esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
When the flow velocity was increased, the resonant frequencies of the numerical transfer function shifted toward the low-frequency region. Additionally, the transfer functions at the resonances of the even mode shapes were distinctly predicted, and their magnitudes increased as the flow velocity increased. The numerical transfer function was in good agreement with those of the analytical models. Figure 9 depicts the predicted flow velocities obtained using Equation (9). The predicted results are corroborated by the input values. The flow velocity also has only minimal measurement error, regardless of resonant frequency. At higher velocities, the transfer function method was more accurate because the magnitude and resonant frequency of the pipe were considerably different from those of the zero-flow velocity pipe. On the other hand, the accuracy of the prediction method at the lower flow velocities dropped at low frequencies. However, when the forced frequency of the excitation sources was higher, the predicted velocities at high frequencies were more accurate because of the inclusion of the transient components of \(\hat{k}_{b,2}\), \(\hat{k}_{b,3}\), and \(\hat{k}_{b,4}\), as shown in Figure 3. Similarly, the measurement accuracies of electromagnetic flowmeters decreased when the flow velocities were lower [24]. Further, the flowrate in the unsteady condition results in measurement errors at low frequencies because of the turbulent intensity [25]. Therefore, to increase the precision of measurement in the transfer function method, the forced frequency of excitation sources must be increased. Figure 7: Numerical mode shapes of the fluid-conveying pipe when the flow velocity increased: (**a**) 1 st, (**b**) 2 nd, (**c**) 3 rd, and (**d**) 4 th mode shapes.
## 3 Learning-Based Path following Control with Parametric Uncertainties
### Observation Space of the Path following Control Environment  
The agents trained for the path following control task should minimize certain errors between the ROboMObil and the path, which is assumed to be provided by the ROboMObil's path planning module [15; 18]. More specifically, the agents should minimize the vehicle's lateral position error \(e_{y}^{\text{P}}\) and orientation error \(e_{\psi}\) to the path. Furthermore, the agents should closely track the demanded velocity in the tangential direction of the path, i.e., minimize the velocity error \(e_{v_{x}}^{\text{P}}\) (cf. Appendix A).  
To successfully minimize these errors and learn the control task, the agents require a suitable observation space during training that contains all the necessary information regarding the environment. In this work, the observation vector \(s_{k}\), also called the state, is chosen based on our previous work in [14]. Here, the aforementioned errors \(e_{y}^{\text{P}}\), \(e_{\psi}\) and \(e_{v_{x}}^{\text{P}}\) as well as the velocity error \(e_{v_{y}}^{\text{P}}\) in the lateral direction of the path (cf. Appendix A), are provided for the observation vector. Furthermore, the path curvature \(\kappa_{\text{P}}\) and the front and rear steering angles \(\delta^{\text{f}}\) and \(\delta^{\text{r}}\) of the ROboMObil are incorporated into the observation vector \(s_{k}\). Lastly, the observation vector \(s_{k}\) in the time step \(k\) is extended with the observation \(\sigma_{k-1}\) of the aforementioned values from the previous time step \(k-1\), which incorporates beneficial rate information into the learning process. This leads to the observation vector \(s_{k}\) being  
\[\mathbf{s}_{k}=\Big{[}e_{y,k^{\prime}}^{\text{P}}e_{v_{x},k^{\prime}}^{\text{ P}},e_{v_{y},k^{\prime}}^{\text{P}},e_{\psi,k},\kappa_{\text{P},k}^{\text{f}}, \mathbf{\delta}_{k^{\prime}}^{\text{f}},\mathbf{\ \sigma}_{k-1}\Big{]} \tag{1}\]  
with  
\[\mathbf{\sigma}_{k-1}=\Big{[}e_{y,k-1}^{\text{P}},e_{v_{x},k-1}^{\text{P}},e_{ v_{y},k-1}^{\text{P}},e_{\psi,k-1},\kappa_{\text{P},k-1},\delta_{k-1}^{ \text{f}},\delta_{k-1}^{\text{f}}\Big{]}. \tag{2}\]  
Figure 2: The performance of agents during execution for different values of a representative dynamics parameter \(\xi\). (**Left**): The true parameter \(\xi_{1}\) of the vehicle in the first environment equals the value \(\xi_{\text{train}}\) applied during the simulation-based training and the agent shows a satisfying path following control performance. (**Right**): The actual vehicle parameter \(\xi_{2}\) in the second environment differs from \(\xi_{\text{train}}\) and the agent shows a poor path following control performance.
