esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 5 Conclusions  
This paper provides a detailed introduction to an undifferenced NRTK positioning model considering the constraints of time-varying atmospheric characteristics. Starting from the UD observation equation, the generation of error correction information for single reference stations and multiple reference stations, interpolation of error correction information, and user error correction and positioning are introduced in detail. In undifferenced NRTK, the ambiguity fixing of the reference station is the starting point for generating high-precision error correction information and high-precision positioning. Therefore, this paper studied the time-varying characteristics of atmospheric parameters for the ambiguity fixing of the reference station. The atmospheric delay error separated from the original observations contains a large amount of observation noise, which affects the determination of the atmospheric error PSD. This paper reduced the impact of observation noise by increasing the difference interval. The estimated PSD was used to constrain the atmospheric parameters in the ambiguity fixing. Compared with the empirical PSD, the estimated PSD in this paper improved the ambiguity fixing time by an average of 18.4%, and increased by 11.7% the ambiguity fixing success rate. The performance of the reference station ambiguity fixing was far better than with the empirical PSD method. Finally, the user error was corrected through the UD atmospheric error generated after the ambiguity fixing of the reference station, and user high-precision real-time dynamic positioning was achieved.  
[PERSON] conceived the idea and designed the experiments with [PERSON], [PERSON], [PERSON] and [PERSON]; [PERSON] also wrote the main manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the paper. All components of this research were carried out under the supervision of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 19: User positioning error sequence.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Nos. 42030109, 42074012) and the open fund of the State Key Laboratory of Satellite Navigation System and Equipment Technology (No. CEPNT-2018 KF-13), and it was supported by the LiaoNing Revitalization Talents Program (Nos. XLYC2002101, XLYC2008034, XLYC2002098).  
**Data Availability Statement:** The authors gratefully acknowledge the Heilongjiang CORS network for providing the multi-GNSS data. The other datasets are available from the corresponding author on reasonable request.  
**Acknowledgments:** The authors gratefully acknowledge the Heilongjiang CORS network for providing the multi-GNSS data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Influence of Fatigue Loading on the Durability of the Conveyor Belt
## 3 Testing and Modeling of Conveyor Belt Fatigue Behavior
As for the second category, it consists of phenomenological models of residual stiffness/strength. These models propose evolutionary connections that describe the gradual degradation of the stiffness or strength of the textile composite specimens in terms of measurable characteristics. Residual stiffness models take into account the degradation of the elastic characteristics of materials during fatigue testing. The stiffness of the specimen can be measured frequently or continuously during the fatigue test and can be determined without damaging the specimen. These models can be deterministic where an exact stiffness value is predicted, or they can be statistically evaluated where a distribution of stiffness values is predicted. Residual strength models describe the degradation of the initial static tensile strength during fatigue testing. They were developed from the need to define the remaining life of the material. Statistical models such as the two-parameter Weibull distribution are most often used to describe the residual strength and probability of fracture of a textile composite after a defined number of cycles. As for the third category, it consists of models of progressive damage. These models describe the degradation of the textile composite in direct relation to the specific damage. They relate one or more variables to measurable damage, quantitatively taking into account the progression of the actual damage mechanisms. The most important outcome of all fatigue models is the prediction of fatigue life. Each of the mentioned categories uses its own criteria for determining the moment of ultimate fracture, and thus the fatigue life. One of the general fatigue life models of textile composites that can be applied to tension/tension testing of conveyor belt specimens is the [PERSON]'s variable stress ratio fatigue life model [39]. The S-N diagram is described by the following relation:  
\[\sigma_{MAX}=b\log(N)+c \tag{2}\]  
where:  
* \(\sigma_{MAX}\)--the maximum value of the stress amplitude;
* \(N\)--number of cycles to fracture;
* \(b\) and \(c\)--fourth-order polynomials in function of an arbitrary function \(R\)\". For the tension/tension loading case, the stress ratio is 0 < \(R\) < 1, while the arbitrary function is calculated as \(R\)\" = 4 + \(R\). Stated fourth-order polynomials in case of variable stress ratio \(R\
eq\mathit{const. }\) are calculated as follows:  
\[b = K(R^{\prime\prime})^{4}+L(R^{\prime\prime})^{3}+M(R^{\prime\prime})^{2}+N(R^ {\prime\prime})+P \tag{3}\] \[c = Q(R^{\prime\prime})^{4}+T(R^{\prime})^{3}+U(R^{\prime})^{2}+V(R^ {\prime\prime})+W \tag{4}\]
# Driver Identification and Detection of Drowsiness while Driving
## 3 Requirements and Technologies
### Convolutional Neural Networks  
Once the vehicle is started and the driving begins, the monitoring of relevant facial features is initiated using the camera. In order to achieve this, image processing techniques are employed to determine the level of drowsiness, typically through the application of machine learning techniques. Some of these techniques are convolutional neural networks (CNNs), support vector machines (SVMs) [30], and hidden Markov models (HMMs) [31]. In order to create a drowsiness detection model, one of those three types of techniques must be trained using labeled features and outcomes. As mentioned earlier, the models applied in this work use a CNN, which is an exclusive class of deep neural networks that perform exceptionally well when used for image classification.  
