esa-sceva/satcom-chunk-collection huggingface dataset:
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 4 Results and Discussion
### Ppp  
In order to investigate the contribution of LEO to accuracy and convergence of high-accuracy positioning with carrier-phase measurements, two PPP experiments have been performed with BeiDou-3 or combined BeiDou-3 and LEO measurements. The simulated code and phase measurements with 1 m and 2 mm random noise were processed with precise satellites' orbit and clock. The ionosphere delay has been corrected with ionosphere-free combination, and the dry troposphere delay was corrected with the Saastamoinen model and global mapping functions (GMF), and the wet delay has been estimated per hour. Besides, coordinates and receiver clock, the constant and inter-system bias were estimated.  
Figure 5 shows the positioning error in north, east, and up direction for the two solutions of the selected station. In general, the inclusion of LEO can significantly accelerate the positioning convergence. The convergence time can be shorted to 1 min 9 s from 28 min 35 s. The results indicates the most great advantage of LEO constellation, which can greatly accelerating the convergence speed of the ambiguities, and providing promising way for real-time PPP application.  
Figure 6 further show the variations of kinematic positioning errors of CENT station in 1 h after the convergence of PPP for BeiDou-3 only and BeiDou-3 as well as LEO combined solutions. It is easy to notice that the positioning accuracy is within 5 cm in horizontal direction, whereas the it is within 8 cm in vertical direction. However, once the LEO data used, the variations of kinematic positioning errors become stable, and it is below 5 cm in all of the three direction. Statically, the RMS of position accuracy has been improved from 1.12, 0.74, and 2.05 cm to 0.65, 0.59, 1.23 cm in east, north, and up direction, once the LEO satellites are used to augment the BeiDou-3. The simulation results of all these three stations are listed in Table 8. This clearly demonstrate the advantage of LEO used as the navigation system.  
Figure 5: _Cont._Figure 5: \(Cont.\)  
Figure 5: Time series of kinematic PPP positioning errors for BeiDou-3 and BeiDou-3 as well as LEO combined solutions. (**a**) CENT BeiDou-3 only; (**b**) CENT BeiDou-3 + LEO; (**c**) POTS BeiDou-3 only; (**d**) POTS BeiDou-3 + LEO; (**e**) NTUS BeiDou-3 only; (**f**) NTUS BeiDou-3 + LEO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multicolumn{3}{c}{**BDS**} & \multicolumn{3}{c}{**BDS + LEO**} \\ \cline{2-7}  & **North** & **East** & **Up** & **North** & **East** & **Up** \\ \hline CENT & 0.74 & 1.12 & 2.05 & 0.59 & 0.65 & 1.23 \\ POTS & 0.93 & 0.99 & 1.96 & 0.50 & 0.72 & 1.44 \\ NTUS & 0.54 & 0.68 & 1.33 & 0.63 & 0.49 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 8: PPP precision of both BDS and BDS + LEO after the ambiguity convergence (cm).
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on the Dehydration Parameter  
Recent research has revealed the impact of hydration on OCT images by conventional polarization-insensitive OCT [83, 84]. A method for assessing early enamel lesions with the dehydration parameter (DH) based on the integrated reflectivity has been presented [85]. The dehydration parameter refers to the positive difference between the two OCT signals of tooth enamel under dry and hydrated conditions, i.e., the integrated area between the two signals.  
[PERSON] et al. [85] detected sound and demineralized bovine enamel blocks after 3, 9, and 15 days of demineralization using 1310-nm SS-OCT with axial/lateral resolution 11/17 \(\upmu\)m, and calculated the dehydration parameters. The experimental results showed DH for sound and demineralized bovine enamel ranged from 272(204) to 3304(751), and a strong linear correlation (\(R^{2}=0.9922\)) between the dehydration parameter and the square root of demineralization day. The benefit of this method is that the DH calculation does not involve determination of the cut-off depth, and the evaluation results are not influenced by  
Figure 5: Evaluation algorithm for the volume of residual enamel. (**a**) 2D images with the applied image window; (**b**) Pixels that meet the predetermined intensity cut-off points; (**c**) 3D volumetric image. This figure was adapted from [82].  
