esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Construction of Intelligent Automatic Blasting Vibration Monitoring System
### Neural Network Model Based on the Fuzzy-Rough Set
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Guideline of**} & \multicolumn{3}{c}{**Description of Decision**} \\ \cline{2-4}  & **0** & **1** & **2** \\ \hline a(kg) & \textless{}30 & 30–50 & \textgreater{}50 \\ b(kg) & \textless{}200 & 200–300 & \textgreater{}300 \\ c(m) & \textless{}20 & \textgreater{}20 & \\ d(m) & \textless{}10 & 10–20 & \textgreater{}20 \\ e(m) & \textless{}0.5 & \textgreater{}0.5 & \\ f(ms) & \textless{}0.5 & 0.5–0.7 & \textgreater{}0.7 \\ g & \textless{}0.5 & 0.5–0.7 & \textgreater{}0.7 \\ h & 0–180 & \textgreater{}180 & \\ i(m) & \textless{}0.3 & 0.3–0.8 & \\ X(cm/s) & \textless{}0.5 & 0.5–1 & \textgreater{}1 \\ Y(cm/s) & \textless{}0.5 & 0.5–1 & \textgreater{}1 \\ Z(cm/s) & \textless{}0.5 & 0.5–1 & \textgreater{}1 \\ F(Hz) & \textless{}100 & 100–200 & \textgreater{}200 \\ \hline \hline \end{tabular} a: maximum quantity for detonating period; b: total quantity for detonating period; c: horizontal distance; d: vertical distance; e: the shortest distance from packet to the free surface; f: differential time; g: the integrity of excavated and unexcavated layer; h: transmission medium; i: types of explosive.  
\end{table} 
Table 2: The reduction of decision and attribute. For the duration of blasting vibration frequency, the uncoordinated rate of size is in the following order: b > a > d = g > c > e = f = h = i. It can be concluded that the total charge of blasting construction has the greatest influence on it, the secondary influencing factors are elevation difference and section charge, the horizontal straight distance and rock integrity coefficient have relatively little influence, while the size of resistance line, blasting time and medium condition of the propagation site have the least influence. Therefore, among all the conditional attributes, the micro-difference time and propagation medium have the least influence on the three decision attributes of blasting vibration, so it is not considered in the parameter prediction of blasting vibration. To achieve the optimal prediction model establishment, three artificial neural network models were respectively constructed according to the site conditions. The topological structures of Model-Stratum, Model- Hollow, and Model- Building are shown in Figure 2.  
Among all the input parameters, the integrity of excavated and unexcavated rock, the relative position of the measuring point, and the excavated space and the relative height of the floor are fuzzy, which means, the data category needs to be fuzzy. (1) The fuzzification of excavated and unexcavated rock's integrity  
This paper uses the integrity coefficient \(K_{v}\) to quantify the integrity of rock mass, which can effectively reflect the development degree of rock mass structure. \[K_{v}=\big{(}V_{pm}/V_{pr}\big{)}^{2} \tag{6}\]  
where \(V_{pr}\) is the compressional wave velocity of rock acoustic waves and \(V_{pm}\) is the compressional wave velocity of rock mass acoustic waves.
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## 3 Results and Discussion
### Particle Migration and Deposition Experiment
#### 3.2.3 Experimental Process  
The test environment is maintained at room temperature with an ambient humidity not exceeding 30%RH. The time interval between removing the sample from the dryer and using it in the experiment should not exceed 30 min. The particle transport experiment will be conducted using silica (SiO\({}_{2}\)) ellipsoidal particles with diameters of 10 \(\upmu\)m and 40 \(\upmu\)m under a wind speed of 12 m/s working conditions. The specific experimental procedures are as follows:  
1. Ensure that the components of the particle migration deposition simulation experimental platform are reliably connected and effectively bonded to the ground;
2. When the machine is turned on, the particle migration and deposition simulation experimental platform self-checks the working state, and the ventilation is self-cleaning;
3. Open the flow field generator and particle collection system through the software operation of the host computer; the system automatically configures the fan speed according to the set conditions, after the flow field is stable for 3 min, where the wind speed in the particle migration and deposition pipeline is collected through the hot-wire anemometer to meet the experimental setting; if it has been reached, the next experimental process can be carried out; if it is not reached, continue to wait for 5 min and re-measure; if the measured wind speed is still not reached, the experimental conditions need to be re-compared, and the power supply should be turned off and the wind power device and the pipeline interface of the experimental platform should be carefully checked, and step 2 should be repeated to re-start the self-test;
4. Open the lid of the particle disperser, put in the pretreated particle samples that meet the quality of the experimental calculation, tighten the lid of the chamber, turn on the gas-phase disturbance through the host computer software, close the disturbance device after a sufficient disturbance, and after a delay of 10 s, the gas-solid two-phase flow mixture is evenly put into the flow field through the deflector, the sensor group collects the simulation data, and the velocity measurement device is triggered synchronously to measure the velocity of the particles and flow field, and the single experiment is completed after the experimental termination condition is reached;
5. Carefully take out the sampling plate, clean the pipe, and prepare for the next experiment.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
###### Abstract  
Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.  