## 5 Discussion
Firstly, the model's performance is tied closely to the dataset derived from South Korean roads, and this may impact its generalizability to other regions with different road conditions. Furthermore, the segmentation of road sections, with lengths as short as 50 m, may contribute to a high incidence of zero crashes. Future research could explore the use of longer or more homogeneously defined segments to further validate the model's generalizability and assess its predictive performance across varied road segment lengths. Additionally, this study did not incorporate extensive validation techniques, such as robust training/testing splits, which could enhance the model's robustness and applicability to unseen data. We suggest that future studies should apply advanced validation methodologies, including cross-validation and separate training and test datasets, to strengthen the model's reliability and reduce the risk of overfitting. Moreover, the current model does not account for temporal dynamics, such as changing traffic patterns, infrastructure modifications, or evolving traffic regulations, all of which can influence crash risks over time. Future iterations of the model could integrate temporal variables to capture these evolving risk factors, enhancing the model's long-term predictive relevance. Expanding the data sources to include real-time traffic data, weather conditions, and socio-economic factors could provide a more comprehensive analysis of factors influencing crash occurrences. Finally, to improve the model's interpretability, advanced techniques such as Individual Conditional Expectation (ICE) and Accumulated Local Effects (ALE) plots could complement SHAP values, offering deeper insights into predictor-variable relationships. Addressing potential biases in underlying crash data, such as underreporting, is also essential to ensure fairness and accuracy in model predictions.
## 7 Impact Assessment of EVs
### Environmental Impacts  
The transportation sector is widely recognized as the second-largest contributor to carbon emissions. Consequently, governments have been increasingly prioritizing EVs in response to the urgent need to address the rapid fluctuations in the global climate. The widespread implementation of EVs in densely populated urban areas is crucial for achieving the electrification of the transportation sector. The transportation revolution has resulted in a significant reduction in carbon dioxide emissions, thereby leading to positive environmental outcomes. The establishment of a connection between EVs and electrical grids is crucial in this context. Furthermore, the adoption of EVs within a vehicle-to-grid (V2G) ecosystem enhances the resilience and sustainability of a society through the utilization of clean and reliable energy sources. The advancements in EV technology have the potential to reduce reliance on fossil fuels, thereby making a significant contribution towards creating a more sustainable global environment [130; 131]. The integration of EVs with renewable energy sources (RESs) yields many environmental benefits. The V2G technology is an indispensable component for the realization of a sustainable energyrevolution. When evaluating the environmental impact of EVs, it is crucial to acknowledge their substantial reduction in carbon dioxide emissions. One of the primary factors that contribute to the lower greenhouse gas emissions of EVs in comparison to internal combustion engine vehicles is the utilization of highly efficient electric motors. EVs are preferable to gas-powered ones since they have a smaller environmental impact. However, we analyze some of the environmental benefits and drawbacks of EVs.  
POSITIVE IMPACTS:  
EVs do not produce any air-polluting emissions from their tailpipes;  
EVs do not add to noise pollution because of their low operating volumes;  
EVs are better for the planet because they do not require engine oil;  
In order to prevent \"corrosion, crumbling, and failing early\" and the associated high maintenance costs, EV brake pads are designed differently;  
EV makers have, historically, prioritized the use of recyclable and biodegradable components;  
EV chargers powered by renewable energy emit less emissions than gas stations. Charging stations can keep \"fuel\" nearby, unlike petrol stations.  