surface reflections. Although this method has the potential to quantitatively assess early enamel caries, there are few relevant studies, and optimization of the methodology requires evaluation of large amounts of demineralized and remineralized enamel.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 3 Background Knowledge  
### I/O Virtualization in KVM  
In the traditional I/O driver model, the device driver is used for the interaction between the OS kernel and the physical device, and the OS calls the device driver in the local environment to control the physical device to work. However, in I/O virtualization technology, because there are multiple VMs sharing the same hardware device, the VMM is required to simulate the virtual network card for use by the VM. For example, in the full virtualization technology, the VMM uses pure software to simulate the behavior of the virtual device. The VMM intercepts all I/O requests from the VM, and then invokes the physical driver to interact with the physical device to complete I/O operation. The traditional way of KVM's I/O virtualization is to use QEMU to simulate I/O devices. As shown in Figure 1a, the I/O _Trap Code_ located in the KVM module intercepts the I/O request of the VM and places it on the I/O _sharing page_, and notifies the QEMU program in user space to obtain the I/O request. Then, the QEMU program calls the _Hardware Simulation Code_ to simulate the I/O operation and returns the result to the I/O _sharing page_, which the VM can read in the KVM module. QEMU pure software simulation can simulate a variety of hardware devices and can provide a completely consistent environment for VMs and hardware platforms, so compatibility is better.  
The pure software simulation of QEMU which has a poor performance due to its costs needs to intercept all I/O requests of VM. In KVM, the _virtio_ para-virtualization framework is generally used to improve I/O performance. The _virtio_ para-virtualization framework divides the device driver into two parts by means of front-end and back-end separation. The front-end driver is the driver module of the VM, and the back-end driver is located in the _QEMU_ program. In addition, the _virtio_ layer and the _virtio_-_ring_ layer are defined between the front-end and back-end drivers for communication between the two. In the para-virtualization framework, only the back-end drivers located in QEMU programs have direct access to hardware, while the front-end drivers in VMs access hardware devices indirectly through the back-end drivers. I/O para-virtualization provides a lightweight analog device to the VM, and all I/O requests of the VM are processed by the QEMU program. Therefore, the _virtio_ framework eliminates the process of capture and simulation of I/O request in pure software simulation mode, and the VM can communicate directly with the I/O module of _QEMU_. As shown in Figure 1b, the KVM module notifies the back-end driver of the _QEMU_ to retrieve data by interruption and stores it in the virtual queue of the _virtio_ layer. Finally, the KVM module notifies the front-end driver of the VM to retrieve data in the _virtual queue_.
## 4 Discussion
In future experiments, we will benchmark the optimized models on low-power hardware, such as Jetson Nano or ARM-based devices, and evaluate their practical deployment feasibility by measuring key performance indicators, including inference time (latency), memory usage, frames per second (FPS), and detection accuracy (mAP) on benchmark datasets. These metrics will help assess the model's efficiency, resource consumption, and performance in real-time applications. The evaluations will help us determine the trade-off between model size, accuracy, and real-time processing capability, ensuring that YOLOv9-ARC remains efficient and effective even in environments with limited computational resources. In summary, this study demonstrates the effectiveness of the YOLOv9-ARC model in detecting low-quality fake facial images, achieving significant improvements through the integration of AKConv and CBAM. However, future research must address challenges related to dataset diversity, computational efficiency, robustness in complex environments, and adaptability to emerging deepfake technologies. By focusing on these areas, the practical application value of fake facial image detection models can be further enhanced, contributing to the development of more reliable and scalable solutions.
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 3 Results and Discussion
### Density  
Figure 6 illustrates the epoxy mortar's density. It was evident that the cement mortar sample's typical density was 2200 kg/m\({}^{3}\)[36]. Due to the higher specific gravity of fillers relative to epoxy resin, the density of epoxy mortar increased as the filler content increased. Steel slag samples had the highest reported density at around 2240 kg/m\({}^{3}\), whereas PVC waste samples with epoxy resin had the lowest density at about 1280 kg/m\({}^{3}\). The porous nature of PVC waste increased the air gaps in epoxy mortar, which may be the cause of the decrease in density. Overall, epoxy mortar samples including marble dust (2000 kg/m\({}^{3}\)), sand (1760 kg/m\({}^{3}\)), and PVC waste (1280 kg/m\({}^{3}\)) had lower densities than epoxy mortar samples containing steel slag (2240 kg/m\({}^{3}\)). These findings demonstrated that the ratio of resin to filler had a significant impact on the density of epoxy mortar. Due to the identical specific gravities of the filler types taken into consideration in this study, their impacts on density are minimal.
## 2 The Mixture Model for the Analysis of Time Series  
This section presents some preliminaries regarding the methodology of mixture models for clustering. For a comprehensive discussion, refer to [PERSON] and [PERSON] [15]. A sample is given of \(n\) observed time series \((t_{1},y_{1}),\ldots,(t_{n},y_{n})\), where \(y_{i}(t)\) is the response (e.g., energy consumption) for the \(i\)th individual time series given the time (predictor) \(t\). The time series of the index \(i=1,\ldots,n\) is observed at the time values \((t_{i1},\ldots,t_{im_{i}})\) with \(t_{i1}<\ldots<t_{im_{i}}\). Notably, \((t_{i1},\ldots,t_{im_{i}})\) may change from one time series to another.  