autonomous driving; dead reckoning; differential drive kinematics; pure pursuit +
Footnote †: journal: Applied Sciences  
1
Footnote 1: College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China 1  
2
Footnote 2: School of Engineering and Design, Technical University of Munich, Garching bei Munchen, 85748 Munich, Germany 2  
3
Footnote 2: School of Engineering and Design, Technical University of Munich, Garching bei Munchen, 85748 Munich, Germany 2  
3
Footnote 3: Mechlab, Dresden University of Applied Sciences, 01065 Dresden, Germany 3  
Footnote 4: Correspondence: [EMAIL_ADDRESS]
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 1 Introduction
It is noted that the ultrasonic wave used for through-metal communication is generally in the range of hundreds of kHz, which is two to three orders of magnitude higher than audio frequency. In this paper a DL algorithm of dual-path recurrent neural network (DPRNN) was investigated to undertake this task for an ultrasonic through-metal communication system. Additionally, to evaluate the validity of the DPRNN algorithm, the conventional LMS approach is comparatively employed to equalize the same channel. The communication system assisted with the trained DPRNN model exhibited a higher signal-to-noise ratio (SNR) signal transmission than the one only using LMS. Finally, the transmission of an image with \(100\times 100\) pixels was achieved in a 50 mm long aluminum channel to verify the advantage of the DPRNN strategy, and a 24.8 dB peak-signal-to-noise ratio (PSNR) and 95% structural similarity (SSIM) were, respectively, obtained for the recovered image.
## 3 Complex Natural Global Cohesive Element Model Setup  
The model size was 50 m \(\times\) 50 m, with uniformly distributed in situ stress loaded on the boundary, as depicted in Figure 6. The maximum in situ horizontal stress \(\sigma_{H}\) was in the vertical direction, and the minimum in situ horizontal stress \(\sigma_{h}\) was in the horizontal direction. A 1-m-long perforation was prefabricated at the center of the model with an injection node at the midpoint. Fracturing fluid entered the model from the injection node and flowed through the perforation. About five hundred natural fractures were randomly distributed in two symmetrical directions with certain restrictions as follows: (1) the fracture length was in a specified range (1 m, 3 m); (2) the fracture length spacing range was (1 m, 2 m); (3) the fracture width spacing range was (2 m, 3 m); and (4) the x coordinate of the starting point was half length of the model diagonal to ensure that the natural fracture network center was at the center of the model. The natural fracture network model was programmed in Python, and the Newton-Raphson algorithm was used to analyze the diffusion and stress of the coupling pore fluid in the model. Modelling the initiation and propagation of hydraulic fracture intersecting with natural fractures network is a strongly nonlinear problem, involving the multi-field coupling effect of stress field and seepage; so, a powerful solver is needed. The calculation process was implemented in the finite element commercial software ABAQUS. Figure 7 shows the principle of the global cohesive element model. Two steps were included in the coupled pore fluid diffusion and stress analysis: (1) a geostatic step, which established equilibrium between the initial pore pressure and in situ stresses and removed the initial deformation; (2) a soil step, which simulated the hydraulic fracture initiation and propagation under the coupling of the rock skeleton and pore fluid. Natural fractures were assumed to be closed as the weak planes and assigned relatively smaller rock mechanical parameters, until driven by the fluid pressure under the Traction-Separation Law.  
Figure 6: Schematic representation of discontinuous natural fractures network model.  
In order to make the simulation results more valuable for field tests, the model parameters referred to the real parameters of the Jiaoye No. 1 well in the Wufeng-Longmaxi Formation in Fuling, Sichuan Province, China [49]. The formation geologic parameters and material properties are listed in Table 1.
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
## 5 Future Research Lines and Applications
### Scalable and Modular Array Design  
The 16-element array presented in this work serves as a foundational module that can be scaled to accommodate larger arrays, addressing the need for high-gain, high-resolution antenna systems. Modular scaling of the array is feasible due to the precision and repeatability offered by AM, which enable the creation of identical, easily integrated units. This modular approach simplifies the assembly of larger arrays without the need for additional alignment or calibration, which would typically introduce phase errors and degrade overall performance. By designing each array segment as a self-contained module with its own feeding network, larger arrays can be assembled to meet the needs of high-resolution radar or long-range communication systems.  