NEGATIVE IMPACTS:  
Electricity for EV chargers come from power plants that generate electricity from fossil fuels. In places like California, where the power grid is already strained during the summer, this could lead to more rolling blackouts;  
Manufacturing EV batteries leads to habitat destruction, pollution, and water scarcity;  
Battery metals like nickel, lithium, and cobalt require a lot of power to extract. These minerals are typically mined in places with poor environmental standards;  
Although EV batteries were not developed with recycling in mind, technology to facilitate recycling is improving rapidly;  
The weight and torque of EVs cause premature tire wear. More frequent tire purchases lead to more pollution.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction
### Implicit Synthetic Co-Visibility-Cluster-Guided Dense 3D Reconstruction
#### 2.1.2 Multi-View Implicit Synthetic Co-Visibility Cluster Generation  
We now have the foundation for synthesizing new-view color images and depth images. However, we need to determine which viewpoints to output as supplements. As previously mentioned, images with overlaps greater than \(\xi_{dense}\) (usually set at 80% as an empirical value) are considered dense. If every image within a co-visibility cluster has overlaps with the surrounding images greater than \(\xi_{dense}\), we can consider the entire cluster to be sufficiently dense and capable of providing good modeling results. To achieve this, we establish a mathematical model. First, we select an image frame \(I_{k}\) from the co-visibility cluster and identify the set of images \(\{I_{l},\dots\}\) within the same cluster that share a co-visibility relationship with it. We find images in the set where the feature overlap with the current image is less than \(\xi_{dense}\). In the previous assumption, image captures were all performed on the same plane, and the generation of viewpoints only required determining the plane coordinates. In the plane coordinates, when the center-to-center distance between two rectangles is less than a certain value, the overlap is guaranteed to be greater than \(\xi_{dense}\). Through simple set deduction, we can derive that when the center-to-center distance  
\[D\leq(1-\xi_{dense}\frac{1}{2})\sqrt{W^{2}+H^{2}} \tag{4}\]  
where \(W\) and \(H\) are the width and height of the image, respectively, the overlap between each image is greater than \(\xi_{dense}\).  
Therefore, when generating the plane coordinates of new viewpoints, we only need to calculate the equation of the line connecting image frame \(I_{k}\) and the images in the co-visibility set \(I_{l}\), as well as the Euclidean distance \(D_{kl}\). We sample new viewpoints along the line at intervals of \(\left|\frac{D_{bl}}{(1-\zeta_{dense}\epsilon^{2})\sqrt{W^{2}+H^{2}}}\right|-1\) where the spacing between viewpoints is \(D_{bl}/\left|\frac{D_{bl}}{(1-\zeta_{dense}\epsilon)\sqrt{W^{2}+H^{2}}}\right|\). We apply this process to generate new viewpoints for every image within the co-visibility cluster that has a shared co-visibility relationship. Combined with height and camera angle information, we input the obtained RGB images and depth maps into the NeRF model. This process produces a highly dense dataset of RGBD aerial viewpoints for the subsequent reconstruction phase, ensuring the availability of data.
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## 3 SHPB Experiment and HOSS Model Setup
### Ensemble Kalman Filter
**Initialization**: An ensemble of parameter values \(\mathbf{p}_{i}\), \(i=1,\ldots,N\) is provided by the latin hypercube sampling strategy, where \(\mathbf{p}_{i}=\left(\sigma_{t,i}^{\max,},E_{t,j}\right)\);
2. **Simulation:** For each ensemble member \(\mathbf{p}_{i}\) simulate HOSS for the SHPB experiment, where the output is a stress versus time curve (\(\mathbf{x}_{i}\) for \(i=1,\ldots,N\));
3. **Assimilation:** Using the HOSS output from the ensemble \(\mathbf{x}_{i}\) for \(i=1,\ldots,N\) and observations \(\mathbf{y}^{\rho}\) from the SHPB experiment, assimilate using the Kalman analysis Equations (3) and (4), where this will provide updated parameter values \(\mathbf{p}_{i}^{\sigma}=\left(\sigma_{t,i}^{\max,\rho},E_{t,j}^{\sigma}\right)\) for all ensemble members;
4. **Cycle:** Feedback the updated parameter values \(\mathbf{p}_{i}^{\sigma}\) to HOSS and repeat Steps 2 and 3 until we see convergence of the error between the HOSS output and SHPB experiment (\(\|\bar{\mathbf{x}}-\mathbf{y}^{\rho}\|<tol\), where \(\bar{\mathbf{x}}\) is the ensemble average);
5. **Forecast:** Once convergence is achieved, perform a \"forecast\" of the HOSS model (best simulation with estimated parameter values). The workflow is illustrated in Figure 3, where there are the five basic steps as in the description above. Figure 3: Data assimilation cycle for HOSS with EnKF for estimating key model parameters. The default parameters are passed to HOSS. If the error between the model output against observations is large, then the assimilation is performed, which provides new parameter values. The assimilation cycle is performed until the error between the model output and observations is small enough (a certain tolerance).