A CNN includes essential components such as an input, an output, and a hidden layer with the potential for many layers. The input layer receives all inputs, whereas the last layer is the output layer, which provides the desired output. All intermediate layers are referred to as hidden layers. The hidden layers depend on the analyzed use case. These layers are convolved through a filter that multiplies the 2D matrices of the layer and the filter. The final layer has two nodes and is also fully connected.  
The following layers constitute the unique architecture of the CNN model that was used:  
* Two layers of 32 nodes, with a kernel size of 3;
* A total of 64 nodes in the convolutional layer, with a kernel size of 3;
* A total of 128 nodes for the fully connected layer.  
Figure 2: Possible camera placement options and selected final camera placement inside the car.  
The rectified linear unit (ReLU) [32] function is commonly used as the activation function for hidden layers in CNN models. However, in this work, the ReLU function was not applied to the output layer but was used in all the other layers. Instead, the Softmax function was applied just before the output layer.
## 2 Materials and Methods
### Physical Parameters
The entropy of energy is considered as a measure of abrupt changes in a signal. The spectral centroid and spectral spread characterize the spectral position and shape. Spectral centroid, \(C_{i}\), is the center of gravity of the spectrum, and the \(C_{i}\) value of the \(i\)th audio frame is defined as:  
\[C_{i}=\ \frac{\sum_{k\ =\ 1}^{W_{L}}kX_{i}(k)}{\sum_{k\ =\ 1}^{W_{L}}X_{i}(k)}. \tag{5}\]  
Spectral spread, \(S_{i}\), is the second central moment of the spectrum and is defined as:  
\[S_{i}=\ \sqrt{\frac{\sum_{k\ =\ 1}^{W_{L}}(k-C_{i})^{2}X_{i}(k)}{\sum_{k\ =\ 1}^{W_{L}}X_{i}(k)}}. \tag{6}\]  
Spectral entropy measures the peakedness of the spectrum and has been used to discriminate between speech and music. The spectral entropy is calculated in a similar way to the entropy of energy in the frequency domain. Spectral flux, \(F_{(i,i-1)}\), is a measure of the variability of the spectrum over time and is defined as:  
\[FL_{(i-1)}\ =\ \sum\
olimits_{k\ =\ 1}^{W_{L}}(NE_{i}(k)-NE_{i-1}(k))^{2}, \tag{7}\]where \(NE_{i}(k)\) is the \(k\)th normalized digital Fourier transform coefficient at the \(i\)th frame. Spectral rolloff is a measure of the skewness of the spectral shape. The spectral rolloff is defined as the frequency below which a certain percentage (usually around 90%) of the distribution is concentrated. The spectral rolloff frequency is usually normalized so that it takes values between 0 and 1. We calculated \(\tau_{1}\), \(\varphi_{1}\), \(\mathrm{W}_{\varphi(0)}\), \(\tau_{\mathrm{e}}\), loudness, sharpness, roughness, ZCR, entropy, spectral centroid, spectral spread, spectral entropy, spectral flux, and spectral rolloff to characterize the birdsongs and insect songs qualitatively. The integration interval or windows length used for the analysis was 0.5 s, and the running step was 0.1 s in all calculations. The average values of physical parameters were used as representative values thereafter. The analyses were carried out using a MATLAB-based program (Mathworks, Natick, MA, USA).