In future designs, this modular approach could allow for the stacking or interlocking of multiple array units, enabling flexible configuration and incremental performance upgrades. Such scalable configurations are highly desirable in radar and remote sensing, where resolution is critical. AM can support this modular architecture by maintaining the necessary accuracy across a large number of identical components, ensuring that the performance characteristics of each module remain consistent throughout the array.
## 1 Introduction
On the other hand, Swin-Transformer based on window self-attention has high computational complexity and insufficient inter-window information interaction when processing large-scale seismic profiles, limiting its ability to model long-range contextual information [10; 11; 12]. Additionally, with increased network depth, traditional skip connection methods are prone to gradient degradation, significantly degrading the model's reconstruction performance for weak reflection signals in high-noise environments. To tackle these challenges, we propose Swin-ReshoUnet, an improved Swin-Unet model enhancing seismic signal reconstruction via architectural innovations. The encoder introduces a hierarchical convolution module with cascaded kernels of varying sizes to build a multi-scale feature pyramid, boosting multi-scale feature capture. The decoder sub stitutes ORCA for traditional W-MSA and SW-MSA, enabling efficient feature aggregation via regional coordinate encoding and global context mapping to improve long-range event horizon recovery. The skip connections integrate a residual channel attention mechanism with dual-pooling fusion (global average and max pooling) to generate adaptive channel weights, combined with SiLU activation for optimized gradient propagation, alleviating deep-network gradient degradation. These improvements enable Swin-ReshoUnet to achieve end-to-end optimization from low-level feature extraction to high-level semantic integration, offering an effective solution for seismic reconstruction under complex geology. The workflow of seismic data processing covers multiple links such as data restoration and inversion modeling. This study focuses on the reconstruction of seismic profile signals, aiming to obtain high-quality seismic profile signals. It should be noted that this study did not involve the inversion of geological structures and wave propagation characteristics, but the high-quality reconstruction results can lay a foundation for subsequent inversion analysis. The structure of this paper is organized as follows: Section 1 is the introduction. Section 2 introduces the base network and the improved model. Section 3 covers data preparation and experimental results on both synthetic and official datasets. Section 4 presents the conclusions.
# Laser Use in Creating Orthodontic Adhesion to Ceramic Surfaces
## 4 Discussion
### The Effect of Laser on Feldspathic Ceramic  
Feldspathic ceramic is most commonly included in studies, as it is most often used in prosthodontics [15; 16; 17; 18; 19; 20; 21; 22; 23]. Only one research, published by [PERSON] in 2013 [15], focuses on fractional CO\({}_{2}\) laser use by testing different intensities of 10 W, 15 W, 20 W and comparing them to hydrofluoric acid. The results obtained showed that the shear bond strength obtained by using 10 and 15 W laser was significantly higher than the acid group; deglazing, meaning the removal of the smooth exterior layer of ceramics, helped in laser-treated ceramic surfaces. Removing the glaze layer of the restoration is considered to generally achieve better adhesion in laser-treated ceramic surfaces, as the high amounts of energy emitted by lasers seem to be better absorbed by the opaque surfaces [33]. By thermocycling for 5000 cycles, the reliability of the study developed by [PERSON] [17] increases, concluding that combined used of HF and laser achieved significantly higher shear bond strength than laser groups and just Er:CrYSGG laser met minimal criteria for orthodontics, when used alone. [PERSON] and his team [18] showed in 2015 that Er:Yag and hydrofluoric acid had similar results, higher than air particle abrasion, which contradicts a study published in 2013 [19], that shows Er:Yag does not allow us to eliminate hydrofluoric acid application. [PERSON], in 2013 [20], shows that Er:Yag with power of 1.6 W had higher SBS than hydrofluoric acid or 3.2 W power. When testing feldspathic ceramics with Nd:Yag laser and Ti:Sapphire Femtosecond laser, [PERSON] [21] shows that this type of laser is relatively ineffective compared to hydrofluoric acid, air particle abrasion and femtosecond laser, but [PERSON] [22] contradicts these results, showing similar results of the same type of laser to hydrofluoric acid.  
When comparing Nd:Yag to Er:Yag, [PERSON] and his team [23] show better, significantly higher results for Nd:Yag.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 2 Derivation of the Formula for Laminated T beam
### Centroid Position in 2D Theoretical Analysis
#### 2.4.2 Wide Flange  
The sum of moments on the web is calculated as shown in Equation (61). The moment of the flange is derived using Equation (35) and shown in Equation (70).  
\[M_{flange}=b(N_{x}Z_{1}+M_{x})=b\Bigg{(}\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+D_{11, f}\Bigg{)}\kappa_{x} \tag{70}\]  
From Equations (61) and (70), the sum of the T-beam moments can be obtained.  
\[M_{beam}=M_{web}+M_{flarge}=\left[\left(\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{Z_{c}^{ 2}h}{A_{11,w}^{-1}}\right)+b\left(\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+D_{11,f} \right)\right]\kappa_{x}=EI_{eff}\kappa_{x} \tag{71}\]  
The effective stiffness of the beam can be obtained as well.  
