision, M.W.; Validation, P.L.; Visualization, Y.W.; Writing--original draft, P.L.; Writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Thanks to my multi-vehicle tracking enthusiasts for their suggestions on my program. Thanks to the KITTI team for evaluating my submission of the tracking results.  
The authors declare no conflict of interest.
# Study on the Influence of Moisture Content and Void Ratio on the Disintegration of Red Clay
## 4 The Influence of Moisture Content and Void Ratio on Disintegration
### Distribution Law of Natural Soil Moisture  
Figure 5 and Table 3 show the distribution of water molecules in various soil types with varying levels of moisture contents and void ratios under natural conditions.  
According to Figure 5, it can be seen that the majority of soil water molecules in their natural state exhibit a bimodal distribution (except for the unimodal distributions at 24%\(-\)1.00, 24%\(-\)1.15, and 28%\(-\)1.10). When e = 1.0, the main peak area increases from 697.08 to 1041.81 as the moisture content increases, with increases of 13.6%, 12.8%, and 16.5% between adjacent moisture contents. When e = 1.1, the main peak area increases by 15.5%, 14.0%, and 14.1% as the moisture content increases. The main peak area is generally smaller than that of e = 1.0, mainly because as the void ratio increases, large pores gradually increase, the soil structure becomes looser, and more gravitational water is free in the large pores, while water is reduced in small pores. When e = 1.15, the main peak area increases as the moisture content increases. The increases between adjacent moisture contents are 13.1%, 14.5%, and 13.7%, respectively. These absolute value increases smaller than e = 1.1. In its natural state, soil contains both combined water and gravitational water in pores. The small pores are not entirely filled with combined water, while the large pores cannot be completely filled with gravitational water. Hence, the main peak or secondary peak area of the T\({}_{2}\) spectrum cannot be used to determine the content of combined water in the soil.