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 3 Establishment of the Sample Dataset of Oceanic Phenomena  
### Sample Dataset Construction  
We select five types of phenomena, oceanic eddies, rain cells, ship wakes, oceanic fronts, and oil spills as objects to be detected. The proposed MFNN performs detection by automatically learning the features of oceanic phenomena using a multilevel network model. Because the process of network learning features needs to be driven by training data, the quality of the training dataset is the key factor that affects the accuracy of the detection results. Therefore, we created a dataset of SAR images containing five oceanic phenomena.  
The original dataset of images of oceanic phenomena is derived from SAR images acquired by the Sentinel-1 satellite in 2015-2017. From these images, we identified SAR images containing oceanic phenomena. To better distinguish the image content and to improve the accuracy of phenomena recognition, we enhance the contrast of the images and adjust the gray level of the images to ensure that the oceanic phenomena are prominent. Finally, we cropped the SAR images containing oceanic phenomena. To retain more information in the images, we do not filter the SAR images during the cropping process. The resulting oceanic phenomena dataset is not only rich in physical features such as structure and scale, but also has significant diversity in visual features and texture features.
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 2 Materials and Methods
### Mechanical Properties Tests  
Tensile tests were carried out to research the effects of process parameters on the mechanical properties of welded joints. The samples were cut from blanks by wire-electrode cutting according to ISO 6892-1:2019 [26]. The dimensions of the tensile samples are shown in Figure 3. The samples before and after quenching were tested on an SHT4106 universal material test machine for the tensile strength of the welded joints. The tensile tests were performed with the loading speed of 1 mm/min and were repeated four times.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Power** & **1800, 2200, 2600, 3000, 3400 W** \\ \hline Welding speed & 65, 75, 100, 125, 150 cm/min \\ Flow rate of shielding gas & 15 L/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process parameters used in the welding process.  
Figure 1: Microstructure of BR1500 HS high-strength steel.  
Figure 2: Welding process: (**a**) schematic illustration; (**b**) field experiment.  
The Vickers hardness tests were performed on the cross-section of welded joints using an HV-1000A Vickers hardness tester, with a load of 500 g, and a holding time of 15 s. The distribution of microhardness test points is shown in Figure 4. The distance between every two test points was 0.1 mm. Each point was tested three times and the average value of the three measurements was taken as the result.
# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 4 Experimental Results and Analysis  
### Human Ear Datasets  
In order to train and test the model of the YOLOv5s-MG-CBAM-F method proposed in this paper, three different human ear datasets, namely, CCU-DE, USTB and EarVN1.0, were used. In order to facilitate the comparison of performance between different methods, the selection of three datasets is the same as that in Reference [17]. There are 3274, 7700 and 3201 pictures in the CCU-DE, USTB and EarVN1.0 human ear datasets, respectively. The training set, validation set and test set are divided according to 3:1:1. Because the selected human ear datasets have different characteristics of data size, category, attitude change, resolution, gender, dynamic and static, etc., it can detect the performance of the proposed method very well.
## 2 Ranging Principle and Analyses of the Questions
### Question 1  
As shown in Figure 2, the satellite time, \(t_{0}\), references the 1 PPS, which is the same as in Figure 1. In addition, \(t^{\prime}{}_{0}\) is obtained by sampling the 1 PPS using the \(f_{c}\). A phase difference, \(\Delta p\), exists between \(t_{0}\) and \(t^{\prime}{}_{0}\). Furthermore, \(t_{2}\) is also referenced to the 1 PPS and \(t^{\prime}{}_{2}\) is obtained by a count of the \(f_{c}\) to the measurement point time after the commencement of \(t^{\prime}{}_{0}\). Likewise, a phase difference, \(\Delta p\), also exists between \(t_{2}\) and \(t^{\prime}{}_{2}\).  
Figure 1: One-way pseudo-range measurement process.  
Figure 2: Uncertainty of the \(\Delta p\) between \(t_{0}\) and \(t^{\prime}{}_{0}\) with the FPA.  