A mixture model for a time series assumes that pairs \((t_{i},y_{i})\) are obtained from \(K\in\mathbb{N}\) probability density components. A discrete random variable \(Z\in\{1,\ldots,K\}\) indicates the component from which the pair \((t_{i},y_{i})\) is drawn. The following parametric density function describes a general mixture model:  
\[f(\mathbf{y}_{i}|t_{i};\mathbf{\theta}) = \sum_{k=1}^{K}\alpha_{k}f_{k}(\mathbf{y}_{i}|t_{i};\mathbf{\theta}_{k}), \tag{1}\]  
where the coefficients \(\alpha_{k}\) are defined by \(\alpha_{k}=P(Z_{i}=k)\) and represent the mixing probabilities such that \(\alpha_{k}>0\) for each \(k\) and \(\sum_{k=1}^{K}\alpha_{k}=1\). Additionally, \(\mathbf{\theta}_{k}\) (\(k=1,\ldots,K\)) is the vector of the parameters for the \(k\)th component density \(f_{k}(\mathbf{y}_{i}|t_{i};\mathbf{\theta}_{k})\), which can be chosen to represent the time series for each group \(k\). In our approach, the component density is a normal regression mixture [20] and can include spline and polynomial regression [21] and B-spline as a special case [22].  
The advantages to using spline and polynomial regression in time-series clustering include:  
* Smoothing: These techniques can help smooth data and reduce noise, which can be particularly useful when dealing with datasets that contain outliers.
* Robustness: Spline and polynomial regression are robust to missing data and differences in sampling times between time-series data.
* Ease of interpretation: The use of spline and polynomial regression can make it easier to interpret the results of the regression analysis.
* Prediction: These techniques can be useful for predictions, particularly when dealing with nonlinear relationships.
* Computational efficiency: Spline and polynomial regression can be computationally efficient, particularly when dealing with large datasets.  
Spline regression is a highly suitable and adaptable form of regression characterized by its ability to account for nonlinear relationships between variables. This technique is commonly employed when the dataset contains outliers or when the relationship between variables cannot be accurately represented by a linear equation. In contrast, polynomial regression is a more inflexible form of regression used to model higher-order polynomial relationships between variables. This method is typically employed when the data have a well-defined and predictable relationship that can be accurately modeled by a higher-degree polynomial.
## 1 Introduction  
Thermal conductivity represents a significant thermophysical parameter of materials. Over the years, there have been many different methods developed for use in characterization [1, 2, 3, 4, 5, 6, 7, 8]. In geological exploration [9, 10, 11, 12], aerospace thermal protection systems [6, 13, 14, 15], and the selection of insulation materials [16, 17, 18], there are strict restrictions on the thermal conductivity of materials, and so it is necessary to measure the thermal conductivity of materials under various conditions accurately. Traditional methods often suffer from lengthy measurement times and potential inaccuracies [19, 20, 21]. In this context, the enhancement of testing efficiency while ensuring measurement accuracy has emerged as a central research focus in thermal property-testing technologies. Transient plane heat source (TPS) technology is a highly efficient approach to thermophysical property measurement, having gained considerable application in practice [22, 23, 24, 25]. It was initially developed by Prof. [PERSON] [3, 26, 27, 28] in Sweden as a patented method based on the transient hot wire method and transient tropical methods, also known as the 'hot disc' method. The method's principal advantages include the ability to measure a wide variety of materials' thermal conductivities, as well as a relatively short testing time, high accuracy, and a straightforward sample preparation process.  
Recent advancements in TPS technology have focused on enhancing measurement precision and efficiency through novel approaches and materials. For example, integrating machine learning algorithms with TPS data analysis has shown promise in accurately predicting thermal conductivity in complex materials [29; 30]. Furthermore, emerging studies on the impact of nanomaterials in thermal fluids indicate that innovative formulations can significantly boost thermal performance, necessitating the precise measurement capabilities offered by TPS methods [31; 32; 33]. In the meantime, researchers have worked to improve the TPS technique by combining various kinetic models to enhance measurement precision and efficiency, optimizing sensor designs and increasing heat transfer measurement accuracy [34; 35].  