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 3 Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
### Multi-Cue Fusion by Weighted Entropy  
The utilization of multiple features enables the acquisition of complementary information for object representation. While various cues may be employed, appearance and motion information remain the most intuitive and critical for cross-frame data association. However, these features are highly susceptible to changes in target characteristics, which diminish their discriminative capacity. To address this limitation, we employ these two widely used features governed by weighted entropy, a metric designed to quantify the uncertainty in their characterization capabilities, to construct a balanced cost matrix for association matching. This framework evaluates the similarity between existing tracks and new detections by integrating feature-specific uncertainties, thereby enhancing the robustness of the data association process against target variations.  
We begin by formulating the likelihood of state \(Y_{t}\) of the object conditioned on the observation \(O_{t}\) given by the detections in the \(t\)-th frame as follows:  
\[p(O_{t}|Y_{t})=\exp\biggl{(}-c\Big{\|}(F_{t}-\overline{F}_{t})-UU^{T}(F_{t}- \overline{F}_{t})\Big{\|}_{2}^{2}\biggr{)} \tag{25}\]  
where \(c\) denotes a normalization constant, \(U\) is the projection matrix of the PCA subspace [16], \(F_{t}\) represents the object feature, and \(\overline{F}_{t}\) represents the empirical mean of historical object features across previous frames. The combined assessment of the \(i\)-th object in the \(t\)-th frame is defined as follows:  
\[Y_{i}^{fused}=\alpha\widetilde{Y}_{i}^{app}+(1-\alpha)\widetilde{Y}_{i}^{mot} \tag{26}\]  
where \(\widetilde{Y}_{i}^{app}\) and \(\widetilde{Y}_{i}^{mot}\) denote the normalized evaluations of the state, and \(0\leq\alpha\leq 1\) is the coefficient applied to \(\widetilde{Y}_{i}^{app}\).  
To reduce the perturbations of the discriminative capability of features, we formally introduce weighted entropy [5] as a metric to quantify the uncertainty of the features:  
\[H(\alpha)=-\sum_{i}Y_{i}^{fused}\lg Y_{i}^{fused} \tag{27}\]  
Smaller \(H(\alpha)\) indicates greater discriminative capacity of the state evaluation metric. Consequently, we formulate the fusion coefficient optimization as a minimization problem of \(H(\alpha)\). And the optimized \(\alpha\) enables effective multi-cue fusion and subsequent construction of the assignment cost matrix.
## 3 Discussion and Results
### Optimization of Morphing Leading Edge
The flow transition behavior can be seen in the velocity contour plots of one of the DNLE airfoils, as shown in Figure 18a for an angle of attack of 4\({}^{\circ}\) and in Figure 18b for an angle of attack of 10\({}^{\circ}\). The comparison of baseline airfoil and DNLE configurations at a 10\({}^{\circ}\) angle of attack shows that higher gradients are formed for the DNLE airfoil in comparison with those of the baseline airfoil, illustrated in Figure 10. However, the trailing edge flow separation region remains the same for both airfoils. In addition, the Turbulent Kinetic Energy (TKE) for the baseline airfoil and for the DNLE airfoils by use of the Transition \((\gamma-R\epsilon_{\theta})\) SST turbulence model are shown in Figure 18c,d. It is evident that the leading edge is propagating the energy towards the downstream; thus a considerable decrease is observed in the wake turbulence. The TKE occurring in the baseline airfoil, which starts from the upper surface and moves towards the trailing edge was not observed in the DNLE-optimized airfoil at an angle of attack of 4\({}^{\circ}\), as seen in Figure 18c.