In general, the 1 PPS is obtained by a count of the 10.23 MHz and \(f_{c}\) is obtained by the frequency multiplication of the 10 MHz onboard atomic clock. If there is no frequency or phase skew, \(\Delta p\) remains fixed and can be calibrated in advance as the zero value of the equipment. However, with the FPA of the 10.23 MHz clock, a phase shift occurs between the 1 PPS and the \(f_{c}\). When the phase shift is not an integer multiple of \(f_{c}\), \(\Delta p\) changes. Assuming that the 10.23 MHz is shifted forward or backward by the phase, \(\phi_{\rm A}\), then \(\Delta p^{\prime}\) denotes \(\Delta p\) after the phase shift. It can be expressed as:  
\[\Delta p^{\prime}\!=\left\{\begin{array}{l}rem\left(\left|\frac{\Delta p+P_{ A}}{1/f_{c}}\right|\right),\hskip 28.452756 ptP_{A}\to forward,\\ 1/f_{c}-rem\left(\left|\frac{\Delta p-P_{A}}{1/f_{c}}\right|\right),\ P_{A} \to backward,\end{array}\right. \tag{2}\]  
where \(rem\left(\bullet\right)\) represents the function of the remainder. When the frequency adjustment of the 10.23 MHz occurs, \(\Delta p^{\prime}\) denotes \(\Delta p\) can be expressed as:  
\[\Delta p^{\prime}\!=\left\{\begin{array}{l}rem\left(\left|\frac{\Delta p+| \Delta f|t/\left(10.236\epsilon+|\Delta f|\right)}{1/f_{c}}\right|\right), \Delta f\geq 0,\\ 1/f_{c}-rem\left(\left|\frac{\Delta p-|\Delta f|t/\left(10.236\epsilon-| \Delta f|\right)}{1/f_{c}}\right|\right),\Delta f<0,\end{array}\right. \tag{3}\]  
where \(\Delta f\) represents the frequency adjustment size and \(t\) is the time after the commencement of the adjustment. According to Equations (2) and (3), the phase uncertainty between \(t_{0}\) and \(t^{\prime}_{0}\) can be up to one working clock cycle. As per Equation (1), the ranging error is increased to an \(f_{c}\) clock cycle. If the ISL payload working clock frequency is larger, the range error is smaller. However, considering the power consumption and the timing reliability of the processing chip, it is impossible to increase the working frequency by a considerable margin.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.4 Medical Imaging  
In the domain of medical imaging, where the accuracy and reliability of segmentation are paramount, DDPM finds application in denoising medical images and generating synthetic datasets. The model's ability to handle noise and capture underlying distributions makes it valuable in enhancing the quality of medical imaging data [43; 54].
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 5 Results
### Statistical Analysis for Coverage Threshold  
To analyze the coverage radius threshold, we adopt the statistical approach introduced in [56], which uses the concept of quartiles to estimate the coverage radius from a dataset. Observations arranged in ascending order are divided into four equal parts to obtain quartiles. The first quartile (Q1) comprises the initial 25% of the data. The second quartile (Q2) is the median, representing the range between 25% and 50% of the data. The third quartile (Q3) spans the range from 50% and 75%, while the fourth quartile (Q4) covers 75% and 100% of the data. In the context of determining the limit coverage radius for a moving end node that sends data to the gateway (uplink communication), we focus on Q3.  
Figure 18 shows the representative graphic for the 75 th percentile for SF7, SF8, and SF9.  
The coverage radius threshold was established based on the 75 th percentile values of SF7, SF8, and SF9, ensuring a reliable communication link between mobile objects uplinking data to end nodes and the gateway. These values were measured at 954.3 m, 905.3 m, and 975.8 m, respectively.  
Our analysis focuses on dynamic network links, where the final device is in motion, simulating a boat with a speed of approximately 25 km/h. The Q3 values represent the coverage radius considering continuous information transmission and reception at the LoRa gateway.  
For values below Q3, the coverage radius is conservative. For distance radius values above the Q3 values, the information sent in uplink would still be received by the gateway but not continuously. In all cases of SFs measured, there was a connection between the end node and the gateway along the entire route, with information sent confirmed by its RRP values.