This study aims to establish a three-dimensional model of the transient planar source method using finite element modeling (FEM) and to compare the simulated average temperature rise data of PMMA samples with experimental results, thereby validating the feasibility of the model. Additionally, the model will be employed to predict the thermal properties of other materials. The simulation work also seeks to clarify the distribution of current density within the sensor and its relationship with the heat source distribution, providing theoretical support for the optimization of geometric structures and parameters in future thermal performance testing.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.5 Segmentation Fractal-Based Texture Analysis (SFTA)  
SFTA is a two-staged feature extraction technique where, in the first stage, Two-Threshold Binary Decomposition (TTBD) is applied on a grayscale image to produce binary images. The Multilevel Otsu algorithm is used in TTBD to calculate some threshold values from the image's gray level distribution. The algorithm first determines a threshold value that ensures that the image's intra-class variance is minimized. This is applied to all regions of the image to obtain \(n_{t}\) threshold values. Decomposition is carried out by the selection of pairs of threshold values and the application of a two-threshold segmentation, as shown in Equation (4) [50].  
\[I_{b}(x,y)=\begin{cases}1&t_{1}<I(x,y)\leq t_{u}\\ 0&\text{otherwise}\end{cases} \tag{4}\]  
\(I(x,y)\) and \(I_{b}(x,y)\) are pixel intensities of grayscale and binary images, respectively, at \((x,y)\) pixel position. \(t_{l}\) and \(t_{u}\) are the upper and lower threshold values. A total of \((n_{t}-1)\) binary images are produced based on the procedure. In addition, simple thresholding is applied to produce \(n_{t}\) binary images summing up to \((2n_{t}-1)\) binary images. In the second step, three features are extracted from each binary image, namely, Area (\(A\)), Mean Gray Level (\(V\)), and Fractal Dimension (\(D\)). Area (\(A\)) represents the number of white pixels in the binary image, Mean Gray Level (\(V\)) is the average brightness of gray image pixel positions corresponding to white pixel positions in the binary image, and Fractal Dimension (\(D\)) is a measure of boundary complexity. A total of \((6n_{t}-3)\) features are extracted for an input image in SFTA [18]. An \(n_{t}\) value of 8 was used, thereby extracting 45 features from each input image.
## 1 Introduction
PSO excels at global optimization and does not rely on gradient information, making it well-suited for optimizing BPNN structures. Specifically, PSO can optimize the initial weights and biases of BPNNs, thereby improving convergence speed, mitigating overfitting, and enhancing overall accuracy. The idea of combining PSO with BPNN stems from the need for a more robust and adaptive surrogate model capable of overcoming the limitations of traditional numerical and approximation methods. Building on the results of previous studies and the demonstrated potential of PSO and BPNN, this paper proposes a method that integrates PSO with a BPNN for turbine component analysis. The proposed PSO-BPNN approach utilizes PSO to optimize the convergence and accuracy of BPNN models, while MCS is employed for failure probability calculation. This integration not only reduces computational costs but also improves the efficiency of addressing high-dimensional and nonlinear reliability problems. The proposed method is validated through numerical and practical engineering examples, with a specific focus on the reliability analysis of an aero-engine high-pressure turbine casing. The results demonstrate the potential of this method to serve as an effective and efficient tool for structural reliability analysis in broader engineering applications. The remainder of this paper is organized as follows. Section 2 introduces the fundamental principles of structural reliability analysis and key concepts. A brief overview of the PSO-BPNN method is introduced in Section 3, and the application in reliability analysis of the PSO-BPNN method is also elaborated at the same time. Numerical and engineering examples are given in Section 4 to verify the accuracy, efficiency, and engineering applicability of the proposed method. Section 5 discusses the advantages, limitations, and potential improvements of the proposed PSO-BPNN method. Finally, Section 6 presents the conclusions of this paper.
## 4 Evaluations
### Statistics  
The following describes the post-processing of the metrics and, namely, the statistical analysis of the collected data. The performance metrics measured during the trials, i.e., time, commands, and Cartesian travel, were first grouped based on participant ID, what object to grasp (strawberry or bottle), and whether there was a single object or multiple objects in the scene. This resulted in four groups for each participant, with six samples for each of the three control schemes. Many repetitions per group were performed to avoid problems with outliers. To also avoid problems with pseudo replication, i.e., artificially inflating the number of samples and hence the power in the statistical analysis, only the mean of these samples is used for each group in the following statistical analysis.  
All the measured performance metrics were found to be positively skewed and hence log transformed. Afterwards, the normality of the transformed data was then confirmed using [PERSON]'s test. A one-way repeated measures ANOVA with the three control schemes were factors used to test for significance. [PERSON]'s test was used to test for sphericity and in the case where sphericity was violated, the Greenhouse-Geisser correction was used. Post hoc analysis was conducted for each of the metrics which showed significance in the repeated measures ANOVA test. These post hoc tests consisted of pairwise comparisons among the different conditions, i.e., the three control schemes, using Bonferroni correction.  
The data from the TLX and INTUI questionnaires were tested for significance using the nonparametric Friedman test. This was followed by a post hoc analysis using the Wilcoxon signed rank tests between each unique pairing of the three control schemes. Bonferroni correction was applied in this post hoc analysis as well. Nonparametric tests were used, as both questionnaires rely on an ordinal scale. It should be noted that a significance level of \(p=0.05\) is used throughout the discussion of the results in regard to whether a result was statistically significant or not.