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 3 Results and Discussion
### Structural Characterization of the Samples
#### 3.1.2 Fourier Transform Infrared (FT-IR) Spectroscopy  
The FT-IR spectra of the nanocomposites are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Mean Crystallite Size (nm)** \\ \hline MNPs35@SiO2 & 7.5 \(\pm\) 0.3 \\ MNPs21@SiO2 & 6.3 \(\pm\) 0.2 \\ MNPs15@SiO2 & 10.6 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average crystallite size.  
Figure 1: X-ray diffraction (XRD) patterns of the magnetic nanocomposites obtained.  
The spectra indicate the presence of both iron oxide and silica in the composite materials, due to the identification of characteristic bands of both components: the typical stretching vibration of Fe-O [14] shifted to 570 cm\({}^{-1}\) due to the Fe-O-Si bond formed, antisymmetric (1090 cm\({}^{-1}\)) and symmetric (800 cm\({}^{-1}\)) Si-O-Si stretching bands, Si-O-Si or O-Si-O bending (460 cm\({}^{-1}\)) and Si-O stretching (950 cm\({}^{-1}\)) [26].  
The saturation magnetization values are of the same order and the slight differences are caused by the variation of the silica coating thickness (content), since each sample contains a different amount of magnetic iron oxide, as per gram. The values of Ms, however, indicate a good response to an external magnetic field of the synthesized materials. This facilitates the separation of the material from the solution, an important feature in the application of this type of material as adsorbent for heavy metal ions from wastewater.
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 2 Related Works
### Incremental Update  
[PERSON] et al. [22] proposed a two-phase update approach, where phase 1 updates the internal switches and phase 2 updates the ingress switches. The ingress switches append version tags to incoming packets to determine whether the legacy rules or the new rules subsequently process the packets. When all packets with old version tags are processed, the legacy rules are deleted.  
[PERSON] et al. [23] proposed an elegant policy composition abstraction based on a transactional interface with all-or-nothing semantics: all new policies are either installed all or none. There, conflicts between concurrent policy updates are avoided, and consistency on the data plane is maintained.  
To reduce storage overhead, [PERSON] et al. [24] proposed a consistent update approach with rule redundancy reduction, named CURE. CURE prioritizes switches according to their usage pattern and schedules updates based on priority. It maintains packet-level consistency through a multi-level queue-based approach.  
The above incremental update schemes update the network in multiple rounds. A portion of the flow rules or a subset of the switches is updated in each round. The update cycle for incremental updates has been shortened compared to ordered updates, but it is still relatively long. Moreover, the incremental update requires the controller to be involved throughout the process and extra flow-table space to accommodate the legacy rules.
# Autonomous Navigation of Robots: Optimization with DQN
## 3 State of Technology
### Kinematics  
In this subsection, as part of the analysis of the mobile robot, the kinematics of the Turtlebot3 Burger is presented.  
The kinematic analysis in mobile robots allows one to know the orientation and position in an environment without considering the forces that generated any change; from this information, it can be determined where the device is or where it will go. The main purpose is to represent the linear speed of the robot and the orientation angle based on the speeds of the wheels and the geometry of the robot [93].  
Due to the fact that the robot moves only on the plane, the analysis is taken as two-dimensional. The differential drive of the wheels and the distance between them are also considered; the parameters to be determined are the orientation \(\theta\) and the \(X_{o},Y_{o}\) position respect to the absolute origin, all depending on the known variables.  
The speed of the robot \(V_{R}\) is given as the average of the linear speeds of each wheel; they are presented as \(V_{1}\) and \(V_{2}\) in Figure 2. The calculation of \(V_{R}\) is shown in Equation (1).  
\[V_{R}=R\frac{V_{1}+V_{2}}{2} \tag{1}\]  
The angular speed of the robot is described in Equation (2), while the components of the movement \(x^{\prime},y^{\prime}\) are described in (3), (4) and the new orientation as a function of time in (5).  
\[\omega=R\frac{V_{2}-V_{1}}{L} \tag{2}\]  
\[x^{\prime}=x_{o}+V_{R}\cos\theta \tag{3}\]  
\[y^{\prime}=y_{o}+V_{R}\sin\theta \tag{4}\]  
Figure 1: Turtlebot3 Burger in Gazebo.  