\[EI_{eff}=\left(\frac{h^{3}}{12A_{11,w}^{-1}}+\frac{Z_{c}^{2}h}{A_{11,w}^{-1}} \right)+b\left(\frac{Z_{1}^{2}}{A_{11,f}^{-1}}+D_{11,f}\right) \tag{72}\]  
The strain of the flange is as follows  
\[\left\{\begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \gamma_{xy}\end{array}\right\}=\left\{\begin{array}{c}Z_{1}+\xi\\ Z_{1}\frac{A_{11}^{-1}}{A_{11,f}^{-1}}\\ Z_{1}\frac{A_{11}^{-1}}{A_{11,f}^{-1}}\end{array}\right\}\kappa_{x} \tag{73}\]  
The stress for each laminate can be calculated using Equation (69).
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 1 Introduction
In [17], a method of computing the line of sights based on NMEA sentences was also presented. In recent years, smartphones have become ubiquitous, and the accuracy requirement for smartphones has become more demanding in applications as diverse as agriculture, transport, and social apps. In this paper, we present details of the DGNSS technology for smartphones from which raw GNSS measurements are not accessible. The computation burden of the technology is significantly large for smartphones. We proposed a DGNSS infrastructure that corrects the standalone position from smartphones using the corrections from the reference station. A Client/Server architecture is presented to cope with a mass of DGNSS positioning requests efficiently. Here, we describe the derivation of the DGNSS based on NMEA messages and show how to solve the technical problems when implementing the infrastructure. Specifically, we address issues such as the fundamentals of the DGNSS based on NMEA messages, the essential assumptions made on the smartphone GNSS chipsets, and the infrastructure implementation issues. Different experiments are carried out to assess the performance of the infrastructure considering different effects, including the ionospheric effects, the multipath effects, and different smartphone types.
## 3 Results and Analysis
### Cross-Domain Comparative Evaluation  
The proposed OMRoadNet framework exhibited superior performance in cross-domain road extraction through rigorous validation against four state-of-the-art models (pix2 pix, CycleGAN, UNIT, and MUNIT). Quantitative evaluations in Table 1 reveal OMRoadNet's dominance across all metrics, achieving 92.16% precision and a 80.77% F1-score, which surpass the second-best model by 4.3% and 2.0%, respectively. These technical advantages are further corroborated by the spectral-spatial analysis in Figure 5, which demonstrates OMRoadNet's capability to resolve three critical challenges faced by conventional methods.  
Here, pix2 pix, constrained by its supervised learning paradigm and urban training data bias, generated structurally distorted road networks with grid-like patterns (38.9% over-connected segments) that deviated from mining road topology. CycleGAN introduced spectral artifacts in material transition zones, such as coal-dust interfaces, achieving only 0.68 SSIM compared to OMRoadNet's 0.81. Despite the improved geometric adaptability, UNIT and MUNIT suffered from topological disconnections in complex branching scenarios, with 23.6% fragmented roads in validation cases, while OMRoadNet maintained continuous connectivity through eight-neighborhood verification.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **IoU (\%)** \\ \hline pix2 pix & 78.85 & 68.77 & 73.47 & 58.06 \\ CycleGAN & 81.63 & 68.79 & 74.66 & 59.57 \\ UNIT & 85.16 & 71.80 & 77.91 & 63.81 \\ MUNIT & 82.89 & 75.01 & 78.76 & 64.96 \\ OMRoadNet & 92.16 & 71.89 & 80.77 & 67.75 \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics of open-pit mine road extraction results (comparative experiments).  
A critical advantage of OMRoadNet lies in its self-training mechanism, which reduced false positives by 51.3% relative to CycleGAN through iterative pseudo-label refinement. The morphology-aware feature disentanglement module enhanced road connectivity by 34.7% compared to UNIT, as validated by graph-based topological analysis. Notably, OMRoadNet demonstrated exceptional robustness in dynamic width variations (8-35 m), sustaining IoU > 65%, where other models degraded below 58%.  
Case studies from Figure 5 (Row 4) provide operational evidence. In transient haul road scenarios, OMRoadNet preserved temporary branch integrity (yellow arrows in visualization) while suppressing false detection from adjacent ore stockpiles (red bounding boxes). Baseline models exhibited two primary failure modes: (1) structural discordance, where pix2 pix misidentified 15 m-wide conveyor belts as roads, and (2) domain-shift artifacts, in which CycleGAN generated salt-and-pepper noise (blue bounding areas) under dust coverage, elevating FP rates to 21.8%. These results systematically validated OMRoadNet's effectiveness in overcoming the feature dissociation problem through hybrid adversarial learning and geometric rule constraints.