After deriving Equations (3)-(5) as a function of time, they can be presented as a space of state in (6).  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}\cos\theta&0\\ \sin\theta&0\\ 0&1\end{bmatrix}\begin{bmatrix}V_{R}\\ \omega_{R}\end{bmatrix} \tag{6}\]  
Finally after replacing (1) and (2) in (6), Equation (7) is obtained as the matrix representation of the angular velocities of each tire of the robot.  
\[\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}\frac{R\cos\theta}{2}&\frac{R\cos \theta}{2}\\ \frac{R\sin\theta}{2}&\frac{R\sin\theta}{2}\\ \frac{1}{L}&\frac{-R}{L}\end{bmatrix}\begin{bmatrix}\omega_{2}\\ \omega_{1}\end{bmatrix} \tag{7}\]
# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 2 Surface Quality and Barkhausen Noise Sensor Experiment Setup  
The fabrication of seven Charpy samples (displayed in Figure 1) without notches and with various manufacturing parameters (RPM, Feed rate, Lateral offset) was performed to deliberately modify the surface roughness and also \"simulate\" machining in a controlled area on irradiated samples that results in different surface conditions. The corresponding measured standard surface roughness parameters (\(R_{\text{a}}\), \(R_{\text{z}}\), \(R_{\text{sm}}\)) are shown below and are used as the calibration samples for the Non-Destructive Evaluation (see Table 1).  
Charpy samples of gradually increasing roughness were prepared from ferromagnetic steel in the shape of rectangular prisms (\(10\times 10\times 55\) mm). Table 1 provides the information of how such samples were produced using a combination of different rotational and thrust forces (feed rates).  
This not only gives different surface conditions, but it can also present different stresses within the surface due to different machined parameters (instead of effective cutting, a dragging effect can result) [8]. The machining of choice was face milling and the material for the samples was embrittled by thermal processing [28]. The quality of the surfaces for the samples corresponds to the machining process (milling) and grooves or even the scratches provided by that process with the objective of using different rotational and feed rates to obtain different surface roughness conditions. If one of those parameters, namely the rotational speed, are not fixed at a constant speed then a by-product of machining will exist, such as machine-induced residual stress. The final sample (sample 28) was produced from direct Electro-Discharge Machining, which resulted in the highest surface roughness value.  
The ferrous yokes were attached to the surface of the samples either directly or over a thin spacer. It was found that the unwanted influence of the rough surface can be reduced by using a nonmagnetic spacer. The spacer can reduce or modify the feedback response, which inherently can influence the measurement. In fact, they substantially reduce the scatter of experimental points accompanied by a slight decrease of the overall degradation functions sensitivity. Spacers, in particular if they are thick, are able to modify the shape of the measured signals qualitatively and to bring about a considerable increase of sensitivity, especially in the degradation functions computed from the signal derivatives [8]. At the same time of mitigating against the surface quality, the received signal is reduced. This reduction is proportional to the change in the thickness of the applied spacer.
## 6 Results and Discussion  
### Comparison of Augmented and Real Signals  
To assess the quality of the augmented data, we compared basic statistics between EEG signals acquired from real recordings and those generated by the simulator. Table 4 summarizes the mean values, standard deviations, skewness, and kurtosis for the clean signals, artifact-contaminated signals, and individual channels. The results indicate an overall similarity in the statistical characteristics of the real (real) and augmented (aug) signals. For example, for clean EEG, the mean was 0.02 \(\upmu\)V compared to 0.26 \(\upmu\)V for the augmented version, with standard deviations of 9.24 \(\upmu\)V and 9.65 \(\upmu\)V, respectively. In the EEG channel with artifacts, the mean values were \(-\)0.77 \(\upmu\)V and 1.80 \(\upmu\)V, and the standard deviations were 24.17 \(\upmu\)V and 34.70 \(\upmu\)V for the real and augmented signals, respectively. Skewness and kurtosis measures were also similar; for instance, for the artifact-affected channel, skewness values were 0.09 and 0.16, while kurtosis values were 5.4 and 5.5. Despite some local differences across individual channels, the augmented data exhibit an overall distribution consistent with the real data, confirming their suitability for further analysis and EEG signal modeling.